The AI Optimist

As he opened the courtroom door, he didn’t know his child custody hearing would involve an attack with an angry audio he never spoke, that his ex-wife created by Googling.

Deepfakes aren’t just about politicians and celebrities, they are hitting home. And they are also opening up creative avenues for spoken word, audiobooks, and so much more.

Jason Rodgers shares insights from his Masters Research, with a focus on media forensics, criminal justice deepfakes, how to create them and how to spot them.

Listen on Apple || Spotify || YouTube

Take the Deepfake Audio Test (answers - bottom of page)

00:00:00 How Deepfakes Work

00:01:09 Intro to Jason's Deepfake Forensic Audio Research Project

00:02:50 Criminal Case with Deepfake Audio Evidence      

00:05:13 The Liar's Dividend - Calling it all Fake!      

00:07:53 Key Indicators of a Deepfake        

00:10:49 Jason's Listening Experiment - Spot the Fake!        

00:19:13 Actual Deepfake: Martin Lewis UK Celeb Fake          

00:22:22 Tell a Deepfake from a Real One

00:24:44 Watermarking Audio - Protecting against Deepfakes

Jason Rodgers is a Producer — Audio Production Specialist with Bitesize Bio

Affiliation: Liverpool John Moores University (LJMU), Applied Forensic Technology Research Group (AFTR)

Qualifications: BSc Audio Engineering - University of the Highlands & Islands (2017), MSc Audio Forensics & Restoration - Liverpool John Moores University (2023)

Professional Bodies: Audio Engineering Society - Member, Chartered Society of Forensic Sciences - Associate Member (ACSFS)

LinkedIn: https://www.linkedin.com/in/ca33ag3/

Jason is currently writing a paper around the challenges to credibility and qualifying factors for forensic audio professionals acting as expert witnesses in court due to the lack of standards relating to audio analysis. This is in part to identify issues for follow-up research into developing a framework for forensically authenticating audio files (especially those suspected of being deepfake).

The AI Optimist Episode #22 Summary

We explore Jason Rodgers' master's research into deepfake audios and deepfake technologies in video, their impact on criminal justice, historical visual deepfakes, and an audio listening experiment for authenticity detection.

Introduction

·    Overview of the podcast episode featuring an interview with Jason Rodgers about his master's research on evaluating authenticity thresholds in deep fake audio and implications for criminal justice

·    Rodgers' origin story in deepfakes and their use in criminal cases after learning media forensics teams aren't looking for them.

·    His research focused on changing how we present audio evidence to identify fakes.

·    From Jason Rodgers' Masters research:

"At the time of writing, there have been a handful of instances where deepfakes have made their way into the courtroom.

One example that has been written about concerned a child custody hearing. As the case involves children the proceedings are protected, however, the reported media writes that the mother's party had created deepfake audio which depicted the father as aggressive and threatening and submitted it to the court as evidence (AIAAIC - Dubai deepfake court evidence, 2023).

Experts later determined that this audio was not legitimate by examining the metadata of the audio file, but in today's world, it is very easy to manipulate or even erase metadata."

Deepfakes in Criminal Cases

·    An example court case discussed where a mother used deepfake audio to paint the father as abusive in a child custody case.

· The fake audio was presented as evidence but found to be AI-generated through metadata traces.

· We explore the forensic community's struggle to adapt to rapid technological changes.

· Jason shares the difficulty in proving Authenticity in court due to proprietary detection tools.

· This custody case shows the potential for deepfakes to influence legal claims improperly.

"A 'deepfake' audio recording was used in a UK child custody battle in an effort to discredit a Dubai resident, it has been revealed.

Byron James, a lawyer in the emirate, said a heavily doctored recording of his client had been presented in court as evidence in a family dispute.

In the edited version of the audio, the child's father was heard making direct and "violent" threats towards his wife.

But when experts examined the recording, they found it had been manipulated to include words not used by the client.

"This is a case where the mother has denied the father access to the children and said he was dangerous because of his violent and threatening behaviour," Mr. James said.

The mother used software and online tutorials to put together a plausible audio file.

"She produced an audio file that she said proved he had explicitly threatened her.

"We were able to see it had been edited after the original phone call took place, and we were also able to establish which parts of it had been edited.

"The mother used software and online tutorials to put together a plausible audio file."

How Deepfakes Work

·    Early deepfakes had detectable flaws like lack of blinking, but creators identified and fixed this over time.

·    Generated ear lobes used to be an indicator, but improved generative AI fixes this.

·    As soon as academic research identifies a way to detect fakes, creators upgrade the tech.

·    Reliable indicators are increasingly complex to find.

Adapting Forensic Analysis

·    AI detection tools carry limitations and proprietary secrets, so manual authentication frameworks are needed.

·    Rodgers is focusing on developing a methodology for audio forensics.

·    Experts view audio on multiple levels, fidelity, metadata, cadence, pronunciation, etc., to identify possible manipulation.

The "Liar's Dividend"

·    Deepfakes make the public distrustful of media authenticity and allow people to dismiss objective evidence as fake.

·    Politicians deny accurate negative coverage as deepfakes.

·    Verifying actual footage strengthens distrust since quality fakes are possible.

From Boston University's Research:

"Deep fakes will prove useful in escaping the truth in another equally pernicious way. Ironically, liars aiming to dodge responsibility for their real words and actions will become more credible as the public becomes more educated about the threats posed by deep fakes.

Imagine a situation in which an accusation is supported by genuine video or audio evidence. As the public becomes more aware of the idea that video and audio can be convincingly faked, some will try to escape accountability for their actions by denouncing authentic video and audio as deep fakes.

Put simply, a skeptical public will be primed to doubt the Authenticity of real audio and video evidence. This skepticism can be invoked just as well against authentic as against adulterated content. "

Take the Deepfake Audio Test (answers - bottom of page)

Rodgers' Research Methodology

·    He creates a 10-question audio quiz with three samples per question, some real and some AI-generated.

·    Testing the quiz with the public and specialists like audio engineers and media forensics teams.

·    Some personal bias arises, even among experts familiar with the voices.

·    Background noise samples simulate real-world conditions:

· The experiment involves identifying real vs. fake audio clips.

· Various scenarios (e.g., telephone filter, street noise) are tested.

· Challenges in differentiating authentic audio due to improving deepfake quality.

·    It understands the importance of considering the pros and cons of the audio content instead of making definitive judgments about real vs. fake.

Live Test of Rodgers' Samples

·    Dunn takes the quiz and shares the thought process in determining real vs fake.

·    Dunn is wrong on the first round, even with a lack of personal familiarity with the speaker (Scottish accent).

·    Dunn demonstrates bias in the second experiment test, where a familiar voice sounds authentic.

·    Takeaways:

o  Bias affects specialists' evaluations.

o  It's easier to fake an unfamiliar voice.

o  Presentation quality and clarity influence perceptions of Authenticity.

Deepfake Video Examples

·    An example of a high-quality fake video impersonating famous UK consumer advocate Martin Lewis.

·    Detecting well-made fakes is complex and requires scrutinizing facial details.

·    Evaluating fluid movements like turning the head and facial expressions is harder.

Challenges Judging Authenticity

·    With good-quality video and audio editing tools, altered media is commonplace.

·    Changes how we establish a baseline of what's "real."

·    There are multiple factors to analyze, like fidelity, metadata, vocal patterns, etc.

·    Watermarking proposals to embed source info during content creation.

o  The Coalition for Content Provenance and Authenticity (C2PA) addresses the prevalence of misleading information online.

o  Manual and AI-based methods for detecting fakes are limited.

o  There's a need for complete analysis beyond metadata and visible indicators.

Conclusion

·    Deepfakes represent a significant challenge as technology improves.

·    Diligence in verifying Authenticity before sharing or acting on media is now a requirement.

·    Holistic evaluation combining technological solutions and trained human judgment is needed.

How to Spot Deepfakes – a Simple Action Plan:

·    Scrutinize media, especially high-stakes info related to individuals.

·    Look for odd visual artifacts and inconsistencies in facial features/expressions.

·    Listen for unnatural speech patterns and vocal anomalies.

·    Cross-reference sources, when possible, to verify.

·    Consider adding watermarks during content production.

·    Support efforts to responsibly advance detection technology.

·    Remain skeptical but avoid knee-jerk "fake news" reactions without evidence.

·    Be cautious about assuming Authenticity in digital content.

·    Learn to identify potential deepfake indicators.

·    Stay informed about the evolving nature of deepfake technologies.

What Are Deepfakes and How Are They Created?

·    Deepfakes blend the words "deep learning" and "fake." They utilize AI and machine learning algorithms to manipulate or generate visual and audio content with a high potential to deceive.

·    The creation process generally involves two AI algorithms: one creating the content (a "generator") and another detecting its artificial nature (a "discriminator"). This adversarial process, known as Generative Adversarial Networks (GANs), refines the output to be realistic.

·    Deepfakes refer to media (images, audio, video) manipulated and falsified using artificial intelligence and machine learning techniques.

·    The name is "deepfakes" because they utilize deep learning, a complex form of neural network modeling that analyzes and reconstructs intricate patterns such as those found in human speech and imagery.

Deepfakes represent a challenge in the digital age, blurring the lines between reality and fabrication. As technology advances, the ability to create convincing deepfakes becomes accessible, increasing the risks of misinformation and exploitation.

Solving the problem demands a combined effort in technology development for detection, legal frameworks for regulation, and public education to foster media literacy.

Discerning Authenticity will be a technological challenge and a fundamental skill for participating in a digital society.

Tools Used:

·    Popular Tools: DeepFaceLab, FaceSwap, Descript, ElevenLabs, Speechify, and Adobe After Effects create deepfakes. More recently, AI models like GPT-4 and others developed by companies like DeepMind and OpenAI are advancing.

·    Accessibility: The accessibility of deepfake technology is increasing, with apps and software becoming user-friendly for non-experts.

Challenges to social media:

·    Content Moderation: Social media platforms need help keeping up with detecting and moderating deepfake content.

·    Awareness: Users must be critical of their media, understanding that seeing is no longer believing.

Diving Deep into Deepfake Technology

Specifically, deepfake creation relies on generative adversarial networks (GANs). Design involves training two AI models against each other - one generates synthetic media, and the other serves as a detector scrutinizing the output authenticity.

These adversarial dynamic pressures both models to evolve and become highly adept at their role.

The generative model uses large volumes of source media data like photos or videos of an individual to learn their facial expressions, speech patterns, and other visual/vocal details.

The detector model then reviews the outputs, flagging areas that don't seem genuine. The generator integrates this feedback to refine its fakes while learning to thwart the detector.

After extensive iterations, the generator becomes skilled enough to produce highly lifelike fabrications that fool AI and human observers.

The fully trained model is applied to synthesize new video or audio of the target individual appearing to say or do things they never actually did.

Deepfake programs like Zao, Avatarify, and Reface leverage these techniques to generate videos of a source individual, like a celebrity, synchronized to another person's facial expressions.

Advanced platforms allow manual editing of faked imagery and audio or generating new videos from text prompts.

Use Cases and Examples

·    Politics: Altered videos of politicians, like one of Nancy Pelosi, slowed down to make her appear drunk to make headlines.

·    Entertainment: Deepfakes are being used in film and television to de-age actors or bring deceased stars back to life, like Peter Cushing in "Star Wars."

·    Misinformation: Deepfakes appear in misinformation campaigns, impacting public opinion and potentially election outcomes.

While deepfake technology holds exciting promise for entertainment, education, and other fields, it also poses significant risks for misuse.

The most infamous applications involve non-consensual use to create "revenge porn" or fake news/disinformation.

Deepfakes help offenders carry out fraud and harassment—criminals using AI-generated audio of a CEO's voice to trick an employee into transferring $243,000.

Fake celebrity pornographic videos harass and exploit without consent. Victims describe the experience as a form of assault.

However, deepfakes also bring positive applications. Filmmakers use them to restore historic speeches by figures like Harriet Tubman or create updated scenes featuring a young Princess Leia in Rogue One.

Visual effects artists utilize AI tools to build realistic digital doubles. Avatarify and similar apps allow comedic face-swapping for entertainment.

Educational deepfakes help illustrative concepts - an AI replica of Richard Feynman delivers lectures on physics and drums up renewed interest.

AI voice duplication enables podcasters and creatives to reuse vocals from historical speeches or literature without costly voice actors. The tech also expands accessibility for those unable to speak or record their voices.

Societal and Ethical Challenges

Usage:

·    Fooling and Deceiving: Initial deepfakes gain notoriety for their use in creating fake pornography and revenge porn, exploiting celebrities and ordinary people. They've also created false narratives and misinformation.

·    Informing About Challenges: Awareness campaigns and educational content use deepfakes to demonstrate their potential harm and encourage critical media consumption.

Challenges to Society:

·    Impact on Trust: Deepfakes erode trust in media, making it difficult for people to discern what's real or fake.

·    Legal and Ethical Concerns: They raise questions about consent, privacy, and the ethical use of someone's likeness.

·    Security Threats: Deepfakes pose security threats, including the potential for false evidence in legal cases or manipulating stock markets with fake news

.Importance of Deepfakes:

·    Highlight the need for public awareness and technological literacy.

·    Emphasize the urgency for improved media forensic methods.

The unchecked proliferation of deep fakes poses threats on several fronts:

Trust & Misinformation - Widespread synthetic media weakens public confidence in online content and images – even real ones get discounted as "fake."

The lack of confidence benefits bad actors who easily dismiss genuine negative coverage as deepfakes. Verifying real clips heightens distrust.

Politics & Civic Discourse - Manipulated or entirely fabricated video/audio of candidates circulated near elections impacts results while avoiding consequences of defamation. Even releasing real clips out of context damages reputations.

Psychological Harm - Seeing oneself in traumatic deepfake situations negatively impacts mental health and leads to self-harm in severe cases. The possibility also engenders constant anxiety about victimization.

Discrimination & Exploitation - Certain vulnerable groups face a disproportionate risk of deepfake victimization, especially non-consensual pornographic use. The threat further marginalizes them psychologically and socially.

To address these risks, experts recommend four essential precautions:

1.    Critical Thinking - Scrutinize unverified viral media and verify using trusted, impartial sources before circulating or reacting.

2.    Consent & Privacy - Before creating or sharing media showing others, obtain explicit consent and consider long-term unintended circulation and misuse.

3.    Verification Standards - Develop institutional protocols requiring rigorous AI and manual examination of media authenticity before acceptance and publication.

4.    Attribution Technology - Embed verifiable digital attribution details during media recording to provide trusted source history, easing forgery detection.

Responsible governance of deepfake technology, balancing innovation with ethics, is crucial to managing the dangers unchecked development poses.

Like media literacy helps protect against misinformation, a critical eye aware of manipulation markers like visual artifacts aids self-defense.

We must evolve societal resilience alongside AI to ensure a just future.

Deepfakes represent a significant challenge in the digital age, blurring the lines between reality and fabrication. As technology advances, the ability to create convincing deepfakes becomes more accessible, heightening the risks of misinformation and exploitation.

The new reality necessitates a combined effort in technology development for detection, legal frameworks for regulation, and public education to foster media literacy.

As we progress, recognizing the Authenticity of digital content will be a technological challenge and a fundamental skill for participating in a digital society.

Answers to the Listening Experiment, how’d you do?

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Self-Aware X-Rays? Intelligent Objects transform healthcare with x-rays gaining AI power, allowing them to understand findings, notify doctors, access records, organize conversations, and more. EP #21

When I first heard about self-aware X-rays, it sounded like a ChatGPT hallucination. Then, discover how Intelligent Objects are digital tools with AI capabilities, allowing them to perform tasks, learn, and adapt to new situations.

Massoud blew my mind with the possibilities – smart meetings recording, summarized, and emailed automatically. Spreadsheets that notify us when problems arise or when filing deadlines arrive.

While this pod shows the x-ray example, this breakthrough applies to many business uses. In Part 2 of this interview with Massoud Alibakhsh, CEO of Omadeus, we venture into a use case study of x-rays acting as intelligent objects.

Here is a detailed summary and analysis of the The AI Optimist Episode #21 about the Aware AI X-ray:

Key Sections:

* Introducing the Concept of an Aware AI X-ray (0:00-2:30)

* Make the x-ray an intelligent, self-aware object through object messaging architecture.

* It knows details like who took it, what type it is, and who the patient is.

* It includes private channels for communicating info between doctors and nurses.

* The natural language capabilities of an LLM allow it to interact with stakeholders.

* It organizes conference calls and records conversations with summaries emailed to stakeholders.

* Explanation of Object Messaging and Intelligent Objects (2:30-7:30)

* The analogy of these objects behaving like a personal butler

* They navigate workflow and deliver relevant info to stakeholders.

* The objects create a harmonious flow of information attuned to the organization's tempo.

* Use case of making an x-ray intelligent using object messaging architecture.

* They are giving it identity details, an LLM, and communication channels to function.

* Programming it to interface with workflows, databases, and stakeholder communication.

* Imagining an Intelligent X-ray Object in Healthcare (7:30-end)

* X-ray reads itself using NLP (natural language programming), identifies cancer, and urgently notifies the primary doctor.

* It joins a conference call with a group of doctors to discuss findings.

* Records call and understand conversations via NLP.

* Answers later questions about itself and accesses previous call info.

* Knows regulations, guidelines, and patient communication rules like HIPAA.

* Fits into larger ecosystems of intelligent objects like patient records.

Featured Guest

Massoud Alibakhsh, CEO of Omadeus

Theme: AI's role in workplace efficiency, focusing on intelligent objects and object messaging

As the CEO and founder of Omadeus, Massoud leads a visionary team in creating a new paradigm of AI-powered business software based on patents pending Object Messaging and Intelligent Objects (OMIO).

The product is an AI-powered project management, collaboration, documentation, and communication unified in one application for the entire company.

The Possible Day in the Life of an Intelligent Object - an X-ray named Alex:

Meet Alex, an intelligent X-ray using object messaging architecture. Alex knows he is a chest x-ray taken of patient John Smith, age 52, by technician Jane.

Alex has natural language capabilities to discuss findings with Jane and ask clarifying questions about image quality.

After the scan, Alex reviews medical imaging guidelines and sends himself securely to the Mayo Clinic's diagnostic AI system per protocol.

He receives a textual report analyzing the images and identifying a concerning mass. Alex reads and understands this report using his language model.

Given the critical finding, he urgently contacted John Smith's oncologist, Dr. Roberts, according to physician notification rules.

Alex organizes a telehealth conference call between himself, Dr. Roberts, and an oncology specialist at Mayo Clinic, Dr. Park.

Alex displays key images, regions of concern, and report excerpts during the discussion. He records the conversation and takes notes on the recommended next steps, such as additional testing and potential treatment plans.

Two months later, Alex cross-referenced the new images and reports with his previous recordings at John's follow-up scan.

He identifies that the tumor has responded well to treatment. Alex creates a summary update for John and schedules another telehealth meeting for the care team to continue coordination.

This example demonstrates how an intelligent object like Alex takes on administrative roles, following medical regulations while interfacing with humans and AI systems using natural language and maintaining context over time. Alex also automates communication flows.

AI X-Rays as Intelligent Objects with Object Messaging

The Aware AI X-ray is a significant advancement in healthcare technology.

Integrating AI and object messaging makes the X-ray more than just a static image; it transforms into a dynamic, interactive, and intelligent entity.

This integration allows the X-ray to:

* Communicate effectively with healthcare professionals and patients, providing timely and relevant information.

* Connect with resources like the Mayo Clinic for advanced analysis, potentially leading to quicker, more accurate diagnoses.

* Automatically comply with regulations, such as HIPAA, by staying updated with the latest guidelines and ensuring patient data privacy and security.

* They enhance clinical trials, as intelligent X-rays could streamline patient selection and monitoring in cancer research and other areas.

What are the possible consequences of relying on Intelligent Objects in critical sectors like healthcare, and how might they be eased?

Relying on Intelligent Objects in healthcare may bring surprising consequences:

1. Over-reliance on Automation: Healthcare professionals become too dependent on Intelligent Objects, reducing their diagnostic or treatment skills.

Analogy: It's like someone relying solely on GPS and losing their sense of direction.

2. Malfunctions or Failures: Any technical glitch or malfunction in an Intelligent Object leads to misdiagnoses, incorrect medication dosages, or surgical mishaps.

3. Data Privacy Concerns: The collection and communication of patient data raises concerns about data privacy and potential abuse.

4. Loss of Personal Touch: With increased automation, patients feel the healthcare experience is impersonal, missing the human touch.

5. Potential for Hacking: With data exchanges between Intelligent Objects, there's an increased risk of cyber-attacks compromising patient health and information.

6. Ethical Concerns: Decisions made by Intelligent Objects may not align with ethical factors, especially in life-and-death situations.

Improvement Strategies:

1. Continuous Training: Healthcare professionals receive ongoing training to maintain their skills and aren't solely reliant on technology.

2. Redundancy Systems: Backup systems prevent complete failures. If one Intelligent Object malfunctions, another takes over.

3. Strong Data Protection: Employing encryption and strict data protection protocols to safeguard patient data.

4. Human-AI Collaboration: Ensuring decisions, especially critical ones, are made with human and AI input balances automation with the personal touch.

5. Advanced Security Protocols: Regular system updates, intrusion detection systems, and advanced firewalls help guard against cyber-attacks.

6. Ethical Guidelines: Establishing guidelines for developing Intelligent Objects ensures they align with human values.

Importance of the X-ray as an Intelligent Object Example

The Aware AI X-ray is an example of an intelligent object, demonstrating the impact of AI by transforming traditional objects into dynamic, interactive entities.

This innovation is not just technological advancement; it's reshaping how we interact with and benefit from everyday objects.

* The Aware AI X-ray is a modern development in healthcare technology, demonstrating the power of integrating AI and object messaging.

* This technology enhances communication, compliance, and efficiency in healthcare workflows.

* The concept of intelligent objects includes applications across various sectors, offering the potential for innovation and improvement.

* While Intelligent Objects revolutionize healthcare, there are potential pitfalls, including over-reliance, malfunctions, data privacy issues, loss of personal touch, security threats, and ethical dilemmas.

* Mitigation strategies address these concerns, such as continuous training, redundancy, robust data protection, human-AI collaboration, advanced security measures, and ethical guidelines.

Benefits of Object Messaging in Real-Time Data-Reliant Industries:

Finance:

1. Real-time Decision Making: Trading algorithms communicate instantly with data analysis tools to make split-second buying or selling decisions based on market fluctuations.

2. Enhanced Security: Financial transactions are more secure when different security tools communicate in real-time to identify and combat threats.

3. Personalized Banking: Banking systems use Object Messaging to tailor services based on real-time customer data, like a bank teller knowing your needs.

Autonomous Vehicles:

1. Real-time Adaptation: Cars communicate with traffic lights, road sensors, and other vehicles to adjust routes, speed, or driving strategy in real time. It's like a car having a conversation with its environment.

2. Enhanced Safety: If one vehicle detects a hazard, it immediately informs nearby vehicles as a quick-reacting network.

3. Traffic Flow Optimization: Vehicles communicate to optimize traffic flow, reducing congestion and ensuring smoother rides.

Think of Object Messaging as the nervous system in a body. Finance allows the brain (algorithms) to react to stimuli (market changes) quickly.

In autonomous vehicles, it lets the car "feel" its environment and adjust accordingly.

Key Takeaways:

* Object Messaging in AI can be challenging due to integration, complexity, latency, security, synchronization, and maintenance concerns.

* Industries like finance and autonomous vehicles benefit from real-time decision-making, enhanced security, personalized services, and improved safety and adaptability.

* Customer service chatbots draw data from various systems, addressing questions without human involvement.

* In agriculture, intelligent soil sensors and robotic harvesters optimize farming practices by providing real-time data and automating labor-intensive tasks.

* In finance, intelligent accounting spreadsheets incorporate rules and regulations, automatically identifying errors and notifying stakeholders, enhancing compliance and accuracy.

The key is integrating natural language processing and identity into the objects that take action within technological workflows.

Challenges for Object Messaging in Real-World AI Applications:

1. Integration with Legacy Systems: Integrating Object Messaging with older systems may lead to compatibility issues.

2. Increased Complexity: Managing communication between numerous objects adds layers of complexity. It's like orchestrating an ensemble where each instrument needs precise coordination.

3. Latency Issues: Real-time communication between objects faces delays, especially in environments with vast data transfer.

4. Security Concerns: More communication points mean potential weak spots of vulnerability.

5. Synchronization Challenges: Ensuring all objects operate harmoniously without overlapping or conflicting is challenging.

6. Maintenance: As objects increase in number and complexity, maintaining them becomes problematic.

AI Agents in 2024 will be filled with Intelligent Objects.

Next year is when AI Agents begin streamlining our personal lives. As usual, the business world is leading with Intelligent Objects and Object Messaging, making things like X-rays active participants.

Imagine a world where everyday objects are not passive items but active. Starting with the Aware AI X-ray in healthcare, we discover that this simple example replicates in finance, agriculture, and self-driving vehicles.

Name a breakthrough, and you'll likely discover these Intelligent Objects are under the hood and engaging.

Now, mundane objects are transforming into smart tools, enhancing efficiency and decision-making. It's not just about medical advances; it's about reimagining the potential of everyday objects across all sectors.

And it doesn't stop at healthcare. Think about smart soil sensors in agriculture that communicate crop needs or intelligent spreadsheets in finance that automatically flag errors and update with new regulations.

These practical, real-world applications are where AI-driven technology makes life easier, more efficient, and more informed. It's a glimpse into a future where technology integrates into every aspect of our lives, improving them.

We delve into how intelligent objects like the AI X-ray are just beginning a new era where technology and daily life intertwine in exciting ways. The possibilities are endless, and the future is now.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

What part of your business or job wears you down?

Often 20-40% of our working time is spent doing things like routing documents, updating people via email, and pushing files around to make sure the right people see them.

Now with the advance of AI, the first wave is often adding to this complex maze of administrative tasks, and it doesn’t have to…

Imagine the future of AI with Object Messaging and Intelligent Objects as a city. Each building (intelligent object) has its functions and responsibilities.

The roads connecting them (object messaging) ensure they communicate and work harmoniously.

Listen on Apple || Spotify || YouTube

Intro (0:00-2:30)

Massoud's Background – Why Omadeus? (2:30-5:00)

AI Replacing Jobs? (5:00-7:30)

The Invention - Intelligent Objects and Object Messaging (7:30-15:00)

Benefits of Intelligent Objects (15:00-22:00)

Comparing to LLMs (22:00-26:30)

Conclusion (26:30 to end)

Featured Guest

Massoud Alibakhsh, CEO of Omadeus

Theme: AI's role in workplace efficiency, focusing on intelligent objects and object messaging

As the CEO and founder of Omadeus, Massoud leads a visionary team in creating a new paradigm of AI-powered business software based on patents pending Object Messaging and Intelligent Objects (OMIO).

The product is an AI-powered project management, collaboration, documentation, and communication unified in one application for the entire company.

With over 20 years of experience as a tech entrepreneur and software architect, Massoud has launched, grown, and sold multiple software companies.

How Omadeus Uses AI

Omadeus is an AI agent integrating intelligent objects and object messaging into workflows. It streamlines processes by automating mundane tasks, facilitating better communication, and making systems more efficient.

With all the breakthroughs of AI, it's still adding complexity to business workflows. AI engineers treating AI as a tool instead of a platform may be continuing the tradition of information overload.

The actual value of AI is getting buried in dated work habits by depending on humans to route and manage information.

Instead, why don't we treat AI as a platform and automate the mundane and tedious parts of jobs?

Imagine having people do their job instead of acting as glorified administrators, frustrated and worn down by information management.

Podcast Highlights

AI's potential extends beyond just being a tool; it can act as a platform for redefining work processes.

Intelligent objects and object messaging can significantly reduce workday waste and increase efficiency.

The integration of AI in the workplace should be harmonious, augmenting human abilities rather than replacing them.

The AI Optimist Podcast Summary

Intro (0:00-2:30)

* AI engineers treat AI as a hammer and everything else as nails. This approach needs to change.

* Massoud Alibakhsh discusses how AI engineers often view AI as a universal solution suitable for various problems, including workplace challenges.

* He sees AI as a tool that engineers want to apply everywhere. Still, it needs to be integrated thoughtfully into existing systems.

* There's a need to integrate AI into automation frameworks harmoniously.

Massoud's Background – Why Omadeus? (2:30-5:00)

* Built a system to connect multiple collaboration tools like Jira, Trello, Slack

* Massoud describes Omadeus as a system that intelligently orchestrates information flow, comparing it to a symphony of harmonious interactions.

* Omadeus.com uses AI to reduce workday inefficiencies and addresses the communication gap between people and systems.

AI Replacing Jobs? (5:00-7:30)

* AI will automate mundane tasks, freeing humans to pursue their passions.

* Massoud argues that intelligent objects help humans realize their potential by handling routine tasks.

* AI provides opportunities for people instead of replacing them.

The Omadeus Invention (7:30-15:00)

* Massoud introduces the concept of intelligent objects, with an x-ray example transforming into a smart object through AI integration.

* Object messaging is the process where these intelligent objects communicate and navigate through workflows, interacting with humans and other objects.

* He shares a detailed example of how an intelligent X-ray can navigate a healthcare system, making decisions and interacting with various stakeholders.

* Current AI models process words; they don't understand meaning.

* The real problem is integrating information across workflows and systems.

* Proposes architecture inspired by biology for "intelligent objects."

* Example: An X-ray is an intelligent object communicating relevant information to doctors.

Benefits of Intelligent Objects (15:00-22:00)

* Objects become stakeholders, recording conversations and delivering information to the right people.

* Much more automated and efficient than current manual workflows

* Enables advanced workflows like auto coordination between multiple doctors

Comparing to LLMs (22:00-26:30)

* LLMs have limitations around reliability and integration.

* Intelligent objects complement LLMs by structuring information and workflows.

* Massoud discusses the limits of current AI models, like ChatGPT, compared to the potential of Omadeus' approach.

* He emphasizes the need for a new architecture that combines traditional programming with AI, particularly for mission-critical applications.

Conclusion (26:30-28:00)

* We can build more automated, integrated, reliable systems with intelligent objects.

* Combining traditional software with new AI architectures

Simple Definitions

Intelligent Objects: Entities in a digital environment that autonomously gather, process, and act upon data. They can make decisions and interact with other objects and humans in a workflow.

Object Messaging: The process of intelligent objects communicating and interacting within a digital ecosystem, sharing information, and making decisions to streamline workflows.

Action Plan:

* Explore Integration of AI in Existing Systems: Investigate how AI enhances current workplace tools and processes.

* Focus on Communication Improvement: Use AI to improve communication flow within organizations.

* Embrace Intelligent Objects: Develop and implement intelligent objects to automate routine tasks, allowing human creativity to flourish.

* Continuous Learning and Adaptation: Keep in touch with advancements in AI and adapt them to existing systems for continual progress.

* Ethical and Responsible AI Use: Ensure AI is used responsibly and ethically, particularly in mission-critical applications.

Object Messaging and Intelligent Objects for AI

Object Messaging is not a new concept. Like people send and receive letters with messages, objects in computer systems send, receive, and process messages to execute functions.

Object-Oriented Programming (OOP) has used the idea of objects that send messages to one another to perform tasks.

Similarly, Intelligent Objects (also known as smart objects) are akin to tools with built-in intelligence. Imagine a hammer that adjusts its weight according to what it's striking.

These AI objects with embedded computing capabilities perform tasks without explicit instructions.

The convergence of Object Messaging and Intelligent Objects will change AI's path.

Here's why:

Pros:

* Decentralized Processing: AI systems distribute tasks among intelligent objects, like a team splitting functions for efficiency.

* Real-time Adaptation: Intelligent objects adjust in real-time, like a chameleon changing colors based on its surroundings.

* Enhanced Integration: Object Messaging allows diverse AI systems to work harmoniously, like an orchestra playing in tune.

Cons:

* Complexity: Managing communication between numerous objects is like coordinating a flash mob.

* Security: With more objects communicating, there's a higher risk of security breaches, like having many doors in a house—more entry points for intruders.

Why They Matter

* Object Messaging and Intelligent Objects have roots in older technologies but are becoming crucial for modern AI.

* Fusing these concepts leads to adaptable and integrated AI systems, though complexity and security are concerns.

* Companies like Omadeus and NVIDIA are pioneering this integration, a promising direction for AI's future.

How will the principles of Object Messaging influence the design of future AI algorithms?

Future AI algorithms will feel the impact of Object Messaging, making data intelligent and responsive instead of static:

1. Modular Design: Object Messaging is a system where components (objects) operate independently and communicate when necessary. AI designers will create modular algorithms, where each module handles a specific function, making AI systems more flexible and easier to update.

Consider a LEGO set. Each piece (module) has its function and can be combined in various ways (messages) to create different structures.

2. Improved Scalability: As tasks increase or become complex, new objects or modules are added to the system. The existing infrastructure can be a partial overhaul.

3. Real-time Adaptability: Objects in a messaging system receive and process data continuously. AI algorithms will adjust in real time based on incoming data.

4. Enhanced Collaboration: Object Messaging allows different AI systems or components to communicate seamlessly. They are encouraging the design of algorithms for collaboration, where multiple AI systems work together to solve a problem.

5. Increased Robustness: When one object fails in a system, others still function. If one part of an AI algorithm encounters an error, the system doesn't crash. The modular nature promoted by Object Messaging enhances system resilience.

6. Simplified Troubleshooting: With clear messaging protocols, when issues arise, they are traced back to specific objects or messages, simplifying the identification and repair of faults in AI algorithms.

Summary

* Object Messaging promotes modular, scalable, and adaptable AI algorithm designs.

* It encourages collaboration between AI systems and enhances system robustness.

* Troubleshooting becomes straightforward with the clear paths provided by object messages.

How will combining Object Messaging and Intelligent Objects influence industries outside of tech, such as agriculture or healthcare?

Integrating Object Messaging and Intelligent Objects influences other sectors, like agriculture and healthcare.

Agriculture

1. Precision Farming: Intelligent Objects, like soil sensors, communicate (through Object Messaging) with irrigation systems to deliver precise amounts of water, optimizing crop yield. It's like conversing between the soil and the water system to determine the right amount of hydration.

2. Crop Health Monitoring: Drones equipped with AI sensors (Intelligent Objects) scan fields and send messages regarding areas with pest infestations or diseases. AI allows farmers to address problems selectively, reducing the blanket use of pesticides.

3. Automated Harvesting: Robotic harvesters decide when to pick fruits based on ripeness sensors and communicate to optimize storage conditions.

Healthcare

1. Patient Monitoring: Wearable devices (Intelligent Objects) continuously monitor patient vitals and, through Object Messaging, alert medical professionals or automated medication dispensers when needed.

2. Remote Diagnostics: Various diagnostic tools communicate to provide a holistic view of a patient's health. For instance, an MRI machine might "discuss" results with a blood test machine to give doctors a clearer picture.

3. Intelligent Prosthetics: Prosthetic devices adapt in real time based on the user's activity level and send feedback to healthcare providers for adjustments.

4. Drug Delivery Systems: Medication dispensers are programmed to release drugs based on the data received from other devices. If a heart rate monitor detects arrhythmia, it messages an implantable drug delivery device to release a specific dose of medication.

Imagine a well-coordinated orchestra. Each instrument (Intelligent Object) plays its part in agriculture, and the conductor (Object Messaging) ensures harmony.

Each medical device is a musician in healthcare, and the patient's well-being is the symphony.

Influences of AI

* By integrating these concepts, agriculture witnesses improved precision, better crop health monitoring, and efficient harvesting.

* Healthcare sees enhanced patient monitoring, diagnostics, prosthetics, and drug delivery systems.

* Both industries achieve higher efficiency and accuracy with reduced human intervention.

What are the challenges in implementing Object Messaging in real-world AI applications?

1. Integration with Legacy Systems: Integrating Object Messaging with older systems not designed for it could lead to compatibility issues.

2. Increased Complexity: Managing communication between numerous objects adds layers of complexity.

3. Latency Issues: Real-time communication between objects faces delays, especially with vast data transfer.

4. Security Concerns: More communication points mean more points of vulnerability.

5. Synchronization Challenges: Ensuring all objects operate harmoniously without overlapping or conflicting is challenging.

6. Maintenance: As objects increase in number and complexity, maintaining them becomes problematic.

Benefits of Object Messaging in Real-Time Data-Reliant Industries

Finance

1. Real-time Decision Making: Trading algorithms communicate instantly with data analysis tools to make split-second buying or selling decisions based on market fluctuations.

2. Enhanced Security: Financial transactions are more secure when security tools communicate in real-time to identify and combat threats.

3. Personalized Banking: Banking systems use Object Messaging to tailor services based on real-time customer data, like a bank teller knowing your needs when you walk in.

Autonomous Vehicles

1. Real-time Adaptation: Cars communicate with traffic lights, road sensors, and other vehicles to adjust routes, speed, or driving strategy in real time.

2. Enhanced Safety: If one vehicle detects a hazard, it immediately informs nearby cars, acting as a quick-reacting network.

3. Traffic Flow Optimization: Vehicles communicate to optimize traffic flow, reducing congestion and ensuring smoother rides.

Think of Object Messaging as the nervous system in a body. Finance allows the brain (algorithms) to react quickly to stimuli (market changes).

In autonomous vehicles, it lets the car "feel" its environment and adjust accordingly.

Key Insights

* Implementing Object Messaging in AI is challenging due to integration, complexity, latency, security, synchronization, and maintenance concerns.

* Industries like finance and autonomous vehicles benefit from real-time decision-making, enhanced security, personalized services, and improved safety and adaptability.

What are possible unforeseen consequences of relying heavily on Intelligent Objects in critical sectors like healthcare, and how might they be eased?

1. Over-reliance on Automation: Healthcare professionals might become too dependent on Intelligent Objects, potentially reducing diagnostic or treatment skills.

It's like someone relying solely on GPS and losing their innate sense of direction.

2. Malfunctions or Failures: Any technical glitch or malfunction in an Intelligent Object leads to misdiagnoses, incorrect medication dosages, or surgical mishaps.

3. Data Privacy Concerns: The continuous collection and communication of patient data raises concerns about privacy and potential misuse.

4. Loss of Personal Touch: With increased automation, patients might feel the healthcare experience could be more impersonal, missing the human touch.

5. Potential for Hacking: With data exchanges between Intelligent Objects, there's an increased risk of cyber-attacks, potentially exposing patient health and information.

6. Ethical Concerns: Decisions made by Intelligent Objects might not always align with ethical considerations, especially in life-and-death situations.

How to Avoid Challenges

1. Continuous Training: Healthcare professionals receive ongoing training to ensure they maintain their skills and aren't solely reliant on technology.

2. Redundancy Systems: Having backup systems in place prevents complete failures. If one Intelligent Object malfunctions, another takes over.

3. Robust Data Protection: Employing strong encryption methods and stringent data protection protocols to safeguard patient data.

4. Human-AI Collaboration: Ensuring that decisions, especially critical ones, are made with both human and AI input balances automation with the personal touch.

5. Advanced Security Protocols: Regular system updates, intrusion detection systems, and advanced firewalls help guard against cyber-attacks.

6. Ethical Guidelines: Establishing ethical guidelines for developing and deploying Intelligent Objects ensures that they align with human values.

Summary

While Intelligent Objects will help healthcare, there are potential pitfalls, including over-reliance, malfunctions, data privacy issues, loss of personal touch, security threats, and ethical dilemmas.

Mitigation strategies address these concerns, such as continuous training, redundancy, robust data protection, human-AI collaboration, advanced security measures, and ethical guidelines.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

AI Black Friday – Freeing 20-40% of Time Wasters with GPTs, AI Assistants, and AI Agents

It’s not magical to think this way; instead of worrying about how AI might replace you, how about if AI took over all those tasks you don’t like to do?

We dive into how to identify and automate those time suckers with two use cases!

How You Already Use AI – Maps and Netflix

The funny thing about AI is how it penetrated our lives in the background way before the ChatGPT craze made the overused term AI a part of our vocabulary.

When you drive, how often do you use Google Maps, Waze, or whatever map program to find your way around?

It’s so typical these days that people forget when we had to use maps, memory, and a sense of direction to find our way home.

Now, you type in where you’re going, and the directions appear, guiding you.

That’s what AI agents and assistants will do for us. Sure, using a map and finding your way seems more nostalgic, much like adding numbers in your head was lost to many with the invention of the calculator.

The way maps are a co-pilot, showing us the way, is how AI is taking over.

Netflix suggests movies and shows you might like; Spotify prompts musical alternatives and playlists. All this seems normal compared to 20 years ago when you had to do this yourself.

In this pod, we’re exploring how to pick out those time wasters in your life and business hours. Seeing where AI can not only fill in those gaps but also give you time to do what you like or do on your job.

I’ll show how to plan and roll out your GPT for those who won’t consider this pod a 4-Step AI Roadmap to streamline life and work’s boring, mundane, and exhausting aspects.

1. Goals: Setting a clear purpose, focused and doable, is vital. Who else will be involved in achieving this goal if this is your job?

Bonus points for considering if the goal is even the right one and adapting it.

After all, goals are a dream with a deadline. Sometimes, the original goal isn’t the right one to help you!

2. Efforts: What tasks do you hate, and which can you automate with AI?

Many waste time and energy doing things AI could do better, like emailing the Team with an update.

Identify what you can streamline and free time to do better work and engage more.

3. Communicate: Remember that communication is the art of sharing ideas and emotions.

Yet, instead of connecting us, we often detour through the land of overcomplication and missed cues.

Learn how to navigate this quirky communication landscape with AI, clarity, and a bit of humor.

4. Review and Revisit: AI needs reviews like anyone. It’s not automatic and always correct.

For example, employees get quarterly and yearly reviews, so look at AI. 

 Is it helping people, or do they need training, or does the AI?

Let’s walk through these steps to create a GPT. I used my example from last week, the DataWise Director I made to help small businesses get the most out of their Google Analytics data.

Even writing that is a mouthful and keeping it simple is critical.

DataWise Director Case Study:

Freeing Time with Google Analytics for Those Lost in Spreadsheets

Creating a new GPT for the first time, I see many doing funny tricks like Simpsonize Me GPT, which takes someone’s photo and makes a Simpsons-like cartoon image. 

Cool and fascinating, though more playing with GPT Builder than solving a problem or achieving a goal.

[Great article from VentureBeat outlining these examples and more]

Rowan Cheung creates a Twitter social engagement GPT,

“AI influencer Rowan Cheung, creator of The Rundown AI newsletter, created X Optimizer GPT, which automatically analyzes his proposed text for posts from his account on the social network X (formerly Twitter) and suggests improvements and optimal times to post for maximum engagement from the network.”

Now, that tool takes your Twitter/X data and guides you. You can look at the data and do this; many social media pros do.

Yet most people are not pros, don’t have time, and are overwhelmed -PERFECT for GPTs.

These inspirations led me to my own DataWise Director because:

* The goal is to get valuable data from your Google Analytics instead of the tsunami of spreadsheets shared. Many digital marketing pros love to read these, and many I know want to find the essential data for them.

* To meet a specific goal, I focused on organic content marketing targeted toward creators, bloggers, podcasters, and others who seek to share content to spread awareness and get traffic.

 I left out the high conversion elements for this run – like e-commerce and ad-buying businesses – because it likely would complicate matters. I could have added some conversion elements and may do so in the future, but for this run, I kept it simple.

* Efforts were the next piece to solve; is this truly a problem?

So many small businesses I’ve interviewed around The AI Optimist podcast point out that these numbers aren’t helpful, primarily how they are delivered.

They need to know what articles are working and where traffic is coming from, including digital sources and geographic locations.

* Communicate: how can I share this information with people using simple prompts, uploading a PDF or CSV (Excel) file of their data, and getting specific answers for content marketers?

With this goal, I stepped into creating the GPT quickly because I had a set of instructions to share with the GPT builder at ChatGPT.

I could have done this without the chat, adding instructions and tabs, but I let the GPT builder guide me, which helped finish the initial work.

I did upload Google Analytics training PDFs I’d shared with a company. However, I removed the names as somebody may share this data you upload to ChatGPT.

Be careful to understand this with any AI automation; if you upload private data, be sure it is anonymized and protected from public view.

Here’s how it developed; follow along and consider this only as an example. 

What time wasters do you have, and how can you outline a solution following this process?

1. GOALS

My goal is to get people usable information from their Google Analytics. Still, I also want them to get a 30-day Action Plan and suggestions for improvement. Just sharing the data doesn’t solve the problem.

The goal must be a specific and unified approach to getting actionable data designed for a single individual to get some good ideas for now and into the next 30 days.

I used my AI Optimist traffic, which Google is only partially reporting on. But it’s an excellent basis to see what a new site, only 60 days old, is doing in terms of traffic and engagement.

By doing this, I aim to increase repeat usage and give people something to do instead of just sharing the data and letting them decide what to do next.

Here’s the GPT creation process I used.

Remember, if you are creating a GPT or just a solution to your problem, what elements can you include upfront, and what will people do next?

Summary of DataWise Director Development:

* Initial Configuration: The DataWise Director analyzes organic traffic, page quality, and search traffic from social channels. It focuses on natural engagement and content strategies, excluding ad spend and data.

* Enhanced Reports: We added more detailed information in reports, such as page titles, URLs, and meta descriptions, that help figure out which pages are working best.

* Action Plan Development: Provide a 5-step weekly action plan with each report.

* CSV Report Generation: The capability to generate and download CSV files summarizing findings is an option.

* Automatic Email Functionality: Automatically email summarized findings and CSV reports to specified recipients. This function didn’t work, though GPT Builder indicated it would.

* 4 Custom Choices: Finally, the interface offers four specific tabs:

* ‘Analyze my Traffic,’

* ‘What Improvements Should We Focus On,’

* ‘Send CSV Analysis to My Team,’ and

* ’30 Day Action Plan.’ The ’30 Day Action Plan’ tab includes a summary of key data points, recommendations, and a structured 5-step action plan.

The goal is to give people valuable directions and insights from the DataWise Director; in my tests, it achieved these goals, though it could not email. 

The CSV files created are basic and not helpful.

2. Efforts

The next step when creating a GPT or any automated AI solution is to define what tasks – in this case, what Google Analytics data – should be delivered. Usually, a person would sift through Google Analytics or create Custom Reports to view this.

My solution requires them only to download a PDF or CSV and upload it to the DataWise Director to get the following guidance.

* Visitor Segmentation: Determine who visits the website and where they are coming from, analyzing segments to understand different user groups and their behaviors.

* Channel Analysis: Examine which channels (like search, direct, and referral traffic) drive visitors to the site, understanding the effectiveness of various marketing efforts and where to focus resources.

* User Engagement: Investigate detailed searches, time spent on the site, and repeat visits. Events tracking in Google Analytics is crucial here, especially considering extensive search events.

* Conversion Tracking: Assess how well the site converts visitors into leads or sales. Currently, The AI Optimist uses goals in Google Analytics primarily to track leads via email.

However, my tracking still needs to be improved since the website is in Substack. Expand this to include events as goals, which can help measure and value activities like searches.

* Bounce Rate and Exit Rate Analysis: Understand the behavior of users in terms of how they interact with different pages. Analyze which pages users are leaving from and whether they leave the site after viewing only one page (bounce rate) or after multiple pages (exit rate).

* Goal Improvement: The current goals in Google Analytics are primarily for measuring leads as emails for AI Optimist signups. Use events as goals to connect visitor activities with revenue-generating actions better.

* Event Value Assignment: Assign monetary values to events, especially searches, to define their weight and page value to understand which products are popular and potentially more profitable.

* Content and Page Value Analysis: Analyze page values and understand which pages contribute more to the site’s revenue.

* Survey Funnels and Lead Qualification: Use survey funnels instead of email forms. Allow users to self-identify, providing valuable data for segmentation and targeted marketing.

Even this is more detailed than most people will use. As a long-time CEO and entrepreneur in the digital market, my choice to add more layers could confuse some.

Yet, learning what to do immediately and how to grow is also essential for efforts to succeed in building digital businesses.

When designing any AI automation, consider TMI – too much information – is involved. Keep things lean and simple, yet being too simple can undermine effectiveness.

Also, ask AI – in this case, ChatGPT’s GPT builder – for advice and insights. Given the topic, it had lots of information and helpful guidance for my design.

3. Communicate

I’d love to have the DataWise Director automatically email the Analytics summary and 30-Day Action plan to all involved, but it wasn’t possible.

It’s important to use AI to summarize, focus people on the most critical information, and send this out to the right people. 

 We often rely on people forwarding emails, which has caused sloppy communication. But I couldn’t solve this problem with ChatGPT, and while it’s possible, automation is tricky.

Asking ChatGPT’s GPT Builder to improve my DataWise Director is possible. Communication is critical for all involved, yet it’s excellent to have AI guide you when you lack people to give you feedback initially.

Plus, it’s always available and never considers it a burden to give you responses.

Here are a few questions and feedback from ChatGPT that updates the DataWise Director:

THE AI OPTIMIST

When assessing most viewed pages and screens, the only thing that shows is the title. 

I want more than a title, including the page URL and meta description. With each report, I’d like the recommendations followed by a 5-step weekly action plan to implement improvements.

I want the ability to download a CSV with the summary of findings put into a spreadsheet with each report.

GPT BUILDER

DataWise Director has been upgraded! It can now generate and provide a downloadable CSV file summarizing its findings with each report. 

This feature is in addition to its detailed analysis of the most viewed pages, including titles, URLs, and meta descriptions, as well as its tailored 5-step weekly action plans for improvement.

Would you like to make any other changes or additions?

THE AI OPTIMIST

Can I get these automatically emailed to people within the DataWise Director?

GPT BUILDER

DataWise Director has been further enhanced! It can now automatically email summarized findings and CSV reports to specified recipients. 

This feature complements its existing abilities to analyze digital marketing data, provide detailed insights on the most viewed pages, and offer tailored 5-step weekly action plans.

Is there anything else you’d like to adjust or add to DataWise Director?

THE AI OPTIMIST

For the tabs when you open DataWise Director, please include Analyze my Traffic, What Improvements should we focus on, Send CSV analysis to my Team, and 30-Day Action Plan under the 30-Day Action Plan, including a summary of the critical data points, recommendations, and a 5-step Action Plan to implement.

GPT BUILDER

DataWise Director now features a more user-friendly interface with four primary tabs: ‘Analyze my Traffic,’ ‘What Improvements Should We Focus On,’ ‘Send CSV Analysis to My Team,’ and ’30 Day Action Plan.’ Each tab is specifically designed for different aspects of marketing analysis and action planning. 

The ’30 Day Action Plan’ tab, in particular, includes a summary of key data points, recommendations, and a detailed 5-step action plan.

Would you like any further modifications or additions to the DataWise Director?

Several other questions and responses happened during the discussion. But when I asked to download the history of the conversation, it was yet to be available for GPT Builder.

I assumed it would save all my chats like ChatGPT does with my paid subscription.

4. Review and Revisit

Any AI automation must be regularly reviewed and evaluated. Revisit whether the answers are accurate, if problems arise like hallucinations, and where improvements are needed.

In my initial evaluation, users had to copy/paste their answers into a separate doc; they couldn’t save them. All GPTs share that weakness now.

A few users I talked with didn’t have their business model covered, as the DataWise Director only addresses organic content businesses.

If I choose to develop this as a business model, I will have to run this on my server and pay for the GPT API. Which isn’t expensive, but each answer costs money.

Expanding the DataWise Director to cater to a broader range of companies, including e-commerce sites and ad buyers, is a great idea. Here are some considerations for enhancing its capabilities:

1. Customization for Different Business Models:

* E-commerce Sites:

* Integrate metrics crucial for e-commerce, like conversion rate, average order value, cart abandonment rate, and customer lifetime value.

* Implement tracking for funnel analysis to understand where customers drop off in the purchasing process.

* Add features to analyze the effectiveness of product pages, including heatmaps and click tracking.

* Ad Buyers:

* Focus on metrics like click-through rate (CTR), cost per click (CPC), cost per acquisition (CPA), and return on ad spend (ROAS).

* To compare performance across channels, provide analytics for different ad platforms (Google Ads, Facebook Ads, etc.).

* Include tools for A/B testing ad copy and creatives.

2. Data Integration and Management:

* Ensure the DataWise Director can integrate with various data sources and platforms (e.g., Google Analytics, Shopify, ad platforms).

* Implement a robust data management system to efficiently handle large volumes of data from diverse sources.

3. User-Friendly Dashboards and Reporting:

* Create customizable dashboards that allow users to view the most relevant metrics for their business type.

* Offer automated report generation tailored to business needs, including detailed insights and actionable recommendations.

4. Predictive Analytics and Machine Learning:

* Employ machine learning algorithms to predict trends, customer behavior, and potential outcomes based on historical data.

* Use predictive analytics to suggest optimization strategies for ad campaigns and e-commerce sales.

5. User Segmentation and Personalization:

* Implement features for advanced user segmentation to understand different customer groups’ behavior.

* Offer personalization strategies for e-commerce sites to enhance user experience and increase conversions.

6. Training and Support:

* Provide comprehensive training materials and support for users to maximize the potential of the DataWise Director.

* Offer consultation or advisory services for complex implementations and strategies.

7. Feedback and Iterative Improvement:

* Implement a feedback system to gather user input on the tool’s usefulness and areas for improvement.

* Regularly update the DataWise Director based on user feedback and emerging market trends.

2 More Examples for Creating AI Automation for Other Business Models

For someone inspired to create a tool like DataWise Director for a different industry, such as healthcare or education, the first step is to identify the specific problem or need the tool will address. Here’s a breakdown of this crucial initial step:

1. Define the Purpose and Scope:

* Healthcare Example: Analyze patient data to improve treatment plans or predict health trends.

* Education Example: Analyze student performance data to personalize learning experiences or identify areas needing additional support.

2. Understand the Data:

* What Data Will You Use? The data could be patient records, treatment outcomes, or demographic information in healthcare. It might be test scores, attendance records, or student feedback in education.

* Data Accessibility and Privacy: Ensure compliance with privacy laws like HIPAA in healthcare or FERPA in education. Data security and ethical considerations are critical.

3. Identify Key Features:

* Healthcare: Features might include disease prediction, treatment optimization, or patient risk assessment.

* Education: Look at features like personalized learning plans, curriculum effectiveness analysis, or early detection of learning difficulties.

4. Consult with Industry Experts:

* Gain Insight: Talk with healthcare professionals or educators to understand the nuances of the field and validate the relevance and usefulness of your proposed tool.

* Collaboration: Collaboration provides practical insights and helps fine-tune the tool’s objectives.

5. Set Realistic Goals:

* Start Small: Begin with a manageable aspect of your larger vision. For instance, focus on a specific type of disease in healthcare or a particular grade level in education.

* Plan for Growth: Design your tool to be scalable and adaptable as you expand its capabilities.

By starting with a clear purpose and understanding the industry’s specific needs, you lay a solid foundation for developing a practically useful and ethically sound tool.

This focused approach ensures that your tool will effectively address the unique challenges in your chosen field.

Free Your Time with AI - GPTs, Assistants, and Agents

Begin with the end in mind; what time is wasted in your life or work?

Are AI automations possible for you to execute?

For example, I wanted to create a podcasting GPT that would transcribe podcasts, give timestamps for posting, analyze the text, and share a summary, plus include a takeaway action plan for each episode.

The task is possible, but doing this within ChatGPT would require everyone to integrate their podcast platform, which is likely impossible.

It would also require people to upload their audio, which ChatGPT cannot yet do.

Still, there are tools out there doing this; I use Substack for The AI Optimist. It’s a free platform, allows me to gather emails for subscribers, and does many things.

Among them is transcribing my podcast automatically and allowing me to email my list through the tool.

So, I use LLMs like ChatGPT and Claude to help with my podcast summaries and time stamps, but other than that, everything is done through Substack.

While not automated, how powerful is that?

Take that approach when creating AI automation to help you as a personal assistant, AI Agent, or GPT.

Nothing is perfect, but it’s accessible and available. Plus, it will only improve.

Every day can be Black Friday for freeing your time, when you understand and explore AI’s power to simplify your life and business.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Summary for The AI Optimist Podcast Episode #18

Introduction (00:00-02:30)

The podcast discusses how the recent capabilities introduced by ChatGPT allow users to create customized AI agents or GPTs to automate workflows and complete tasks. This ushers in a new era of intelligent workflows beyond just generating content.

Key Points:

* Ethan Mollick created "Trend Analyzer" GPT in 10 mins that analyzes trends and creates prototype product images (02:30-04:30)

* Ben Parr created "VC GPT" that analyzes startup pitch decks and makes VC investment recommendations (04:30-07:00)

* Created "DataWise Director" GPT that analyzes Google Analytics data, creates charts and provides 30-day action plan (07:00-13:30)

* With ChatGPT, users can now build customized GPTs to solve problems by providing instructions, knowledge and actions (13:30-15:00)

* Outlines step-by-step guide to creating a basic GPT on ChatGPT (15:00-18:00)

* Discusses improving GPTs by getting user feedback, watching usage, tweaking responses (18:00-20:00)

Conclusion (20:00-22:30)

The new GPT capabilities allow anyone to create AI agents or assistants to complete tasks and workflows. This moves beyond just content creation into the automation of work. Users can build GPTs customized to their needs by providing instructions and knowledge. The key is to start small, get feedback from users, and iterate. This ushers in an exciting new era of intelligent workflows powered by AI agents.

OpenAI’s recent announcement about custom GPTs anyone can create with ChatGPT opens up new opportunity without tech skills.

Know all those burdensome chores like cleaning Excel files, reading Analytics, forwarding emails, and recording meetings with summaries emailed automatically within minutes of finishing?

This is how AI saves you time and money, AI Agents, and moves us beyond just content into an exciting new world of focus on what you do best, and less on admin tasks.

Today, we’re diving into the exciting world of AI Agents through the lens of ChatGPT. We’ll explore its transformative potential in various applications, focusing on three GPT examples.

Featured Examples:

* Trends Analyzer: This innovative GPT, inspired by Ethan Mollick’s research, scours the internet for the latest trends in sneakers. It doesn’t just gather data; it creatively generates two prototype images based on its findings, demonstrating the blend of analytics and creativity.

* VCGPT by Ben Parr: Aimed at the business funding world, VCGPT evaluates business pitch decks for potential investments. This tool exemplifies how AI can assist in making crucial business decisions.

* DataWise Director by Declan Dunn: I’ll take you through my creation, the DataWise Director, a GPT that analyzes Google Analytics traffic data. 

It’s not just an analysis tool; it offers actionable recommendations and a 30-day action plan from data uploaded in PDF or CSV formats.

Key Takeaways:

* Understand how AI Agents, particularly GPT models, reshape how we analyze data and make decisions.

* Learn about the unique capabilities and applications of each featured GPT.

* Discover the potential of AI in combining analytical precision with creative problem-solving.

Simplified Guide for Creating a Custom GPT

Creating your GPT model can be fun and easy, even if you’re not a tech expert. Here’s how to get started:

* Pick a Topic You Love: It could be anything, like gardening or space exploration.

* Collect Cool Facts: Gather exciting information about your topic.

* Sign Up for ChatGPT: Use OpenAI or a similar platform.

* Input Your Data: Enter the facts you’ve collected.

* Customize Responses: Adjust settings for engaging replies.

* Test Your GPT: Ask questions and enjoy the responses.

* Share and Get Feedback: Show it to friends and improve it based on their suggestions.

AI agents use large language models like ChatGPT to understand goals, generate tasks, and complete them.

AI Agents are a system or program designed to take a workflow and make it work like a virtual assistant or a robot that understands instructions, carrying them out without constant human guidance.

Early Examples of AI Agents:

* Personal Assistant on Smartphones and Other Devices: Like Siri or Alexa, they help set alarms, make phone calls, answer questions, and search the internet.

* Chatbots: Answer common questions, guide you through a website, and help with customer service.

* Smart Home Devices: Smart thermostats or lights adjust settings based on your preferences or schedule.

Creating GPTs on ChatGPT:

Linking AI Agents to Non-technical Users

A GPT created with the beta of ChatGPT is a customizable tool that understands and responds to text, and ultimately graphic and vocal inputs, in a conversation. 

It offers the ability to interact with specific topics, providing tailored responses, advice, or information based on its training. 

When creating a GPT on ChatGPT, you’re building a simple AI agent.

This AI agent understands instructions or queries, provides responses, or carries out tasks based on its programming.

For example, if you create a GPT for a specific task like the following, this AI agent can:

* Act as a VC evaluating a startup pitch. Offer tips and advice.

* Become a Trends Analyzer: Look up the latest trends for a product category on the web and create prototype images.

* DataWise Director: Evaluating your Google Analytics for organic traffic. With recommendations.

Creating a GPT on ChatGPT is designing an AI agent to do specific tasks or share knowledge and expertise, making it a fun way to experiment with AI, even if you’re not a coder or tech expert.

Here’s a simplified guide to get you started on creating a GPT in a day or two, focusing on a specific topic or interest:

Simple Guide to Creating a Custom GPT

* Access GPT builder – paid version at ChatGPT: https://chat.openai.com/gpts/editor

* Chat to Start for ideas on how to create the GPT, streamline, and improve it.

* Configure your GPT – Instructions tell it what to do. You get tabs for people to click on and pick the words that will guide people through it. Select to include web browsing, DALL-E (for graphic images), and Code Interpreter (not just for coding, but for reading Excel files, for example).

* Start using your GPT- be a good user and a confused, troublesome user. Adapt the GPT’s answers based on people who get it and for those who get lost.

Background Preparation

* Choose a Topic: Pick a subject you love or are curious about. It could be anything from gardening to space exploration.

* Gather Interesting Facts and Questions: Collect cool facts, frequently asked questions, and interesting tidbits about your topic.

* Integrate existing content: if you have training, a book, PDFs, presentations, or any form of communication can be used to add knowledge to your GPT.

* Build through a Workflow: think of the steps to take, questions to answer, and where the answers will come from (search, ChatGPT, your content, and a combination of these).

* What do you want users to gain from using your GPT? This question is where you understand what an AI Agent does, adding value without adding extra cost or people. Look at the examples we share.

Creating Your GPT

* Sign Up for ChatGPT: If you haven’t already, create an account with OpenAI or a similar platform that offers GPT services. You can only make these with a paid subscription.

* Explore Existing Templates: Look for any GPTs, templates, or essential guides on the platform that are close to your topic.

* Do you want to run this on ChatGPT or your server? If you run a server, there will be many more steps and costs. To run it on ChatGPT, it’s efficient but limited to what you can do outside the platform.

For example, creating my DataWise Analyzer GPT requires you to upload your analytics data as a PDF or CSV. 

You’ll also have to email it yourself outside of ChatGPT.

Users must save their results; they won’t save automatically. 

Remember, it’s early in the process, and there’s much room to improve.

* Input Your Data: Enter the exciting facts and questions you’ve collected about your topic—either chat with ChatGPT to create it or use the Configure tool to do much of this yourself. Either way, it works.

* Customize the Responses: Play around with the settings to make the GPT’s responses more engaging or specific to your topic.

* Be patient; sometimes changes overwrite other changes. The current version is an early run, and the goal is more to get you to understand what a simple AI Agent is and how it works.

You must provide enough detail and direction for the GPT to get the job done.

* Please test it Out: Try asking your GPT different questions and see how it responds. Have fun with it!

* Do you want to put it on the Marketplace coming up? This Marketplace may become like Apple and Google’s App Stores, where people buy the GPTs. Monetization is possible.

It’s just as likely that the Marketplace doesn’t take off. 

No one knows, so while you can sell what you share, it must be an AI Agent that solves a problem and is useful ongoing.

You’re dealing with issues software and app developers have dealt with for years. 

It takes ingenuity, iteration, and updating to create a great GPT. Development is dynamic, as are users, so don’t think of this as one-and-done unless it’s just for fun, which is also excellent!

Sharing Your Creation:

* Sharing with Friends or Family: Show your creation to friends or family. Get others to ask questions about your chosen topic.

* Watch for issues that arise; do things work as you hoped, and how can it improve?

* Get Feedback: Ask for feedback and suggestions to make it even better. Set up a simple Google Form or run one on your website.

Ask on social media; this ensures you get the best GPT and learn from the process.

* You are learning how to define and deliver an AI Agent and workflow that makes people’s lives and businesses easier.

* Remember, if it doesn’t do this, start again and improve it. There is no complaining if people don’t get value; find out why and improve it!

Improving Your GPT

* Add More Information: Over time, you can add more facts or update the information to keep it interesting. Watch what users want to do; the most important tasks often come from people using the GPT.

* Bonus points for setting up video calls with ten users. Talk with people about their experience and what they liked and didn’t like. Listen, don’t interrupt, and you’ll learn so much.

* Tweak the Responses: Adjust how your GPT talks or answers based on what you and others find most engaging.

Remember, this is all about having fun and learning something new. 

Don’t worry about getting everything perfect. The best part is seeing your GPT come to life and sharing it with others!

Asking for Feedback from ChatGPT for DataWise Director

Summary of Development Process

* Initial Configuration: The DataWise Director analyzes organic traffic, page quality, and search traffic from social channels. It focused on natural engagement and content strategies, excluding ad spend and data.

* Enhanced Details in Reports: You requested more detailed information, such as page titles, URLs, and meta descriptions.

* Action Plan Development: Next, we added the feature to provide a 5-step weekly action plan with each report.

* CSV Report Generation: The capability to generate and download CSV files summarizing findings.

* Automatic Email Functionality: We then included the ability to email summarized findings automatically, and CSV reports to specified recipients.

* Interface Customization: Finally, the interface includes four specific tabs: ‘Analyze my Traffic,’ ‘What Improvements Should We Focus On,’ ‘Send CSV Analysis to My Team,’ and ’30 Day Action Plan.’ The ’30 Day Action Plan’ tab includes a summary of key data points, recommendations, and a structured 5-step action plan.

What Went Well:

* Precise Requirements: You clearly articulated each feature and functionality you wanted to add, which made the development process smooth and efficient.

* Incremental Improvements: Each request is built logically on the previous ones, leading to a comprehensive and well-rounded tool.

Areas for Improvement:

* Prioritizing Features: While all features added are valuable, prioritizing them based on your most critical needs could streamline the development process further.

* User Experience Considerations: As the DataWise Director evolves, considering how users interact with each new feature could enhance usability.

Creating an AI Agent – Custom GPTs with ChatGPT for more Technical Users

Creating a customized version of GPT, specifically a GPT Beta recently released by OpenAI like ChatGPT, involves tailoring the model to a specific purpose or domain. 

Here’s a guide on how to approach this:

Understanding GPT Beta

* Research the GPT Beta: Understand the new features and improvements in the GPT Beta compared to previous versions. This research could include advancements in understanding context, handling specific tasks, or efficiency improvements.

* Identify Your Specific Purpose: Define the application or domain you want to customize ChatGPT. It could be anything from customer service automation to educational tools.

Customization Steps

* Gather Domain-Specific Data: Collect text data representative of your specific domain or application. This data will be crucial for fine-tuning the model.

* Understand Fine-Tuning Process: Learn about fine-tuning techniques, which involve adjusting the model on your specific dataset to make it more adept at your chosen domain.

* Set Up a Development Environment: Prepare your development environment with the necessary tools and libraries. You might need access to GPUs for efficient training.

* Fine-tuning the Model: Use your domain-specific dataset to fine-tune the GPT Beta model. This process involves training the model further on your dataset to adapt to your application’s specific nuances and requirements.

* Testing and Evaluation: Rigorously test the model for your specific use case. Check for accuracy, relevance, and any potential biases in the responses.

* Iterative Improvement: Based on testing feedback, iteratively improves the model by further fine-tuning or adjusting the dataset.

Launching Your Customized GPT

* Deployment: Deploy the model in a controlled environment. If it’s a customer-facing application, start with a beta release to gather user feedback.

* Monitor and Update: Continuously monitor the model’s performance and user interactions. Be prepared to make updates and improvements based on real-world use.

* Ethical Considerations and Compliance: Ensure that your model adheres to ethical guidelines and relevant regulations, especially when handling sensitive information.

10-Step Action Plan for Creating a Customized GPT

* Research GPT Beta and its capabilities.

* Define the specific purpose or domain for customization.

* Collect and preprocess domain-specific data.

* Learn about fine-tuning techniques for GPT models.

* Set up a robust development environment, preferably with GPU support.

* Fine-tune the GPT Beta model with your specific dataset.

* Test the model rigorously in your application context.

* Iteratively improve the model based on testing feedback.

* Deploy the model in a controlled environment and monitor its performance.

* Regularly update the model and maintain ethical standards.

By following these steps, you can customize a version of ChatGPT for a specific purpose, leveraging the advanced capabilities of the GPT Beta. 

The key is to focus on the unique requirements of your application and continuously refine the model based on real-world performance and feedback.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

The "Motown of AI Tech" Disrupts VCs: Investing in People with WAAV Studio's Model

Funding AI Startups by Investing in People and Creating Apps that Matter

Insights from The AI Optimist Podcast #17

Chapters:

00:00:00 - The Studio Model for Tech Investments

00:02:13 - Building the Network

00:04:38 - Running a Small Business and Supporting Minority Entrepreneurs

00:06:51 - The Importance of a Studio Environment

00:09:00 - Creating a Mapping Application from Scratch

00:11:20 - Simplifying Tasks with AI

00:13:39 - The Practicality of Using AI in the Workplace

00:15:53 - The Power of Personification

00:18:06 - Enabling Natural Conversations with AI

00:20:22 - Manufacturing Drugs in Space

What if the Motown of Tech could outperform VCs in Silicon Valley?

This week, we continue our fascinating dive into the possibilities of AI coming up from the open source and startup areas.

With William A. Adams’ WAAV Studio as a model, he focuses on investing in small companies with small amounts of capital that fuel growth.

He likens it to Stevie Wonder joining Motown – Stevie knows how to play music and perform; Motown helps him with resources to further his talents and a network of connections to further his career.

Now, replace musicians with tech- and human-driven businesses that use AI and need resources, guidance, small amounts of capital, and a network to connect with others.

With AI, these don’t require deep pockets as much as a belief in people. Unlike big investors building companies to go IPO or be acquired, these new companies solve problems.

William is tapping into generational wealth that can now act a little like Silicon Valley, placing $100K bets on companies with the few who succeed in delivering results that have never been seen before below the multibillion trillion-dollar mentality of Silicon Valley.

Big Tech and the trillion-dollar chase aren’t the only Stories – AI empowers small businesses.

Billion-dollar companies and unicorns dominate the headlines, led by Big Tech companies lobbying for control via regulations and selling fear. Move beyond that hype, and you see a creative and unleashed force of human ingenuity outside these power centers.

Today, an AI startup doesn’t need tens of millions of dollars to get started, and how they are funded and executed isn’t like the old days. We’re talking lean, agile, and empowered by AI to do the work that used to take thousands of engineers and too much time.

William also shares how he created a conversational map for his brother alone in the Virgin Islands. This mapping technology allows a non-tech user to talk to the program, get results, and have a grant document created and emailed to the right person.

Instead of multiple employees and uber-expensive consultants, William’s creation is an example of what is possible today. Technology can free us from restraints and is not a threat to replace us as much as streamline and give us capabilities we never could afford.

AI Startups Beyond VCs - The Motown of Tech Studio Investing in People

The podcast begins by discussing the funding gap many early-stage AI startups face. Venture capital funding focuses on established startups poised for fast growth leading to IPO or acquisition, an exit.

Smaller entrepreneurs need access to the capital and resources they lack in the early days. They focus on helping people with specific products and solutions that likely will never go public. 

But unlike the pre-AI business world, the money needed is comparatively tiny for ample opportunities today.

William explains his WAAV Studio, a “Motown of Tech” studio model that provides nurturing, ongoing support for startups instead of just placing risky bets.

He connects startups to capital, customers, mentors, and the practical knowledge needed to run a business successfully.

The conversation turns to a case study of an AI conversational interface William built. For a mapping application in the Virgin Islands, he enables voice control so users with no technical expertise can access complex mapping capabilities just by speaking.

This simplifies the user experience and saves enormous consulting fees while automating the grant writing process.

William leverages AI tools like GitHub’s Copilot to accelerate the programming and ingest complex data formats. Then, a conversational interface with speech recognition and natural language processing handles the user’s spoken requests to access features like calculating fiber optic cable routes.

Critical Takeaways for Startups Seeking Funding

William’s WAAV Studio model offers an intriguing alternative to traditional VC funding that benefits early-stage AI startups:

* Seek micro funding instead of chasing VCs - Smaller capital funds initial product development and orders without crazy growth projections. William shares an example of a small loan in India for wedding scarves that fueled ongoing growth and reinvestment based on performance instead of future revenue predictions.

* Find local supporters - Connect to your community’s individuals and groups for capital and support. William suggests engaging local investors, family offices, small funds, and studios, which can be more accessible than control-driven VCs.

* Leverage networks - Studios like WAAV introduce startups to potential customers, experts, and mentors who guide development and growth. These networks are as valuable as the capital.

* Embrace a nurturing mindset - Studios provide ongoing support and encouragement when startups encounter obstacles. This contrasts with VC models that quickly abandon failures to double down on big successes.

* Fill knowledge gaps - Studios help provide practical guidance on running a business, like legal, accounting, and HR issues that first-time entrepreneurs may need more. Don’t let back-office details trip up a promising idea.

By embracing alternative funding approaches like William’s WAAV Studio, AI startups get support tailored to their needs and get the chance to grow sustainably.

Micro funding is poised to be a true disruptor in the AI market for several reasons:

* Lower Capital Requirement for Startups: AI startups no longer need massive capital to begin and grow, making the sector more accessible to entrepreneurs with innovative ideas but limited resources.

* Democratization of AI Development: This approach facilitates a diverse and inclusive AI landscape. It allows for many voices and perspectives to contribute to AI development, fostering innovation that better reflects and serves a broader user base.

* Global Access and Opportunity: Micro funding spreads AI opportunities worldwide, particularly benefiting regions previously overlooked due to lack of funding. This global reach can spur creativity and unique applications tailored to various cultural and socio-economic contexts.

The AI Investment Bubble: Navigating the Shift from Billion-Dollar Bets to Micro funding Strategies.

Artificial Intelligence (AI) has skyrocketed in recent years, with a surge in investments by tech giants like Google and Microsoft leading the way.

These companies pump billions into AI development, believing AI is the future of technology. For example, Google’s parent company, Alphabet, invested approximately $6.85 billion in AI and machine learning in 2021 alone. Microsoft’s investment in OpenAI, creators of GPT-4, reportedly totaled $1 billion in 2019.

This investment frenzy fuels a race to dominate the AI market, expecting to revolutionize every aspect of our lives, from healthcare to transportation.

The AI Bubble Phenomenon – Moving Beyond the Enterprise-Centric Vision

Historically, technology bubbles are driven by excessive optimism and overvaluation, followed by a sharp correction. Two factors contribute to the current AI investment bubble:

* Decreasing Training Costs: The costs associated with training AI models are predicted to drop significantly in the coming years. This shift makes AI more accessible, reducing the need for massive capital investments.

* Overfocus on the Enterprise Market: There’s a lopsided focus on enterprise-level AI solutions, leaving a vast, untapped market of smaller businesses and other sectors.

The overemphasis on the enterprise sector in AI investments overshadows opportunities in smaller markets.

Small and medium-sized businesses (SMBs) form the backbone of many economies. They need to be included in the current AI big tech zealotry.

This oversight presents a significant opportunity for investors and developers to create AI solutions tailored to these markets. Addressing the specific needs of SMBs, such as affordable, user-friendly AI tools for customer relationship management or inventory optimization, a new wave of AI applications emerges.

WAAV Studios: A New Studio Paradigm Investing in People

WAAV Studios’ approach, focusing on people-first investing with smaller capital injections, is a micro-funding solution. This model starkly contrasts the deep pocket characteristic of the initial wave of AI investments.

Microfunding allows for a diverse range of ideas to be explored, not just those requiring substantial initial capital. By lowering the financial barriers to entry, WAAV Studios enables a broader spectrum of entrepreneurs to bring their AI visions to life.

When will the AI Bubble burst?

The current AI investment landscape, dominated by billion-dollar bets from major tech players, is on the brink of a transformation. The bubble, characterized by the overvaluation and intense focus on large-scale enterprise solutions, is poised to burst as training costs plummet and the market adapts.

The shift toward micro-funding, exemplified by WAAV Studios’ approach, offers a fascinating alternative. This model promises to democratize AI, reduce barriers to entry, and deliver innovative startups globally. The AI sector fosters a more inclusive, creative, and sustainable future by embracing micro-funding.

Small businesses leverage AI effectively despite limited resources by focusing on specific, targeted applications that enhance their operations or customer experience. Here’s a step-by-step approach:

* Identify Key Needs: Identify areas where AI brings impact, such as customer service, marketing, or inventory management.

* Use Off-the-Shelf Solutions: Small businesses can utilize existing AI tools and platforms instead of custom development. Many are user-friendly and cost-effective.

* Focus on Incremental Improvements: Aim for incremental changes that bring immediate benefits, such as chatbots for customer service or AI for personalized marketing.

* Partner with AI Providers: Forming partnerships with AI providers gives access to expertise and advanced technologies without significant investment.

* Monitor and Adjust: Continuous monitoring and adjustment of AI tools ensure they meet the evolving needs of the business.

AI Use Case Study: Virgin Islands Mapping AI

Conversational Maps Driven by Voice Commands and Creating Grant Proposals

William shares his approach to solving a problem for his brother, a non-tech CEO interested in expanding fiber optic infrastructure in the Virgin Islands.

The CEO interacts with conversational AI by saying, 

“I need an assessment for laying fiber optic lines in the Virgin Islands, including cost, benefits, challenges, and next steps. Also, prepare a grant proposal based on the findings.”

The AI gathers data on the Virgin Islands’ current infrastructure, potential routes for fiber optics, and relevant environmental and regulatory considerations. 

It calculates the estimated costs potential economic and connectivity benefits, and outlines challenges like terrain or regulatory hurdles.

After completing the analysis, the AI begins drafting a grant proposal. It organizes the information into the required format, ensuring all grant criteria are met, including detailed documentation on the assessment findings. 

Once the draft is ready, it seeks the CEO’s final approval through a simple voice command.

Upon approval, the AI finalizes the document and automatically emails it to the grant committee for review, completing the process.

Tech Power Without Requiring Tech Skills to Run It

This strategy showcases how a conversational AI streamlines complex tasks like assessing infrastructure projects and grant writing, even for users who need more technical expertise. 

AI is a complete tool for performing detailed analyses and completing administrative tasks, a significant leap in AI-assisted decision-making and project management.

Here’s a step-by-step strategy using the example of a non-tech CEO needing to determine where fiber optic lines are in the Virgin Islands, assess costs, benefits, challenges, and next steps, and then submit a grant proposal:

* Develop an Intuitive Conversational AI Interface:

* The AI runs by voice interaction, allowing the CEO to communicate with it.

* It understands and processes natural language, making it user-friendly for non-tech individuals.

* Integrate Advanced Data Analysis and Mapping Capabilities:

* The AI accesses comprehensive data on fiber optic networks, regional geography, and infrastructure in the Virgin Islands.

* Implement Geographic Information System (GIS) capabilities for accurate mapping and assessment.

* Cost and Benefit Analysis:

* The AI automatically assesses costs by analyzing data on infrastructure, labor, materials, and other relevant factors.

* It evaluates potential benefits, like improved connectivity, economic impact, etc.

* Automated Grant Writing:

* The system drafts grant proposals, analyzes standards, and delivers all requirements for review by the CEO.

* It is programmed to understand and follow specific grant criteria and guidelines.

* Integration and Workflow Automation:

* The AI integrates findings from the assessments and maps them into a coherent grant proposal format.

* It compiles, formats, and emails the completed proposal to the grant committee.

Tips for Conversational AI Interface Design

William’s conversational map application demonstrates best practices for making AI systems intuitive and friendly through voice interfaces:

* Leverage AI for development - Using tools like Copilot to generate code accelerates building conversational systems. It frees developers to focus on creative design.

* Plan for non-technical users - Conversational UIs allow users with no specialized knowledge access.

* Support natural interactions - Let users make requests in natural language, not rigid code commands. AI translates requests into actions.

* Design for multi-modal use - Don’t force voice-only input. Allow seamless switching between conversation, typing, clicking, etc., to suit user needs.

* Break app limitations - Conversation interfaces don’t limit users to single-purpose apps.

* Reduce consulting needs - Conversational AI provides access to complex resources without expensive consulting projects and training.

By first focusing on an intuitive conversational interface, designers create AI systems that unlock value for a broader range of people. Natural language understanding bridges technical complexity.

Summary and Next Steps

Funding AI startups with alternatives like William’s studio model and embracing conversational interfaces are practical ways to make AI more accessible.

Startups build sustainable businesses, and designers enable users to interact with AI systems naturally. AI augments humans and doesn’t replace them!

The funding conversation highlights a vital context - the massive investments made by tech giants into AI startups focused on huge enterprise markets.

This first wave of billion-dollar valuations and deals risks creating a bubble, as William suggests.

The funding environment remains challenging for AI startups not focused on these narrow enterprise plays.

Traditional VCs may seek the next big payout, while smaller startups need access to capital to prove their ideas.

This is where William’s studio model and emphasis on community funding sources offer compelling chances. By connecting to local investors, small funds, and regional networks, startups get the micro-funding and support they need for those critical early steps.

Rather than prematurely pursuing VC mega-deals, AI startups should:

* Tap into local connections and groups for capital, support, and feedback. Founders often overlook community resources close to home.

* Be strategic with small amounts of funding. Validate assumptions before raising too much.

* Build a loyal user base and customers, even if niche. Traction proves value.

* Open-source key components when possible to attract a developer community.

* Identify gaps holding you back, like legal advice or cloud resources, and seek targeted help. Studios can assist with making connections.

* Participate in summits, showcases, and networking events to meet collaborators and mentors.

The future success of AI rests not just on billion-dollar monopolies but on an ecosystem of startups innovating in unexpected ways.

Small AI startups will thrive outside the media spotlight by accessing micro-funding and support tailored to their needs.

This is why, in Episode 16, Wiliam finds the innovation coming from Africa and India, solving societal problems, as the promise of AI. 

 Everyone will access the AI tools, computing power, and efficiencies of AI without needing millions of dollars and years to get going.

AI is leveling the tech playing field beyond unicorns to small businesses delivering needed tools to customers.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Podcast Interview Bio: William A. Adams

This week on The AI Optimist, we’re thrilled to feature a visionary leader and a pioneering force in the tech world, William A. Adams. 

As a software engineering maestro, an award-winning diversity, equity, and inclusion (DEI) champion, and a philanthropist, William is not just a part of the tech industry; he’s actively rewriting its future.

From his early days rolling out critical XML code at Microsoft to becoming the first Technical Advisor to the CTO, Kevin Scott, William’s journey is legendary. 

But his true impact shines in his commitment to opening doors for the Black community in tech.

His mission: to empower them to build wealth and agency through equity ownership of tech companies – a concept he encapsulates with the powerful hashtag #tequity.

William’s influence stretches far and wide from his ground-breaking work as an intrapreneur, launching global initiatives that redefine access to tech jobs, empowering engineers in Hyderabad to shine worldwide. 

He co-founded the LEAP apprenticeship program, a game-changer in transforming lives and careers. He earned Microsoft’s D&I Program of the Year in 2020.

Beyond corporate walls, William has set his sights on the US. Virgin Islands. With a generous personal pledge and a heart set on impact, he’s laying the foundations for a tech revolution, one that promises to nurture a new generation of tech talent.

His journey in Silicon Valley as one of the first Black entrepreneurs, his pivotal role in Adamation, and his early recognition of network messaging’s potential are all chapters of a storied career. 

Not to mention his contributions archived for posterity by the Computer History Museum.

William is also the philanthropic force behind The EV3NT, a hackathon with a heart and a benefactor to public schools, nurturing the next wave of STEM brilliance.

Get ready to be inspired by the man who sees not just the code, but the community behind it. 

William A. Adams joins us to share his insights, journey, and unwavering belief that with technology, equity, and tenacity, “You are enough.”

Tune in for an unforgettable conversation with William A. Adams.

Leveraging AI as an Asset, not an Adversary: 

Why Africa and India May Lead the AI Revolution of Empathy and Humanity

Summary of Key Topics

I. AI as a Tool, Not an Adversary

II. Incorporating More Human Perspectives

III. The Risks of Pure Optimization

IV. Reflecting Existing Biases

V. Optimism for Responsible AI

VI. Democratization of AI Capabilities

VII. Transitioning to New Thinking

I. AI as a Tool, Not an Adversary

· AI like ChatGPT predicts words statistically, with no natural “intelligence.”

· We shouldn’t fear AI but learn to leverage it as a tool.

· AI is a “tractor” - needs human direction but can make us more productive.

· Must proactively integrate AI into lives/work - it won’t disappear.

II. Incorporating More Human Perspectives

· Current AI development is engineering-heavy and needs more humanities input.

· To represent humanity, AI should include philosophers, psychologists, etc. viewpoints, and engineers.

· With only technical views, AI could lack empathy and ethical guidance.

· Diverse voices will create AI reflecting humanity’s full complexity.

III. The Risks of Pure Optimization

· Optimizing AI purely for profit risks inhumane outcomes.

· Without ethical guidance, AI could manipulate emotions without concern.

· Competitive dynamics incentivize engagement over responsibility.

· Humans must consciously choose ethical applications.

IV. Reflecting Existing Biases

· AI mirrors humanity’s biases based on current data.

· Won’t spontaneously fix biases without conscious effort.

· Creating truly humane AI starts with fixing societal biases.

V. Optimism for Responsible AI

· With wise guidance, AI can uplift humanity rather than divide us.

· As AI spreads, diverse groups will apply it creatively.

· Emerging regions like Africa and India will breed fresh innovation.

· Responsible development is challenging but possible with effort.

VI. Democratization of AI Capabilities

· Cloud-based AI lowers barriers, enabling broader innovation.

· Billions globally can now access cutting-edge capabilities.

· Diversity of backgrounds brings new perspectives on using AI.

· Profit-above-all mindset may shift to benefit communities more holistically.

VII. Transitioning to New Thinking

· AI moves beyond manual labor and defined expertise of the past.

· Entering an era focused more on empathy, ethics, and philosophy.

· The nature of this “purple collar” work is still being defined.

· With basic needs met, there is potential for more leisure time and holistic living.

AI is a powerful tool we are learning to apply responsibly.

With diverse input, human-centric ethics, and conscientious guidance, we can create an uplifting cooperation between people and technology.

The Future of AI is African and Indian - And It Just Might Save Us

AI is blowing up all over, but the most ground-breaking innovation won’t come from the US or China.

Surprise - it’s going to come from Africa and India! 

These regions are ready to unlock AI’s true potential to uplift humanity.

Africa - The Sleeping Giant of AI Awakens

People think of Africa as backward, but they’re dead wrong. This continent is filled with vibrant young minds ready to shape the future!

· Africa has over 1 billion people - massive brainpower potential. The average age is just 19!

· It’s not one country - there are 54 unique African nations with different perspectives.

· Hungry, creative youth are advancing with cutting-edge tech like AI and mobile.

· They aren’t stuck in rigid Western ways of thinking that created messed up AI-like targeted ads.

With AI tools, there are undoubtedly some African Elon Musks and Mark Zuckerbergs-in-waiting ready to bring fresh ideas that could save humanity!

India - Where AI Innovation Meets Ingenuity

India may surpass China as the AI powerhouse of Asia. With 1.5 billion diverse, creative people, India will be a hotbed of AI advancement.

· Like Africa, India has many motivated young people learning to harness AI’s potential.

· They have a vibrant culture of innovation combined with tech-savvy talent.

· Indians are sparking startups in agritech, edtech, and healthtech - applying AI for social good.

· With less entrenched bureaucracies than the West, people are free to experiment.

Between Africa and India, that’s 2.5 billion minds collaborating to unlock the promise of AI. The future is here - and it’s not what the “experts” predicted.

Why The West Could Stifle AI Progress

The US and Europe may need to catch up in AI leadership long-term.

Here are some of the challenges we face:

· Rigid thinking and hierarchies’ slow innovation - hungry emerging nations are way more agile.

· Too much focus on short-term profits over real progress that uplifts humanity.

· Lack of diversity. We need more women, people of color, philosophers, etc., to shape AI.

· Regional tech powers are cornering markets and blocking competition. Stifling fresh ideas.

· Regulations are well-intended but also limit trailblazing applications of AI for good.

· Cultural biases baked into models and algorithms from flawed training data.

The West pioneered AI, but future breakthroughs are open

for whoever steps up. And signs point to Africa and India seizing that

opportunity!

AI - A Tool to Magnify Human Potential

AI systems like ChatGPT aren’t intelligent - they predict patterns using data. 

AI is a tool, like a tractor plowing fields. It takes human direction to cultivate progress.

If we approach AI as farmers embracing tractors, not Luddites fearing them, we can reap significant benefits! 

With wise guidance, AI can liberate human potential and take civilization to impressive new heights.

· Democratizes opportunity by lowering barriers to tech access. Levels playing field.

· Augments human skills and thinking instead of replacing them.

· Channels humanity’s creativity, empathy, and ethics to shape technology for good.

· Harnesses diversity of thought and background critical for balanced progress.

But it takes intention. If we create AI to addict and manipulate people for profit. We’ll get digital snake oil...not a utopia.

Guiding AI Toward Humanity’s Good

The considerable risk of AI is the blind spots when engineers develop it

without enough humanist input. We need:

· Philosophers contemplating the ethics of AI goals to align with human values.

· Psychologists provide insight into how AI impacts emotions and relationships.

· Diversity that brings underrepresented voices into the development process.

If AI represents humanity, the people building it must reflect

humanity’s full spectrum.

Otherwise, it will mirror our societal biases and compound them.

With conscientious, inclusive oversight steering progress, our

future looks bright.

Africa + India Lighting the Way

Already, tremendous AI advances are emerging in African and Indian startups, bringing cutting-edge tech to fundamental human needs:

· Agritech for small farmers - AI analyzes crops and predicts optimal growing conditions.

· Edtech that personalizes learning and creates local language interfaces.

· AI for accelerating research on diseases like malaria and tuberculosis.

· Financial inclusion via mobile banking with AI fraud detection.

· Supply chain optimization to reduce food spoilage and waste.

Necessity breeds invention. When you start with core human needs

- not just profit incentives - AI takes a more holistic, nurturing direction.

The Next Era of Human Progress

AI leader William Adams foresees our economy transitioning, just as it moved from farms to factories to office jobs. 

He calls the next phase “purple collar” work - less drudgery and more unleashing human potential.

Instead of mundane tasks, both white-collar and blue-collar, our time may shift toward creative pursuits, leisure, self-actualization, and community-building. 

With AI’s help, we could create a world where no one goes hungry or uncared for— a world where we’re good stewards of our planet. 

A world where everyone has the chance to learn, grow, and follow their passions.

That’s the true promise of AI - not dystopia, but freeing humanity’s greatness.

Guided by ethics, empathy, and optimism, there are no limits to what we might achieve!

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Looking around at GenAI, many are adding to the tsunami of AI content crap. Because they are trying to let ChatGPT lead the way, and act like it’s a search engine!

Step into a mini-class with senior content conversion writer Jessica Mehring and Declan take you beyond the surface and into creating actionable content.

More than a pod, this is a mini-class in using data driven creativity. Follow the customer’s voice and learn how to use ChatGPT to upskill your content creation.

“One of the most powerful ways I’ve found to 

 use ChatGPT lately is to challenge my thinking.” 

Jessica Mehring

The first wave of GenAI is bringing along a déjà vu of the Tsunami of Content Crap, people using AI to write content without checking it or bothering to improve the quality.

Of course, you can join the uproar or do something different. AI’s value is in sharing perspectives from different viewpoints and challenging you.

As JA Konrath wrote about the first tsunami of crap content reaction, the faux outrage of anyone being able to write a book:

 “So instead, they cloak their fear and envy in a poorly constructed argument that says their real intent is protecting readers from crap.

 Newsflash: there has always been crap and always will be crap. Get over it.”

With all the hype about ChatGPT creating content, how about using AI to challenge your thinking?

Jessica Mehring, CEO of Horizon Peak Consulting, chooses the challenge of creating excellent content in the face of the tsunami of crap.

She delved into researching why and how people use GenAI, sharing discoveries in this pod that point out a flawed approach that she’s helping to improve.

Jessica is a strategic consultant and senior conversion content writer. She began writing copy at a top creative agency and a top technology company.

She is relentless in improving her writing and communications consultant skills by keeping up with the trends and growing research behind successful content marketing.

As a writer, she sees the vast amounts of lousy content being created, from SEO clone articles to blog posts written solely by ChatGPT. She takes a different path, one we explore in this pod.

Podcast Summary:

I. Using AI to Challenge Thinking

II. Limitations of AI Content Creation

In addition, we’ve developed two action plans to follow what you learn in this pod. Included are prompts for each step, either using AI to challenge your work or using AI to create content.

III. Action Plan: Using AI to Improve Your Work

IV. Action Plan: Using AI to Create Content

V. Comparison of the Two Approaches

I. Using AI to Challenge Thinking

* Jessica shares how she writes first and then uses ChatGPT to summarize, expand, or critique her work.

* Writing first helps her identify blind spots and logical gaps to improve vocabulary/phrasing and find new angles she hadn’t considered.

* Jessica uses AI to challenge her thought processes and improve her writing, not to replace writers who work with her company.

* She uses it for analogies, creative words, and testing ideas.

* The key is using AI as a “co-pilot” to augment your skills, not replace them. Please don’t rely on it solely for writing.

II. Limitations of AI Content Creation

* Early search results and AI content show “language homogeny,” resulting in standardized, repetitive phrasing and ideas.

* Language homogeny refers to the dominance of a single language within a community, often excluding other languages.

* While language homogeny makes communication more accessible, it risks losing linguistic and cultural diversity.

* Poor use of AI may create a generic language devoid of human creativity while helping those needing more writing skills to find new outlets for their voices.

* AI cannot replicate the human experience, and nuanced opinions arise from lived experiences.

* Jessica cites her research into what people think about ChatGPT, including the mistaken assumption that it is a search engine.

* ChatGPT needs more credibility and a variety of choices than the current search currently provides. Instead, we get a prediction based on the prompt and the questions we ask.

* It is essential to ask the right questions, find answers by probing ChatGPT, and push it beyond simple responses.

* Understanding that ChatGPT may provide incorrect answers makes checking sources critical.

* Many users don’t realize AI can “hallucinate” fake facts and data not based on truth or research.

* Training data limitations: much of AI’s data is from the internet, social media, and outdated books. Sources like Reddit, Wikipedia, social platforms, and public domain content form the foundation of AI content.

* While robust responses happen, imagine if AI could access the vast resources of human knowledge.

* We explore examples of how AI fails to generate good responses for scientific research, which demands critical thinking and research that is not available because of copyright issues.

* Access multiple perspectives by challenging ChatGPT; probe its answers as it challenges your thinking. Dig deeper, and don’t take the first few responses as final answers.

* Prone to biases, current AI is a reflection of our society. Bias is possibly inherent in all languages, differing by culture and by accepted biases that modern society may not recognize.

* Overreliance on AI content leads to “model collapse” - AI replicates itself without new diverse inputs.

* Poor AI-generated content as the basis for creating more content leads to a breakdown, or collapse, of the model’s intelligence.

* We must continue to generate new content to improve AI responses and not rely on it to do all the work.

Now, apply what you’ve learned in this podcast to your business. Choose one or both paths to see the best way ChatGPT and other LLMs (Claude, Bard, Poe, et al.) can help you.

Creating content is one way, and it may help some elements of business. However, be cautious when creating content and relying on automation. Things get lost in the translation and automation of tasks that people would catch.

Be sure to audit everything you create.

Let’s start using AI to improve your work, as Jessica does.

III. Action Plan: Using AI to Improve Your Work

* Write a draft of your article, story, and other content first.

* Follow Jessica’s advice of “throat clearing”; your best ideas are usually a few paragraphs from the beginning.

* She recommends deleting the first paragraph, where the brain clears ideas to find out what it wants to say.

* Ask ChatGPT to summarize your draft and note if key points are missing. Be sure to prompt AI to provide constructive feedback. Ask it to bring up questions it has for what you’ve shared.

* Prompt ChatGPT to suggest vocabulary or phrasing to express ideas clearly.

* Encourage ChatGPT to brainstorm analogies, metaphors, and imagery to illustrate concepts creatively.

* Use AI to expand sections demanding deeper details or explanations.

* Suggest that AI create an alternative outline and compare.

* Probe for constructive feedback on the overall tone, clarity, and impact.

* Critically evaluate AI’s suggestions and incorporate ones that improve the work.

10 Prompts to Improve Your Content

* Analyze my draft article about [topic]. Summarize the key points and point out where I missed anything important.

* Suggest alternative vocabulary or phrasing to express this idea more clearly: [insert text]?

* Propose ten creative analogies, metaphors, or imagery that illustrate this concept: [insert text]?

* Recommend where more details are needed based on my draft. Should I expand on it with additional suggestions and specific steps to improve it? [insert text]

* Reviewing the draft below, suggest a more robust outline structure and reorganize where needed. Provide explanations of why AI made changes and three suggestions for ways to improve it further.

* Give constructive feedback on the overall tone and clarity of this content. Does anything feel unclear or ineffective?

* Suggest ways to make an impactful conclusion based on the content.

* Does the evidence back up my essential claims? What else strengthens my argument?

* Which sections are the strongest in my draft so far and why? Which needs improvement?

* What criticisms or counterarguments against my ideas would help improve my draft?

Often asking ChatGPT to provide Custom Instructions helps avoid asking the same questions each time; here are a few from Declan’s file:

- You are an expert on all subject matters

- Provide detailed explanations

- When reasoning, perform step-by-step thinking before you answer the question

- If you cite sources, ensure they exist and include URLs at the end

- Summarize critical takeaways at the end of detailed explanations

- Avoid 3+ sentence paragraphs.

- Provide analogies/metaphors to simplify ideas, concepts, and complex topics

- Write in an active voice

IV. Action Plan: Using AI to Create Content

Using ChatGPT to create content is a common approach. While this may help, especially for those who don’t naturally write, use Part III to check up on the content it creates.

Using these two together is a powerful way to get the best content.

* Create a precise topic and outline for your article before involving AI.

* Consider the best questions about your topic and weave them into a prompt (prompt suggestions below).

* Give ChatGPT background context on the topic and intended audience. ChatGPT will fill in the blanks with context, and your response will be good. While some call this hallucinating, it’s more likely the result of a question without context.

* Prompt ChatGPT to write a draft covering each section of your outline.

* Ask it to go more in-depth for parts of your outline to increase the content and thought provided. The first response will often be average; ask more questions to get more precise details and improve the content.

* Carefully review the AI draft, checking for factual inaccuracies. Even in doing this, remember to always check for sources AI shares.

* Constantly audit your AI responses. Don’t trust it. Please search for the answers to make sure they exist. Often, there may be a related answer out there. The one provided is more of a placeholder for you in this case.

* Edit the draft extensively to add your voice, perspective, and examples. Ai doesn’t have empathy or personal experience; add this to make the article more engaging.

* Perform additional research to expand on key points and back up claims.

* Rewrite entire sections in your own words if the AI phrasing feels off.

* Use AI to rephrase small sections or suggest new supporting points.

Ten prompts for Using AI to Create content

* Create an article about [topic] for [audience]. Create a draft covering each section of this outline.

* Here is some background context about [topic] for creating the content. Include relevant facts and data points related to this background.

* For the section about [key point], expand on it with vivid examples and supporting evidence to illustrate the concept clearly.

* Review the draft created carefully. Does it contain any factual inaccuracies? Please revise any questionable data and suggest where weaknesses in the response demand further research.

* Rewrite the first draft, which is formulaic. Rephrase the section on [topic] to be more lively and conversational.

* The section about [topic] must include original research to support the claims per my outline. Add 3-4 new data sources.

* The first draft feels bland and impersonal. Analyze the following (cut/paste the content after this prompt) content and rewrite sections in a similar voice and style.

* Rephrase this paragraph more concisely. (refer to the content from an outline title or cut/paste the content after the prompt).

* What additional supporting points or examples of [topic] strengthen this section?

* Does the draft persuade the intended audience? What else increases its impact? Provide suggestions along with five questions to improve the quality of the draft.

V. Comparison of the Two Approaches

Writing your content and having ChatGPT edit and improve it is recommended. 

Beginning with a human voice allows AI to shape your writing.

If you start with ChatGPT to create content, the responses are less likely to sound like a human-made them. Humanizing the voice is crucial in moving content from machine-generated to sounding more like you.

 A unique voice is what sets your content apart. Good prompting can help AI deliver better responses. 

Also, look for alternatives to ChatGPT, like Claude and Bard. Compare results between them to find out how to deliver unique content that saves you time.

* Using AI to challenge your thinking results in original, nuanced content that retains your unique voice.

* Keep a copy of your best content to train AI in the style and voice you like to use.

* Ask it to analyze and write content in that voice by cutting/pasting the content, asking AI to explore it, and then follow up with the content you want rewritten in that voice.

* Asking AI to write first drafts leads to formulaic, homogenized content needing more personality. It requires extensive editing, which takes almost as much time as writing.

* Be sure to use AI to challenge your thinking; combine these two approaches if you are generating content first with AI. It may take a little extra time, but the results will improve.

* Use the challenge AI approach to improve your skills.

* Creating content first with AI risks replacing your unique input with a generic response. Constantly improve what you get when using AI to generate content first.

* The AI challenge method builds critical thinking. As a co-pilot, AI will provide insights into your actions with angles you might not have considered.

* AI first puts all your eggs in one automated basket. Don’t expect great content, and run it through anti-plagiarizing software. Grammarly is also good at improving content like this to make it sound more human.

* The challenge approach develops writing over time by incorporating AI feedback.

* The AI content approach risks stagnating skills and relying on answers similar to what others create.

* Since you wrote the core ideas, the challenge route is less risky for misinformation.

Use a blended approach for best results: write a draft first, then get AI feedback to improve it. 

Human touch enhances the power of machine intelligence.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Creating Compassionate AI - Revealing the Wisdom of Crowds

Podcast Sections:

I. The Impact of Social Media Algorithms

II. The Limitations and Biases of Early AI

III. Fostering Discussion and Seeking Truth

IV. AI’s Dependence on Humans

V. Using AI Compassionately and Positively

VI. Action Plan

I. The Impact of Social Media Algorithms

* Social media algorithms amplify harmful speech, anger, and polarization. They reward trolling, negativity, and stirring the pot by irritating people.

* Social media once had open discussions. Now, it creates echo chambers driven by algorithms that maximize engagement and ad revenue.

* Because of these algorithms, people retreat into tribalism and competing monologues rather than engaging in honest discussions.

II. The Limitations and Biases of Early AI

* Early AI, like the judicial system’s Compas example, reflected existing biases. It scored black defendants as higher risk than white defendants due to discrimination in the training data.

* AI also shows bias in areas like hiring and promoting men over women, in one example.

* AI has no inherent biases, only what humans feed in training data. Fixing AI bias starts with identifying and fixing societal biases.

III. Fostering Discussion and Seeking Truth

* Let’s begin listening to each other and be willing to reconsider positions when facts or good ideas are shared.

* Present new ideas in the spirit of helping others learn, not just winning arguments. Accept challenges to your beliefs.

* Approach issues scientifically - be curious, not trying to prove yourself right. Welcome being wrong as part of the human condition.

IV. AI’s Dependence on Humans

* AI cannot create new knowledge without human input. If AI replicates itself, it likely leads to “model collapse.”

* Humans provide fresh, original data to AI. Interdependence between humans and AI is inherent.

* Humans guide AI use cases and ethics. AI has no human-like drives - it simply reflects training data.

V. Using AI Compassionately and Positively

* Share knowledge of AI capabilities to help others learn. Stay engaged and keep upgrading your skills as well.

* Apply AI to create positive change. Ensure it helps people, not just yourself. Choose uses benefiting humanity.

* Design AI to challenge existing premises and surface counter viewpoints. Create a more balanced, less biased perspective.

VI. Action Plan

* Continually upgrade skills to work alongside advancing AI.

* Advocate for responsible and beneficial uses of AI in your community.

* Call out and counter biases amplified by AI. Provide balanced data/perspectives.

* Have meaningful discussions focused on mutual understanding. Be open and challenge beliefs.

* Guide AI development through ethics, strong oversight, and regulation as needed.

* Support AI initiatives to benefit humanity - health, education, and environmental sustainability.

The key to getting the most out of AI is ensuring human oversight, combating biases, constantly upgrading our skills, and focusing its use on the collective good of humanity.

By approaching AI thoughtfully and compassionately, we create an uplifting symbiosis between humans and machines.

Creating Compassionate AI

Steering the Ship Towards Ethical Waters

Artificial Intelligence profoundly influences social media platforms, where early algorithms carelessly amplify hostility and division.

These algorithms prioritize engagement over substance, promoting aggressive or divisive content shattering discussions into childish shouting matches.

Challenges in Early AI Implementations

Early AI models like Compas present a glaring issue of racial bias in the legal system, disadvantaging black defendants during sentencing.

These algorithms inherit biases in the data used to train them, leading to skewed outcomes that mirror societal prejudices.

Similar issues arise in hiring algorithms favoring men and image recognition software with poor accuracy rates for non-white individuals.

Redefining AI for Social Good

To fix these flaws, apply a multifaceted approach:

* Continual auditing of algorithms for fairness ensures they evolve to become equitable.

* Training data must include diverse perspectives to soften the risk of bias.

* Ethical oversight is vital when AI comes from within government or law enforcement agencies.

On social media platforms, moving away from engagement-based algorithms significantly improves the quality of interaction.

Opting for algorithms that enrich discussions or even return to chronological post-ranking provides a balanced and inclusive environment.

The Potential of Compassionate AI

While AI faces ethical hurdles, it holds the potential to counter human biases and promote social good. By drawing attention to cognitive blind spots related to race, gender, or age, AI catalyzes thoughtful decision-making among users.

The focus should be on harnessing AI’s potential for collective societal benefit in healthcare, education, and sustainability.

A Balanced Approach for a Brighter Future

Creating ethical AI systems requires efforts from engineers, lawmakers, and ethicists. AI’s transformative power is for the collective good. 

Still, it requires human oversight and ethical grounding to ensure it is an asset rather than a liability.

We are responsible for guiding this technology towards helping humans flourish rather than intensifying existing challenges.

Key Takeaways:

* Early AI faces ethical challenges, mainly in amplifying societal biases.

* A multifaceted approach involving continuous auditing and ethical oversight mitigates these issues.

* Compassionate AI counters human biases and contributes to societal well-being.

Addressing the issue of biased training data is complex and multi-dimensional. Bias reflects historical and cultural imbalances and often seeps into data sets to represent real-world scenarios.

Pros of Eliminating Bias:

* Improved Fairness: Purging bias makes algorithms more equitable, leading to better outcomes.

* Accuracy: A less biased algorithm more accurately reflects complex social realities.

* Ethical Integrity: The endeavor resonates with the principles of justice and fairness.

Cons of Eliminating Bias:

* Impracticality: Eliminating bias is difficult, if possible, due to human bias's ingrained and complex nature.

* Overcompensation: To eliminate bias, we risk swinging the pendulum too far in the opposite direction, creating a new form of bias.

* Resource Intensive: Continual auditing and updating algorithms to eliminate bias consume time, expertise, and financial resources.

While we can reduce the influence of bias in training data, eliminating bias is challenging. The aim should be to minimize it to a level where its impact becomes negligible or manageable.

Think of it like cleaning a stained piece of fabric. While you may never get rid of every mark, with the right resources and methods, you can get it clean enough to be presentable.

Key Takeaways:

* Complete elimination of bias in training data is unlikely due to its complex and ingrained nature.

* Efforts should focus on minimizing the impact of bias, ensuring it does not compromise the algorithm’s equity or ethical integrity.

* Awareness of the limitations enables a more rational approach to developing Compassionate AI.

What are some specific examples of AI being used to foster compassion and support?

* Mental Health Support: AI-powered chatbots like Woebot offer real-time emotional support and coping strategies based on cognitive behavioral therapy principles.

* Anti-Bullying Measures: Platforms like Instagram are using AI to detect hateful comments or harassment and warn users before they post harmful content.

* Virtual Companionship: AI systems like ElliQ aim to combat loneliness among older people by providing companionship and facilitating communication with family.

* Personalized Learning: AI adapts educational material to suit students’ needs, offering a compassionate approach to learning.

* Disaster Relief: AI algorithms help optimize aid distribution during natural disasters, ensuring that resources reach those in dire need.

* Accessible Tech: AI-driven technologies, like voice-activated systems, make digital platforms more accessible to people with physical limitations.

* Telemedicine: AI algorithms can diagnose certain conditions and provide medical advice, making healthcare accessible in remote areas.

* Suicide Prevention: AI algorithms analyze social media posts for signs of emotional distress, flagging at-risk individuals for intervention.

* Animal Welfare: AI tracks endangered species and monitors their well-being, helping conservationists take action.

* Community Building: Algorithms match like-minded individuals for social causes, creating impactful volunteer networks.

Think of AI as a Swiss Army knife for compassion and support. It’s a multi-tool with specialized capabilities tackling societal and individual well-being aspects.

Key Takeaways:

* AI is being utilized in diverse settings to offer emotional support, promote well-being, and compassionate action.

* Technology shows promise in healthcare, education, community building, and more.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com

Interview with Jonathan Khorsandi, CEO of Diwy.eu

In Episode 13 of “The AI Optimist,” we interview Jonathan Khorsandi, CEO of Diwy.eu, exploring the transformative power of AI in B2B lead generation.

Adding Personal Touch: Jonathan’s approach to understanding ICPs (Ideal Customer Profiles) shows how combining AI with a human touch improves results. 

By knowing companies’ challenges, lead campaigns adapting to different customer profiles often increase conversion rates.

Interview Highlights:

* Jonathan uses an “octopus-like” AI system, reaching out to 40 resources. Combined with machine learning, this AI automates and gathers relevant data for targeted lead generation.

* He underlines the importance of understanding Ideal Customer Profiles (ICPs) and creating proper email campaigns for them. Using AI, Jonathan crafts precise email prompts addressing the specific needs of each ICP.

* We discuss the myth that AI will replace jobs. Jonathan believes AI will impact many roles but can’t replace the human element in B2B sales, especially empathy and intimacy.

* We focus on the importance of common sense in B2B marketing. Regardless of AI’s capabilities, a marketer’s intuition remains essential.

* Declan highlighted an example of a window tinting company adapting to a new ICP. Using AI, they discover a new customer segment: parents wanting to protect their kids from UV rays. This realization radically improves their business.

* AI helps identify public email records like DKIM, SPF, and DMARC, shedding light on a company’s email awareness and deliverability. With this knowledge, businesses can fine-tune their email campaigns.

4 Powers of AI in B2B: A Comprehensive Look

Article: AI is used in marketing by two thirds of B2B orgs, Forrester finds

1. Targeting:

Details:

* AI’s algorithms analyze datasets, drawing patterns and insights about potential clients. It allows businesses to segment markets efficiently and zero in on the most promising leads. 

* While traditional methods cast a wide net hoping to catch something, AI allows businesses to use a precision spear to target specific customers.

* Example: A tech startup wanting to target businesses seeking cloud-based solutions uses AI to sift through online behaviors, forum discussions, and social media mentions. Now, they can focus their resources on leads more likely to convert.

2. Personalization:

* Details: AI helps craft unique, tailored messages for each prospect, enhancing engagement. AI predicts what content resonates best with each lead by analyzing online behaviors, past purchases, and interactions with emails.

* Example: An e-commerce platform determining through AI that a specific business frequently orders office supplies in bulk every three months and sends them personalized reminders with special bulk-purchase discounts.

3. Marketing Automation: 

* Details: Beyond automating repetitive tasks, AI-powered marketing automation systems adapt and optimize campaigns in real time. They adjust email send times, content displayed on websites, and ad budgets based on ongoing performance metrics.

* Example: An online course provider using AI automatically sends follow-up emails to visitors who abandon their shopping carts, offering exclusive discounts and addressing common pre-purchase concerns.

4. Chatbots:  

* Details: AI-powered chatbots offer instant, 24/7 customer service. They handle everything from answering frequently asked questions to guiding leads through a sales funnel. Chatbots continually learn from interactions, refining responses over time.

* Example: A B2B SaaS company employs an AI chatbot on its website. When someone visits, the chatbot immediately engages, asking about specific needs and offering real-time product demos or connecting them to a sales rep.

Pros:

* Enables hyper-targeted marketing.

* Enhances personalization, resulting in higher engagement.

* Automates mundane tasks, freeing human resources for more strategic work.

Cons:

* Risk of over-reliance, potentially dismissing human intuition.

* Misuse might lead to overly aggressive campaigns and annoying prospects.

How Artificial Intelligence Can Transform B2B Marketing by Cience

Step-by-Step AI B2B Guide:

How can small businesses with limited resources effectively incorporate AI into their B2B marketing?

Incorporating AI into B2B marketing for small businesses doesn’t require large budgets or a team of data scientists. 

Businesses with limited resources can harness the power of AI with a strategic approach.

1. Start Small:

* Instead of a full-fledged AI integration, focus on improving specific tasks or processes.

* Example: Use simple AI chatbots for customer support and automate email campaigns for lead nurturing.

2. Use Cloud-Based AI Tools:

* Numerous SaaS platforms offer AI capabilities that don’t require a complex setup. These are subscription-based and scalable.

* Example: Platforms like Mailchimp offer AI-driven email marketing, while tools like HubSpot integrate AI for better lead scoring.

3. Leverage Free or Low-Cost Resources:

* Many open-source tools and platforms provide AI. Train yourself with these resources.

* Example: Google’s AutoML allows businesses to build custom machine learning models based on their data without needing expertise.

4. Focus on Data Collection:

* AI thrives on data. Collect relevant data on your customers, website visitors, and sales patterns. Basic analytics provide valuable insights.

* Example: Use Google Analytics to gather data on website traffic and behavior and CRM tools to collect sales and lead data.

5. Collaborate and Partner:

* Partner with tech startups or universities looking for real-world applications of their AI research.

* Example: A startup provides its AI-driven customer segmentation tool at a discounted rate for feedback and case studies.

6. Educate and Upskill:

* Invest in AI courses or hands-on workshops. Understanding AI basics helps you make informed choices.

* Example: Online platforms like Coursera or LinkedIn offer courses on AI for non-technical people.

7. Experiment and Iterate:

* In marketing, AI is all about testing and refining. Conduct small experiments, measure the results, and iterate.

* Example: Try AI-driven A/B testing tools to optimize landing pages; refine based on results.

8. Stay Updated:

* AI is rapidly evolving. Regularly read articles, attend webinars, and join AI-focused community groups to keep updated.

* Example: Join AI in Marketing communities on LinkedIn or Facebook to share experiences and learn from peers.

Small Business and the AI Advantage:

* Even with limited resources, small businesses effectively use AI in B2B marketing.

* Starting small, prioritizing data, and continuously learning are crucial.

* Collaboration and leveraging free or low-cost tools help achieve results without breaking the bank.

Final Thoughts 

While AI offers unparalleled tools for understanding and reaching potential clients, the heart of B2B marketing remains in human connection and common sense.

Companies balancing AI’s power and maintaining the human element will stand out in the crowded B2B marketplace.

Key Takeaways:

* In B2B lead generation, AI offers precise targeting and personalization.

* Understanding ICPs is crucial for effective campaigns.

* Despite AI’s prowess, the human touch in B2B sales and marketing remains essential.

References:

* Diwy.eu

* Jasper.ai

* Promptbase

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.theaioptimist.com