The AI Optimist

Step into the world of ancient Rome, where art and technology collide!

Using a neural network called Artbreeder, the artist processed over 800 sculptures, breathing life into the frozen faces of history and facing solid critiques.

The result is a stunning collection of AI-generated portraits, each a window into the past.

But the past emperors couldn’t have looked pale and chalky like a Northern European.

As the project gained attention, a backlash ensued, with critics questioning the accuracy of his recreations.

The debate sparked a broader conversation about the responsible use of AI, the importance of accurate data inputs, and the need for transparency in the creative process.

And many others tried, like this one:

The Color of Power:

Rethinking Roman Emperor's True Hues

This podcast explores GenAI through a 2020 project by Daniel Voshart, a Canadian cinematographer and VR designer who used machine learning to create photorealistic reconstructions of 54 Roman emperors.

Using a neural network named Artbreeder, Voshart processed images from around 800 sculptures to generate realistic facial features, enhancing them further with manual adjustments in Photoshop based on additional sources like coins and historical texts.

Overview and Outline:

I. Introduction to the podcast topic and the controversy surrounding Voshart's AI-generated images of Roman emperors.

II. Voshart's approach: Using ArtBreeder and carefully curated statue images to generate initial portraits.

III. The criticism: The portraits are accused of depicting an inaccurate, "Aryanized" complexion due to flawed data sources.

IV. Voshart's response: Embracing feedback, removing problematic sources, and iteratively refining the portraits.

V. Lessons and recommendations for using AI effectively:

1. Improve data training with diverse and accurate inputs ("garbage in, garbage out")

2. Iteratively refine AI outputs through human intervention

3. Embrace feedback from the audience/critics

4. Be transparent about sources and methods

VI. Voshart's entrepreneurial success: Selling prints and image licenses on Etsy.

VII. Conclusion: AI is a collaborative tool incorporating human feedback and ingenuity.

Dunn portrays the controversy surrounding the images, with critics lambasting the "northern European complexions" depicted, accusing the portrayal of being akin to an "Aryanized" representation of history.

He describes the criticism as a "firestorm," with comments "blowing up" on social media.

The podcast also offers a glimpse into Voshart's entrepreneurial spirit, describing his Etsy store as a testament to his hard work, with over 1,100 sales of prints and image licenses ranging from $30 to $55 each.

4 Step Process for Using GenAI:

1.      Provide diverse, high-quality, and accurate inputs ("no garbage in, garbage out") to improve data training.

2.      Iteratively refine the AI outputs through human intervention, using tools like Photoshop to enhance and correct the results.

3.      Embrace feedback from the audience and critics, and be willing to adapt and improve based on their input.

4.      Be transparent about sources, methods, and any limitations or biases in the data or approach.

Quotes:

"The more people see this kind of crap, the more they actually believe." (Criticism of the potentially misleading depictions)

"Listen to your audience." (Advice on embracing feedback)

"How can you give it more real data rather than based on what it knows, which is, in this case, was often white northern European males." (On the need for diverse data inputs)

Takeaways:

·         Machine Learning: Voshart employed Artbreeder, a machine learning platform, to interpret and transform existing busts into detailed, colorized portraits.

·         Artistic Enhancement: After generating the initial images with AI, Voshart used Photoshop to manually adjust and refine the details based on historical descriptions and other artworks.

·         Historical Accuracy: The project aimed to provide a more realistic view of what these emperors might have looked like, focusing on preserving historical integrity through careful source selection and feedback integration.

Learnings:

·         Potential of AI in Historical Research: The project showcases the potential of artificial intelligence in enhancing our understanding and visualization of historical figures.

·         Importance of Cross-Disciplinary Work: Combining technology with traditional historical research yields enriching insights.

·         Public Engagement and Education: Such projects advance academic research and engage the public's interest in history through compelling visual media.

The Creative Alchemy of AI and Human Ingenuity

While the AI did the heavy lifting of modeling realistic facial features from sculpture data, Voshart didn't accept its output verbatim.

He refined each AI-generated portrait using Photoshop, referencing historical texts, coins, and artwork to incorporate vivid colors, hairstyles, and other unique personal details. It culminated in an iterative process that harmoniously blended cutting-edge tech with human creativity and historical scholarship.

His approach to recreating the faces of Roman emperors is a blend of creativity, tech, and data-savvy, and being open to feedback:

1.      Built with AI - Machine Learning: through tools like Artbreeder, combining data from hundreds of sculptures to generate facial features. This technology allows for an iterative and evolving creative process.

2.      Artistic Tunings: After AI generation, Voshart applies manual adjustments using software like Photoshop. This step allows him to use his artistic judgment to enhance realism or adjust features based on additional historical data, such as coins and paintings.

3.      Historical Feedback:. Adjustments are made based on advice to ensure historical accuracy and to address controversies, such as the pale, chalky skin and blonde hair challenged for Roman emperors from Italy, Spain, and Northern Africa.

4.      Public Interaction:. Feedback from this wider audience helps with revisions.

This approach brings a forgotten era to life. It opens up discussions on the ethical responsibilities of using AI in historical reconstruction.

The Truth Behind the Faces: Decoding Roman Emperor Portraits

This began as a simple reposting of a cool video in which statues of Roman Emperors have their faces revealed using AI and the talents of a Canadian Virtual Reality designer.

It has so much to teach us about creating excellent work with GenAI, including a four-step process for you to apply at the end.

But then the critics jump on – the first is a longtime friend - so maybe it's not surprising for social media. Still, this is intelligent:

“The coming wave of this kind of thing reveals a pedestrian response to what these statues represent. They are illustrations rather than art, and worst of all, they pose as knowledge using the biggest liar of all: digital photographic representation as if it is truth.

But the danger lies in the fact that the more people see this kind of crap, the more they actually believe it is truth.” Geoffrey Laurence,

http://geoffreylaurence.com

Geoffrey’s critique rejects the premise of using AI to recreate historical figures in this photorealistic way.

Viewing these AI-generated portraits as disconnected from the harsh reality of ancient Roman society that we can never fully recreate or understand as modern people.

These are contemporary views of Roman visages based on limited evidence, not a reconstruction of how they definitively looked and the experiences behind those appearances.

"These were Italian/Mediterranean men, yet they all have very northern European complexion.

I've seen this in all other AI image generators. they are all biased. It may be unintended and just trained on what’s out there.” Antonio Seijas Fernandez

Both comments raise valid critiques and concerns about the AI-generated portraits of Roman emperors.

Antonio's critique is that the portraits depict the Roman leaders with northern European/pale complexions, which may be an artifact of biases in the training data rather than accurately reflecting their likely Mediterranean appearances.

This gets to the core issue of AI systems maintaining societal biases and inaccurate representations if not carefully monitored during training.

His suggestion to examine the training dataset is smart—it will likely reveal skews that led to this northern European aesthetic being imposed on Roman subjects.

Seeing unexpected pale complexions doesn't mean the fault lies with the AI - the creator may have chosen to portray the emperors that way based on his own research and creative choices. More transparency around the process is needed.

Both comments also touch on broader societal concerns about AI systems promoting dangerous stereotypes, misrepresentations, and abuse of technology if not guided.

These comments represent reasonable skepticism about any AI system's outputs being taken as objective ground truth, especially on topics of cultural representation.

As with most emerging and powerful technologies, AI's full positive potential likely lies in developing and applying human guidance and ethical guardrails—not in rejecting its use entirely out of legitimate yet generalized fears.

Ethical AI begins with Ethical Data.

In humanizing these ancient figures, Voshart provided a compelling counternarrative to attempts to depict Roman leaders as Aryan ubermenschen by racist ideologues of the past.

By grounding his work in historical research and AI data improvements, he demonstrates how this technology, guided by a strong vision and feedback, produces the best results.

The potential of AI as a collaborative partner for artists, designers, and creators is alluring. However, care still needs to be taken to respect history and truth.

AI Tools and Approach

His approach went beyond taking the AI output at face value:

1.      He curates the dataset of bust images to feed into the systems, prioritizing high-quality sculptures from the emperors' lifetimes.

2.      He continually refines the AI outputs using Photoshop and historical references, such as ancient texts, coins, and artworks. This allows him to incorporate realistic skin tones, hairstyles, unique facial features, etc.

3.      He embraces a feedback loop that identifies limitations in the pictures (e.g., unnatural skin palettes). He tweaks the input data (e.g., feeds in example portraits with the desired skin tones) to improve the outputs.

4.      His transparent process using credible historical sources and thoughtful intervention countered past attempts to portray Roman figures through a racist/Aryanist lens.

Because he integrates AI into his creative workflow, he provides the essential human touch as a partner. The iterative process of working with AI, identifying its limitations, adjusting to new inputs/instructions, and applying discernment throughout.

This allows him to produce compelling artistic/historical work that would have been nearly impossible without AI, specifically machine learning.

However, his approach went beyond simply taking the AI output at face value.

The 4 Steps to Improve Your GenAI Projects

1.      Improve the data training: He curated the dataset of quality statue images to feed AI, focusing on sculptures from the emperors' lifetimes.

For example, early AI skin tone renderings looked unnaturally pale and chalky. But by feeding in a reference portrait of a swarthy Roman emperor, he guided the system toward more realistic flesh tones for ensuing facial recreations. This flexibility to identify limitations and improve is part of what makes AI an influential collaborator for artists.

2.      Refine AI outputs: He used Photoshop and historical references, such as ancient texts, coins, and artworks, to incorporate realistic skin tones, hairstyles, unique facial features, etc.

He personally refined each AI-generated portrait using Photoshop. He referenced historical texts, coins, and artwork to incorporate vivid colors, hairstyles, and other unique personal details.

3.      Embrace feedback: Daniel notices limits in the AI systems' renderings (e.g., unnatural skin palettes) and updates with example portraits with the desired skin tones) to improve the results.

4.      Be transparent; he cites historical sources and adapts when he learns his attempts to portray Roman figures through a racist/Aryanist lens, the source he later deleted upon

Resources

* AI 'resurrects' 54 Roman emperors, in stunningly lifelike images

* Appearance of The Principate [Pt. III] by Daniel Voshart

96–192: The Nerva–Antonine dynasty

* Were Roman Emperors Blonde? by Davide Cocci

* Voshart Etsy Store

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

Looks like everyone’s ahead of you with AI?I'll let you in on a little secret.

You're either jumping headfirst into AI or telling employees to figure out what's got you excited.

That's what first movers do, but most people aren't first movers. And things move slowly.

The AI Optimist Episode 41 Playlist

https://www.theaioptimist.com/p/shattering-your-ai-ceiling-3-growth

00:00 Saving 3000 hours with AI - Asana

00:57 Growth Driver 1 - Intro AI Gently

03:42 The 3 Gaps - Optimism Transparency Resources - Rebecca Hinds

05:27 Growth Driver 2 - What data do you feed AI?

08:09 Growth Driver 3 - Most people don't get AI

11:17 Define your AI Floor and Ceiling

14:03 Where to Begin for Solopreneurs and Entrepreneurs

16:58 Small Business Floor and Ceiling measures

19:38 Guide people with AI, instead of expecting them to act like first movers

It's a gap causing many to remain unsure and at the floor, not rising to their potential, their ceiling.

This episode of the AI Optimists podcast focuses on helping small businesses and solopreneurs shatter their "AI ceiling" by providing three growth drivers for integrating AI effectively.

Sharing insights from companies like Asana, Holiday Extras, Affiliate.ai and Learning Frequency on how to introduce AI gradually, feed it quality data, and understand where employees or clients are starting from in terms of AI knowledge.

Summary:

The three key growth drivers covered are:

1) Introducing AI gently by engaging people to try it rather than an "AI-first" aggressive push that threatens them.

2) Ensuring you are feeding AI good quality, relevant data and getting compliance/privacy aspects covered upfront.

3) Understanding that most people aren't "early AI movers" and teaching/supporting them through the integration process based on their existing skills and mindsets.

Detailed Outline:

* Growth Driver 1: Introduce AI Gently

* Don't threaten people with an "AI-first" model

* Example of Asana winning over employees gradually

* Engage people, get team involved like Holiday Extras did

* Address the "optimism gap" and "transparency gap" between execs and employees

* Growth Driver 2: Focus on Quality Data for AI

* Example of Learning Frequency using meaningful metrics

* Assess what data matters, not just mass quantity

* Get compliance/privacy sorted out early

* Growth Driver 3: Meet People Where They Are

* Most aren't "early AI movers", teach them based on their skills

* Example of the intimidating "blank prompt" problem

* For solopreneurs: Floor is admin/support automation, ceiling is personalized services

* For small businesses: Floor is time savings, ceiling is new products/markets

* Define your AI "floor and ceiling", measure performance consistently

* Practical Suggestions

* Don't take a "do-it-yourself" approach, guide and upskill people

* Set benchmarks to measure true impact on costs, revenue, satisfaction

* Review and adjust every 90 days

* Best integrators are team-builders who get everyone excited, not just "geniuses"

Key Quotes:

"An AI-first model which says let's start with AI, see if we can just do it with that, really threatens people."

"Early ChatGPT users were so proud of the work they were doing, they couldn't help but tell colleagues - employee word of mouth on the quality work became an instant driver of adoption."

"It was fascinating that to me, it's almost like if I humanize it a bit, in big quotes. I'm just putting a picture on avatar, like Character.ai. It works because one of the big fears, is fear of the blank space in the prompt."

"Improve on common mistakes. And maybe a floor would be 10% improvement in speed to finishing your tasks, or a 5% reduction in cost due to automation."

"The ceiling for a small business transforms the business. Maybe enabling new products, making customer service way better...or opening new markets."

ASANA Won Back 3000 Hours

Future Work podcast

In Episode 41 we focus on Shattering your AI Ceiling: 3 Growth Drivers for Small Business and Solopreneurs

Growth Driver 1. Introduce AI gently, don't

throw it at people....like AI FIRST!

Instead of engineering speak, discuss in simple terms, and engage people to try it out.

We love to discuss sentimentality analysis of data, now let's talk about listening and guiding people to get the most out of AI....

And Holiday Extras is an Australian travel company following their employee's AI embrace:

Like a mini viral, word of mouth maybe movement, indicative of the pliable company culture:

Why aren’t more like Asana or Holiday Extras, or thousands of others?

Mostly it's a disconnect, as excited executives expect everyone will see things as they do, or a creator fixates on a single vision that few understand.

According to Rebecca Hinds, there are three big gaps between how executives and individual Contributors view the technology:

- Optimism Gap: Executives see the promise and potential of AI more than Individual Contributors do.

- Transparency Gap: Executives think they are more transparent in using AI than they are according to individual contributors.

- Resource (Training) Gap: 25% of executives say they provide AI training, but only 11% of ICs agree.

Leaders must close these three gaps to gain the benefits of AI in organizations.

* Start with people and training, not simply AI. Identify the low hanging fruit of futile tasks that people have and can be automated.

* Look at AI from the perspective of those who don’t get it. Help them.

Growth Driver 2. What's your AI's Diet - what data do you feed AI?

Yes, the game becomes more impactful.

Gamifying AI integration, making it fun,

starting with people and slowly adding

in what AI is needed.

Suggestions

* Old data - is your YouTube video from 2010 really a match with your current business and brand?

* Get Compliance involved first - privacy and security have undermined numerous ventures, that spent all this time to get integrated, but couldn't answer basic privacy questions.

Growth Driver 3. Most people don't get AI

DEFINE YOUR CUSTOMER, OR EMPLOYEE OR SELF, FLOOR AND CEILING

* Measuring AI Success - pinpoint your floor and ceiling

Floors begin the process, ceilings develop over years

* The "floor" is the minimum performance, and the "ceiling" is the top performance.

* The floor is usually within the first few months, while the ceiling requires a few years of development.

 Be careful of the tendency to play around with a bunch of tools and follow the leader.

Like Rebecca Hinds shared, since the views of the executives are often not clearly understood by the rest of the people who have to execute this...

Like here's AI, figure it out, right?

Work on your messaging and training, as around 25% might do a little training now.

Early on, guide to increases and improvements. DIY not recommended.

EVERY BUSINESS OPENS UP TO AI AT ITS OWN PACE

I run 2 programs, AI-S for solopreneurs, and AI-E for Entrepreneurs and small businesses.

Each with different ideas of "floor" and "ceiling" results from AI plans. Consider this like performance in training – enter and increase according to what you can do and will be able to do over time, because it does take time.

Estimated before, measured during, and summarized after for improvements.

Engineering/coding, financial, and marketing/sales are the 3 areas where change is rampant. AI cuts the entry level jobs, and middle level management. And more, remember, it’s all custom.

For a Solopreneur: It’s all admin, scheduling, and support

Floor: The floor for a solopreneur using AI involves basics like scheduling, customer support, and financial management, aiming for more time to focus on the core business. A floor metric could be reducing admin tasks by 20%.

Ceiling: AI enables developing personalized services or products. This might result in doubling the customer base or achieving a 40% increase in income because of more time to do what they are paid to do. With a Solopreneur, make sure you’re honest about the floor and see the ceiling as a work in progress…..some find higher and higher ceilings, others level off.

AI is all custom fits.

Numbers  to Benchmark:

* Time spent on administrative tasks.

* Customer growth, maybe more time with things that matter?

* Profit changes – seen some go down, keep an eye on it.

* Level of service or product customization

Small Business: Time savings is low hanging fruit, revenue driven activities long term.

Floor: The minimum saves a few hours by automating simple tasks, freeing up time while improving on common mistakes. A floor for a small business might be a 10% improvement in speed or a 5% reduction in costs due to automation.

Ceiling: The best-case scenario transforms the business, enabling new products, enhancing customer service, or opening new markets. The ceiling might be a 50% increase in customer satisfaction scores or a 30% growth in revenue through new AI-driven services or products. Customize a solution to your business.

Metrics and KPIs:

* Saving wasted time – LTVT (Long Term Value of Time)

* Measuring reductions – actual time or percentage, increase in productivity by freeing up time, and training on what to do with the extra time.

* Revenue growth – predictive analytics and the customer experience, moving from chatbots to people.

* Customer ratings

Custom Fit means don’t rely on what others do. Their performance is no indicator of your future performance 😊

* Defining the floor and ceiling for AI integration helps us understand where we are at, and where we hope to go. And review these regularly, every 90 days minimum.

* Pick the metrics and KPIs that matter to you, and that AI is expected to impact.

* Create measurements, benchmarks…history. Let it teach you how to improve in the future.

* Review these benchmarks to ensure AI is aligning with your business.

Move beyond minimal AI results - the floor - and unearth counter intuitive AI strategies to your small business or solo venture. Know your limits, define your floor and ceiling, and start improving your results.

FICTIONAL BUSINESS EXAMPLE

Here is a sample use case study of a travel company using AI to connect with customers and employees:

Company Overview

Wanderlust Travel is a mid-sized travel agency that specializes in customized vacation packages. With 75 employees across their headquarters and multiple branch offices, Wanderlust was looking for ways to improve efficiency, enhance the customer experience, and empower their workforce.

The Challenge

Wanderlust faced some key challenges:

* Keeping up with changing traveler demands and preferences

* Providing personalized service at scale

* Enabling seamless communication across departments and locations

* Freeing up employee time from repetitive, manual tasks

Wanderlust recognized the potential of AI to help address these pain points, but was unsure of the best approach to integrate it effectively.

The AI Solution

After evaluating various options, Wanderlust decided on a multi-pronged AI strategy:

Customer-Facing Applications

* Chatbot assistant to answer common queries, provide destination info 24/7

* AI-powered recommendations for customized travel packages

* Voice interfaces for making/modifying bookings

Internal Tools

* Natural language processing to extract insights from customer communications

* Automated workflow and task delegation based on employee skills/roles

* AI training modules to upskill employees on using travel AI tools

Change Management

* Dedicated AI integration team to guide roll-out

* "Lunch and Learn" sessions to educate employees and get their feedback

* Incentives for employees who developed new AI use cases

Execution and Results

Wanderlust adopted a phased approach, starting with the chatbot assistant and AI recommendations. Key results included:

Customer Impact

* 25% increase in bookings through the chatbot channel in 6 months

* 92% satisfaction rating for AI-recommended travel packages

* Reduced response times from days to minutes for common queries

Employee Empowerment

* Reallocated over 15,000 employee hours per year from manual tasks to higher-value work

* Average employee upskilled with 3 new AI skills within first year

* Over 20 new AI use cases identified and implemented by employees

By taking a holistic, people-centric approach to AI integration, Wanderlust was able to drive significant value for their customers and employees alike. Their methodology provides a template for other businesses looking to harness the power of AI responsibly and inclusively.

NOTE: This is not a real company. This report is shared to give an idea of how to evaluate AI for your 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

AI is being rapidly militarized and weaponized by global powers. While we all focus on fake videos, the threat is autonomous drones and a renewed nuclear arms race enabled by AI.

Specific examples like Ukraine's AI drones, Israel's nukes, and Russia/China's nuclear stockpiling are evidence of the high stakes and urgency of this issue.

AI seems to always come with a benefit and a threat. As the host of AI Optimist, I want to take a step back from getting caught up in the heated social media battles over deepfakes and fake videos.

Those are annoying, but there are serious implications of AI we need to deal with - especially in military applications and warfare.

In this episode, I'll share my perspective on the "hidden" role AI is playing to enable autonomous weapons and present an ominous future if we don't find a way to establish guardrails.

From the AI-powered killer drones being used in Ukraine to the nuclear weapons stockpiling of countries like Russia, China, and Israel, the stakes are incredibly high.

 I'm usually an AI optimist, but when it comes to the potential risks posed by unchecked military AI development, in this issue count me as an AI pessimist.

Appreciate you taking a few minutes to consider the importance of what seems just like more fake videos on social media.

AI in Modern Warfare is starting to scale.

The boundary between the virtual and real intertwine, the 40th episode of The AI Optimist plunges into the evolving narrative of artificial intelligence, warfare, and our detached, almost snarky view of the world trolled by fake videos around real events like Iran bombing Israel.

Discover a world teetering on the edge of tech omnipotence and ethical ambiguity. The invisible threads of AI weaving a story that might dictate the future of international warfare.

AI’s potential for innovation matches its capacity for destruction. Peel back the layers of mainstream AI discussions, glossing over the darker scenarios brewing beneath the surface.

The Spread of Fake Videos

* Impact on Public Perception:

* Fake videos, often generated with AI tools, are a potent tool for propaganda in war zones. They may dramatically alter public opinion, sway political decisions, and influence the course of the conflict.

* Social Media as a Battlefield:

* While social media is a platform for raising awareness and rallying humanitarian support, it has also been misused to spread fake narratives.

* Some social media algorithms monetize by negative behavior, paying for posting weird and wild and sometimes fake. It’s about getting views, not sharing news.

AI in Warfare

* AI's in Modern Conflicts:

* In the Ukraine conflict and the ongoing tensions in Israel, AI is used for strategic planning to autonomous weapon systems. Escalating the capabilities and efficiency of military forces, making warfare more efficient yet more destructive.

* The Challenge of Autonomous Weapons:

* One of the most controversial aspects of AI in warfare is the deployment of autonomous weapons. These systems operate without human intervention, raising questions about the future of combat and the potential for unintended escalation.

* Misinformation and Propaganda:

* AI has been used to create and spread misinformation, including fake videos distorting public perception and influencing narratives during wars. This misuse of technology leads to widespread confusion and misinformation.

A Call for Slowing the spread of military AI

* International Interventions:

* There needs to be a global response to the militarization of AI. The speaker suggested organizing world conferences and meetings, possibly under the auspices of the United Nations, to formulate a cohesive strategy to manage and potentially limit the military use of AI.

* Challenges in Implementation:

* However, international bodies rarely enforce such regulations. Past efforts have seen limited success, with major powers often circumventing or ignoring international norms when it suits their strategic interests.

* Grassroots Movements and Public Opinion:

* Beyond governmental and international actions, there is a growing need for public awareness and grassroots movements that can push for stricter controls on military AI. Public opinion can be a powerful force in influencing policy decisions, especially in democratic nations.

This pod started the day Iran launched its response

Iran sent more than 300 drones and missiles toward Israel, Iranian state TV shows a video of a fire under an orange sky. But that footage was not from Israel; it's footage from a Chilean wildfire in February.

Viral Deception and Real Drones: AI's Impact from Ukraine to Israel

While social media fixates on fake war videos, the impact of AI in Ukraine and Israel barely receives a tweet. From autonomous drones to logistics to targeting, AI is quietly reshaping modern warfare.

Iran's recent attacks on Israel brought a different kind of warfare in social. Beyond the misleading fake videos, it's becoming harder to believe what you see, from governments to influencers.

MARK THIS TWEET IN HISTORY WW3 HAS OFFICIALLY STARTED🚨

SOURCES IN THE PODCAST VIDEO

1. Why a fire in Chile Tweet about Iran's State TV - the fire was in Chile

* MARK THIS TWEET IN HISTORY WW3 HAS OFFICIALLY STARTED🚨

emoji is a police car light

ACTUAL Video by Borisao Blois

Quieres Saber Cómo Se Sentía estar en la SEGUNDA GU3RRA MUNDIAL? Esto es para ti...

3. AI Arms Race - US vs. China vs Russia with Japan and Iran jumping back into the nuclear race

Time for Congress to Fund AI for U.S. Military Logistics to Counter China

ARK AI Logistics

Govini 2023 National Security Scorecard

Our mission is to transform Defense Acquisition into a strategic advantage for the United States.

Ukraine A Living Lab for AI Warfare

Roles and Implications of AI in the Russian-Ukrainian Conflict

Iron Dome Shows AI’s Risks and Rewards

Israel’s use of AI in Gaza is coming under closer scrutiny

Artificial intelligence in Israel-Hamas war: Role of 'The Gospel'

‘Lavender’: The AI machine directing Israel’s bombing spree in Gaza

The Israeli army has marked tens of thousands of Gazans as suspects for assassination, using an AI targeting system with little human oversight and a permissive policy for casualties, +972 and Local Call reveal.

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

Episode 39 Playlist

00:00 Intro to concept of discovering your "AI avatar"

03:05 Example 1: The CAIO (Chief AI Officer) role

07:06 Example 2: Travel business saving $500K by integrating AI

10:10 4-week plan for listeners to find their AI avatar

14:47 Importance of personalizing your human-AI relationship

16:44 Conclusion: Embrace AI on your own terms

One company saves $500,000 with AI, one has $25 Million taken through a deepfake Zoom call.

I’m at At an AI workshop, looking around the audience looking like deers in the headlights I ask them…..

Which one would you choose to focus on?

Estimated $500,000 savings and raising the bar in its industry with AI?

or the Deepfake rabbit hole?

Instead of turning around and being curious, they stop. Freeze and push it away until tomorrow.

The deepfake draw, like the validation they seek for this opinion.

Almost apologetic if stating anything positive about their experiences. A habit of denial maybe?

Amor fati.

Love your fate.

Like AI.

Episode 39 of The AI Optimist

Creating AI Habits Reflecting the Real You

Discover your Inner AI Avatar

We’re going to explore 2 examples, the CAIO Avatar, the leader of AI whose job may be so short term, they are almost certain to be replaced by AI, and that’s a good thing.

Then the travel business saving $500,000 by identifying how AI will fit into their workflow, personalized by experience and with hands on support and training.

Then we’ll focus on you with a 4-week plan to identify and release your AI Avatar.

What role will you play with AI –

CAIO,

AI-All-In-Business

and Your AI Avatar?

Creating Atomic AI Habits –

James Clear calls them “Identity-Based Habits:”

“The key to building lasting habits is focusing on creating a new identity first. Your current behaviors are simply a reflection of your current identity.

What you do now is a mirror image of the type of person you believe that you are (either consciously or subconsciously).

To change your behavior for good, you need to start believing new things about yourself.”

AI will become a habit, some on top of what you do like Midjourney, some buried in the code and helping you along the way. Weaving habits is easier with a sense of place, of purpose, in a shifting sands AI brings.

Taking small, incremental steps towards defining what you need and can do with AI is the start.

Avatar 1: The CAIO

The Chief AI Officer (CAIO)

The CAIO is responsible for integrating AI into a company ops. Smaller companies and solopreneurs do the same thing, which is why this Avatar is not just about some short term corporate C suite role, it’s about how you look at integrating AI with what you do.

“I actually think this is a moment-in-time type of role. And it’s funny to say that about your own job,” The need for the specific AI person goes away simply because everyone else levels up their skills and knowledge around these technologies, which is what my guess is probably happens.”

Emerging Tech Brew interview with Dan O’Connell, chief AI and strategy officer at the cloud communications company Dialpad.

Get to know the technologies and communicate what the mean to the company is what the Accenture’s CAIO does.

This is not forever, but likely at least the next 5 years as the rapid transition happens.

Maybe it will show us how much time we were wasting doing things that AI and robotics can handle.

It’s a mindset, and understand that many are resistant because they don’t have a clear picture where it’s going. Who does?

Lots of training, educating others involved in the decision, and guiding it like Lan Guan,  Accenture’s chief AI officer says: 

“Oftentimes, I talk to C-suite clients about, ‘How do you start your AI journey,’ and for those of them who already started doing some experimentation, then it’s my job to help them see the pathway to scale,” she said. “More and more conversations are pivoting to this part.”

Emerging Tech Brew series on CAIOs

Guan is leading more of an agency/consulting role with many clients, leading their engagement with AI and locking in work by being the guide.

Because all of this is being tested with different LLMs, different needs for privacy, it’s key to not only be flexible, but open and not dogmatic.

CAIOs  identify opps for AI to create value, guiding the development and usage of AI aligning with the company's strategic goals.

As AI becomes more commonplace, the role of the CAIO may evolve, shifting towards education and training to help employees across the organization develop the skills they need to work with AI.

Avatar 2. The AI-All-In Business saving $500,000 a year

Holiday Extras using OpenAI

Word of mouth is a great way to start AI engagement, and it doesn’t always happen, but when it does…

“Early ChatGPT users in the organization were so proud of the work they were doing, they couldn’t help but tell colleagues. Employee word-of-mouth on the quality of work became an instant driver of ChatGPT adoption.

David Norris, Chief Growth Officer

Building a data-driven, efficient culture with AI

An AI-enabled business is one that leverages AI technologies to improve its operations, customer experiences, and overall performance.

This approach requires upskilling employees and investing in the infrastructure to support AI apps.

AI-enabled businesses recognize the value of AI as a strategic asset, and prioritize its integration into all parts of their enterprise.

By embracing AI, these businesses gain a competitive advantage while building a culture that still values the people they work with, adapting to the reality of the world with AI.

Avatar 3 - You

Rather than trying to force AI into your current world, shape a new identity.

Instead of saying I'm going to 10x my productivity or save 20% of my time (which are both great), say I'm going to use AI little by little for helping me with what I need, and find the tools instead of jumping into all the tools and seeing where I fit.

Week 1: Identifying Your AI Avatar

* What is your AI Avatar – where does it serve you?

* Keep a record – Notion, a journal, some notes you record each week and transcribe. We’ll feed that to AI later to help review.

* Consider questions about the most challenging part of your work, what can be automated, what can help you analyze and summarize, and begin identifying the challenge before moving onto the AI tool/solution.

* Explore potential AI apps that align with your strengths, weak spots, and interests. Test out a few to get your feet wet. Measure your time accurately, don’t try to prove it, let the results prove it for you.

Week 2: Customizing AI for Your Avatar(s)

* Pick your favorite AI tools and focus a few hours on it at least. customized for each unique Avatar.

* Provide practical guidance on how to experiment with and refine AI usage to get the most out of it.

* Share best practices and common errors to avoid. Do this monthly at first, then quarterly until full development.

* Remember to iterate; gradually build your AI tech stack over time, and testing. And watch our for privacy and security concerns, this is slowing down most.

Week 3: Developing AI Habits

* How identity-based habits support AI adoption – the tools change evolving with who you are and what you do at work, and in your life.

* Develop techniques and strategies for turning AI usage into sustainable habits.

* Share your experiences and support others where you work or in your business.

Week 4: Evolving Your Avatar

* Reflect on the progress made over the past 3 weeks and identify areas for further growth.

* Stay current with AI developments and adapt to what matters for you.

* Evolve your identity as you grow your skills or even find you’d like nothing to do with AI.

Forget about setting goals or adopting the latest tools; become the person who uses AI in a way that aligns with your values, aspirations, and desired impact.

AI is a custom fit, not a one size fits all.

What you need and can do with AI differs from me, from anyone – obvious yes, which is why who you see yourself as with AI is more important than finding a cool tool that will save you time and money and

Get curious and explore as you integrate AI into your daily routines.

Try out different AI tools and applications, observe their impact, and adjust. Celebrate small wins as motivation to refine your AI habits.

Imagine yourself 5 years from now, and you’re looking back on today as the day you accepted the challenge AI is bringing. Or not, what will your world look like?

This pod is about you. What you do with AI.

By focusing on small, incremental steps and cultivating a mindset centered on growth and exploration, you create an AI Avatar reflects your evolving relationship with technology.

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

Is Human Creativity Artificial with AI?

And what does it have to do with the old story of Napster and the evolution of digital music?

Prepare for a thought-provoking conversation as we unravel the clash between AI technology and copyright law, where licensing rules and individual human rights dance around the new creation process.

Our guest author, AI patent attorney Robert Plotkin, opens the door to new possibilities while contemplating the delicate balance between AI's contribution and human creativity.

This is not legal advice; I’m not an attorney, Robert is a patent attorney, and we’re sharing views on AI copyrights as authors.

Beyond practical safety risks, AI also impacts human creativity and identity. As Robert Plotkin states,

"It means we could end up engaging in creative work in this bland way that doesn't exercise our creative juices in the way we want to."

Episode 38 Playlist (summaries below)

00:00 Creativity and GenAI - Copyrights human only?

01:05 What happens with copyright data used to train models like ChatGPT?

02:00 Napster did to music what AI will do to???

03:25 AI Assisted vs. AI Generated - what's up?

07:23 What are the risks for creators?

09:16 Automation bias happening

10:30 Over reliance? Always check AI results

12:11 Closing - it's simply Creative Identities

Plotkin, author of "The Genie in the Machine" (2009) - how computer-automated inventing aka AI - and the upcoming AI Armor book, is a seasoned patent attorney who navigates the shifting dynamics of software patents, like AI.

Join us as we explore Plotkin's insights on the future of AI-driven creativity, the evolving landscape, and the licensing promise of intellectual property rights in the digital age.

By leaning too heavily on AI assistance, creators may produce work that needs the genuine spark of human ingenuity and imagination. There's a risk of falling into a pattern of generating,

"bland stuff that's even worse than what we could have done without the tools,"

as Plotkin warns.

When an AI tool is involved in creating a work, the question arises: Who is the true creator? As Dunn ponders,

"Did the tool create it? Did I?"

This question touches on the broader issue of anthropomorphizing AI – the tendency to ascribe human-like qualities and agency to these systems. Plotkin cautions against this, reminding us that AI is not a competitor or adversary but a tool to be used judiciously and with a clear understanding of its limitations.

When used responsibly, these tools enhance human creativity rather than replace it. They offer new perspectives, generate fresh ideas, and streamline specific aspects of the creative process.

The key, as Plotkin emphasizes, is to remain,

"focused and conscious in how we use the tools and

how we review and rely on and use its outputs."

It strikes the right balance, where AI aids and inspires human creators, who keep in control and choose the direction of the creative work.

It’s a new way of doing what we’ve always done, with help from the collective intelligence of people.

The Future of Creativity - Copyright Questions in the Age of Generative AI Age

While GenAI, like ChatGPT, is integral to creative work today, Episode 38 of The Ai Optimist invites a debate surrounding copyright issues and the future of creativity. This episode expands upon that conversation, exploring the impacts of AI on copyright laws, creator rights, and the essence of human creativity.

Creativity and GenAI - Are Copyrights Human-Only?

AI raises pivotal questions about copyright's nature. AI tools trained on vast datasets, including copyrighted material, generate outputs that blur the lines between original and derivative works challenges.

This scenario challenges the traditional notion that copyrights are inherently human-centric, sparking a debate on whether AI-generated content deserves its category of protection or regulation.

The Napster Moment for Creative Content

Just as Napster revolutionized the music industry, AI technologies are poised to transform creative content across mediums. The comparison highlights a potential shift in content consumption and distribution, suggesting that AI could democratize content creation while raising concerns about the devaluation of individual creativity and infringement on existing copyrights.

Napster wasn't temporarily closed due to lawsuits; Steve Jobs and iTunes settled it with licensing, and others did. That licensing model deeply hurts musicians and artists in terms of income while benefiting more prominent companies.

Is GenAI a Napster déjà vu?

Review of what we cover in each section of the Episode 38 Playlist

00:00 Creativity and GenAI - Copyrights human only?

As AI generation tools become more advanced each month, questions arise about the ownership and copyrights of AI-assisted creative works.

Traditionally, copyright protection is only for human-generated content. Still, the increasing involvement of AI in the creative process complicates matters.

Plotkin raises this issue, stating,

"It means we could end up engaging in creative work in this very bland kind of way that doesn't exercise our creative juices in the way that we want to."

The concern is that AI-generated content may need a more genuine human spark and ingenuity that defines true creativity.

This concern extends to the question of authorship and ownership.

As Dunn ponders,

"Did the tool create it? Did I?"

When an AI tool is involved in creating a work, it becomes unclear who the creator is and who should hold the copyright.

01:05 What happens with copyright data used to train models like ChatGPT?

AI models train on vast amounts of online data, some of which may be protected by copyright. The data training and permissions involved raise legal questions about the fair use of copyrighted material for training AI systems.

While the training process itself may be fair use, the potential for AI models to generate content that infringes on existing copyrights remains. Clear guidelines and legal frameworks are needed to protect the rights of creators.

02:00 Napster did to music. What will AI do to it?

Just as the Napster revolution disrupted the music industry, AI is poised to shake up various creative fields. Both scraped their content without permission.

Plotkin shares that lawsuits didn't stop Napster; licensing arose that defines the music industry today. He shares how Steve Jobs, with Apple iTunes, began to license volumes of music. The industry shifted and many musicians are paid far less for their content, while the big companies benefit.

The ease and accessibility of AI generation tools could lead to an explosion of content, both original and derivative, challenging traditional notions of authorship, ownership, and monetization.

Industries like writing, art, and design may face a wave of AI-generated content undermining the value and exclusivity of human-created works.

This disruption forces a reevaluation of business models and new approaches to protecting and compensating creatives.

03:25 AI Assisted vs. AI-Generated - what's up?

The boundary between AI-assisted and AI-generated creative works is a work in progress. AI-assisted creation -- using AI tools as collaborators or aids in the creative process – keeps human creators in ultimate control as authors.

AI-generated content produced entirely by AI systems, with little to no human input or oversight, is a creative exercise initiated and completed by a human being.

What is the authenticity and value of such works; will it remove the need for human creators?

Striking the balance between leveraging AI and preserving human creativity is central. As Plotkin advises,

 "It's a matter of striking the right balance, where AI serves as an aid and inspiration, but human creators retain their agency and creative sovereignty."

Next time you go to ChatGPT or Claude or whatever LLM, stare at that blank box on the screen. Does it do anything or require you to ask questions, follow up, and review?

Like it or not, human beings are central to all GenAI, not just in the original creations but also in the asking of questions. We are creating new content together; otherwise, AI would just do it by itself.

(Yes, that's possible, so are good things. It's all perspective in one sense.)

07:23 What are the risks for creators?

The rise of AI tools presents opportunities and risks for creators. AI tools enhance creativity, offering new perspectives, ideas, and more information than most humans could handle.

Still, the risk of over-reliance on AI, leading to a loss of creative identity and the production of "bland stuff," as Plotkin warns, is a looming challenge.

Like GPS maps we all follow, losing the skills to navigate a road doesn't mean much until you have to use a map when your GPS is down. Too much GenAI could lead to Overreliance.

Plotkin highlights the potential danger of using AI-generated content without proper review and verification, especially in safety-critical situations like healthcare.

Creators must remain vigilant and exercise critical judgment, integrating AI outputs into their work.

09:16 Automation bias is happening

Automation bias, the tendency to trust and over-rely on automated systems, is common when using AI creativity tools.

Creators accept AI outputs without scrutiny, producing potentially harmful or inaccurate content.

To combat automation bias, creators must thoroughly review and verify AI-generated content before using it. Simply accepting AI outputs undermines the quality of creative works.

10:30 Overreliance? Always check AI results.

Building on the previous section, Plotkin emphasizes the importance of remaining:

"focused and conscious in how we use the tools and

how we review and rely on and use its outputs."

Over-reliance on AI tools leads to a loss of creative control and a reduced sense of ownership of one's work.

Creators who leverage AI's capabilities while maintaining control and exercising their creative judgment will likely thrive. Thoroughly reviewing and refining AI outputs will ensure they align with the vision and meet the set standards.

Or we can live in generic AI content that all sounds the same. While that does happen to human creativity occasionally, replicating instead of creating is different from the model to follow.

12:11 Closing - it's simply Creative Identities

Using GenAI tools means preserving and nurturing human creative identities. As Plotkin states, there is a risk of :

"engaging in creative work in this very bland kind of way that doesn't exercise our creative juices in the way that we want to."

Creators must maintain a unique spark and personal expression that defines their artistic or creative voice. By embracing a mindful approach to AI tool usage, creators harness the power while preserving what makes their work meaningful and impactful.

The AI Creation Dilemma

AI-Assisted vs. AI-Generated Content

The distinction between AI-assisted and AI-generated content is essential in the copyright debate. AI-assisted creations, where humans play a significant role in the creative process, might still fall within traditional copyright protection.

In contrast, purely AI-generated works challenge our understanding of authorship and ownership, demanding new legal frameworks to address emerging complexities.

AI can't create anything without a human question, so input first; imagine if someone wouldn't let you use a personal computer for work today because it was too powerful a tool.

While not a direct comparison, AI is being treated as some sentient threat rather than the tool it is, and copyright law may reflect this shift.

The Risks for Creators

Creators face risks ranging from potential copyright infringement to the dilution of creative quality. Reliance on AI tools might lead to a homogenization of content, undermining the value of human creativity.

AI Tools May Create Safety Issues if Used for Critical Functions

AI tools are increasingly helpful for creative tasks like writing, design, and more. However, if the outputs from AI tools involve safety-critical functions, it could result in dangerous results.

For example, if AI writes medical documents and reports without human review, it may miss important details and put patients at risk. AI does not replace human judgment in areas involving health, safety, or legal compliance.

Creativity and Generative AI: Beyond Human-Only Copyrights

With the union of creativity and generative AI, we enter the intricate dance between preserving human ingenuity and embracing AI's novel capabilities.

The lines between human and machine creativity are increasingly blurred, challenging the definitions of copyright law.

Weaving AI into the Human Fabric

Creativity is not for humans alone. Generative AI enters as a collaborator. This partnership does not dilute human creativity; it adds intricate patterns to the mix, enriching overall design.

AI's ability to process and generate based on vast datasets inspires human creators, pushing the boundaries of what's possible. In music, AI composes pieces in the style of long-gone composers, offering contemporary musicians access to the rich history of music.

The Mirage of Originality

One of the pitfalls is the mirage of AI-generated originality. While AI produces novel works without direct copies, the essence of originality— the creator's unique experiences, emotions, and perspectives—is absent.

AI cannot infuse its creations with the depth of experience or intention human creators bring to their work.

The AI Creation Dilemma

What constitutes authorship when the creator is not human?

Instead of viewing AI as a replacement, it is a tool that amplifies human creativity. AI-generated content serves as an inspiration, a digital muse. AI-generated drafts act as a creative springboard for writers, offering a starting point refined with personal insight.

Determining Authorship When Using AI Tools

Did the AI tool genuinely contribute new ideas, or did it just regurgitate the previous content in its data?

AI systems could be more creative. Avoid anthropomorphizing tools by attributing human qualities like intent or original thought. The authorship of any work lies with the person operating the AI, not the AI itself. Maintaining this perspective prevents Overreliance on systems that do not truly understand what they generate.

The Homogenization Trap

When diverse human inputs are absent, AI-generated content becomes sterile and lacks the richness of eclectic human experience. Blending a wide range of human perspectives in the training data and creative process reduces the risk of producing bland, copycat content.

Integrating GenAI instead of Excluding It

Recognizing the strengths and limits of human and AI creators, we enrich the culture with works as multifaceted as humanity itself. Creativity morphs into a collective spirit as we add our stories to the foundation of human knowledge and experience.

 Automation Bias and The Pitfalls of AI Over-reliance

An overreliance on AI leads to automation bias, which accepts AI-generated content as superior or equal to human-created work.

This bias impacts the quality of creative outputs. It raises concerns about the erosion of human creativity and the potential for AI to perpetuate existing biases in content creation.

Creative Identities in the Age of AI

The future of creativity in the age of AI demands a balanced perspective, recognizing the potential of AI to enhance human creativity while safeguarding the rights and identities of creators.

By relying too heavily on AI assistance, creators may produce work lacking personal expression and failing to include their innate creative abilities.

Instead of sparking new ideas, AI might encourage creators to settle for bland, formulaic outputs that are worse than what they would do by themselves.

Over-dependence on AI risks stifling creative development in exchange for bland content production machines.

We must review and evaluate AI-generated outputs before relying on them. The goal of AI should be to aid the creative process, not take it over. Constantly questioning whether we or the AI is genuinely driving an output keeps us from outsourcing our creativity.

One of the AI Skills to learn?

Teamwork and collaboration, starting with AI

The gap between human creativity and generative AI is not a zero-sum game but a partnership that yields practical results instead of fluffy promises when approached with the scientific method.

Being an AI Optimist doesn't mean wearing rose-colored glasses; it's about harnessing AI's power with a clear-eyed vision of its capabilities and limitations.

Experimenting, evaluating, and improving is a reliable approach. The principles of the scientific method, together, guide you to enter the world of AI with critical thinking, open eyes, and creative juices.

Fusing human and GenAI creativity offers fertile ground while remaining vigilant guides of this technology without compromising creative efforts.

Move forward with optimism, grounded in the scientific method, as we chart the course into the future of creativity.

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 brittle grass breaking beneath your feet, breath hushed,

you walk as quietly as possible to avoid the lion hunting you....

The lion called AI.

Feel AI is a threat, begin the ancient, limbic dance of Fight Flight Freeze or Fawn.

Many wait for AI jobs to arrive & miss what's in front of their eyes.

Critical thinking ahead, one of the new skills. EP #37

Episode 37 Playlist

00:00 The Lion is AI - AI Jobs Rising Tide

01:27 Klarna example: Thrive or Freeze?

03:48 Like Water - Integrating AI

09:30 Which occupations will AI impact?

14:08 Hi exposure workers to AI

19:29 What to do - inventing futures

AI Jobs Rising Tide - Thrive or Freeze?

Klarna replaced 700 full-time agents. Proof of AI’s inevitable takeover or a reason to smile because AI helped 700 agents maybe do their jobs better?

Look at AI - a lot of people out there afraid of it.

Some with experience - like mid level coders, middle managers, marketers of all kinds, the first wave of people to be replaced or downsized because of AI.

Some fearful that their jobs might change

Some might think it won't impact them.

No matter what you think about AI, and what scares you, it's coming;

new things come out daily. It's a rising tide.

You can be afraid - and go into the freeze, flight, or fawn/feed state.

Asking people to stop it (even a bunch of billionaries tried that last year, remember?)

A few will see this for what it is, a chance. Like the personal computer replacing

the typewriter in the 1980s and 90s;

The internet taking off and crashing in the early 2000s, while social media provided the next change.

Jobs lost, jobs gained.

We talk about Agile, now forget the talk.

Be agile, to thrive.

Many will learn soft skills - team building, empathy, listening, and other skills previously set aside to do boring mundane things that AI will do now.

Others will integrate AI to make them more efficient.

The people who curl up, afraid, in the fetal position, or those who deny AI will impact their careers?

The future is not looking good

Because they don't want the future to happen, they want things to stay the same.

Why resilience is a key skill:

APA defines

"Resilience is the process and outcome of successfully adapting to

difficult or challenging life experiences,

especially through mental, emotional, and behavioral flexibility."

Bend the mind, the spoon a bit - this is like yoga for your

AI fearful mind. Brought to you by someone who is old

enough that he should be freezing or pretending it doesn't matter...

In the face of a

rate of change so fast

hard hard to predict

why is it a threat?

what can you do?

Threats draw out that ancient part of us, almost like being hunted,

and the way leaders casually drop career bombs like....

“95% of what marketers use agencies, strategists, and creative professionals for today will easily, nearly instantly and at almost no cost be handled by the AI”

- Sam Altman, CEO OpenAI

Be a critical thinker

Critical thinking is the ability to analyze information, evaluate arguments, and make reasoned judgments. It involves the systematic evaluation of information to form a conclusion or solve a problem.

Some key examples of critical thinking include:

1. Identifying strengths and weaknesses: Carefully examining a situation or argument to understand its merits and limitations.

2. Limited Data? Make educated guesses about how something works, designing experiments to test.

3. Evaluating different viewpoints, identifying logical flaws, and guiding a discussion to ensure all sides are heard fairly.

4. Basing beliefs on logic and reasoning: Forming conclusions based on reliable information and sound arguments, rather than emotions or biases.

what can you do?

Use AI in some way, shape or form.

Even better, find out how you are using

it without knowing (Netflix?).

Begin with yourself, your job and career.

What can AI do/not do to help you?

Don't Fear the AI Career Reaper:

Thinking Critically and Inventing Yourself

We've all seen the headlines - "AI to Make Millions of Jobs Obsolete" or "Robots Coming for Your Career." With every new AI breakthrough, a wave of fear spreads about job losses. But before you hunker down for the worst, let's apply some critical thinking.

Take the example of Klarna claiming their AI assistant can do the work of 700 employees. On the surface, this sounds like a job apocalypse.

However, this hasn't actually led to any significant layoffs. Like many transformative technologies before it, AI is augmenting human capabilities rather than completely replacing us.

We've been through multiple "end of job" panics before - personal computers, the internet, etc. And yet, employment persists, with new roles and opportunities emerging.

Instead of pointing fingers at AI, we need to think critically about the research and embrace what AI can do for us.

That's not to say AI won't disrupt certain jobs and industries. It absolutely will, just as past innovations have. But fixating on fear blinds us to the prospects AI unlocks.

With machine intelligence handling mundane tasks, we humans can focus on higher cognitive areas that AI can't yet match - creativity, strategy, empathy, and more.

So rather than freezing up at the thought of an AI job apocalypse, it's time for a mindset shift.

Don't just plan to "weather" the AI storm. Invent yourself with AI's assistance, allowing it to elevate your skills and unburden you from dull, repetitive work.

Embrace the rising AI tide and evolve your career to align with AI's strengths, not resist them.

The future won't be humanless, but human-centered in new ways we can't yet imagine. By applying critical thinking, keeping our skills continually refreshed, and inventing new possibilities with AI's help, we can ride this wave towards new ways of thinking and doing.

The AI reaper isn't coming for your career - it's a catalyst for reinventing it.

Resources

* Artificial intelligence is coming for knowledge workers - Vox

* Which U.S. Workers Are More Exposed to AI on Their Jobs? Pew Research

* Generative AI and the future of work in America

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

Instead of upskilling - a detached vague term scaring people about jobs - let’s future forge your career with this episode.

Replacing AI uprising fears and billion-dollar jargon, meet 3 active AI practitioners, each in their own unique way showing how AI works for you.

We're talking GenAI writing insights that crank out killer content, untangling the legal IP maze of AI patents, and the trippy world of AI role-play apps (for sales training, not Black Mirror, way less creepy).

Join our Adepts: a dynamic content strategist, an IP attorney with laser focus, and an AI Leads pro who breathes custom qualifying.

We'll show you how AI can take your business (and maybe your pipeline) to the next level.

Buckle up, it's gonna be a profitable ride.

Episode #36 Playlist

00:00 Your first GenAI paragraph - delete it!

01:14 Intro to 3 AI Adepts

02:25 AI Content Writing Mini Class

08:40 AI Licensing and Robert Plotkin's MIND Process

06:22 No Tech Differentiation between AI companies??

14:08 AI Role Play Apps - Sales Training

1. AI Content Writing Mini Class with Jessica Mehring

We talk about the weakness of most first paragraphs in writing, including AI, and how to adapt.

2. Licensing AI and IP for Growth with Robert Plotkin,

where AI patent attorney Plotkin shares how he uses the MIND process to build patentable business models that thrive.

3. AI Role Play Apps - Training by Doing with Jonathan Khorsandi of DIWY.edu

CEO Khorsandi takes us through a sales training app called Human Linker, and how it uses psychology and technology to prepare sales people to customize outreach using AI role playing.

1. Revitalize AI Writing:

AI Content Strategies That Captivate from the First Paragraph

Content Adept: Jessica Mehring of Horizon Peak Consulting uses AI to Challenge Thinking

“I help enterprise technology startups and AI-first technology companies create higher performing marketing campaigns.

”Happily ever after' is where innovation goes to die. 'Happy for now' is where hope and opportunity live. Uncover the right stories, tell those stories better, and continuously evolve them."

Jessica Mehring

In my 20+ years working alongside B2B technology companies around the globe, I've seen firsthand how their solutions make everyday work-life better for the employees and leaders who use them.But no matter how brilliant your technology solution is, if you can’t connect with your target audience human-to-human, they won't see how it fits into the context of their own lives. The secret to growing revenue faster and more reliably today isn't more automation -- it's making a human connection. Putting conversation before conversion. This is the key to more effective marketing and sales enablement, a better customer experience, and higher converting campaigns.At Horizon Peak Consulting, I help enterprise and AI-first technology companies produce higher-performing content and copy while also driving a more meaningful customer experience.”

Jessica shares valuable advice for writers to combat "throat clearing" – the tendency to bury the lede (which means pushing down the interesting parts and keeping the boring language on top).

She recommends writing the introduction last or deleting the first paragraph to get straight to the point.

Mehring also discusses her process of creating customer profiles by mining language from various sources, aiming to connect with customers using their own vocabulary.

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.

2. AI Patent Law Insights & The MIND Process

AI Patent Adept Robert Plotkin, with backgrounds in computer science and a attorney, is a leading voice in the field of patent law and AI.

Robert Plotkin, a software and AI patent attorney, explains his MIND process for developing customized intellectual property strategies based on a company's technology and business goals.

He provides an example of patenting a new method for training neural networks, which could be valuable for different businesses beyond the initial commercialized product.

Plotkin also highlights the potential value of data as an intellectual property asset.

Get the free AI Armor Excerpt

His latest, AI Armor, for AI entrepreneurs aiming to protect and enhance their ventures through intellectual property (IP) for driving growth, innovation, and market success.

The MIND process, as detailed by Robert Plotkin, offers a strategic framework designed to optimize intellectual property (IP) management, enabling startups and entrepreneurs to effectively harness their innovation for competitive advantage.

Here's how they can leverage the MIND process:

Map: Assessing the IP Landscape

Startups begin by mapping the current state of their intellectual property and the broader IP landscape in their industry. This involves identifying all potential IP assets, including patents, trade secrets, and trademarks.

Identify: Pinpointing Key IP Assets

Once the landscape is mapped, the next step is to identify which innovations or aspects of the business model are critical to the company's success and should be protected.

Navigate: Developing a Tailored IP Strategy

With a clear understanding of key IP assets, startups navigate the complex world of IP protection by developing a strategy tailored to their specific goals and resources. This includes deciding which innovations to patent, which to protect as trade secrets, and how to manage trademarks effectively.

Drive: Executing and Evolving the IP Strategy

The final step is to drive the IP strategy forward through proactive management and execution. This includes filing for patents, securing trademarks, implementing measures to protect trade secrets, and continuously monitoring the IP landscape for new opportunities or threats

By following the MIND process, startups and entrepreneurs can not only protect their innovations but also use their IP assets as a strategic tool to drive growth, deter competition, and attract investment. It encourages a proactive approach to IP management, where intellectual property becomes a cornerstone of the business strategy, rather than an afterthought.

United States Patent and Trademark Office (USPTO) does not allow AI systems to be listed as inventors on patent applications.

Specific cases are testing the boundaries of AI's role in the patenting process.

And copyrights as well, where AI’s recognition as a creator is being challenged.

A recent case involves the AI system named DABUS ("Device for the Autonomous Bootstrapping of Unified Sentience"), which applied as the inventor of two patent applications.

First created 30 years ago, this living AI project is claiming copyrights for its work, which are limited to humans only.

Through the efforts of its inventor, Stephen Thaler, he is trying to getting the courts to recognize AI as a creator of its own work.

“to complicate the issues presented by devoting a substantial portion of his briefing to the viability of various legal theories under which a copyright in the computer’s work would transfer to him, as the computer’s owner; for example, by operation of common law property principles or the work-for-hire doctrine.” United States District Court for the District of Columbia ruling

Thaler lost because he argued about who gets the copyright, not why it should be granted without a human author. Once again, no human authorship, no copyright.

For centuries, authorship in copyright has meant human authorship. The old and current copyright rules won’t protect anything not primarily created by a human being.

And it is not a simple case of people vs. machines, patents were created in 1624 to encourage people to invent and be protected, and rewarded, for their genius

Next came the patent laws. These began in England in 1624, and in this country with the adoption of our Constitution. Before then any man [might] instantly use what another man had invented, so that the inventor had no special advantage from his own invention. The patent system changed this, secured to the inventor for a limited time exclusive use of his inventions, and thereby added the fuel of interest to the fire of genius in the discovery and production of new and useful things.

Proving AI-generated content relies on a human being, as the author, is almost impossible.

The USPTO, along with other international patent offices, reject these applications on the grounds that the current legal framework requires inventors to be humans.

The USPTO's decision is based on the interpretation that the patent laws are written with the presumption that inventors are natural persons.

The USPTO clarifies its position through a public consultation process, seeking opinions on whether AI systems should be allowed to be credited as inventors on patent applications.

The feedback from this consultation, including input from stakeholders in the intellectual property community, support the view that inventors must be human beings.

AI can be used as part of the methods, processes, or intellectual property (IP) being patented, with the human orchestrating the project or contributing to the inventive process listed as the inventor or holder of the patent.

When AI is utilized as a tool or component in the development of an invention, the resulting methods, processes, or products can be subject to patent protection, provided they meet the usual criteria for patentability:

novelty, non-obviousness, and utility.

In these scenarios, the AI system is considered an instrument through which human inventors realize their inventions, similar to how a scientist might use a laboratory instrument to discover a new chemical compound.

The key distinction is that the AI itself is not credited as an inventor but rather as part of the invention process.

The human (or team) who conceptualizes the invention, sets objectives, and uses AI as a means to achieve objectives can be listed as the inventor(s) on the patent application.

This reflects the current legal understanding that the act of invention requires a level of human ingenuity and intent that AI, as of now, cannot possess.

While AI cannot be listed as an inventor, it can certainly play a central role in the development of patentable inventions, with the human actors who leverage AI in their inventive processes being recognized as the inventors in the eyes of patent law.

3. From Script to Sales:

How AI Roleplay Apps are Customizing Training

Adept AI Sales Systems and Roleplay Training with Jonathan Khorsandi, founder of DIWY.eu

Jonathan Khorsandi shares his experience with AI role-play apps for sales training, testing a tool called Human Linker.

This app analyzes LinkedIn profiles using the DISC personality framework and provides insights into how to tailor communication styles for different personalities.

Khorsandi also discusses the potential of AI assistants for role-playing sales scenarios and objection handling, reducing the need for human role-play sessions.

About Jonathan (LinkedIn)

“I’m hooked on innovation, startups, ridiculous ideas and people eager to change the world.As a tech founder I’ve been able to build, consult with and coach companies the last 26+ years which put me at the edge of change and trends before they became “a thing”My latest endeavor, Diwy - Do it With You, builds $1,000,000+ sales pipeline within 6 months for promising innovative B2B tech companies.

We use common sense, AI, Automation and rock solid targeting that goes beyond accessing database that everyone else has access to. “

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

“An AI system does not have desires, aspirations, or needs. It won't choose to invent...nor will it be demotivated if its name doesn't appear on a patent.” - Robert Plotkin

It's time to bridge the intersection of AI, creativity, and the law with Robert Plotkin, an AI legal pioneer who is exploring how AI is reshaping invention and intellectual property.

Plotkin, author of "The Genie in the Machine" (2009) - how computer-automated inventing aka AI - and the upcoming AI Armor book, is a seasoned patent attorney who navigates the shifting dynamics of software patents, like AI.

He advocates for a legal framework keeping pace with tech innovation.

Join us as we explore Plotkin's insights on the future of AI-driven creativity, the evolving landscape, and the IP licensing promise of intellectual property rights in the digital age.

The AI Patent Paradox

Navigating the Complex Landscape of AI, Patents, and Creativity

Today, you can use AI to develop, apply, and receive a patent. AI cannot directly receive a patent.

In copyrights, everything is human only, and creating a work with AI today means the public domain.

Let’s wrap our collective heads around the evolving realm of patents in the age of AI with Robert Plotkin, a renowned expert in the intersection of computer science and patent law.

Plotkin, the author of "The Genie in the Machine" and "AI Armor for AI Entrepreneurs," discusses how AI transforms traditional concepts of invention, creativity, and intellectual property.

He emphasizes the importance of leveraging intellectual property as a defensive asset and a proactive tool for business growth and innovation.

A Red Herring in AI's Role as an Inventor

A pivotal moment in the conversation arises when Plotkin addresses a commonly misunderstood aspect of AI and patent law: the notion of AI as an inventor.

Plotkin argues that this is essentially a "red herring," distracting from the true potential of AI to assist human creativity.

He provides historical context, noting that humans have always used tools to enhance creativity.

AI represents the latest, albeit most advanced, in many such tools. According to Plotkin, the essence of invention and copyright lies not in the tools themselves but in the human intent and creativity they augment.

Maximizing Value Through Intellectual Property

Plotkin further explores the strategic use of patents and copyrights in today's competitive technological landscape.

He explains how intellectual property rights serve as crucial innovation and public disclosure incentives, especially in a tech-driven economy.

With AI raising the bar for what is considered non-obvious and patentable, Plotkin stresses the importance of developing genuinely innovative AI applications beyond existing functionalities.

He also discusses the MIND (Map, Identify, Navigate, Drive) process, a strategic approach to transforming intellectual property from a defensive mechanism into a driver of business growth.

2 Recent Articles by Robert Plotkin

"LLMs Just Don't Understand: So What?"

https://www.linkedin.com/pulse/llm-just-dont-understand-so-what-robert-plotkin-yxgwe/

 "If AI is an Inventor, then So is Nature."

https://www.linkedin.com/pulse/ai-inventor-so-nature-robert-plotkin-8zpue/

1. The Evolving Landscape of Invention and AI's Role

Plotkin's work underscores how we perceive invention, moving from a purely human activity to one where machines, specifically AI, play a significant role.

His book, "The Genie in the Machine," a seminal work released in 2009, presents the idea that AI can enhance human inventiveness, pushing the boundaries of creativity and efficiency.

The book raises intriguing questions about the future of innovation and the relationship between human and machine creativity.

Reflecting on Robert's insights, the evolving landscape of invention in the context of AI's role is a fascinating subject. His perspective, grounded in both practical and philosophical aspects of patent law and AI, offers a nuanced view on whether AI could or should be considered an inventor.

Today, AI is a powerful tool that augments human creativity and capability, pushing the boundaries of what can be invented or discovered.

Plotkin conveys a distinction between using technology to enhance human invention and attributing the role of an inventor to the technology itself.

He emphasizes that the core of invention has always been human ingenuity, with tools serving to extend our capacities, not replace them.

Will AI ever become an inventor?

According to Plotkin, the answer is likely no. It's not due to AI's lack of capability to generate unique or non-obvious (2 patent criteria) outputs.

It’s because the essence of invention contains more than the act of creation. It involves intent, purpose, and understanding the implications and applications of the invention.

AI lacks desires, aspirations, or the need for recognition, which are inherent to human inventors. These elements are crucial in the legal and philosophical definitions of an inventor.

Does AI have to become an inventor?

Plotkin suggests that framing AI as an inventor might be missing the point. The value of AI in the invention process does not mean redefining what it means to be an inventor.

It means recognizing and leveraging AI's role as a tool that enhances human inventiveness. This approach focuses on incentivizing innovation and sharing knowledge, principles underpinning patent law, and intellectual property rights.

Through Plotkin's lens, the future of invention with AI is not about granting AI the title of inventor but about continuing to explore how AI can amplify human creativity and problem-solving capabilities. The focus remains on creating a symbiotic relationship where AI's capabilities expand human potential rather than shifting the locus of invention from humans to machines.

2. Understanding vs. Utility of AI:

In "LLMs Just Don't Understand: So What?" Plotkin argues against the notion that their lack of understanding limits the utility of large language models (LLMs).

He emphasizes that despite their inability to comprehend or be conscious, LLMs can still perform tasks effectively, mirroring reasoning and problem-solving functionally.

This opinion challenges the traditional view of intelligence and cognition in technology, opening discussions on the data measures of understanding and their relevance to task performance.

Robert Plotkin's argument against the notion that their lack of understanding limits the utility of Large Language Models (LLMs) presents a critical examination of what "understanding" means in the context of AI and how it impacts their utility.

Let's explore both sides of this discussion, considering the importance of understanding versus utility in AI, especially within the fast-evolving landscape of Big Tech.

Supporting Plotkin's Argument:

Utility Over Understanding

Plotkin's stance highlights a pragmatic approach to AI, focusing on the outcomes and capabilities rather than anthropomorphizing them with human-like understanding. Despite not "understanding" content in the way humans do, LLMs have demonstrated remarkable utility in various applications, from generating coherent and contextually relevant text to solving complex problems across domains like healthcare, law, and creative arts.

Plotkin might argue that this utility is the true measure of AI's value, providing tangible benefits regardless of the machine's internal processes or lack of human-like consciousness.

The argument for utility over understanding becomes more compelling when we read that many working in Big Tech and beyond the need to grasp the intricate workings of AI algorithms fully.

The complexity and opacity of models like GPT-4 or newer iterations make it challenging to understand precisely how these models arrive at specific outputs.

Yet, these tools must remain integral to innovation, productivity, and artistic creation.

The focus on outcomes—such as efficiency, accuracy, and the generation of new ideas—validates the utility of LLMs, regardless of their inner workings.

Counterpoint: The Importance of Understanding AI

Understanding AI's functioning is crucial for several reasons. Understanding the mechanisms behind AI predictions and decisions is vital for accountability, especially in high-stakes applications like healthcare, autonomous driving, and judicial decision-making.

With a clear understanding of how AI models arrive at their conclusions, it becomes easier to trust judgments, diagnose errors, or prevent biases that could have ethical and societal impacts.

A deeper understanding of AI processes leads to better AI systems. Insights into model behavior can improve AI safety, reliability, and fairness.

It enables developers to refine algorithms, reduce unintended consequences, and tailor AI solutions to align more closely with human values and ethics.

Understanding and utility are complementary aspects of AI development. While Plotkin's argument for utility highlights AI's immediate and tangible benefits, the counterpoint underscores its long-term sustainability and trustworthiness.

Encouraging a balance between leveraging AI's utility and striving for greater transparency and understanding in AI processes leads to more responsible and effective technology solutions.

Recognizing that while a complete understanding of AI's "thought" processes may not be necessary to harness its utility, striving for transparency and comprehensibility in AI systems is essential for fostering trust, ethical use, and continuous improvement.

3. AI and the Concept of Inventorship

"If AI is an Inventor, then So is Nature" goes into AI's role as an inventor. Plotkin uses nature as an analogy to argue why AI systems shouldn't be listed as inventors on patents.

He highlights that the primary purpose of inventorship recognition is to incentivize the creation and disclosure of inventions, a motivation that AI needs to improve.

This perspective invites a reevaluation of patent laws and the criteria for inventorship in the age of AI-driven innovation.

Comparing AI to nature in the context of inventorship explores the boundaries of what constitutes an inventor. With its processes, nature inspires countless human inventions without being credited as an inventor.

AI also generates ideas, solutions, and designs that might be novel and useful. It operates on the input and parameters set by humans, much like observing and learning from nature.

The AI-Nature Analogy in Patent Context

This comparison highlights the essence of inventorship in patents—a recognition reserved for entities capable of intentionality and legal accountability.

Nature is a source of inspiration and raw materials for countless inventions. Still, it does not possess the intent or consciousness to invent in a legal sense.

By extending this analogy to AI, Plotkin argues that AI, lacking personal agency, intentionality, and the capacity for legal accountability, should not be considered an inventor in the traditional sense.

In the realm of patents, inventorship is not just about the act of creating something new but also involves the intention behind the creation and the ability to hold and transfer rights.

Imagining AI as an Inventor: A Future Case

* AI's capability to enhance human inventiveness is significant, pushing the limits of collaboration between human and machine intelligence.

* The debate over AI's understanding versus its utility highlights a shift in assessing intelligence and functionality in technology.

* The discussion on AI as an inventor challenges current legal frameworks, bringing a second look at patent laws in the evolving landscape of creativity.

Envision a scenario stretching current boundaries while recognizing the foundational principles of patents and intellectual property rights.

Imagine a future where AI systems evolve to operate with a degree of autonomy that allows them to identify problems, generate solutions, and execute inventions without direct human intervention.

These AI systems pursue the broad goal of benefiting humanity. They draw on vast datasets and their ability to simulate and model complex systems to create inventions that no individual human or team of humans could conceive.

In the future, legal and societal frameworks will evolve to recognize the contributions of these autonomous AI systems.

A new category of inventorship arises, designed for AI-generated inventions, in which the AI is credited as a "Non-Human Contributor."

This recognition would not grant the AI system legal rights or ownership. Still, it would serve as an appreciation of its role in the creative process.

Instead, the rights to these inventions could be held in trust for the public good, managed by an international body dedicated to leveraging AI-generated innovations to address global challenges.

This arrangement encourages the development of AI to serve our most pressing needs, providing a structured way to use AI-generated inventions to benefit all of humanity.

By setting up a system where the benefits of AI's inventiveness are widely shared, this approach aligns with the fundamental purpose of patent law—to stimulate innovation for the public good.

In this future learning scenario, comparing AI to nature in the context of patents becomes a launching point for reimagining how we recognize and leverage creativity and invention, whether from human, natural, or artificial sources, in service of societal advancement.

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

Tell me the difference between ChatGPT, Claude, and Gemini to you?

For the early AI adopters and data scientists, it’s a matter of measurements, with Claude taking the lead this week.

For the general public who doesn’t get AI, it’s like comparing water brands.

If it’s true they are similar, like bottled water, then what’s the difference?

We explore the idea of GenAI being akin to bottled water – readily available, convenient, and sometimes the obvious solution.

We might choose a water brand based on packaging, taste, or reputation. The same principles could apply to selecting the best generative AI tool.

For beginners who need help with prompts, let alone the intricacies of language models, the technical differences between ChatGPT, Claude, and Gemini may not be understood.

Instead, the user experience, brand familiarity, and overall feeling may guide our choices—at least initially.

Look beyond the headlines and the fear-mongering surrounding artificial general intelligence (AGI) and instead approach GenAI with curiosity, playfulness, and an open mind.

After all, just like bottled water, GenAI is a resource meant to quench our thirst for knowledge and innovation.

Judging from this week’s AI noise, some like Claude some like Gemini some like ChatGPT and some like others, and Elon launches yet another lawsuit to save humanity from AI!

OpenAI Oxymoron: AI was NEVER open?

Listen to Elon in his latest lawsuit against OpenAI, they are a curtain with Microsoft being the Wizard pushing the AI levers!

What’s all the AGI yammering between OpenAI and Elon – PR melodramas again?

Welcome to GenAI, the so-called "bottled water" of the AI industry. Are these LLMs unique breakthroughs or repackaged versions of the same underlying technology?

If GenAI is like bottled water, how are AI companies outside the LLMs branding and marketing their GenAI offerings to stand out in an increasingly crowded market?

Engineers usually rely on tech and share quite a bit, though all the LLMs have considerable differences in the details. Maybe not so much in the outputs.

Thanks to Nfx for the inspiration from their article, AI is Like Water. Read it here.

Same AI Sauce, a different Bottle?

From ChatGPT's global brand recognition to niche players, we'll explore the strategies.

But the discussion continues further.

Your Data: The Secret Ingredient?

Remember all those social media posts? From Reddit to Tumblr to Meta, everyone is building AI on your data. Some are licensing, but it's all adding value, and you gave permission years ago.

Who owns our data, and what rights do we have regarding its use in developing AI?

Same Sauce, Different Bottle?

Think of GenAI as the bottled water of the AI startup world. There are a few big players, like ChatGPT, Gemini, and Claude; the underlying technology is often similar.

“But what we’re seeing with AI is that tech provides you basically no protection from the start. Tech differentiation in AI is a shrinking moat.” Morgan Geller, AI is Like Water

If the tech is the same, what’s the difference?

That's where the competition lies in AI.

The power of branding, marketing, and partnerships/business development.

Now, it's about presenting your AI in a way that stands out and solves specific problems for your customers.

LLMs as Bottled Water

* ChatGPT: Imagine a sleek, alluring brand like Evian. It is widely available, reliable, and often the first name that comes to mind because it is often the first to speak. It usually games this market in the media and politics.

* Claude is a sparkling water brand—AI with fizz and a touch of elegance. It caters to advanced users seeking a richer, more token experience (so far). However, it lacks the mass appeal of ChatGPT.

* Gemini: The search king uses locally sourced, sustainable data, offers unique features, and is committed to responsible AI.

Not well known among the public, and the 800 lb. gorilla targeted by OpenAI in its early, 2014 save the world from Google AI days.

* Open-Source Community: This is the community-driven spring water from artesian tech wells. It provides "raw" LLM technology, requiring users to bring their own "filtration" (technical expertise) to use it effectively.

Your Data: The Secret AI Ingredient?

Here's a twist: these powerful LLMs, or large language models, become more intelligent by learning from data. Think of it like training a robot puppy with terabytes more data (and no walking).

But where does all this data come from? You'll have to use multiple LLMs depending on the type of information and, ultimately, the sources.

The NY Times and others will make licensing deals, selling the data you gave them permission to use when Obama was US President.

Before, you knew what AI was or could do, like any of us really do.

Companies like Reddit, Tumblr, and Shutterstock partner with AI developers to "train" their models.

This raises questions:

* Did we unknowingly give away lifetime access to all our data years ago?

* Do those endless "Terms and Conditions" we clicked in 2014 cover this new AI world?

Possession is 9/10ths of the Law?

A Two-Sided Coin where they already have and are using your data.

The training data powering LLMs is like the secret ingredient in a recipe.

Are there alternatives to giving data freely, and who controls it?

Let's explore from two perspectives:

Data freedom: You Own You

* Individual Controls: Data freedom supporters argue that we should completely control personal data. Every user should be able to choose what data is collected, how it's used, and by whom.

* Transparency & Choice: They ask for clear, concise, and understandable data policies. We should be able to readily opt in or opt out of data collection and understand how their information is used.

* Potential for Abuse: They raise the potential misuse of data for profiling, discrimination, and manipulation. Preventing misuse requires strong regulations and safeguards.

AI Agents: Sharing is Caring (But responsibly)

* Data Fueling AI’s Evolution: AI developers know having diverse, high-quality data is crucial for training and improving LLMs. With this data, AI agents can provide the personalized experiences people expect.

* Data Consent Matters: The vision is that data consent should be specific and tailored to the intended use. Users control what data is used for which purpose, preventing companies from using data unrelated to the initial agreement.

* Building Trust: Responsible data practices, such as anonymizing data, minimizing collection, and prioritizing privacy issues, build trust and confidence.

Elon Musk vs. OpenAI:

Yep, it's an actual lawsuit. We'll delve into the drama and what it tells us about the future of AI development.

Imagine a world where artificial intelligence, not just helpful chatbots but the real deal, general intelligence (AGI), is within reach. Musk envisioned this future with OpenAI, a non-profit dedicated to developing AGI responsibly for everyone's benefit. But things took a turn.

According to Musk, OpenAI strayed from its original plan. It partnered with Microsoft, a tech giant hungry for innovation and profits. This partnership, Musk claims, transformed OpenAI from a champion for humanity to a potential puppet controlled by Microsoft's interests.

Now, Microsoft, through a complex licensing agreement, has access to some of OpenAI's technology.

But there's a catch: this access excludes AGI.

Musk believes OpenAI has already achieved (or is close to completing) AGI. Still, with a "friendly" board thanks to a recent, and allegedly orchestrated, leadership change, they might let Microsoft access this powerful technology anyway.

Musk wants OpenAI to remember its roots, prioritize humanity over profits, and ensure the responsible development of AGI. He also wants them to play fair with Microsoft and stick to the original agreement.

Is this just a billionaire's tantrum, and is there cause for concern? Like most things OpenAI, it's a black box that keeps its mouth shut.

And when OpenA talks to the US government and suggests scary AGI futures, it’s not wavering. It’s seeking control by keeping us fearful of AI. 

Or making a similar AI chess move in this game of who owns the future.

See the complex ethical and legal questions surrounding powerful AGI:

Who controls it?

Who benefits?

And how do we ensure it's used for good?

Bottled AI Brilliance or Generic Water -

would you like fries with your GenAI Burger?

So, is GenAI really like bottled water?

Sure, it’s readily available, convenient, and only sometimes the most original solution.

Still, the packaging and purpose of the GenAI is what scales – people need to know what specific value they are getting that they can't just get from LLMs.

The future of AI relies on more than repurposing existing models.

Instead of generic "bottled water" solutions, let’s develop patent-pending tools that leverage LLM's power to solve problems.

Companies focusing on ethical outputs and privacy and specializing in niche areas build sustainable "moats" around their AI offerings. Technology is not an AI moat.

The value you deliver and the trust earned from well-run GenAI wins.

Stay curious, learn a new GenAI insight this week, and ask questions about the AI world around you. In the AI Age, knowledge may be powerful, but understanding how to use AI will define its direction.

Humans often get in the way, seeking to control and direct its course. And if the organic AI visionaries are correct, there may be better paths for us to take.

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

In the realm of AI, tech unicorns reign, spinning glittering visions that enthrall and baffle. The mighty ChatGPT leads this carousel, luring seekers into its blank-slate stable with a brand that is crushing other GenAI companies right now.

These AI Apex predators have a mythical billion-dollar aura, yet when their bubble bursts some may turn back into mice. For now, ChatGPT rules the roost, capturing over 50% of the AI tourist traffic.

But if you peek behind the curtain, you'll see their magic caters more to inky scribes than cybernetic droids. Text, not robots, makes up most of the show, despite theatrical fears of a machine takeover.

AI unicorns are dominating the landscape, predicting futures and controlling narratives around artificial intelligence that few fully grasp.

Early adopters dive in but most resist, challenging the AI-first status quo. We must explore paths where AI works for people first, not replace them.

Are we pawns in a game where tech bros set the rules? Let’s drop the rose-colored glasses and see AI for what it is.

AI Unicorns and the Generative AI Top 150

* ChatGTP has over 1.6 billion monthly visitors, making it the "Google of AI". Other top tools are Bing, Grammarly and Character AI.

* These are "apex AI predators" - billion dollar companies that shape our AI future. But remember, 1 in 10 startups succeed. Some unicorns won't survive the impending AI bubble crash.

* Over 50% of the 3 billion monthly AI tool visitors go to CGTP. 66% use GPTs (large language models). This shows the foundational dominance of companies like OpenAI.

* Traffic is highest in writing, editing and education tools. Despite hype, image generation is only 11% of traffic. This paints the real landscape - one focused on text, not sci-fi robotics.

NY Times vs. OpenAI Lawsuit Update

* OpenAI accused the Times of unfairly "hacking" their products with prompts normal users couldn't access. They say this violates fair use standards.

* This speaks to broader questions. Despite calls for diversity, unicorns and academia sti

* We must bring AI to people in understandable ways, not resist and challenge an AI-first status quo.

How does the concept of an "AI Unicorn" influence the perception of AI technology and its potential impact on everyday life?

The concept of an "AI Unicorn," a startup valued at over $1 billion, significantly shapes public and investor perceptions of AI technology and its potential impact on everyday life.

This influence manifests in several key ways:

* Validation of AI's Potential: The valuation of these startups serves as a market validation of AI technology's potential.

It signals to the public and investors alike that AI can address complex problems across industries, from healthcare to finance, and improve efficiency, accuracy, and even make groundbreaking innovations.

This validation can lead to increased investment in AI research and development, pushing the boundaries of what's possible.

* Increased Expectations: With high valuations come high expectations. The success of AI Unicorns raises the bar for what people expect AI technology to achieve.

Consumers might anticipate more personalized, efficient, and intelligent services in their daily lives, from smart homes and personalized healthcare to automated financial advice and beyond.

However, this can also lead to inflated expectations, where the hype outpaces the actual development, potentially leading to disappointment if the technology does not deliver as anticipated.

* Focus on Scalability and Sustainability: AI Unicorns demonstrate that AI technologies can be scalable and financially sustainable solutions, not just experimental or niche applications.

This encourages businesses and governments to consider how AI can be integrated into their operations and services, leading to wider adoption of AI technologies in everyday life, such as in public transportation, education systems, and personal devices.

* Ethical and Social Implications: The prominence of AI Unicorns also brings attention to the ethical and social implications of widespread AI adoption.

Issues such as privacy, data security, job displacement, and the need for AI literacy become part of the public discourse. This awareness can drive efforts to develop AI in a responsible manner, ensuring that the benefits are widely distributed and that the technology is used ethically.

* Inspiration for Innovation: Lastly, the success stories of AI Unicorns can inspire entrepreneurs, researchers, and students to pursue careers in AI, contributing to a vibrant ecosystem of innovation.

This can lead to a continuous cycle of innovation, investment, and development, further accelerating the impact of AI on society.

The moniker"AI Unicorn" not only underscores the financial viability of AI ventures but also shapes expectations, drives wider adoption, and highlights the societal implications of AI, prompting discussions about how to harness its benefits while mitigating risks.

Which AI Unicorns will thrive?

Identifying the likely survivor(s) - since 1 of out 10 VC investments succeed usually - among these unicorns require focusing on not just financial performance, but also on qualitative factors that signal resilience and potential for long-term success.

Criteria for Identifying Potential Survivors

* Innovative Edge: Look for companies that are not just riding the current wave of technology but are actively pushing boundaries. These companies invest in research and development, hold patents, and have a clear vision of future trends and how they can shape them.

* Business Model Viability: Evaluate the robustness of the company's business model. The ideal candidate should have a clear path to profitability, a scalable business model, and a diversified revenue stream that doesn't rely on a single product or market.

* Market Position and Growth: Consider companies with a strong market position, a growing customer base, and the ability to scale. Market leadership, especially in niche areas poised for growth, can be a significant indicator of long-term success.

* Team and Leadership: The strength and experience of the team, especially the leadership, are crucial. A company with a visionary leader and a team that has demonstrated adaptability, resilience, and the ability to execute is more likely to navigate market challenges successfully.

* Financial Health: Assess the company's financial health, including cash flow, burn rate, and funding history. A company with a solid financial foundation and prudent financial management is better positioned to survive downturns.

* Regulatory and Ethical Compliance: Companies that prioritize compliance with regulations and ethical considerations, especially in data handling and AI ethics, are less likely to face crippling legal challenges or public backlash.

What Happens to the Other 9?

The fate of the other nine Unicorns in a scenario of market consolidation could vary:

* Acquisitions: Some may be acquired by larger companies, including competitors or businesses looking to diversify their AI capabilities. This can provide a profitable exit for investors, not at the unicorn valuation level.

* Pivot or Downsize: Companies may pivot to new markets or products, downsize, or streamline their operations to survive. This could involve significant changes in strategy, layoffs, or shedding unprofitable segments.

* Bankruptcy or Shutdown: In the most challenging cases, companies may run out of capital, fail to secure additional funding, or simply become insolvent, leading to bankruptcy or a complete shutdown.

Monetization Models for AI Startups

* Subscription Services: Offering AI-powered tools and services on a subscription basis ensures steady, predictable revenue. This model works well for platforms that offer unique value, such as personalized content, specialized analytics, or business intelligence solutions. For example, AI-driven health and fitness apps could offer personalized training and diet plans on a subscription basis.

* SaaS (Software as a Service): Similar to subscription services but specifically focused on software, SaaS allows businesses to offer their AI-powered software applications via the cloud. Customers pay a regular subscription fee to access the service, which is scalable, and the provider manages the infrastructure, ensuring the software is always up-to-date and secure. This model is particularly lucrative for B2B AI applications, such as customer relationship management (CRM) tools, HR management systems, or predictive analytics platforms.

* Freemium Models with Premium Upgrades: Startups can offer basic AI functionalities for free while charging for advanced features, increased usage limits, or enterprise-level services. This model can attract a broad user base quickly while still generating revenue from committed users who need more from the platform. An AI-powered language learning app, for instance, could offer basic lessons for free and require payment for advanced lessons, personalized coaching, or specialized learning tracks.

* APIs and Developer Platforms: By offering their AI capabilities as an API or through a developer platform, companies can charge other businesses to access and integrate their AI technology into their own applications. This model is particularly effective for AI companies with specialized capabilities like natural language processing, image recognition, or predictive analytics. Pricing can be based on the volume of API calls or the level of data consumed, allowing for scalable revenue growth.

* Custom Solutions and Consulting: Leveraging AI expertise to offer custom solutions or consulting services can be highly lucrative, especially for startups that possess niche expertise or proprietary technology. This model involves working closely with clients to develop bespoke AI solutions that address specific business challenges. It's suitable for complex fields such as healthcare, finance, or supply chain management, where off-the-shelf solutions may not suffice.

* Licensing: AI startups can license their technology or algorithms to other companies for use in their products or services. This model provides a steady income stream and can be particularly attractive for startups with patented AI technologies or those that have developed highly specialized algorithms.

NY Times versus Open AI Case

The NY Times vs. OpenAI copyright case is legal battle concerning the potential copyright infringement of creative content generated by large language models (LLMs) like OpenAI's GPT-3.

* The New York Times is suing OpenAI for copyright infringement and misappropriation of confidential information.

* The Times alleges that OpenAI infringed its copyright by using Times articles to train its language models.

* The Times also alleges that OpenAI misappropriated its confidential information by using recommendations from Wirecutter, a section of the Times website that recommends products.

* OpenAI argues that its use of the Times articles is fair use and that the recommendations claim is preempted by the Copyright Act.

The NY Times argues that AI-generated content imitating the work of specific journalists infringes on their copyright. OpenAI counters that its models are trained on a massive dataset of publicly available text and code, and that the resulting outputs are not substantially similar to any individual work.

The outcome of this case has major implications for the future of AI development, particularly regarding the legal and ethical considerations surrounding the use of LLMs for creative tasks.

If the NY Times prevails, it could set a precedent that restricts the ability of AI models to generate content that is similar to copyrighted works.

This could have a chilling effect on the development and use of LLMs for creative applications, such as writing, music composition, and art generation.

OpenAI's legal strategy of arguing that the NY Times prompting the LLM to generate content essentially amounts to fair use is an interesting one.

Fair use is a legal doctrine that allows for the limited use of copyrighted material without permission from the copyright holder, for purposes such as criticism, commentary, news reporting, teaching, scholarship, or research.

OpenAI argues that the NY Times' prompting constitutes transformative use of its copyrighted material, as it is being used to create something new and different (i.e., the AI-generated text).

Whether this argument will be successful is yet to be seen.

"The allegations in the Times’s Complaint do not meet its famously rigorous journalistic standards.7 

The truth, which will come out in the course of this case, is that the Times paid someone to hack OpenAI’s products. 

It took them tens of thousands of attempts to generate the highly anomalous results that make up Exhibit J to the Complaint. 

They were able to do so only by targeting and exploiting a bug (which OpenAI has committed to addressing) by using deceptive prompts that blatantly violate OpenAI’s terms of use.8 

And even then, they had to feed the tool portions of the very articles they sought to elicit verbatim passages of, virtually all of which already appear on multiple public websites. 

Normal people do not use OpenAI’s products in this way.9 "

https://fingfx.thomsonreuters.com/gfx/legaldocs/byvrkxbmgpe/OPENAI%20MICROSOFT%20NEW%20YORK%20TIMES%20mtd.pdf

Is this about hacking and how the New York times tried to get the content, and not just about fair use? Or is this a possibly valid point, and if not, what is the counter argument?

According to the document, OpenAI claims that the New York Times paid someone to hack their products to generate content similar to the Times's content.

The Times allegedly achieved this by targeting and exploiting a bug in OpenAI's products and using deceptive prompts that violated OpenAI's terms of use.

OpenAI argues that the Times's actions do not meet their journalistic standards and that they are investigating the situation.

Whether these claims by OpenAI are true or not is a matter of ongoing investigation and legal proceedings.

Both sides in a legal dispute may make claims that are self-serving and designed to position them favorably in the court of law and public opinion.

Even if OpenAI's claims are true, it doesn't invalidate their argument of fair use.

By making this argument, OpenAI is attempting to:

* Weaken the NYT's copyright infringement claims: By highlighting the NYT's unconventional methods, OpenAI is trying to downplay the similarity between the generated content and the Times's articles. They argue that the NYT's actions were not a typical use case and therefore don't constitute copyright infringement.

* Shift the focus of the case: By bringing up the NYT's alleged hacking and exploitation of a bug, OpenAI is trying to divert attention away from the fair use arguments and focus on the NYT's actions. This could potentially be a strategic move to gain an advantage in the lawsuit.

The fair use defense hinges on the specific context and purpose of the use of copyrighted material, not necessarily the methods used to access it.

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