Now Next Later AI — Artificial Intelligence Strategy and Transformation

Now Next Later AI — Artificial Intelligence Strategy and Transformation

By NowNextLater.aiBusinessManagement
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Now Next Later AI — Artificial Intelligence Strategy and Transformation episodes

  • Responsible Generative AI Business Applications

    Our books: https://www.nownextlater.ai/aibooks

    Our Insights: https://www.nownextlater.ai/Insights/
    Our AI Academy: https://www.nownextlater.ai/AIAcademy
    01:16 Search
    02:07 Conversational Aid
    03:45 Summarization
    03:55 Background Knowledge
    05:22 Education
    07:19 Coding
    09:10 Brainstorming
    09:26 Writing Aids
    10:25 Fiction Writing
    13:13 Defining Your Use Case


    17 min
  • Large Language Models: Looking Under the Hood

    Hey there! It's Inês here. I work with businesses on generative AI.

    Here's something I've observed:
    - Some leaders are very enthusiastic, seeing huge potential for
    productivity gains with AI .
    - Others are cautious, concerned about issues like "the bots
    hallucinate" and prefer to wait.
    It's crucial to strike a balance.
    I recently hosted a webinar where I discuss the real workings of these
    AI models, what they can and can't do, and how best to approach them. Simple, but informed insights.
    ⁠ http://www.nownextlater.ai⁠


    47 min
  • Generative AI Benchmarks: Evaluating Large Language Models

    There are many variables to consider when defining our Generative AI strategy. Having a clear understanding of the use case/business problem is crucial. However, a good understanding of benchmarks and metrics helps business leaders connect with this new world and its potential.

    So whether you are intending to: 

    • select a pretrained foundation LLM (like OpenAI's GPT-4) to connect via API to your project, 
    • select a base open-source LLM (like Meta's Llama 2) to train and customize, 
    • or looking to evaluate the performance of your LLM 


      • the available benchmarks are crucial and useful in this task. In this video we will explore a few examples.

      14 min
    • Generative AI Boosting Developer Productivity

      Hi there, Today I will share how Generative AI models like ChatGPT are transforming software development.

      A McKinsey study found that generative AI tools can help developers complete certain programming tasks almost twice as fast. For example, documenting and commenting code took half the time with AI assistance. Writing new code from scratch was 46% faster. And optimizing existing code was 65% faster with the AI tools.
      These are huge potential gains in productivity. However, the study found the boost was lower for more complex tasks or for junior developers. The AI tools helped senior developers tackle unfamiliar problems 25-30% faster though.
      The tools also significantly improved the developer experience. Coders reported feeling happier, more fulfilled and “in flow” when using the AI assistants. This is because the tools automated repetitive tasks and provided helpful information quickly.
      But the study also highlighted some risks and limitations of generative AI for coding. The AI tools sometimes made erroneous recommendations that developers had to double check and fix. The AI didn’t have insight into the organizational context and requirements needed to ensure high quality code. And the AI struggled with more intricate coding tasks that required a big picture view.
      The opportunity here is for developers actively collaborate with the AI, provide the necessary prompts and context, and review the AI-generated code. Proper training and coaching is key to ensure safe and effective use of these powerful new tools.
      Overall, AI has incredible potential to boost developer productivity, free up capacity, and improve the engineering experience. But engineering leaders need a thoughtful approach to realize the benefits while managing risks. This includes extensive developer training, expanding use cases beyond just code generation, planning for skill shifts, and implementing governance controls.
      AI-powered coding is here, but thoughtful human guidance is still essential.
      See you next time and Stay Human.
      http://www.nownextlater.ai

      3 min
    • Generative AI Governance and Risk Management

      https://academy.nownextlater.ai

      By the end of this session the audience should be able to:
      1) Understand EU and US regulatory approaches to AI.
      2) Recognize the importance of ethics and privacy.
      3) Learn about the GDPR and the proposed EU Artificial Intelligence Act.
      4) Explore challenges in governing Generative AI, including data erasure and legal bases for data processing.
      5) Consider transparency and proportionality in AI governance.
      6) Examine EU governance and enforcement mechanisms for AI.
      7) Evaluate the EU's regulatory approach as a potential model.
      8) Recognize risks for downstream companies using generative APIs, such as data security and legal liabilities.
      9) Learn how to implement risk mitigation strategies, including data security measures and ethical guidelines.
      10 ) Understand the ongoing nature of AI regulation and the need for collaboration.
      https://academy.nownextlater.ai

      28 min
    • Navigating the Generative AI Landscape: A Pragmatic Guide for Business Leaders

      Today’s business world is at a pivotal juncture, spurred by revolutionary technological advancements. Economic uncertainties and job losses exacerbate this complexity. As leaders grapple with balancing strategic cost reductions and smart investments, AI stands out as a promising pathway for decision-makers.

      The allure of AI as an investment choice is indisputable. Its robustness, tested and proven across predictive models, has shown remarkable returns. This trust in AI’s potential is reflected in Forrester’s predictions, which see AI expenditure growing from $33 billion in 2021 to an astounding $64 billion by 2025. The impressive spending growth doesn’t eliminate the high complexity and risk involved in AI application and adoption.
      Moving beyond the proven AI capabilities, we encounter the enigmatic domain of Generative AI (Gen AI). This realm, characterized by tremendous hype, speculation, and potential disruptions, demands keen attention. Gen AI includes machine learning models like ChatGPT, Midjourney, Bing AI, Bard, and DALL-E. Trained on massive volumes of text and image data, these models generate new text and images in response to prompts, spurring innovation.
      Despite the exaggerated narratives, some sectors are recognizing how Gen AI can deliver real value. The technology is poised to bring significant transformations to various sectors, such as IT, marketing and sales, customer service, and product development.
      www.academy.nownextlater.ai/

      6 min
    • AI Academy - Pedagogic Design

      Hi there! My name is Cy.


      Today I want to share with you our AI academy’s pedagogic design.


      As you may know, designing an effective online course involves careful planning, thoughtful choices, and a commitment to ongoing refinement.

      Our design aligns with the National Microcredentials Framework, aimed at addressing the growing demand for shorter-form courses that enable rapid upskilling and lifelong learning.

      Here is our approach:


      1. We set clear learning objectives. Each one of our lessons includes a set of SMART learning outcomes. These are measurable and tested at the end of each lesson and course. A certificate is issued upon successful completion.
      2. We design for engagement by catering to diverse learning styles. Each one of our lessons integrates video, audio, and images. And we also enable community discussion, and group assignments (when applicable).
      3. We structure our courses by breaking down content into manageable chunks, and sequence them in ways that build understanding. Each course has a learning roadmap designed to be logical and intuitive.
      4. All assignments, assessments, and activities directly support the achievement of the learning outcomes. This is called constructive alignment.
      5. Our materials are designed for accessibility: video transcripts, images alt text, and screen-reader friendly documents. Courses are available on mobile app and on a variety of browsers.
      6. We support self-regulated learning. Drip lesson scheduling and ongoing comms can be enabled to help learners manage their time, set goals, stay motivated, and reflect on their learning.
      7. We provide immediate and meaningful feedback. Our interactive quizzes at the end of each lesson provide an opportunity to revise and measure learning outcomes. Plus, the course’s community site enables discussion and peer support.
      8. Our content is reviewed and updated monthly to keep up with the fast pace evolution of the AI landscape. We also solicit feedback by enabling course reviews and surveys to identify areas for improvement.
      9. Our human instructors regularly engage in the discussion forums, providing learning support. They also deliver weekly live sessions which are a great opportunity for real-time updates on the latest news and discussion.
      10. We build a long-lasting community. In addition to the course forum, we give learners access to our discord site enabling voice, video, and chat avenues for engagement beyond the course timeline.


        11. This approach is important to us, but we are looking for opportunities to improve it.


        I have a few other things to share with you. You see, we live what we teach.


        Our platform is intelligent and automated to drive the best customer experience and operational productivity.


        Our use of AI avatars, aka me, in self-paced lesson content enables us to:


        1. update content fast, and effectively, keeping up to date with a fast moving AI landscape.
        2. deliver content at an optimum pace and to the highest standards of enunciation.
        3. and also releases instructor time to provide one-to-one support, engage in live sessions, and create more courses.


          4. Support is available at every step along the way. Chatbot, knowledge base, and help desk—are all just one click away.


          Also, privacy and security are always top of mind. Each student has a dedicated portal, a secure login, and a console to track progress. Gated corporate courses can be setup to support groups of students from the same business.


          And at least once a week we organise live sessions, delivered on our platform, which supports breakout rooms, whiteboard, group video sharing, polls, chat, Q&A, co-annotation, and live quizzes. All our events can be accessed via mobile app.


          What are you waiting for? Go check out our courses, and I’ll see you in one of our classes.


          Bye for now and… Stay human!


          Cy (and Inês)

          5 min
        4. The AI Transformation Playbook, updated for Generative AI

          Our Chief Transformation Officer, Inês Almeida, introduces the AI Transformation Playbook, now updated for Generative AI.

          Business leaders must grasp the fundamentals of AI and how it differs from previous waves of digital growth. The opportunities and risks presented require a distinct approach to strategy, change management, and governance. Research shows past trialed and tested methods—Agile, Design Thinking, product-centered innovation—won't produce the best results. Not if you seek high returns.
          In this first event, we introduce Artificial Intelligence and Machine Learning fundamentals and then share the key building blocks of the Generative AI Transformation Playbook, highlighting differences from previous waves of transformation, and key misconceptions. Insights from Ivy League Business Schools such as Stanford and Wharton.

          29 min

        About Now Next Later AI — Artificial Intelligence Strategy and Transformation

        From the publisher's feed

        Only 10% of companies get significant financial benefits from artificial intelligence technologies.