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5 Reasons Foundational Models Are The Future of AI

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Read Time:2 Minute, 48 Second

We’ve all seen firsthand how technological advancements can drive profound changes in our economy. In this era of digital transformation, one of the most promising developments in artificial intelligence (AI) is the rise of foundational models, like GPT-4 from OpenAI. These large-scale machine learning models, trained on diverse Internet text, show an unprecedented versatility and are now being considered as foundational building blocks for a range of AI applications. In this blog post I will present 5 reasons why foundational models represent the future of AI.

  1. Flexibility and Generalization
    One of the key reasons foundational models are the future of AI is their inherent flexibility and generalization ability. Unlike narrow AI models that are trained for a specific task, foundational models can be fine-tuned to perform a variety of tasks, from translation and summarization to coding assistance and content generation. This flexibility means that businesses can use a single foundational model to power a wide array of applications, reducing the need for multiple specialized models.
  2. Economies of Scale
    From an economic perspective, foundational models offer significant economies of scale. Training a large-scale AI model is resource-intensive, requiring substantial computational power and energy. Once a foundational model is trained, however, it can be fine-tuned for various applications at a fraction of the cost of training a new model from scratch. This cost efficiency is especially beneficial for small and medium-sized businesses, which may lack the resources to develop their own AI models.
  3. Democratizing AI
    Foundational models are also playing a critical role in democratizing AI. By offering pre-trained models that can be fine-tuned for different tasks, organizations developing foundation models are making it possible for more people and businesses to leverage the power of AI. This can drive innovation and competition, leading to better products and services and fostering economic growth.
  4. Accelerating AI Research and Development
    Foundational models are not only useful in practical applications but also serve as valuable tools for AI research and development. They can be used as benchmarks to measure the progress of AI technology and to explore new methods and techniques in machine learning. Moreover, the insights gained from training and fine-tuning these models can help researchers better understand the inner workings of AI, leading to more robust and reliable AI systems in the future.
  5. Mitigating AI Risks
    Despite their potential, foundational models also pose risks, such as the generation of harmful or misleading content. By focusing on these models, researchers and developers can concentrate their efforts on mitigating these risks. For example, they can develop better methods for detecting and preventing harmful outputs, and they can work on creating more transparent and accountable AI systems. This risk mitigation is a critical aspect of ensuring the responsible and ethical use of AI.

The rise of foundational models represents a significant milestone in the evolution of AI. Their flexibility, economies of scale, and potential to democratize AI make them a powerful tool for leveraging AI. However, to fully harness their potential, it’s crucial to continue researching and addressing the risks associated with these models. As we move forward into the future of AI, foundational models will undoubtedly play a pivotal role in shaping our digital economy. Let’s embrace this future with both anticipation and a sense of responsibility, ensuring that the benefits of AI are shared widely and equitably.

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Posted On : Published By : Shawn Harris
Category:

A Framework for Assessing the Implications of Large Language Models on Society

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Read Time:4 Minute, 15 Second

tl;dr – Large Language Models (LLMs) like GPT-4 are transforming our sociocultural interactions, pushing technological boundaries in AI, creating economic shifts through automation and new job roles, raising environmental concerns due to energy-intensive training, influencing political landscapes potentially through propaganda generation, and posing new legal questions about content responsibility and copyright. As we leverage these powerful models, it’s crucial to navigate these challenges responsibly, ethically, and sustainably, ensuring a future that aligns with our shared values.

___

Introduction
The advent and expanding use of Large Language Models (LLMs), like OpenAI’s GPT-4, has the potential to redefine society in many ways. We’re already reading about how lawyers are using LLMs to help with citing cases, with unpredictable results. 😉LLMs can write like humans, compose poetry, answer trivia, translate languages, do complex math, and even write code. But what are the broader implications of these AI advancements? Let’s delve into this using the STEEPL framework, which stands for Sociocultural, Technological, Economic, Environmental, Political, and Legal factors.

Sociocultural Implications
LLMs are poised to transform our sociocultural interactions significantly. As these AI models become more prevalent, they could change the way we communicate with technology. We’re already seeing this transition with digital personal assistants, automated customer service, and AI-generated entertainment content.

However, these advancements bring along challenges. The ability of LLMs to generate human-like text raises concerns about digital literacy and information discernment. There’s a risk of spreading misinformation or manipulating public opinion if these tools are used unethically. It’s crucial to build robust systems and practices to ensure ethical use and to educate the public about these technologies.

Technological Implications
LLMs represent a quantum leap in AI and machine learning. They’re pushing the boundaries of what’s possible with natural language understanding and generation. Their development will likely inspire further research and innovation in related fields.

Yet, as we advance, we must also address associated challenges. LLMs underscore the need for improvements in AI transparency, interpretability, and fairness. We need to ensure that as AI becomes smarter, it doesn’t become a black box, and its decisions and processes remain understandable to us.

Economic Implications
From an economic perspective, LLMs could be a game-changer. They have the potential to automate tasks traditionally performed by humans, leading to significant cost savings and efficiency gains for businesses. However, this automation could also lead to job displacement in some sectors.

On the flip side, this disruption is likely to create new roles related to the development, deployment, and regulation of LLMs. Therefore, while we might see some jobs becoming obsolete, new ones will also emerge, driving the need for reskilling and upskilling in the workforce.

Environmental Implications
The environmental impact of LLMs is a significant concern. Training these models is computationally intensive and consumes a substantial amount of energy. As we reap the benefits of these powerful models, we must also be mindful of their carbon footprint.

Therefore, researching more energy-efficient training methods is paramount. While making AI smarter, we also need to strive to make it greener.

Political Implications
In the political sphere, LLMs can be a double-edged sword. On the one hand, they could be used to automate the generation of propaganda or misinformation, influencing political discourse and election outcomes. On the other hand, they could also be used to streamline administrative processes and increase transparency.

Moreover, the development and control of these technologies raise questions about power dynamics between countries. Addressing these issues will require international cooperation and thoughtful regulation.

Legal Implications
Lastly, the rise of LLMs poses new legal questions. For instance, who should bear the responsibility when an LLM generates harmful or illegal content? How should copyright law handle text generated by these models? These questions will need careful consideration as we navigate the legal landscape of AI.

Conclusion
LLMs are undoubtedly shaping the future of AI. As we stand on the cusp of this AI revolution, it’s essential to consider these sociocultural, technological, economic, environmental, political, and legal implications. Navigating these challenges will require a concerted effort from researchers, policymakers, and society at large.

In this brave new world of AI, we must strive for a balance. Let’s embrace the possibilities that LLMs offer while also addressing the challenges they present. Let’s work towards ensuring that these technologies are used responsibly and ethically, and that their benefits are accessible to all.

Remember, technology itself is neither good nor bad; it’s how we use it that makes the difference. As we continue to advance in our AI journey, let’s ensure that we’re not just creating smart machines, but also a future that reflects our shared values and aspirations. You can add an additional E to the end of the STEEPL Framework, making it STEEPLE. The last E standing for Ethical. This final factor touches a few areas, enough so that I think I will write a separate post on E.

Thank you for joining me in this exploration of the implications of Large Language Models. I invite you to engage in this conversation and share your thoughts and insights. The future of AI is a journey we’re all on together, and every perspective matters.

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Family Computer Vision Model – Step 1

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Read Time:16 Second

We started on a project tonight to build a computer vision model that will classify a few family members including the dogs. We used Teachable Machine to get a model built, and will now be exporting a Keras model to run in TensorFlow. Love teaching the kids the power of AI. More to come…

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Velocity As A Model; With Deliberate Speed

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Read Time:1 Minute, 8 Second

I believe in on boarding ways of thinking via models to help drive faster, hopefully  consistent practical decisions…to quickly say, ah it’s just another one of those. Most models on their own will lead you astray. However, applying multi-model thinking has statically improved outcomes. Here’s another model to add, from Shane Parrish’s The Great Mental Models Volume 2.

Velocity as a model; with deliberate speed

“The concept that underpins using velocity as a model is displacement in a direction. If we take a step forward, we have velocity. If we run in place, we just have speed. Thus, our progress in a given area is not about how fast we are moving now but is best measured by how far we’ve moved relative to where we started. To get to a goal, we cannot just focus on being fast, but need to be aware of the direction we want to go.”

“Velocity challenges us to think about what we can do to put ourselves on the right vector, to find a balance between mass and speed to move in the direction of our goals. Gains come from both improving your tactics and being able to adjust to and respond to new information.”

“Being able to move in the right direction is a lot more useful than going fast in the wrong one.”

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Posted On : Published By : Shawn Harris
Category:

Force Field Analysis

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Read Time:53 Second

Force Field Analysis essentially recognizes that in any situation where change is desired, successful management of that change requires applied *inversion. Here is a brief explanation of this process:

1) Identify the problem
2) Define your objective
3) Identify the forces that support change towards your objective
4) Identify the forces that impede change towards the objective
5) Strategize a solution! This may involve both augmenting or adding to the forces in step 3, and reducing or eliminating the forces in step 4.

*Inversion is a powerful tool to improve your thinking because it helps you identify and remove obstacles to success. The root of inversion is “invert,” which means to upend or turn upside down. As a thinking tool it means approaching a situation from the opposite end of the natural starting point. Most of us tend to think one way about a problem: forward. Inversion allows us to flip the problem around and think backward from objective. Sometimes it’s good to start at the beginning, but it can be more useful to start at the end.


#leadership #mentalmodels

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12 Places To Intervene In A System, To Drive Systemic Change

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Read Time:5 Minute, 55 Second

After reading quite a few books on systemic racism. I was compelled to find a book on the discipline of Systems Thinking. I found “Thinking in Systems” by Donella Meadows to be a highly read and rated choice on the topic. Given the complex nature of systemic racism and racist actions, how do you tackle it. I believe systems thinking can provide a framework for doing just that. Given that we can’t just change a system directly, in “Thinking in Systems,” Donella Meadows outlined a list of interventions you can lever to influence the system. She sorts the leverage points in increasing order of effectiveness, from the easiest to lever/least long term impact on the system; to the hardest to lever/most effective to long term impact on the system.   The easiest and least effective is effecting Numbers (e.g. effecting #’s and %’s); the hardest and most effective is Transcending Paradigms (which is almost spiritual), but 2nd to the hardest/most impactful is Paradigms (i.e. changing the societal culture around how we consider each other). I think racism needs to be attacked from the top and bottom, that is starting with Numbers AND Paradigms; converging where they do.

There are 12 interventions you can take to impact change in systems:

12. Numbers

Numbers (like subsidies, taxes, standards, minimum wage, research investments) define the rate at which things happen in the system.

11. Buffers

Buffers are stabilizing stock, relatively to flows. Big buffers make the system more stable, small buffers make it more subject to change. A good example of buffer is the money you keep in the bank: it helps you manage extraordinary expenses.

10. Stock-and-Flow Structures

This represent the structure of the system itself, how material stocks move through the system itself, and while changing it can in theory change a lot, in practice it’s very hard to do so. For example the baby-boom put strain on the elementary system, then high school, then jobs, then housing and then retirement, and there’s nothing changeable in that.

9. Delays

They determine how much time passes between the moment a change is made on the system, and the moment when the effect of the change happens. You can clearly see how a long delay makes everything challenging, so being able to shorten it could lead to lots of benefits, if possible. Changing delays can have a big impact, but similar to flows structure they are very hard to change. If there’s energy shortage and you need to build a power plant, that takes time.

8. Balancing/Negative Feedback Loops

A balancing feedback loop is a self-correcting logic composed by three elements: a goal to keep, a monitoring element, and a response mechanism. It is a mechanism that tries to keep a specific measurement around a specific goal. For example a thermostat has a goal temperature and it turns heating on to keep that temperature. While it is relatively simple to spot a loop in terms of mechanics, it’s harder in general. For example a law that grants more protection for whistle-blowers is something that makes the feedback loop that controls the neutrality of a democracy stronger.

7. Reinforcing/Positive Feedback Loops

Reinforcing feedback loops are built similarly to negative feedback loops, but instead of keeping a variable stable around a goal, they aim to reinforce it: the more it works, the more it gains power to work more. For example giving bonuses for every sales done is an incentive to sell more (even if we know that it damages the system as a whole more than the benefits of it), or the more you have in the bank the more interest you earn. Positive feedback loops are usually perceived as positive, but since they keep growing they can build up and damage the system in the long run if they aren’t controlled in some other way.

6. Information Flows

Creating new balancing or reinforcing feedback loops, changing how information is propagated and how it’s made visible in the system, these are all changes in the information flows structure.

For example if you put the energy counter clearly visible to a family you make them more aware of how much they are consuming, and the effect is that they consume less. This basically creates a new negative feedback loop without changing any other parameter in the system.

5. Rules

The rules of the system define its scope, its boundaries, its degrees of freedom. Incentives, punishments, constraints, are all rules of a system. Examples are everywhere, from the constitution (a set of do / do nots) to free speech to game rulebooks. These are strong leverage points, and they can be both written and unwritten.

4. Self-Organization

This is the power to add, change, evolve or self-organize system structure. In biological systems that power is called evolution. In human economies it’s called technical advance or social revolution. In systems lingo it’s called self-organization. These are structural transformation of the system, usually due to new elements appearing, such as the currency or the computer. Variability, diversity, experimentation are usually a key element to make a system evolve, but they are hard to accept because they make “lose control” on the system given what they bring to the table is something new and as such still unknown.

3. Goals

Goals have the power to transform and define each and every leverage point above. If you’re creating a system, like an organization, it’s relatively easy to see the goals because usually there’s someone to set them, and if there isn’t, then the organization is likely to have a problem. Leaders, managers, heads of state, have the power to modify or set new goals. If someone with this power says that the goal is to get a man on the Moon, well, a lot of the other variables are going to change to accommodate this goal.

2. Paradigms

Everything, including goals, arise in specific mindsets, social contexts, beliefs. In a country with a low rate of tax evasion, you need very few rules that try to address that, you probably don’t even need to have “avoid tax evasion” as a goal anywhere. These beliefs can be changed, and while in societies this can take a long time, in individuals can be a matter of an instant. Changing the paradigms from which a complex system emerge can be done by pointing out anomalies and failures. You work on active change, building more and more the new one. You don’t spend time with reactionaries.

1. Transcending Paradigms

No paradigm however is true in an absolute sense, our understanding of this infinite universe is limited. So every paradigm can be embraced, and changed, and treated as a relative variable. There isn’t just a change from an old system to a new system, there’s the possibility of an infinity of them.

This won’t be easy but it’s required, and together we can truly make lasting change, this time.


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Category:

My 2019 Reading List/Book Recommendations

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Read Time:5 Minute, 24 Second

One of my goals for 2019 was to read 26 books, effectively one every other week. Well, I ended the year having completed 18. I fell short of my goal; still feel like I satisfied my CQ (Curiosity Quotient), with completing one of my other goals of doing a deep dive in to ML/DL, so it’s no longer a black box. Done!

Below are the list of books I completed in 2019. For 2020, I plan on doing 10% better than 2019 so will be targeting 20 books. Currently, I’m reading “Aligning Strategy and Sales” by Frank V. Cespedes, then “The Model Thinker” by Scott E. Page.

The List:

1) “Neuroscience of Leadership” – Provides insight in to the chemical reactions taking place within each of us; what triggers them, and how they manifest themselves in everyday interactions with others. Engender oxytocin in others, not cortisol.
 
2) “Strategy Beyond the Hockey Stick” – Looks at the Power curve of economic profit, and how companies can move us the curve,l through Endowment, Financial starting point; Trends, Right industry, right time; Movement, Reallocation of resources.
 
3) “The Gift of Black Folks” – Power book on the significant impact of the “Negro” in the making of America. From exploration, revolutionary war, slavery and all skilled labor, civil war, invention, and beyond. Goes through the mid-1920’s.
 
4) “The Master Algorithm” – Become a savvy consumer of AI/ML/DL, avoiding the pitfalls that kills data projects; anticipate next.
 
5) “The 12 rules of life.” Fundamentally about putting your own “house” in order impact those around you.
 
6) “Applied Artificial Intelligence” – A practical guide to leveraging AI in the Enterprise. Next up, “The Art of Facilitation.”
 
7) “The Machine Stops” by E.M. Forster. This science fiction book written in 1909, presents a world where most humans no longer can live on Earth’s 
surface; living below the surface in a “standard room.” All human needs are met by the Global Machine. Any communication is by instant messaging/video conferencing machine. Prescient? We hope not!
 
8) “Superintelligence” by Nick Bostrom. The more optimistic view of what AI can bring to humanity; with the possibility that we may not be able to get to AGI.
 
9) “The Path Made Clear” by Oprah Winfrey. Shared this before. I hope you read it.
 
10) “The Mueller Report” by Robert S. Mueller III and the special counsel’s office, U.S. Department of Justice. I think American should read this, and not just take others opinions.
 
11) “Skin in the Game” by Nassim Taleb. This book hinges on the idea that you can not truly make significant decisions without having “skin in the game.” That is, making a decision under the prospects of both being impacted if there is a good turn out, and being impacted if there is a negative turn out. Nassim cites several examples in the book of cases where decisions were made without this symmetry, where a given decision with a negative outcome did not impact the deciders, but several people removed from them.
 
12) “Unlocking the Customer Value Chain” by Thales Texera. This is a great book for looking at value creation from a customer-first lens, instead of inside->out. Thales is clear to layout why technological innovation is not enough, and that business model innovation has been the real disruptor. Thales presents examples of startups who decouple the customer value chain to capture value in a net new ways.
  • Key steps to decoupling:
    1. Identify a target segment, and map their CVC activities in hyper-detail. [50%⌛of steps taken]
    2. Classify the CVC activities (e.g. Value creating, Value charging, Value eroding)
    3. Identify weak links between CVC activities. Links that are logical to decouple should be targeted.
    4. Break the weak links.
    5. Predict how incumbents will respond, then take preemptive action.
13) “Made in America” by Sam Walton. As I read this book, in so many cases, I could have replaced “Walmart” with “Amazon.” In many ways, the beliefs and motivations of Jeff Bezos, are the same ideals Sam Walton held. I could imagine that Sam would have looked at today’s marketplace and would see a tremendous opportunity, and exciting competitive challenge. This book was a great read and really gives you insight in to what set the culture of Walmart, thus why they have been so successful.
 
14) “Dare to Lead” by Brene Brown. Brene is a refreshing voice in leadership training, asking leaders to lead bravely and foster a courageous workplace. I think this book can be summed up in Brene’s own voice “Leadership is not about titles or the corner office. It’s about the willingness to step up, put yourself out there, and lean into courage. The world is desperate for braver leaders. It’s time for all of us to step up.”  Brene has a ton of great content at  https://daretolead.brenebrown.com/
 
15) “The Book of Why” by Judea Pearl. Judea reminds us that data is dumb. Telling us what has already happened, being able to predict what could happen, but without understanding Why, causation. Though there’s a lot of work in the area of cause, “AI” doesn’t quite get P(y|do(x)) > P(y). Instead it is a lot of P(y|x)… correlation. This book can further explain what is “AI,” what it is not, and what it could be.
 
16) “The Value of Everything” by Mariana Mazzucato. The Value of Everything rigorously scrutinizes the way in which economic value has been determined and reveals how the difference between value creation and value extraction has become increasingly blurry. Mariana Mazzucato argues that this blurriness allowed certain actors in the economy to portray themselves as value creators, while in reality they were just moving existing value around or, even worse, destroying it.
 
17) “Questions are the Answers” by Hal Gregersen. In this book Hal lays out why having the right answer is not what’s most important; instead asking the right question is. Endeavor to use more “?”, than “.”. 
 
18) “Principles” by Ray Dalio. This book should is a must read. Ray I think it’s a classic though only published in 2017. Ray believes that life, management, economics, and investing can all be systemized into rules, or principles. The book is broken up into two sections, Life Principles, and Work Principles.
 
Enjoy!

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Category:

My object detection and classification model, running on Raspberry Pi 4. Progress!

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Fun little project his weekend, building a object detection and classification solution for less than $100. Though this pic only shows “person” and “book” classifications, the model can classify some 90 objects! The Tensorflow Lite model is running on a 4GB Raspberry Pi 4 w/ 128GB Sdcard. The camera is a Arducam, which I need to work on the resolution for but it didn’t impact the detection or classification, and ran at ~2.0 fps. Running on a Pi I have a give and take between model performance and accuracy, given the limited resources, but will push to see how resource hungry a model I can run on it.  More to come…


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Wondering what’s real about artificial intelligence? – BrainTrust Live! Episode 54

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Wondering what’s real about artificial intelligence? Today on BrainTrust LIVE, we’re fortunate to have Cynthia Holcomb, founder/CEO of Prefeye, and Shawn Harris, Customer Partnerships & Strategy, SmartLens — two retail practitioners who are working with their clients on real A.I. solutions. They’ll give us the lowdown — more specifically, on how retailers can currently use AI for personalization, the limitations that are frustrating them at present, and what does the future holds.

Recording on Facebook: https://www.facebook.com/retailwire/videos/745383885894757/


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Throwback Thursday: ECC Life & Style w/ Jarvis Green on Patriots All-Access

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Read Time:16 Second

 

 

 

I enjoyed doing this segment with Jeff Lahens, my former business partner, and Jarvis Green, good friend and 2X Super Bowl champion with The New England Patriots… now owner of Oceans 97.

 

 

 

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“…most of all: nothing without skin in the game.” ~ Nassim Nicholas Taleb

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This.

“No muscles without strength, friendship without trust, opinion without consequence, change without aesthetics, age without values, life without effort, water without thirst, food without nourishment, love without sacrifice, power without fairness, facts without rigor, statistics without logic, mathematics without proof, teaching without experience, politeness without warmth, values without embodiment, degrees without erudition, militarism without fortitude, progress without civilization, friendship without investment, virtue without risk, probability without ergodicity, wealth without exposure, complication without depth, fluency without content, decision without asymmetry, science without skepticism, religion without tolerance, and, most of all: nothing without skin in the game.” ~ Nassim Nicholas Taleb


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Category:

Innovation Strategy Questions

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When seeking to develop an innovation strategy, here are some questions you should get answered.

  1. How do I see emerging trends before they become problematic?

  2. How do I generate a robust pipeline of new growth ideas to consider?

  3. How do I identify and focus on the highest-potential opportunities in areas like blockchain and AI?

  4. How can I motivate traditional company management to realize the need for digital transformation?

  5. How do I evaluate competitive signals in a noisy, buzzword-filled market?

  6. How do I get the middle layer of my company to embrace change?

  7. How do I bring outside ideas into my organization?

  8. When does it make sense to be a fast-follower? And when does it not?

  9. How do I decide whether to build, buy or partner?

  10. Should I start a venture capital fund?


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4: AI for Everyone – AI and Society – Notes

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Read Time:1 Minute, 15 Second

Introduction

  • Hype

  • Limitations

    • Bias

    • Adversarial attacks

  • Impact on developing economics and jobs

A realistic view

  • Goldilocks rule for AI:

    • Too optimistic: Sentient/AGI, killer robots

    • Too pessimistic: AI cannot do everything, so an AI winter is coming

      • as opposed to the past, AI is creating value today.

    • Just right: Can't do everything, but will transform industries

  • Limitations of AI

    • performance limitations. (limited data issues)

    • Explainability is hard (instructible)

    • Biased AI through biased data

    • Adversarial attacks

Discrimination/Bias

  •     

  • Biases

    • Bias against women and minorities in hiring

    • Bias against dark skinned people

    • banks offering hiring interest rates to minorities

    • reinforcing unhealthy stereotypes

  • Technical solutions

    • "Zero out" the bias in words

    • Use more inclusive data

    • More transparency and auditing processes

    • More Diverse workforce

Adversarial attacks

  • Minor perturbation to pixels can lead and AI to have a different B output.

  • Adversarial defenses

    • Defenses exist; incur some performance cost

    • There are some applications that will remain in an arms race.

Adverse uses of AI

  • DeepFakes, fakes can move faster than the truth can catch up

  • Undermining of democracy and privacy, oppressive surveillance

  • Generating fake comments

  • spam vs. anti-spam, fraud vs. anti fraud

AI and developing economies

  • AI will eliminate lower rung opportunities. The development of leapfrog opportunities will be required. Think how countries jumped to mobile phones, mobile payments, online education, etc.

  • US and china leading, but still a very immature space.

  • Use AI to strengthen country's vertical industries.

  • More public-private partnerships

  • invest in education

AI and Jobs

  • AI is automation on steroids.

  • Solutions

    • Conditional basic income: provide a safety net but incentivize learning

    • Lifelong learning society

    • Political solutions

Conclusion

  • What is AI?

  • Building AI projects

  • Building AI in your company

  • AI and society


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3: AI for Everyone – Building an AI company – Notes

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Read Time:3 Minute, 13 Second

Introduction

Case Study: Smart Speaker

  • “Hey Device, tell me a joke”
    • Steps (AI Pipeline):
      1. Trigger work/wakeword detection A) Hey device”? -> B) 0/1
      2. Speech recognition A) Audio -> B) “tell me a joke”
      3. Intent recognition A) Joke? vs, B) time?, music?, call?, weather?
      4. Execute joke
    • These could be 4 different teams
  • “Hey device, set timer for 10 minutes”
    • Steps (AI Pipeline):
      1. Trigger work/wakeword detection A) Hey device”? -> B) 0/1
      2. Speech recognition A) Audio -> B) “Set timer for 10 minutes”
      3. Intent recognition A) “set timer for 10 minutes -> B) Timer
      4. Execute
        1. Extract duration 
          1. “Set timer for 10 minutes”
          2. “Let me know when 10 minutes is up”
        2. Start Timer with set duration
  • Challenge:
    • Each function is a specialized piece of software.
    • This requires companies to train users on what the speaker can, and can not do.

Case study: Self driving car

  •     Steps for deciding how to drive
    1. Image/Radar/Lidar
      • Car detection
      • Pedestrian detection
    2. Motion planning 
      • Steer/acceleration/Brake
  • Key Steps
    1. Car detection (supervised learning)
    2. Pedestrian detection (supervised learning)
    3. Motion Planning (SLAM – Simultaneous localization and mapping)
  • Challenge:
    • Each function is a specialized piece of software.

Roles in AI teams

  • Software Engineers (30% +)
  • Machine Learning Engineer. focused on A -> B mapping
  • Applied ML Scientist: Using State of the art to today’s problems
  • Machine Learning Researcher. Extend the state-of-the-art in ML
  • Data Scientist. Examine data and provide insights. Make presentation to team/executives. Some may be Machine Learning Engineer.
  • Data Engineers. Organize data. Make sure data is saved in an easily accessible, secure, and in a cost effective way
  • AI Product Manager. Help define what to build. What feasible and valuable

AI Transformation Playbook

  1. Execute pilot projects to gain momentum
    • Success is more important than value
      • Need to get the flywheel moving
    • Show traction within 6 to 12 months (quiz said 6 to 10)
    • Can be in-house or outsourced
  2. Build an in-house AI team
    • Can be under: CTO, CIO, CDO, or CAIO
    • Have a central AI center of excellence. Matrix them in to start, untill understanding of AI is throughout the org
    • CEO should provide funding to start, not from BU.
  3. Provide broad AI training
  4. Develop an AI strategy
    • You do this at step 4 to gain concrete experience, vs starting with an academic strategic approach to something so new.
    • Leverage AI to create and advantage specific to your industry sector
    • Design strategy aligned with the “Virtuous Cycle of AI”
      • Better product -> More users -> More data -> [Repeat]
    • Create a data strategy
      • Strategic data acquisition
      • Unified data warehouse/lake
    • Create network effects and platform advantages
      • In industries with “winner take all/most” dynamics, AI can be an accelerator
    • Leverage classic frameworks as well. Low cost/ focus
    • Consider humanity.
  5. Develop internal and external communication
    • AI can change a company and its products
    • Investor relations. to properly value your company
    • Government relations. to align on regulations.
    • Consumer/use education
    • Talent/recruitment
    • Internal communications. to address questions and concerns.

AI pitfalls to avoid

  • Don’t
    • Expect it to do everything
    • Hire 2-3 ML engineers and expect then to come up with use cases.
    • expect it to work the first time\
    • Don’t expect Traditional planning process to apply yo AI
    • Don’t wait for a superstar, get going with what you have today.
  • Do
    • Be realistic
    • Beginners should be linked with business
    • Work with  the AI team to develop new timelines, KPIs, etc

Taking your first steps

  • Get friends to learn about AI
    • Courses
    • Reading group
  • Start brainstorming projects (no project too small)
  • Hire a few ML/DS people to help
  • Hire or appoint an AI leader
  • Discuss with CEO/Board possibilities of AI transformation
    • Will the company be more valuable, or more effective if we are good at AI.

Supervised learning

Unsupervised learning

Transfer learning

GANs

Knowledge graphs


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Quick Take: The Great Decoupling of Retail

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Read Time:47 Second

There is a great decoupling happening in retail, a structural change. Similar to the decoupling that the computing industry went through, going from being vertically integrated to horizontal specialist. What does this mean for retailers? Retailers need to be clear on what their unique selling proposition is, that is why do customers choose them vs their competitor, or substitute; then double down on those things. Is it your wide assortment, price, convenience, customer service, maybe safety now, or some thing less rational. Everything else should be considered for outsourcing to horizontal specialist, those who are optimized to delivery a particular service, or product.

Within any company, typically the most valuable resources are centered around the making and the selling organizations. There is no difference in retail; instead it’s the merchandising and store operations organizations, and I would add human resources to being core. Most other functions should be evaluated for their need to be an in-house capability.


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2: AI for Everyone – Building AI Projects – Notes

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Read Time:2 Minute, 40 Second

Introduction

  • Starting an AI project

    • Workflow of projects

    • Selecting AI projects

    • Organizing data and team for the projects

Workflow of a machine learning project

How do you build, say a speech recognition engine

  • Key Steps:

    1. Collect Data: people saying “Alexa”, and other words

    2. Train model: learns A to B mapping… audio clip to “word”

      • many iterations

    3. Deploy the model: implement in to a smart speaker

      • Will collect new data (get data back), to  maintain /update the model

How do you build, say a self driving car

  • Key steps:

    1. Collect Data: images – > positions of other cars, draw rectangles around cars

    2. Train model: need to iterate and precisely identify cars

    3. Deploy model: may learn that golf carts are identified and positions well. keep iterating.

Workflow of a data science project

output: actionable insights

Optimize a sales funnel

  • Key steps:

    1. Collect Data: where are people coming from, time of day, machines type, etsc…

    2. Analyze the data: Iterate many time to get good insights insights from the data collected.

    3. Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.

Optimizing a manufacturing line

  • Key steps:

    1. Collect Data: clay supplier, mixing time, ingredients, lead times, relative humidity, temperature, kiln duration, etc…

    2. Analyze the data: Iterate many time to get good insights insights from the data collected. 

    3. Suggest hypotheses/actions: Deploy changes, re-analyze new data periodically.

Every job function needs to learn how to use data

  • Use data to optimize workflows through data science based analysis, and to take on tasks with machine learning (remember less than a second), Inputs (A) to Output (B).

  • From Sales, recruiting, marketing, to agriculture, and beyond DS and ML are having huge impacts

How to choose an AI project

  • Bring together a cross-functional team knowledgeable in AI, plus domain experts.

  • Brainstorming framework:

    • Think about automating “tasks,” vs automating “jobs.”

    • what are the main drivers of business values?

    • What are the main pain point in your business?

    • Note: you can make progress without big data

      • Having more data almost never hurts

      • Data makes some business [Google, Facebook, Netflix, Amazon] defensible.

      • With small datasets, you can still make progress. The amount of data you need is problem dependant.

  • Due diligence on project

    • What AI can do + Valuable for your business

      • Technical diligence

        • Can AI system meet desired performance (e.g. accuracy, speed, etc)

        • How much data is need to meet performance goals

        • Engineering timline

      • Business diligence

        • Current business: Lower costs

        • Current business: Increase revenue ( getting more people to check out)

        • New business: New product or business

      • *Ethical diligence*

        • money vs impact on society

  • Build vs. buy

    • ML projects can be in-house or outsourced

    • DS projects are more commonly in-house

    • Some things will be industry standard, avoid building those.

  • “Don’t sprint in front of a train.”

    • some times it makes sense to adopt another’s platform or approach than to build your own. resource constraints, capability constraints…

Working with an AI team

  • Specify your acceptance criteria

    • Goal: defects with 95% accuracy…How do you measure accuracy

      • Test Set (n1000): labelled training dataset to measure performance. 

    • Training Set: Pictures with labels

      • Learn mapping from A to B

    • Test Set: Another data set to test the mappings. Often more than 1 test set will be requested.

  • Pitfall of expecting 100% accuracy. Discuss with AI engineers what’s reasonable.

    • Limitations of ML

    • Insufficient data

    • Mislabeled data

    • Ambiguous labels

Technical Tools for AI teams

  • CPU vs. GPU [Great for deep Learning/Neural Networks] Nvidia

  • Cloud vs. On-prem, ….Edge [Processor, where data is collected.]


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1: AI for Everyone – Introduction to Artificial Intelligence – Notes

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Read Time:1 Minute, 12 Second

What is AI?

What is Machine Learning?

  • Supervise Learning… Input (A) to output (B) learning

What is Data?

  • Need to determine what is A (Input – Features) and what is B (Output) on the inputs
  • Acquiring data via manual labeling, observation, available downloads
  • Once you start collecting data, start feeding it to a AI team for a feedback loop to start. Start this early. 
  • You can not assume value in data, because you have a lot of it. Becareful, have an AI with to asses
  • Garbage in garbage out exists here too

The Terminilogy of AI

  • AI(ML, et al (DL/NN, et al))…Data Science cuts across all of these.

What makes an AI company?

What machine learning can and cannot do

  • Technical diligence: looking at the data, look at the input, and output A and B, and just thinking through if this is something AI can really do.
    • Can NOT do market research and write an extended market report
  • Complex interactions require lots of examples. the system would either remain vague in response, or return incoherent responses.
  • What makes an ML problem easier
    1. Learning s “simple” concept <= 1 second of human thought…Cause we have to formulate? why?
    2. Lots of data available
  • AI has a hard time of inferring intention of action in context A= hand gesture, B= stop, hitchhiker, left turn on a bike, hello, etc…

Deep Learning

  • Deep Learning and Neural Network are used interchangeably

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