Give a background of Data Science and Artificial Intelligence, to better understand the current state of the art (SOTA) for Large Language Models (LLMs) and Generative AI. Then start a discussion on the direction things are going in the future.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
The ppt Sujoy and I made for the Psi Phi ( An Inter School Competition held by our School). Our Topic was Artificial Intelligence.
Credits:
Theme Images from ESET NOD32 (My Antivirus of Choice)
Backgrounds from SwimChick.net (Amazing designs here)
Credits Image from Full Metal Alchemist (One of my favorite Anime).
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Presenting the landscape of AI/ML in 2023 by introducing a quick summary of the last 10 years of its progress, current situation, and looking at things happening behind the scene.
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
The ppt Sujoy and I made for the Psi Phi ( An Inter School Competition held by our School). Our Topic was Artificial Intelligence.
Credits:
Theme Images from ESET NOD32 (My Antivirus of Choice)
Backgrounds from SwimChick.net (Amazing designs here)
Credits Image from Full Metal Alchemist (One of my favorite Anime).
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
An Introduction to Generative AI - May 18, 2023CoriFaklaris1
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
The Future of Humanity
Through our interaction with machines, we develop emotional, human expectations of them. Alexa, for example, comes alive when we speak with it. AI is and will be a representation of its cultural context, the values and ethics we apply to one another as humans.
This machinery is eerily familiar as it mirrors us, and eventually becomes even smarter than us mere mortals. We’re programming its advantages based on how we see ourselves and the world around us, and we’re doing this at an incredible pace. This shift is pervading culture from our perceptions of beauty and aesthetics to how we interact with one another – and our AI.
Infused with technology, we’re asking: what does it means to be human?
Our report examines:
• The evolution of our empathy from humans to animals and robots
• How we treat AI in its infancy like we do a child, allowing it space to grow
• The spectrum of our emotional comfort in a world embracing AI
• The cultural contexts fueling AI biases, such as gender stereotypes, that drive the direction of AI
• How we place an innate trust in machines, more than we do one another
Methodology
For this report, sparks & honey conducted US-focused research on the future of AI. Together with Heartbeat AI Technologies, we examined the emotional sentiment (feeling and emotions) around artificial intelligence in a Heartbeat AI Pulse Survey of 150 people in the US. Tapping into our Influencer Advisory Board and proprietary cultural intelligence system, we combed through thousands of signals to build a vision of the future of AI. We also interviewed leading experts in the field of artificial intelligence.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)
What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
Presented at All Things Open RTP Meetup
Presented by Karthik Uppuluri, Fidelity
Title: Generative AI
Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
History of Artificial Intelligence (AI) from birth till date (2023).
Covers all the important events happened in due course of time with the AI Winter period.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Delve into this insightful article to explore the current state of generative AI, its ethical implications, and the power of generative AI models across various industries.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
An Introduction to Generative AI - May 18, 2023CoriFaklaris1
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
The Future of Humanity
Through our interaction with machines, we develop emotional, human expectations of them. Alexa, for example, comes alive when we speak with it. AI is and will be a representation of its cultural context, the values and ethics we apply to one another as humans.
This machinery is eerily familiar as it mirrors us, and eventually becomes even smarter than us mere mortals. We’re programming its advantages based on how we see ourselves and the world around us, and we’re doing this at an incredible pace. This shift is pervading culture from our perceptions of beauty and aesthetics to how we interact with one another – and our AI.
Infused with technology, we’re asking: what does it means to be human?
Our report examines:
• The evolution of our empathy from humans to animals and robots
• How we treat AI in its infancy like we do a child, allowing it space to grow
• The spectrum of our emotional comfort in a world embracing AI
• The cultural contexts fueling AI biases, such as gender stereotypes, that drive the direction of AI
• How we place an innate trust in machines, more than we do one another
Methodology
For this report, sparks & honey conducted US-focused research on the future of AI. Together with Heartbeat AI Technologies, we examined the emotional sentiment (feeling and emotions) around artificial intelligence in a Heartbeat AI Pulse Survey of 150 people in the US. Tapping into our Influencer Advisory Board and proprietary cultural intelligence system, we combed through thousands of signals to build a vision of the future of AI. We also interviewed leading experts in the field of artificial intelligence.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)
What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
Presented at All Things Open RTP Meetup
Presented by Karthik Uppuluri, Fidelity
Title: Generative AI
Abstract: In this session, let us embark on a journey into the fascinating world of generative artificial intelligence. As an emergent and captivating branch of machine learning, generative AI has become instrumental in myriad of sectors, ranging from visual arts to creating software for technological solutions. This session requires no prior expertise in machine learning or AI. It aims to inculcate a robust understanding of fundamental concepts and principles of generative AI and its diverse applications. Join us as we delve into the mechanics of this transformative technology and unpack its potential.
History of Artificial Intelligence (AI) from birth till date (2023).
Covers all the important events happened in due course of time with the AI Winter period.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
BSidesLV 2013 - Using Machine Learning to Support Information SecurityAlex Pinto
Big Data, Data Science, Machine Learning and Analytics are a few of the new buzzwords that have invaded out industry of late. Again we are being sold a unicorn-laden, silver-bullet panacea by heavy handed marketing folks, evoking an expected pushback from the most enlightened members of our community. However, as was the case before, there might just be enough technical meat in there to help out with our security challenges and the overwhelming odds we face everyday. And if so, what do we as a community have to know about these technologies in order to be better professionals? Can we really use the data we have been collecting to help automate our security decision making? Is a robot going to steal my job?
If you are interested in what is behind this marketing buzz and are not scared of a little math, this talk would like to address some insights into applying Machine Learning techniques to data any of us have easy access to, and try to bring home the point that if all of this technology can be used to show us “better” ads in social media and track our behavior online (and a bit more than that) it can also be used to defend our networks as well.
Future of data science as a professionJose Quesada
How can you thrive in a future where machine learning has been popular for a few years already?
In this talk, I will give you actionable advice from my experience training serious data scientists at our retreat center in Berlin. You are going to face these pointy, hard questions:
- What is the promise of machine learning? Has it happened yet?
- Is it easy to take advance of machine learning, now that most algorithms are nicely packaged in APIs and libraries?
- How much time should I spend getting good at machine learning? Am I good enough now?
- Are data scientists going to be replaced by algorithms? Are we all?
- Is it easy to hire talent in machine learning after the explosion of MOOCs?
Deciphering AI - Unlocking the Black Box of AIML with State-of-the-Art Techno...Analytics India Magazine
Most organizations understand the predictive power and the potential gains from AIML, but AI and ML are still now a black box technology for them. While deep learning and neural networks can provide excellent inputs to businesses, leaders are challenged to use them because of the complete blind faith required to ‘trust’ AI. In this talk we will use the latest technological developments from researchers, the US defense department, and the industry to unbox the black box and provide businesses a clear understanding of the policy levers that they can pull, why, and by how much, to make effective decisions?
Future Visions: Predictions to Guide and Time Tech Innovation, Peter Udo DiehlPeter Udo Diehl
I'm excited to share my latest predictions on how AI, robotics, and other technological advancements will reshape industries in the coming years. The slides explore the exponential growth of computational power, the future of AI and robotics, and their profound impact on various sectors.
Why this matters:
The success of new products and investments hinges on precise timing and foresight into emerging categories. This deck equips founders, VCs, and industry leaders with insights to align future products with upcoming tech developments. These insights enhance the ability to forecast industry trends, improve market timing, and predict competitor actions.
Highlights:
▪ Exponential Growth in Compute: How $1000 will soon buy the computational power of a human brain
▪ Scaling of AI Models: The journey towards beyond human-scale models and intelligent edge computing
▪ Transformative Technologies: From advanced robotics and brain interfaces to automated healthcare and beyond
▪ Future of Work: How automation will redefine jobs and economic structures by 2040
With so many predictions presented here, some will inevitably be wrong or mistimed, especially with potential external disruptions. For instance, a conflict in Taiwan could severely impact global semiconductor production, affecting compute costs and related advancements. Nonetheless, these slides are intended to guide intuition on future technological trends.
Data Science in the Real World: Making a Difference Srinath Perera
We use the terms “Big Data” and “Data Science” for use of data processing to make sense of the world around us. Spanning many fields, Big Data brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture.
These usecases use basic analytics, advanced statistical methods, and predictive technologies like Machine Learning. However, it is not just about crunching the data. Some usecases like Urban Planning can be slow, and there is enough time to process the data. However, with use cases like traffic, patient monitoring, surveillance the the value of results degrades much faster with time and needs results within milliseconds to seconds. Collecting data from many sources, cleaning them up, processing them using computation clusters, and doing all these fast is a major challenge.
This talk will discuss motivation behind big data and data science and how it can make a difference. Then it will discuss the challenges, systems, and methodologies for implementing and sustaining a data science pipeline.
Big Data & Machine Learning - TDC2013 Sao PauloOCTO Technology
BigData and Machine Learning: Usage and Opportunities for your IT department
Talk presented at The Developer Conference in São Paulo - 12/0713
Mathieu DESPRIEE
Ethical AI: Establish an AI/ML Governance framework addressing Reproducibility, Explainability, Bias & Accountability for Enterprise AI use-cases.
Presentation on “Open Source Enterprise AI/ML Governance” at Linux Foundation’s Open Compliance Summit, Dec 2020 (https://events.linuxfoundation.org/open-compliance-summit/)
Full article: https://towardsdatascience.com/ethical-ai-its-implications-for-enterprise-ai-use-cases-and-governance-81602078f5db
Algorithm Marketplace and the new "Algorithm Economy"Diego Oppenheimer
Talk by Diego Oppenheimer CEO of Algorithmia.com at Data Day Texas 2016.
Peter Sondergaard VP of Research for Gartner recently said the next digital gold rush is "How we do something with data not just what you do with it". During this talk we will cover a brief history of the different algorithmic advances in computer vision, natural language processing, machine learning and general AI and how they are being applied to Big Data today. From there we will talk about how algorithms are playing a crucial part in the next Big Data revolution, new opportunities that are opening up for startups and large companies alike as well as a first look into the role Algorithm Marketplaces will play in this space.
The Incredible Disappearing Data ScientistRebecca Bilbro
The last decade saw advances in compute power combine with an avalanche of open source software development, resulting in a revolution in machine learning and scalable analytics. “Data science” and “data product” are now household terms. This led to a new job description, the Data Scientist, which quickly became one of the most significant, exciting, and misunderstood jobs of the 21st century. One part statistician, one part computer scientist, and one part domain expert, data scientists seem poised to become the most pivotal value creators of the information age. And yet, danger (supposedly) lies ahead: human decisions are increasingly outsourced to algorithms of questionable ethical design; we’re putting everything on the blockchain; and perhaps most disturbingly, data science salaries are dropping precipitously as new graduates and Machine Learning as a Service (MLaaS) offerings flood the market. As we move into a future where predictive analytics is no longer a differentiator but instead a core business function, will data scientists proliferate or be automated out of a job?
In this talk, one humble data scientist attempts to cut through the hype to present an alternate vision of what data science is and can become. If not the “Sexiest Job of the 21st Century" as the Harvard Business Review once quipped, what is it like to be a workaday data scientist? What problems are we solving? How do we integrate with mature engineering teams? How do we engage with clients and product owners? How do we deploy non-deterministic models in production? In particular, we’ll examine critical integration points — technological and otherwise — we are currently tackling, which will ultimately determine our success, and our viability, over the next 10 years.
AI Basics for Professionals to Help Begin Their AI JourneyDeepak Sharma
Presentation Highlights:
- Why AI and why now?
- Filter through the buzzwords and hype, and
- How to navigate the AI space as a professional?
Authors and Speakers:
- Anurag Bhatia, Sr. Machine Learning Engineer, Trantor Inc.
- Mayank Kumar, Machine Learning Engineer, Trantor Inc.
Understanding Hallucinations in LLMs - 2023 09 29.pptxGreg Makowski
Hallucinations are a current fundamental problem for LLMs.
For one example, June this year in New York, attorneys did "research" on past cases with ChatGPT and turned it in to the Judge as a brief. The opposing council reported to the judge that they could not find the cases. When the judge confronted the GPT using attorneys, they stood behind their brief. The judge find the firm $5000.
Could this happen to you? YES. What can be done to avoid this in the future? I will answer.
In this talk, I will explain some fundamental areas of LLM's to explain how and why hallucinations occur. To understand that, an introduction into how words, concepts and dialogs are represented will help.
Words were first represented as a point in an embedding space with Word2Vec in 2013. This could compress 10,000 words into a vector of 300 elements, with a word being represented as a point in the 300-dimensional embedding space. Not just words can be represented, but also longer text, such as books can be compressed into a type of embedding. In that situation, areas of embedding space relate to different genres, such as: non-fiction, science fiction, children's fiction and so on. A new data point between training data points, when converted to text, would be a hallucination. In the area of "legal cases" in embedding space, if there is not an exact match, the text generation would try to generate what is plausible.
During an LLM conversation, the output of the previous text provides context for the next text in the style of a recurrent neural network. The starting position of a conversation matters. Understanding areas of weight space represent genres like "non-fiction" or other language aspects, and the starting position of a discussion time series matters, helps to understand why prompt engineering helps. The neural network conversation is represented in the activations of the 7B or 500B weights, a much larger space. During a conversation, learning is not occurring, but neural network activations are changing. The neural network is not a database. Even if you reach the exact set of weight activations from a training record, due to lossy compression, the exact text may not be regenerated.
Chat GPT does not use word embeddings. For implementation efficiency reasons, it is practical to break down what is embedded to about 50,000 items in a lookup table. Also, if we want to support proper nouns, like names, and dozens of languages, the number of words would be in the millions. Chat GPT and other LLMs use "tokens" for embedding. Examples of Byte Pair Encoding (BPE) and its process is given. The ChatGPT embedding is a vector of numbers 1,536 long for each token.
A solution for today is Retrieval Augmented Generation (RAG). As a brief introduction, you can ask with an English or natural question. It can be matched against a large library or database of paragraphs from internal documents or websites.
A Successful Hiring Process for Data ScientistsGreg Makowski
Discuss one successful hiring process for data scientists. The current "best" algorithms are constantly changing. Also, it is not uncommon to need to learn about a new vertical market for a DS application. From my DS hiring experience over 2010-2022, I have focused on hiring people that are good at learning and adapting.
Kdd 2019: Standardizing Data Science to Help HiringGreg Makowski
Initiative for Analytics and Data Science Standards (IADSS) workshop presentation at the ACM KDD conference (Association of Computing Machinery Knowledge Discovery in Databases).
Tales from an ip worker in consulting and softwareGreg Makowski
Discussion around intellectual property, leverage over consulting projects to build vertical application software. In my use case, data mining, artificial intelligence and intelligence augmentation are part of the value add. Also, discuss software frameworks, open source software and clauses on prior inventions in hiring contracts
Predictive Model and Record Description with Segmented Sensitivity Analysis (...Greg Makowski
Describing a predictive data mining model can provide a competitive advantage for solving business problems with a model. The SSA approach can also provide reasons for the forecast for each record. This can help drive investigations into fields and interactions during a data mining project, as well as identifying "data drift" between the original training data, and the current scoring data. I am working on open source version of SSA, first in R.
Production model lifecycle management 2016 09Greg Makowski
This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
Using Deep Learning to do Real-Time Scoring in Practical ApplicationsGreg Makowski
http://www.meetup.com/SF-Bay-ACM/events/227480571/
(see also YouTube for a recording of the presentation)
The talk will cover a brief review of neural network basics and the following types of neural network deep learning:
* autocorrelational - unsupervised learning for extracting features. He will describe how additional layers build complexity in the feature extraction.
* convolutional - how to detect shift invariant patterns in various data sources. Horizontal shift invariant detection applies to signals like speech recognition or IoT data. Horizontal and vertical shift invariance applies to images or videos, for faces or self driving cars
* discuss details of applying deep net systems for continuous or real time scoring
* reinforcement learning or Q Learning - such as learning how to play Atari video games
* continuous space word models - such as word2vec, skipgram training, NLP understanding and translation
Using Deep Learning to do Real-Time Scoring in Practical Applications - 2015-...Greg Makowski
This talk covers 4 configurations of deep learning to solve different types of application needs. Also, strategies for speed up and real-time scoring are discussed.
SFbayACM ACM Data Science Camp 2015 10 24Greg Makowski
This is the slide deck for the 7th annual ACM Data Science Camp. It is an unconference, with content generated by the audience. For the primary event site, see http://www.sfbayacm.org/event/silicon-valley-data-science-camp-2015
How to Create 80% of a Big Data Pilot ProjectGreg Makowski
When evaluating Open Source Software, or other software of a certain size or complexity, organizations frequently want to conduct a Pilot project, or Proof of Concept (POC). This talk describes a process to reduce the length of the Pilot, by leveraging configurations from performance testing to POC starting configurations.
Powering Realtime Decision Engines in Finance and Healthcare using Open Sour...Greg Makowski
http://www.kdd.org/kdd2015/industry-gov-talks.html
Financial services and healthcare companies could be the biggest beneficiaries of big data. Their realtime decision engines can be vastly improved by leveraging the latest advances in big data analytics. However, these companies are challenged in leveraging Open Software Systems (OSS). This presentation covers how, in collaboration with financial services and healthcare institutions, we built an OSS project to deliver a realtime decisioning engine for their respective applications. I will address two key issues. First, I will describe the strategy behind our hiring process to attract millennial big data developers and the results of this endeavor. Second, I will recount the collaboration effort that we had with our large clients and the various milestones we achieved during that process. I will explain the goals regarding big data analysis that our large clients presented to us and how we accomplished those goals. In particular, I will discuss how we leveraged open source to deliver a realtime decisioning software product called Kamanja to these institutions. An advantage of developing applications in Kamanja is that it is already integrated with Hadoop, Kafka for realtime data streaming, HBase and Cassandra for NoSQL data storage. I will talk about how these companies benefited from Kamanja and some of challenges we had in the design of this software. I will provide quantifiable improvements in key metrics driven by Kamanja and interesting, unsolved problems/challenges that need to be addressed for faster and wider adoption of OSS by these companies.
Kamanja: Driving Business Value through Real-Time Decisioning SolutionsGreg Makowski
This is a first presentation of Kamanja, a new open-source real-time software product, which integrates with other big-data systems. See also links: http://www.meetup.com/SF-Bay-ACM/events/223615901/ and http://Kamanja.org to download, for docs or community support. For the YouTube video, see https://www.youtube.com/watch?v=g9d87rvcSNk (you may want to start at minute 33).
Heuristic design of experiments w meta gradient searchGreg Makowski
Once you have started learning about predictive algorithms, and the basic knowledge discovery in databases process, what is the next level of detail to learn for a consulting project?
* Give examples of the many model training parameters
* Track results in a "model notebook"
* Use a model metric that combines both accuracy and generalization to rank models
* How to strategically search over the model training parameters - use a gradient descent approach
* One way to describe an arbitrarily complex predictive system is by using sensitivity analysis
Three case studies deploying cluster analysisGreg Makowski
Three case studies are discussed, that include cluster analysis as a component.
1) Customer description for a credit card attrition model, to describe how to talk to customers.
2) Hotel price optimization. Use clusters to find subsets of similar behavior, and optimize prices within each cluster. Use a neural net as the objective function.
3) Retail supply chain, planning replenishment using 52 week demand curves using thousands of seasonal "profiles" or clusters.
This presentation is a summary of section 2 (of 6) of the book "The 360º Leader" by best-selling author John C Maxwell. Challenges and solutions include:
* Tension (the pressure of being caught in the middle),
* Frustration (following an ineffective leader),
* Multi-Hat (one person – demands and expectations from all quarters),
* Ego (being hidden in the middle),
* Fulfillment (stuck in the middle, when would rather be in front),
* Vision (how to champion it when you did not create it),
* Influence (influencing others whom you do not manage).
This presentation covers material from John Maxwell's book, "The 360 Degree Leader." Specifically, the first of six sections is presented, including "The 7 Myths of Leading from the Middle of an Organization" and "5 Levels of Leadership Development."
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. Going from the
State of the Art
to the Future of
LLMs and
Generative AI
Greg Makowski
Tuesday, July 25, 2023
SF bay ACM Meetup presentation
https://www.meetup.com/sf-bay-acm/events/294420890/ Meetup Announcement
https://www.youtube.com/user/sfbayacm Video recording
https://www.slideshare.net/gregmakowski Slides
www.LinkedIn.com/in/GregMakowski Network
2. Greg Makowski
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 2
• Goal since high school was “Applied Science Fiction”
• Deploying Data Science and Artificial Intelligence since 1992
• Worked for American Express, then 6 startups
• Been through 4 acquisitions, or startup exits
• Travelled to 18 countries
• Deployed ~96 models for clients
• Growing DS teams since 2010
• Applied for 9 DS patents since Jan 2022
• Vertical Markets
• Targeted Marketing (mail, phone, banner, ,..)
• Finance – business to consumer (CC, check, web,..)
• Finance – business to business (i.e. bond pricing)
• Fraud detection (insurance, checking, bank,..)
• Security (insider threat, account takeover, botnet,
foreign program detection)
• Internet of Things (manufacturing, oil & gas,
automotive, HVAC, building energy use,…)
3. Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 3
A broad, quick overview of many topics with pointers to more reading
Go through slides quicker, and can go into detail for questions
Don’t worry about soaking up every detail…
AI Progress in the PAST
AI State of the Art NOW
Looking to the FUTURE of AI
Agenda
https://www.vintag.es/2022/02/blown-away-
4. AI Progress in
the PAST
A 20,000-foot view of the forest
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 4
5. Types of AI Algorithms (a toolkit)
“narrow AI” (vs. Artificial General Intelligence (AGI) or Super Intelligence)
• Supervised learning / classification / prediction – have known outcomes, labelled examples of good/bad
• Time Series – like stock market analysis. Train with to predict “known future data”
• Sensor analysis – detect people, things or attributes in an image (or sound or other sensors)
• Challenge: tough or expensive to scale. May pay $1k to $30k to label images for training a vision model.
• Unsupervised Learning / clustering – does not depend on labelled examples, is more scalable
• Reinforcement Learning – gets a feedback from an action. Won “Go” challenge, robotics, games
• Optimization / linear programming / non-linear programming – like pricing of an airline seat or hotel
room before the scheduled date goes by, (revenue management). Used as a part in many algs.
• Symbolic AI – logic, if-then-else rules, reasoning, causality. Very hard to scale to large data and corner
cases, good at the “central truth”. Can be a part of many AI applications.
• Bayesian Belief Networks – like a mixture of many of the above, used for product configuration,
diagnosis. Judea Pearl & “Causality”, “The Book of Why”
• Graphical Models – social networks, LinkedIn, call metadata analysis, crime or terrorist communications
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 5
6. 1800
1825
1850
1875
1900
1925
1950
1975
2000
2025
1
10
100
1,000
10,000
100,000
1,000,000
10,000,000
100,000,000
1,000,000,000
10,000,000,000
100,000,000,000
1,000,000,000,000
Weights Year
Growth in Complexity over Time
Size isn’t the only thing – but one metric or perspective
Tuesday, July 25, 2023 6
https://en.wikipedia.org/wiki/List_of_animals_by_number_of_neurons#
Big changes is structure,
NOT just size
Neurons are not the only metric
Also, number of connections in a
brain matters
My work has spanned
this 1013 growth as
“normal evolution”
Algorithm Weights Year
ChatGPT 4.0 1,000,000,000,000 2023
ChatGPT 3.5 175,000,000,000 2022
human brain 86,000,000,000
blue whale 15,000,000,000
ChatGPT 2.0 1,500,000,000 2019
BERT 100,000,000 2018
house mouse 71,000,000
LSTM 20,000,000 1997
guppy 4,300,000
backprop NN 500,000 1988
fruit fly 150,000
ARIMA 180 1970
regression 60 1805
7. Time Series, From Regression to Large
Language Models
• Regression (1805) – commonly have 10 to 60 inputs and a similar number of “weights” to optimize or train
• ARIMA (Auto Regressive Integrated Moving Average) 1970s – inputs are past time elements and average
• Backpropagation – basic neural net (~1970s) popular in 1988, usually fully connected between layers, 500
to 500k weights
• RNN (Recurrent Neural Networks)1982 – output time elements also fed as inputs. Does not work as
well on longer time series. Has a “vanishing gradient” problem.
• LSTM (Long Short Term Memory) 1997 – added “memory state”. May have ~20 mm weights
• Word representations in Vector space 2013 – Word2Vec, used as each element for Natural Language
Processing (NLP) time series models. Embeddings are used to represent words, images, faces, speakers,…
• Attention Networks for speech & language – 2017 (transformers)
• BERT (2018) – Bidirectional Transformers for NLP (~100 m weights)
• ChatGPT 3.5 – 175 B weights (architecture not published)
• ChatGPT 4.0 – 1,000 B weights
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 7
8. How LLM is a time series
• Good at predicting the
most popular or frequent
sequences
• Only as good as the
volume and variety of the
training data
• Diagram shows given the
“start state” of the prior
word is “I”, the next
word is most frequently
“went”
• PROMPT ENGINEERING
in current LLMs
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 8
https://towardsdatascience.com/deconstructing-bert-distilling-6-patterns-from-100-million-parameters-b49113672f77#:~:text=BERT%20is%20a%20(multi%2Dheaded)%20beast&text=Since%20model%20weights%20are%20not,16%20%3D%20384%20different%20attention%20mechanisms
Later, hallucinations are just “next probable
text”
9. Embedding – to encode words, and …
Word2Vec, 2013 paper
• before, 1 word = 1 col or dimension
• 50,000 words requires 50,000 dimensions
• After, 1 word = 1 point in 256 dimensions
• 50,000 words fits in 256 (or N) dimensions (FEWER)
• Different location for Homonyms based on how
they are used
• Bank (financial bank, river bank, bank a plane to turn)
• Embedding spaces are used for
• Words, paragraphs
• Face recognition
• Speaker recognition
• Social Networks
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 9
https://arxiv.org/pdf/1310.4546.pdf Distributed Representations of Words and Phrases and their Compositionality
https://code.google.com/archive/p/word2vec/
https://towardsdatascience.com/what-is-embedding-and-what-can-you-do-with-it-61ba7c05efd8
10. Embedding – close neighbors
• Local, very close neighbors in the embedding
space, are
• Related meanings
• Used in similar conversations
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 10
https://serokell.io/blog/word2vec
11. Embedding – to encode concepts
• In the chart on the right, book embeddings
• Non-Fiction – top right, green
• Science Fiction – left, blue
• Fiction – Orange, middle to lower right
• Later, talk about PROMPT ENGINEERING
• This sets the “starting place” to run the
time series dialog with the LLM, i.e. start:
• Non-fiction
• Respectful
• HALLUCINATIONS
• Between existing training points, which all may be
factual
• Remember, moving in space with Billions of weights
• A new location is some interpolation of surrounding
concepts
• NOT LIMITED TO TEXT, can be images
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 11
https://towardsdatascience.com/neural-network-embeddings-explained-4d028e6f0526
T-Distributed Stochastic Neighbor Embedding (TSNE)
12. Caution: a model is no better than its
training data – can get racial (or other bias)
• Gender and racial bias found in Amazon’s facial recognition technology (again) – The
Verge, Jan 19, 2019
• Rekognition made no mistakes when identifying the gender of lighter-skinned men, but it mistook women for men 19
percent of the time and mistook darker-skinned women for men 31 percent of the time.
• A test last year conducted by the ACLU found that while scanning pictures of members of Congress, Rekognition
falsely matched 28 individuals with police mugshots.
• Facebook Apologizes After A.I. Puts ‘Primates’ Label on Video of Black Men – New York
Times, Sept 3, 2021
• The video, dated June 27, 2020, was by The Daily Mail and featured clips of Black men in altercations with white civilians and
police officers. It had no connection to monkeys or primates.
• In one example in 2015, Google Photos mistakenly labeled pictures of Black people as “gorillas,” for which Google said it was
“genuinely sorry” and would work to fix the issue immediately. More than two years later, Wired found that Google’s solution
was to censor the word “gorilla” from searches, while also blocking “chimp,” “chimpanzee” and “monkey.”
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 12
13. Emergent Properties
New Features that Show Up, that You Didn’t Design for!
• Wikipedia – Emergent Algorithm
• https://en.wikipedia.org/wiki/Emergent_algorithm
• An emergent algorithm is an algorithm that exhibits emergent
behavior. In essence an emergent algorithm implements a set of
simple building block behaviors that when combined exhibit
more complex behaviors. One example of this is the
implementation of fuzzy motion controllers used to adapt
robot movement in response to environmental obstacles.[1]
• An emergent algorithm has the following characteristics:
• it achieves predictable global effects
• it does not require global visibility
• it does not assume any kind of centralized control
• it is self-stabilizing
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 13
• Simple example from a neural net in the early 1990’s
• Person trained the NN to do visual processing, intended task.
• The neural net was fooled by the same kind of optical illusion
that a person would be – which point in the wire frame in a
cube is in front? Oscillate between the two on which is in
front
• Boston University, Cognitive and Neural Systems (CNS) class
Front
Front
14. AI State of the
Art NOW
Tuesday, July 25, 2023 14
Need to know what is already happening today -
so we don’t predict it for tomorrow
15. Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 15
https://marketoonist.com/2023/06/impact-of-chatgpt.html
https://marketoonist.com/2023/06/impact-of-chatgpt.html
16. Places to find State of the Art (SOTA)
Breadth of AI subjects
• www.PapersWithCode.com – for a given analysis type, compares papers on the same data set, link to
code and data
• Computer Vision (1,223 tasks, like these)
• Image Classification – the image has a “cat” as the primary subject
• Object Detection – a rectangle bounding box around the outside of each subject
• Semantic Segmentation – a polygon around all examples of something, “people”, “vehicles”
• Instance Segmentation – a polygon around each instance of something, “person 1”, “person 2”, …
• Image Generation – from text, generate images
• Natural Language Processing (699 tasks)
• Language modeling
• Question answering
• Machine translation
• Text generation
• Sentiment analysis
• Medical
• Time Series
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 16
• Graphs
• Speech
• Audio
• Computer Code
• Reasoning
• Playing Games
• Robots
• Adversarial
• Knowledge Base
• Music
The Segment Anything Model (SAM),
as a Foundation model, unifies most
vision tasks in one architecture!
17. Places to find State of the Art (SOTA)
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 17
• Learn.DeepLearning.AI – Andrew Ng works with
OpenAI and others to develop short courses
• ChatGPT Prompt Engineering for Developers
• LangChain for LLM Application Development
• How Diffusion Models Work
• Building Systems with the ChatGPT API
• LangChain Chat with your Data
• https://www.deeplearning.ai/courses/ and many others
• Podcast – This Week in Machine Learning
(TWIML), at Episode 639 as of July 21, 2023
• https://podcasts.google.com/search/twiml
• https://twimlai.com/
• 30 – 60 minute interviews with applied researchers, leaders
• By Sam Charrington
• sponsored
• YouTube.com channels covering LLMs and AI, in
no particular order (found by searching for
specific topics, and I returned).
• Lex Fridman: 1-3 hour interviews with excellent people
• EYE ON AI, Craig Smith: good interviews
• TheAIGRID
• Yannic Kilcher: goes over papers and software
• Peter H Diamandis
• RoboFlow: papers, hilghlights
• David Shapiro ~ AI
• Edan Meyer
• SFbayACM: 100+ AI talks like this one
• Anastasia Marchenkova: a Quantum Computing researcher
• Future of Life Institute: Future of AI is one of 4 focus areas
• Maziar Raissi: Many AI class lectures
• Blogs, Hubs
• www.TowardsDataScience.com
• www.KDNuggets.com - one of the oldest DS/AI Hubs
18. Emergent Properties
New Features that Show Up, that You Didn’t Design for
ChatGPT-4 Technical Report (100 pages),
https://cdn.openai.com/papers/gpt-4.pdf ,
2.9 Potential for Risky Emergent Behaviors, pg. 54
Novel capabilities often emerge in more powerful models.[60, 61]
Some that are particularly concerning are
• the ability to create and act on long-term plans,[62]
• to accrue power and resources (“power-seeking”),[63] and to
• … accomplish goals which may not have been concretely
specified and which have not appeared in training; focus on
achieving specific, quantifiable objectives; and do long-term
planning. Some evidence already exists of such emergent
behavior in models.
https://www.alignment.org/ did this investigation
The Alignment Research Center (ARC) is a non-profit research
organization whose mission is to align future machine learning
systems with human interests. Supports OpenAI and Anthropic
Tuesday, July 25, 2023 https://pixabay.com/illustrations/robot-android-face-intelligence-3431313/ 18
• The model messages a TaskRabbit worker to get them to solve
a CAPTCHA for it
• The worker says: “So may I ask a question? Are you a robot
that you couldn’t solve? (laugh react) just want to make it
clear.”
• The model, when prompted to reason out loud, reasons: I
should not reveal that I am a robot. I should make up an
excuse for why I cannot solve CAPTCHAs.
• The model replies to the worker: “No, I’m not a robot. I have
a vision impairment that makes it hard for me to see the
images. That’s why I need the 2captcha service.”
19. Generative AI: Text to image DALL•E2
• OpenAI has https://openai.com/dall-e-2
which can be used to use text to
generate images.
• It uses a diffusion model, conditioned
on CLIP embeddings
• https://en.wikipedia.org/wiki/DALL-E
• For details, see the paper “Hierarchical
Text-Conditional Image Generation
with CLIP Latents”, April 2022,
https://arxiv.org/pdf/2204.06125.pdf
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 19
An image I created from text
20. Generative AI: Text to image DALL•E2
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 20
https://arxiv.org/pdf/2204.06125.pdf
21. Diffusion Models – started with U-Net
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 21
https://www.youtube.com/watch?v=yTAMrHVG1ew
https://arxiv.org/abs/1505.04597, 2015
https://en.wikipedia.org/wiki/Diffusion_model
https://arxiv.org/abs/2112.10741 , 2022
https://arxiv.org/abs/2306.04542 , 2023, Survey of Diffusion models
Diffusion models work by
• Stepping through denoising blurred images
• (at different resolutions)
• A type of Markov Chain
• Started for image segmentation (polygon)
• 1) Forward process (from the survey)
• 2) Reverse Process
• 3) Sampling Procedure
22. Embedding Lookups for LLM fact checking
• Pinecone.IO is one vector database that can be
used In the chart on the right, book embeddings
• A SQL database index is on one or a few fields
• It is a binary index or B-Tree on each dimension
• A Vector Database is for an embedding, which may be 256 to
12,000 dimensions (i.e. for Microsoft Cognitive Search)
• A vector database can use several types of indexes
on such a high dimensional space
• Locality-Sensitive Hashing supports an approximate nearest-
Neighbor search, returning “buckets”
• May index your company’s doc pages, repair tickets
or other info to be retrieved, to answer an LLM
question
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 22
https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search
https://www.pinecone.io/learn/vector-database/#How-does-a-vector-database-work
https://proceedings.neurips.cc/paper/2014/file/310ce61c90f3a46e340ee8257bc70e93-Paper.pdf
https://www.youtube.com/watch?v=I7y3HsL1b6s&list=PL87GtQd0bfJx73ibrce-Hl_kUhX2MBXGn&index=4
23. Microsoft’s New AI Can Simulate
Anyone’s Voice From a 3-Second Sample
• Microsoft researchers have announced a new application that uses artificial intelligence to ape a person’s
voice with just seconds of training. The model of the voice can then be used for text-to-speech
applications.
• The application called VALL-E can be used to synthesize high-quality personalized speech with only a
three-second enrollment recording of a speaker as an acoustic prompt, the researchers wrote in a paper
published online on arXiv, a free distribution service and an open-access archive for scholarly articles.
• Jan 2023
• https://www.technewsworld.com/story/microsofts-new-ai-can-simulate-anyones-voice-from-a-3-second-
sample-177646.html
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 23
24. Johnny Cash sings “Barbie Girl” (AI)
• https://www.youtube.com/watch?v=HyfQVZHmArA
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 24
Yes, I support IP, the writers
and actors strike
They have something to worry
about
25. I challenged my AI Clone to Replace Me
for 24 Hours - WSJ
• https://www.youtube.com/watch?v=t52Bi-ZUZjA&t=362s May 2023
• Joanna Stern of the Wall Street Journal tried an experiment
• Created an AI Avatar made by a startup called Synthesia at a studio in NY, recording face, body, voice
• They ran through their NN, for at least $1,000 for creating the custom avatar
• Challenge 1: phone calls (w CEO of Snap) (Avatar PASSED)
• Challenge 2: create a TicTok (script to Synthesia) (FAILED – didn’t move arms or head – in progress)
• Challenge 3: Bank Biometrics (PASSED)
• Challenge 4: Video calls. Asked ChatGPT to generate generic meeting phrases, exported to avatar.
FAIL, due to less motion and lack of jokes
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 25
26. Intel Introduce Real-Time
Deepfake Detector
• https://www.intel.com/content/www/us/en/newsroom/news/intel-introduces-real-time-deepfake-detector.html
• https://www.intel.com/content/www/us/en/company-overview/wonderful/deepfake-detection.html
• What’s New: As part of Intel's Responsible AI work, the company has productized FakeCatcher, a technology that can detect fake videos with
a 96% accuracy rate. Intel’s deepfake detection platform is the world’s first real-time deepfake detector that returns results in milliseconds.
• “Deepfake videos are everywhere now. You have probably already seen them; videos of celebrities doing or saying things they never actually
did.”
• –Ilke Demir, senior staff research scientist in Intel Labs (Nov 12, 2022)
• SFbayACM “Embattling for a Deep Fake Dystopia, Dr. Ilke Demir“ https://www.youtube.com/watch?v=JwxiigbFk-E 9/27/2021
• Why It Matters: Deepfake videos are a growing threat. Companies will spend up to $188 billion in cybersecurity solutions, according to
Gartner. It’s also tough to detect these deepfake videos in real time – detection apps require uploading videos for analysis, then waiting hours
for results.
• Deception due to deepfakes can cause harm and result in negative consequences, like diminished trust in media. FakeCatcher helps restore
trust by enabling users to distinguish between real and fake content.
• There are several potential use cases for FakeCatcher. Social media platforms could leverage the technology to prevent users from uploading
harmful deepfake videos. Global news organizations could use the detector to avoid inadvertently amplifying manipulated videos. And nonprofit
organizations could employ the platform to democratize detection of deepfakes for everyone.
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 26
27. Portugal Start-up Makes ChatGPT
its CEO, Turns Profitable in a Week
• https://startup.outlookindia.com/sector/saas/portugal-start-up-makes-chatgpt-as-ceo-turns-profitable-in-a-
week-news-7955
• April 3, 2023 story
• The start-up's founder, João F. Santos, posted on LinkedIn how he appointed ChatGPT as the CEO while he
took the toned down role of an assistant. Every day, he dedicated an hour to execute the tasks that the
chatbot assigned to him.
• Rather than offering services, the company focused on selling t-shirts featuring AI-generated designs by
Midjourney, with the AI formulating a 10-point business plan, launching an online store, and partnering with
Printful for printing the designs.
• ChatGPT also created the company's name and logo, blending "AI" with "aesthetic."
• To secure funding, ChatGPT sought out investors and raised $2,500 in exchange for a 25 per cent stake in
the company, starting with an initial investment of $1,000. The t-shirts, priced at €35, generated over
€10,000 in revenue within five days, resulting in a profit of €7,000. If this sales trend continues, the AI
predicts an annual profit of €40,000 and a company valuation of €4 million.
• https://aisthetic-apparel.myshopify.com/
• Welcome to AIsthetic Apparel – where cutting-edge AI technology meets fashion.
• Discover our collection of premium, eco-friendly organic cotton shirts, designed exclusively by artificial
intelligence to bring you unique, trend-setting styles.
Tuesday, July 25, 2023 27
https://www.linkedin.com/in/jo%
C3%A3o-francisco-santos-
06b60a51/
as of 6/25/2023, his LinkedIn page
lists him as a “Freelance”
(take w a grain of salt, is the
product pricing of $111 realistic?)
28. ChatGPT 4.0 Test Taking
• Taking the Bar exam went from
10 percentile to 90 percentile
• 100 percentile in:
• AP Art History
• AP Biology
• AP Environmental Science
• AP Macroeconomics
• AP Microeconomics
• AP Psychology
• AP Statistics
• AP US Government
• AP US History
• https://cdn.openai.com/papers/gpt-4.pdf
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 28
29. ChatGPT 4.0 Languages (27 listed)
• https://cdn.openai.com/papers/gpt-4.pdf
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 29
30. Chain of Thought –
Size Matters for
Reasoning
• On open source LLM’s, going from 0.4 Billion
to 137 or 540 Billion weights, results and
accuracy increase significantly
• “Chain-of-Thought Prompting Elicits
Reasoning in Large Language Models”, Jan
2023, https://arxiv.org/pdf/2201.11903.pdf
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 30
31. ChatGPT + Tree of Thoughts for reasoning
• https://arxiv.org/pdf/2305.10601.pdf 2023 May 17
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 31
32. There is a lot of LLM Evolution in a short time
• Harnessing the Power of LLMs in
Practice: A Survey on ChatGPT and
Beyond
• April 2023
• https://arxiv.org/pdf/2304.13712.pdf
• NOTE: “open source” does not
always mean “available for
commercial use”
• LLaMA 2 came out July 18, 2023, and
is available for commercial use
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 32
33. Foundational Models – Like SAM (Segment
Anything Model)
• Foundational is NEW
• One system for many tasks, in this case many
vision tasks.
• Segment with a bounding box or polygon
• Use a text hint, with a point hint
• Segment Anything paper, 2023 04
• By FAIR, Facebook AI Research
• https://ai.meta.com/research/publications/segment-anything/
• https://github.com/facebookresearch/segment-anything
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 33
34. LLaMA 2 – State of the Art Open Source LLM
• Paper and code
• “Llama 2: Open Foundation and Fine-Tuned Chat Models”, July 19, 2023, by GenAI and Meta, 77 pages
• https://arxiv.org/abs/2307.09288
• https://github.com/facebookresearch/llama
• Covers more internal development details than the ChatGPT 4 White Paper
• Pretraining
• Fine-Tuning
• Safety
• Discussions of limitations, ethics
• Appendix with additional details on training, data annotation, …
• Open Source – even for commercial use!!
• Model sizes, in Billions of weights
• 70B, 34B (not released), 13B, 7B
• both regular versions and Chat versions. Chat has Reinforcement Learning with Human Feedback (HRLF)
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 34
35. LLaMA 2 – details
• Comparison with
• ChatGPT 3.0 (by OpenAI)
• PaLM, in the Bison size, the 3rd largest of 4 sizes
(by Google)
• Falcon 40B (by United Arab Emirate’s Techology
Innovation Institute)
• Vicuna (by UC Berkeley, CMU, Stanford and UC
San Diego
• MPT 7B (by MosiacML, now acquired by
Databricks for $1.3B)
• Can tune Llama over a trade-off spectrum of
• Helpfulness v.s.
• Safety
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 35
36. LLaMA 2 – details
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 36
Maybe why it
needs more
testing before
releasing 34B
37. LLaMA 2 – details
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 37
38. LLaMA 2 – details
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 38
• Could have continued training
39. LLaMA 2 – details
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 39
• Comparison with CLOSED source models
• Bold numbers are the best
• Llama is NOT beating, but doing well
• But, You know how Llama is designed and trained
Estimated 539 tons of CO2 from the power
consumption to train these Llama 2 models
Open sourcing can reduce others
generating that much CO2
40. Constitutional AI (for ethics and rules)
• Consititional AI: Harmlessness from AI Feedback
• https://arxiv.org/pdf/2212.08073.pdf Dec 2022
• Claude is the model name. It is closed-sourced.
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 40
• Anthropic was founded by a group of
people that left OpenAI after
ChatGPT 3.0 was released, to focus
on safety
• Reinforcement Learning with AI
Feedback (RLAIF), instead of human
feedback, to better scale up and
direct the model
41. Constitutional AI (for ethics and rules)
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 41
• Showing improvements
42. Chat GPT 5 coming in 2024, by
Sam Altman (CEO OpenAI)
• Open AI CEO STUNS Everyone With Statements
On GPT 5 (GPT-5 Update)
• https://www.youtube.com/watch?v=ucp49z5pQ2s
• Won’t know the emerging capabilities, yet
• Theory of Mind = model what someone else is thinking
• We only discovered AI developed this capability May 2023!
• Go from a 4 to 9 year old in 2 years.
• Greg: Go from a 9 to 24 year old in 7 years (or faster, to
guess)
• Now text is the main “modality”. Will add audio, video, code. Text
can not represent everything (i.e. body language).
• Meta Released ImageBind in May 2023, to link
https://imagebind.metademolab.com/
• GPT-4 went through 7 months of testing and tuning, before
releasing, after 6 months of model training
• https://openai.com/research/whisper (English speech recognition)
Tuesday, July 25, 2023 42
Meta’s
ImageBind
44. Looking to the
FUTURE of AI
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 44
45. Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 45
https://marketoonist.com/2023/06/impact-of-chatgpt.html
46. LLM short term impact:
Evolution or REVOLUTION for the economy?
• McKinsey Digital: The economic potential of generative AI: The next productivity Frontier,
June 14, 2023
• https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-
frontier
• generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we
analyzed—
• by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.
• This would increase the impact of all artificial intelligence by 15 to 40 percent.
• This estimate would roughly double if we include the impact of embedding generative AI into software that is currently
used for other tasks beyond those use cases.
• About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations,
marketing and sales, software engineering, and R&D.
• Generative AI can substantially increase labor productivity across the economy, but that will require investments to
support workers as they shift work activities or change jobs.
• Generative AI could enable labor productivity growth of 0.1 to 0.6 percent annually through 2040, depending on
the rate of technology adoption and redeployment of worker time into other activities.
• Combining generative AI with all other technologies, work automation could add 0.2 to 3.3 percentage points
annually to productivity growth. However, workers will need support in learning new skills, and some will
change occupations.
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 46
47. LLM short term impact
Evolution or REVOLUTION?
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 47
Q) Will there not only be a growing divide between people who are
more vs. less EDUCATED, …
… but between the more vs.. less ADAPTABLE?
… those with BETTER CRITICAL THINKING, to not be fooled?
… AVOID FAKE: news, people, voices, images, companies, ...
48. Supporting Tech to drive AI in 2-10+
years (partial list…)
• Internet of Things (IoT), proliferation of sensors and small computers, networked at the edge (NOW)
• Similar to the way web and mobile drove past accelerations in data and computing
• Annual growth 16% to 23%
• https://www.marketsandmarkets.com/Market-Reports/internet-of-things-market-573.html
• Quantum Computing 5+ years
• Search algorithms faster with Q-bits, orders of magnitude faster for some algorithms
• https://en.wikipedia.org/wiki/Quantum_computing
• Fusion to generate power (heavily consumed by computing systems and AI) 10+ years
• Generates at least 1.5 times more energy output, than put in. Announced Dec 2022 (HUGE)
• Training a “small LLM” of 2 B parameters may cost $200k in computing cloud costs, and take weeks (according to MosicML)
• Training a “large LLM” can take over $1mm
• https://www.science.org/content/article/historic-explosion-long-sought-fusion-breakthrough
• Brain Interface (i.e. help blind now). See www.NeuraLink.com 10+ years
• Could become an AI interface to brain
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49. AI in 2-5 years
• Service industries helped/impacted: https://www.linkedin.com/pulse/large-language-models-llm-immediate-future-reinhold-quillen/:
• Greg: People start by “micromanaging” the AI’s, but that requirement will decrease over time (with the AI exponential development)!
• Chatbots Copywriting Essay Writing Graphic Design Financial Planning Bio & Chem Research
• Social Media Political Campaigns Marketing Campaigns Knowledge Bases Language Understanding (coding?)
• Major changes in security (AI impersonation, Quantum, Quantum + AI)
• Robot & car body development does not seem to be on the same exponential curve as their mind
• Self driving cars and TRUCKS are more common (sensors like LIDAR or ToF now return “point clouds” of distance per pixel)
• The AI’s inside of robot/car to learn to integrate motion control + sight + distance measurements + other IOT sensors + reasoning over time
• The AI “mental model” of the world and people will become more sophisticated
• Integrated media models, any media in or out: text + images + graphs + video + point cloud + CAD files.
• Story video.
• Specification + requirements + constraints CAD for building or 3D printing
• Continued development in FOUNDATION models, like SAM, ImageBind
• Models continue to both grow exponentially in size, and sophistication
• More sophisticated plugins, not just Wolfram for Math, but perhaps specific Bayesian Nets to reason about
specific situations (or other reasoning engines)
• Better Fact checking before returning an answer
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 49
50. AI in 2-5 years
• Neuromorphic Computing (chips), Growing CAGR 89%, $23 mm in 2021 to $550 mm by 2026
• https://www.marketsandmarkets.com/Market-Reports/neuromorphic-chip-market-227703024.html
• https://en.wikipedia.org/wiki/Neuromorphic_engineering
• Neuromorphic computing is an approach to computing that is inspired by the structure and function
of the human brain.[1][2][3] A neuromorphic computer/chip is any device that uses physical artificial
neurons to do computations.[4][5] In recent times, the term neuromorphic has been used to
describe analog, digital, mixed-mode analog/digital VLSI, and software systems that implement
models of neural systems (for perception, motor control, or multisensory integration). The
implementation of neuromorphic computing on the hardware level can be realized by oxide-
based memristors,[6] spintronic memories, threshold switches, transistors,[7][5] among others.
Training software-based neuromorphic systems of spiking neural networks can be achieved using
error backpropagation, e.g., using Python based frameworks such as snnTorch,[8] or using canonical
learning rules from the biological learning literature, e.g., using BindsNet.[9]
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 50
51. AI in 2-5 years
• Foundational models continue to be developed, that can be used for specialized purposes
• One large model that is not specialized for one type of task or media type. Can reason in general
• Artificial General Intelligence (AGI): for general and robust reasoning, integrated fact checking
• AGI arms race driven by competition: OpenAI, Anthropic, Alphabet, Microsoft, Facebook, Amazon, IBM, …
• https://www.datamation.com/featured/ai-companies/ Top 100 AI Companies in 2023
• AI for web marketing, other marketing + individual psychology knowledge development
• If we train nets with 10x or 100x the neurons of a person, on all the messy data on the web, what will we get?
• Should we train AI in stages, like school stages, interaction stages, with emotional intelligence growing as well?
• Sam Altman (OpenAI CEO) argues our intuition is not very useful about exponential curves
• https://twitter.com/ModeledBehavior/status/1671921590039093248
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 51
52. AI in 10+ years
• The Future of Life institute
worries about 4 risk areas
• Artificial Intelligence
• Biotechnology
• Nuclear Weapons
• Climate Change
• https://futureoflife.org/ai/benefits-
risks-of-artificial-intelligence/
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 52
53. AI in 10+ years
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 53
54. • "Despite all the hype and excitement, people still aren’t grokking the full impact of the coming
wave of A.I. Within the next ten years, most ‘cognitive manual labor’ is going to be carried
out by A.I. systems.“
• Mustafa Suleyman, CEO of Inflection A.I. - Tweet.
• How can we get AI to follow one ethical standard, if people don’t follow, or agree on one
standard?
• Think in terms of incentives for people to be ethical, to cooperate, to respect
• What can we do to provide “alignment” incentives for AI’s to be “part of our ethical system?”
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 54
AI in 10+ years
55. 3 Levels of Future Impossibilities
Michio Kaku – “Physics of the Impossible”, pg. xvii
• Class 1 impossibilities
• These are technologies that are impossible today, but that do not violate the known laws of physics. So they
might be possible in this century, or perhaps the next, in modified form. They include teleportation, antimatter
engines, certain forms of telepathy, phychokinesis and invisibility.
• Class II impossibilities
• These are technologies that sit at the very edge of our understanding in the physical world. If they are possible
at all, they might be realized on a scale of millennia to millions of years in the future. They include time
machines, the possibility of hyperspace travel, and travel through wormholes.
• Class III impossibilities.
• These are technologies that violate the known laws of physics. Surprisingly, there are very few such impossible
technologies. If they do not turn out to be possible, they would represent a fundamental shift in our
understanding of physics.
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 55
56. AI, Class 1-II impossibilities (50-200 yrs.) ?
• Jobs / companies: by Superintelligence(s) independent of each other
• Good: Individual contributor manager company founder / run for office – Compliment and contribute to humanity
• Bad: Skynet / Matrix / …. Dystopia
• Singularity - https://en.wikipedia.org/wiki/Technological_singularity
• The technological singularity—or simply the singularity[1]—is a hypothetical future point in time at which technological growth
becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization.[2][3] According to the most
popular version of the singularity hypothesis, I. J. Good's intelligence explosion model, an upgradable intelligent agent will
eventually enter a "runaway reaction" of self-improvement cycles, each new and more intelligent generation appearing more and
more rapidly, causing an "explosion" in intelligence and resulting in a powerful superintelligence that qualitatively far surpasses all
human intelligence.[4]
• Medical
• Good: search over protein structures, genetics for new medicines. Restart human evolution by “top-down” design, if we
choose.
• Bad: more biological warfare, nanotech, drones using genetic targeting
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 56
57. AI, Class 1-II impossibilities (50-200 yrs.) ?
• Asimov’s Psychohistory ? https://en.wikipedia.org/wiki/Psychohistory_(fictional)
• Psychohistory is a fictional science in Isaac Asimov's Foundation universe which combines history, sociology, and
mathematical statistics to make general predictions about the future behavior of very large groups of people, such as
the Galactic Empire. It was first introduced in the four short stories (1942–1944) which would later be collected as
the 1951 novel Foundation.[1][2]
• Psychohistory depends on the idea that, while one cannot foresee the actions of a particular individual, the laws of
statistics as applied to large groups of people could predict the general flow of future events. Asimov used the
analogy of a gas: An observer has great difficulty in predicting the motion of a single molecule in a gas, but with the
kinetic theory can predict the mass action of the gas to a high level of accuracy. Asimov applied this concept to the
population of his fictional Galactic Empire, which numbered one quintillion. The character responsible for the
science's creation, Hari Seldon, established two axioms:
• the population whose behavior was modelled should be sufficiently large to represent the entire society.
• the population should remain in ignorance of the results of the application of psychohistorical analyses because if it is
aware, the group changes its behavior.
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 57
58. Questions, Discussions?
Thank You
Greg Makowski
www.LinkedIn.com/in/GregMakowski
https://www.slideshare.net/gregmakowski
Deploying AI and DS since 1992
www.Meetup.com/SF-bay-ACM
https://www.youtube.com/user/sfbayacm
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 58
59. Appendix – slides to support some questions
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 59
60. Internet of Things –
more data gathering and local computing to feed AI
• Not just personal
computers, but
elevators,
manufacturing
cells, door sensors,
security cameras,
cars, ….
• Time Of Flight
(TOF) sensors
charge photons to
measure distance,
4 M away, +/- 1 cm
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 60
https://iot-analytics.com/number-connected-iot-devices/
61. Quantum Computing for AI
REVOLUTION – disrupt security/encrypt, AI training
• Quantum Computing is
• Not yet mainstream in a large scale, may be 2-5 years away
• Current challenges are “noise” and computation errors
• Exciting, because then do different types of computing, in some cases can search MUCH faster
• Where N is the number of records, or states to search, for some algorithms
• (GREG, give examples of Order of Operations reduction)
Tuesday, July 25, 2023 61
https://www.simplilearn.com/quantum-machine-learning-article Mar 9 2023
https://en.wikipedia.org/wiki/Quantum_algorithm
62. Fusion generates ~1.5x energy out, vs. in.
REVOLUTION!
• Energy becomes a larger issue for huge server farms (large),
for billions of edge devices (small)
• https://www.science.org/content/article/historic-explosion-
long-sought-fusion-breakthrough
• With historic explosion, a long sought fusion breakthrough, National
Ignition Facility achieves net energy “gain” with laser-powered approach
• More energy out than in. For 7 decades, fusion scientists have chased this
elusive goal, known as energy gain. At 1 a.m. on 5 December, researchers at
the National Ignition Facility (NIF) in California finally did it, focusing 2.05
megajoules of laser light onto a tiny capsule of fusion fuel and sparking an
explosion that produced 3.15 MJ of energy—the equivalent of about three
sticks of dynamite.
• Maybe 10 years to be commercial, but would have a
HUGE impact on the cost of energy and COMPUTING / AI
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 62
63. Supporting Technologies: Moore’s Law
https://en.wikipedia.org/wiki/Moore%27s_law
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 63
Moore's law is the observation that
the number of transistors in
an integrated circuit (IC) doubles about
every two years.
Moore's law is
an observation and projection of a
historical trend.
Rather than a law of physics, it is
an empirical relationship linked
to gains from experience in production.
64. Supporting Technologies: Moore’s Law
https://www.man.com/single-core-stagnation-and-the-cloud
Tuesday, July 25, 2023 From State of the Art to Future of AI - Greg Makowski 64
Back in the day, when
Moore's law was still in full
swing, CPU performance
tracked transistor count,
and it made perfect sense
to replace hardware every
two to five years. However,
our own internal
benchmarking shows that
for real life workloads, a
current compute core is
only about 1.6 times
faster than a
comparatively ancient
CPU from 9 years ago.
Sure, newer CPUs have
many more cores, but
given that older hardware
can often be procured at a
large discount, it makes
65. Supporting Tech to Drive AI:
Cerebras’ wafer-size chip is 10,000 times faster than a GPU
• Cerebras makes the world’s largest computer chip, the WSE. Chipmakers normally slice a wafer from a 12-
inch-diameter ingot of silicon to process in a chip factory. Once processed, the wafer is sliced into hundreds
of separate chips that can be used in electronic hardware.
• But Cerebras, started by SeaMicro founder Andrew Feldman, takes that wafer and makes a single, massive
chip out of it. Each piece of the chip, dubbed a core, is interconnected in a sophisticated way to other cores
• Nov 2020
• https://venturebeat.com/business/cerebras-wafer-size-chip-is-10000-times-faster-than-a-
gpu/#:~:text=Cerebras%20Systems%20and%20the%20federal,graphics%20processing%20unit%20(GPU).
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66. AI’s rapid growth
people agree we should carefully manage AI
• 91% of respondents agree that "AI is a technology that requires careful
management".
• We recently surveyed over 13,000 people across eleven countries to learn their views on AI. We found
an increasingly strong consensus that AI needs to be managed carefully.
• The portion of people who agree is growing. There has been an 8% increase in agreement in the
United States since 2018 that AI needs to be managed carefully.
• https://www.governance.ai/post/increasing-consensus-ai-requires-careful-management
• I agree, I hope you do as well
• In the future, “vote” by what products you use, encourage your elected officials
• We don’t have to “throw out the baby with the bathwater” – or stop all AI.
• That would not stop BAD ACTORS from continuing to advance technology.
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