Future of AI - 2023 07 25.pptx

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

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,…)

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-

AI Progress in
the PAST
A 20,000-foot view of the forest

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

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

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

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
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”

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
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

Embedding – close neighbors
• Local, very close neighbors in the embedding
space, are
• Related meanings
• Used in similar conversations
https://serokell.io/blog/word2vec

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
https://towardsdatascience.com/neural-network-embeddings-explained-4d028e6f0526
T-Distributed Stochastic Neighbor Embedding (TSNE)

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.”

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
• 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

AI State of the
Art NOW
Need to know what is already happening today -
so we don’t predict it for tomorrow

https://marketoonist.com/2023/06/impact-of-chatgpt.html

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
• 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!

Places to find State of the Art (SOTA)
• 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

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.”

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
An image I created from text

Generative AI: Text to image DALL•E2
https://arxiv.org/pdf/2204.06125.pdf

Diffusion Models – started with U-Net
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

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
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

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

Johnny Cash sings “Barbie Girl” (AI)
• https://www.youtube.com/watch?v=HyfQVZHmArA
Yes, I support IP, the writers
and actors strike
They have something to worry
about

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

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.

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.
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?)

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

ChatGPT 4.0 Languages (27 listed)
• https://cdn.openai.com/papers/gpt-4.pdf

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

ChatGPT + Tree of Thoughts for reasoning
• https://arxiv.org/pdf/2305.10601.pdf 2023 May 17

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

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

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)

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

LLaMA 2 – details
Maybe why it
needs more
testing before
releasing 34B

LLaMA 2 – details

LLaMA 2 – details
• Could have continued training

LLaMA 2 – details
• 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

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.
• 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

Constitutional AI (for ethics and rules)
• Showing improvements

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)
Meta’s
ImageBind

Meta’s (Facebook) ImageBind
• https://www.youtube.com/watch?v=ucp49z5pQ2s, minute 7
Meta’s ImageBind

Looking to the
FUTURE of AI

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.

LLM short term impact
Evolution or REVOLUTION?
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, ...

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

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

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]

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

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/

AI in 10+ years

• "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?”
AI in 10+ years

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.

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

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.

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

Appendix – slides to support some questions

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
https://iot-analytics.com/number-connected-iot-devices/

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)
https://www.simplilearn.com/quantum-machine-learning-article Mar 9 2023
https://en.wikipedia.org/wiki/Quantum_algorithm

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

Supporting Technologies: Moore’s Law
https://en.wikipedia.org/wiki/Moore%27s_law
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.

Supporting Technologies: Moore’s Law
https://www.man.com/single-core-stagnation-and-the-cloud
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

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).

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.

Future of AI - 2023 07 25.pptx

Future of AI - 2023 07 25.pptx

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