20240411 QFM009 Machine Intelligence Reading List March 2024Matthew Sinclair
The document provides a summary of topics related to machine intelligence that were discussed in March 2024, including NVIDIA's Project GR00T which aims to create a general-purpose foundation model for humanoid robots, DeepMind's SIMA which explores using generative AI in 3D virtual environments, Meta's development of large AI clusters to support advanced model training, and an open-source desktop tool for interacting with large language models. The summary also mentions articles on understanding the abilities of large language models, security concerns regarding AI metacognition, and innovative defense strategies against AI attacks.
Artificial Intelligence can Offer People Great Relief from Performing Mundane...JPLoft Solutions
AI refers to the recreation of human-like intelligence in machines created to function like humans and mimic their actions. Artificial Intelligence solutions can be applied to any device that exhibits traits similar to the human brain, such as the capacity to learn and analytical thinking.
As technology is outdoing it self everyday, newer jobs are coming to light. Scroll through to know about the futuristic jobs and the skills required to make it a career.
This document provides an overview of artificial intelligence (AI) and its applications in enterprises. It examines real use cases for AI, challenges, and opportunities. Key areas where AI can provide value for enterprises are enterprise intelligence, computer vision, and conversational AI. Enterprise intelligence involves analyzing multiple internal and external datasets to extract insights, predictions, and recommendations. Computer vision allows machines to "see" and interpret images. Conversational AI allows machines to communicate using natural language. The document also provides case studies of how companies like Stripe and DBS are using AI.
Machine learning and artificial intelligence are two of the most rapidly growing and transformative technologies of our time. These technologies are revolutionizing the way businesses operate, improving healthcare outcomes, and transforming the way we live our daily lives. Learn more about it in the PPT below!
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
The document provides an overview of generative AI, including its key concepts and applications. It discusses transformer models versus neural networks, explaining that transformer models use self-attention to capture long-range dependencies in sequential data like text. Large language models (LLMs) based on the transformer architecture have shown strong performance in natural language generation tasks. The document outlines the evolution of generative AI techniques from early machine learning to modern large pretrained models. It also surveys some commercial generative AI applications in industries like healthcare, finance, and gaming.
This document discusses generative AI, including what it is, how it works, challenges, and potential business uses. Some key points:
- Generative AI can automatically generate new text, images, videos and other content based on training data, rather than just categorizing data like other machine learning.
- It uses large language models trained on vast datasets to generate human-like responses to prompts. While this allows for many potential business uses, challenges include lack of transparency, privacy/security issues, and the risk of factual inaccuracies.
- Generative AI could be used by businesses for tasks like document processing, writing code, augmenting human work, and creating marketing content. Industries like insurance, legal,
AI and Machine Learning: Shaping the Future of Technology
Artificial Intelligence (AI) and Machine Learning (ML) have emerged as revolutionary technologies that are transforming various industries and aspects of our daily lives. From predictive analytics to autonomous vehicles, these advancements are driving innovation and shaping the future of technology. In this article, we’ll delve into the intricacies of AI and Machine Learning, exploring their significance, applications, challenges, and potential for the years ahead.
FAQs
What is the difference between AI and Machine Learning?
AI encompasses the broader concept of simulating human intelligence, while Machine Learning is a subset that focuses on training machines using data.
How does AI impact job markets?
AI can automate routine tasks but also create new job roles that require expertise in AI development, maintenance, and ethical considerations.
What are some ethical concerns with AI?
Bias in AI algorithms, data privacy breaches, and the potential for AI to make critical decisions without human intervention raise ethical questions.
Can AI replace human creativity?
While AI can assist in creative tasks, human creativity remains irreplaceable, as it involves complex emotions, experiences, and subjective interpretations.
Is AI only for tech-savvy industries?
No, AI’s applications span diverse sectors, from healthcare and finance to agriculture and entertainment, driving innovation across the board.
In recent years, AI and Machine Learning have garnered widespread attention due to their potential to replicate human cognitive functions. AI refers to the simulation of human intelligence processes by machines, enabling them to perform tasks that typically require human intelligence, such as problem-solving, decision-making, and language understanding. Machine Learning, a subset of AI, involves training machines to learn from data and improve their performance over time without explicit programming.Machine Learning is based on the principle of allowing machines to learn from data. It involves supervised learning (where models learn from labeled data), unsupervised learning (finding patterns in unlabeled data), and reinforcement learning (reward-based learning). The ability of machines to learn and adapt makes them highly versatile.AI enhances business efficiency by automating tasks and optimizing processes. Chatbots provide instant customer support, while AI-driven analytics assist in data-driven decision-making, giving companies a competitive edge.AI and Machine Learning are reshaping industries, economies, and societies at an unprecedented pace. As we stand at the intersection of human ingenuity and technological innovation, the future promises breakthroughs that will redefine the boundaries of possibility.
While technological advances say they are on the brink of achieving that perfect artificial intelligence, we are not quite there yet. Fortunately for us, an AI does not need to be irreproachable, just better than a human. Take connected cars, for instance. An AI-based driver may not be mistake-proof, but it is certainly less imperfect than a human driver.
This is very much the case in cybersecurity where IT experts are changing the rules of the game using Machine Learning.
Introduction to Artificial Intelligence.pptxRSAISHANKAR
My name is R. Sai Shankar. In here, I'm publish a small PowerPoint Presentation on Artificial Intelligence. Here is the link for my YouTube Channel "Learn AI With Shankar". Please Like Share Subscribe. Thank you.
https://youtu.be/3N5C99sb-gc
DeepMind achieved multiple breakthroughs in 2021 related to our prediction, including:
- Proposing a method using neural networks and human collaboration to generate conjectures in mathematics. This led to solving a long-standing conjecture and proving a new theorem.
- Approximating the density functional theory in materials science using a neural network trained on mathematical constraints.
- Repurposing AlphaZero to discover new deterministic matrix multiplication algorithms by framing it as a reinforcement learning problem.
- Developing a deep reinforcement learning system to stabilize plasma in nuclear fusion experiments, bringing controlled fusion closer to reality.
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Produced by Nathan Benaich and Air Street Capital team
In the landscape of technological evolution, Generative Artificial Intelligence stands at the forefront, reshaping our interactions with technology, creativity, and the world at large. As we teeter on the brink of a new era, the trajectory of Generative AI promises to redefine industries, reshape human experiences, and unlock unprecedented possibilities.
Generative AI's Ascendance:
Empowered by advanced machine learning techniques, Generative AI possesses the remarkable ability to create, innovate, and simulate, once thought to be exclusive to human intellect. Deep learning, anchored in neural networks and algorithms, has paved the way for machines not only to comprehend but also autonomously generate content.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
This document discusses techniques for identifying fake news using social network analysis. It first reviews literature on existing fake news identification methods that use feature extraction from news content and social context. Deep learning models are then proposed to classify news as real or fake using datasets of news and social network information. The implementation achieves 99% accuracy on binary classification of news. Social network analysis factors like bot accounts, echo chambers, and information spread are discussed as enabling the spread of fake news online.
UNCOVERING FAKE NEWS BY MEANS OF SOCIAL NETWORK ANALYSISpijans
The short access to facts on social media networks in addition to its exponential upward push also made it
tough to distinguish among faux information or actual facts. The quick dissemination thru manner of sharing has more high quality its falsification exponentially. It is also essential for the credibility of social media networks to avoid the spread of fake facts. So its miles rising research task to robotically check for
misstatement of information thru its source, content material, or author and save you the unauthenticated
assets from spreading rumours. This paper demonstrates an synthetic intelligence primarily based completely approach for the identification of the fake statements made by way of the use of social network
entities. Versions of Deep neural networks are being applied to evalues datasets and have a look at for
fake information presence. The implementation setup produced most volume 99% category accuracy, even
as dataset is tested for binary (real or fake) labelling with multiple epochs.
Researchers at DeepMind achieved several breakthroughs in 2021 related to their prediction that they would make advances in physical sciences, including proposing a new method for data-driven conjecture generation in mathematics, improving the approximation of density functional theory in materials science, and applying reinforcement learning to control the magnetic coils of a fusion reactor tokamak more effectively. DeepMind has also deployed their AlphaFold protein structure prediction system at an unprecedented scale by predicting structures for 200 million proteins, vastly expanding the potential for scientific discoveries across many fields leveraging this protein structure database. A new method called ESMFold was also developed that can predict protein structures directly from sequences alone without relying
Trendcasting for 2019 - What Will the Tuture of Tech HoldBrian Pichman
Join Brian Pichman of the Evolve Project as he highlights this year’s most significant technology trends and what it means for 2019. What changes are on the horizon? What technologies falling to the wayside? What technologies are on the verge of significant changes? What technologies should we expect to see flourish in the upcoming year?
From Alexa and Siri to factory robots and financial chatbots, intelligent systems are reshaping industries. But the biggest changes are still to come, giving companies time to create winning AI strategies
AI leadership. AI the basics of the truth and noise publicLucio Ribeiro
There are 6 things I identified in the last 2 Years I have been working in AI.
The Problem is - Hysteria
The lack of context is leading to Noise
The Noise is distracting from the attention and urgency where AI should really be
Executives want a Solution and Directions.
THE GOOD NEWS IS: You don’t need to know the HOW to do, leave this to the tech dudes. You need to know the WHY?
You need to create a culture of enablement. A culture of Data
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2. QFM021: Machine Intelligence
Reading List June 2024
We kick off this month’s reading list with the transformative potential of AI in executive
roles. If AI Can Do Your Job, Maybe It Can Also Replace Your CEO (nytimes.com)
highlights AI’s growing capability to manage high-level decision-making tasks
traditionally reserved for CEOs, suggesting a future where AI could play a pivotal role in
corporate leadership, albeit with human oversight to ensure strategic alignment and
accountability. If it can take the jobs of call centre staff, designers, and software
engineers, is there something so special about executive jobs that leaves them immune?
Another theme is the drive to understand the inner workings of gen-AI systems more
deeply. Here’s what’s going on inside an LLM’s neural network (arstechnica.com),
unveiling how AI models like Claude operate on the inside. These studies reveal the
intricate patterns within neural networks, enhancing our ability to interpret and
potentially steer AI behaviour in critical applications such as security and bias mitigation.
We then examine the practical experience of deploying AI at scale with What We
Learned from a Year of Building with LLMs (Part I) (oreilly.com). The O’Reilly article
provides lessons from a year of building with LLMs, emphasizing the importance of
robust prompting techniques and structured workflows.
Finally, this month’s list touches on AI deployment's ethical and operational
considerations. What’s the future for generative AI? The Turing Lectures with Mike
Wooldridge (youtube.com) examines the importance of addressing bias, misinformation,
and ethical concerns in AI’s advancement.
As always, the Quantum Fax Machine Propellor Hat Key will guide your browsing. Enjoy!
Key:
: Mentions technology
: Talks about technology in real-world use cases
: Talks about details of machine intelligence technologies
: Using and working with machine intelligence technologies in software
: Programming new machine intelligence concepts and implementations
Source: Photo by vackground.com on Unsplash
2
3. If AI Can Do Your Job, Maybe It Can Also
Replace Your CEO (nytimes.com): The article
discusses how artificial intelligence (AI)
might not only replace routine jobs but also
high-level executive roles, including CEOs.
With AI's capability to analyse markets,
automate communication, and make
dispassionate decisions, some companies
are already experimenting with AI leadership
to cut costs and increase efficiency, though
human oversight remains necessary for
accountability and strategic thinking.
#AI #Automation #Leadership
#CorporateManagement #FutureOfWork
3
4. Here’s what’s really going on inside an LLM’s
neural network (arstechnica.com): Anthropic's
recent research unveils how the Claude LLM's
neural network operates by mapping millions
of neurons' activities, revealing that concepts
are represented across multiple neurons. This
mapping process, using sparse auto-
encoders and dictionary learning algorithms,
helps identify patterns and associations in
the model, providing partial insights into its
internal states and conceptual organisation.
#AI #MachineLearning
#NeuralNetworks
#ArtificialIntelligence #Research
4
5. Scaling Monosemanticity - Extracting
Interpretable Features from Claude 3 Sonnet
(transformer-circuits.pub): Researchers at
Anthropic have successfully scaled sparse
autoencoders to extract high-quality, interpretable
features from the Claude 3 Sonnet language
model, demonstrating that the technique can
handle state-of-the-art transformers. These
features are diverse, covering concepts from
famous people to programming errors, and are
crucial for understanding and potentially steering
AI behaviour, especially in safety-critical areas
such as security vulnerabilities and bias.
#AI #MachineLearning
#NaturalLanguageProcessing #Safety
#AIResearch
5
6. What is the biggest challenge in our
industry? (thrownewexception.com): The
biggest challenge in the tech industry is the
anxiety caused by layoffs and the fear of AI
replacing jobs, leading to mental health
issues like burnout. Leaders can help by
fostering open communication, leading
positively, leveraging new technologies,
investing in continuous learning, and
collaborating with HR to support their
teams.
#TechIndustry #AI #MentalHealth
#Leadership #Layoffs
6
7. What We Learned from a Year of Building
with LLMs (Part I) (oreilly.com): Over the past
year, the authors built real-world
applications using large language models
(LLMs) and identified crucial lessons for
developing effective AI products. They
emphasise the importance of robust
prompting techniques, retrieval-augmented
generation, structured workflows, and
rigorous evaluation and monitoring to
overcome the complexities and challenges
inherent in leveraging LLMs for practical use.
#AI #MachineLearning #LLM
#TechInnovation #ProductDevelopment
7
8. Achieving the Self-Thinking Business
(linkedin.com): The article discusses Honu's
development of a "Self-Thinking Business"
model through the introduction of a Cognitive
Layer that bridges the gap between current AI
capabilities and true business autonomy. This
new layer aims to transform AI from tactical
automation tools into strategic decision-
makers by providing a comprehensive,
contextual understanding of business data
and operations, reducing the need for
extensive data and compute resources.
#AI #BusinessAutomation
#CognitiveLayer #AutonomousAgents
#Innovation
8
9. What's the future for generative AI? The
Turing Lectures with Mike Wooldridge
(youtube.com): Mike Wooldridge, a
Professor of Computer Science at the
University of Oxford, discusses the current
capabilities and future potential of
generative AI, highlighting both its
transformative possibilities and the
significant challenges it presents, including
issues of bias, misinformation, and ethical
concerns.
#GenerativeAI #FutureTech
#AIChallenges #MachineLearning
#TechEthics
9
10. Introducing Generative Physical AI --
youtube.com: NVIDIA introduced
Generative Physical AI, a technology
enabling robots to learn and refine their
skills in simulated environments,
leveraging NVIDIA's AI supercomputers and
robotics platforms. This development aims
to minimise the gap between simulation
and real-world application, enhancing the
autonomy and functionality of future
robotics.
#NVIDIA #GenerativeAI #Robotics
#AItechnology #Computex2024
10
11. Grounding - Enhance GEN AI with YOUR DATA
(youtube.com): The article discusses
techniques for grounding generative AI
models to ensure their outputs are accurate
and reliable by integrating real-world data,
employing human oversight, and using
multiple models to verify results. These
methods are crucial for preventing errors in
fields like healthcare, finance, and legal
services, and involve strategies like Retrieval-
Augmented Generation (RAG) and
Reinforcement Learning from Human
Feedback (RLHF).
#AI #GenerativeAI #AIAccuracy
#AITrustworthiness #GroundingAI
11
12. Generative AI Handbook: A Roadmap for Learning
Resources -- genai-handbook.github.io: The
Generative AI Handbook offers a comprehensive
roadmap for learning about modern artificial
intelligence systems, particularly focusing on large
language models and image generation. It organises
existing resources like blogs, videos, and papers into
a textbook-style presentation aimed at individuals
with a technical background who seek to deepen
their understanding of AI fundamentals and
applications. The handbook emphasises the
importance of foundational knowledge to effectively
use and adapt to rapidly evolving AI tools and
techniques.
#GenerativeAI #AIHandbook
#MachineLearning #AIeducation
#DeepLearning
12
13. The Future of AI: In a recent LinkedIn post,
Matt Webb shared his thoughts on the
future of AI and its applications. Matt is
focused on the smaller, more ubiquitous
aspects of AI, such as home hardware and
managing intelligent agents.
#AI #FutureOfWork #Innovation
#Technology #LinkedIn
13
14. Back To Atoms: AI has always been seen as
the technology of the future but it has
finally arrived with ChatGPT and Large
Language Models (LLMs). This post reflects
on the journey of AI, the realization of its
'magic,' and the implications it may have on
the software industry and our future. The
author speculates that the next wave in
technology may bring us back to focusing
on tangible, real-world innovations.
#AI #TechFuture #ChatGPT #LLM
#Innovation
14
15. My personal AI research agenda, mid 2024
(and a pitch for work): Matt Webb shares
his latest work with AI agents, specifically a
smart home assistant demonstrating
emergent behaviour. He discusses the
simplicity of creating sophisticated AI
behaviours with minimal code and outlines
his personal AI research interests, including
human-AI collaboration, simple agents
acting in the world, and tiny, ubiquitous
embedded intelligence.
#AI #Research #SmartHome
#TechInnovation #Collaboration
15
16. The Next Great Scientific Theory is Hiding
Inside a Neural Network: Miles Cranmer
discusses the potential of neural networks
to uncover groundbreaking scientific
theories. The lecture delves into the
expanding applications of machine learning,
from text generation to construction
infrastructure. Highlighting the intersection
of AI and scientific discovery, this talk
envisions a future where neural networks
become pivotal in advancing knowledge.
#NeuralNetworks #MachineLearning
#AI #ScientificDiscovery
#Innovation
16
17. Transforming Customer Support and Sales
with Mendable's AI Solutions: Mendable
introduces Firecrawl, a tool that converts
websites into LLM-ready markdown or
structured data. Their platform offers various
AI capabilities to streamline customer support
and sales through AI-powered knowledge
bases, secure data integrations, enterprise-
grade security, and detailed customer
interaction insights. They also support custom
AI model training and have free and enterprise
pricing plans.
#AI #CustomerSupport
#SalesEnablement #EnterpriseSecurity
#AIModelTraining
17
18. Why Apple is Taking a Small-Model Approach
to Generative AI: Apple introduced its new
generative AI offering, Apple Intelligence, at
WWDC 2024. Unlike larger models from
competitors, Apple’s approach focuses on
smaller, customized models integrated
seamlessly with its operating systems to
prioritize a frictionless user experience. Apple
Intelligence is designed to handle various
tasks while maintaining privacy and
efficiency, with the speech generation and
image creation models being processed on-
device for speed and user focus.
#Apple #GenerativeAI #WWDC2024 #AI
#Privacy
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19. Sober AI is the Norm: The article discusses the
current state of AI, emphasizing the need for
'Sober AI' amidst the hype surrounding
advanced artificial intelligence technologies.
Highlighting observations from the Databricks
Data+AI Summit, it points out that most AI
work is mundane, involving data preparation
and pipeline management rather than
groundbreaking advancements. The writer
argues that even these seemingly modest
applications hold significant value in driving
practical business intelligence solutions.
#AI #BusinessIntelligence
#DataScience #TechSummit
#MachineLearning
19
20. Can LLMs invent better ways to train LLMs?:
Sakana AI explores using Large Language Models
(LLMs) for inventing better ways to train
themselves, termed LLM². They leverage
evolutionary algorithms to develop novel
preference optimization techniques, significantly
improving model performance. Their latest
report introduces 'Discovered Preference
Optimization (DiscoPOP)', achieving state-of-the-
art results across various tasks with minimal
human intervention. The approach promises a
new paradigm of AI self-improvement, reducing
extensive trial-and-error efforts traditionally
required in AI research.
#LLMs #AIResearch #DeepLearning
#EvolutionaryAlgorithms #DiscoPOP
20
21. SWE-bench: Can Language Models Resolve
Real-World GitHub Issues?: The SWE-bench
project investigates the ability of language
models to automatically resolve GitHub
issues. It uses a dataset comprising 2,294
issue-pull request pairs from 12 popular
Python repositories, with evaluations based
on unit test verification. The leaderboard
showcases various models and their
performance on this task, with Amazon Q
Developer Agent currently leading.
#LanguageModels #GitHub
#Automation #MachineLearning
#Python
21
22. Will We Run Out of Data? Limits of LLM Scaling
Based on Human-Generated Data: Epoch AI has
estimated the total supply of human-generated
public text at about 300 trillion tokens. They project
that, at the current rate of usage, language models
will exhaust this data stock by 2026 to 2032, or
even earlier with high-frequency training. Their
forecast also explores the impact of different
training strategies on data consumption, noting that
models trained beyond computed-optimal levels
might leverage more data to enhance training
efficiency. The discussion includes possible avenues
to sustain AI progress, such as developing synthetic
data, tapping into other forms of data, and
improving data efficiency.
#AI #Data #MachineLearning #Research
#EpochAI
22
23. Reverse Turing Test Experiment with AIs:
This video showcases an experiment
where advanced AIs try to determine who
among them is the human. Created in Unity
and featuring voices by ElevenLabs, it
presents a reverse Turing Test scenario.
The experiment aims to explore how AI
identifies human traits.
#AI #TuringTest #ReverseTuringTest
#Unity #ElevenLabs
23
24. I Will Piledrive You If You Mention AI Again:
The article explores the author's frustration
with the overhyping of AI technologies in
professional software engineering. With
formal training in data science, the author
critiques how AI initiatives are often
pushed by individuals lacking in-depth
understanding, leading to a culture of hype
and grift. He emphasises the gap between
genuine technological advancements and
the superficial, profit-driven pushes that
dominate the industry landscape today.
#AI #TechIndustry #Hype
#DataScience #Critique
24
25. Gen AI Testing and Evaluation with ARTKIT: As
Generative AI (Gen AI) systems become more
integrated into critical processes, their testing
and evaluation gain importance for ensuring
safety, ethics, and effectiveness. ARTKIT, an
Automated Red Teaming and testing toolkit,
facilitates this by automating key steps like
generating prompts, interacting with systems,
and evaluating responses. It aids in creating
testing pipelines that offer insights into Gen AI
system performance, highlighting areas that
require improvement. However, human-driven
testing remains essential for a comprehensive
evaluation.
#GenerativeAI #AI #Testing #Evaluation
#Ethics
25
26. Why we no longer use LangChain for building
our AI agents:: Octomind shares their
experience using LangChain for building AI
agents and why they decided to replace it
with modular building blocks. The article
highlights the limitations and complexity
introduced by LangChain's high-level
abstractions and demonstrates how simpler
code with minimal abstractions improved
their productivity and made the team happier.
It suggests that often a framework might not
be necessary and advocates for a building-
block approach for AI development.
#AI #Tech #LangChain #AIDevelopment
#Coding
26
27. OpenAI's GPT-5 Pushed Back To Late 2025,
But Promises Ph.D.-Level Abilities:
OpenAI's long-awaited GPT-5, initially
rumored for release in late 2023 or
summer 2024, is now projected for late
2025 or early 2026. Mira Murati, OpenAI's
CTO, outlined the system's capabilities,
comparing it to having Ph.D.-level
intelligence in specific tasks, a leap from
GPT-4's high schooler-level smartness.
#OpenAI #GPT5 #AI #TechNews
#ArtificialIntelligence
27