Artificial (General) Intelligence and Machine Learning
Why does AI matter? Simply put, AI is the ability of a computer to perform tasks that would normally require human intelligence, such as understanding natural language and recognising objects. Machine learning, on the other hand, is a subset of AI that deals with the ability of computers to learn from data and improve their performance over time.
They are becoming increasingly important as we collect more and more data. With so much data being generated every day, we need AI and machine learning to help us make sense of it all and extract valuable insights.
In addition, AI and machine learning are also transforming a number of industries. For instance, they are being used to develop self-driving cars, improve healthcare, and create more personalized experiences online.
If you’re interested in getting started with AI and machine learning, there are a number of ways to do so. You can start by taking some online courses, such as those offered by Coursera and Udacity. Alternatively, you can attend a conference or meetup, or start with a book such as “AI: A Modern Approach”
Artificial Intelligence Safety and Alignment: ensuring it all goes well
When it comes to artificial intelligence (AI), safety and alignment are critical. Without taking the proper precautions, AI can pose a serious threat to humans and the world at large.
There are three key areas to focus on when it comes to AI safety and alignment: control, values and goals.
First, it’s important to have control over AI systems. This means having the ability to shut them down if necessary and prevent them from causing harm.
Second, AI systems must be aligned with human values. This means that they should be designed to promote the welfare of humans, not harm them.
Third, AI systems should have goals that are compatible with human values. For example, an AI system designed to cure cancer would have a goal that is compatible with human values.
Levels of AGI: Operationalizing Progress on the Path to AGI DeepMind
Most definitions focus on capabilities rather than processes. Achieving AGI does not require human-like thinking, consciousness, or brain-like mechanisms. The focus should be on what an AGI system can do:
- AGI defined by capability scope (generality) and quality (performance), not human-like traits.
- Emphasize cognitive breadth and depth over physical tasks.
- Potential over deployment to avoid non-technical barriers.
- AGI benchmarks should mirror valued human tasks, not just AI metrics.
- Progress marked by gradations in generality and performance, aiding risk assessment.
- Proposes a 5-tier performance, 2-tier generality taxonomy for AGI tracking.
A short introduction to machine learning by Richard Ngo
AI solving real world tasks
But what is a neural network? | Part 1, Deep Learning
11. Introduction to Machine Learning
How To Get Started With Machine Learning? | Two Minute Papers
- Two minute (+) papers has excellent video explainers of many relevant AI and machine learning papers
Illustrated Guide to Transformers Neural Network: A step by step explanation
- one of the most impactful recent papers
Andrej Karpathy: The spelled-out intro to neural networks and backpropagation: building micrograd
Andrej Karpathy: The spelled-out intro to language modeling: building makemore
Andrej Karpathy: Let’s build GPT: from scratch, in code, spelled out.
Deep Learning State of the Art (2020)
Reinforcement Learning Lecture Series: Introduction to Reinforcement Learning [1/13] (2021 DeepMind x UCL)
RL Course by David Silver: Introduction to Reinforcement Learning
Deep Learning: Introduction to Machine Learning Based AI
Using AI to accelerate scientific discovery - Demis Hassabis (Crick Insight Lecture Series)
(ML 1.1) Machine learning - overview and applications
Temporal difference learning by Rich Sutton
Reinforcement Learning from Human Feedback: From Zero to chatGPT
Ilya Sutskever: OpenAI Meta-Learning and Self-Play | MIT AGI
Sam Altman of Open AI: The Future of AI Research from DALL·E to GPT-3
- Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
- Software 2.0 by Andrej Karpathy (AI as a new programming paradigm)
- A short introduction to machine learning by Richard Ngo
- The AI Revolution: The Road to Superintelligence on Wait But Why
- LLM Powered Autonomous Agents
- AI Wiki: Machine Learning Models Explained
- Distill is an amazing, oftentimes interactive web-native machine learning journal
- Algorithms for Reinforcement Learning
- Supervised Machine Learning: Regression and Classification by Andrew Ng
- Learning from Data: caltech course
- Machine learning reddit posts
- Neural Networks and Deep Learning by Michael Nielsen
- Demis Hassabis ideas, collected by Sonia Joseph and Jeremy Nixon
- Transformer Tutorial by Huggingface
- Seminar on Large Language Models (COMP790-101 at UNC Chapel Hill, Fall 2022)
- A (long) peek into reinforcement learning (Weng, 2018)
- Alignment Research Engineer Accelerator Resources
- OpenAI proposed scaling laws in 2020 with this paper
- Training Compute-Optimal Large Language Models by DeepMind
- New Scaling Laws for Large Language Models, lesswrong, Apr 2022
Product + AI
Podcast and videos
- Alignment Newsletter Podcast
- Alignment Podcast by Future of Life Institute
- Lex Fridman Podcast hosts a lot of great AI figures