Decentralized AI + science


Artificial (General) Intelligence and Machine Learning

Machine Learning Models

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:

Machine Learning Models

A short introduction to machine learning by Richard Ngo

AI solving real world tasks Machine Learning Models

AI Techniques Machine Learning Models

Videos/Courses

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

Illustrated Guide to Transformers Neural Network: A step by step explanation

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

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Papers

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Podcast and videos

Transformers

Machine Learning Models