THE MASTER ALGORITHM – How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos
Micro-summary: the ultimate master algorithm is an algorithm (or machine learning algorithm) that can learn anything (in minutes or seconds) given enough data, especially non-linear models or phenomena.
Everyone a coder
Currently, machine learning algorithms do two things: one, where they improve the existing processes in order to do them more accurately and faster, and two, where machine learning can do entirely new things that have never been done before. For example, if you give a computer enough data about a particular health condition, it will learn in less than a minute how to diagnose a patient for that condition much better than any top doctor can do. In the future, machine learning algorithms will be embedded in everything from day one, in the same way as your subconscious mind with its neural network, which works in a similar way and learns all the time. At the moment, in order to program a computer, you need to know how to code or be a computer scientist. In the near future, anyone will be able to program a computer without any knowledge of coding because machine learning is learning the natural language and will enable you to understand your English or whatever language you choose to speak. You’ll just need to explain in plain English what you want your computer to do.
The ultimate master algorithm is a machine learning system capable of learning anything in seconds or minutes, provided it has sufficient data. It excels at modelling non-linear patterns and complex phenomena, making it a game-changer in automation and intelligence. The principles of AND, OR, and NOT play a fundamental role in how these algorithms function, deciding what conditions must be met (AND), offering flexible alternatives (OR), and excluding irrelevant or harmful data (NOT).
Everyone a Coder with AND, OR, and NOT
At present, machine learning algorithms serve two primary purposes:
- Enhancing Existing Processes – Machine learning optimises current systems, making them faster and more accurate.
- Creating the Unprecedented – It enables solutions to problems never tackled before.
For instance, if fed enough medical data, a machine learning algorithm could diagnose a health condition in under a minute, outperforming top doctors.
The future of machine learning is intrinsically linked to the AND-OR-NOT logic:
- AND: Algorithms must process data AND validate information before drawing conclusions.
- OR: They must assess multiple possibilities OR alternative solutions to improve accuracy.
- NOT: They must filter out noise and irrelevant data to avoid misleading outcomes.
Looking ahead, machine learning will be embedded in everyday technology from the outset, much like how the subconscious mind continuously learns and adapts. At present, coding requires specialised knowledge, but soon, anyone will be able to “program” a computer simply by speaking in natural language. Instead of writing complex code, users will instruct machines in plain English, allowing for seamless automation and personalised AI-driven assistance.
In this rapidly evolving landscape, coding will no longer be a barrier—everyone will be a coder, without needing to code. The AND-OR-NOT model will continue to shape AI’s ability to understand, adapt, and refine decision-making processes, making technology more accessible and intelligent.
Read more Mastering Life and Decision-Making: How AND, OR, and NOT Accelerate Learning and Speed Reading
10,000 hours for humans vs seconds for AI
For a human being to master something, it takes the famous 10,000 hours, which is 3 hours/day for 10 years, not for a machine learning (MI) algorithm, though. An algorithm developed by researchers at Moorfields Eye Hospital in London can diagnose common signs of eye diseases with 94% accuracy, and it took less than one minute for the algorithm to learn that! as opposed to 10,000 hours of human laborious training. Another algorithm developed by researchers in Los Angeles is 90% accurate at predicting suicides, and again, it took minutes to train itself to do it. AI is used to create art and paintings and even whole films from scratch in minutes. In 2017, AlphaZero, a software program developed by the Alphabet-owned (Google) AI research company DeepMind, self-learnt chess from scratch by playing against itself, without any input regarding the rules of the chess – in just four hours – and defeated Stockfish 8 (the world’s chess computer champion for 2016).
Another great example of the power of the learning machine algorithms is a writing software developed by Philip M Parker, which can write any book or your whole PhD in 20 minutes! I know it’s difficult to believe – watch his TED talk.
Many predict that soon, AI might take over humans because it will be able to do everything humans do better, quicker and more accurately, including being more conscious.
Symbolists, connectionists, evolutionaries, Bayesians, and analogizers
Pedro Domingos suggests that in order to develop the Master Algorithm, different rival schools of thought need to be considered and combined, and these are the symbolists, connectionists, evolutionaries, Bayesians, and analogizers. Each tribe has identified individual problems and offered solutions, but the Master Algorithm must solve all five problems, not just one. For example, Douglas Hofstadter, one of the top analogizers, says that all of the intelligence is just analogy. By the way, he’s the creator of Hofstadter’s Law, which states that a task always takes longer to complete than you expect, even when you take into account Hofstadter’s Law. Hofstadter is the author of Surfaces and Essences – Analogy as The Fuel and Fire of Thinking.
| Machine learning tribes | Problem | Solution |
|---|---|---|
| Symbolists | Knowledge composition | Inverse deduction |
| Connectionists | Credit assignment | Backpropagation |
| Evolutionaries | Structure discovery | Genetic programming |
| Bayesians | Uncertainty | Probabilisitic inference |
| Analogiezers | Similarity | Kernel machines |
A shorter video clip version summarising his book