Machine Learning for Humans🤖👶

Simple, plain-English explanations accompanied by math, code, and real-world examples.

Vishal Maini
Aug 19, 2017 · 10 min read
This series is available as a full-length e-book! Download here. Free for download, contributions appreciated (paypal.me/ml4h)

Roadmap

Part 1: Why Machine Learning Matters. The big picture of artificial intelligence and machine learning — past, present, and future.

Who should read this?

  • Technical people who want to get up to speed on machine learning quickly
  • Non-technical people who want a primer on machine learning and are willing to engage with technical concepts
  • Anyone who is curious about how machines think
This series is a guide for getting up-to-speed on high-level machine learning concepts in ~2-3 hours.If you're more interested in figuring out which courses to take, textbooks to read, projects to attempt, etc., take a look at our recommendations in the Appendix: The Best Machine Learning Resources.

Why machine learning matters

Artificial intelligence will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic.

(Vinyals & Le, 2017)
Professional Go player Lee Sedol reviewing his match with AlphaGo after defeat. Photo via The Atlantic.
See the full match at The International 2017, with Dendi (human) vs. OpenAI (bot), on YouTube.
Google Translate overlaying English translations on a drink menu in real time using convolutional neural networks.
A bold proclamation by London-based BenevolentAI (screenshot from About Us page, August 2017).

The semantic tree: artificial intelligence and machine learning

Machine learning is one of many subfields of artificial intelligence, concerning the ways that computers learn from experience to improve their ability to think, plan, decide, and act.
The AI effect: what actually qualifies as “artificial intelligence”?The exact standard for technology that qualifies as “AI” is a bit fuzzy, and interpretations change over time. The AI label tends to describe machines doing tasks traditionally in the domain of humans. Interestingly, once computers figure out how to do one of these tasks, humans have a tendency to say it wasn’t really intelligence. This is known as the AI effect.For example, when IBM’s Deep Blue defeated world chess champion Garry Kasparov in 1997, people complained that it was using "brute force" methods and it wasn’t “real” intelligence at all. As Pamela McCorduck wrote, “It’s part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something — play good checkers, solve simple but relatively informal problems — there was chorus of critics to say, ‘that’s not thinking’”(McCorduck, 2004).Perhaps there is a certain je ne sais quoi inherent to what people will reliably accept as “artificial intelligence”:"AI is whatever hasn't been done yet." - Douglas HofstadterSo does a calculator count as AI? Maybe by some interpretation. What about a self-driving car? Today, yes. In the future, perhaps not. Your cool new chatbot startup that automates a flow chart? Sure… why not.

Strong AI will change our world forever; to understand how, studying machine learning is a good place to start

The technologies discussed above are examples of artificial narrow intelligence (ANI), which can effectively perform a narrowly defined task.

Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control. — I.J. Good, 1965

You may have heard this point referred to as the singularity. The term is borrowed from the gravitational singularity that occurs at the center of a black hole, an infinitely dense one-dimensional point where the laws of physics as we understand them start to break down.

We have zero visibility into what happens beyond the event horizon of a black hole because no light can escape. Similarly, after we unlock AI’s ability to recursively improve itself, it’s impossible to predict what will happen, just as mice who intentionally designed a human might have trouble predicting what the human would do to their world. Would it keep helping them get more cheese, as they originally intended? (Image via WIRED)
Image from Kurzweil’s The Singularity Is Near, published in 2005. Now, in 2017, only a couple of these posters could justifiably remain on the wall.
There are complex, high-stakes questions about AI that will require  our careful attention in the coming years.How can we combat AI’s propensity to further entrench systemic biases evident in existing data sets? What should we make of fundamental disagreements among the world’s most powerful technologists about the potential risks and benefits of artificial intelligence? What will happen to humans' sense of purpose in a world without work?

How to read this series

You don’t necessarily need to read the series cover-to-cover to get value out of it. Here are three suggestions on how to approach it, depending on your interests and how much time you have:

  1. Focused approach. Jump straight to the sections you’re most curious about and focus your mental energy there.
  2. 80/20 approach. Skim everything in one go, make a few notes on interesting high-level concepts, and call it a night. 😉

About the authors

“Ok, we have to be done with gradient descent by the time we finish this ale.” @ The Boozy Cow in Edinburgh

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On Twitter? So are we. Feel free to keep in touch — Vishal and Samer 🙌🏽.

Machine Learning for Humans

Demystifying artificial intelligence & machine learning.

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