## The Fundamental Limits of Machine Learning

Neat and interesting read from Nautilus

A few months ago, my aunt sent her colleagues an email with the subject, “Math Problem! What is the answer?” It contained a deceptively simple puzzle.

She thought her solution was obvious. Her colleagues, though, were sure their solution was correct—and the two didn’t match. Was the problem with one of their answers, or with the puzzle itself?

My aunt and her colleagues had stumbled across a fundamental problem in machine learning, the study of computers that learn. Almost all of the learning we expect our computers to do—and much of the learning we ourselves do —is about reducing information to underlying patterns, which can then be used to infer the unknown. Her puzzle was no different.

As a human, the challenge is to find any pattern at all. Of course, we have intuitions that limit our guesses. But computers have no such intuitions. From a computer’s standpoint, the difficulty in pattern recognition is one of surplus: with an endless variety of patterns, all technically valid, what makes one “right” and another “wrong?”

The problem only recently became of practical concern. Before the 1990s, AI systems rarely did much learning at all. For example, the chess-playing Deep Thought, predecessor to Deep Blue, didn’t get good at chess by learning from successes and failures. Instead, chess grandmasters and programming wizards carefully crafted rules to teach it which board positions were good or bad. Such extensive hand-tuning was typical of that era’s “expert systems” approach.

To tackle my aunt’s puzzle, the expert systems approach would need a human to squint at the first three rows and spot the following pattern:

1 * (4 + 1) = 5

2 * (5 + 1) = 12

3 * (6 + 1) = 21

The human could then instruct the computer to follow the pattern x * (y + 1) = z. Applying this rule to the final line yields a solution of 96.

Despite expert systems’ early success, the manual labor required to design, tune, and update them became unwieldy. Instead, researchers turned their attention to designing machines that could infer patterns on their own. A program could inspect, say, thousands of photos or market transactions and tease out statistical signals suggesting a face or an impending price spike. This approach quickly came to dominate, and has since powered everything from automated postal sorting to spam filtering to credit card fraud detection.

And yet. With all their successes, these machine learning systems still needed engineers in the loop. Consider again my aunt’s puzzle. We assumed that each line has three relevant components (the three numbers in the line). But there’s a potential fourth element: the result on the previous line. If that attribute of a line—that feature, in machine learning parlance—is in bounds, then another plausible pattern emerges:

0 + 1 + 4 = 5

5 + 2 + 5 = 12

12 + 3 + 6 = 21

By this logic the final answer should be 40.

So which pattern is right? Both, of course—and neither. It all depend on which patterns are allowed. You could also find a pattern by taking the first number times the second number, adding one-fifth of three more than the previous answer, and rounding to the nearest integer. (It’s weird, but it works!) And if we allow features that consider the visual forms of the numbers, perhaps we could come up with some pattern involving strokes and serifs.

Adafruit publishes a wide range of writing and video content, including interviews and reporting on the maker market and the wider technology world. Our standards page is intended as a guide to best practices that Adafruit uses, as well as an outline of the ethical standards Adafruit aspires to. While Adafruit is not an independent journalistic institution, Adafruit strives to be a fair, informative, and positive voice within the community – check it out here: adafruit.com/editorialstandards

Adafruit is on Mastodon, join in! adafruit.com/mastodon

Stop breadboarding and soldering – start making immediately! Adafruit’s Circuit Playground is jam-packed with LEDs, sensors, buttons, alligator clip pads and more. Build projects with Circuit Playground in a few minutes with the drag-and-drop MakeCode programming site, learn computer science using the CS Discoveries class on code.org, jump into CircuitPython to learn Python and hardware together, TinyGO, or even use the Arduino IDE. Circuit Playground Express is the newest and best Circuit Playground board, with support for CircuitPython, MakeCode, and Arduino. It has a powerful processor, 10 NeoPixels, mini speaker, InfraRed receive and transmit, two buttons, a switch, 14 alligator clip pads, and lots of sensors: capacitive touch, IR proximity, temperature, light, motion and sound. A whole wide world of electronics and coding is waiting for you, and it fits in the palm of your hand.

Have an amazing project to share? The Electronics Show and Tell is every Wednesday at 7pm ET! To join, head over to YouTube and check out the show’s live chat – we’ll post the link there.

Join us every Wednesday night at 8pm ET for Ask an Engineer!

Join over 36,000+ makers on Adafruit’s Discord channels and be part of the community! http://adafru.it/discord

CircuitPython – The easiest way to program microcontrollers – CircuitPython.org

Maker Business — “Packaging” chips in the US

Wearables — Enclosures help fight body humidity in costumes

Electronics — Transformers: More than meets the eye!

Python for Microcontrollers — Python on Microcontrollers Newsletter: Silicon Labs introduces CircuitPython support, and more! #CircuitPython #Python #micropython @ThePSF @Raspberry_Pi

Adafruit IoT Monthly — Guardian Robot, Weather-wise Umbrella Stand, and more!

Microsoft MakeCode — MakeCode Thank You!

EYE on NPI — Maxim’s Himalaya uSLIC Step-Down Power Module #EyeOnNPI @maximintegrated @digikey

New Products – Adafruit Industries – Makers, hackers, artists, designers and engineers! — #NewProds 7/19/23 Feat. Adafruit Matrix Portal S3 CircuitPython Powered Internet Display!

Get the only spam-free daily newsletter about wearables, running a "maker business", electronic tips and more! Subscribe at AdafruitDaily.com !