Scientific American 201905

60 Scientific American, May 2019

Input: Thousands

of cat photographs

Each layer of the network

learns to identify progressively

more complex features

Training

Images

broken

into pixels

Result: Ability to recognize a cat

Output: Image label

Cat

Input: Sets of

different defined

groupings

Training

Pretraining

Input: A few cat photographs

Result: Ability to recognize a cat faster

Cat

Result: Ability to generate convincing cat image

Discriminator

is randomly given

either a real or

a fake cat image

Training

Discriminator

judges whether the

image is real. If not,

in what ways is it not

real? Feedback is fed

to the generator.

Real

Fa ke

Fake (generated)

cat image

Noise

Discriminator

Generator

Input: Random

noise and a class

Cat

Result: Ability to isolate and reconstruct elements

Training

Input: Primitive elements with multiple variables

Bottleneck is gradually loosened

Many AI researchers got into the field because they want to
understand, reproduce and ultimately surpass human intelli-
gence. Yet even those with more practical interests think that ma-
chine systems should be more like us. A social media company
training its image recognizers, for example, will have no trouble
finding cat or celebrity pictures. But other categories of data are
harder to come by, and machines could solve a wider range of
problems if they were quicker-witted. Data are especially limited
if they involve the physical world. If a robot has to learn to manip-
ulate blocks on a table, it can’t realistically be shown every single
arrangement it might encounter. Like a human, it needs to ac-
quire general skills rather than memorizing by rote.
In getting by with less input, machines also need to be more
forthcoming with output. Just the answer isn’t enough; people
also want to know the reasoning, especially when algorithms pass
judgment on bank loans or jail sentences. You can interrogate hu-
man bureaucrats about their biases and conflicts of interest; good
luck doing that with today’s AI systems. In 2018 the European
Union gave its citizens a limited right to an explanation for any
judgment made by automated processing. In the U.S., the Defense
Advanced Research Projects Agency funds an “Explainable AI” re-
search program because military commanders would rather not
send soldiers into battle without knowing why.
A huge research community tackles these problems. Ideas
abound, and people debate whether a more humanlike intelli-
gence will require radical retooling. Yet it’s remarkable how far re-
searchers have gone with fairly incremental improvements. Self-

improvement, imagination, common sense: these seemingly quint-

essential human qualities are being incorporated into machines,

at least in a limited way. The key is clever coaching. Guided by hu-

man trainers, the machines take the biggest steps themselves.

DEEP NETWORKS

More than Most fields of science and engineering, AI is highly cy-

clical. It goes through waves of infatuation and neglect, and meth -

ods come in and out of fashion. Neural networks are the as cen-

dant technology. Such a network is a web of basic com put ing

units: “neurons.” Each can be as simple as a switch that tog gles on

or off depending on the state of the neurons it is con nect ed to.

The neurons typically are arrayed in layers. An ini tial layer ac -

cepts the input (such as image pixels), a final layer pro duces the

output (such as a high-level description of image con tent), and

the intermediate, or “hidden,” layers create arith me tic com bina-

tions of the input. Some networks, especially those used for prob-

lems that unfold over time, such as language recog nition, have

loops that reconnect the output or the hidden layers to the input.

Illustration by Brown Bird Design

Network

Effects

For all their immense power,

neural networks still have

frustrating limitations. For

classifying images, the net ­

work takes in the image pix­

els, processes them through

multiple stages, and outputs

the probabilities of the vari­

ous labels the image might be

given. Fine­tuning the inter ­

connections typi cally takes

thousands of sample images.

How exactly the network

performs the classifications is

lost in the tangle of wiring.

Several new techniques fix

these shortcomings

Meta-Learning

To reduce the amount of training data, researchers can

prime the network by giving it practice exercises of the

same general type. The network does not retain any of the

information but gradually gets better at solving whatever

new tasks it is given. It learns how to learn.

George Musser is a contributing editor to Scientific American

and author of Spooky Action at a Distance (Farrar, Straus and Giroux,

2015) and The Complete Idiot’s Guide to String Theory (Alpha, 2008).

© 2019 Scientific American