Artificial Intelligence: A Guide for Thinking Humans

by Melanie Mitchell

The issue that worries him is really one of complexity. He fears that AI might show us that the human qualities we most value are disappointingly simple to mechanize.

such minds of infinite subtlety and complexity and emotional depth could be trivialized by a small chip, it would destroy my sense of what humanity is about.”

While assuming that these AI researchers underestimated humans, had I in turn underestimated the power and promise of current-day AI?

try to sort out how far artificial intelligence has come, as well as elucidate its disparate—and sometimes conflicting—goals. In

The proposed study was, they wrote, based on “the conjecture that every aspect of learning or any other feature of intelligence can be in principle so precisely described that a machine can be made to simulate

Artificial intelligence inherits this packing problem, sporting different meanings in different contexts.

Indeed, the word intelligence is an over-packed suitcase, zipper on the verge of breaking.

distinctions. Instead, it has focused on two efforts: one scientific and one practical. On the scientific side, AI researchers are investigating the mechanisms of “natural” (that is, biological) intelligence by trying to embed it in computers.

On the practical side, AI proponents simply want to create computer programs that perform tasks as well as or better than humans, without worrying about whether these programs are actually thinking in the way humans think. When asked if their motivations are practical or scientific, many AI people joke

But since the 2010s, one family of AI methods—collectively called deep learning (or deep neural networks)—has risen above the anarchy to become the dominant AI paradigm.

AI is a field that includes a broad set of approaches, with the goal of creating machines with intelligence. Deep learning is only one such approach.

better understand these various distinctions, it’s important to understand a philosophical split that occurred early in the AI research community: the split between so-called symbolic and subsymbolic AI.

symbolic AI program’s knowledge consists of words or phrases (the “symbols”), typically understandable to a human, along with rules by which the program can combine and process these symbols in order to perform its assigned task.

Symbolic AI of the kind illustrated by GPS ended up dominating the field for its first three decades, most notably in the form of expert systems, in which human experts devised rules for computer programs to use in tasks such as medical diagnosis and legal decision-making.

In contrast, subsymbolic approaches to AI took inspiration from neuroscience and sought to capture the sometimes-unconscious thought processes underlying what some have called fast perception, such as recognizing faces or identifying spoken words.

Roughly speaking, a neuron sums up all the inputs it receives from other neurons, and if the total sum reaches a certain threshold level, the neuron fires. Importantly, different connections (synapses) from other neurons to a given neuron have different strengths; in calculating the sum of its inputs, the given neuron gives more weight to inputs from stronger connections than inputs from weaker connections.

Neuroscientists believe that adjustments to the strength of connections between neurons is a key part of how learning takes place in the brain.

Rosenblatt’s idea was that the perceptron should similarly be trained on examples: it should be rewarded when it fires correctly and punished when it errs. This form of conditioning is now known in AI as supervised learning. During training, the learning system is given an example, it produces an output, and it is then given a “supervision signal,” which tells how much the system’s output differs from the correct output. The system then uses this signal to adjust its weights and threshold.

Perhaps the most important term in computer science is algorithm, which refers to a “recipe” of steps a computer can take in order to solve a particular problem.

The network shown in figure 4 is referred to as “multilayered” because it has two layers of units (hidden

networks that have more than one layer of hidden units are called deep networks.

However, unlike in a perceptron, a unit here doesn’t simply “fire” or “not fire” (that is, produce 1 or 0) based on a threshold; instead, each unit uses its sum to compute a number between 0 and 1 that is called the unit’s “activation.”

If the sum that a unit computes is low, the unit’s activation is close to 0; if the sum is high, the activation is close to

called back-propagation—for training these networks. As its name implies, back-propagation is a way to take an error observed at the output units (for example, a high confidence for the wrong digit in the example of figure 4) and to “propagate” the blame for that error backward (in figure 4, this would be from right to left) so as to assign proper blame to each of the weights in the network. This allows back-propagation to determine how much to change each weight in order to reduce the error.

Symbolic systems can be engineered by humans, be imbued with human knowledge, and use human-understandable reasoning to solve problems.

subsymbolic systems tend to be hard to interpret, and no one knows how to directly program complex human knowledge or logic into these systems. Subsymbolic systems seem much better suited to perceptual or motor tasks for which humans can’t easily define rules.

mind.14 We’re back to the philosophical question I was discussing with my mother: Is there a difference between “simulating a mind” and “literally having a mind”?

the deep in deep learning doesn’t refer to the sophistication of what is learned; it refers only to the depth in layers of the network being trained.

David Hubel and Torsten Wiesel were later awarded a Nobel Prize for their discoveries of hierarchical organization in the visual systems of cats and primates (including humans) and for their explanation of how the visual system transforms light striking the retina into information about what is in the scene.

convolutional neural networks, or (as most people in the field call them) ConvNets.

the design of ConvNets is based on several key insights about the brain’s visual system that were discovered by Hubel and Wiesel in the 1950s and ’60s.

the role of these backward connections is not well understood by neuroscientists, although it is well established that our prior knowledge and expectations, presumably stored in higher brain layers, strongly influence what we perceive.

is inaccurate to say that today’s successful ConvNets learn “on their own.” As we saw in the previous chapter, in order for a ConvNet to learn to perform a task, a huge amount of human effort is required to collect, curate, and label the data, as well as to design the many aspects of the ConvNet’s architecture.

The fear is that if we don’t understand how AI systems work, we can’t really trust them or predict the circumstances under which they will make errors.

one of the hottest new areas of AI is variously called “explainable AI,” “transparent AI,” or “interpretable machine learning.”

Calling this an “intriguing property” of neural networks is a little like calling a hole in the hull of a fancy cruise liner a “thought-provoking facet” of the ship. Intriguing, yes, and more investigation is needed, but if the leak is not fixed, this ship is

If deep-learning systems, so successful at computer vision and other tasks, can easily be fooled by manipulations to which humans are not susceptible, how can we say that these networks “learn like humans”

And computer vision isn’t the only domain in which networks can be fooled; researchers have also designed attacks that fool deep neural networks that deal with language, including speech recognition and text analysis.

Clever Hans has become a metaphor for any individual (or program!) that gives the appearance of understanding but is actually responding to unintentional cues given by a trainer.

The AI researcher Andrew Ng has optimistically proclaimed, “AI is the new electricity.” Ng explains

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”

the Great AI Trade-Off. Should

Loss of privacy is not the only danger

face-recognition systems can make errors. If your face is matched in error, you might be barred from a store or an airplane flight or wrongly accused of a crime.

significantly higher error rate on people of color than on white people.

To what extent should AI research and development be regulated, and who should do the regulating?

Simply leaving regulation up to AI practitioners would be as unwise as leaving it solely up to government agencies.

believe that regulation of AI should be modeled on the regulation of other technologies, particularly those in biological and medical sciences, such as genetic engineering. In those fields, regulation—such as quality assurance and the analysis of risks

Could machines themselves be able to have their own sense of morality, complete enough for us to allow them to make ethical decisions on their own, without humans having to oversee them?

Asimov’s stories often focused on the unintended consequences of programming ethical rules into robots.