Just putting ‘artificial’ and ‘intelligence’ together in the same term is enough to get people pretty excited.
For some, ‘Artificial Intelligence’ can only be a misnomer. True intelligence is uniquely human, biologically evolved, embodied, necessarily shaped by environment and social relationships, non-algorithmic, and unknowable by mere human consciousness. Anything that becomes possible for human-built computers is by definition not really artificial intelligence.
For others, natural intelligence is simply computation performed on a relatively slow biological computer, that took hundreds of thousands of years to evolve. It is only a matter of time before the exponential improvement in computing technology will allow AI to surpass the power of human brains.
The loaded nature of the term AI has also led to a variety of definitions, and identification of subcategories such as Narrow AI and Artificial General Intelligence. Sometimes AI is differentiated from terms such as ‘machine learning’ or ‘automation’.
I prefer a simple and pragmatic definition for AI – technology that can perform tasks previously requiring human intelligence. This definition does not address the limits or scope of AI, it simply acknowledges that we have developed and will continue to develop systems that perform tasks previously requiring human intelligence.
This definition will be too broad for some people’s taste. After all, electronic calculators fit the definition, and nobody considers them AI. However, I think of AI as a pursuit rather than a destination, with a leading edge that continues to advance. In practice, when we talk about AI, we are usually talking about technology near the leading edge.
In a previous post, I addressed why I think AI is neither comparable to human intelligence, nor a threat to humans. But I also think the leading edge of AI, deep neural networks, is very impressive.
Deep neural networks map patterns in data to outputs that represent some useful interpretation of the data, such as the identity of a face or the translation of a spoken sentence. In a sense, this capability is pretty simple; these AIs can be dismissed as mere ‘curve fitters‘. What makes deep neural networks so useful?
Here are three things that give these AIs ‘superpowers’:
- Patterns are everywhere
- Data is abundant
- AI learning extends human programming.
Patterns are everywhere
Recognizing patterns is central to the way we humans live, work, and play. For example:
- Patterns in our environment tell us what we can eat, where we can find food, when we need to take shelter, and how to turn the wheel of our car
- Social patterns bond children to mothers, attract mates, expose cheaters
- Humans impose patterns on their environment – constellations in the stars, orbital mechanics – to enrich understanding and guide exploration
- Patterns of language – spoken, written, schematic – communicate ideas and directions, and preserve the growing body of human knowledge
- Patterns are used by detectives to fight crime, and by financial analysts to make money
- We amuse and enrich ourselves through patterns in music and art, and in puzzles and games.
Data is abundant
Much of our reality these days is represented digitally, on the web or in databases. This gives unprecedented access to information about the patterns central to our lives. If only we had enough eyes and brains to examine and digest this huge volume of data! But this task is a perfect fit for deep neural networks: feed them enough data and they can discover extremely complex patterns.
For example, automatic speech-to-text recognition has been revolutionized by deep neural networks. One such network with 5 billion connections is possible only because it could be trained with lots of data: 3 million audio samples, together with 220 million text samples from a 495,00-word vocabulary.
AI learning extends human programming
Obviously, it takes humans to program deep neural networks. But these networks are programmed to ‘learn’, in the sense that they adjust their own parameters during the training process.
The fact that very large deep neural networks can be trained and give good results is a relatively recent discovery in AI. Why these networks work so well is not well understood theoretically, but extensive experimentation has led to innovative designs and good results. This work has been carried out by a thriving, innovative community of AI researchers and engineers, who are building and extending a shared body of open-source software, datasets, and results.
One of the things observed in these experiments is that as deeper neural networks have become feasible, human engineers have needed to do less preprocessing of the inputs to the networks, to identify important features in the data. By letting the networks ‘learn’ what features are important, better results are obtained with less human programming.
An example is automatic speech-to-text recognition, mentioned above. For decades engineers developed these systems using approaches that drew on linguistic analysis of human vocalization and language: speech as composed of elemental sounds, phonemes, which are then built up into words and sentences, all governed by language syntax and semantics. Up through the early 2000’s, systems mirrored this analysis: sounds were mapped to phonemes and possible words, sometimes using neural networks, then symbolic or statistical models of language were used to predict, correct, and make sense of words and sentences.
As effective deep neural networks became available, engineers put more and more of the linguistic analysis burden on the networks. Eventually, networks were trained to directly map sound (digital time-frequency plots) to words, resulting in a dramatic improvement in accuracy.
Whether or not AI is really ‘intelligent’, AI research and development continues to move the limit of machine capability.