From the Turing Test to Deep Learning: Artificial Intelligence Goes Mainstream

From the Turing Test to Deep Learning: Artificial Intelligence Goes Mainstream

From the Turing Test to Deep Learning: Artificial Intelligence Goes Mainstream

This year, the Association for Computing Machinery (ACM) celebrates 50 years of the ACM Turing Award, the most prestigious technical award in the computing industry.  The Turing Award, generally regarded as the ‘Nobel Prize of computing’, is an annual prize awarded to “an individual selected for contributions of a technical nature made to the computing community”. In celebration of the 50 year milestone, renowned computer scientist Melanie Mitchell spoke to CBR’s Ellie Burns about artificial intelligence (AI) – the biggest breakthroughs, hurdles and myths surrounding the technology.

EB: What are the most important examples of Artificial Intelligence in mainstream society today?

MM: There are many important examples of AI in the mainstream; some very visible, others blended in so well with other methods that the AI part is nearly invisible.  Web search is an “invisible” example that has had perhaps the broadest impact. Today’s web search algorithms, which power Google and other modern search engines, are imbued with AI methods such as text processing with neural networks, and searching large-scale knowledge representation graphs. But web search happens to quickly and seamlessly that most people are unaware of how much “AI” has gone into it.

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Another example with large impact is speech recognition. With the recent ascent of deep neural networks, speech recognition has improved enough so that it can be easily used for transcribing speech, texting, video captioning, and many other applications.  It’s not perfect, but in many cases it works really well.

There are many other natural language AI applications that ordinary people use every day: email spam detection, language translation, automated news article generation, and automated grammar and writing critiques, among others.

Computer vision is also making an impact in day-to-day life, especially in the areas of face recognition (e.g, on Facebook or Google Photos), handwriting recognition, and image search (i.e., searching a database for a given image, or for images similar to an input image).

We’re all familiar with so-called “recommendation systems,” which advise us on which books, movies, or news stories we might like, based on what kinds of things we’ve already looked at, and on what other people “like us” have enjoyed.

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Another sophisticated, but often invisible, application of AI is to navigation and route planning—for example, when Google Maps tells us very quickly the best route to take to a given destination. This is not at all a trivial problem, but, like web search, is available so easily and seamlessly that many people are unaware of the AI that has gone into it.

 



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