These technologies exhibit some facets of human intelligence. Those are examples of Narrow AI in practice. Examples of narrow AI are things such as image classification on a service like Pinterest and face recognition on Facebook. What we can do falls into the concept of “Narrow AI.” Technologies that are able to perform specific tasks as well as, or better than, we humans can. General AI machines have remained in the movies and science fiction novels for good reason we can’t pull it off, at least not yet. You’ve seen these machines endlessly in movies as friend - C-3PO - and foe - The Terminator. This is the concept we think of as “General AI” - fabulous machines that have all our senses (maybe even more), all our reason, and think just like we do. Artificial Intelligence - Human Intelligence Exhibited by Machines King me: computer programs that played checkers were among the earliest examples of artificial intelligence, stirring an early wave of excitement in the 1950s.īack in that summer of ’56 conference the dream of those AI pioneers was to construct complex machines - enabled by emerging computers - that possessed the same characteristics of human intelligence. Let’s walk through how computer scientists have moved from something of a bust - until 2012 - to a boom that has unleashed applications used by hundreds of millions of people every day. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. Over the past few years AI has exploded, and especially since 2015. Frankly, until 2012, it was a bit of both. In the decades since, AI has alternately been heralded as the key to our civilization’s brightest future, and tossed on technology’s trash heap as a harebrained notion of over-reaching propellerheads. From Bust to BoomĪI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at the Dartmouth Conferences in 1956 and birthed the field of AI. The easiest way to think of their relationship is to visualize them as concentric circles with AI - the idea that came first - the largest, then machine learning - which blossomed later, and finally deep learning - which is driving today’s AI explosion - fitting inside both. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. All those statements are true, it just depends on what flavor of AI you are referring to.įor example, when Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. ![]() Artificial intelligence is already part of our everyday lives. Artificial intelligence is science fiction. This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland.Īrtificial intelligence is the future.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |