The Logic Behind AI’s All Time Highs

The Logic Behind AI’s All Time Highs

Summary:To cut down on AI research process times, firms are investing in hardware – like coprocessors and GPUs.AI will help companies cut costs and improve revenues.VC’s don’t expect AI to slow down anytime soon, they are quite excited.Imagine a time when computers start to beat humans at their own games, like world class grandmaster chess players, the best Go players or trivia champions. Well, this actually happened years ago.With the power of artificial intelligence and big data, computers are able to solve complex scenarios like this much faster than humans. Watson and Deep Blue, developed by IBM, or AlphaGo, developed by Alphabet subsidiary DeepMind, were the systems behind these experiments and are likely the most well-known examples of artificial intelligence (AI). While not a small feat at their time, these experiments now represent a fraction of what artificial intelligence can accomplish today.The MacroAI is coming of age, but its roots date back to 1666 when Gottfried Leibniz (a German philosopher, mathematician, and all around Renaissance man) theorized that all ideas are basically a combination of a small amount of concepts.¹ Similar to how humans and computers can recognize numbers. The number “8” for example is comprised of two little o’s stacked on top of each other vertically or how all of physical life is made up of the relatively small amount of elements in the periodic table. Breaking things down into its components, that being physical objects or numbers, is what Leibniz essentially theorized and how data scientists actually view and solve problems today.This is relevant to AI because neural networks (one of the many AI research techniques) break...