2016: the year of hyper-times and disruption

2016 was a year of controversies, big data, big fail. A year of hope, progress, solutions. A year of crazy dreams, astonishing technology come true. A year of inventing, pushing boundaries and dreams. 2016 gave us faster access, virtual reality, better medicine, robots everywhere and a worried workforce. 2016 pushed Artificial Intelligence to uncomfortable heights, rockets and probes to unthinkable frontiers. 2016 was lightning fast, and yet…. change will never be this slow again…

IBM’s Watson scores a hit with Alex Da Kid like it’s no big deal

Watson, IBM’s flamboyant AI unit is getting the hang of it. It wins at Jeopardy, writes Real Time advertising stories, drops them in an ideal context and solves mathematical puzzles like there is no tomorrow. It helps doctors detecting and diagnosing extreme rare diseases and sequenced cancer DNA. As it got a bit bored, it teamed up with Alex Da Kid to write a winning song for Elle King, Wiz Khalifa, and X Ambassadors.

5 years in a beat

Jeopardy champion Watson analyzed a whopping five years’ worth of a broad range of texts, from newspaper editorials,  to movie scripts, novels and court rulings.  It distilled significant cultural themes out of this plethora of data and crosslinked it with social media sentiment.  As an afterthought, it also scanned all music from the same period.

Beat

Watson then used its own musical algorithm, Beat, to create original music scores, linked to emotional promters. Alex Da Kid used Watson’s musical musings to building organic songs.

“Not Easy” featuring Wiz Khalifa,  X Ambassadors, and Elle King makes you wonder…

Yes, your robot dreams. And so should you.

We all dream. Luckily, we do not always know what other people dream, and our box of dreams is conveniently closed to the peeping of the people around us. Frankly, neuroscience discovered that our dreams are pretty much a dark and doom affair anyway. But DeepMind, Google’s powerfull AI (artificial Intelligence) dreams… and wakes up a lot wiser.

Reply awkward situations

Scientists and psychologists discovered that dreams, from a purely neuroscientific perspective were mostly negative or threatening.   That awkward dream of you being lost in a maze, of turning up way late at work completely naked, or incapable of outrunning that fire spitting beast  are more rule than exception.  The latest neuroscience theories claim that dreams strengthen the neuronal traces of recent events. Negative feelings help cement memories deeper into the brain. This enhances memory formation.  Long story short: your brain packages learnings, plays them back in your unconsciousness, and links the learning to a negative emotion (the bad dream) so the learning gets filed more properly in your memory. Neat.

Play it again, Sam

Often we get the same dream over and over again, with slight variations in the storytelling. Our brain is trying to find a solution for a complex problem. Neuroscientists are convinced that this helps you finding the solution quicker. Quick replay, fast forward. Add variation. Repeat. Learn, store. Dream.

Unsupervised learning

Contrary to other AI’s, that are getting fed controlled pathways to the solutions, Googles Deep Mind is programmed to learn by itself. It detects and plots its own way to a solution, learning to solve complex puzzles all by itself. DeepMind experiments on its own to detect how different variations of action within a situation affects the outcome. That is how it learns.  Unsupervised learning eats up time, because it involves variations and experimentation.  However, the learning method allows a lot of future time gain: the robot learns how to learn, and becomes able to learn by itself. Self-learning is a requirement in the stages to true intelligence.

Dreaming is parallel processing

DeepMind uses a dream like state to highlight certain extremely challenging parts of its deep learning,  and repeat them endlessly, adding slight variations. Rather than solving the whole puzzle over and over again, it will replay peculiar parts, and variate, much like humans do while they dream.  After a certain number of loops, the challenge is solved, the method learned, the result stored for future reference: expertise is achieved. The researchers at DeepMind were able to boost DeepMind’s speed of learning with a whopping 10x speed.

Better Robots through better understanding humans

So, adding a dream feature enables AI’s to function better and quicker. Dreamlike loops are one of the first deeplearning on how to learn that machines learn to master. It reminds me of my good friend John C. Havens who in all his wisdom says that “to build better machines, we will have to better understand humans”. My question is: who will understand who the quickest?

Sssst…. your robot dreams, probably about you!

DeepMInd dreams

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