Live from the EU Open Innovation Conference, and Viva Tech in Paris
June 28, 2017 – ISSUE #17
NEWS FOR SMART DATA-DRIVEN AUGMENTED CREATIVE PEOPLE
I’ve been in Europe giving the closing keynote address at the EU Open Innovation 2.0 Conference and then attending the 2nd Viva Tech in Paris. I managed to escape to visit the amazing ‘Picasso Primatif’ show at the Musee du Quai Branley.
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NEWS YOU NEED TO KNOW
“A fresh way of seeing things, rather than a new set of arguments” Wittgenstein.
The NY Times thinks the company’s $13.4 billion deal for Whole Foods is the latest signal of Amazon’s ambitions to have a hold on nearly every facet our lives – here’s the startling list of all the things Amazon does/owns. It took Amazon only 18 years from IPO to equal Walmart’s market capitalization – and then just two more years to double that valuation: the most remarkable case of wealth creation in history. After the deal was announced, Amazon’s stock price rose by more than the price it is paying for Whole Foods – Amazon basically bought the country’s sixth-largest grocery store for free.
“Machines take me by surprise with great frequency” Alan Turing.
Most of us regard self-driving cars, voice assistants, and other artificially intelligent technologies as revolutionary. For the next generation, however, these wonders will have always existed. AI for them will be more than a tool; in many cases, AI will be their co-worker and a ubiquitous part of their lives. We need to prepare the next generation for jobs in the AI economy.
The Machine Learning Paradox: Nothing says machine learning can’t outperform humans, but it’s important to realize perfect machine learning doesn’t, and won’t, exist. To train a machine learning system, you start with a lot of training data: millions of photos, for example. You divide that data into a training set and a test set. You use the training set to “train” the system so it can identify those images correctly. Then you use the test set to see how well the training works: how good is it at labeling a different set of images? If you train your system so it’s 100% accurate on the training set, it will always do poorly on the test set and on any real-world data. Building a system that’s 100% accurate on training data is a problem that’s well known to data scientists: it’s called overfitting. It’s an easy and tempting mistake to make, regardless of the technology you’re using.
There’s no such thing as a single idea about “Artificial Intelligence”. Pedro Domingo groups five different approaches around “Five Tribes of AI”: 1) Symbolists (inspired by symbolic logic and semantics to fill in the blanks between what I already know and what I don’t already know); 2) Connectionists (inspired by the neuronal architecture of the brain; probabilistic, weighted neurons firing in a neural network to produce a single action/answer); 3) Evolutionaries (inspired by evolutionary biology and genetics – to produce variations and let selection happen); 4) Bayesians (inspired by testing statistical models of probability against previous evidence; the probability of this, if that); and 5) Analogizers (inspired by psychology; to optimize a function in light of constraints)
Philosophers have been keen to contribute to debates about what uniquely distinguishes human mentality from that of the animal: from having a soul, being aware of ourselves, time or death, through being rational, linguistic or conceptual beings, to being jokers, tool-users, self-recognizers, other-recognizers, inhabitants of an objective world, truth trackers, capable of meta-cognition, pursuers of moral, aesthetic or epistemic goals for their own sake and so on. Herewith, the stupendous intelligence of honey badgers and the thoughts of a spider web.
Albert Einstein said: “Make things as simple as possible, but not simpler.” That advice, quoted in Machine, Platform, Crowd, is well followed by Andrew McAfee and Erik Brynjolfsson in their latest business book, which tries to make sense of the “technology surge” that is bewildering so many executives. The two academic authors from MIT, who became the pin-up boys of the Davos crowd for their previous book on The Second Machine Age (2014), do a neat job of scanning the technological horizon and highlighting significant landmarks. This is a clear and crisply written account of machine intelligence, big data and the sharing economy.
Then there’s Prince (he could do anything – here’s Soul Train from 1994) and George Michael (“All we have to do now / Is take these lies and make them true somehow”) – to both great artists: Rest In Peace.