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I can just see Peter from saying. "Well, what about Artificial Stupidity, heh? heh? heh?" What if an AI was taught to make bad decisions that lead to failure? heh? I bet the dataset is larger than that of smart decisions.




























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This is the era when we are fascinated by individual isolated AI news. The next era is when these isolated AI breakthroughs start integrating. This will be know as the "The Next WOW Moment". LOL We won't believe it.










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Yahoo releases massive 13.5TB web-browsing data set to researchers

Yahoo’s business may be struggling, but millions of people still visit its site to read the news every day. That gives the company unique insights into browsing and reading habits, and today the company has released a huge swath of that data. The “Yahoo News Feed dataset” incorporates anonymous browsing habits of 20 million users between February and May of 2015 across a variety of Yahoo properties, including its home page, main news site, Yahoo Sports, Yahoo Finance, Yahoo Movies and Yahoo Real Estate.

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Sony wants to push AIs to learn from their own experiences
Artificial intelligence is being put through rigorous training. Major technology companies like Microsoft, Google, IBM and Amazon have invested heavily in machine learning techniques that teach systems how to think and react like humans. Now Sony is stepping in to introduce a new layer of learning that it believes will power the next generation of AIs. Read more
Amazon opens up its product recommendation tech to all

For a company like Amazon, product recommendations are hugely important. They can be the difference between a one-off order and an unexpected spending spree. The company has spent years adapting its algorithms to produce the most relevant suggestions, but now it wants help. It’s taken the wraps off DSSTNE – its Deep Scalable Sparse Tensor Network Engine (pronounced destiny) – and is asking for companies, researchers and developers to make its artificial intelligence framework even more powerful.

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Google's Deepmind AI beats Go world champion in first match

Google’s Deepmind artificial intelligence has done what many thought it couldn’t: beat a grandmaster at the ancient Chinese strategy game Go. The “AlphaGo” program forced its opponent, 33-year-old 9-dan professional Lee Sedol, to resign three and a half hours into the first of their five-match battle. While Deepmind has defeated a Go champion before, it’s the first time a machine has beaten a world champion.

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Prisma's neural net-powered photo app arrives on Android

When Prisma Labs said you wouldn’t have to wait long to use its Android app outside of the beta test, it wasn’t joking around. The finished Prisma app is now readily available on Google Play, giving anyone a chance to see what iOS users were excited about a month ago. Again, the big deal is the use of cloud-based machine learning to turn humdrum photos into hyper-stylized pieces of art – vivid brush strokes and pencil lines appear out of nowhere. Give it a shot if you don’t think your smartphone’s usual photo filters are enough.

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Apple iOS 10 uses AI to help you find photos and type faster

Apple is making artificial intelligence a big, big cornerstone of iOS 10. To start, the software uses on-device computer vision to detect both faces and objects in photos. It’ll recognize a familiar friend, for instance, and can tell that there’s a mountain in the background. While this is handy for tagging your shots, the feature really comes into its own when you let the AI do the hard work. There’s a new Memories section in the Photos app that automatically organizes pictures based on events, people and places, complete with related memories (such as similar trips) and smart presentations. Think of it as Google Photos without having to go online.

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Google's new head of search is an AI research leader

There’s a changing of the guard underway at Google… and it could have big ramifications for how the company tackles its main business. Senior VP of search (and early employee) Amit Singhal is retiring on February 26th, and he’s being replaced by John Giannandrea, the VP who leads the company’s artificial intelligence and research work. In the process, Google is folding its research division into search – it’s now an integral part of how Google operates.

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Apple buys AI firm that detects emotions in facial cues

If it wasn’t already clear that Apple is getting serious about artificial intelligence, it is now. The company has confirmed that it bought Emotient, a fledgling outfit that uses AI to gauge emotions based on facial expressions. As usual, the Cupertino crew isn’t saying what its plans are. However, Emotient’s specialty is in detecting your overall sentiment, like contentedness or frustration. Combined with the AI-powered assistant tech from VocalIQ, it wouldn’t be shocking if Apple is working on helper software that genuinely understands your moods and reactions. There’s certainly pressure to do so – with both Facebook and Google working on AI-driven chat assistants, Apple might not want to feel left out.

[Image credit: Getty Images]

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The Coming Revolution in the Social and Health Sciences

In the last decade we’ve come to realize that the application of the scientific method in so-called “Soft” Sciences is fundamentally broken. One of the symptoms of this brokenness is the Replication Crisis–nobody can reproduce the results of anybody else’s experiments. There are many identifiable reasons for this (for starters the modifier ”soft” would seem to indicate that we should think of Sociology, Psychology, and Psychiatry as “not really sciences” when in fact they are the opposite: they are the study of incredibly complex topics which we don’t yet understand very well), but the only useful thing to observe is that it’s practically impossible to untangle the ways our humanness and our systems (i.e. demands of politics and capitalism and our own in-built biases) are breaking this science. 

We can, however, see the source of this brokenness. It is the post-hoc processing of statistical data. One of the realizations that’s come from the study of the Replication Crisis is that one can use perfectly acceptable statistical methods to prove that precognition exists* or that listening to When I’m Sixty-Four by the Beatles makes you younger. Machine learning can end this brokenness. While the popular conception of Machine Learning is as an early form of AI (and it certainly is that too), it is fundamentally a statistical processing engine. Paraphrasing a data scientist I heard speak about a year ago, “The miracle of machine learning is that I no longer have to clean my data. I don’t need to decide what goes in or out. Everything goes in.” As long as you do not withhold data from a machine learning algorithm, it can identify the trends in your data, free from unconscious (or conscious) bias. 

This again points to the problems of referring to the soft sciences this way. Calling them “soft” makes it seem as if it is right for us to substitute our human judgement for the parts of the data collection or statistical regression that seem to fill in the gaps where the science is deficient. But the science is deficient because of the gaps in our human judgment. Again, Soft Sciences are not soft because science isn’t a good method, they are soft sciences because we don’t yet understand them well enough.

What fundamental things about our selves, our bodies, our relationships, or our societies–what things that we currently accept without question–are about to be shattered? And, more importantly, how will we respond to them? Will the revelations be greeted with the same mixture of credulous and incredulous reactions that we respond to, say, an industry-funded study that rBGH is good for us? Or we will somehow immediately recognize the new nature of machine-learned statistical revelation?

(A seed of this idea came from my friend Ben Bauermeister, who has a really good idea for a machine-learning thriller novel based on this.)

*You might believe ESP is real and that this reality could be demonstrated scientifically. Totally fine. I’m going to talk about that tomorrow. 

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The Alien Style of Deep Learning Generative Design – Intuition Machine – Medium

“What happens when you have Deep Learning begin to generate your designs? The commons misconception would be that a machine’s design would look ‘mechanical’ or ‘logical’. However, what we seem to be finding is that they look very organic
[..]
The Dreamcatcher system allows designers to input specific design objectives, including functional requirements, material type, manufacturing method, performance criteria, and cost restrictions. Loaded with design requirements, the system then searches a procedurally synthesized design space to evaluate a vast number of generated designs for satisfying the design requirements.
[..]
these designs do not exist for the sake of style. Rather, these designs are actually the optimal solutions to multiple competing design requirements”