I was recently consulting with a publishing company that is exploring various ways to digitize and contextualize its content. Knowing that some of the company's competitors had signed deals with IBM's Watson, I asked several executives why they had not done a Watson deal themselves. "We think that the market for AI software is rapidly commoditizing, and we believe we can assemble the needed capabilities ourselves at much lower cost," was this company's party line. Some particularly knowledgeable managers mentioned that they expected the company would instead make use of open source cognitive software made available from various providers. These potential open source providers are not small vendors; they include, for example, Google, Facebook, Microsoft, Amazon, and Yahoo.

Phone (US): 1 (425) 440-5100 ... (long distance cost may apply) (1C4A) Unfamiliar with how to do this on Skype? Add the contact "join.conference" to your skype contact list first. To participate in the teleconference, make a skype call to "join.conference", then open the dial pad (see platform-specific instructions below) and enter the Conference ID: 843758# when prompted. You can indicate that you want to ask a question verbally by clicking on the "hand" button, and wait for the moderator to call on you; or, type and send your question into the chat window at the bottom of the screen. Just add the room as a buddy - (in our case here) summit_20160317@soaphub.org ... Handy for mobile devices!

A few years back, artificial intelligence reached the point at which it could recognise objects in images and answer questions about them. But it turns out that when an AI looks at an picture, it sees totally different things to humans. In experiments conducted at Facebook and Virginia Tech, researchers found significant differences between what humans and computers looked at when asked a simple question about an image. Lawrence Zitnick and a team of computer vision experts first asked human workers on Amazon's Mechanical Turk platform to answer basic questions about a photo. The photo began blurred, but the worker could click around to sharpen it in different areas.

A basic problem in information theory is that of transmitting information reliably over a noisy channel. An analogous problem in computing machines is that of reliable computing using unreliable elements. This problem has been studies by von Neumann for Sheffer stroke elements and by Shannon and Moore for relays; but there are still many open questions. The problem for several elements, the development of concepts similar to channel capacity, the sharper analysis of upper and lower bounds on the required redundancy, etc. are among the important issues. Another question deals with the theory of information networks where information flows in many closed loops (as contrasted with the simple one-way channel usually considered in communication theory).

The race to acquire artificial intelligence talent has inverted the "laws" of M&A, with pre-revenue AI firms such as UK-based Magic Pony being sold to Twitter for about 10m per employee. Magister Advisors, the global M&A advisory firm to the technology industry, notes that AI firms without revenues are more valuable than those with, as buyers look for pristine competitive advantage, and that Britain is amongst top tier for AI innovation. Twitter just paid 150m for 14-person Magic Pony, a UK-based AI visual search company barely anyone had heard of before the deal. At 10m per employee it marks a high water mark in AI for what is essentially a team acquisition. Magister has tracked 26 AI driven deals since 2014 in the US, Europe and Israel, 11 of which involved companies with less than 50 employees which were acquired largely, or entirely, for the team and capability. Across all 11 deals, the median price paid per employee has reached 2.4m, meaning a high quality AI company with 40 employees would be valued at near 100m - even if it had little or no revenue.

There's a lot that artificial intelligence can do, but understanding human behavior isn't one of the strong suits. A team at MIT's Computer Science and Artificial Intelligence Laboratory wants to change that. Researchers essentially turned computers into couch potatoes by feeding them hundreds of hours of footage from popular TV shows like "The Office," "Scrubs," and "Desperate Housewives," NPR reported Tuesday. Each clip ends with one of four actions: a hug, a kiss, a high five, or a handshake. Predict which one is about to happen.

If you've ever learned to program, you've probably written a Fizz Buzz test. With Fizz Buzz, you print the numbers from 1 to 100, except if it is divisible by 3, you print "fizz"; if it's divisible by 5, you print "buzz"; and if it's divisible by 15 you print "fizzbuzz." This trivial coding problem is typically achieved with a couple of if statements and checking whether each number can be divided by 3 or 5. In his recent blog post "Fizz Buzz in TensorFlow," Grus imagines he's asked to solve Fizz Buzz as part of a job interview. But instead of taking the obvious approach, he uses TensorFlow, the open-source machine learning library developed by Google.

So everyone in China has been following the European Championship in France, and this time it's the game between France and Iceland that made a lot of noise, generating 2.8 billion media impressions! It was fascinating for Chinese people as they really admired this team from Iceland. They discovered that Iceland is a country of only 330 thousand people, which is just a city for them. Shanghai for example has 25 million inhabitants! For a country so small to reach that stage of a Soccer Championship was just amazing for them.

Dr. Arno Candel, Chief Architect, H20.ai In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will show live demos of how to train sophisticated machine learning models on large datasets to solve common business problems. He will show how data scientists and application developers can use modern software tools to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech and marketing.