At the On Point Summit, Chitra Dorai, IBM Fellow, opened with a beautiful story. In 2013, I remember watching IBM's Watson beat two human champions on Jeopardy in real time. In 2014, I remember taking a few of my clients to meet IBM's Watson. I watched her be asked and answer questions in real-life. It was unbelievable to see technology respond correctly to human questions -- as any human genius would.

From a practical point of view, the following experiments were carried out in Keras and run on an amazon GPU instance. I've been amazed by the power of neural network models. Intuitively, I don't have an explanation of how they are so powerful at learning representations of text. It makes some sense that a model with so many parameters could reproduce the results here. What's impressive is that they can be trained by simple stochastic gradient decent.

Matt Bencke is CEO of Spare5. Previously, he worked at Getty Images, Microsoft and Boeing. Most artificial intelligence models are built and trained by humans, and therefore have the potential to learn, perpetuate and massively scale the human trainers' biases. This is the word of warning put forth in two illuminating articles published earlier this year by Jack Clark at Bloomberg and Kate Crawford at The New York Times. Tl;dr: The AI field lacks diversity -- even more spectacularly than most of our software industry.

When you write down the laws of physics mathematically, you can describe all of them using functions with basic properties. As such, a neural network doesn't need to understand every possible function (like a conventional computer would) to generate an answer -- it just needs to know some fundamentals. The network can use each of its layers to approximate each step toward the solution, reaching a conclusion faster than a conventional computer when the solution involves a hierarchical structure (such as when you're mapping cosmic radiation). The insights could lead to better-designed artificial intelligence systems that do a better job of exploiting their inherent advantage. Moreover, it could help you understand your own mind.

When you think about how a neural network can beat a Go champion or otherwise accomplish tasks that would be impractical for most computers, it's tempting to attribute the success to math. Surely it's those algorithms that help them solve certain problems so quickly, right? Researchers from Harvard and MIT have determined that the nature of physics gives neural networks their edge. When you write down the laws of physics mathematically, you can describe all of them using functions with basic properties. As such, a neural network doesn't need to understand every possible function (like a conventional computer would) to generate an answer -- it just needs to know some fundamentals.

How do you transform your enterprise from being data-incompetent to data-driven? How do you incorporate and leverage your legacy data assets with your latest Big Data assets? How do you successfully implement Data Quality, Data Governance, and Master Data Management into your existing business and IT structures without causing undue chaos or friction? How do you consume 64 pounds of candy in five and a half days? Such thought-provoking, data-centric, and sugar-infused questions were only a few of the countless that were asked, pondered, examined, tested, queried, joined, updated, and ultimately answered (and eaten) during the DATAVERSITY Enterprise Data World (EDW) 2016 Conference.

Seeing how AI programmes use algorithms to interpret images, and replicate our human connection in the modern world: that's the premise of Tate Britain's new exhibition, Recognition. The exhibition uses artificial intelligence technologies (such as object and facial recognition) to pair photos from news agency Reuters with paintings from the Tate collection. Visitors are able to view the virtual gallery created by the programme, and learn about why it chose each specific art/photo match, as well as share their favourite choices made by the machine. They can also help out the AI by making their own comparisons between Reuters' real-time news images and the museum's archives. The aim of the project is to find out whether, over the course of three months, the AI programme can learn and improve on its pairings, using its own observations as well as the input of Tate visitors.

These are the questions I often meet in my daily work with innovative startups and corporates all trying to assess the trajectories with higher potentials and market opportunities. AI is a true game changer. It has showed extraordinary advancements in the last couple of years thanks to a new technique called "deep learning" which allows machines to become superintelligent by crunching myriads of examples and data rather than being explicitly programmed. Independently of the specific deep learning technique, the idea of taking advantage from extensive amounts of computing power and giant quantities of data to mimic human brains and neuronal systems is already proving very effective to power internet search engines, block spam emails, translate web pages, recognize voice commands, etc. Moreover the recent success of AI is opening the route to applications in a number of industries: improved vision systems for example are up to boost disruption in the automotive and mobility worlds with self-driving cars, retailers logistics with smarter delivery drones, the surveillance universe and medical diagnosis scientific breakthroughs with powered image recognition. In addition dynamic-reaction systems and Automated Teller Machines can be used in massive clusters of white-collar jobs also in traditionally conservative industries such as in legal, banking, health-care, journalism, etc., sectors where human answers, expert advice, reports, editorial summaries can be substituted by Intelligent Machines.

Why we urgently need to measure AI's societal impacts How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people's everyday lives. AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see.

It's been a busy summer in the artificial intelligence (A.I.) space, but the most interesting A.I. opportunities might not be coming from the biggest names. You may have heard about Tesla's self-driving cars that made headlines twice, for vastly different reasons -- a fatal crash in Florida in which the driver was using the Autopilot software, and claims by a Missouri man that the feature drove him 20 miles to a hospital after he suffered a heart attack, saving his life. Or you might have heard of Apple spending 200 million to acquire machine learning and A.I. startup Turi. A smart drone defeated an experienced Air Force pilot in flight simulation tests. IBM's Watson diagnosed a 60-year-old woman's rare form of leukemia within 10 minutes, after doctors had been stumped for months.