Artificial intelligence is making a lot of promises for even the near future. We already have access to digital assistants like Microsoft's Cortana and Apple's Siri. It has already made great improvements in the healthcare industry. AI can even compose music, dream, and beat us at complex games like Go. All of these advancements are incredible, but why did they start appearing recently, and how far will we be able to push AI? Modern AI has been a thought since the first digital computer was created around the 1940s.

But most people seem far more interested in what you can tell them about the future. The agricultural and industrial revolutions were about changing the world outside us. This century will be about changing the world inside us – making new entities with new kinds of brains. We are quickly acquiring powers that were always thought to be divine. I grew up in a small industrial suburb of Haifa, in Israel.

THE descent into a post-truth world continues at a depressing rate. The latest winner of the pants-on-fire award is former US presidential candidate Newt Gingrich. In an interview with CNN after a speech in which Donald Trump wrongly claimed that violent crime was rising, Gingrich cherry-picked the facts – then abandoned them altogether. "The average American does not think crime is down," he said. "As a political candidate, I'll go with what people feel."

Apache PredictionIO (incubating) is an open source Machine Learning Server built on top of state-of-the-art open source stack for developers and data scientists create predictive engines for any machine learning task. Apache PredictionIO (incubating) can be installed as a full machine learning stack, bundled with Apache Spark, MLlib, HBase, Spray and Elasticsearch, which simplifies and accelerates scalable machine learning infrastructure management. A summary of the changes in each release before migrating to Apache Software Foundation are on GitHub here. Apache PredictionIO (incubating) is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.

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.