When Donald Trump opens his mouth, the output can seem like the work of a demented Markov chain, a poorly trained algorithm trying its hand at rhetoric. Key words--"great again," "let me tell you," "we don't win anymore"--end up strung together by exceptionally weak ligaments. His syntax seems generated on the fly, word to word, each stumbling straight into the next, bound by the barest loyalty to grammar. As only he could, Trump's brought the state of political speech down to the state of the art of machine speechwriting. This past winter, a graduate student at the Technical University of Denmark earned significant attention for the politicians he was crafting in Python.

In April, a group of Finnish farmers outfitted a spindly black drone with a remote-controlled chainsaw and filmed it decapitating snowmen. They called it "Killer Drone." More formally, it was a DJI S1000. This spring, marine biologists flew a drone over the Sea of Cortez to capture samples of the fluid sprayed from the blowholes of blue whales. It was a DJI Inspire 1.

Intel wants to take on Google's Tensor Processing Unit and Nvidia's GPUs in machine learning computing with improvements to its Xeon Phi mega-chips. The company will add new features to Xeon Phi to tune it for machine learning, said Nidhi Chappell, director of machine learning at Intel. Machine learning, a trendy technology, allows software to be trained to do tasks like image recognition or data analysis more efficiently. Intel didn't disclose when the new features will be added, but the next version of Xeon Phi will come by 2018. Intel's already behind chip rivals in machine learning, so it may have to speed up the next Xeon Phi release.

Blackboard Inc. and IBM (NYSE: IBM) today announced a collaborative agreement for IBM to manage Blackboard's datacenters and cloud infrastructure. The two companies will also work together to develop innovative educational solutions, taking advantage of IBM Watson's cognitive computing technology and Blackboard's broad capabilities suite. Under the agreement, IBM will manage much of Blackboard's technology infrastructure, including the company's 28 global data centers and its existing public cloud footprint. IBM will also provide support for Blackboard's expanding use of the public cloud. Blackboard will leverage IBM's expertise and software to offer customers some of the most flexible, reliable, security-rich and resilient environments available.

The workshop program at the Eleventh Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment was held November 14–15, 2015 at the University of California, Santa Cruz, USA. The program included 4 workshops (one of which was a joint workshop): Artificial Intelligence in Adversarial Real-Time Games, Experimental AI in Games, Intelligent Narrative Technologies and Social Believability in Games, and Player Modeling. This article contains the reports of three of the four workshops.

AAAI-18 will be February 4-10, 2018. it draws upon, and contributes

CRF, HMM, MEMM - that I can do for sequence tagging, never tried it for something like this, it's probably extra hard. Although, I find probabilistic graphical models lacking and have redirected my efforts towards Learning to Search methods. If you check Structured models for fine-to-coarse sentiment analysis by McDonald et al. (2007), you'll see their structured prediction model is a CRF that is a bit hierarchical. You can use Leon Bottou's sgdcrf and adapt the model to get their model (little changes in the forward-backward and viterbi). The complexity of learning and inference for a single example for sentiment of document, paragraphs and sentences is O(M · (M2 P PM2 T)) O(M3 P T), where M is number of possible categorical values, P is number of paragraphs and T the average number of sentences in the paragraph. That's slow as fuck, although still fast if sgdcrf is used (about 500-2000 sentences per second).

Software faults raise questions about the validity of brain studies (arstechnica.com) Is "Python Machine Learning" by Sebastian Raschka a good book?

In 2010, a startling rumor filtered through the number theory community and reached Jared Weinstein. Apparently, some graduate student at the University of Bonn in Germany had written a paper that redid "Harris-Taylor"--a 288-page book dedicated to a single impenetrable proof in number theory--in only 37 pages. The 22-year-old student, Peter Scholze, had found a way to sidestep one of the most complicated parts of the proof, which deals with a sweeping connection between number theory and geometry. "It was just so stunning for someone so young to have done something so revolutionary," said Weinstein, a 34-year-old number theorist now at Boston University. Mathematicians at the University of Bonn, who made Scholze a full professor just two years later, were already aware of his extraordinary mathematical mind.

Ashok Goel wants to expand high-quality education to "millions" more people over the internet. It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities -- and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider.