Earlier this week, Microsoft CEO Satya Nadella published an article on Slate that outlined his positions on artificial intelligence (A.I.) and machine learning. If you're interested in those fields, it's well worth a read, because Nadella's position as head of one of the world's biggest technology companies means his opinion could have considerable influence on how the A.I. field evolves in coming years and decades. Nadella is, to put it mildly, an A.I. optimist. He thinks self-learning machines will allow humanity to conquer "disease, ignorance, and poverty." "I would argue that perhaps the most productive debate we can have isn't one of good versus evil," he wrote.

First it was whimsical poems, then full-length movies. But, now artificial intelligence is writing sports articles. The Associated Press announced it is expanding the publication's coverage to include Minor League Baseball and will use automated software to cover the 10,000 games. This AI reporter is capable of analyzing data from the games, pulling out the most important highlights to formulate a well-constructed and informative stories. The Associated Press announced it is expanding the publication's coverage to include Minor League Baseball and will use automated software to cover the 10,000 games, like the Altoona Curve.

The purpose of the report is to detail the process of training an Artificial Neural Network to control a robot. This report will be divided into several sections. The goal of this report is to demonstrate the ability of an ANN to control a robot to stand. In the previous reports the GA was used to evolve an ideal Artificial Neural Network topology, which was then refined via backpropagation learning. For this report the same techniques will be applied to the process of training an ANN to control a simulated robot, referred to simBot in this report.

These are my notes and observations after attending the Safety in Artificial Intelligence (SafArtInt) conference, which was co-hosted by the White House Office of Science and Technology Policy and Carnegie Mellon University on June 27 and 28. This isn't an organized summary of the content of the conference; rather, it's a selection of points which are relevant to the control problem. As a result, it suffers from selection bias: it looks like superintelligence and control-problem-relevant issues were discussed frequently, when in reality those issues were discussed less and I didn't write much about the more mundane parts. SafArtInt has been the third out of a planned series of four conferences. The purpose of the conference series was twofold: the OSTP wanted to get other parts of the government moving on AI issues, and they also wanted to inform public opinion. The other three conferences are about near term legal, social, and economic issues of AI. SafArtInt was about near term safety and reliability in AI systems.

Researchers at Purdue University are approaching virtual reality with a concept that uses powerful learning algorithms from a "deep learning" software that they are calling DeepHand. Specifically, the research team is addressing the problem of accurate hand tracking in virtual reality and augmented reality and proposing an interesting solution involving neural networks and a multitude of 3D sensors. The thought process behind this experiment makes sense given the increasing importance of powerful and accurate hand tracking in augmented reality and human-computer interfaces. In both augmented reality and virtual reality, better hand tracking means a better user experience. In real life, hand movements are something that we generally take for granted (i.e.

ALPHA's prowess is impressive, but equally amazing is the tiny computer that runs it. For such a complicated set of decision-making algorithms, ALPHA requires very little processing power, running on a 35 Raspberry Pi minicomputer. ALPHA uses what are called "fuzzy logic algorithms" to form a "Genetic Fuzzy Tree" system that breaks big problems down into smaller chunks so the system can evaluate which variables are relevant to a particular decision and which of those are most important. This allows the system to work more efficiently and rapidly.

The companies have integrated the automation technology into Watchwith's data-driven advanced advertising products. "What used to potentially require thousands of man-hours is now an automated process within the Watchwith platform," Watchwith says in a statement. By embedding artificial intelligence into the video advertising inventory creation process, Watchwith MAF gives TV networks and premium video publishers the power to create, manage and sell contextually relevant native video advertising at scale. "And the result is the highly scalable, native digital video advertising solution the TV industry needs to compete with Facebook, YouTube, Snapchat and other native digital video distribution platforms."

Watchwith has acquired the Arris Media Analysis Framework (MAF), a cloud-based machine learning and automated metadata generation platform. MAF was developed in Arris research labs, and the technology analyzes, tags and describes video at a frame level, which eliminates manual tagging. The companies have integrated the automation technology into Watchwith's data-driven advanced advertising products. "What used to potentially require thousands of man-hours is now an automated process within the Watchwith platform," Watchwith says in a statement. The combined solution is able to automatically determine the optimal timing and location within a TV episode to deliver in-program advertising and tune-in messages.

Chemists and mathematicians from the Skolkovo Institute of Science and Technology (Skoltech) and Moscow State Universite (MSU) have suggested checking the composition of medical plants by means of machine learning technologies, the Skoltech press service said. They have come up with automatizing computer assisted data analysis based on high-performance liquid chromatography and mass spectrometry. "Machine learning is when a computer can be taught to analyze the chemical composition of herbal medicine based on the previously known data on chemical analysis," Skoltech said. According to the researchers, the market of herbal remedies has been rapidly developing in the recent years, as it provides an alternative to synthetic drugs. But there are still no existing effective methods of plant material quality control.