Manufacturing sites worldwide use industrial robots to streamline and automate operations. Japan has led the world in industrial robot technology, and now is about to spur an even greater evolution by combining this technology with an open source deep learning framework developed in Japan. Deep learning enables industrial robots to make judgments in complex operational situations by learning from past examples. Moreover, the learning can be shared between robots to increase efficiency. Watch this video to learn how this innovative approach will deliver previously unavailable levels of advanced automation in manufacturing.

A new federal commission on artificial intelligence is being led by Democrats. The National Security Commission on Artificial Intelligence (AI), a federally appointed commission, held its first meeting Monday chaired by former Google executive and billionaire Eric Schmidt, a major donor and informal adviser to former President Barack Obama. The commission's vice chairman is Robert Work, who was deputy defense secretary in the Obama administration. Additionally, the AI commission has hired as a staff member Ylli Bajraktari, a former National Security Council staff member under Mr. Obama. Mr. Bajraktari also was a former aide to Obama Defense Secretary Ash Carter.

If an automated car had to choose between crashing into a barrier, killing its three female passengers, or running over one child in the street -- which call should it make? When three U.S.-based researchers started thinking about the future of self-driving cars, they wondered how these vehicles should make the tough ethical decisions that humans usually make instinctively. The idea prompted Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan to design an online quiz called The Moral Machine. Would you run over a man or a woman? By 2069, autonomous vehicles could be the greatest disruptor to transport since the Model T Ford was launched in Detroit in 1908.

If you shop on Amazon, an algorithm rather than a human probably set the price of the service or item you bought. Pricing algorithms have become ubiquitous in online retail as automated systems have grown increasingly affordable and easy to implement. But while companies like airlines and hotels have long used machines to set their prices, pricing systems have evolved. They have moved from rule-based programs to reinforcement-learning ones, where the logic of deciding a product's price is no longer within a human's control. If you recall, reinforcement learning is a subset of machine learning that uses penalties and rewards to incentivize an AI agent toward a specific goal.

Should we be scared of artificial intelligence (AI)? Some notable individuals such as legendary physicist Stephen Hawking and Tesla and SpaceX leader and innovator Elon Musk suggest AI could potentially be very dangerous; Musk at one point was comparing AI to the dangers of the dictator of North Korea. Microsoft co-founder Bill Gates also believes there's reason to be cautious, but that the good can outweigh the bad if managed properly. Since recent developments have made super-intelligent machines possible much sooner than initially thought, the time is now to determine what dangers artificial intelligence poses. What is applied and general artificial intelligence?

Machine learning has the potential to democratize access to credit. It can expand the pool of people qualified to obtain credit -- most notably low- and moderate-income (LMI) borrowers -- and decrease the cost of that credit. It also can increase access to credit and reduce systemic risk by allowing different banks to analyze different factors, and thereby generate different results in a way that the existing, FICO-based system discourages. The greatest current obstacle to this development is pressure from the banking regulators to continue adhering to the status quo system, lest machine learning produce an unfortunate outcome. Perversely, that system already contains the very flaws that regulators have expressed about machine learning.

But with a huge volume of posts popping up on these sites each day, it's difficult for even this combination of people and machines to keep up. AI still has a long way to go before it can reliably detect hate speech or violence online. Machine learning, the AI technique tech companies depend on to find unsavory content, figures out how to spot patterns in reams of data; it can identify offensive language, videos, or pictures in specific contexts. That's because these kinds of posts follow patterns on which AI can be trained. For example, if you give a machine-learning algorithm plenty of images of guns or written religious slurs, it can learn to spot those things in other images and text.

The Indian pharmaceutical industry has emerged as a significant contributor to the global pharmaceutical industry. As per the report by the Federation of Indian Chamber of Commerce and Industries (FICCI), "India is the largest provider of generic drugs globally. Presently over 80 per cent of the antiretroviral drugs used globally to combat AIDS (Acquired Immune Deficiency Syndrome) is supplied by Indian pharmaceutical firms." In 2017, the Indian pharmaceutical industry was valued at $33 billion. The industry is expected to grow at a CAGR of 22.4 per cent over 2015–20 to reach $55 billion.

The key to successfully applying artificial intelligence in the real world is to get up close and personal with the people who'll to use it, executives agreed during a roundtable discussion about A.I. during Fortune's Brainstorm Design conference in Singapore last Thursday. "The reason we all wear black shirts, by the way, is that you can't see the blood splatters on them, because we need to be so close to the action that we're getting covered in blood," said Sean Carney with a smile. Carney, who indeed was wearing a black dress shirt, is chief design officer for Philips, which claims substantial market share for A.I.-driven healthcare tools. Many clinicians worry that new technologies will put them out of a job. It's the wrong mindset, Carney said.

Back in 2015, a hitchhiker was murdered on the streets of Philadelphia. It was no ordinary crime. The hitchhiker in question was a little robot called Hitchbot. The "death" raised an interesting question about human-robot relationship - not so much whether we can trust robots but whether the robots can trust us. The answer, it seems, was no.