Something that reminds me of this is the framework of generative adversarial networks. The generator is updated to try to trick the discriminator (by gradient descent to minimize its accuracy), which is then updated again to deal with the new samples. This framework has been very popular in the past few years, but is very tricky to use in practice, and there's a lot of ongoing research (e.g. this paper from earlier this year and this one just published two days ago) in getting them to work more reliably. You could imagine, maybe, having human annotators do some kind of label smoothing for the discriminator: true data set samples get the label 1, terrible samples get label -1, okay ones get -.75, great ones get 0, maybe the best ones get labeled as .5 or even 1. I haven't thought through the consequences of this too much, but it might help.

Meet Jill Watson, a first-time teaching assistant at Georgia Tech assigned to moderate an online forum for a computer science class. Jill was 1 of 9 TAs assigned to help answer questions about coursework and projects from the 300 students enrolled in the advanced course. During the first few weeks in January, Jill really struggled. This was Knowledge-Based Artificial Intelligence, after all, a course with the goal to "build AI agents capable of human-level intelligence and gain insights into human cognition." It was also a requirement for graduate students to earn their master's degree.

Nvidia (NASDAQ:NVDA) and Mercedes-Benz parent Daimler AG (OTCPK:DDAIF) (OTCPK:DDAIY) announced a partnership for AI powered Mercedes cars. These cars, which are expected to be available within the next year, will feature self-driving capability as well as an active driver assistance function called "Co-Pilot." Co-Pilot may well be the most intelligent human-machine interface ever devised. The announcement at CES of the Nvidia/Daimler partnership came only a day after Nvidia CEO Jen-Hsun Huang gave his keynote at CES. Since the announcement of the Nvidia/Tesla (NASDAQ:TSLA) partnership last year, the automobile industry has been waking up to the fact that the race to build the first commercially available self-driving car is all but won.

Despite spending more money on security than ever, organisations struggling with a widespread cybersecurity skills gap are often told how technologies like big data, analytics, machine learning, and artificial intelligence can aid them in protecting their data or critical infrastructure from attackers. Organisations ranging from startups to established large corporations are investing in the building of AI systems to bolster defences by analysing vast amounts of data and helping cybersecurity professionals identify far more threats than would be possible if they were left to do it manually. But the same technologies that improve corporate defences could also be used to attack them. It's the simplest method of cyberattack available -- and there are schemes on the dark web which put all the tools required to go phishing into anyone's hands. It's simply a case of taking an email address, scraping some publicly available personal data to make the phishing email seem convincing, then sending it to the victim and waiting for them to bite.

In billiards, you must call your shot ahead of time. You don't get any points for luck. With that in mind, here are my on-the-record predictions for 2017. Self-driving cars, previously existing only in the world of science fiction, are becoming a reality. Today taxis are driving themselves in Singapore.

A new proof-of-concept study by researchers from the University of California San Diego succeeded in training computers to "learn" what a healthy versus an unhealthy gut microbiome looks like based on its genetic makeup. Since this can be done by genetically sequencing fecal samples, the research suggests there is great promise for new diagnostic tools that are, unlike blood draws, non-invasive. As recent advances in scientific understanding of Parkinson's disease and cancer immunotherapy have shown, our gut microbiomes – the trillions of bacteria, viruses and other microbes that live within us – are emerging as one of the richest untapped sources of insight into human health. The problem is these microbes live in a very dense ecology of up to 1 billion micobes per gram of stool. Imagine the challenge of trying to specify all the different animals and plants in a complex ecology like a rain forest or coral reef – and then imagine trying to do this in the gut microbiome, where each creature is microscopic and identified by its DNA sequence.

Picture a virtual assistant that helps find directions, schedules appointments or plays music, and the soothing yet robotic sound of a female voice likely comes to mind. From Apple's Siri to Amazon's Alexa, a majority of the world's most popular virtual assistants have female personas. But that's starting to change as a growing number of consumers -- and companies -- turn to digital assistants. Some developers are going against the grain, creating chatbots and messaging apps that no longer conform to one gender and challenging a tradition of female digital assistants that some say display submissive personalities. Making virtual assistants female by default can be bad for business and perpetuate stereotypes, these chatbot developers say, so they're offering more options to consumers.

BY PATRICK WATSON: The news headlines after last week's jobs report proved it again: Life is hard (and getting harder) for low-skilled workers. Illegal immigrants take their service industry jobs. Robots take their manufacturing industry jobs. According to the media, many low-skilled workers have simply given up. Their lives are terrible, and everyone wants to help.

Machine Learning, somehow, emerged from Artificial Intelligence.

Artificial intelligence (AI) on its own isn t enough to compete -- companies need industry-specific solutions to business problems. So said Martin Schroeter, IBM Corp. (NYSE: IBM) s company senior vice president and chief financial officer, on the company s quarterly earnings call Thursday afternoon. Cognitive computing technology (IBM s term for AI) is just "table stakes," said Schroeter, claiming that his company is going the extra mile. IBM is building datasets for Watson to serve specific industries, including healthcare and finance. "You need more than public data or algorithms to solve real-world problems," Schroeter said.