The cutting-edge number-crunching capabilities of artificial intelligence mean that AI systems are able to spot diseases early, manage chemical reactions, and explain some of the mysteries of the Universe. New research emphasizes how easily AI models can be trained for malicious purposes as well as good, specifically in this case to imagine the designs for hypothetical bioweapon agents. A trial run with an existing AI identified 40,000 such bioweapon chemicals in the space of only six hours. In other words, while AI can be incredibly powerful – and much, much faster than humans – when it comes to spotting chemical combinations and drug compounds to improve our health, the same power can be used to dream up potentially very dangerous and deadly substances. "We have spent decades using computers and AI to improve human health – not to degrade it," the researchers write in a new commentary.

AI's sophisticated ability to analyze numbers means that AI systems can detect diseases early, perform chemical reactions and unlock some of the mysteries of the universe. But there are downsides to this staggering and almost limitless artificial brain power. New research underscores how easy it is to train artificial intelligence models for malicious and good purposes, especially in the context of visualizing the design of speculative biological weapons agents. A pilot experiment using existing artificial intelligence identified 40,000 of these bioweapon chemicals in just 6 hours. In other words, while AI can be very powerful--and much faster than humans--it is likely to use the same powers when it comes to finding chemical and pharmaceutical compounds to improve our health.

Moore's law is coming to an end. At least, that is what a large proportion of hardware designers believe. However, the great open challenges of science and engineering require ever more powerful processors. To meet these needs, tech giants like Google or Nvidia are turning to the development of ASICs (Application Specific Integrated Circuits). Unlike ordinary processors, these chips can perform only a reduced set of operations but at unparalleled speed.

Google says it has developed a way of using deep reinforcement learning (RL) to create computer chip floorplanning in just six hours -- a complicated feat that typically requires humans months to achieve. The chips Google's AI develops are on par or superior than those humans can create, the team explained in its paper published in the journal Nature on Wednesday, June 9. In a first for one of its commercial products, Google's research is being used for the company's upcoming tensor processing unit (TPU) chips, which are optimized for AI computation. So Google's AI method to design chips can eventually be used to improve and quicken the future development of AI. "Our method was used to design the next generation of Google's artificial intelligence (AI) accelerators, and has the potential to save thousands of hours of human effort for each new generation," the team said. The major breakthrough is that Google's AI method can be used for chip "floorplanning," which, as the paper said "Despite five decades of research, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts."

Deepfake artist Hao Li created this Putin deepfake, which was shown at an MIT conference this week.AP Photo/Alexander Zemlianichenko; MIT Technology Review A deepfake pioneer said in an interview with CNBC on Friday that "perfectly real" digitally manipulated videos are just six to 12 months away from being accessible to everyday people. "It's still very easy you can tell from the naked eye most of the deepfakes," Hao Li, an associate professor of computer science at the University of Southern California, said on CNBC's Power Lunch. "But there also are examples that are really, really convincing." He continued: "Soon, it's going to get to the point where there is no way that we can actually detect [deepfakes] anymore, so we have to look at other types of solutions." Li created a deepfake of Russian president Vladimir Putin, which was showcased at an MIT tech conference this week.

In January, I started a weekly newsletter on the impact automation (AI and robotics) is having on jobs. I did this mostly because I wanted to keep better track of what's happening in this space, and I thought that a weekly publication deadline might help me do this. I'm only six weeks in, and there have already been so many developments in the field that I have to be selective about what I include. Writing about this stuff week on week, several trends have emerged that paint a picture of a world waking up to the huge changes automation is going to bring. Here are some of the ways these trends have unfolded so far in 2017.

If you think artificial intelligence was huge in 2016, just wait. While you can't discount the achievements made by AI in the past year -- autonomous driving, AlphaGo's victories, the world's saddest assistant -- 2017 stands to best each of them. And we've started with January off with a bang (and an AI that's now $800,000 richer, sorta). All of this pales in comparison to what's to come. As amazing as current AI seems, it's a compounding effect that uses previous advancements to build upon the last until AI ultimately starts to learn from its mistakes and get smarter than every. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us.