BPM 31510 is just another cancer drug in human development trials, except for one thing. Scientists didn't toil away in labs to come up with it; artificial intelligence did. The cancer drug development process is costly and time-consuming. On average, it takes 24 to 48 months and upwards of 100 million to find a suitable candidate. Add that to the fact that 95% of all potential drugs fail in clinical trials, and the inefficiencies of the whole drug-discovery machine really become apparent.

Every job has its more boring components. In libraries, one of those jobs is scanning the shelves, looking for missing and misplaced books, and taking stock of what's available. For human workers, this is time-consuming, repetitive and boring, all of which can contribute to wandering attention. The autonomous robotic shelf-scanning platform, or AuRoSS, is in development by the Institute for Infocomm Research of Singapore's Agency for Science, Technology and Research. It uses laser mapping to navigate a library, and RFID tags placed on books to scan the collection.

You'll read these next three paragraphs in my voice. Better get busy reading, or get busy dying. On June 14, America escaped from the primary season. All the wardens found was a "Make America Great Again" hat, a hollowed-out copy of "Hard Choices" and an old rock hammer, damn near worn down to the nub. I remember thinking the nominees would be decided by mid-March.

The best minds in the business--Yann LeCun of Facebook, Luke Nosek of the Founders Fund, Nick Bostrom of Oxford University and Andrew Ng of Baidu--on what life will look like in the age of the machinesThe traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

If you write marketing or advertising text for a living, you may want to get a second job skill. That's because software that writes text is here, and it is tackling a growing list of assignments. Several companies offer software that regularly churns out thousands of stories and reports based on structured data, like financial results. Ads that literally write themselves emerged last week, as IBM announced a new service based on its Watson supercomputer. A program called Quakebot has generated earthquake stories for the LA Times.

DVDs were first launched in Japan, Travelocity opened up as the first online booking agent, eBay, Ask Jeeves also opened their online doors and the Spice Girls had their first UK number-one. You get the point (well except the Spice Girls bit) – it was an inflection point with technology, and 20 years later we are that same point again with AI, sometimes referred to as cognitive technologies. At the time I was fortunate to work for a very innovative company who had developed the first generation of SaaS solution for managing supply chains. So as you can imagine I spent a lot of my time sat in meetings trying to convince executives the internet was going to change the world and they needed to innovate. What we forget (which seems silly now) is many of these execs either dismissed the technology, or worse, had convinced themselves they get it and have a solid plan.

The U.S. spends 270 billion on incarceration each year, has a prison population of about 2.2 million and an incarceration rate that's spiked 220 percent since the 1980s. But with the advent of data science, White House officials are asking experts for help. On Tuesday, June 7, the White House Office of Science and Technology Policy's Lynn Overmann, who also leads the White House Police Data Initiative, stressed the severity of the nation's incarceration crisis while asking a crowd of data scientists and artificial intelligence specialists for aid. "We have built a system that is too large, and too unfair and too costly -- in every sense of the word -- and we need to start to change it," Overmann said, speaking at a Computing Community Consortium public workshop. She argued that the U.S., a country that has the highest amount of incarcerated citizens in the world, is in need of systematic reforms with both data tools to process alleged offenders and at the policy level to ensure fair and measured sentences.

Taxpayers spend 39 billion a year on jailing 2.3 million people, making the U.S. the country with the highest incarceration rates in the world. And while technology is radically reshaping every aspect of our economy and society, none of our advances in computing and data are helping to stem the tide of mass incarceration. At a workshop in the capital last Tuesday, White House senior adviser Lynn Overmann of the Office of Science and Technology Policy called on the technologists of the country to figure out how to use data and technology to end widespread incarceration, according to Government Technology. Overmann wants artificial intelligence and machine learning programs that improve screening processes, scan body camera footage for police misconduct and make sentencing more fair. "I represented a client who was looking at spending 40 years of his life in prison because he stole a lawnmower and a weed-eater from a shed in a backyard," she said.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Several startups have already begun experimenting with various approaches to using AI for security. One of the most interesting approaches is using the human body as a paradigm: invaders such as microbes and viruses are defended by our immunity system, so why not copy the same mechanism? Gerald Edelman, the Nobel laureate who discovered how the immunity system works, would have been proud of such creative out-of-the-box thinking. Indeed, Edelman had the unique insight that the human brain may also be working on a similar principle: neurons forming groups through adaptation to regular or frequent signals. Machine Learning seems to verify his insight where neural nets monitor systems and user behaviour for anomalies and thus "learn" how to defend effectively against real threats.