John Nhial was barely a teenager when he was grabbed by a Sudanese guerrilla army and forced to become a child soldier. He spent four years fighting, blasting away on guns almost too heavy to hold, until one day the inevitable happened: he was seriously injured, treading on a landmine while he was on morning patrol. "I stepped on it and it exploded," he recalled. "It threw me up and down again – and then I tried to look for my leg and found that there was no foot." His comrades carried him back to base camp, but there was hardly any medical care available. It took 25 days before he received proper treatment, during which time he developed tetanus down one side of his body.

The Tokyo Institute of Technology, also known as Tokyo Tech, has revealed that the TSUBAME 3.0 supercomputer scheduled to be installed this summer will provide 47 half precision (16-bit) petaflops of performance, making it one of the most powerful machines on the planet for artificial intelligence computation. For Tokyo Tech, the use of NVIDIA's latest P100 GPUs is a logical step in TSUBAME's evolution. The original 2006 system used ClearSpeed boards for acceleration, but was upgraded in 2008 with the Tesla S1040 cards. In 2010, TSUBAME 2.0 debuted with the Tesla M2050 modules, while the 2.5 upgrade included both the older S1050 and S1070 parts plus the newer Tesla K20X modules. Bringing the P100 GPUs into the TSUBAME lineage will not only help maintain backward compatibility for the CUDA applications developed on the Tokyo Tech machines for the last nine years, but will also provide an excellent platform for AI/machine learning codes. In a press release from NVIDIA published Thursday, Tokyo Tech's Satoshi Matsuoka, a professor of computer science who is building the system, said, "NVIDIA's broad AI ecosystem, including thousands of deep learning and inference applications, will enable Tokyo Tech to begin training TSUBAME 3.0 immediately to help us more quickly solve some of the world's once unsolvable problems."

Suppose you enter a dark room in an unknown building. You may panic about some potential monsters lurking in the dark. Or just turn on the light, to avoid painfully bumping into the furniture. The dark room is the future of artificial intelligence (AI). Unfortunately, there are people who believe that, as we step into the room, we may run into some evil, ultra-intelligent machines. Fear of some kind of ogre, such as a Golem or a Frankenstein's monster, is as old as human memory.

More than half of mobile collaboration providers will support chatbots in their offerings by the end of 2017, according to new analysis from Aragon Research. The company evaluated 16 major mobile collaboration providers as part of its Tech Spectrum report on the topic, and found that while'several' companies analysed already have chatbot capabilities more will follow before the year's end. "Messaging, the core of mobile collaboration, is becoming a top priority in the enterprise. But enabling collaboration via mobile is not enough – it also needs to be secure and in the near future, increasingly automated via chatbots," the company notes. The prevalence of chatbots in making enterprise tasks more efficient is growing increasingly pronounced.

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There is an urgent need to expand the AI epistemic community beyond the specific geographies in which it is currently clustered. Artificial Intelligence (AI) is no longer the subject of science fiction and is profoundly transforming our daily lives. While computers have already been mimicking human intelligence for some decades now using logic and if-then kind of rules, massive increases in computational power are now facilitating the creation of'deep learning' machines i.e. algorithms that permit software to train itself to recognise patterns and perform tasks, like speech and image recognition, through exposure to vast amounts of data. These deep learning algorithms are everywhere, shaping our preferences and behaviour. Facebook uses a set of algorithms to tailor what news stories an individual user sees and in what order.

In the future, Artificial Intelligence can be utilized to eliminate inherent human biases that often influence important decisions surrounding employment, government policy, and even policing. At the event, Professor Iris Bohnet stated that every person has biases that inform their decisions. These biases can affect whether a candidate for a job is chosen or not. As a result, Bohnet suggested that by using algorithms, employers could choose the best candidates by using AI to focus on the candidate's qualifications rather than by basing decisions on gender, race, age or other variables. However, the panel also discussed the fact that even algorithms can have bias.

Element AI has acquired the entire team at MLDB.ai, an open source machine learning database. The acquisition includes all staff, MLDB.ai's complete product line, and customer base. The company will continue to be developed as an open-source project and leverage Element AI's resources. The Pro version of MLDB.ai will be open-sourced, as will some plugins associated with the processing of LiDAR datasets. The company said in a blog post that it will be winding down support contracts over the next six months, and replacing it with an expanded presence on free, community-based support channels.

IBM (NYSE: IBM) today announced IBM Machine Learning, the first cognitive platform for continuously creating, training and deploying a high volume of analytic models in the private cloud at the source of vast corporate data stores. Even using the most advanced techniques, data scientists – in shortest supply among today's IT skills1 – might spend days or weeks developing, testing and retooling even a single analytic model one step at a time. IBM has extracted the core machine learning technology from IBM Watson and will initially make it available where much of the world's enterprise data resides: the z System mainframe, the operational core of global organizations where billions of daily transactions are processed by banks, retailers, insurers, transportation firms and governments. IBM Machine Learning also for the first time deploys Cognitive Automation for Data Scientists from IBM Research to assist data scientists in choosing the right algorithm for the data by scoring their data against the available algorithms and providing the best match for their needs. The service also considers various circumstances – such as what the algorithm is needed to do and how fast it needs to produce results. Clients are beginning to see the value in IBM Machine Learning for z/OS.

AI is about to go mainstream. It will show up in the connected home, in your car, and everywhere else. While not as glamorous as sentient beings that turn on us in futuristic theme parks, the use of AI in fraud detection holds major promise. Keeping fraud at bay is an ever-evolving battle where both sides, good and bad, are adapting as quickly as possible to determine how to best use AI to their advantage. There are currently three major ways that AI that is used to fight fraud, corresponding to how AI developed as a field.