AI for recruiting is the application of artificial intelligence to the recruitment function that is designed to automate some part of the recruiting workflow, especially repetitive high-volume tasks. The main benefits of AI for recruiting include saving recruiters' time by automating high-volume tasks and improving quality of hire through standardized job matching. The major challenges of AI for recruiting include requiring a lot of data, the potential to learn human biases, and skepticism of new technology by HR professionals. The future applications of AI for recruiting are intelligent screening software that automates resume screening, recruiter chatbots that engage candidates in real-time, and digitized interviews that help assess a candidate's fit. AI for recruiting will change the recruiter role through augmented intelligence which will allow recruiters to become more proactive in their hiring, help determine a candidate's culture fit, and improve their relationships with hiring managers by using data to measure KPIs such as quality of hire.

Today's data watchers estimate that we create at least 2.7 million terabytes of data per day -- enough to double the digital universe every two years. Our digital, sharing economy has given rise to a universe of apps, all generating bytes and bytes of data that, for the most part, no one looks at or uses. How will we begin to meaningfully utilize this overwhelming amount of data? The consensus answer is with articificial intelligence; AI will draw sense from this data junkyard, thereby enabling groundbreaking progress in applications from self-driving cars and medical diagnoses to national security and scientific discoveries. But the reality is that, beyond some specialized exceptions, AI as a broad discipline has been largely stagnant for decades.

Canada's BlackBerry Ltd (BB.TO) will open an autonomous driving research center on Monday, as it tries to make itself an indispensable under-the-hood piece of the automotive industry's weaponry in the self-driving vehicle arms race. Prime Minister Justin Trudeau will attend the launch by the Waterloo, Ontario-based smartphone pioneer, the prime minister's office said on Sunday. A Blackberry spokeswoman deferred any comment on the project until Monday. Blackberry, once known for its phones but now betting its future on the more profitable business of making software and managing mobile devices after largely ceding the smartphone market to the likes of Apple and Samsung, is expanding subsidiary QNX's Ottawa facility to focus on developing advanced driver assistance and autonomous vehicle technology. After a detour where QNX's industrial-focused software was used to reinvent the now-discarded BlackBerry phone operating system, BlackBerry is focused on how its embedded software interacts with the explosion of sensors, cameras and other components required for a car to drive itself.

NEC Corp. said it will develop cancer drugs using artificial intelligence, aiming to put new vaccines using peptides into practical use within the next eight years as key substances in next generation immunotherapy. A peptide is a short chain of amino acids with an enormous number of variations and helps increase cells that attack cancer. Whether a pattern works depends on the patient's leukocyte group, so NEC will use its AI technology to quickly choose the right type of peptide, the company said Monday. Under joint research with Yamaguchi and Kochi universities, NEC has already discovered a peptide that the developers hope will be effective for liver cell and esophageal cancer. A new company established for the project, Cytlimic Inc., plans to commercialize effective peptide vaccines in cooperation with pharmaceutical companies, NEC said.

Almost one-third of large companies will employ smart machines by 2021, predicts analyst firm Gartner Inc. in a new report. Gartner defines "smart machines" as technologies that include artificial intelligence, machine learning, cognitive computing, deep learning and intelligent automation. Smart machine use will be especially transformative in the consulting and systems integration services space, predicts Gartner, where spending could reach $29 billion by 2021. "Smart machines will profoundly change the way work is done and how value is created," said Susan Tan, research vice president at Gartner. "From dynamic pricing models and fraud detection, to predictive policing and robotics, smart machines have broad applicability in all industries."

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks. Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt. Evaluation function tells how good the machine learning model is.

It's been an unusual year in tech: Apart from maybe Snapchat's elusive Spectacles, there hasn't been a truly game-changing, mass-market piece of technology that's totally dominated the conversation. Instead, the biggest companies in tech, especially Facebook, Microsoft, Google, and Amazon, spent their 2016 hyping up something a little more abstract: their ongoing quests to build a so-called "general artificial intelligence," and all the extra smarts they're adding to the product while they're at it. If you ask the biggest tech companies in the world, the rise of artificial intelligence is just as big a deal, if not more so, than the transition from desktop PCs to mobile computing. Google CEO Sundar Pichai, a very vocal fan of AI, has said that he envisions a world in which every user gets their own personal Google. Facebook and Microsoft, too, have made a big point of showing how artificial intelligence is improving their own apps, enabling stuff (like helping you design better PowerPoints) that would never have been otherwise possible.

Pulling these threads together, recently a gaming AI defeated a US Air Force expert combat pilot in simulated combat.

How to survive the age of Artificial Intelligence? Part 2: "World urban population will double in 2050. So, what should be done? With an exploding world population increasingly becoming urban, new technologies like AI threatening the job market, what should we do? I've read up multiple blogs and articles available out there and couldn't figure out a clear answer – most of them stopped at scaremongering. So, here's an attempt to start finding the solutions. These in no way are concrete solutions, but I see it as a start for ideas and thoughts. As I write this blog, sweeping changes are being made in the world political scene. Donald Trump is the president-elect of USA, Theresa May is busy finding solutions to wriggle out of the BREXIT mess, Modi with one stroke of an executive order has demonetized a major chunk of currencies (Rs) in India. The common theme is CHANGE. We need similar sweeping changes in our policies to be prepared for the future. Future work for humans is not heavy lifting, but creative. Our policies should start focusing on "How could a driver earn his living in the near future?", "How could they contribute to knowledge & creativity?", not "How many unskilled workers got trained as plumbers?" What are they going to do & how will they survive. Does our current rote-based learning system prepare them for future? Focus should be on improving basic needs at rural areas and discourage urbanisation. Policies should also focus on de-urbanisation, like "How to incentivise skilled people like doctors, teachers etc to move to rural areas?" Let's only hope this becomes mainstream policies (like Paris climate agreement). Voters should start demanding action from the elected representatives and start probing on their thoughts. Cost of treating the damage to environment (e.g., air & water pollution) and humans (e.g., depression, medicare) will prove to be very costly. Do we leave everything to our policy makers? We could also start with small changes. Atleast for workers in the knowledge industry this flexibility exists. Learning new skills is going to be very critical in the coming years. There are business & social opportunities in skill development & education. Read part-1 of the series: Why smart city is a dumb strategy? Read part-2 of the series: "World urban population will double in 2050.

They found that the trained system included an intermediate processing step that represented a face's degree of rotation -- say, 45 degrees from center -- but not the direction -- left or right. This property wasn't built into the system; it emerged spontaneously from the training process. But it duplicates an experimentally observed feature of the primate face-processing mechanism. The researchers consider this an indication that their system and the brain are doing something similar. "This is not a proof that we understand what's going on," says Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines (CBMM), a multi-institution research consortium funded by the National Science Foundation and headquartered at MIT. "Models are kind of cartoons of reality, especially in biology. So I would be surprised if things turn out to be this simple. But I think it's strong evidence that we are on the right track."