The TechCrunch conference is an event that swoops in on the startup scenes of the US and Europe, gathering for appraisal the technology world's rising movers, shakers and innovators. It's widely regarded as the place to get a feel for what's happening at the industry's grassroots – which is exactly what PwC Innovation Manager Marina Paronetto headed to London to do. As it turns out, it was the brains behind the machine – artificial intelligence – that was the star of 2016's show. It was said in sarcasm, but MC Jordan Crook couldn't have been more accurate when she said that TechCrunch's theme for its latest Disrupt conference was artificial intelligence. From what I saw, every solution on show here featured an element of AI.

Predictive analytics and machine learning working separately or together can be just what a company needs to succeed. But understanding how they work is key to figuring out how they can help businesses thrive. So, what is predictive analytics? Datafloq's Mark van Rijmenam uses the car metaphor, according to which traditional, descriptive analytics is like looking at the rear-view mirror to see what has happened, while predictive analytics is using a navigation system to tell you what will happen, and prescriptive analytics is a self-driving car that knows how to take you to your destination. This metaphor, while easy to comprehend, may also be deceptively simple.

In this tutorial, you discovered how to backtest machine learning models on time series data with Python.

Digital assistants are increasingly making their way into our everyday lives, not only via mobile devices, but also through home devices, cars, and more. As their scope widens, the technology powering them must evolve and deepen to fit people's ever-growing expectations and pace of life. With the intelligence of these assistants maturing, businesses will see new uses that go beyond today's simple question-and-answer interactions (Question: What is today's weather? Answer: Sunny and 64 degrees Fahrenheit).This new generation of assistants will have implications beyond our original thinking -- but it all starts with the tech. Voice artificial intelligence (Voice AI) involves the application of artificial intelligence techniques to voice-based interactions, enabling users to converse with systems in a flexible and collaborative way.

If you've seen a robot manipulation demo, you've almost certainly noticed that the robot tends to spend a lot of time looking like it's not doing anything. It's tempting to say that the robot is "thinking" when this happens, and that might even be mostly correct: Odds are that you're waiting for some motion-planning algorithm to figure out how to get the robot's arm and gripper to do what it's supposed to do without running into anything. This motion-planning process is one of the most important skills a robot can have, and it's also one of the most time consuming. Researchers at Duke University, in Durham, N.C., have found a way to speed up motion planning by three orders of magnitude while using one-twentieth the power. Their solution is a custom processor that can perform the most time-consuming part of the job--checking for all potential collisions across the robot's entire range of motion--with unprecedented efficiency.

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."