Uber has admitted that there is a "problem" with the way autonomous vehicles cross bike lanes, raising serious questions about the safety of cyclists days after the company announced it would openly defy California regulators over self-driving vehicles. An Uber spokeswoman said on Monday that engineers were working to fix a flaw in the programming that advocates feared could have deadly consequences for cyclists. Uber began piloting its self-driving vehicles in its home town of San Francisco last week, despite state officials' declaration that the ride-share company needed special permits to test its technology. On day one, numerous autonomous vehicles – which have a driver in the front seat who can take control – were caught running red lights and committing a range of traffic violations. Self-driving cars: Uber's open defiance of California shines light on brazen tactics Despite threats of legal action from the department of motor vehicles (DMV) and California's attorney general, Kamala Harris, Uber refused to back down on Friday, claiming its rejection of government authority was "an important issue of principle".

Drone boats belonging to the U.S. Navy have begun learning to work together like a swarm with a shared hive mind. Two years ago, they would have individually reacted to possible threats by all swarming over like a chaotic group of kids learning to play soccer for the first time. Now the drone boats have showed that they can cooperate intelligently as a team to defend a harbor area against intruders. The U.S. Office of Naval Research (ONR) held its latest robot swarm demonstration in the lower Chesapeake Bay off the Virginia coast for about a month. Four drone boats showed off their improved control and navigation software by patrolling an area of 4 nautical miles by 4 nautical miles. If they spotted a possible threat, the swarm of roboboats would collectively decide which of them would go track and trail the intruder vessel.

If you ask Elon Musk, it's possible we already have. That's right: Artificial Intelligence is creeping into every area of our lives. AI is now a big part of the Microsoft strategy. Your least favorite but most used application of all time (that would be PowerPoint) can generate slides automatically with just a click and can write captions, making us all seem a little superfluous in some ways. In a New Yorker article this week (that is not online quite yet), there was a side comment about how a factory in China figured out how to produce the same number of parts using robots and with around 50,000 fewer employees.

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.