Cisco announced a partnership Monday with bot-building platforms Gupshup and API.ai that allows thousands of bots to quickly join Cisco Spark and Cisco Tropo platforms. It also turns up the intensity in competition between enterprise team communication chat apps like Skype and Slack. The announcement was made during Cisco Live, a four-day Cisco event taking place in Las Vegas this week. Gupshup built an SMS social network of more than 50 million users, mainly in India, before becoming an enterprise messaging service company in 2010. Today, it processes four billion messages a month.

To learn more about opportunities in applied AI, join us at the O'Reilly Artificial Intelligence Conference, September 26-27, 2016 in New York. One of the truisms of modern AI is that the next big step is to move from supervised to unsupervised learning. In the last few years, we've made tremendous progress in supervised learning: photo classification, speech recognition, even playing Go (which represents a partial, but only partial, transition to unsupervised learning). Unsupervised learning is still an unsolved problem. As Yann LeCun says, "We need to solve the unsupervised learning problem before we can even think of getting to true AI."

It always has, since the beginning of my forays into Computer Science. I did two startups in the nineties, at the heart of which were AI-driven innovations. The world's recent embrace of AI, thus, also fascinates me. Driverless cars are all the rage these days. Uber is chomping at the bit to replace the headache of having to pay anything at all to their drivers, having driven the labor cost down already to the minimum.

Neural networks are changing the Internet. Inspired by the networks of neurons inside the human brain, these deep mathematical models can learn discrete tasks by analyzing enormous amounts of data. They've learned to recognize faces in photos, identify spoken commands, and translate text from one language to another. They're helping to choose what you see when you query the Google search engine or visit your Facebook News Feed. All this is sharpening the behavior of online services.

Carmakers and tech companies are in a race to put autonomous vehicles on the road, and it's time for regulators to tap the brakes. This month the National Highway Traffic Safety Administration revealed that it is investigating two crashes involving Tesla vehicles allegedly operating on autopilot. Tesla's autopilot feature is a semi-autonomous system that uses cameras, radar and sensors to steer the car, change lanes, adjust speed and even find a parking space and parallel park. It's not supposed to turn a Tesla sedan into a self-driving car, but there's ample evidence on YouTube of people driving with their hands off the steering wheel, playing games and even climbing into the back seat while their car is hurtling down a freeway. You and your daughter are riding in a driverless car along Pacific Coast Highway.

Tech giant Google has added to its vast technology services stable by acquiring Paris-based startup Moodstocks, which has developed machine learning based image recognition technology for smartphones. The acquisition by Google will see it add the recognition technology of Moodstocks to its already large number of services it offers which use machine learning, such as Google Translate, Smart Reply in Inbox and the Goggle app. With many of its services already relying on machine learning technologies, Google's acquisition of Moodstocks will help with implementing visual recognition. The Moodstocks team of engineers and researchers, based in Paris, developed new algorithms for Visual pattern recognition and machine learning, as well as a technology for the recognition of images and objects via mobile devices. Head of the R&D Center of Google France, Vincent Simonet, wrote in a blog on July 7 to announce the deal, said that while great steps forward were taken by Google in terms of Visual recognition, there was still much to be done in this area, stating that the company expects that to be where Moodstocks comes into its own.

Today machine learning is centre stage in the world of technology, and thousands of scientists and engineers are applying machine learning to an extraordinarily broad range of domains. However, making effective use of machine learning in practice can be daunting, especially for newcomers to the field. Over the last five decades, researchers have created literally thousands of machine learning algorithms. Traditionally an engineer wanting to solve a problem using machine learning must choose one or more of these algorithms to try, often constrained those algorithms they happen to be familiar with, or by the availability of software implementations. In this talk we view machine learning from a fresh perspective which we call'model-based machine learning', in which a bespoke solution is formulated for each new application.

Following the death by bomb-armed-robot of the suspect in last week's cop killings in Dallas, which apparently involved an improvised setup that the local police department's robot wasn't built to be used for, a public outcry over police access to high tech weaponry has erupted. "The Dallas Police Department's unprecedented use of an explosive-laden robot to kill an armed suspect ushers in a new phase in the militarization of U.S. police departments," reports the Los Angeles Times. The article goes on to point out that there have been similar uses of modified machines originally built as anti-bomb robots, particularly in the military. But Dallas is certainly the most high profile domestic policing use so far, and experts say it could have lasting implications. "If lethally equipped robots can be used in this situation, when else can they be used?"

While everyone keeps saying that robots are not a job security threat, it is also true that robots are steadily getting better at tasks. Enter this little bad boy, the robot that won Amazon's Picking Challenge. A team of engineers from Netherland's TU Del have won this year's challenge, both in the picking and stowing finals. They dubbed their creation "Delft." The cool thing is their robot is no ordinary warehouse bot; it relies on a suction cup, a "two-fingered" gripper, and the combination of deep learning artificial intelligence and depth-sensing cameras to get the job done.

Years ago, I was working on a project that needed to be adaptive. Essentially, the software needed to learn and get better at a frequently repeated task over time. I'd read about neural networks and some early success people had achieved with them, so I decided to try it out myself. That marked the beginning of a life-long fascination with AI. AI is a really big deal.