The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Another year, another swath of technology announcements, celebrity appearances and video game reveals at the annual Electronic Entertainment Expo. The world's largest video game trade show hosted its 22nd event Tuesday through Thursday at the Los Angeles Convention Center. Throughout the week, major video game publishers such as Microsoft, Sony and Ubisoft held press conferences to reveal their upcoming projects while more than 70,000 investors, notable gamers and members of the press roamed the show floors to play exclusive game demos and meet with industry leaders. In addition to dozens of video game title announcements, E3 2016 highlighted several shifts in direction for the gaming industry and was host to a number of surprises, good and bad. Here's what you need to know: The average shelf life for video gaming consoles is around six years.

In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various other data analyses. In this section want to recommend a way for installing the required Python-packages packages if you have not done so, yet. Otherwise you can skip this part. Although they can be installed step-by-step "manually", but I highly recommend you to take a look at the Anaconda Python distribution for scientific computing. Anaconda is distributed by Continuum Analytics, but it is completely free and includes more than 195 packages for science and data analysis as of today.

Let's assume we are really into mountain climbing, and to add a little extra challenge, we cover eyes this time so that we can't see where we are and when we accomplished our "objective," that is, reaching the top of the mountain. Since we can't see the path upfront, we let our intuition guide us: assuming that the mountain top is the "highest" point of the mountain, we think that the steepest path leads us to the top most efficiently. We approach this challenge by iteratively "feeling" around you and taking a step into the direction of the steepest ascent -- let's call it "gradient ascent." But what do we do if we reach a point where we can't ascent any further? I.e., each direction leads downwards?

Local Motors, the company behind the first 3-D printed car, just debuted its first self-driving vehicle. The shuttle-like vehicle, called Olli, can fit up to 12 passengers and is also the first autonomous vehicle to use Watson -- IBM's machine-learning platform -- to communicate with passengers. That means Olli can respond to voice commands, like "Hey, Olli, please take me downtown," and also answers questions using IBM's speech-to-text, natural-language classifier, entity extraction and text-to-speech technology. The Watson-powered shuttle will even be able to recommend destinations to its passengers. And so begins the transition from human bus drivers to robot bus drivers.

Humans have weighed the pros and cons of robots for decades. On the one hand, they automate processes that can be tedious and slow for humans. On the other hand, they do jobs humans might otherwise be paid for. But the likelihood you'll lose your job to a robot at some point in your life is slim, right? Well, Foxconn, the largest contract electronics manufacturer in the world, announced last month that it automated 60,000 jobs in one of its factories, replacing human workers with robots.

Self-driving vehicles are the Holy Grail of the transportation technology of the next age. Big companies like Google, Tesla, General Motors, and even the US Government are accelerating research into the field. But it seems like a small startup beat them to the punch. Arizona-based startup Local Motors has just revealed a 3D-printed, autonomous, electric shuttle bus that is partially recyclable. The electric vehicle, which can carry up to 12 people, is equipped with IBM Watson Internet of Things (IoT) for Automotive, which is IBM's car-focused cognitive learning platform.

SugarCRM has put AI at the core of its product plans and is working on a new intelligence service along with a Siri-like agent named Candace. Tapping the company's recent acquisitions of Stitch and Contastic, the new technology will be designed to help businesses spend less time entering data into their customer relationship management software and more time learning from and acting upon it. SugarCRM is scheduled to demonstrate the new capabilities Wednesday at its SugarCon conference in San Francisco. Nominate your analytics project today! "In the CRM space, we want people to focus on what they're good at: relating to others, such as customers and partners," Rich Green, SugarCRM's chief product officer, said in an interview last week.

Do join our debate chaired by Will Hutton, author of'How Good We Can Be', on The Future with AI: Will it be good for us? The UK has a wealth of capability in AI techniques and their application, but a future with AI raises many questions. Will AI really take our jobs or augment them? Will AI lead to the concentration of power into a small number of hands? Which is our biggest worry – smart machines or dumb machines?

Roger Schank, an experienced computer and cognitive scientist with long experience in artificial intelligence research, is continually offended when media present simple tools like chatbots as examples of AI. "Key word analysis that enables responses previously written by people to be found and printed out, is not AI," as Schank sees it. And he complains, "We are in a situation where machine learning is not about learning at all, but about massive matching capabilities to produce canned responses." Schank worries that a bubble of hype about AI will lead, as it has in the past, to an "AI winter" -- when disillusionment from unfulfilled expectations causes interest and research funding in AI to dry up. Given the breadth of investment now in business, military, and consumer AI applications, perhaps this time may be different. Which is not a minor problem.