With the proliferation of data and technology -- which has led to improvements in artificial intelligence and natural language interpretation -- it's now safe to assume we are rapidly entering the age of the chatbots. When we're conversing with a company online, we're quickly getting to the point where we don't know if we're talking to a human or a machine. Recently, I wanted to find out why my cell phone bill was going up. I figured online chat was the best way to see if my supplier could help. It started out fine, albeit a little fishy. I hope you are having a great day!

Artificial intelligence (AI) - and robotics - has been around for a long time but it has not yet disrupted white collar work very much yet - or has it? And surely our sales profession is safe - no robot could ever sell - or could it? As it turns out AI development has accelerated in recent year and it is now contributing considerably to the sales world and influencing significant change. I asked Tony Hughes to explain how and asked him for his futuristic view of what AI and robotics will mean to us in the sales world - and how soon can we expect it to impact us? He said that the impact of AI on sales was already significant - so much so that within only 4 years (2020) 85% of business to business (B2B) sales transactions will occur without human intervention resulting in the redundancy of about 25% of current B2B sales roles in that time.

"The tagline is, Do you want to be a machine learning ninja?" says Christine Robson, a product manager for Google's internal machine learning efforts, who helps administer the program. "So we invite folks from around Google to come and spend six months embedded with the machine learning team, sitting right next to a mentor, working on machine learning for six months, doing some project, getting it launched and learning a lot." For Holgate, who came to Google almost four years ago ago with a degree in computer science and math, it's a chance to master the hottest paradigm of the software world: using learning algorithms ("learners") and tons of data to "teach" software to accomplish its tasks. For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by "neural nets" that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some cases, super humans.

When we see two people meet, we can often predict what happens next: a handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. This week researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before.

The U.S. robotics expert tapped to head Toyota's Silicon Valley research company says the 1 billion investment by the giant Japanese automaker will start showing results within five years. Gill Pratt told reporters that the Toyota Research Institute is also looking ahead into the distant future when there will be cars that anyone, including children and the elderly, can ride in on their own, as well as robots that help out in homes. Pratt, a former program manager at the U.S. military's Defense Advanced Research Projects Agency, joined Toyota Motor Corp. first as a technical adviser when it set up its artificial intelligence research effort at Stanford University and MIT. He said safety features will be the first types of AI applications to appear in Toyota vehicles. Such features are already offered on some models now being sold, such as sensors that help cars brake or warn drivers before a possible crash, and cars that drive themselves automatically into parking spaces or on certain roads.

But there are other'fashion-genic' people as well, living in this world, who take their dressing more seriously than Mark Zuckerberg (sorry Mark;-)). After all, "your dressing sense is the first thing anyone can see and it's the first impression that counts right?". Just to assist and improve the quest of such fashion lovers, we have created the most insane AI engine ever. You or your friend may have missed what Tom Cruise wore in MI5 in its famous aeroplane shot (albeit the scene was meant to enjoy the action and not fashion), but this AI once trained will never miss such fine detail. I am sure it might be bugging you to not know the fine details that go behind this awesome engine; I mean how can a dumb computer recommend a person's jewelry, shoes or even jeans with a fashion sense rivaling that of a fashion curator?

On June 28, Brian Eno will launch a new video experience for the title track of his latest album The Ship, which was released in April. What's different about this music video is, according to Eno, it isn't really a music video at all, but rather a visual experience informed by and created with artificial intelligence. "Just as I'm excited about the possibilities of artificial intelligence and new technologies, I'm so incredibly and numbingly bored with videos and the traditional music videos, that I just couldn't imagine wanting to do that," said Eno, on stage at Cannes Lions. "So really, this is an attempt to say, is there some other way we can do this thing?" The legendary artist and producer said that he's interested in finding out what new technologies can do, primarily because they so often can do something nobody ever thought they could do.

Here we'll take a look at the basic things you need to know in regards to search engine optimisation, a discipline that everyone in your organisation should at least be aware of, if not have a decent technical understanding.

Robots will learn the same way as AI algorithms, through iteration and exploration. Unlike software, however, a robot that's trying new things can actually kill someone. To prevent negative side effects, researchers need to penalize unwanted changes to the environment, while still allowing a robot some leeway to explore and learn. For instance, if a bot is focused just on cleaning, it may "engage in major disruptions of the broader environment [like breaking a wall] if doing so provides even a tiny advantage for the task at hand." To solve that, it proposes solutions like simulated and constrained exploration, human oversight, and goals that heavily weigh risk.