Engineers at CTA.ai, an imaging-technology start-up in Poland, are trying to popularize a more comfortable alternative to the colonoscopy. To do so, they are using computer chips that are best known to video game fans. The chips are made by the Silicon Valley company Nvidia. Its technology can help sift speedily through images taken by pill-size sensors that patients swallow, allowing doctors to detect intestinal disorders 70 percent faster than if they pored over videos. As a result, procedures cost less and diagnoses are more accurate, said Mateusz Marmolowski, CTA's chief executive.

In essence, people who interact with advanced technology want to be able to trust it appropriately, and then act on that trust. In interpersonal relationships, and otherwise, humans act largely based on trust. For example, a supervisor asks a subordinate to accomplish a task based on several factors that indicate they can trust them to accomplish that task. When consumers make purchases, they do so with trust that the product will perform as promised. Likewise, when using something like an autonomous vehicle, the user must be able to trust it appropriately in order to use it properly. With the rapid advancement of the capabilities of intelligent computing technology to do tasks that were previously assumed to be too complicated for computers, there has been much recent discussion regarding how humans can trust this technology - although the connection to trust is not always made explicit, per se.

Today's artificial assistants are typically prompted to perform tasks through direct, imperative commands such as \emph{Set a timer} or \emph{Pick up the box}. However, to progress toward more natural exchanges between humans and these assistants, it is important to understand the way non-imperative utterances can indirectly elicit action of an addressee. In this paper, we investigate command types in the setting of a grounded, collaborative game. We focus on a less understood family of utterances for eliciting agent action, locatives like \emph{The chair is in the other room}, and demonstrate how these utterances indirectly command in specific game state contexts. Our work shows that models with domain-specific grounding can effectively realize the pragmatic reasoning that is necessary for more robust natural language interaction.

The concept of virtual assistance has been in consumers' mind for more than half a century, first introduced through Sci-Fi movies such as 2001: A Space Odyssey (1968) featuring virtual assistant HAL and infamous quote "I'm sorry Dave, I'm afraid I can't do that." Portrayed as a must-have feature by the consumer tech industry, our research at Ovum shows that digital assistants, in fact, fall short of consumer expectations and can't do what they want. A survey conducted by Ovum in the UK and the US in May 2017 revealed that more than 50 percent of consumers don't find digital assistants useful (see Figure 1). The digital assistant population will equal today's human population in 2021. Instead, Google Assistant will dominate the native (i.e.

New research from Accenture shows that £218 million has been invested in insurance technology startups in the first half of 2017 – up from £7.8 million the previous year. This represents an increase of over 2500% in year-over-year figures. Investment in insurance technology startups, or insurtech, have been bolstered by some recent, extremely large investments including the £180 million invested in Gryphon, an insurtech startup focused on critical illness and income protection. Just last week, Coya, a Berlin-based digital insurance provider founded in 2016 managed to raise $10 million (approx £7.7 million) from a variety of investors from Silicon Valley and Europe. Various new technologies are being applied to the insurance industry, which has historically been largely administrative and paper intensive.

Machine learning has already extended into so many aspects of daily life that it can be handy for us to memorize a set of go-to examples of its impact on certain industries. For instance, we might think of fraud detection as the canonical example of machine learning in the financial sector. Or we might think of Watson's cognitive approach to oncology as the canonical example of machine learning in healthcare. Or, yet again, we might point to recommendation engines at Netflix and Amazon as canonical examples of machine learning in retail. If you are interested to try out new IBM's Watson Machine Learning service - click here To try the Data Science Experience - subscribe for the free trial Certainly, those are tremendous demonstrations of the power of the technology -- and in aggregate, they give a sense of machine learning's pervasive presence in our lives.

Daimler has announced its self-driving'friendly' electric Smart car, geared toward urban ride sharing, will debut at next month's Frankfurt Motor Show in Germany. The car, dubbed the Smart Vision EQ ForTwo, is a fully-autonomous vehicle with no steering wheel or pedals that riders can hail with an app, much like an Uber taxi. Passengers can then pick fellow riders based on their mutual interests, and the car will even suggest topics of conversation to stop things getting awkward. The outside of the car sports a panel on the front that displays welcome messages, destinations, who it is picking up and even warnings to other cars and pedestrians. Daimler has announced its self-driving electric Smart car (pictured), geared toward urban car sharing, will debut at next month's Frankfurt Motor Show in Germany The vehicle's side doors open upwards over the rear axle and will display news and weather updates.

The rapid improvements in technology have transformed how we work and in some cases made our jobs easier, but the change is also having another effect – removing the need for a human worker altogether. The technology boom is showing no sign of slowing either. A study carried out by Deloitte, the Big Four accountancy firm, and the University of Oxford in 2014 predicted that 35% of the jobs we do today in the UK could go to robots by 2034. Here are some jobs from yesteryear that have already gone and some we are starting to see being phased out. The days of manual telephone switchboards which required a switchboard operator to physically connect calls wire to wire, are well over. An operator would have to insert phone plugs into the relevant jacks, and would also be able to participate in the call.

If you ask the internet to name the world's toughest car, you get the same answer every time: the Toyota Hilux. It is utterly unyielding: there are endless tales of the lengths people will go to in trying to destroy it, most famously by Top Gear. In a breathless example of vehicle cruelty, Clarkson and chums took an elderly 1988 model with 200,000 miles on the clock and tried to batter it to death. They dropped it, drowned it, crashed it into a tree and an office, flattened it with a caravan and then hit it with a wrecking ball. That legendary sturdiness has seen it driven to both the North and South poles – and all points in between.

Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. In the movie "2001" I found the scariest moment was when astronauts David Bowman and Frank Poole met in the EVA pod to discuss the artificially intelligent HAL 9000 computer's behavior -- and HAL reads their lips. In the paper Lip Reading Sentences in the Wild, researchers Joon Son Chung, of Oxford University, Andrew Senior, Oriol Vinyals, and Andrew Zisserman, of Google, tested an algorithm that bested professional human lip readers. Soon, surveillance videos may not only show your actions, but the content of your speech.