Gartner predicts that by 2018 a full 30 percent of our interactions with technology will be through "conversations" with smart machines. Major players such as Microsoft, Facebook and Google are now focused on empowering development of smart bots, and IBM's Watson is looking to find ways to integrate its cognitive computing platform deeper into businesses. We are being inundated with conversational tools and we're seeing a considerable, but not entirely surprising, shift of operations to this new way of human-machine interaction. Increasingly, we are depending on these systems for both routine daily tasks and sophisticated business interactions. However, we have already seen these new tools stumble.

It's been just about six months since IBM closed its acquisition of The Weather Company, but it's not resting on its laurels. This week Big Blue moved to leverage The Weather Company's go-to-market strength to launch Deep Thunder, a machine learning-driven weather model developed by IBM Research to help industries ranging from aviation and agriculture to retail better predict the business impact of weather. "One of the greatest things about being part of IBM is having a relationship with IBM's Research arm," says Mary Glackin, head of Science & Forecast Operations for The Weather Company. The Weather Company is actually merging its existing Rapid Precision Mesoscale (RPM) model -- a numerical weather prediction system based on the Advanced Research Weather Research and Forecast System (WRF-ARW) -- with Deep Thunder. RPM generates forecasts up to 24 hours ahead, with updates every three hours in the U.S. and every six hours outside the U.S. Precipitation forecasts are calculated from half-hourly instantaneous precipitation forecasts provided by RPM.

Amazon Web Services is increasingly gaining industry attention, and the company's recent hire of Alex Smola to lead the artificial intelligence initiative for AWS is expanding even more its infrastructure as a service cloud offerings. While Amazon Web Services has been around since 2006 its customer base varies widely from that of large cloud leaders Microsoft (NASDAQ:MSFT) and IBM (NYSE:IBM). Additionally, a large part of the Amazon Web Services business includes its own web business Amazon.com In the first quarter, Amazon reported sales of 29.13 billion with sales from Amazon Web Services accounting for 2.57 billion. The majority of AWS customers are startups seeking low-cost solutions without the burden of a full suite of enterprise infrastructure.

Beleaguered enterprises are struggling to keep pace with cyber threats, and small and medium-sized businesses are hit hardest of all due to limited resources. A recent survey by the Federation of Small Business (FSB) found 66% of those questioned had been a victim of cybercrime over the past two years, and only 4% had an incident response plan in place in anticipation of an attack. For many, cyber security takes them into unfamiliar territory and depletes the time spent on core business activities. This has seen an over-reliance upon point solutions, poor attention to patching and updates, and a failure to apply strategic business-specific security controls. To make matters worse, the potential attack surface is only set to widen as the Internet of Things sees sensors and IP-enabled tech insinuate themselves into every niche of society, even the small business.

We know we have a problem, but will AI be the solution or an added burden? "A recent survey by the Federation of Small Business (FSB) found 66% of those questioned had been a victim of cybercrime over the past two years, and only 4% had an incident response plan in place in anticipation of an attack."

Intel's strategy for tackling the AI CPU market, where it is facing competition from leading GPU makers and potentially also big customers that make their own specialized processors for this purpose, such as Google, rests to a great extent on designing systems that scale out rather than up. The latter, according to the chipmaker, is the conventional but inefficient approach to architecting these systems. Software code in today's machine learning systems (machine learning is one of the most active subfields in the development of artificial intelligence) is tough to scale and usually lives in a single box, Charles Wuischpard, VP of the Intel Data Center Group and general manager of the giant's HPC Platform Group, said. Companies generally buy high-power scale-up systems filled with GPUs. "In a way, there's an efficiency loss here," he said on a call with reporters last week.

As the driverless car gets closer to reality, so too does the dilemma of how to insure the car and its owner. The auto insurance industry faces upheaval in the next 25 years as the migration to autonomous safety features -- and ultimately a self-driving car -- shifts more of a car's accident risk from the driver to the vehicle, analysts said. The number of accidents is expected to drop sharply because currently more than 90% of accidents are caused by driver error. The U.S. market for personal auto insurance policies, which currently generates 200 billion in premiums a year, could shrink substantially, some experts predict. "There are going to be dramatic changes," said Joe Schneider, a managing director at KPMG who's part of the accounting firm's task force studying the issue.

In the midst of a continuing refugee crisis across much of the globe, June 20 marks World Refugee Day, an occasion that seeks to raise awareness for the plight of refugees across the world. And thanks to a partnership between Blippar, the augmented reality and artificial intelligence company, and UN refugee agency UNHCR, that awareness is going digital. Currently, the Blippar app allows people to "blipp," or scan objects they want to learn more about, thereby accessing informational or entertaining content about the world around them. As part of the new campaign, Blippar is asking users to show their support for refugees by blipping their hands. This scan of support translates into an instant signature on the UNHCR #WithRefugees petition, which requests that national governments act with solidarity and shared responsibility when it comes to the migrant crisis.

You'll find the Future of Humanity Institute down a medieval backstreet in the centre of Oxford. It is beside St Ebbe's church, which has stood on this site since 1005, and above a Pure Gym, which opened in April. The institute, a research faculty of Oxford University, was established a decade ago to ask the very biggest questions on our behalf. Notably: what exactly are the "existential risks" that threaten the future of our species; how do we measure them; and what can we do to prevent them? Or to put it another way: in a world of multiple fears, what precisely should we be most terrified of? When I arrive to meet the director of the institute, Professor Nick Bostrom, a bed is being delivered to the second-floor office. Existential risk is a round-the-clock kind of operation; it sleeps fitfully, if at all. Bostrom, a 43-year-old Swedish-born philosopher, has lately acquired something of the status of prophet of doom among those currently doing most to shape our civilisation: the tech billionaires of Silicon Valley.

GE unveiled a machine data system for power plants it claims can increase a facility's efficiency of operation by 1.5%, reduce carbon dioxide emissions by 3%, and reduce coal consumption by 67,000 tons for each megawatt of electricity produced. GE's Digital Power Plant for Steam suite was introduced at the Minds Machines conference in Paris June 14, where GE executives also revealed the results of a hardware and software upgrade using the technology at the Chivasso power plant in Northern Italy. The plant, run by A2A Group, was restarted in November 2015 after a three-year shutdown. Digital Power Plant for Steam is one of the first application suites to sit atop GE's Predix machine data analytics platform and yield practical, industrial results. The reference to "steam" in the product's name reflects the fact that gas and coal-fired power plants produce steam to drive the turbines that generate electricity used in households and industry in most societies.