Over July 4th weekend in 1981, several hundred game nerds gathered at a banquet hall in San Mateo, California. Personal computing was still in its infancy, and the tournament was decidedly low-tech. Each match played out on a rectangular table filled with paper game pieces, and a March Madness-style tournament bracket hung on the wall. The game was called Traveller Trillion Credit Squadron, a role-playing pastime of baroque complexity. Contestants did battle using vast fleets of imaginary warships, each player guided by an equally imaginary trillion-dollar budget and a set of rules that spanned several printed volumes. If they won, they advanced to the next round of war games--until only one fleet remained. Doug Lenat, then a 29-year-old computer science professor at nearby Stanford University, was among the players. But he didn't compete alone. He entered the tournament alongside Eurisko, the artificially intelligent system he built as part of his academic research. Eurisko ran on dozens of machines inside Xerox PARC--the computer research lab just down the road from Stanford that gave rise to the graphical user interface, the laser printer, and so many other technologies that would come to define the future of computing. That year, Lenat taught Eurisko to play Traveller. Doug Lenat says his common-sense engine is a new dawn for AI. The rest of the tech world doesn't really agree with him.

In recent years, global business leaders have increasingly embraced the power of the cloud, with its 24/7 availability, scalable performance and variable costs. But the merger of these on-demand capabilities with artificial/machine intelligence (AI/MI) is a new phase in digital innovation that has not yet been sufficiently appreciated by senior management. New research from the Leading Edge Forum (LEF) shows how the combination of cloud computing and machine intelligence is shaping the business models of the future, marking a new phase in digital innovation. AI advances used to stem mostly from academic investigations and the expert system initiatives of large organisations, but there's been a fundmental shift in recent years and progress is now driven by the consumer marketplace. See also: The droids you're looking for: the AI tech that will make up the intelligent enterprise'Virtually every type of human intelligence is becoming the basis of a scalable, cloud-based, global business model.

Google is making it easier for companies to take advantage of the machine learning (ML) revolution with an offering that allows creation of custom machine learning models. The company on Wednesday showed off its new AI product that it is making available to folks outside of the Internet giant. With machine learning, applications are able to learn or adjust on their own without help from human developers. According to Tech Crunch, the search giant's chairman, Eric Schmidt said that Google believes machine learning is "what's next." With the new cloud service, the Internet giant will make it easier to employ some of the machine learning tech the company already uses to power features like Smart Reply in Inbox.

Google is hoping to push machine learning as a mainstream business tool, with today's release of a set of preview products that take advantage of its cloud platform. Cloud Machine Learning relies on the open-source TensorFlow library, released late last year. Google said developers can use the company's tools such as Cloud Dataflow, BigQuery, Cloud Dataproc, Cloud Storage and Datalab, to train its machine learning. However, it is also offering pre-trained machine learning models with application programming interfaces. The company has been able to leverage large amounts of user and customer data stored in the applications it offers for the pre-trained models.

It's well understood that machine learning is eating the software world, so it's no surprise to see tech startups like Cosmify and LodgIQ emerging from stealth today with plans to leverage big unstructured data for a competitive advantage. San Francisco-based Cosmify came out of stealth today with a new solution that uses machine learning to jumpstart knowledge discovery across a range of information sources. The company's solution is designed to scan, analyze, and visualize unstructured data sources, such as documents, tweets, user data, chat logs, and photographs. Machine learning algorithms create a model of the data sources, and then maps all relevant relationships between them based on individual words or properties, according to Cosmify. Users can then explore the model to find outliers, discover behavioral trends, and predict future results.

Maybe the machines won't take over, but Eric Schmidt, chairman of Google parent company Alphabet, thinks machine learning might. The combination of cloud, crowdsourced information, and machine learning "will be the basis for every fundamental and hugely successful IPO win in the next five years," he said during this morning's keynote at GCP NEXT, the developer conference for Google Cloud Platform. Schmidt is prone to sweeping statements. But having watched computing transform many times in 45 years -- he was a Sun Microsystems bigwig when the company launched Java -- he said he felt qualified to predict that machine learning could lead to truly new innovations, the kind that can't yet be envisioned. Google is now offering the technology to cloud customers in the form of Cloud Machine Learning, an alpha application launched today.

As artificial intelligence (AI) technologies such as the Amazon Echo and self-driving cars are hitting the market, they are poised to become an essential part of society. For people around the world, especially college students, this could mean some big changes. Once created, an AI can be used either internally or externally. Internal interfaces are located on a cloud and can access other devices and software that is used in the home such as a TV or smartphone. Technology like the Amazon Echo, which can access apps that are downloaded on a smart phone, is an example of this, but in comparison this technology is rudimentary compared to what others have developed.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Twitter started the trend of'People to Follow'. This later got replicated by other platforms such as Facebook, Linkedin, Quora and GitHub. This cool feature lets you connect with the rockstars of various domains and get an access to what is going on their end without bothering them much. For the influencers, this has become an effective way to communicate with their followers. The lives of people on GitHub doesn't appear to as tempting as you would observe on other platforms, but if you love coding, programming and data science, you'll surely enjoy the company of 9 million users on this platform!

IBM's Watson AI product is mostly rolled out live with machine learning halted to avoid "losing control" of its behaviour, Europe CTO Duncan Anderson has confirmed. Anderson said the idea of AI always learning and adjusting its behaviour is still something people are "a bit nervous about". "At the moment, you stop the learning before it goes live, so you don't get any surprises," Anderson told Computing at our Big Data & Analytics Summit 2016 in London. The aim, explained Anderson, is to "get a sensible kind of answer" from an AI in line with the business's expectations. He added that Watson's modular-based learning updates are now so advanced in specific areas of industry that it's now possible to sell "off the shelf" versions of the AI that can immediately get to grips with traditional tasks in a given area.