Our Top Picks from Cannes 2017 Film Feature Just Why Did King Arthur: Legend of the Sword Flop So Bad?

More than 80 years ago, a robot could be found in the unlikely surroundings of Kettering, directing traffic and smoking a cigarette. Today, the story of Robert the Robot is little known, even in the Northamptonshire town where he was once a celebrity. Yet in the 1930s, his fame reached as far as Czechoslovakia and the United States, where he even featured in Time magazine. And the reason he came to be? "Someone bet me £5 I could not make a robot in three weeks," inventor Charles Lawson, who had a radio shop, told a newspaper at the time. The electrical engineer's creation first hit headlines in the Northamptonshire Evening Telegraph, which proclaimed: "Kettering robot'enjoys' smoke."

Farming is, by far, the most mature industry mankind has created. Dating back to the dawn of civilization, farming has been refined, adjusted and adapted -- but never perfected. We, as a society, always worry over the future of farming. Today, we even apply terms usually reserved for the tech sector -- digital, IoT, AI and so on. So why are we worrying?

The continuing development of Semantic Web technologies and the increasing user adoption in the recent years have accelerated the progress incorporating explicit semantics with data on the Web. With the rapidly growing RDF (Resource Description Framework) data on the Semantic Web, processing large semantic graph data have become more challenging. Constructing a summary graph structure from the raw RDF can help obtain semantic type relations and reduce the computational complexity for graph processing purposes. In this paper, we addressed the problem of graph summarization in RDF graphs, and we proposed an approach for building summary graph structures automatically from RDF graph data. Moreover, we introduced a measure to help discover optimum class dissimilarity thresholds and an effective method to discover the type classes automatically. In future work, we plan to investigate further improvement options on the scalability of the proposed method.

When on the subject of successful leaders, it is evident that the how and why of success help derive inspiration. Forbes has come up with its second list of game changers--those who are not happy with the status quo and are transforming their industries by making a difference to the lives of their consumers. The youngest game changers are two contenders--Evan Spiegel, 26-year-old CEO of Snap, and Stripe cofounders John (26) and Patrick Collison (28). The oldest is 75–year-old Christo Wiese, chairman of South Africa-based retail company Steinhoff. These are the innovative trailblazers who are redefining and reimagining the lives of people, and taking the requisite action to bring that vision to life. Forbes embarked on this initiative to acknowledge not just typical corporate achievements such as profits and growth, which are largely limited to employees and shareholders, but also the flair for making a true difference to the larger society.

Today, only 25% of rare diseases are accurately diagnosed1. With SOPHiA's applications for exome analysis, this is set to change. The exome is the protein-coding region of the human genome, which represents just 1% of the genome but contains approximately 85% of known disease-causing genetic variants2. SOPHiA takes exome sequencing to new heights, allowing for unmatched analytical performances to detect, annotate and pre-classify disease-related genetic variants over all protein-coding regions of the human genome. Exome sequencing generates large amounts of sequenced data.

Genetic mutations already in the population today may make the X-men movies seem less like science fiction than you think. It turns out that there are some genetic mutations that seemingly give some people superhuman abilities. For example, some people have a very rare genetic mutation that makes muscle cells grow bigger and divide more than usual, resulting in a condition where people, and even children, can look like body builders. A gene mutation is a permanent alteration in the DNA sequence that makes up a gene, such that the sequence differs from what is found in most people. Mutations can be beneficial, harmful or neutral depending on their context and location.

San Francisco: Jensen Huang, co-founder, president and chief executive officer of Santa Clara-based Nvidia Corp., says that the rapid adoption of artificial intelligence (AI) technologies such as machine learning, deep learning, natural language processing and computer vision augur well for the growth prospects of his company. His confidence stems from the fact that Nvidia designs the chips that can deliver the extra computing power that clients need in an algorithm-driven world, which is increasingly using these AI technologies to make business sense of the voluminous data that users generate and thus gain a competitive edge. These chips, called graphics processing units (GPUs), helped Nvidia fuel the growth of the personal computer gaming market almost two decades back. Huang hopes the increasing use of GPUs for AI will help his company repeat the success. Huang argues that even when you increase the number of central processing unit transistors in a computer, they result in a small increase in application performance, whereas GPUs, which are specifically designed to handle multiple tasks simultaneously, make them more suitable for high-performance computing tasks.

ARM is unveiling its first Dynamiq processor designs today, and the company said that the family will boost artificial intelligence performance by more than 50 times over the next three to five years. The new family aims to spread AI processing from the edge to the cloud. The processors include the ARM Cortex-A75, which delivers massive single-thread compute performance at the high end; the ARM Cortex-A55, a high-efficiency processor; and the ARM Mali-G72 graphics processor, which expands the possibilities for virtual reality, gaming, and machine learning on premium mobile devices, with 40 percent more graphics performance. ARM's partners are expected to launch chips in 2018. To better handle AI processing, ARM realized that it needs to make basic changes to the computing architecture, with faster, more efficient, and distributed intelligence between computing at the edge of the network (like in smartphones and laptops) and in the cloud-connected data centers, said Nandan Nayampally, vice president and general manager of the Compute Products Group at ARM, in a blog post. That AI technology also needs to be secure, as recent survey data shows 85 percent of global consumers are concerned about securing AI technology, Nayampally said.

Designed to predict and map potential crimes, Solution House Software has announced the launch of their new artificial intelligence (AI) module for Incident Desk. Solution House Software director, Tiaan Janse van Rensburg, says the Incident Desk Predictive Analysis module will initially focus solely on crime, but future versions may be expanded to include other industries such as facility management and maintenance. The narrow focus according to Janse van Rensburg will "allows us to achieve better results and hone the AI engine even further to apply it to other incident management areas." Incident Desk uses a multi-tenant model which combines numerous customer areas, properties and buildings into one solution. This includes urban areas, central improvement districts, neighbourhood watch initiatives, estates, shopping malls and even schools.