At a recent gathering of startup founders at his home in Silicon Valley, Larry Ellison, Oracle's indefatigable executive chairman, CTO, and cofounder, had some fun with the wacky notion that the cloud platform, service, and infrastructure giant had somehow become a "dinosaur." Ellison told his guests that making Oracle ever-more successful and proving the naysayers wrong are always top of mind, joking that he thought about bidding on the giant Tyrannosaurus Rex fossil exhibit in the American Museum of Natural History and displaying it in the Oracle lobby. But a very dangerous dinosaur," he said. At Oracle OpenWorld this month, Oracle executives, partners, and customers revealed just how dangerous an innovator the company continues to be. They laid out cutting-edge products and services powered by a range of emerging technologies, including machine learning, predictive analytics, blockchain, and the Internet of Things. What follows are just six highlights. Calling machine learning a "technology that's every bit as revolutionary as the internet," Ellison described how Oracle is building machine learning-based databases and applications that get smarter as they take in more and more data. Machine learning is based on algorithms that can learn from data without rules-based programming. "The more data we have these computer systems look at, we say we're'training' the computer system," Ellison said during his second keynote at Oracle OpenWorld, on October 3. "And as the computers begin to identify patterns in the data, identify abnormalities in the data, they then can make predictions.

Late in 2015, Gilberto Titericz, an electrical engineer at Brazil's state oil company Petrobras, told his boss he planned to resign, after seven years maintaining sensors and other hardware in oil plants. By devoting hundreds of hours of leisure time to the obscure world of competitive data analysis, Titericz had recently become the world's top-ranked data scientist, by one reckoning. "Only when I wanted to quit did they realize they had the number-one data scientist," he says. Petrobras held on to its champ for a time by moving Titericz into a position that used his data skills. But since topping the rankings that October he'd received a stream of emails from recruiters around the globe, including representatives of Tesla and Google.

IBM Watson has partnered with two of Sweden's leading law firms, MAQS and Lindahl, as well as legal knowledge management consultancy, VQ, to build an AI-driven contract review and advice system. The new AI tool, which is called True Agreement, is at present focused on Swedish shareholder agreements and has been trained to identify the type of document, find key clauses and then provide advice on aspects of those clauses as they are surfaced by IBM Watson's natural language processing capability. The new venture was revealed at the VQ forum event in Stockholm, yesterday and is the first joint venture legal AI project of its kind in Scandinavia. Also, MAQS revealed that it is now working with UK legal AI company, Luminance, becoming one of several law firms in the region now making use of the due diligence-focused AI company's platform. MAQS Knowledge Manager, Hans Hedkvist told Artificial Lawyer: 'The idea came about jointly between myself, the head of IT at Lindahl and Helena Hallgarn of VQ.' 'We saw that AI was coming and we wanted to do something in Swedish.

HPE announced new purpose-built platforms and services capabilities to help companies simplify the adoption of Artificial Intelligence, with an initial focus on a key subset of AI known as deep learning. Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of deep learning, enterprises need a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data. Many organizations lack several integral requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems. Based on the HPE Apollo 6500 system in collaboration with Bright Computing to enable rapid deep learning application development, this solution includes pre-configured deep learning software frameworks, libraries, automated software updates and cluster management optimized for deep learning and supports NVIDIA Tesla V100 GPUs.

In addition, the Internet Society was asked to send written comments, which are reprinted here. AI is not new, nor is it magic. "Intelligent" technology is already everywhere – such as spam filters or systems used by banks to monitor unusual activity and detect fraud – and it has been for some time. What is new and creating a lot of interest from governments stems from recent successes in a subfield of AI known as "machine learning," which has spurred the rapid deployment of AI into new fields and applications. It is the result of a potent mix of data availability, increased computer power and algorithmic innovation that, if well harnessed, could double economic growth rates by 2035. So, governments' reflection on what good policies should look like in this field is both relevant and timely.

You can't really give a conference keynote in 2017 without staking some sort of claim on AI, so Databricks was smart to keep its credibility, and let its geeky co-founder and CTO Matei Zaharia officially open the Spark Summit Europe 2017 yesterday. Instead, Zaharia spoke about streaming data and deep learning -- the engineers and developers in the room ate it up. The conference, organized by Databricks, creators of Apache Spark, brought more than 1200 enthusiasts to Dublin, Ireland this week to learn about what new features and functions will be added to the open source project. The short answer, according to Zaharia is cost based optimization, Python and R improvements, Kubernetes support and more. But that's not what the C-Suite executives whose companies leverage Databricks enterprise-ready, feature-filled version of Spark wanted to know about.

Researchers at the University of Helsinki and the Helsinki University Hospital (HUH), Finland, have developed software based on machine learning, which can independently interpret EEG signals from a premature infant and generate an estimate of the brain's functional maturity. Published in the journal Scientific Reports, the method is the first EEG-based brain maturity evaluation system in the world. It is more precise than other currently understood methods of evaluating the development of an infant's brain, and enables the automatic and objective monitoring of a premature infant's brain development. "We currently track the development of an infant's weight, height and head circumference with growth charts. EEG monitoring combined with automatic analysis provides a practical tool for the monitoring of the neurological development of preterm infants and generates information which will help plan the best possible care for the individual child," says Professor Sampsa Vanhatalo from the University of Helsinki, who led the research.

Connections between nodes of fully connected neural networks are usually represented by weight matrices. In this article, functional transfer matrices are introduced as alternatives to the weight matrices: Instead of using real weights, a functional transfer matrix uses real functions with trainable parameters to represent connections between nodes. Multiple functional transfer matrices are then stacked together with bias vectors and activations to form deep functional transfer neural networks. These neural networks can be trained within the framework of back-propagation, based on a revision of the delta rules and the error transmission rule for functional connections. In experiments, it is demonstrated that the revised rules can be used to train a range of functional connections: 20 different functions are applied to neural networks with up to 10 hidden layers, and most of them gain high test accuracies on the MNIST database. It is also demonstrated that a functional transfer matrix with a memory function can roughly memorise a non-cyclical sequence of 400 digits.

Reading articles about artificial intelligence (AI) often leaves you with that'late to a mixer' feeling: everybody else looks comfortably established, and there's no way I'm ever going to be able to establish a foothold, I'll just grab a few salmon puffs and go home. Don't despair, because despite how it seems, studies show that your competition are not the AI whizzes that they appear. Only 15% of companies are using AI in their customer service strategies, according to the results of a TATA consultancy services survey published in April this year. Slow and steady will not win the AI race, experts warn! Access-AI have tapped some of the finest minds in the tech industry, and here you can read what they had to say about what's causing some of the biggest businesses around to drag their heels: At this point I think it has become a very common truth that any CIO or CTO candidate who would not have one of their first planks as integrating ML and AI would not get the job.

Russia has been hit by a wave of reports of a giant UFO in the sky last night with spectacular pictures of an enormous glowing ball illuminating northern Siberia. Social media erupted with claims of'aliens arriving' and locals in far flung parts of the country told of'shivers down their spines'. While the source of the light remains unclear, some have suggested that it was the the trace of a rocket launched by the Russian military that caused this extraordinary phenomenon in the night sky. While the source of the light remains unknown, local experts suggest there were two possible reasons for the eerie spectacle in the Siberian night sky. The first was that a vivid display of the Northern Lights - or Aurora Borealis - was underway.