CHICAGO – August 10, 2017 – Seyfarth Shaw LLP in connection with its subsidiary SeyfarthLean Consulting announced today that it is one of the first law firms to select iManage Extract. Seyfarth will use the Artificial Intelligence (AI) platform to perform document review and comparison across all practice areas. Seyfarth will utilize an enhanced feature of the product to train and have complete control of the robot. Using the self-service portal will allow the firm to speed up the data extraction process, increasing productivity within the organization. To read more, visit the full press release here.

Golfers wanting to shoot below par or tennis players looking to smash their way past opponents should focus on their backswing, a new study suggests. Scientists assessed the speed at which people learned the basic skills in both golf and tennis, and found that those with consistent backswings perfected their techniques twice as quickly. Tennis star Roger Federer and golfer Rory McIlroy have climbed to the top of their game partly due to their powerful - and consistent - lead-in movement. Tennis players who were able to perform consistent lead-in motions were able to perfect their techniques twice as quickly as those who couldn't. The study demonstrates that any immediately preceding movement needs to be consistent to achieve fast learning.

It's clear that technology now plays a central role in every child's life and their expectations on how they will use innovative technology when they enter the workplace are extremely high Most businesses and business leaders realise that technology is the future. It will help improve almost every aspect of people's lives and of course, impact business processes. It appears that this belief is shared by the majority of teenagers, those who have grown up with the innovation created by technological breakthroughs: think smartphones and iPads. In a survey of 1,000 12-15 years olds based in the UK, they revealed what they think they'll be using when they enter the workplace in the next 10-15 years. What's more, just over a quarter (26%) of youngsters think commercial space travel will be a thing by 2032, and over one in five (22%) think they'll be using computers linked to their brains.

Machine learning and AI are expected to be big with retailers in the coming year, as more than 86% of CMO's say they are planning to invest in it in 2017, according to a study by Persado that was emailed to Retail Dive. Almost half (47%) of respondents said they plan to invest up to $50 million in the technology, while a quarter plan to spend over $100 million on it. Effective customer engagement topped the list of benefits that marketers hope to receive from an investment in AI, where engagement "is considered foremost as a driver of revenue (37%) and to a much lesser extent, brand awareness (13%)," the report states. Despite the possibility of using technology to drive personalization, only 22% of those surveyed felt skilled at emotionally engaging customers through their content, something the researchers believe might be a result of data collection that is limited to demographic and location data. There's no question that AI is a growing field for retailers.

Tesla is close to testing a prototype driverless electric truck in California and Nevada, leaked emails have revealed. The long haul semi-trucks will drive themselves and move in'platoons' that automatically follow a lead vehicle. The plans were revealed in a discussion of potential road tests between Elon Musk's firm and the Nevada Department of Motor Vehicles (DMV). California officials are also meeting with Tesla on Wednesday'to talk about Tesla's efforts with autonomous trucks,' DMV spokeswoman Jessica Gonzalez told Reuters. Tesla is close to testing a prototype autonomous electric truck (teaser image pictured), leaked emails have revealed.

About this course: If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.

I've been following cybersecurity startups and hackers for years, and I suddenly discovered how hackers are always ahead of the rest of us -- they have a better business model funding them in their proof of concept (POC) stage of development. To even begin protecting ourselves from their well-funded advances and attacks, cyberdefense and artificial intelligence (AI) technologies must be funded at the same level in the POC stage. Today, however, traditional investors not only want your technology running, they also need assurances that you already have a revenue stream -- which stifles potential new technology discovery at the POC level. And in some industries, this is dangerous. Consider the fast-paced world of cybersecurity, in which companies are offered traditional funding avenues as they promote their product's tech capabilities so people will invest.

As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, Matrix-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb? I would take the fame, I suppose, but perhaps the critics are right.

The complexity facing business has never been greater. Information pours into databases at unprecedented speed and from sources unimaginable even just a few years ago. Information on customer sentiment, employee performance, market movements, work-in-progress status, financial positions, project completion, and countless other sources is about to be dwarfed by the data generated by the Internet of Things (IoT). There's gold in this data, but most businesses don't have the tools to extract it. In their 2016 Big Data Dilemma report, members of the U.K. House of Commons Science and Technology Committee wrote: "Despite data-driven companies being 10% more productive than those that do not operationalize their data, most companies estimate they are analyzing just 12% of their data."

Motivation: The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects--such as a gene and a disease--can be related in different ways, for example, directly via gene-disease associations or indirectly via functional annotations, chemicals and pathways. Different ways of relating these objects carry different semantic meanings. However, traditional methods disregard these semantics and thus cannot fully exploit their value in data modeling. Results: We present Medusa, an approach to detect size-k modules of objects that, taken together, appear most significant to another set of objects. Medusa operates on large-scale collections of heterogeneous data sets and explicitly distinguishes between diverse data semantics. It advances research along two dimensions: it builds on collective matrix factorization to derive different semantics, and it formulates the growing of the modules as a submodular optimization program. Medusa is flexible in choosing or combining semantic meanings and provides theoretical guarantees about detection quality. In a systematic study on 310 complex diseases, we show the effectiveness of Medusa in associating genes with diseases and detecting disease modules. We demonstrate that in predicting gene-disease associations Medusa compares favorably to methods that ignore diverse semantic meanings. We find that the utility of different semantics depends on disease categories and that, overall, Medusa recovers disease modules more accurately when combining different semantics.