A study by Deloitte has suggested that technology is already leading to job losses in the UK legal sector, and some 114,000 jobs could be automated within 20 years. Professor Richard Susskind, a technology consultant and co-author of The Future of the Professions: How Technology Will Transform the Work of Human Experts, predicts unprecedented upheaval in a profession where the working practices of some lawyers and judges have changed little since the time of Charles Dickens. "One question lurking in all this is whether someone can come in and do to law what Amazon did to bookselling," he says. "We won't see anything as dramatic, but we will see incremental transformations in areas like the way documents are reviewed and the way legal risk is assessed." Big law firms are pouring money into AI as a way of automating tasks traditionally undertaken by junior lawyers.

The buying process begins and ends with consumers and what they go through as they recognize needs, figure out ways to solve their needs, make purchase decisions, process information, and implement their plans. While there are plenty of variables in this process, big data and machine learning are significantly affecting the ways consumers behave and make decisions about what they want and need to purchase. The only way to really understand your customers is to get better insight into how they really behave. You can turn to surveys to find out more and generalizations based on demographics can be helpful, too. In reality, though, today's businesses need to collect the most data on how customers behave in real time and how that affects real-time decision making.

It sounds like something out of a science-fiction movie, but the truth is it's your inevitable reality. The rise of artificial intelligence, or AI, is poised to change the way we think of productivity. That's according to a new report from Accenture, which predicts that AI could boost productivity by up to 40 percent by 2035. Companies are already investing heavily in AI, and not just in the U.S. Put simply, AI is defined as the ability of a machine to mimic intelligent human behavior. It's essentially any sort of technology that is able to make sense of its environment and surroundings, and then act and react accordingly. One of the benefits AI will bring is taking care of mundane responsibilities so human employees can concentrate on others.

This month's seminar you will learn a unique approach that can protect your enterprise or your industrial systems from cyber threat. Join ACG on the 20th of October at 12:00 pm to take a look at some of the most advanced cyber security tools in the world for combating the cyber security threat. We will review the latest technology tools available today to help businesses mitigate the risk of operating your business in today's high-threat environment. We will also look at the latest threats to cell phone technology and other personal devices. We will discuss the value of workshops with security awareness education.

These elementary AIs only know what we tell them, and if that data carries a bias of any kind, so too will the system trained on it. Google is looking to avoid such awkward and potentially serious situations systematically with a method it calls "Equality of Opportunity." Machine learning systems are basically prediction engines that learn the characteristics of various sets of data and then, given a new bit of data, assign it to one of several buckets: an image recognition system might learn the difference between different types of cars, assigning each picture a label like "sedan," "pickup truck," "bus," etc. The consequences of that particular mistake are likely to be trivial, but what if the computer is sorting through people instead of cars, and categorizing them for risk of default on a home loan? People who fall outside the common parameters are disproportionately likely to fall afoul of what the system learns are good bets from the rest of the data set -- that's just how machine learning operates. "When group membership coincides with a sensitive attribute, such as race, gender, disability, or religion, this situation can lead to unjust or prejudicial outcomes," wrote Google Brain's Moritz Hardt in a blog post.

With Uber recently launching a trial of self-driving cars in Pittsburgh, it's the question everyone, not just attorneys, is now asking, "In the case of an accident, who's the legally responsible'driver' in a driver-less car?" Artificial Intelligence (AI) and the Internet of Things (IoT) are beginning to learn on their own and make independent decisions based on that learning, triggering new questions of responsibility and accountability. Among AI and IoT's many challenges in becoming mainstream technologies, the most important ones may be around building a legal framework for when the responsible party is no longer an easily identifiable person or company. To start this discussion on the legal questions to be answered in a world increasingly populated by autonomous drones, robots, and vehicles, we reached out to three leaders in the AI space – Stanford's Sudha Jamthe, CityMD's Ramu Kannan, and Kimera Systems' Mounir Shita (we've included their bios and contact information at the end of this article). Here's what we asked them, and their striking responses: AI means different things to different people. There are people who think of AI as a sensationalized topic that will build robots who will take over the world.

The Japanese robot-maker Fanuc has teamed up with the computer technology specialist Nvidia to apply artificial intelligence (AI) to robotics to boost productivity and to bring new capabilities to automated factories. The partners will use AI to give robots the ability to teach themselves to perform tasks faster and more efficiently, and to work together, so that a task that would previously have taken one robot eight hours to complete, could now be done by eight robots in an hour. The robots will be able to learn on their own, instead of being programmed painstakingly--for each function they need to perform. The technology is based on --deep learning"--software, accelerated using Nvidia GPUs (graphics processing units), which will support AI in the cloud, in data centres and embedded in devices. The AI will be implemented on Fanuc--s Field (Fanuc Intelligent Edge Link and Drive) platform, which combines AI with edge computing to process --edge-heavy-- sensor data from machines to allow them to collaborate intelligently and flexibly.

Machine learning is kind of magic right? But is it the kind of magic that can make us rich? And I don't mean lucrative consulting gig rich, I mean digging valuable metals out of the ground rich. Also I'd been meaning to try out some transfer learning and looking around for a good topic to try it on. Transfer learning is where you take a pre-trained convolution (or other) network and use it for your task.

Neural networking with advanced parallel processing is beginning to take root in a number of markets ranging from predicting earthquakes and hurricanes to parsing MRI image datasets in order to identify and classify tumors. As this approach gets implemented in more places, it is being customized and parsed in ways that many experts never envisioned. And it is driving new research into how else these kinds of compute architectures can be applied. Fjodor van Veen, deep learning researcher at The Asimov Institute in the Netherlands, has identified 27 distinct neural net architecture types. The differences are largely application-specific. Neural networking is based on the concept of threshold logic algorithms, which were first proposed in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, a logician.

Through the development of highly advanced software, companies will be able to operate faster with less staff and on less money as there will no longer be a need for salaries, holiday pay or need for equipment in the office. Although there may be huge advantages for companies through these advances, there's a huge amount of pressure on staff among various industries that could easily be replaced. We take a look at some of the technology that's currently being used in the office; and ones that are already replacing the need for humans. Many office-based jobs are turning to the help of virtual digital assistants to help with workloads. They are an advanced piece of automated software that have the ability to assist employees across different industries and are made with artificial intelligence and voice technology to be able to understand users in a range of languages.