Consumer adoption of artificial intelligence-powered virtual assistants will increase, but some brands don't see the capabilities fully replacing humans. The number of active users of virtual assistants will grow to 2 billion consumers in 2021, up from 390 million last year, according to new research from Tractica. Global revenue generated by virtual assistants will grow to 16 billion by 2021, up from 2 billion last year. The majority (75%) of revenues will come from the consumer side of the market, according to Tractica. The Asia Pacific region will lead the market in revenue and multiply in value by 10 times to account for more than 5 billion by 2021. "The consumer and enterprise use cases for virtual digital assistants are proliferating rapidly thanks to accelerated innovation and scalability of underlying technologies, such as natural language processing and artificial intelligence," Mark Beccue, principal analyst at Tractica, said in a statement.

We are in the midst of a digital revolution, where banking, commerce and security is becoming more and more complex every day. Technology is evolving continually, consumers are adopting new technologies easily and the competitive landscape is changing regularly. To progress and stay ahead in the financial industry, it is imperative that banks evolve, either by leveraging their existing strengths or developing new technological capabilities. A deep understanding of their systems using artificial intelligence, automation and investing in technologies that matter to their customers, can help banks stay relevant and innovate. At Infosys, we have invested significantly in technologies that help banks innovate using new tools and technologies while strengthening their core offerings.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

New Delhi: While artificial intelligence is making waves globally, Cognitive Business Intelligence (BI) is the next stage of machine learning to design and analyse unstructured data, video, images and human language, say experts. According to them, we are generating data but is this data being analysed to create insights which could help in running businesses more effectively is the real concern for businesses. "This potential can be leveraged using Business Intelligence. Using BI, we can understand what really the data means," said Nikhilesh Tiwari, Co-Founder, Helical Insight, the world's first open source Business Intelligence (BI) framework. At the moment, less than 0.5 per cent of all data is ever analysed and used globally.

Scott graduated from Cardiff University with a degree in English Literature and a diploma in Magazine Journalism. He has a keen interest in disruptive tech, sport and the media. It's official, 2016 has been the year of the AI startup acquisition. Tech giants Apple, Intel, Twitter and Microsoft have all spent large sums to bring artificial intelligence startups, and their expertise, inhouse. Four of the biggest AI startup acquisitions of the last five years have come from the UK, starting with Google's purchase of Deep Mind in 2014 for a reported 400 million.

SmartHire is an open source applicant prioritization system powered by machine learning. The system was created to help organizations go through large backlogs of applicants and find the best candidates for their open positions. The system learns who the best candidates are based on who has previously made it through the hiring process. SmartHire is built on Node.js, SmartHire is powered by a number of dependencies.

The term "machine learning" covers a grab bag of algorithms, techniques, and technology that are by now pretty much everywhere in modern life. However, machine intelligence has recently started to be used not just for identifying problems but to build better products. Amongst the first is the world's only beers brewed with the help of machine intelligence, which went on sale a few weeks ago. The machine learning algorithms uses a combination of reinforcement learning and bayesian optimisation to assist the brewer in deciding how to change the recipe of the beer, with the algorithms learning from experience and customer feedback. Perhaps the most obvious intrusion of machine learning into the physical world is the voice recognition that drives Apple's Siri, or Amazon's Alexa.

There's no human instinct more basic than speech, and yet, for many people, talking can be taxing. Unfortunately, many behaviorally-based voice disorders are not well understood. In particular, patients with muscle tension dysphonia (MTD) often experience deteriorating voice quality and vocal fatigue ("tired voice") in the absence of any clear vocal cord damage or other medical problems, which makes the condition both hard to diagnose and hard to treat. But a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital (MGH) believes that better understanding of conditions like MTD is possible through machine learning. Using accelerometer data collected from a wearable device developed by researchers at the MGH Voice Center, researchers demonstrated that they can detect differences between subjects with MTD and matched controls.

This is a long overdue blog post on Reinforcement Learning (RL). You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over the last year: I worked through Richard Sutton's book, read through David Silver's course, watched John Schulmann's lectures, wrote an RL library in Javascript, over the summer interned at DeepMind working in the DeepRL group, and most recently pitched in a little with the design/development of OpenAI Gym, a new RL benchmarking toolkit. So I've certainly been on this funwagon for at least a year but until now I haven't gotten around to writing up a short post on why RL is a big deal, what it's about, how it all developed and where it might be going. It's interesting to reflect on the nature of recent progress in RL. Similar to what happened in Computer Vision, the progress in RL is not driven as much as you might reasonably assume by new amazing ideas. In Computer Vision, the 2012 AlexNet was mostly a scaled up (deeper and wider) version of 1990's ConvNets. Similarly, the ATARI Deep Q Learning paper from 2013 is an implementation of a standard algorithm (Q Learning with function approximation, which you can find in the standard RL book of Sutton 1998), where the function approximator happened to be a ConvNet. AlphaGo uses policy gradients with Monte Carlo Tree Search (MCTS) - these are also standard components.

While major players keep betting on the producer vs explorer model to access data, a revolution is on its way fuelled by three main factors: startups, machine learning and natural language. Here is how entrepreneurs and technology are changing how we do business intelligence and shifting the paradigm of an old industry. "Over the next few years, BI vendors are expected to start playing a quick game of catch-up with the virtual personal assistant market. Initially, BI vendors will enable basic voice commands for their standard interfaces, followed by natural language processing of spoken or text input into SQL queries. Ultimately, "personal analytic assistants" will emerge that understand user context, offer two-way dialogue, and (ideally) maintain a conversational thread."