In this study we consider learning a reduced dimensionality representation from datasets obtained under multiple views. Such multiple views of datasets can be obtained, for example, when the same underlying process is observed using several different modalities, or measured with different instrumentation. Our goal is to effectively exploit the availability of such multiple views for various purposes, such as non-linear embedding, manifold learning, spectral clustering, anomaly detection and non-linear system identification. Our proposed method exploits the intrinsic relation within each view, as well as the mutual relations between views. We do this by defining a cross-view model, in which an implied Random Walk process between objects is restrained to hop between the different views. Our method is robust to scaling of each dataset, and is insensitive to small structural changes in the data. Within this framework, we define new diffusion distances and analyze the spectra of the implied kernels. We demonstrate the applicability of the proposed approach on both artificial and real data sets.

Everyone today talks about AI, big data and machine learning, yet most do not delve into the fundamental properties of how they will operate and how they might be an actual threat to asset managers. Some view technological methods as tools to assist them instead of being such a threat, and it would help provide both perspectives of the argument. Deep learning is a branch of machine learning that uses particular architectures of neural networks. These are artificial networks that attempt to actually replicate how the neural structures in human brains operate. Such methods have successfully been applied to areas such as computer vision – i.e. image processing and classification – as well as speech recognition. The techniques are readily available to any undergraduate student willing to learn the process.

NEW DELHI: HDFC Bank today announced the launch of an electronic virtual assistant (EVA), an artificial intelligence-driven chatbot, for customer services. Eva is India's first AI-based banking chatbot and can answer millions of customer queries across multiple channels instantly, HDFC Bank said in a statement. Eva can assimilate knowledge from thousands of sources and provide answers in simple language in less than 0.4 seconds, it said. "Within the first few days of its launch, Eva has answered over 1 lakh queries from thousands of customers from 17 countries across the globe," it claimed. With the launch of Eva, it said, bank's customers can get information on its products and services instantaneously and it also becomes smarter as it learns through its customer interactions.

They speak with Albert Efimov, Chief Roboticist at Skolkovo, about creating an innovation cluster for robotics companies; Anastasia Uryasheva, CEO of Tsuru, about a waterproof unmanned aerial vehicle; and Borris Rozman, General Director of GNOM, about underwater vehicles.

Oxford University researchers have estimated that 47 percent of U.S. jobs could be automated within the next two decades. But which ones will robots take first? First, we should define "robots" (for this article only) as technologies, such as machine learning algorithms running on purpose-built computer platforms, that have been trained to perform tasks that currently require humans to perform. With this in mind, let's think about what you'll do after white-collar work. Oh, and I do have a solution for the short term that will make you the last to lose your job to a robot, but I'm saving it for the end of the article.

Fortune magazine last week released a list of "50 Companies Leading the AI Revolution," and the prestigious list includes three hot Israeli companies in the artificial intelligence sector: Logz.io, Fortune's infographic includes only six countries and features an equal number of notable AI companies from Israel (population 8.5 million) as China (population 1.38 billion) and the United Kingdom, and more than France and Taiwan. Only the United States has more companies on the graph. Fortune relied on research firm CB Insights' AI 100 list of the most promising artificial intelligence startups globally, based on factors like financing history, investor quality, business category, and momentum. The CB Insights list also includes Israeli companies Prospera Technologies (ag-tech at work in Spain, Mexico, and New York) and Chorus.ai

In the hallowed hallways of Morgan Stanley's office in Canary Wharf, men in crisp tailored suits pass each other, briskly chatting away on their smartphones, while attractive young women in black pencil skirts and long legs scamper through the halls with take-away salads from Chop'd, dressing on the side. Walking these very halls has been the aspiration of many, the full-time MBA candidate who was looking for a career change and "wanted to get into investment banking". Just what is investment banking, you might ask? Well, the money-making part of the operation is called "the front office". Let's just say that if you meet a 9 at All Bar One in Canary Wharf and she asks you if you're in a front office role, just say yes.

In 1952, Professor Sandy Douglas created a tic-tac-toe game on the EDSAC, a room-sized computer at the University of Cambridge. One of the first ever computer games, it was developed as part of a thesis on human-computer interaction. Forty-five years later, in 1997, another milestone occurred when IBM's Deep Blue machine defeated Garry Kasparov, the world chess champion. This was followed by Watson, again created by IBM, which appeared on the Jeopardy! Yet another breakthrough was Google's DeepMind AlphaGo, which in 2016 defeated the Go world champion Lee Se-dol at a tournament in South Korea.

Over the past few years technology has been finding a way to produce more and more innovative ways to diagnose disease, but now it has a new party trick – disease detection by voice analysis – and it's just as it sounds, if you excuse the pun. You talk, it analyses your voice and screens for illnesses. We're all used to being able to tell when someone's ill by just listening to their voice – on the phone for example. If someone has a cold then they sound nasal, and we know that and we can ask them if they have a cold. If they're slurring then again we can hear it and we can ask them what's wrong.

With AI in a full-fledged mania, 2017 will be the year of reckoning. Pure hype trends will reveal themselves to have no fundamentals behind them. Paradoxically, 2017 will also be the year of breakout successes from a handful of vertically-oriented AI startups solving full-stack industry problems that require subject matter expertise, unique data, and a product that uses AI to deliver its core value proposition. Over the past year a mania has risen up around'bots.' In the technical community, when we talk about bots, we usually mean software agents which tend to be defined by "four key notions that distinguish agents from arbitrary programs; reaction to the environment, autonomy, goal-orientation and persistence." Enterprises have decided to usurp the term'bot' to be mean'any form of business process automation' and create the term'RPA', robotic process automation.