Did you hear the one about the affordable yet efficient human lawyer and its robot counterpart? One is complete myth and will never happen while the other might be just around the corner thanks to artificial intelligence (AI). Everybody loves a silly lawyer joke but the joke may be on us because the lawyer or barrister is one of the professions least likely to be replaced by automation and it may also be one that will benefit most from AI and machine learning. There is an interesting website called willrobotstakemyjob.com where you can enter various jobs and see the probability that automation will, at some point in the future, render certain professions obsolete or not. This is calculated using a methodology developed by Oxford University researchers looking at the future of employment.

Volkmar Denner, CEO of the Germany-headquartered Bosch Group which supplies technology and services, said that the rise of Artificial Intelligence (AI) is expected to be like any other industrial revolution and will lead to a shift in job qualification profiles. Denner said that AI will have the potential to create high value jobs and added that the Bosch Group was bullish about its Indian operations. "Last year we had set up an AI centre in Bengaluru, and our newly established Connected Mobility Solutions division is active in India too, where it is helping to shape the mobility of the future. We plan to invest an additional Rs 1700 crores in India in the next three years," said Denner. He further remarked that the company has always believed in giving adequate training to its people to develop new skill sets.

RPA, which covers AI and machine learning capabilities used to handle high-volume, repeatable tasks that once needed humans, is coming. RPA stands for Robotic Process Automation, but don't be confused: it doesn't refer to R2D2, or any of the Kiva robots scurrying around the Amazon warehouse. RPA is language that covers the broad use of software with artificial intelligence (AI) and machine learning capabilities to handle high-volume, repeatable tasks that previously required humans to perform. Given the innovation it represents and the pain points it satisfies, RPA is quickly making its way towards a billion dollar revenue market. In recent months alone, leading startups in the space have raised over $300 million for their RPA systems.

There's more than one way to build a paywall. Over the last year, Swiss news publisher Neue Zürcher Zeitung has been using a payment system that is personalized to the individual based on hundreds of criteria. NZZ requires people to register and eventually, pay. But when readers get these registration and payment messages and how those messages look varies based on predefined rules, dozens of A/B tests and machine learning. "If we're to be successful in paid content, we need to individualize the experience with our product and the product itself, and automate our marketing approach," said Steven Neubauer, managing director at Neue Zürcher Zeitung.

Nick Loui had never been asked about neural networks from a man wearing a violet leotard before. But the CEO and founder of CivicFeed didn't bat an eyelid as he thoughtfully answered the question. Given the context -- a panel at Lightning In A Bottle -- the dress code was A-OK. The question was part of a broader discussion that Loui was having with Dr. Nathan Walworth about the ethics of artificial intelligence and how engineers hold responsibility for coding a bias-free world. Using AI for social good is something Loui, a former web-dev and marketer, feels passionately about.

Of course, I've had a voice assistant on my phone for years, but we rarely talk; the main exception being those evenings where we might interrogate it for fun. Such play will typically stray from affable tyre-kicking -- "What kind of Pokemon is Squirtle?" A few months ago I finally bought a smart speaker: a dedicated conduit through which the Internet can penetrate my kitchen. I was determined to forge a valuable relationship with the hefty, fabric-wrapped cylinder, and began by immediately changing the "wake word" to something less gendered, dropping a syllable in the process. Enthusiastically, I set about testing it carefully in real scenarios, eager to identify our combined strengths.

No longer science fiction: Artificial intelligence and robotics are transforming healthcare Artificial intelligence – AI – is getting increasingly sophisticated at doing what humans do, albeit more efficiently, more quickly, and more cheaply. While AI and robotics are becoming a natural part of our everyday lives, their potential within healthcare is vast. Most of us are barely aware of it, but AI is everywhere we turn – it's in our cars, telling us when it's time for the engine to be serviced based on our driving patterns; it's in our everyday Google searches and the suggestions from Amazon that follow us around the web; it's the chatbot on the end of the telephone in call service centres. In homes, workplaces and clinical environments around the world, intelligent technologies such as AI and robots are supporting, diagnosing and treating people. How we embrace AI and robotics to complement and enhance healthcare services today will define our ability to deliver more effective, efficient and responsive healthcare services that reap improved health outcomes, which enabling individuals to own and manage their daily health needs.

Nobody will buy an electric vehicle unless it's loaded with government subsidies and the computer driven car is a solution to a problem that doesn't exist. And yet car makers are spending kerzillions of dollars to provide these vehicles, mainly on the basis that even though they are not sure if anyone will buy one, they can't risk being blindsided one day by a Silicon Valley upstart making something in his basement. I've still not met anyone who would rather rent a vehicle when he needed it rather than own one, even if it was almost immediately available, and despite the fact we all know it makes no sense to spend all that money on a vehicle that Morgan Stanley says will be used just 4% of the time. I've held these views for some time and felt a bit isolated expressing them, but after reading the latest report from AlixPartners, the global consulting firm, the evidence is mounting that victory for electric cars and autonomous ones is not a foregone conclusion.. "The automotive industry faces the possibility of a monumental capital drain in the near term as hundreds of players, including non-traditional ones, are all pouring unprecedented sums into electric and autonomous vehicles years before those technologies are fully cost-competitive in the market, when consumers are questioning the cost and safety of some of the technologies, and just as the market itself is set to continue a cyclical downturn," AlixPartners said. The AlixPartners study finds that by 2023 a whopping $255 billion will be spent globally developing electric vehicles, and that some 207 electric models are set to hit the market by 2022, many of them destined to be unprofitable due to currently-high systems costs, low volumes and intense competition.

Direct numerical simulation of Stokes flow through an impermeable, rigid body matrix by finite elements requires meshes fine enough to resolve the pore-size scale and is thus a computationally expensive task. The cost is significantly amplified when randomness in the pore microstructure is present and therefore multiple simulations need to be carried out. It is well known that in the limit of scale-separation, Stokes flow can be accurately approximated by Darcy's law with an effective diffusivity field depending on viscosity and the pore-matrix topology. We propose a fully probabilistic, Darcy-type, reduced-order model which, based on only a few tens of full-order Stokes model runs, is capable of learning a map from the fine-scale topology to the effective diffusivity and is maximally predictive of the fine-scale response. The reduced-order model learned can significantly accelerate uncertainty quantification tasks as well as provide quantitative confidence metrics of the predictive estimates produced.

Many recent state-of-the-art recommender systems such as D-ATT, TransNet and DeepCoNN exploit reviews for representation learning. This paper proposes a new neural architecture for recommendation with reviews. Our model operates on a multi-hierarchical paradigm and is based on the intuition that not all reviews are created equal, i.e., only a select few are important. The importance, however, should be dynamically inferred depending on the current target. To this end, we propose a review-by-review pointer-based learning scheme that extracts important reviews, subsequently matching them in a word-by-word fashion. This enables not only the most informative reviews to be utilized for prediction but also a deeper word-level interaction. Our pointer-based method operates with a novel gumbel-softmax based pointer mechanism that enables the incorporation of discrete vectors within differentiable neural architectures. Our pointer mechanism is co-attentive in nature, learning pointers which are co-dependent on user-item relationships. Finally, we propose a multi-pointer learning scheme that learns to combine multiple views of interactions between user and item. Overall, we demonstrate the effectiveness of our proposed model via extensive experiments on \textbf{24} benchmark datasets from Amazon and Yelp. Empirical results show that our approach significantly outperforms existing state-of-the-art, with up to 19% and 71% relative improvement when compared to TransNet and DeepCoNN respectively. We study the behavior of our multi-pointer learning mechanism, shedding light on evidence aggregation patterns in review-based recommender systems.