By the year 2021, there will be more AI-powered digital assistants installed on devices than there are people in the world, according to new research from Ovum. The install base will be higher than 7.5 billion by that time, which is greater that the planet's population as recorded by the US Census Bureau on May 1, 2017. Google Assistant will be the most installed assistant, accounting for 23.3% of the market, the report said. The next most popular assistant will be Samsung Bixby, with 14.5% market share. SEE: Why robots and AI won't replace most jobs any time soon "Ultimately, a digital assistant is just another user interface. It will only be as good as the ecosystem of devices and services that it is compatible with. Partnerships between tech giants and local service providers will therefore be key differentiators," said Ronan de Renesse, practice leader for Ovum's Consumer Technology team and author of the report, in the release.

In this paper we study stochastic quasi-Newton methods for nonconvex stochastic optimization, where we assume that noisy information about the gradients of the objective function is available via a stochastic first-order oracle (SFO). We propose a general framework for such methods, for which we prove almost sure convergence to stationary points and analyze its worst-case iteration complexity. When a randomly chosen iterate is returned as the output of such an algorithm, we prove that in the worst-case, the SFO-calls complexity is $O(\epsilon^{-2})$ to ensure that the expectation of the squared norm of the gradient is smaller than the given accuracy tolerance $\epsilon$. We also propose a specific algorithm, namely a stochastic damped L-BFGS (SdLBFGS) method, that falls under the proposed framework. {Moreover, we incorporate the SVRG variance reduction technique into the proposed SdLBFGS method, and analyze its SFO-calls complexity. Numerical results on a nonconvex binary classification problem using SVM, and a multiclass classification problem using neural networks are reported.

We consider abstract-argumentation-theoretic coalition formability in this work. Taking a model from political alliance among political parties, we will contemplate profitability, and then formability, of a coalition. As is commonly understood, a group forms a coalition with another group for a greater good, the goodness measured against some criteria. As is also commonly understood, however, a coalition may deliver benefits to a group X at the sacrifice of something that X was able to do before coalition formation, which X may be no longer able to do under the coalition. Use of the typical conflict-free sets of arguments is not very fitting for accommodating this aspect of coalition, which prompts us to turn to a weaker notion, conflict-eliminability, as a property that a set of arguments should primarily satisfy. We require numerical quantification of attack strengths as well as of argument strengths for its characterisation. We will first analyse semantics of profitability of a given conflict-eliminable set forming a coalition with another conflict-eliminable set, and will then provide four coalition formability semantics, each of which formalises certain utility postulate(s) taking the coalition profitability into account.

Richard Adler knows a few things about the uneasy relationship between older adults and technology. It's something that Adler, a distinguished research fellow at the Institute for the Future in Palo Alto, Calif., has been studying for more than 25 years. One thing, in particular, has struck him. "The truth is that a lot of older adults are technophobes," he said. "They tend to be classically late adopters of almost any technology."

Battlefield drones are evolving quickly, and there's only so much militaries can do to keep up -- they're not used to competing with consumer tech that can improve in a matter of months. To that end, US Special Operations Command (aka SOCOM) is taking the unusual step of opening a drone hacker lab in Tampa, Florida. The organization's James Geurts hopes this tinkerer mindset will help special forces "get out in front" of future threats that could easily prove overwhelming, such as drone swarms. There are also more direct problems to solve, such as balancing weapon payloads and portability. You may need a drone bigger than something like the tiny, kamikaze-oriented AeroVironment Switchblade (above) to take down a target, but you can't always field more conventional runway-bound drones to do the job.

Summary: Watson is a remarkably flexible and complete AI development platform. To understand how you might build new services for your current employer or imagine your own Watson-based startup, look at these 30 companies that are leading the way. In our recent reviews of historical Watson and the modern Watson of today we concluded that IBM's Watson Group may have the first or at least the current strongest comprehensive AI platform. This is the first time that we know of that all three elements of AI have been brought together in a single user friendly platform: image processing, text and speech processing, and knowledge retrieval. This is not so much a platform for data scientist to use to expand the capabilities of AI as it is a platform for business users (with the aid of data scientists) to exploit the capabilities of modern AI by building new products and services.

Google has announced at its annual conference (I/O 2017) that it has computing pods capable of 11.5 petaflops. They are made of 64 customized TPU (processors specialized for deep learning/AI), each generate 180 teraflops. It is going to be available to other companies via Google cloud. Google has also announced Google.ai, a new website where Google presents its work on AI. It seems likely that Google wants to sell AI as a service.

It is ironic, but perhaps not unexpected, that many organizations who want the benefits of using an Agile approach to deliver software use a waterfall approach to adopting Agile practices: they form plans, they set milestones, and they measure progress by how many teams they have engaged. Old habits die hard, but like most waterfall software projects, most waterfall-style Agile adoption efforts fail to produce the results desired. The problem is that to get the results they want, they have to change their culture and cultures are very hard to change. To paraphrase Peter Drucker, "culture eats Agile for breakfast." Successful approaches are opportunistic and leverage the power of self-organization to achieve lasting change.

What Did Dell EMC Announce Dell EMC announced their initial product, platform solutions, and services for Azure Stack. This includes a Proof of Concept (PoC) starter kit (PE R630) for doing evaluations, prototype, training, development test, DevOp and other initial activities with Azure Stack. Dell EMC also announced a larger for production deployment, or large-scale development, test DevOp activity turnkey solution. The initial production solution scales from 4 to 12 nodes, or from 80 to 336 cores that include hardware (server compute, memory, I/O and networking, top of rack (TOR) switches, management, Azure Stack software along with services. Other aspects of the announcement include initial services in support of Microsoft Azure Stack and Azure cloud offerings.

It might seem like an ambitious goal, but key players in Montreal's rapidly growing artificial-intelligence sector are intent on transforming the city into a Silicon Valley of AI. Certainly, the flurry of activity these days indicates that AI in the city is on a roll. Impressive amounts of cash have been flowing into academia, public-private partnerships, research labs and startups active in AI in the Montreal area. And hopes are high that a three-day conference starting May 24 -- AI Forum -- will help burnish Montreal's reputation as one of the world's emerging AI advanced research centres and top talent pools in the suddenly very hot tech trend. Topics and issues on the agenda include the evolution of AI in Montreal and the transformative impact AI can have on business, industry and the economy.