Any fashion enthusiast will know what an insurmountable task it is to keep up with changing and capricious fashion trends. With ecommerce leading the way for the Indian startup ecosystem, fashion websites have carved a niche for themselves. With Indian ecommerce slated to touch the 100 Bn dollar mark by 2020, the fashion industry stands to own up to 35% worth of shares in the pie. These improving prospects of online fashion retail only add to the increasing competition. Confronted with the challenges of longevity and sustainable growth, fashion sites are keenly turning towards artificial intelligence (AI) to rise over the challenges and provide customers with a customer service that is truly extraordinary and next generation.

You could call it a demonstration of the proliferation of drone use with frightening possibilities. Last week's attack in northern Iraq in which a small drone exploded killing two Peshmerga fighters and badly wounding two members of the French special forces, marks something of an innovation in modern warfare. The US launched the first armed drone attack back in October 2001. Since then the use of armed drones has been the preserve of the most sophisticated military actors in the world. Israel and the US had the early technological lead with Russia and China rapidly developing their own drone industries.

Mizuho Financial Group Inc. has entered talks with information technology startup Metaps Inc. about forming a business alliance on settlement services using advanced financial technology, Jiji Press learned Thursday. Metaps is a leading provider of fintech services, which use artificial intelligence and big data. The envisaged tie-up is aimed at developing new settlement services, sources said. The predecessor of Metaps was established in Tokyo in September 2007. The firm offers online settlement services, including Spike.

The workshop will take participants through the conceptual and applied foundations of the subject.

"I wish I could touch you," Theodore says, laying in bed. Until she speaks up, tentatively. "How would you touch me?" It's a famously poignant scene from the movie Her, as the character Theodore is about to make vocal love to an artificial intelligence living in his ear. But according to half a dozen experts I interviewed, ranging from industrial designer Gadi Amit to the usability guru Don Norman, in-ear assistants aren't science fiction. In fact, a notable pile of discreet, wireless earbuds enabling just this idea are coming to market now. Sony recently released its first in-ear assistant, the Xperia Ear. Intel showed off a similar proof-of-concept last year.

MIT-CHIEF, a not-for-profit student group that promotes cooperation between the United States and China in technology and innovation, is readying its annual conference with a focus on machine learning, new materials and more. The MIT-China Innovation and Entrepreneurship Forum (CHIEF) Annual Conference, to be held Nov. 12-13 at MIT, will feature 6 panels and 6 keynote speeches that in addition to the topics cited above, will hit on energy, advanced manufacturing, healthcare and autonomous driving. Speakers will include those from academia and industry, including venture capital firms, and represent outfits such as Microsoft, Stanford University and AutoX.

John McClurg, Vice President in the Office of Security and Trust (OST), Cylance, told us, "CylancePROTECT is a truly advanced threat prevention solution. It sits on each endpoint within the organization, whether it's a desktop, laptop, mobile device, server, or virtual machine. By applying artificial intelligence, machine learning, and mathematic techniques, it instantly identifies and prevents malware and cyberattacks from executing. Basically, it protects from every threat known and yet-to-be-known, including system- and memory-based attacks, malicious documents, zero-day malware, privilege escalations, scripts, and potentially unwanted programs. The solution boosts the efficiency of your IT resources and reduces user impact throughout your organization. The endpoint security product uses little memory, less than 1% of CPU. It requires no Internet connection or signature updates and is engineered to run with minimal updates and fewer system resources. In addition, it works with Windows and Mac OS, easily integrates into existing security platforms, and is available in OEM and embedded versions for technology partners. It operates in every environment, whether it's 1,000, 10,000, or 100,000 endpoints. James Lantrip, Segment Head, Security, Siemens Industry, Inc., told us, "Siemens has a very customer-centric view.

Affiliate marketing company Impact Radius has locked its sights on artificial intelligence and machine-learning techniques, with aspirations of integrating the technology into attribution modeling and ad fraud detection to monitor campaigns across devices. Overtime programmatic technology combined with unified measurement and optimization will increasingly automate processes, according to Per Pettersen, CEO and co-founder at Impact Radius. "At this point you're really competing on the merit of margin and lifetime value," he said. When asked how artificial intelligence will change affiliate marketing and search, Pettersen said computers will analyze the data and make specific recommendations. "In theory, as systems get more sophisticated, the technology will make buying decisions," he said, without the marketing having to cap the bid.

Multilingual Text Classification (MLTC) is a text classification task in which documents are written each in one among a set L of natural languages, and in which all documents must be classified under the same classification scheme, irrespective of language. There are two main variants of MLTC, namely Cross-Lingual Text Classification (CLTC) and Polylingual Text Classification (PLTC). In PLTC, which is the focus of this paper, we assume (differently from CLTC) that for each language in L there is a representative set of training documents; PLTC consists of improving the accuracy of each of the |L| monolingual classifiers by also leveraging the training documents written in the other (|L| − 1) languages. The obvious solution, consisting of generating a single polylingual classifier from the juxtaposed monolingual vector spaces, is usually infeasible, since the dimensionality of the resulting vector space is roughly |L| times that of a monolingual one, and is thus often unmanageable. As a response, the use of machine translation tools or multilingual dictionaries has been proposed. However, these resources are not always available, or are not always free to use. One machine-translation-free and dictionary-free method that, to the best of our knowledge, has never been applied to PLTC before, is Random Indexing (RI). We analyse RI in terms of space and time efficiency, and propose a particular configuration of it (that we dub Lightweight Random Indexing LRI). By running experiments on two well known public benchmarks, Reuters RCV1/RCV2 (a comparable corpus) and JRC-Acquis (a parallel one), we show LRI to outperform (both in terms of effectiveness and efficiency) a number of previously proposed machine-translation-free and dictionary-free PLTC methods that we use as baselines.

We propose a flexible framework for spectral conversion (SC) that facilitates training with unaligned corpora. Many SC frameworks require parallel corpora, phonetic alignments, or explicit frame-wise correspondence for learning conversion functions or for synthesizing a target spectrum with the aid of alignments. However, these requirements gravely limit the scope of practical applications of SC due to scarcity or even unavailability of parallel corpora. We propose an SC framework based on variational auto-encoder which enables us to exploit non-parallel corpora. The framework comprises an encoder that learns speaker-independent phonetic representations and a decoder that learns to reconstruct the designated speaker. It removes the requirement of parallel corpora or phonetic alignments to train a spectral conversion system. We report objective and subjective evaluations to validate our proposed method and compare it to SC methods that have access to aligned corpora.