Alphabet Jigsaw (formerly Google Ideas) officially unveiled this morning their new tool for fighting toxic speech online, appropriately called Perspective. Powered by a deep-learning model trained on more than 17 million manually reviewed reader comments provided by the New York Times, the model assigns a score to a given passage of text, rating it on a scale from 0 to 100%, similar to statements that human reviewers have previously rated as "toxic." What makes this new approach from Google so different than past approaches is that it largely focuses on language rather than ideas: for the most part you can express your thoughts freely and without fear of censorship as long as you express them clinically and clearly, while if you resort to emotional diatribes and name calling, regardless of what you talk about, you will be flagged. What does this tell us about the future of toxic speech online and the notion of machines guiding humans to a more "perfect" humanity? One of the great challenges in filtering out "toxic" speech online is first defining what precisely counts as "toxic" and then determining how to remove such speech without infringing on people's ability to freely express their ideas.

The very first chatbot, ELIZA, was developed 50 years ago at the Massachusetts Institute of Technology. It simulated a Rogerian psychotherapist, someone who just repeats the human user's words back to the human; it is not very good. In the following decades, chatbots were mostly of academic interest. But in recent years, smartphone-based chatbots have gained wide interest from the industry with high profile products such as the Apple Siri, Amazon Echo, and China's WeChat. In 2016, chatbots are one of the hottest trends in technology.

Microsoft Monday is a weekly column that focuses on updates in regards to the Redmond giant. This week, Microsoft Monday includes details about Windows 10 upgrade revealed, EU still not satisfied with data collection transparency, HoloLens used for designing operating rooms, the launch of Skype Lite, an investment in AirMap, Halo split-screen is returning, new features for the Mail and Calendar apps for Windows 10 and much more! Microsoft has recently announced that a fourth major Windows 10 upgrade is being planned. Microsoft made the announcement at Ignite Australia roughly about two weeks ago. The first three updates are known as November Update, Anniversary Update and Creators Update.

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic treebased recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and node function. We implemented and evaluated a binarytree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification, outperforming state-of-the-art recurrent and recursive neural networks.

Bribery in elections is an important problem in computational social choice theory. However, bribery with money is often illegal in elections. Motivated by this, we introduce the notion of frugal bribery and formulate two new pertinent computational problems which we call Frugal-bribery and Frugal- $bribery to capture bribery without money in elections. In the proposed model, the briber is frugal in nature and this is captured by her inability to bribe votes of a certain kind, namely, non-vulnerable votes. In the Frugal-bribery problem, the goal is to make a certain candidate win the election by changing only vulnerable votes. In the Frugal-{dollar}bribery problem, the vulnerable votes have prices and the goal is to make a certain candidate win the election by changing only vulnerable votes, subject to a budget constraint of the briber. We further formulate two natural variants of the Frugal-{dollar}bribery problem namely Uniform-frugal-{dollar}bribery and Nonuniform-frugal-{dollar}bribery where the prices of the vulnerable votes are, respectively, all the same or different. We study the computational complexity of the above problems for unweighted and weighted elections for several commonly used voting rules. We observe that, even if we have only a small number of candidates, the problems are intractable for all voting rules studied here for weighted elections, with the sole exception of the Frugal-bribery problem for the plurality voting rule. In contrast, we have polynomial time algorithms for the Frugal-bribery problem for plurality, veto, k-approval, k-veto, and plurality with runoff voting rules for unweighted elections. However, the Frugal-{dollar}bribery problem is intractable for all the voting rules studied here barring the plurality and the veto voting rules for unweighted elections.

The income from human labor gets taxed. Founder & and CEO of SoftBank Group, Masayoshi Son, speaks during a keynote speech at the Mobile World Congress in Barcelona on February 27, 2017 in Barcelona. BARCELONA--In 30 years, your shoes will be smarter than you. That's not meant to be an insult but rather what Masayoshi Son believes will be the case as robots outnumber humans and exceed their intelligence in just three decades. The chairman and CEO of Japan's SoftBank Group outlined his vision of the future during remarks made on stage at the Mobile World Congress tech conference underway this week in Barcelona.

Someone made a lot of new friends over the holidays. Her name is Alexa, and along with her acquaintances (perhaps frenemies?) Cortana and Siri, she's about to become as much a part of the daily life of American households as Alfred the butler is at Wayne Manor. Except that Alfred, for all his talents, can't tell Batman exactly what time the cable repairman will arrive this afternoon, or the current balance in his bank account (not that billionaire Bruce Wayne would care). The implications of 2017, the year virtual assistants really arrived, will be immense for business.

The word "Disagree" is seen on the hand of Julia Grabowski during a town hall meeting for Republican U.S. Senator Bill Cassidy in Metairie, Louisiana. News about President Donald Trump -- including an apparently neglected vegetable garden that once belonged to former first lady Michelle Obama -- is inescapable. As The New York Times' Farhad Manjoo wrote, "he is no longer just the message. In many cases, he has become the medium." Mental health professionals in the U.S. have reported that the all-encompassing coverage of the president has induced anxiety and depression, or post-election stress, in many of their patients.

LONDON – Guided by cameras and radars, and negotiating traffic and roundabouts, a self-driving Nissan car took to the streets of London on Monday for the Japanese company's first European tests of an autonomous vehicle. Traveling at up to 50 mph (80 kph) and moving from local streets to a major multilane road, the modified Nissan LEAF electric car showcased the kind of technology many hope to be the future of travel. Britain has been wooing developers of autonomous vehicles, hoping to grab a slice of an industry it estimates could be worth £900 billion ($1.1 trillion) worldwide by 2025. It also recently announced changes to allow for a single insurance policy to cover motorists driving conventionally and in autonomous mode, as it tries to get regulations in place to encourage the uptake of driverless cars from 2020. Britain's flexible approach to testing autonomous vehicles helped Nissan pick London for its first European tests, the director of its research center in Silicon Valley told Reuters. "It's not everywhere in Europe that we can go and drive on the road," Maarten Sierhuis said.