I came to a shocking conclusion while writing about diversity for my book on machine learning: diversity in Artificial Intelligence is not your problem, it's hers. I mean, of course, that the problem is with the English pronoun "hers". There is a bias against "hers" in most major AI systems today, and the source of the bias is the perfect metaphor for bias in AI more broadly. Like you might remember from high school, "hers" is a pronoun. Each word in a sentence belongs to one of a small number of categories: nouns, pronouns, adjectives, verbs, adverbs, etc. One common building block in many AI applications is to identify the right category in raw text. Today, "hers" is not recognized as a pronoun by the most widely used technologies for Natural Language Processing (NLP), including (alphabetically) Amazon Comprehend, Google Natural Language API, and the Stanford Parser. The video shows that in the sentence "the car is hers", Amazon and Google classify "hers" as a noun and the Stanford parser classifies "hers" as an adjective. They don't make the same mistake with the sentence "the car is his", correctly identifying "his" as a pronoun.

A new informative report on the global Artificial Intelligence for Edge Devices Market titled as, Artificial Intelligence for Edge Devices has recently published by Contrive Market Research to its humongous database which helps to shape the future of the businesses by making well-informed business decisions. It offers a comprehensive analysis of various business aspects such as global market trends, recent technological advancements, market shares, size, and new innovations. Furthermore, this analytical data has been compiled through data exploratory techniques such as primary and secondary research. Moreover, an expert team of researchers throws light on various static as well as dynamic aspects of the global Artificial Intelligence for Edge Devices Market. Different leading key players have been profiled to get better insights into the businesses. It offers detailed elaboration on different top-level industries which are functioning in global regions.

Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting low-dimensional representations and concatenating them, followed by a decoding step of the concatenated vector. This accounts for only first-order interactions of the features and ignores higher-order interactions. In this paper we propose a polynomial fusion layer that models the joint representation of the encodings by a higher-order polynomial, with the parameters modelled by a tensor decomposition. We demonstrate the the suitability of this approach through experiments on generated videos evaluated on a range of metrics on video quality, audiovisual synchronisation and generation of blinks.

A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\sim$5\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model the motion of sand dunes. The models are trained on simulated data from community-standard cellular automaton model of sand dunes. Preliminary results show the GAN producing reasonable forward predictions of dune migration at ten million times the speed of the existing model.

We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have been generated by a different policy, or policies. In particular, we introduce a novel doubly-robust estimator for the OPE problem in RL, based on the Targeted Maximum Likelihood Estimation principle from the statistical causal inference literature. We also introduce several variance reduction techniques that lead to impressive performance gains in off-policy evaluation. We show empirically that our estimator uniformly wins over existing off-policy evaluation methods across multiple RL environments and various levels of model misspecification. Finally, we further the existing theoretical analysis of estimators for the RL off-policy estimation problem by showing their $O_P(1/\sqrt{n})$ rate of convergence and characterizing their asymptotic distribution.

It is the present-day darling of the tech world. The current renaissance of Artificial Intelligence (AI) with its sister discipline Machine Learning (ML) has led every IT firm worth its salt to engineer some form of AI onto its platform, into its toolsets and throughout its software applications. IBM CEO Ginni Rometty has already proclaimed that AI will change 100 percent of jobs over the next decade. And yes, she does mean everybody's job from yours to mine and onward to the role of grain farmers in Egypt, pastry chefs in Paris and dog walkers in Oregon i.e. every job. We will now be able to help direct all workers' actions and behavior with a new degree of intelligence that comes from predictive analytics, all stemming from the AI engines we will now increasingly depend upon.

Industry Report "Cognitive Computing Market" provides a clear picture of the Current Market Scenario which includes past and estimated future size with respect to Value and Volume, Technological Advancement, Macro Economical and Governing Factors in the Cognitive Computing market. Cognitive Computing is defined as the technology based on the principle of artificial intelligence, signal processing, machine learning, and natural language processing (NLP) among others technology. It brings human like intelligence for a many business applications which will include big data. Cognitive Computing is a well-known technology basically specialized for processing and analyzing large and unstructured datasets. The major drivers of the cognitive computing market are the advancements in computing platforms like cloud, mobile, and big data analytics which will drive the growth of the market in the forecast period.

Today's largest manufacturing country headed the output league table nearly two centuries ago before being ousted by Britain in the first industrial revolution. China accounts for 20 per cent of global output, followed by the United States with 18 per cent, Japan 10 per cent, Germany 7 per cent and South Korea with 4 per cent, according to the most recent (2015) data from the United Nations Conference on Trade and Development. The UK is ninth with 2 per cent. In the intervening centuries there have been sizeable shifts. China reclaimed its crown after 150 years by overtaking America during the past decade.

Key Questions Answered in this Report: • What is the estimated global artificial intelligence in agriculture market size in terms of value during the period 2018-2024? Global Artificial Intelligence (AI) in Agriculture Market Forecast, 2019-2024 The Global Artificial Intelligence (AI) in Agriculture Market Analysis projects the market to grow at a significant CAGR of 28.38% during the forecast period from 2019 to 2024. The reported growth in the market is expected to be driven by the increasing need to optimize farm operation planning, growing demand to derive insights from emerging complexities of data-driven farming, and rising development of autonomous equipment in agriculture. Artificial intelligence has emerged to be a strong driving force behind the growth of data-driven farming.Regions and countries where agriculture is the major source of livelihood and sustenance, the artificial intelligence technology has led to greater profitability in the farms of those economies. The reduction in expenditure and resultant positive RoI with AI's integration in farm equipment and operations has even reached above 30% in a few countries.

In February 2019, CB Insights announced our third annual AI 100 -- a list of the 100 most promising AI startups across the globe. We take a look at where these companies are now. In 2019, companies from 3 continents and 18 industries made it to the CB Insights AI 100. They were selected from a pool of 3K companies based on a range of criteria, including patent activity, investor profile, news sentiment analysis, market potential, partnerships, competitive landscape, team strength, tech novelty, and more. Since announcing our list, 7 of these startups have been snapped up by major corporations, 4 went on to become unicorns, and several have entered into partnerships with corporations like Microsoft, Oracle, HSBC, and General Electric.