To simplify the path toward enterprise AI, organizations are turning to IBM Watson Studio and Watson Machine Learning. Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business. It helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.
Organizations that are working with artificial intelligence (AI) or machine learning (ML) have, on average, four AI/ML projects in place, according to a recent survey by Gartner, Inc. Of all respondents, 59% said that they have AI deployed today. The Gartner "AI and ML Development Strategies" study was conducted via an online survey in December 2018 with 106 Gartner Research Circle Members – a Gartner-managed panel composed of IT and IT/business professionals. Participants were required to be knowledgeable about the business and technology aspects of ML or AI either currently deployed or in planning at their organizations. "We see a substantial acceleration in AI adoption this year," said Jim Hare, research vice president at Gartner.
One of the essential phrases necessary to understand AI in 2019 has to be "ethics washing." Put simply, ethics washing -- also called "ethics theater" -- is the practice of fabricating or exaggerating a company's interest in equitable AI systems that work for everyone. A textbook example for tech giants is when a company promotes "AI for good" initiatives with one hand while selling surveillance capitalism tech to governments and corporate customers with the other. Accusations of ethics washing have been lobbed at the biggest AI companies in the world, as well as startups. The most high-profile example this year may have been Google's external AI ethics panel, which devolved into a PR nightmare and was disbanded after about a week.
Artificial Intelligence (AI) and machine learning is no more an unheard concept. AI is everywhere now and is slowly taking over routine jobs from human beings. Digital marketers and businesses are implementing AI to improve their rankings, increase sales revenue, and cut operational costs at the same time. AI is placing itself in almost every aspect of our life. Back in the 2000s, who would have thought of controlling their home appliances using Amazon Echo or Google Home?
We hear a lot about AI and its transformative potential. What that means for the future of humanity, however, is not altogether clear. Some futurists believe life will be improved, while others think it is under serious threat. Here's a range of takes from 11 experts. Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia.
This paper introduces a la carte embed-ding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transfor-mation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in con-text to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.
According to the new market research report "Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025", published by MarketsandMarkets, the Artificial Intelligence Market is expected to be valued at USD 21.5 billion in 2018 and is likely to reach USD 190.6 billion by 2025, at a CAGR of 36.6% during the forecast period. Major drivers for the market are growing big data, the increasing adoption of cloud-based applications and services, and an increase in demand for intelligent virtual assistants. The major restraint for the market is the limited number of AI technology experts. Critical challenges facing the AI market include concerns regarding data privacy and the unreliability of AI algorithms. Underlying opportunities in the artificial intelligence market include improving operational efficiency in the manufacturing industry and the adoption of AI to improve customer service.
Generative adversarial networks (GANs) are a recently introduced class of generative models, designed to produce realistic samples. This tutorial is intended to be accessible to an audience who has no experience with GANs, and should prepare the audience to make original research contributions applying GANs or improving the core GAN algorithms. GANs are universal approximators of probability distributions. Such models generally have an intractable log-likelihood gradient, and require approximations such as Markov chain Monte Carlo or variational lower bounds to make learning feasible. GANs avoid using either of these classes of approximations.