Only a few years ago, web search was simple. Users typed a few words and waded through pages of results. Today, those same users may instead snap a picture on a phone and drop it into a search box or use an intelligent assistant to ask a question without physically touching a device at all. They may also type a question and expect an actual reply, not a list of pages with likely answers. These tasks challenge traditional search engines, which are based around an inverted index system that relies on keyword matches to produce results.

Artificial intelligence is quickly becoming a part of daily life. Enterprise implementations of AI-based technologies tripled in 2018, according to Gartner. At the same time, it's reaching ubiquity in consumer-facing applications, helping us write our emails, discover new music, and get on-demand customer support. At every touchpoint, our data is being collected and used to make machines faster and smarter, and that's driving calls for regulation from global citizens, governments, and companies who want to ensure deployments of machine and deep learning algorithms are safe and ethical. While implementing laws to protect consumers from "AI-gone-wild" may seem like a reasonable proposition, it's one that's doomed to fail.

US federal government contract obligations and AI-related investments grew almost 75% to nearly $700 million between fiscal 2016 and 2018 [Federal News Network]. Of those that have adopted an AI-driven marketing solution, 74% reported using AI in an "assistive" fashion, which surfaces insights for marketers to consider during manual decision making. Only 26% of marketers reported using autonomous AI, which can act on its own insights and work collaboratively with marketers (without adding manual work) [Albert and Forrester]. Notable growth came in areas like food and consumer goods (48%), plastics and rubber (37%), life sciences (31%), and electronics (22%) [Robotic Industry Association]. Nearly eight out of 10 enterprise organizations currently engaged in AI and ML report that projects have stalled, and 96% of these companies have run into problems with data quality, data labeling required to train AI, and building model confidence; only half of enterprises have released AI/ML projects into production; 78% of their AI/ML projects stall at some stage before deployment; 81% admit the process of training AI with data is more difficult than they expected; 76% combat this challenge by attempting to label and annotate training data on their own; 63% go so far as to try to build their own labeling and annotation automation technology; 71% report that they ultimately outsource training data and other ML project activities [Alegion and Dimensional Research].

This is not an entirely new problem. Computer programs have always had bugs. Over decades, software engineers have assembled an impressive toolkit of techniques, ranging from unit testing to formal verification. These methods work well on traditional software, but adapting these approaches to rigorously test machine learning models like neural networks is extremely challenging due to the scale and lack of structure in these models, which may contain hundreds of millions of parameters. This necessitates the need for developing novel approaches for ensuring that machine learning systems are robust at deployment.

Ecoprosus India Pvt. Ltd. and Nymo Technologies have signed a US $20 Million deal to deliver a next-generation video analytics platform powered by deep-learning algorithms, coupled with energy efficient and new age technologies. Ecoprosus, has developed a Smart Poles solution for the Smart City infrastructure projects across India. This deployment is centered around a socially responsible philosophy and will endeavour to not only meet the technological challenges of Smart City projects, but provide safe and sustainable solutions that will last for years to come. "Our collective aim in this joint venture was to leverage each-other's strengths and deliver a truly world-class end to end solution that addresses a broad range of socially responsible edicts," remarked BR Anand, CSO and co-founder of Ecoprosus. Ecoprosus specialises in Smart City infrastructure projects and is providing Smart Pole hardware and software designed to be energy efficient and sustainable from inception.

Tech giants like Facebook and governments around the world are struggling to deal with disinformation, from misleading posts about vaccines to incitement of sectarian violence. As artificial intelligence becomes more powerful, experts worry that disinformation generated by A.I. could make an already complex problem bigger and even more difficult to solve. In recent months, two prominent labs -- OpenAI in San Francisco and the Allen Institute for Artificial Intelligence in Seattle -- have built particularly powerful examples of this technology. Both have warned that it could become increasingly dangerous. Alec Radford, a researcher at OpenAI, argued that this technology could help governments, companies and other organizations spread disinformation far more efficiently: Rather than hire human workers to write and distribute propaganda, these organizations could lean on machines to compose believable and varied content at tremendous scale.

The Report Titled on "Global Artificial Intelligence (AI) in Social Media Market – Segmented By Technology (Machine Learning and Deep Learning, …

In the previous blog, I discussed Visual Perception and its both biological and computational aspects. This blog is specifically about computational Visual Perception, also known as Computer Vision. Computer vision has been around for more than 50 years, but recently, we see a major resurgence of interest in how machines'see' and how computer vision can be used to build products for consumers and businesses. The key driving factor behind all these is Computer Vision. In the simplest terms, Computer Vision is the discipline under a broad area of Artificial Intelligence which teaches machines to see.

Multi-simulator training has contributed to the recent success of Deep Reinforcement Learning by stabilizing learning and allowing for higher training throughputs. We propose Gossip-based Actor-Learner Architectures (GALA) where several actor-learners (such as A2C agents) are organized in a peer-to-peer communication topology, and exchange information through asynchronous gossip in order to take advantage of a large number of distributed simulators. We prove that GALA agents remain within an epsilon-ball of one-another during training when using loosely coupled asynchronous communication. By reducing the amount of synchronization between agents, GALA is more computationally efficient and scalable compared to A2C, its fully-synchronous counterpart. GALA also outperforms A2C, being more robust and sample efficient. We show that we can run several loosely coupled GALA agents in parallel on a single GPU and achieve significantly higher hardware utilization and frame-rates than vanilla A2C at comparable power draws.

Unrolled neural networks emerged recently as an effective model for learning inverse maps appearing in image restoration tasks. However, their generalization risk (i.e., test mean-squared-error) and its link to network design and train sample size remains mysterious. Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks. We particularly investigate the degrees-of-freedom (DOF) component of SURE, trace of the end-to-end network Jacobian, to quantify the prediction variance. We prove that DOF is well-approximated by the weighted \textit{path sparsity} of the network under incoherence conditions on the trained weights. Empirically, we examine the SURE components as a function of train sample size for both recurrent and non-recurrent (with many more parameters) unrolled networks. Our key observations indicate that: 1) DOF increases with train sample size and converges to the generalization risk for both recurrent and non-recurrent schemes; 2) recurrent network converges significantly faster (with less train samples) compared with non-recurrent scheme, hence recurrence serves as a regularization for low sample size regimes.