A child who has never seen a pink elephant can still describe one -- unlike a computer. "The computer learns from data," says Jiajun Wu, a PhD student at MIT. "The ability to generalize and recognize something you've never seen before -- a pink elephant -- is very hard for machines." Deep learning systems interpret the world by picking out statistical patterns in data. This form of machine learning is now everywhere, automatically tagging friends on Facebook, narrating Alexa's latest weather forecast, and delivering fun facts via Google search. But statistical learning has its limits.

Our core retail experience hasn't changed much in recent years. We go into a store (or to an online portal), we browse through the available options, try them out and make the purchase. Well, that's where the disruptions are happening thanks to artificial intelligence (AI). It has completely transformed the way we handle our retail experience – both from a customer's perspective as well from a business standpoint. Artificial Intelligence creates an opportunity for retailers to bridge the gap between virtual and physical sales channels. Brands are progressively using Artificial Intelligence to reduce cost, improve efficiency, achieve operational agility and increase the speed of decision making in the retail world. According to IBM's recent study, AI-driven intelligent automation in the retail and consumer products industries is projected to leap from 40 percent of companies today to more than 80 percent in the next three years.

"You must unite behind the science. You must do the impossible. Because giving up can never ever be an option" – Greta Thunberg Most woke Millenials have updated their vocabulary to use terms that more accurately describe the environmental crises facing the world. 'Climate change' has now turned to'climate emergency' – but there are others who haven't yet understood how the situation has worsened over the years. According to a report, seven million people have been displaced globally due to natural disasters including storms and floods between January and June 2019 and the number is estimated to grow more than triple by the end of the year.

I love tech, innovation, AI etc. I worked with it for decades in Latin America, not in Silicon Valley with a positive ecosystem, investors etc. OK?;-) We Human Beings developed robots to serve us, not vice versa.

Deep learning is a fascinating sub field of machine learning that creates artificially intelligent systems inspired by the structure and function of the brain. The basis of these models are bio-inspired artificial neural networks that mimic the neural connectivity of animal brains to carry out cognitive functions such as problem solving. A field with the most impressive results of neuromorphic computing is that of visual image analysis. Similar to how our brains learn to recognize objects in order to make predictions and act upon them, artificial intelligence must be shown millions of pictures before they are able to generalize them in order to make their best educated guesses for images they have never seen before. Professor Cheol Seong Hwang from the Department of Material Science and Engineering at Seoul National University and his research team have developed a method to accelerate the image recognition process by combining the inherent efficiency of resistive random access memory (ReRAM) and cross-bar array structures, two of the most commonly used hardware. Many of us have performed a reversed image search to find information based on a certain image in order to browse similar results.

Preparing employees for jobs of the future will require leaders in business, government, and higher education to work together. That was a major takeaway from a conference at Northeastern's Toronto campus to discuss the results of a Northeastern-Gallup poll on attitudes toward artificial intelligence in the U.S., the U.K and Canada. "We have to do more partnering with universities on that note," Helena Gottschling, the chief human resources officer at the Royal Bank of Canada, told an audience comprised of the three sectors gathered for a conference at Northeastern's Toronto campus this week. She added that employers need to do more to communicate "what we need in our workforce through the universities, so that we're helping to inform the skills and capabilities that the universities are growing through the student populations." The conference follows the publication of a survey conducted by Northeastern and Gallup that revealed an international cross-section of opinions about artificial intelligence as economies around the world undergo the transformative move to automation.

The third annual Modern Artificial Intelligence (AI) seminar series at NYU Tandon, bringing together students and experts to discuss recent advances in the field, wrapped up on December 6 with a presentation by Raia Hadsell, Head of Robotics Research at DeepMind. In the final presentation of the series, sponsored by the Department of Electrical and Computer Engineering and organized by Professor Anna Choromanska, Hadsell explored ways in which human learning can inform machine learning systems to develop highly sophisticated AI to solve complex real-world tasks. The Fall roster kicked off in early October with a lecture by Facebook AI Research's Leon Bottou. The researcher, who harbors the long-term ambition of replicating human-level intelligence, examined causal inference, or finding the relationship between existing facts and objects. Next, on November 14, Francis Bach, researcher at Institut National de Recherche en Informatique et en Automatique (INRIA) in France, spoke about a new generation of "distributed optimization" schemes that are critically needed to scale algorithms to massive data.

Artificial Intelligence is generally seen as a great advance and benefit for mankind. Smart humans, however, see it as our undoing and even possibly our extermination. Much of modern technology has far more prospect for harm than for good. There are a large number of technologies that impose massive costs on life on Earth. The article below from Crime & Power, "A Hard Look At Artificial intelligence" by Jerry Day, sounds like a horror science fiction story.

A new research project in Australia is using motion detectors and muscle sensors to track sheep shearers in an effort to minimize on the-job-injuries. Sheep shearers are six times more likely to be injured in the workplace than the average Australian worker. Data from sensors attached to sheep shearers will be used to model worker movement throughout the workday and test new ways of doing the job without risking injury. The study, a joint project between University of Melbourne and the trade group Australian Wool Innovation, uses sensors to measure electrical activity in muscles. These sensors are placed directly on the skin of the lower back and upper thighs, the ABC reported, while motion detectors are placed around the joints to track a worker's posture and shearing motions.

Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian decision-making workflow. First, Bayesian calibration is performed to estimate model parameters. We then use the posterior distribution from Bayesian analysis to compute optimal firing conditions to hit a target via a single-stage stochastic program. The case study demonstrates the ability of Bayesian hybrid models to overcome systematic bias from missing physics with less data than the pure machine learning approach. Ultimately, we argue Bayesian hybrid models are an emerging paradigm for data-informed decision-making under parametric and epistemic uncertainty.