An era of rapid evolution of structures and devices driven by new capabilities in machine learning, nanoscale experiments, and economic modeling is unfolding, MIT materials researchers revealed during the annual Industrial Liaison Program (ILP) Research and Development Conference. Pointing to progress in areas as diverse as biomedical devices, computing, and energy, Carl Thompson, director of the MIT Materials Research Laboratory and the Stavros Salapatas Professor of Materials Science and Engineering, noted the convergence of advances in nanoscale imaging and computerized prediction of materials structure and behavior with analysis of the likelihood of success in the marketplace. A longstanding problem with green energy sources, including solar and wind, is their power production varies widely and is often mismatched to demand. Thompson noted the work of Jessika Trancik, an associate professor of energy studies who has identified the economic value of various energy storage methods based on their relative costs. These methods include compressed air, pumped water, and vanadium-based flow batteries, in addition to traditional cell-type batteries such as nickel-cadmium, lithium ion and sodium-sulfur combinations.

Machine learning algorithms now underlie much of the software we use, helping to personalize our news feeds and finish our thoughts before we're done typing. But as artificial intelligence becomes further embedded in daily life, expectations have risen. Before autonomous systems fully gain our confidence, we need to know they are reliable in most situations and can withstand outside interference; in engineering terms, that they are robust. We also need to understand the reasoning behind their decisions; that they are interpretable. Aleksander Madry, an associate professor of computer science at MIT and a lead faculty member of the Computer Science and Artificial Intelligence Lab (CSAIL)'s Trustworthy AI initiative, compares AI to a sharp knife, a useful but potentially-hazardous tool that society must learn to weild properly.

Amazon's use of facial recognition has sparked fears of an authoritarian future resembling that described by George Orwell. Privacy advocates and campaigners have said Amazon using facial recognition in its smart doorbells could provide the perfect tool for extreme surveillance. The campaigners called the technology'nightmarish' and'disturbing'. Amazon bought the US-based firm Ring earlier this year. The doorbell company has previously filed for a patent to use facial recognition in its products.

Self-driving cars have come a long way from being a sci-fi fantasy, with the likes of Google, Uber and Tesla all establishing a stake in the race to bring them to the road. But as autonomous vehicles have become more commonplace, so has criticism around a lack of safety in the technology. Uber, Google and Tesla have all exhibited elements of'recklessness' in their development of autonomous vehicles, as shown by the slew of accidents that have recently occurred, according to Gizmodo. Crashes involving self-driving cars have led to injuries and, in some cases, even death. And often, the autonomous vehicles escape the blame for the incident - instead, it has fallen on the human test drivers who were supposed to be watching the road.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. The bar has now been set for robot holiday videos, thanks to FZI. Still waiting for a robot with a cookie to show up at my door.

Listen up, Apple Music subscribers--HomePod isn't the only smart speaker that can handle your tunes anymore. Apple has teamed up with Amazon to allow streaming on Echo devices, so all of your songs, albums, and playlists are good to go. Here's how to set it all up: Setting up Apple Music on your Echo device takes just a few taps. Now you can say, "Alexa, play'Imagine' by Ariana Grande on Apple Music" and it'll start playing. You can also ask Alexa to stream playlists, charts, and Beats 1 radio.

An increasing number of modern antivirus solutions rely on machine learning (ML) techniques to protect users from malware. While ML-based approaches, like FireEye Endpoint Security's MalwareGuard capability, have done a great job at detecting new threats, they also come with substantial development costs. Creating and curating a large set of useful features takes significant amounts of time and expertise from malware analysts and data scientists (note that in this context a feature refers to a property or characteristic of the executable that can be used to distinguish between goodware and malware). In recent years, however, deep learning approaches have shown impressive results in automatically learning feature representations for complex problem domains, like images, speech, and text. Can we take advantage of these advances in deep learning to automatically learn how to detect malware without costly feature engineering?

Decision Tree (CART) - Machine Learning Fun and Easy https://www.udemy.com/machine-learnin... Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART). So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node. To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/

Today, Machine Learning is one of the most important trends in every area of software engineering. No longer limited to researchers and analysts, it's a vital part of everything from cybersecurity to web development. To help you get started with Machine Learning, we've put together this list of 5 free Machine Learning eBooks from Packt. You can download as many of them as you like -- all you'll need to do is register when you download your first title. But there's an important reason it's the first free eBook on this list: Python is the go-to language if you want to develop Machine Learning models.

It's hard to live a day in the developed world without using a Unilever product. The multinational manufactures and distributes over 400 consumer goods brands covering food and beverages, domestic cleaning products and personal hygiene. With so many processes to coordinate and manage, artificial intelligence is quickly becoming essential for organizations of its scale. This applies to both research and development as well as the huge support infrastructure needed for a business with 170,000 employees. Recently it announced that it had developed machine learning algorithms capable of sniffing your armpit and telling you whether you are suffering from body odors.