Engineers program tiny robots to move, think like insects


While engineers have had success building tiny, insect-like robots, programming them to behave autonomously like real insects continues to present technical challenges. A group of Cornell University engineers has been experimenting with a new type of programming that mimics the way an insect's brain works, which could soon have people wondering if that fly on the wall is actually a fly. The amount of computer processing power needed for a robot to sense a gust of wind, using tiny hair-like metal probes imbedded on its wings, adjust its flight accordingly, and plan its path as it attempts to land on a swaying flower would require it to carry a desktop-size computer on its back. Silvia Ferrari, professor of mechanical and aerospace engineering and director of the Laboratory for Intelligent Systems and Controls, sees the emergence of neuromorphic computer chips as a way to shrink a robot's payload. Unlike traditional chips that process combinations of 0s and 1s as binary code, neuromorphic chips process spikes of electrical current that fire in complex combinations, similar to how neurons fire inside a brain.

Machine Learning Engineer - Video


Muse is creating an advanced AI to search the world's video. We are looking for engineers who want to be at the forefront of search and discovery to make searching video as intuitive as recalling a memory in your mind. We are working across the frontiers of storage & distributed file systems, machine learning infrastructure, high performance computing and intuitive user interfaces across (web, mobile, voice and beyond). You will join the machine learning team in sunny Lisbon with a focus on implementing and productionizing the latest machine learning algorithms to analyze video and interpret the knowledge inside video across speech, people, objects, actions and locations. We are agnostic to the algorithms, but care deeply about the computational efficiency of these algorithms in the analysis of a video file.

Data lifting and why it has to be made easy


At the end of 2017, there will be 8.4 billion connected things in use worldwide up 31 percent from 2016, and this figure is expected to reach 20.4 billion by 2020. When Internet of Things (IoT) as an industry took off in India, it spawned a host of startups selling edge devices that could gather and crunch data from corporate customers. These startups ran into one fundamental problem, which was data lifting. The data was so voluminous that these startups took so much time to organise them that they ran out of money to keep the companies afloat. In the end, their services were just organising data for customers with very little insights.

Deep Learning Joins Process Control Arsenal Semiconductor Manufacturing & Design Community


At the 2017 Advanced Process Control (APC 2017) conference, several companies presented implementations of deep learning to find transistor defects, align lithography steps, and apply predictive maintenance. The application of neural networks to semiconductor manufacturing was a much-discussed trend at the 2017 APC meeting in Austin, starting out with a keynote speech by Howard Witham, Texas operations manager for Qorvo Inc. Witham said artificial intelligence has brought human beings to "a point in history, for our industry and the world in general, that is more revolutionary than a small, evolutionary step." People in the semiconductor industry "need to take what's out there and figure out how to apply it to your own problems, to figure out where does the machine win, and where does the brain still win?" At Seagate Technology, a small team of engineers stitched together largely packaged or open source software running on a conventional CPU to create a convolution neural network (CNN)-based tool to find low-level device defects. In an APC paper entitled Automated Wafer Image Review using Deep Learning, Sharath Kumar Dhamodaran, an engineer/data scientist based at Seagate's Bloomington, Minn.

How AI Is Catching Crooks in Call Centers


This article first appeared in Data Sheet, Fortune's daily newsletter on the top tech news. I was in Atlanta Thursday, and for the second time in two weeks I was reminded that Silicon Valley has no monopoly on innovation. I visited a company adjacent to Georgia Tech University called Pindrop, which makes voice authentication and security products used by financial services companies and the like to cut down on fraud. Its AI-driven software listens to customer responses and cuts down on annoying verification questions as well as fraudulent behavior. Pindrop has some mind-blowing capabilities.

Andrew Ng Says Enough Papers, Let's Build AI Now! – Synced – Medium


While the scientific community continues looking for new breakthroughs in artificial intelligence, Andrew Ng believes the tech we need is already here. Stop publishing, and start transforming people's lives with technology!" The three-day conference drew over 1,400 attendees from 17 different countries to the Santa Clara Convention Center. Ng's keynote speech was titled "AI is the new electricity". The number of papers submitted across arxiv-sanity categories such as machine learning, computer vision, and speech recognition has dramatically risen since 2012, says OpenAI's Senior Engineer Andrej Karpathy.

Hot or not: LinkedIn data shows which jobs and skills are on the rise and which are fading


Machine learning is in; Flash is out. Data scientists are in great demand, specialized developers, not so much. These are just a few of the trends LinkedIn picked up in its 2017 Emerging Jobs Report. It's no surprise that jobs in tech are growing faster than any other industry. The fastest growing job over the last five years is machine learning engineer, as the number of open positions on LinkedIn has multiplied by nearly 10X.

Google AI, Kepler spacecraft find first 8-planet system outside of our own


Before a Google machine learning program discovered an eighth planet in an exoplanet system using NASA spacecraft observations, the only other known system with as many planets was our own. Kepler has been scanning for planet systems beyond our solar system since 2009 and has made countless discoveries of planets orbiting stars, but so far none of those systems have contained as many planets as our own. That changed recently with the help of a Google AI program that found an an eighth planet orbiting the sun-like star Kepler 90, NASA scentists announced Thursday. Like Earth, this new planet, Kepler-90i, is the third rock from its sun. But it's much closer to its sun -- orbiting in just 14 days -- and therefore is a scorching 800 degrees Fahrenheit (427 Celsius) at the surface. In fact, all eight planets are scrunched up around this star, orbiting closer than Earth does to our sun. Kepler scientists already knew of seven planets orbiting Kepler 90, but hadn't been able to detect Kepler 90i. Google AI software engineer Christopher Shallue and NASA Sagan Postdoctoral Fellow Andrew Vanderburg used data collected by NASA's exoplanet hunter, the Kepler Space Telescope, to develop a machine-learning computer program. It focuses on weak planetary signals -- so feeble and numerous it would take humans ages to examine. "The machine-learning model was able to look at more signals than it would be reasonable for humans to look at," said Christopher Shallue, a senior software engineer at Google AI. NASA officials said this was the first time an algorithm like this has been used to confirm an exoplanet, and they expect more discoveries. "I'm on the edge of my seat to see what Chris and Andrew learn when they apply their algorithm to those sections of the sky," Kepler project scientist Jessie Dotson said. The Associated Press contributed to this report.

What are machine learning engineers?


We've been talking about data science and data scientists for a decade now. While there's always been some debate over what "data scientist" means, we've reached the point where many universities, online academies, and bootcamps offer data science programs: master's degrees, certifications, you name it. The world was a simpler place when we only had statistics. But simplicity isn't always healthy, and the diversity of data science programs demonstrates nothing if not the demand for data scientists. As the field of data science has developed, any number of poorly distinguished specialties have emerged.

Machine Learning Engineer posted by Lenovo on


Lenovo is a $46 billion global Fortune 250 company and leader in providing innovative consumer, commercial and enterprise technology. Our portfolio of high-quality, secure products and services covers PCs, workstations, servers, storage, smart TVs and a family of mobile products like smartphones (including the Motorola brand), tablets and apps. Everyone here at Lenovo is an integral part of the company, working together, across continents, cultures and innovations, all comprised in a friendly, fast-paced, work environment that focuses on one common goal: to be known as the best in what we do.