If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Hi, I am TuhinBanik; born in North East India and raised in Kolkata. Throughout my childhood days, I had a keen interest in technology and it used to drive me crazy whilst thinking on how techgiants come up with new inventions and technologies. At a very young age, I was curious about the things which might change civilization in the near future. Well, I came out with four main pillars which will make a change in the universe in future. With the above points on my mind, I have come up with a concept under the brand name of ThatWare, where my main vision is to enhance digital marketing with full automation using artificial intelligence and machine learning.
Applying just a bit of strain to a piece of semiconductor or other crystalline material can deform the orderly arrangement of atoms in its structure enough to cause dramatic changes in its properties, such as the way it conducts electricity, transmits light, or conducts heat. Now, a team of researchers at MIT and in Russia and Singapore have found ways to use artificial intelligence to help predict and control these changes, potentially opening up new avenues of research on advanced materials for future high-tech devices. The findings appeared in early February in the Proceedings of the National Academy of Sciences, in a paper authored by MIT professor of nuclear science and engineering and of materials science and engineering Ju Li, MIT Principal Research Scientist Ming Dao, and MIT graduate student Zhe Shi, with Evgenii Tsymbalov and Alexander Shapeev at the Skolkovo Institute of Science and Technology in Russia, and Subra Suresh, the Vannevar Bush Professor Emeritus and former dean of engineering at MIT and current president of Nanyang Technological University in Singapore. Already, based on earlier work at MIT, some degree of elastic strain has been incorporated in some silicon processor chips. Even a 1% change in the structure can in some cases improve the speed of the device by 50 percent, by allowing electrons to move through the material faster.
On March 18, 2018, Elaine Herzberg became the first pedestrian in the world to be killed by an autonomous vehicle after being hit by a self-driving Uber SUV in Tempe, AZ, at about 10 p.m. Video released by the local police department showed the self-driving Volvo XC90 did not appear to see Herzberg, as it did not slow down or alter course, even though she was visible in front of the vehicle prior to impact. Subsequently, automotive engineering experts raised questions about Uber's LiDAR technology.12 LiDAR, or "light detection and ranging," uses pulsed laser light to enable a self-driving car to see its surroundings hundreds of feet away. Velodyne, the supplier of the Uber vehicle's LiDAR technology, said, "Our LiDAR is capable of clearly imaging Elaine and her bicycle in this situation. However, our LiDAR does not make the decision to put on the brakes or get out of her way" ... "We know absolutely nothing about the engineering of their [Uber's] part ... It is a proprietary secret, and all of our customers keep this part to themselves"15 ... and "Our LiDAR can see perfectly well in the dark, as well as it sees in daylight, producing millions of points of information. However, it is up to the rest of the system to interpret and use the data to make decisions. We do not know how the Uber system of decision making works."11
Most of what's out there is either too fluffy or too mathy, either too general or too focused on specific applications, too disconnected from business outcomes and metrics, and too undirected. Jerry Hartanto leads the AI and Self-Service BI Practice at Trace3, a technology solution provider with growing consulting practices including data intelligence, cloud solutions, cyber analytics, devops, and data center solutions. Hartanto's background is in management consulting, corporate/business strategy, marketing and sales, operations and process improvement, and product development and engineering. He has a BS in Electrical Engineering from McGill University, an MS is Electrical Engineering from Johns Hopkins University, and an MBA from the University of Michigan. He can be reached at email@example.com.
My journey into machine learning began in the summer of 2016. It all started at a barbecue party at the home of my fiancé's aunt and uncle's in northern Stockholm. I was sitting outside at a garden table together with the older men of her family. These are old and tough Finish men, her granddad (96 years old) fought in the war against the Russians. As you can imagine, as the new kid on the block, I was keeping a low profile and my mouth shut.
This talk will focus on all the engineering aspects involved in Machine Learning at scale. A common warning shared with aspiring Data Scientists & ML engineers is that 90% of the work is about gathering, cleaning and validating data plus deploying and monitoring models. Yet for a long time most of the open source ML tooling focused on the modelling part. We will first give an overview of the different ML Engineering frameworks out there, both open and closed source. We will then focus in on Kubeflow Pipelines and TFX (Tensorflow Extended), both of which are open source and model agnostic, by giving an end-to-end example highlighting why these frameworks are incredibly powerful.
Dan Huttenlocher SM '84, PhD '88, a seasoned builder and leader of new academic entities at Cornell University, has been named as the first dean of the MIT Stephen A. Schwarzman College of Computing. He will assume his new post this summer. A member of Cornell's computer science faculty since 1988, Huttenlocher has served since 2012 as the founding dean of Cornell Tech, a graduate school in New York City that focuses on digital technology and its economic and societal impacts. Previously, he helped create and then led Cornell's Faculty of Computing and Information Science. Huttenlocher returns to MIT with widely published scholarship in computer science, as well as a strongly interdisciplinary approach to computing.
Imagine a highly sophisticated body armor that is a tough as it is flexible, a shield that consists largely of water, but remains strong enough to prevent mechanical penetration. Now imagine that this armor is not only strong, but also soft and stretchy, so much so that the wearer is able to move their body parts with ease, whether they're swimming in water, walking across the ground or rushing to escape danger. That description might sound like a suit worn by a fictional hero in the DC Comics franchise, but it actually describes portions of a lobster's exoskeleton. Researchers at the Massachusetts Institute of Technology and Harvard believe the soft membrane covering the animal's joints and abdomen ---- a material that is as tough as the industrial rubber used to make car tires and garden hoses ---- could guide the development of a new type of flexible body armor for humans, one designed to cover joints like knees and elbows. The researchers' findings appeared in a recent edition of the journal Acta Materialia.
Robots are progressively being utilized to teach students in the classroom for various subjects crosswise over science, maths and language. Despite the fact that grown-ups may rapidly end up disillusioned with machines that aren't exceptionally insightful or don't talk more than scripted sentences, kids are liable to chat with, tune in to and generally treat even fundamental robots as social creatures, says Tony Belpaeme, a social roboticist at Ghent University in Belgium. Scientists like Breazeal and Belpaeme are attempting to use that association to make robots connect with children as coaches and peep learners. However, according to a research, while students appreciate learning with robots, tutors are somewhat hesitant to utilize them in the classroom. In our examination, which saw staff and students connect with the Nao humanoid robot, tutors believe they were more incredulous of robots being deployed into the classroom.
Flipboard, a news aggregation platform, is an extreme testing ground for AI recommendations, given that it features as many as 300,000 articles each day. Of the 150 people who work for Flipboard, about 40 are focused on tech, engineering and data science, which are the teams responsible for creating and monitoring AI tools. Those tools scan for contextual clues to tag articles by topic and keyword, Cora said. The platform's AI features also weed out identical articles, and block spam domains that attempt to spoof legitimate websites. AI does not manage the entire recommendation process, however.