Earlier this year, artificial intelligence yielded a practical insight: people like to drink coffee in the morning, so workplaces should find efficient ways to serve coffee. That raised a question that's surprisingly deep -- and can cost serious money to ignore: Is AI actually necessary for this problem? is a question that remains largely unasked in Silicon Valley today. We think it's worth asking. To be sure, modern data products owe a lot of their success to artificial intelligence. Well-considered AI unlocks entirely new types of data-driven insights and cuts the time and money needed for manual data analysis. But ill-considered AI can fail -- expensively.

Do you know the way to San Jose? As they previewed earlier this year, Bosch and Mercedes-Benz have commenced trials for an automated ride-hailing service in the Silicon Valley city of San Jose. To start with, autonomous S-Class Mercedes-Benz vehicles (with safety drivers at the wheel) will shuttle "a select group of users" between North San Jose and downtown. The busy San Carlos/Stevens Creek corridor between west San Jose and downtown should be good test for the self-driving tech used by Mercedes and Bosch. Rather than just playing with prototypes, the companies want to create a production-ready SAE Level 4/5 self-driving system that can be built into different makes and models. To do that, they're using a combination of AI, a million-square foot proving ground in Stuttgart and the real world San Jose ride-hailing tests.

The upshot: Mindtech provides a capability for creating fully annotated synthetic training images to complement real images for improved AI training. We've spent a lot of time looking at AI training and AI inference and the architectures and processes used for each of those. Where the AI task involves images, we've blithely referred to the need for training sets; that's easy, right? After all, if you're trying to train your algorithm to recognize a dog, then just give it a bunch of pictures of dogs (OK, tag them with, "This one contains a dog") and then a bunch of pictures without dogs ("This one contains no dog"), and off you go! Right? And the behemoths like Google and Facebook have oodles of images and videos (videos being collections of frames, each of which is an image), thanks to the free stuff willingly served up by unsuspecting users (including images now and 10 years ago to help improve aging algorithms).

Facial recognition technology has advanced swiftly in the last five years. As University of Texas at Dallas researchers try to determine how computers have gotten as good as people at the task, they are also shedding light on how the human brain sorts information. UT Dallas scientists have analyzed the performance of the latest echelon of facial recognition algorithms, revealing the surprising way these programs--which are based on machine learning--work. Their study, published online Nov. 12 in Nature Machine Intelligence, shows that these sophisticated computer programs--called deep convolutional neural networks (DCNNs)--figured out how to identify faces differently than the researchers expected. "For the last 30 years, people have presumed that computer-based visual systems get rid of all the image-specific information--angle, lighting, expression and so on," said Dr. Alice O'Toole, senior author of the study and the Aage and Margareta Møller Professor in the School of Behavioral and Brain Sciences.

Developers and entrepreneurs wanting to grow their businesses ultimately end up recipients of US financing, working for US companies, and the job opportunities and economic growth these technologies bring head elsewhere. An investment support programme will also be set up to complement the 100m fund, leveraging further financial support from EU Member States. The aim is to multiply investments at the national level by involving national promotional banks, incentivising private sector investments, and making Europe more attractive for start-ups to stay and grow in Europe. This is a particular issue for companies based in central, eastern and south-eastern Europe. Recent research shows that due to difficulties with access to finance, nearly half of start-ups in this region choose to leave, and the European Commission, together with the European Investment Fund, has launched a pilot investment programme which leverages EU resources under the InnovFin Equity programme.

Topics like Artificial Intelligence (AI), the internet of things (IoT), and machine learning are getting lots of hype, but digital twin technology might just be the real game-changer. Digital twin software uses aspects of all the trending tech mentioned (AI, IoT, ML) in a unique way that's changing the way businesses optimize production and investment, and the big boys are already heavily invested. A digital twin is a highly advanced simulation that's used in computer-aided engineering (CAE). It's a digital duplicate that represents a physical object or process, but it is not intended to replace a physical object; it is merely to inform its optimization. Other terms used to refer to digital twin technology include virtual prototyping, hybrid twin technology, and digital asset management, but digital twin is quickly winning out as the most popular name.

This theme is also shown in Udemy's top emerging tech skills of 2020, which included Gatsby.js

Artificial intelligence (AI) has changed the face of rapidly growing technology, and every company nowadays is looking to take maximum benefit from it. In this blog, we will discuss the top benefits of artificial intelligence for businesses. Businesses' first and foremost motive is to engage more customers towards their services, provide the best customer experience, and drive maximum revenue out of it. All the businesses quest to find the right way to ease their efforts and maximize their efficiency with minimum equipment. Their hunt for all these ends right away with the AI because of the extensive benefits of artificial intelligence, which eventually helps them to make their business a profitable one.

The Virginia-class, nuclear-powered, fast-attack submarine, USS North Dakota (SSN 784), transits the Thames River as it pulls into its homeport on Naval Submarine Base New London in Groton, Conn - file photo. Bringing massive amounts of firepower closer to enemy targets, conducting clandestine "intel" missions in high threat waters and launching undersea attack and surveillance drones are all anticipated missions for the Navy's emerging Block V Virginia-class attack submarines. The boats, nine of which are now surging ahead through a new developmental deal between the Navy and General Dynamics Electric Boat, are reshaping submarine attack strategies and concepts of operations -- as rivals make gains challenging U.S. undersea dominance. Eight of the new 22-billion Block V deal are being engineered with a new 80-foot weapons sections in the boat, enabling the submarine to increase its attack missile capacity from 12 to 40 on-board Tomahawks. "Block V Virginias and Virginia Payload Module are a generational leap in submarine capability for the Navy," Program Executive Officer for Submarines Rear Adm. David Goggins, said in a Navy report.

Originally published in SAP Blogs, October 16, 2019. For today's leading deep learning methods and technology, attend the conference and training workshops at Deep Learning World Las Vegas, May 31-June 4, 2020. Machine learning algorithms, especially deep learning neural networks often produce models that improve the accuracy of prediction. But the accuracy comes at the expense of higher computation and memory consumption. A deep learning algorithm, also known as a model, consists of layers of computations where thousands of parameters are computed in each layer and passed to the next, iteratively.