AI allows machines to carry out all sorts of tasks that used to be the domain of humans alone. Need to run quality control on a factory production line? Set up an AI-powered camera to spot defects. Machine learning can identify potential tumors from scans and flag them to a doctor. But applications like this are useful only so long as they're fast and secure.

When it comes to building an Artificially Intelligent (AI) application, your approach must be data first, not application first. Dependencies on data cost more than software dependencies, but are constantly overlooked. To build a face detection and/or face recognition model it's important to know available data set and data augmentation approaches to be followed for training the model. Note that there's difference between Face Identification and Face Recognition. It is process of comparing face image with claimed identity, basically it is a "One-to-one matching".

Fearing that a newfangled technology would put them out of work, neighbors broke into the house of James Hargreaves, the inventor of the spinning jenny, and destroyed the machine and also his furniture in 18th-century England. Queen Elizabeth I denied an English priest a patent for an invention that knitted wool, arguing that it would turn her subjects into unemployed beggars. A city council dictated that Anton Möller, who invented the ribbon loom in the 16th century, should be strangled for his efforts. But centuries of predictions that machines would put humans out of work for good -- a scenario that economists call "technological unemployment" -- have always turned out to be wrong. Technology eliminated some jobs, but new work arose, and it was often less grueling or dangerous than the old.

Raytheon Co. announced on Monday it has begun work on a machine-learning technology allowing machines to teach machines through artificial intelligence use. The $6 million contract is one of four, valued at a total of $20.9 million, between the U.S. Defense Research Projects Agency and Raytheon BBN Technologies, SRI International, BBN Technologies, Teledyne Scientific & Imaging and BAE Systems. The new deal calls for development of systems able to communicate information and the conditions of the initial learning, and recommended strategies and situations calling for those strategies. Known as CAML, or Categorical Abstract Machine Language, it uses a process similar to that in a video game; instead of rules, the system offers a list of choices and identification of a goal. By repeatedly playing the game, the system will learn the best way to achieve the goal.

Former Obama administration advisors say the White House regulatory AI principles announced this week are a good start in many ways, but they're incorrect in their oversimplified mandate to avoid overregulation of private business use, and that the Trump administration could face an uphill battle in its appeal to the rest of the world. Though the Trump administration has developed a reputation for blaming the Obama administration when things go wrong or trying to erase Obama-era policy, on artificial intelligence policy, at times the Trump administration has remained strikingly similar to its predecessor. This was evident in the AI research and development strategy plan for federal agencies released in summer 2019. In some instances, like with White House deputy CTO and assistant director of AI at the White House Office of Science and Technology Policy (OSTP) Dr. Lynne Parker who also served in the Obama administration, the same people drive White House AI policy. The list of 10 AI principles are meant to guide US federal agencies as they consider making rules that regulate AI. White House CTO Michael Kratsios said he wants other countries around the world to adopt similar policies.

More than a decade ago, Internet analyst and new media scholar Clay Shirky said: "The only real way to end spam is to shut down e-mail communication." Will shutting down the Internet be the only way to end deepfake propaganda in 2020? Today, anyone can create their own fake news and also break it. Online propaganda is more misleading and manipulative than ever. Deepfakes, a specific form of disinformation that uses machine-learning algorithms to create audio and video of real people saying and doing things they never said or did, are moving quickly toward being indistinguishable from reality.

Designer and Fulbright fellow Stanislas Chaillou has created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans. After an initial study in the potential of AI-generated floor plans, Chaillou's project developed into training and tuning an array of models on specific architectural styles: Baroque, Row House, Victorian Suburban House, & Manhattan Unit. The study reveals how style carries a fundamental set of functional rules that define mechanics of space and control the internal organization of the plan.

Engineers from Duke University and the Institut de Physique de Nice in France have developed a new method to identify objects using microwaves that improves accuracy while reducing the associated computing time and power requirements. The system could provide a boost to object identification and speed in fields where both are critical, such as autonomous vehicles, security screening and motion sensing. It also jointly determines optimal hardware settings that reveal the most important data while simultaneously discovering what the most important data actually is. In a proof-of-principle study, the setup correctly identified a set of 3D numbers using tens of measurements instead of the hundreds or thousands typically required. The results appear online on December 6 in the journal Advanced Science and are a collaboration between David R. Smith, the James B. Duke Distinguished Professor of Electrical and Computer Engineering at Duke, and Roarke Horstmeyer, assistant professor of biomedical engineering at Duke. "Object identification schemes typically take measurements and go to all this trouble to make an image for people to look at and appreciate," said Horstmeyer. "But that's inefficient because the computer doesn't need to'look' at an image at all." "This approach circumvents that step and allows the program to capture details that an image-forming process might miss while ignoring other details of the scene that it doesn't need," added Aaron Diebold, a research assistant in Smith's lab. "We're basically trying to see the object directly from the eyes of the machine."

Show your working: generations of mathematics students have grown up with this mantra. Getting the right answer is not enough. To get top marks, students must demonstrate how they got there. Now, machines need to do the same. As artificial intelligence (AI) is used to make decisions affecting employment, finance or justice, as opposed to which film a consumer might want to watch next, the public will insist it explains its working.

Transformative technology can be powerful not just in its own right, but where different technologies converge. Artificial intelligence, in particular, can be a technology supercharger. The second Insight in our series looking at the digital future (and adapted from an article written for the 2019 Bristol Technology Showcase) considers the transformative power of machine learning. Artificial intelligence, in the form of machine learning or deep learning, relies on finding and mapping the patterns in data and then using more and more data to refine and deepen the accuracy of that model, without the need for human-generated linear hand-coding. Part of the reason why this has become such a powerful tool is the speed and availability of almost limitless computing power, thanks to Moore's law and the development of the cloud, respectively.