"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
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.
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.
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.
Artificial intelligence and machine learning (AI/ML) systems are growing exponentially around the world and is estimated to generate AU$22.17 trillion to the global economy by 2030. The Australian Government's Artificial Intelligence Technology Roadmap, developed by Data61, identified Australia's need for up to 161,000 new specialist AI workers by 2030. Stuart Ayres, NSW Minister for Jobs, Investment, Tourism and Western Sydney, said: "NeurIPS 2021 will propel Australia's research and innovative discoveries to the forefront – bringing with it opportunities for trade and investment and talent attraction as well as helping to build Sydney's brand as an intellectual capital." Dr. Terrence Sejnowski, President of the NeurIPS Foundation, agreed that it was a "significant step" bringing the conference to Australia. It will be the first time NeurIPS is held in the Asia-Pacific region, and only the third time it has been held outside North America.
Together with the rise of the Internet, access to large repositories of data has helped machine learning technology grow exponentially. The incredibly quick pace of growth was unprecedented. As a result, it is obvious that AI will make a significant impact on the world in the years to come. However, with the numerous established and emerging fields of AI around today, such a blanket statement doesn't provide much concrete meaning. What fields and applications of AI are receiving the most investment and development?
AI has the capacity to make decisions in real-time, based on pre-installed algorithms and efficient computing technologies. With an HR department encompassing the human element and AI, companies can provide an enhanced experience for their candidates and employees, writes Khalid Durrani, Digital Marketing Manager, Cubix. "Deep-learning will transform every single industry," said Andrew Ng, a Chinese-American scientist excelling in machine learning and AI. McKinsey's forecast on machine learning backs up his statement claiming that by 2030, AI will have a significant impact of $13 trillion on the global economy. HR professionals understand the importance of optimizing the combination of the human mind and machine learning for a seamless workflow and intuitive work environment.
The chip designer Ambarella has announced two new chips for automotive cameras and advanced driver assistance systems (ADAS) based on its CVflow architecture for artificial intelligence processing. The Santa Clara, California-based company introduced the CV22FS and CV2FS automotive camera (SoC) systems with CVflow AI processing and ASIL-B compliance to enable critical safety applications. Ambarella will also demonstrate applications with its existing chips, as well as a robotic platform and Amazon SageMaker Neo technology to train machine learning models, at CES 2020, the big technology fair in Las Vegas this week. The company, which was made public in 2011, started as a manufacturer of low-power chips for video cameras. But he turned that ability into computer vision experience and launched his CVflow architecture in 2018 to create low-power artificial intelligence chips.