TenD Information System & Technology company with a reputation for the quality skilled professional team and highly satisfied customers in Artificial Intelligence, Blockchain, Robotics, Machine learning, Intelligent ERP systems and multi-domains like Banking & Finance, Oil & Gas, healthcare, Insurance. Innovative health information systems and services that transform the administrative and clinical operations of healthcare organizations of all sizes. The Automation revolution has been awaited for a long time. The success of any engagement is as good as the outcome it produces. TenD is a trailblazer with the vision to provide effective guidance by means of Training, Consulting & Delivery & a wide range of innovative products.

The AI Group of experts at the OECD (AIGO) completed its recommendations in meetings at the World Government Summit in Dubai earlier in February. "The contributions of the AI expert group mark an important milestone in our efforts to ensure that governments and people share the economic and social benefits of AI and understand and minimise the risks," said Andrew Wyckoff, director of the OECD's Science, Technology and Innovation Directorate, which is spearheading the work. The recommendations cover a broad range of public and private policy matters that are being transformed by artificial intelligence systems. They include a common understanding of AI concepts including: what is an AI system? What is the AI system lifecycle?

Could a program detect potential terrorists by reading their facial expressions and behavior? This was the hypothesis put to the test by the US Transportation Security Administration (TSA) in 2003, as it began testing a new surveillance program called the Screening of Passengers by Observation Techniques program, or Spot for short. While developing the program, they consulted Paul Ekman, emeritus professor of psychology at the University of California, San Francisco. Decades earlier, Ekman had developed a method to identify minute facial expressions and map them on to corresponding emotions. This method was used to train "behavior detection officers" to scan faces for signs of deception.

Sweden's Volvo Buses began trial services for its first full-sized driverless electric bus in Singapore on Tuesday, in what its president has dubbed "the world's first." Speaking to CNBC's "Squawk Box," Hakan Agnevall, the president of Volvo Buses, said the vehicle is the "first full-sized, autonomous electric bus in the world." The 12-meter long Volvo 7900 electric bus is jointly developed by the Swedish automobile firm, together with Singapore's Nanyang Technological University (NTU), whose researchers will oversee the artificial intelligence developments of the vehicle. "We really think that autonomous (vehicles) can really transform public transport," Agnevall told CNBC on Tuesday. The zero-emissions vehicle will require 80 percent less energy than its diesel powered counterparts.

The growing capabilities of Artificial Intelligence (AI) are changing the world as we know it. Ideas once confined to the imagination are now becoming a reality, with AI technology creating outputs either largely or entirely independent from human intervention. In 2018, an album called I AM AI was the first of its kind to be entirely composed and produced by AI technology, through a music composition software called Amper. Deep learning networks allow Amper to analyse data to learn chords, notes, genres, tempo and song length to independently compose melodies. A qualified person is an Australian citizen or a person resident in Australia.[1]

Last month, the U.S. Army put out a call to private companies for ideas about how to improve its planned semi-autonomous, AI-driven targeting system for tanks. In its request, the Army asked for help enabling the Advanced Targeting and Lethality Automated System (ATLAS) to "acquire, identify, and engage targets at least 3X faster than the current manual process." But that language apparently scared some people who are worried about the rise of AI-powered killing machines. In response, the U.S. Army added a disclaimer to the call for white papers in a move first spotted by news website Defense One. Without modifying any of the original wording, the Army simply added a note that explains Defense Department policy hasn't changed.

You can no longer believe what you see. Deepfakes, which use artificial intelligence to make people appear to say and do things in videos that they haven't said or done, have been growing more realistic at an alarming rate. And it's a matter of time before they're used to try to circumvent cybersecurity. Steve Grobman, chief technology officer at cybersecurity firm McAfee, and Celeste Fralick, chief data scientist, warned in a keynote speech at the RSA security conference in San Francisco that the tech has reached the point where you can barely tell with the naked eye whether a video is fake or real. They showed a video where Fralick's words were coming out of a video of Grobman's face, even though Grobman never said those words.

We show that a simple randomized sketch of the matrix multiplicative weight (MMW) update enjoys the same regret bounds as MMW, up to a small constant factor. Unlike MMW, where every step requires full matrix exponentiation, our steps require only a single product of the form $e^A b$, which the Lanczos method approximates efficiently. Our key technique is to view the sketch as a randomized mirror projection, and perform mirror descent analysis on the expected projection. Our sketch solves the online eigenvector problem, improving the best known complexity bounds. We also apply this sketch to a simple no-regret scheme for semidefinite programming in saddle-point form, where it matches the best known guarantees.

We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density function is discontinuous with respect to, while further providing runtime checks for boundary crossings. This enables the introduction of new inference engines that are able to exploit gradient information, while remaining efficient for models which are not everywhere differentiable. We demonstrate this ability by incorporating a discontinuous Hamiltonian Monte Carlo (DHMC) inference engine that is able to deliver automated and efficient inference for non-differentiable models. Our system is backed up by a mathematical formalism that ensures that any model expressed in this language has a density with measure zero discontinuities to maintain the validity of the inference engine.

Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures. For the purpose of supporting human operators of chemical plants, we are developing an AI system that can semi-automatically synthesize operation procedures for efficient and stable operation. Our system can provide not only appropriate operation procedures but also reasons why the procedures are considered to be valid. This is achieved by integrating automated reasoning and deep reinforcement learning technologies with a chemical plant simulator and external knowledge. Our preliminary experimental results demonstrate that it can synthesize a procedure that achieves a much faster recovery from a malfunction compared to standard PID control.