He graduated w/Special Honors in ChE & later Cert. in Quality Mgmt. Syndicated research (silicon photonics); writes for trade press and web communities. Served Fortune 1000 and FTSE 250 companies in a variety of projects, including global market/product strategy and most recently deep analytics and forecasting. Following ten years in government research and management (Deputy Director, National Measurement Laboratory (US DoC NIST) and Chief, Chemical Engineering Division of NIST), Mr. Bateman worked at several start-ups in electronics and antennas, resulting in 100s of products and several patents. Mr. Bateman led efforts to bring design and manufacturing of telematics and in-building antennas to China and Malaysia, and was key in creating an Automotive Connectivity Unit in Laird, and led technical diligence for multiple acquisitions and creation of an Infrastructure Antenna Unit.

Given the growing ubiquity of machine learning and artificial intelligence in healthcare settings, it's become increasingly important to meet patient needs and engage users. And as panelists noted during a HIMSS Machine Learning and AI for Healthcare Forum session this week, designing technology with the user in mind is a vital way to ensure tools become an integral part of workflow. "Big Tech has stumbled somewhat" in this regard, said Bill Fox, healthcare and life sciences lead at SambaNova Systems. "The patients, the providers – they don't really care that much about the technology, how cool it is, what it can do from a technological standpoint. "It really has to work for them," Fox added. Jai Nahar, a pediatric cardiologist at Children's National Hospital, agreed, stressing the importance of human-centered AI design in healthcare delivery. "Whenever we're trying to roll out a productive solution that incorporates AI," he said, "right from the designing [stage] of the product or service itself, the patients should be involved." That inclusion should also expand to provider users too, he said: "Before rolling out any product or service, we should involve physicians or clinicians who are going to use the technology." The panel, moderated by Rebekah Angove, vice president of evaluation and patient experience at the Patient Advocate Foundation, noted that AI is already affecting patients both directly and indirectly. In ideal scenarios, for example, it's empowering doctors to spend more time with individuals. "There's going to be a human in the loop for a very long time," said Fox. "We can power the clinician with better information from a much larger data set," he continued. AI is also enabling screening tools and patient access, said the experts. "There are many things that work in the background that impact [patient] lives and experience already," said Piyush Mathur, staff anesthesiologist and critical care physician at the Cleveland Clinic. At the same time, the panel pointed to the role clinicians can play in building patient trust around artificial intelligence and machine learning technology. Nahar said that as a provider, he considers several questions when using an AI-powered tool for his patient. "Is the technology … really needed for this patient to solve this problem?" he said he asks himself. "How will it improve the care that I deliver to the patient?

About 6 million people in the US are currently living with Alzheimer's disease, the most common form of dementia, according to the Alzheimer's Association. Despite being the sixth-leading cause of death in the country, there is currently no known cure for the memory-robbing condition. But diagnosing the disease early can help people seek preventative care and slow its progress. That's why a team of students at USC is developing machine learning tools to detect early-onset Alzheimer's disease using speech patterns, and democratize the diagnosis process. The team started working on the system in spring 2021 as a project for CAIS, the student branch of the Center for Artificial Intelligence in Society, in collaboration with students from MEDesign, the biomedical engineering design group.

In this paper, We present our approach for IEEEBigMM 2020, Grand Challenge (BMGC), Identifying senti-ments from tweets related to the MeToo movement. The modelis based on an ensemble of Convolutional Neural Network,Bidirectional LSTM and a DNN for final classification. Thispaper is aimed at providing a detailed analysis of the modeland the results obtained. We have ranked 5th out of 10 teamswith a score of 0.51491

When initially brainstorming the podcast, Singh said they had to consider how conversational or formal they wanted to make it. Academic podcasts require more attention from listeners to fully grasp the concepts, which Singh said presented a challenge as they first began. The pair ended up rerecording its first episode, as Mathur said they chose a conversational style for a more organic discussion that became a stream of consciousness rather than a structured piece. As the podcast is still in its beginning stages, its first iteration may not be perfect, and Singh said they are looking to gauge feedback and continuously improve their format. Examining ethics alongside AI has been a challenge of presenting the technical information while distilling the topics to a level that anyone can understand, Singh said.

Reprinted with permission from Quanta Magazine's Abstractions blog. Despite its ability to bend both minds and space, an Einsteinian black hole looks so simple a child could draw it. The point is the singularity, an infinitely dense, unimaginably small dot contorting space so radically that anything nearby falls straight in, leaving behind a vacuum. The spherical boundary marks the event horizon, the point of no return between the vacuum and the rest of the universe. But according to Einstein's theory of gravity, the event horizon isn't anything that an unlucky astronaut would immediately notice if she were to cross it.

Consider what healthcare systems are faced with today: People are living longer, but having fewer children. Many countries are ill-equipped to support and care for a growing number of elderly citizens. The prevalence of chronic diseases brings an added layer of complexity into the mix – and healthcare insurers are racing to adapt, notes Arvind Mathur, Chief Information Technology Officer of Prudential Singapore. "Traditionally, insurance has been defined in certain ways, with very specific kinds of products and capabilities," he tells GovInsider. "But as the needs are changing and as our consumers are evolving, those products and solutions are no longer sufficient."

The Andhra Pradesh Government and SAP have signed a MoU here on Thursday to set up a start-up accelerator here. The MoU was signed by SAP Director (Start-up Focus Programme) Mayank Mathur and IT Adviser to AP Government J.A. Chowdary. Mathur said that SAP would strive to create the right kind of eco system in Vizag for the flourishing of start-ups and to develop entrepreneurial spirit among the young. "We will launch the operations initially under our experts based at Bengaluru within a month. They will be visiting Vizag periodically," he said.

This has been made possible by advancements in satellite sensor technology thus enabling the acquisition of land cover information over large areas at various spatial, temporal spectral and radiometric resolutions. The process of relating pixels in a satellite image to known land cover is called image classification and the algorithms used to effect the classification process are called image classifiers (Mather, 1987). The extraction of land cover information from satellite images using image classifiers has been the subject of intense interest and research in the remote sensing community (Foody and Mathur, 2004b). Some of the traditional classifiers that have been in use in remote sensing studies include the maximum likelihood, minimum distance to means and the box classifier. As technology has advanced, new classification algorithms have become part of the main stream image classifiers such as decision trees and artificial neural networks. Studies have been made to compare these new techniques with the traditional ones and they have been observed to post improved classification accuracies (Peddle et al. 1994; Rogan et al. 2002; Li et al. 2003; Mahesh and Mather, 2003).

We describe two successfully working, analog VLSI vision circuits that move beyond pixel-based early vision algorithms. One circuit, implementing the dynamic wires model, provides for dedicated lines of communication among groups of pixels that share a common property. The chip uses the dynamic wires model to compute the arclength of visual contours. Another circuit labels all points inside a given contour with one voltage and all other with another voltage. Its behavior is very robust, since small breaks in contours are automatically sealed, providing for Figure-Ground segregation in a noisy environment. Both chips are implemented using networks of resistors and switches and represent a step towards object level processing since a single voltage value encodes the property of an ensemble of pixels.