Researchers at the Indian Institute of Science (IISc) have developed a design framework to build next-generation analog computing chipsets that could be faster and require less power than the digital chips found in most electronic devices. Using their novel design framework, the team has built a prototype of an analog chipset called ARYABHAT-1 (Analog Reconfigurable technologY And Bias-scalable Hardware for AI Tasks). This type of chipset can be especially helpful for Artificial Intelligence (AI)-based applications like object or speech recognition--think Alexa or Siri--or those that require massive parallel computing operations at high speeds. Most electronic devices, particularly those that involve computing, use digital chips because the design process is simple and scalable. "But the advantage of analog is huge. You will get orders of magnitude improvement in power and size," explains Chetan Singh Thakur, assistant professor at the Department of Electronic Systems Engineering (DESE), IISc, whose lab is leading the efforts to develop the analog chipset.
Recently, Nvidia released a new report called the State of AI in Financial Services. To learn more, I caught up with Pahal Patangia, Global Developer Relations Lead for Consumer Fintech at Nvidia. Below is the transcript of our conversation (slightly edited for clarity). Theodora: Now, I know oftentimes when we think about Nvidia, we think about graphics cards. Nvidia is also a full stack, accelerated computing platform company that has been in the financial services space for 15 years.
Planning problems are, in general, PSPACE-complete; large problems, especially multi-agent problems with required coordination, can be intractable or impractical to solve. Factored planning and multi-agent planning both address this by separating multi-agent problems into tractable sub-problems, but there are limitations in the expressivity of existing planners and in the ability to handle tightly coupled multi-agent problems. This paper presents EGOPLAN, a framework which factors a multi-agent problem into related sub-problems which are solved by iteratively calling on a single agent planner. EGOPLAN is evaluated on a multi-robot test domain with durative actions, required coordination, and temporal constraints, comparing the performance of a temporal planner, OPTIC-CPLEX, with and without EGOPLAN. Our results show that for our test domain, using EGOPLAN allows OPTIC-CPLEX to solve problems that are twice as complex as it can solve without EGOPLAN, and to solve complex problems significantly faster.
There is a church that worships artificial intelligence (AI). Zealots believe that an extraordinary AI future is inevitable. The technology is not here yet, but we are assured that it's coming. We will have the ability to be uploaded onto a computer and thereby achieve immortality. You will be reborn into a new, immortal silicon body.
The introduction of artificial intelligence in the banking sector makes banks efficient, helpful and more understanding. The growing impact of AI in this sector reduces operational costs, improves customer support and process automation. AI-based applications help banks by reducing costs thereby increasing productivity. Also, intelligent algorithms are able to spot inconsistency and fraudulent information in a matter of seconds. According to reports, nearly 80 percent of banks are aware of the potential benefits that AI presents to their sector.
Microneedles (MNs) introduced a novel injection alternative to conventional needles, offering a decreased administration pain and phobia along with more efficient transdermal and intradermal drug delivery/sample collecting. 3D printing methods have emerged in the field of MNs for their time- and cost-efficient manufacturing. Tuning 3D printing parameters with artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is an emerging multidisciplinary field for optimization of manufacturing biomedical devices. Herein, we presented an AI framework to assess and predict 3D-printed MN features. Biodegradable MNs were fabricated using fused deposition modeling (FDM) 3D printing technology followed by chemical etching to enhance their geometrical precision. DL was used for quality control and anomaly detection in the fabricated MNAs. Ten different MN designs and various etching exposure doses were used create a data library to train ML models for extraction of similarity metrics in order to predict new fabrication outcomes when the mentioned parameters were adjusted. The integration of AI-enabled prediction with 3D printed MNs will facilitate the development of new healthcare systems and advancement of MNs’ biomedical applications.
The service has a new responsible AI system that filters out harmful content and helps detect abuse. Additionally, Azure OpenAI Service now offers access to more models, including GPT-3, Codex and embeddings models. Codex can generate code and translate plain language to code, while embeddings make semantic search and other tasks easier. The service also offers new capabilities for customers to fine tune models for more tailored results. Azure OpenAI Service is enabling customers across industries from health care to financial services to manufacturing to quickly perform an array of tasks.
Eric is President of Suki and seasoned technology executive with expertise co-founding and scaling companies including Hotwire and Expedia. I recently wrote about the promise of AI and its potential to play an important role in transforming how physicians interact with technology. Even today, AI is making meaningful inroads in specialties ranging from radiology to cardiology. The potential for AI to help physicians work faster and with greater accuracy has industry analysts predicting explosive 10x growth in this decade alone, with estimates reaching $96 billion in 2028. That said, most physicians are only beginning to become familiar with AI and understand its use cases.
Millions of students attend community colleges every year, with almost 1,300 schools located in every corner of the United States. With their large student bodies, community colleges are a massive source of potential for expanding the artificial intelligence (AI) workforce, but employers and policymakers alike sorely underestimate their potential. If the United States aims to maintain its global lead and competitive advantage in AI, it must recognize that community colleges hold a special spot in our education system and are too important to be overlooked any longer. As detailed in a recent study I co-authored as part of Georgetown University's Center for Security and Emerging Technology (CSET), community colleges have the potential to support the country in its mission for superiority in AI. Community colleges could create pathways to good-paying jobs across the United States and become tools for training a new generation of AI-literate workers.