https://cdn-images-1.medium.com/max/900/0*F_Wgu6ovsI9aLNHG Original Source Here Dubbed as ‘GRAND’, the algorithm eliminates the need for multiple, computationally complex decoders Continue reading on Technicity » AI/ML Trending AI/ML Article Identified & Digested via Granola by Ramsey Elbasheer; a Machine-Driven RSS Bot via WordPress https://ramseyelbasheer.io/2021/10/01/first-silicon-chip-designed-using-a-universal-decoding-algorithm/

Original Source Here By John P. Desmond, AI Trends Editor     Companies that have committed to cloud computing for IT services have shifted from seeing the primary benefit as increased efficiency, to it being increased revenue and improved profitability, according to a new study.     Based on a survey of 1,300 global, C-level executives and decision-makers from 11 industries and six countries, the report was conducted by Wipro FulStride Cloud Services, a unit of the global tech information services company Wipro. The FullStride Cloud Services unit was announced in July along with an investment of $1 billion in cloud technologies, capabilities, acquisitions and partnerships over the next three years.      The report anticipated that the trend of increased benefits from cloud computing will continue as the cloud becomes more intelligent, hyperconnected, and pervasive. The report defined cloud computing leaders as the ...

Original Source Here By John P. Desmond, AI Trends Editor   Since the European Commission in April proposed rules and a legal framework in its Artificial Intelligence Act (See   AI Trends , April 22, 2021), the US Congress and the Biden Administration have followed with a range of proposals that set the direction for AI regulation.     “The EC has set the tone for upcoming policy debates with this ambitious new proposal,” stated authors of an update on AI regulations from  Gibson Dunn , a law firm headquartered in Los Angeles.     Unlike the comprehensive legal framework proposed by the European Union, regulatory guidelines for AI in the US are being proposed on an agency-by-agency basis. Developments include the US Innovation and Competition Act of 2021, “sweeping bipartisan R&D and science-policy regulation,” as described by Gibson Dunn, moved rapidly through the Senate. “While there has been no major shift away from the previous “ha...

Original Source Here By John P. Desmond, AI Trends Editor   Edge computing is booming, with estimates ranging up to $61 billion in value in 2028. While definitions vary, edge computing is about taking compute power out of the data center and bringing it as close as possible to the device where analytics can run.     The devices can be standalone IoT sensors, drones, or autonomous vehicles. “There’s  one thing in common. Increasingly, data generated at the edge are used to feed applications powered by machine learning models,” stated George Anadiotis, analyst, engineer and founder of Linked Data Orchestration of Berlin, Germany, working on the intersection of technology, media and data, writing in a recent account in  ZDnet .     However, “There’s just one problem: machine learning models were never designed to be deployed at the edge. Not until now, at least. Enter  TinyML .”     A fast-growing field of machine learning...

Original Source Here By AI Trends Staff   The auto industry is transforming into a value network, driven by the technological development of electric-connected autonomous and shared (ECAS) vehicles architectures, systems intelligence, new computing paradigms at the edge and swarm capabilities into vehicle domains.   That is from a report from the journal  Frontiers in Future Transportation , a company exploring frontiers of innovation in future transportation systems, with offices in Lausanne, Switzerland.    AI is important to this evolution. “ The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements,” the report states.   The primary manufacturers of electric vehicles have a range of approaches toward incorporating AI.      The leading electric vehicle manufacturer is Tesla with a 15%...

Original Source Here More customers are finding the need to build larger, scalable, and more cost-effective machine learning (ML) inference pipelines in the cloud. Outside of these base prerequisites, the requirements of ML inference pipelines in production vary based on the business use case. A typical inference architecture for applications like recommendation engines, sentiment analysis, and ad ranking need to serve a large number of models, with a mix of classical ML and deep learning (DL) models. Each model has to be accessible through an application programing interface (API) endpoint and be able to respond within a predefined latency budget from the time it receives a request. In this post, we describe an inference architecture, developed in collaboration with the Commerce Einstein Team at Salesforce, built on Amazon Elastic Kubernetes Service (Amazon EKS) to not only address the base prerequisites, but also pack thousands of unique DL models in a scalable architecture. We ...

Original Source Here By Lance Eliot, the AI Trends Insider   Think about all of those moment-to-moment rapid decisions that you make while driving a car.       Go ahead, do a slow-motion post-driving analysis in your mind. Think about a recent trip to the grocery store or perhaps a driving trek to a local mall.       Whether you realize it or not, there were hundreds upon hundreds or likely thousands of minuscule driving decisions that you made, all part of a larger web of driving decisions in the course of a driving journey. And, notably, they all ultimately encompassed some variant of life-or-death considerations.       How so?       Imagine that you are driving along amid city streets on an otherwise ordinary day.       If you decide to take that upcoming right turn just a bit fast, there is a heightened risk that you could inadvertently go awry. You might veer into a pedestrian that is standing...