If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Welcome to General Intelligence, OneZero's weekly dive into the A.I. news and research that matters. War robots today take just too much darn time to control. I know it, you know it, and the U.S. Army knows it. That's why its research branch is cooking up a system that would allow soldiers to give orders to small robotic cars by speaking naturally, as opposed to using specific commands. The robots would be able to understand the soldiers' intent and complete the given task, according to an Army press release.
Right now, the AI chip market is all about deep learning. Deep learning (DL) is the most successful of machine learning paradigms at making AI applications useful in the real world. The AI chip market today is all about accelerating deep learning (DL) – the acceleration is needed during training and during inferencing. The AI chip market has exploded with players: for a recent research report we counted some 80 startups globally with $10.5 billion spend by investors, competing with some 34 established players. Clearly this is unsustainable, but we need to dissect this market to better understand why it is the way it is now, how it is likely to change, and what it all means.
Defense contracts valued at $7 million and above ARMY Moderna TX Inc.,* Cambridge, Massachusetts, was awarded a $1,525,000,000 firm-fixed-price contract for 100 million filled drug production doses of a SARS-CoV-2 mRNA-1273 vaccine. Bids were solicited via the internet with one received. Work will be performed in Cambridge, Massachusetts, with an estimated completion date of March 31, 2022. Fiscal 2020 research, development, test and evaluation (Army) funds in the amount of $1,525,000,000 were obligated at the time of the award. U.S. Army Contracting Command, Aberdeen Proving Ground, Maryland, is the […]
Cyber attacks and threats are considered major disruptors to businesses, nations and consumers alike. Artificial intelligence is seen as a major disruptive force too, but of the positive kind, fuelling a new era of hyper connectivity, hyper intelligence and hyper performance. An increasingly complex business environment is leading organisations to embrace forms of artificial intelligence such as machine learning and facial recognition technology, while using data to build more intimate relationships with consumers. But the flip side of these innovations is that the'attack surfaces' of an organisation are multiplying, creating a fast-growing world of vulnerability to cyber crime that didn't exist before. At the same time, AI use is on the rise among cyber criminals, who are using it to help drive attacks, employing the technology to uncover unsecured points of entry in enterprise networks.
In the new paper Does BERT Solve Commonsense Task via Commonsense Knowledge?, a team of researchers from Westlake University, Fudan University and Microsoft Research Asia dive deep into the large language model to discover how it encodes the structured commonsense knowledge it leverages on downstream commonsense tasks. The proven successes of pretrained language models such as BERT on various downstream tasks has stimulated research investigating the linguistic knowledge inside the model. Previous studies have revealed shallow syntactic, semantic and word sense knowledge in BERT, however, the question of how BERT deals with commonsense tasks has been relatively unexamined. CommonsenseQA is a multiple-choice question answering dataset built upon the CONCEPTNET knowledge graph. The researchers extracted multiple target concepts with the same semantic relation to a single source concept from CONCEPTNET, where each question has one of three target concepts as the correct answer. For example, "bird" is the source concept in the question "Where does a wild bird usually live?" and "countryside" is the correct answer from the possible target concepts "cage," "windowsill," and "countryside."
Core engine performance enhancements accelerate verification throughput by reducing stimulation cycles with matching coverage on randomized test suites. Cadence Design Systems, Inc. today announced that the Cadence Xcelium Logic Simulator has been enhanced with machine learning technology (ML), called Xcelium ML, to increase verification throughput.Using new machine learning technology and core computational software, Xcelium ML enables up to 5X faster verification closure on randomized regressions. Using computational software and a proprietary machine learning technology that directly interfaces to the simulation kernel, Xcelium ML learns iteratively over an entire simulation regression.It analyzes patterns hidden in the verification environment and guides the Xcelium randomization kernel on subsequent regression runs to achieve matching coverage with reduced simulation cycles. Cadence's Xcelium Logic Simulator provides best-in-class core engine performance for SystemVerilog, VHDL, mixed-signal, low power, and x-propagation.It supports both single-core and multi-core simulation, incremental and parallel build, and save/restart with dynamic test reload. The Xcelium Logic Simulator has been deployed by a majority of top semiconductor companies, and a majority of top companies in the hyper-scale, automotive, and consumer electronics segments.Kioxia has effectively utilized Xcelium simulation for a variety of our designs, and it addresses our ever-growing verification needs.
Recently, Python has become the most chosen language for data science and Artificial intelligence--two technological innovation patterns for worldwide organizations to remain competitive in today's era. Truth be told, Python is the quickest developing programming language today, as indicated by Stack overflow's 2019 developer Survey. Known for its meaningfulness and flexibility, every organization, regardless of its size, is using this language. New businesses may upgrade a small design group's workflow by using Python's proficient syntax structure and utilizing its many package libraries. Big organizations may go to python to process mammoth datasets utilizing Artificial Intelligence algorithms.
The Indian Institute of Technology-Roorkee (IIT-R) in partnership with leading online learning platform Coursera on Thursday launched two new online certificate programmes for professionals looking to build skills in data science, Artificial Intelligence (AI) and Machine Learning (ML). The six-month certificate programme in AI and ML will consist of video lectures, hands-on learning opportunities, team projects, tutorials and workshops. The programme will also teach classical ML techniques and provide hands-on programming experience with'Tensorflow' software for model building, robust ML production and powerful experimentation. The certificate programme in data science will help professionals build skills in data science, machine learning, critical thinking, data collection, data visualization and data management. "We are delighted to partner with Coursera to help fulfil the goal of inclusive education of the New Education Policy," Professor Ajit K Chaturvedi, Director, IIT Roorkee, said in a statement.
Samurai is an innovative provider of trading solutions, novel quantamental research, alternative data, and specialized risk/hedging tools. By leveraging Artificial Intelligence (AI) and Machine Learning (ML) with our team's niche experience in market structure analysis and volatility research, Samurai's unique solutions empower our clients to better define risks, identify opportunities, and most importantly, generate outsized returns. We've developed our solutions from the ground up with wealth managers, traders and market participants in mind. With multiple latency options available, a highly scalable infrastructure, and seamless integration, Samurai is flexible and easily deployable in any environment. Engineered by a sophisticated combination of proprietary methodology and niche industry expertise, our clients benefit from decreased volatility, lower market correlations and unmatched results.