Advances in machine learning, data management, and cloud computing are having a significant impact on the market for drone-based mapping and intelligence gathering. Even as satellite-based imaging gains steam, drones appear to be extending their lead closer to Earth. We are in the midst of a renaissance in drone-based aerial intelligence. From counting the number of koalas in the Australian outback to detecting enemy combatants inside of buildings, drones seem to be everywhere at the moment. The surge in drone use is great news for Krishnan Hariharan, the CEO of Kespry, a 30-person California drone AI startup.
June 21, 2021--Researchers in the Center for Research on Entertainment and Learning (CREL) at the University of California San Diego have developed a system to analyze and track eye movements to enhance teaching in tomorrow's virtual classrooms – and perhaps future virtual concert halls. UC San Diego music and computer science professor Shlomo Dubnov, an expert in computer music who directs the Qualcomm Institute-based CREL, began developing the new tool to deal with a downside of teaching music over Zoom during the COVID-19 pandemic. "In a music classroom, non-verbal communication such as facial affect and body gestures is critical to keep students on task, coordinate musical flow and communicate improvisational ideas," said Dubnov. "Unfortunately, this non-verbal aspect of teaching and learning is dramatically hampered in the virtual classroom where you don't inhabit the same physical space." To overcome the problem, Dubnov and Ph.D. student Ross Greer recently published a conference paper on a system that uses eye tracking and machine learning to allow an educator to make'eye contact' with individual students or performers in disparate locations – and lets each student know when he or she is the focus of the teacher's attention.
"Ask forgiveness, not permission" has long been a guiding principle in Silicon Valley. There is no technological field in which this principle has been more practiced than the machine learning in modern AI, which depends for its existence on giant databases, almost all of which are scraped, copied, borrowed, begged, or stolen from the giant piles of data we all emit daily, knowingly or not. But this data is hardly ever rigorously sourced with the subjects' permission. "Because we can," two sociologists tell Kate Crawford in Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence, by way of acknowledging that their academic institutions are no different from technology companies or government agencies in regarding any data they find as theirs for the taking to train and test algorithms. This is how machine learning is made.
Silicon Valley adaptive computing bellwether Xilinx announced its entrance into the growing system-on-module (SOM) market today, with a portfolio of palm-sized compute modules for embedded applications that accelerate AI, machine learning and vision at the edge. Xilinx Kria will eventually expand into a family of single board computers based on reconfigurable FPGA (Field Programmable Gate Array) technology, coupled to Arm core CPU engines and a full software stack with an app store, the first of which is specifically is targeted at AI machine vision and inference applications. The Xilinx Kria K26 SOM employs the company's UltraScale multi-processor system on a chip (MPSoC) architecture, which sports a quad-core Arm Cortex A53 CPU, along with over 250 thousand logic cells and an H.264/265 video compression / decompression engine (CODEC). This may sound like alphabet soup as I spit out acronyms, however, the underlying solution is a compelling offering for developers and engineers looking to give new intelligent systems, in industries like security, smart cities, retail analytics, autonomous machines and robotics, the ability to see, infer information and adapt to their deployments in the field. Also on board the Xilinx Kria K26 SOM is 4GB of DDR4 memory and 245 general purpose IO, along with the ability to support 15 cameras, up to 40 Gbps of combined Ethernet throughput, and four USB 2/3 compatible ports.
Generative Pre-trained Transformer 3 (GPT-3) embraces and augments the GPT-2 model architecture, including pre-normalization, modified initialization, and reversible tokenization. It exhibits strong performance on many Natural Language Processing (NLP) tasks. GPT-3 is an auto-regressive artificial intelligence algorithm developed by OpenAI, an AI-powered research laboratory located in San Francisco, California. It is a massive artificial neural network that takes help from deep learning to generate human-like text and is trained on huge text datasets with thousands of billions of words. It is the third-generation AI language prediction model in the GPT-n series and the successor to GPT-2. In simple words, OpenAI GPT-3 was fed inputs the ways how billions of people write and also was taught how to pick up on writing patterns based on user entry.
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One of the many consequences of the COVID-19 pandemic is a global shortage of the microchips that are essential to electronic devices. The factories that make these chips had to shut down for some of the pandemic, and are struggling to cope with an increase in demand. Some products could be delayed by months. It's too early to know how the shortage will affect the industry in the long term, but the pandemic has focused attention on some key research questions -- including how to make the manufacturing process more resilient to shocks and emergencies. One well-known problem is that microchips are designed in just a handful of companies, including Samsung in South Korea and Intel, NVIDIA and Qualcomm in California. But not all these companies make the chips.
Earlier this month, Chinese artificial intelligence (A.I.) researchers at the Beijing Academy of Artificial Intelligence (BAAI) unveiled Wu Dao 2.0, the world's biggest natural language processing (NLP) model. NLP is a branch of A.I. research that aims to give computers the ability to understand text and spoken words and respond to them in much the same way human beings can. Last year, the San Francisco–based nonprofit A.I. research laboratory OpenAI wowed the world when it released its GPT-3 (Generative Pre-trained Transformer 3) language model. GPT-3 is a 175 billion–parameter deep learning model trained on text datasets with hundreds of billions of words. A parameter is a calculation in a neural network that shapes the model's data by assigning to each chunk a greater or lesser weighting, thus providing the neural network a learned perspective on the data.
Xilinx has introduced its Kria programmable chips and boards for holding AI applications at the edge of the network. This should come in handy for visual applications like smarter cameras. San Jose, California-based Xilinx, which is in the process of being acquired by Advanced Micro Devices (AMD) for $35 billion, has a group of products dubbed the Kria portfolio of adaptive system-on-module offerings for AI at the edge. These are production-ready small form factor embedded boards that enable rapid deployment in edge-based applications. Coupled with a complete software stack and prebuilt, production-grade accelerated applications, Kria adaptive modules are a new method of bringing adaptive computing to AI and software developers.
With a wink and a wave, California-based start-up Qooore has entered fintech with its plans to offer artificial intelligence (AI) powered investment forecasts. The tradetech offers a subscription-based mobile app providing these "smart signals". Its website is a holding page for now and showing it's in early access mode. Qooore says its backend collects "hundreds" of human-made forecasts, market trends, parsed analyst websites and 250 more (unspecified) factors, to generate one balanced forecast. This is all based on its AI-fuelled scoring system and previous forecasts.