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Researchers from Google, Amazon Web Services, UC Berkeley, Shanghai Jiao Tong University, Duke University and Carnegie Mellon University have published a paper titled "Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning" at OSDI 2022. The paper introduces a new method for automating the complex process of parallelising a model with only one line of code. So how does Alpa work? Data parallelism is a technique where model weights are duplicated across accelerators while only partitioning and distributing the training data. The dataset is split into'N' parts in data parallelism with'N' being the quantity of GPUs.
Background: Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines due to the growth of computing power, advances in methods and techniques, and the explosion of the amount of data; medicine is not an exception. Rather than replacing clinicians, AI is augmenting the intelligence of clinicians in diagnosis, prognosis, and treatment decisions. Summary: Kidney disease is a substantial medical and public health burden globally, with both acute kidney injury and chronic kidney disease bringing about high morbidity and mortality as well as a huge economic burden. Even though the existing research and applied works have made certain contributions to more accurate prediction and better understanding of histologic pathology, there is a lot more work to be done and problems to solve. Key Messages: AI applications of diagnostics and prognostics for high-prevalence and high-morbidity types of nephropathy in medical-resource-inadequate areas need special attention; high-volume and high-quality data need to be collected and prepared; a consensus on ethics and safety in the use of AI technologies needs to be built. Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines; medicine is not an exception.
For an autonomous car to drive safely, being able to predict the behavior of other road users is essential. A research team at the Massachusetts Institute of Technology's CSAIL, along with researchers at the Institute for Interdisciplinary Information Sciences (IIIS) at Tsinghua University in Beijing, have developed a new ML system that could one day help driverless cars predict in real time the upcoming movements of nearby drivers, cyclists and pedestrians. They titled their study, " M2I: From Factored Marginal Path Prediction to Interactive Prediction." Qiao Sun, Junru Gu, Hang Zhao are the IIIS members who participated in this study while Xin Huang and Brian Williams represented MIT. Humans are unpredictable, which makes predicting road user behavior in urban environments de facto very difficult.
Copy and paste the image source into your website to display the chart. The value marked a decrease of 29.2% over the previous month of $1.29bn and a drop of 37% when compared with the last 12-month average of $1.45bn. Asia-Pacific held an 18.68% share of the global technology industry artificial intelligence venture financing deal value that totalled $4.89bn in April 2022. China was the top country in Asia-Pacific's artificial intelligence venture financing deal value across technology industry. In terms of artificial intelligence venture financing deal activity, Asia-Pacific recorded 83 deals during April 2022, marking a decrease of 21.70% over the previous month and a rise of 2.47% over the 12-month average.
Earlier this month, DeepMind presented a new "generalist" AI model called Gato. The model can play the video game Atari, caption images, chat, and stack blocks with a real robot arm, the Alphabet-owned AI lab announced. All in all, Gato can do hundreds of different tasks. But while Gato is undeniably fascinating, in the week since its release some researchers have got a bit carried away. One of DeepMind's top researchers and a coauthor of the Gato paper, Nando de Freitas, couldn't contain his excitement.
Developments in the field of artificial intelligence (AI) are moving quickly. The EU is working hard to establish rules around AI and to determine which systems are welcome and which are not. But how does the EU do this when the biggest players, the US and China, often have different ethical views? Political economist Daniel Mügge and his team will conduct research into how the EU conducts its'AI diplomacy' and will sketch potential future scenarios. "Our research is essentially about regulation around AI", says political economist Daniel Mügge.
Artificial intelligence is defined as systems that do not operate according to a designed algorithm but are able to learn from new data. The fact that European policymakers have turned their eyes to the challenges of applying AI technologies is an important step forward, according to Jokūbas Drazdas, director of UAB Acrux Cyber Service, a Lithuanian IT company specialising in AI and cyber security. Europe is lagging far behind the US and China in the development and deployment of AI. In 2020, only 7% of European companies were using AI systems. The US and China are currently trying to accelerate the use of AI in the public and private sectors.
Five teams from the Netherlands, South Korea and China have advanced to the final stage of a competition to see who can grow fresh tomatoes in greenhouses remotely using artificial intelligence. The second Autonomous Greenhouse Challenge, which is organised by Dutch academic powerhouse Wageningen University & Research (WUR) and Chinese multinational conglomerate Tencent, began in September with a 24-hour hackathon involving 21 international teams and more than 200 participants from 26 countries. The five winning teams – Netherlands-based AiCU, The Automators and Automatoes, China'sIUA.CAAS and Korea'sDigilog – will each be given six months' access to a real greenhouse in the Dutch town of Bleiswijk, where from December onwards they will attempt to control and produce a tomato crop from afar by employing AI algorithms to keep inputs like water, nutrients and energy at sustainable levels. September's hackathon, held at WUR, saw an international jury award points to each team based on their composition and competence, their application of AI technology and the net profit they made during a virtual tomato production game. During their pitches, the teamswere given access to a climate model and a tomato crop growth model previously developed by researchers at WUR.
A robotic drone that can travel through air and water, and also attach itself to larger objects with a suction cup, could be useful for tagging wild animals, say its creators. The suction cup is inspired by the remora fish, which attaches itself to larger marine creatures in a symbiotic relationship where the remora eats parasites that would irritate its host and also gets a ride in return. "My original thought was'let's find a point where we can beat nature'," says Li Wen at Beihang University in Beijing. "Let's do a robot that can not only swim and stick underwater, but also can fly into the air and stick in the air. I don't think there are any animals that can do this."