If you are in the ML/AI field, and are interested in enhancing your skills, while networking and learning from Google's ML/AI experts, this is the event for you! Attendees should have prior knowledge and experience with Machine Learning and/or AI technologies. We want to create a learning journey for developers around Google's ML content - from data to decisions.
Before the global pandemic struck in 2020 and the world was turned on its head, artificial intelligence (AI), and specifically the branch of AI known as machine learning (ML), were already causing widespread disruption in almost every industry. The 4 Top Artificial Intelligence Trends For 2021 Adobe Stock The Covid-19 pandemic has impacted many aspects of how we do business, but it hasn't diminished the impact AI is having on our lives. In fact, it's become apparent that self-teaching algorithms and smart machines will play a big part in the ongoing fight against this outbreak as well as others we may face in the future. AI undoubtedly remains a key trend when it comes to picking the technologies that will change how we live, work, and play in the near future. So, here's an overview of what we can expect during what will be a year of rebuilding our lives as well as rethinking business strategies and priorities.
Tesla may be introducing machine-learning training as a web service with its upcoming'Dojo' supercomputer, CEO Elon Musk said on Twitter. Project Dojo was initially revealed by Musk last year and is a supercomputer which Tesla has been working on. The supercomputer has been designed to ingest massive amounts of video data and perform massive levels of unsupervised training on the visual data. The goal of Dojo will be to be able to take in vast amounts of data and train at a video level and do massive unsupervised training of vast amounts of video data. Dojo uses our own chips & a computer architecture optimized for neural net training, not a GPU cluster. Could be wrong, but I think it will be best in world.
Machine learning algorithms can beat the world's hardest video games in minutes and solve complex equations faster than the collective efforts of generations of physicists. But the conventional algorithms still struggle to pick out stop signs on a busy street. Object identification continues to hamper the field of machine learning--especially when the pictures are multidimensional and complicated, like the ones particle detectors take of collisions in high-energy physics experiments. However, a new class of neural networks is helping these models boost their pattern recognition abilities, and the technology may soon be implemented in particle physics experiments to optimize data analysis. This summer, Fermilab physicists made an advance in their effort to embed graph neural networks into the experimental systems.
BERLIN (AP) -- An international team of scientists have joined forces to combat the spread of anti-Semitism online with the help of artificial intelligence. The Alfred Landecker Foundation, which supports the team, said Monday that the project named Decoding Anti-Semitism includes discourse analysts, computational linguists and historians. They will develop a "highly complex, AI-driven approach to identifying online anti-Semitism." The team includes researchers from Berlin's Technical University, King's College in London and other scientific institutions in Europe and Israel. Computers will run through vast amounts of data and images that humans wouldn't be able to assess because of their sheer quantity.
Since its release, GPT-3, OpenAI's massive language model, has been the topic of much discussion among developers, researchers, entrepreneurs, and journalists. Most of those discussions have been focused on the capabilities of the AI-powered text generator. But much about GPT-3 remains obscure. The company has opted to commercialize the deep learning model instead of making it freely available to the public. And though the AI has shown to be capable of many interesting feats, it's not yet clear if GPT-3 will become a real product or will join the endless array of abandoned projects that never found a viable business model. Earlier this month, as reported by users who have access to the beta version of the language model, OpenAI declared the initial pricing plan of GPT-3.
Artificial intelligence (AI) is surpassing human performance in a growing number of domains. However, there is limited evidence of its economic effects. Using data from a digital platform, we study a key application of AI: machine translation. We find that the introduction of a new machine translation system has significantly increased international trade on this platform, increasing exports by 10.9%. Furthermore, heterogeneous treatment effects are consistent with a substantial reduction in translation costs.
Singapore is gradually reopening its borders again after months of coronavirus travel restrictions. As the city-state looks to salvage its battered tourism industry -- which contributes around 4% to its economy -- it's hoped that artificial intelligence (AI) can help the sector bring back visitors safely. Official data shows monthly visitor arrivals were down by 76% between January to July, compared to a year ago. Visitor arrivals in July alone were down more than 99% year-on-year. Even though the Southeast Asian nation remains closed off to most foreigners, officials are now considering lifting restrictions for select groups of visitors.
The machine learning and AI-powered tools being deployed in response to COVID-19 arguably improve certain human activities and provide essential insights needed to make certain personal or professional decisions; however, they also highlight a few pervasive challenges faced by both machines and the humans that create them. Nevertheless, the progress seen in AI/machine learning leading up to and during the COVID-19 pandemic cannot be ignored. This global economic and public health crisis brings with it a unique opportunity for updates and innovation in modeling, so long as certain underlying principles are followed. Here are four industry truths (note: this is not an exhaustive list) my colleagues and I have found that matter in any design climate, but especially during a global pandemic climate. When a big group of people is collectively working on a problem, success may become more likely.