This paper aims to provide a quick review of the methods including the technologies in detail that are currently reported in industry and academia. Specifically, this paper reviews the end-to-end planning, including Tesla FSD V12, Momenta 2023, Horizon Robotics 2023, Motional RoboTaxi 2022, Woven Planet (Toyota): Urban Driver, and Nvidia. In addition, we review the state-of-the-art academic studies that investigate end-to-end planning of autonomous driving. This paper provides readers with a concise structure and fast learning of state-of-the-art end-to-end planning for 2022-2023. This article provides a meaningful overview as introductory material for beginners to follow the state-of-the-art end-to-end planning of autonomous driving in industry and academia, as well as supplementary material for advanced researchers.

In the biggest meeting of women Data Science leaders from across the domain, women professionals from the industry and academia will come together for The Rising. The conference will serve as a forum for exchange for building a better idea for women participating in STEM and will also highlight the achievements and career interests of women in data science. The conference will provide a platform to leading women visionaries to dive into the field of data science and share their perspective on how to build a career in this buzzing field and their role in data science. Through a series of talks and informal sessions, this 2-day conference will empower women and help them in the development of leadership skills. In the biggest meeting of women Data Science leaders from across the domain, women professionals from the industry and academia will come together for The Rising.

SAN FRANCISCO - December 3, 2020 -- Today, MLCommons, an open engineering consortium, launches its industry-academic partnership to accelerate machine learning innovation and broaden access to this critical technology for the public good. The non-profit organization initially formed as MLPerf, now boasts a founding board that includes representatives from Alibaba, Facebook AI, Google, Intel, and NVIDIA, as well as Professor Vijay Janapa Reddi of Harvard University; and a broad range of more than 50 founding members. The founding membership includes over 15 startups and small companies that focus on semiconductors, systems, and software from across the globe, as well as researchers from universities such as U.C. Berkeley, Stanford, and the University of Toronto. MLCommons will advance development of, and access to, the latest AI and Machine Learning datasets and models, best practices, benchmarks and metrics. An intent is to enable access to machine learning solutions such as computer vision, natural language processing, and speech recognition by as many people, as fast as possible.

MLCommons, an open engineering consortium, launches its industry-academic partnership to accelerate machine learning innovation and broaden access to this critical technology for the public good. The non-profit organization initially formed as MLPerf, now boasts a founding board that includes representatives from Alibaba, Facebook AI, Google, Intel, NVIDIA and Professor Vijay Janapa Reddi of Harvard University; and a broad range of more than 50 founding members. The founding membership includes over 15 startups and small companies that focus on semiconductors, systems, and software from across the globe, as well as researchers from universities like U.C. Berkeley, Stanford, and the University of Toronto. MLCommons will advance development of, and access to, the latest AI and Machine Learning datasets and models, best practices, benchmarks and metrics. An intent is to enable access to machine learning solutions such as computer vision, natural language processing, and speech recognition by as many people, as fast as possible.

I really like the expression "being bitten by the SOTA bug". In a nut shell it means that if a large group of people focuses on pursuing a top result on some abstract metric, this metric loses its meaning (a classic manifestation of Goodhart's Law). The exact reason why this happens is usually different each time and it may be very technical, but in ML what is usually occurring is that the models are overfit to some hidden intrinsic qualities of the dataset that are used to calculate the metrics. For example, in CV such patterns are usually clusters of visually similar images. A small idealistic under-the-radar community pursuing an academic or scientific goal is much less prone to falling victim to Goodhart's law than a larger and more popular community. Once a certain degree of popularity is reached, the community starts pursuing metrics or virtue signalling (showing off one's moral values for the sake of showing off when no real effort is required) and the real progress stops until some crisis arrives. This is what it means to be bitten by the SOTA bug. For example, in the field of Natural Language Processing this attitude has lead to irrational over-investment into huge models optimized on public academic benchmarks, but the usefulness of such "progress" is very limited for a number of reasons:

The JAIC is working to bring critical AI detection technology to the first responders who bravely battle wildfires. Increased use of AI will reduce response timelines, increase situational awareness, and save more American lives.

The conference was designed to not only demystify the application of AI in defence, but to also reflect on the responsible use of AI. Approximately 70 individuals from EU member state governments, EU institutions, industry and academia attended the event and the organisers had the pleasure of welcoming speakers from the Estonian Ministry of Defence, the European Commission, the European Defence Agency, the European Parliament, the EU Satellite Centre, the Finnish Ministry of Defence, NATO, the Political and Security Committee and industry and academia. Participants engaged in a stimulating debate about military interoperability, industrial sovereignty and ethics.

While many federal agencies primarily think of artificial intelligence as an emerging technology to support their own missions, they also have a secondary role to play in fueling America's research, development and testing of AI by sharing their data, federal tech leaders said Wednesday. The development of innovative artificial intelligence applications relies on powerful underlying data, which many federal agencies hold via the services they provide to Americans. But both U.S. CIO Suzette Kent and Lynne Parker, assistant director of AI in the White House's Office of Science and Technology Policy, identified agencies' hesitancy to share their data with private and academic partners, as well as other agencies, as a leading challenge limiting the nation's development of meaningful AI solutions. Kent said one of her biggest concerns around AI is figuring out "how we make available the powerful data that are strategic assets of the federal government on behalf of its citizens." Agencies are responsible for handling the data properly, but much of it belongs to the public. "The agencies have a responsibility for the external components -- many of the things … around making data available, responding to request from industry, supporting research and development, whether that is in direct grants or specific topic areas or making data or facilities available to support those sets of activities," Kent said at a Bipartisan Policy Center event.

The federal government plans to spend almost $1 billion in nondefense artificial intelligence research and development in fiscal 2020, according to a supplemental report to the president's budget request. "The U.S. has pushed the boundaries for computational power, we have given our innovators the freedom to thrive, and today we can proudly say America continues to be the leader in artificial intelligence," federal Chief Technology Officer Michael Kratsios said Tuesday at a Center for Data Innovation event. "This new supplement report demonstrates just how diverse and extensive our efforts are." The figure indicates a weighty increase from 2016--when all agencies, including defense, collectively spent about $1 billion on AI. However, immediately after Kratsios' announcement, industry experts and former federal officials weighed in on what more needs to be done to secure and sustain American leadership in AI across the global technological landscape.

The 5th Huawei Asia-Pacific Innovation Day was held in Chengdu, China. This year's Innovation Day is themed "Innovation Enables Asia-Pacific Digitization". More than 200 representatives from government, industry and academia of Asia-Pacific countries and regions got together to discuss innovative 5G technologies and applications, sustainable development, as well as technology, humanity, and nature. As a ubiquitous technology, 5G is the cornerstone of a smart world in which everything is connected. Today, as we usher in the 5G era, we are also at a critical stage of digital transformation across industries worldwide.