Mathematical instruments mark the history of human progress. They've enabled trade and helped navigate oceans, and advanced understanding and quality of life. The latest tool propelling science and industry is AI computing. AI computing is the math-intensive process of calculating machine learning algorithms, typically using accelerated systems and software. It can extract fresh insights from massive datasets, learning new skills along the way. It's the most transformational technology of our time because we live in a data-centric era, and AI computing can find patterns no human could.

"Computing is the first productive force of the digital economy." On July 29, 2022, at the SenseTime Science and Technology Sub-forum of the First Computing Conference in China, Gao Shanshan, director of Shandong Sino US Digital Media International Cooperation Research Center said that, in the era of AI, computing infrastructure keeps changing such industries as finance, medicine and data center. Therefore, AI computing has become the main increment of digital economy development in various countries, and also the foundation of the era of digital economy. Computing represents a new type of productivity. Who owns the computing of the future digital economy industry development will have the ultimate power to lead the development of the digital economy in digital economy.

Improvement of steam engine by Watt triggered the first industrial revolution, and human society entered industrial civilization from agricultural civilization; electromagnetic induction principle triggered the second industrial revolution, and human society entered the age of electricity; the strong power brought by the third industrial revolution marked by information technology and the fourth industrial revolution featured by artificial intelligence deeply influenced our economy and society. In the age of digitalization, data is new means of production and computing is new productivity. Network reaches everywhere, computing exists everywhere and intelligence is used everywhere. Those who have strong computing have the password to win the future. At the new beginning of the age of computing, China is running extremely fast on the new track of digital economy.

Join gaming executives to discuss emerging parts of the industry this October at GamesBeat Summit Next. Oregon State University today announced that Jen-Hsun (Jensen) Huang, CEO of Nvidia, and Lori Huang donated $50 million to the school to build a new innovation complex on campus. The university has also raised a total of $100 million in gifts to launch what will ultimately be a $200 million research and education center with one of the nation's most powerful supercomputers. The center will do research in artificial intelligence, materials science and robotics to solve global challenges in areas such as climate science, oceanography, sustainability and water resources. The complex also will underpin OSU's research and teaching supporting the semiconductor and broader technology industry in Oregon and beyond.

This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware. We characterize the carbon footprint of AI computing by examining the model development cycle across industry-scale machine learning use cases and, at the same time, considering the life cycle of system hardware. Taking a step further, we capture the operational and manufacturing carbon footprint of AI computing and present an end-to-end analysis for what and how hardware-software design and at-scale optimization can help reduce the overall carbon footprint of AI. Based on the industry experience and lessons learned, we share the key challenges and chart out important development directions across the many dimensions of AI. We hope the key messages and insights presented in this paper can inspire the community to advance the field of AI in an environmentally-responsible manner.

Deloitte has launched the Deloitte Center for AI Computing, designed to accelerate the development of artificial intelligence offerings for its clients. The center is built on NVIDIA's DGX A100 systems to create a supercomputing architecture that will help Deloitte's clients in their efforts to become AI-fueled organizations. The accelerated computing platforms feature NVIDIA graphics processing unit technology, along with its networking and software for advanced data processing, analytics and AI by bringing massive parallel processing capability and speed to deep learning, machine learning and data science workloads, the company said. Deloitte's State of AI in the Enterprise survey found that more than half of respondents reported spending more than $20 million over the past year on AI technology and talent. Nearly all adopters said they were using AI to improve efficiency, while mature adopters are also harnessing the technologies to boost differentiation.

In the era of Artificial Intelligence of Things (AIoT), Industrial PC (IPCs) is expected more than just a computer for general data processing. Faced with the increasing workload at the edge, end devices are required to be smart, automated, and interconnected, which reflects on the demands of AI computing and M2M (Machine-to-Machine) communication in small-sized PCs. The demand for AI computing emerged on the account of the decentralization trends in recent years to reduce cloud computing workloads and costs, and to reinforce AI performance at the edge, high-end embedded solutions is a must. But to downsize them and meanwhile support the conditions required by edge environments, like tight spaces and abrupt temperature changes, it's definitely a challenge for IPC manufactures. Computing decentralization also infused diversity and heterogeneity into AIoT framework that further stresses the importance of integration capability.

The 2025 market for AI, including ADAS and robotic vehicles, is estimated at $2.75 billion – of which $2.5 billion will be "ADAS only"... Artificial Intelligence (AI) is gradually invading our lives through everyday objects like smartphones, smart speakers, and surveillance cameras. The hype around AI has led some players to consider it as a secondary objective, more or less difficult to achieve, rather than as a central tool to achieve the real objective: autonomy. Who are the winners and losers in the race for autonomy? "AI is gradually invading our lives and this will be particularly true in the automotive world" asserts Yohann Tschudi, Technology & Market Analyst, Computing & Software at Yole Développement (Yole). "AI could be the central tool to achieve AD, in the meantime some players are afraid of overinflated hype and do not put AI at the center of their AD strategy".

Ursula von der Leyen, the new president of the European Commission, has pledged to introduce GDPR-style legislation to regulate artificial intelligence (AI).

Start-up Blaize (formerly known as Thinci) has announced details of the first true Graph-Native silicon architecture and software built to process neural networks and enable AI applications. The Blaize Graph Streaming Processor (GSP) architecture enables concurrent execution of multiple neural networks and workflows on a single system. It also supports a range of heterogeneous compute-intensive workloads, says Blaize. According to Blaize, the computing architecture meets the demands and complexity of new computational workloads found in artificial intelligence (AI) applications in automotive, smart vision and enterprise computing segments. The Blaize GSP architecture and Blaize Picasso software development platform blends dynamic data flow methods and graph computing models with fully programmable proprietary SoCs.