You've Never Heard of China's Greatest Sci-Fi Novel Thousands of authors. is barely known outside China--but it contains the secret to the country's modernization and malaise. Ma Qianzhu was unsatisfied with Chinese progress. An engineer at a large state-owned enterprise, he belonged to a generation that grew up believing engineering is destiny, that China's future would be built, bolt by bolt, by people like him. Then Ma discovered something extraordinary: a wormhole to the late Ming Dynasty. With more than 500 peers, he commandeered a ship and traveled back in time 400 years, to a preindustrial China wracked by foreign invasion and internal decay. Their mission: trigger an industrial revolution in the past that would, in the future, make modern China great (again).

When a phalanx of the top Silicon Valley executives--Mark Zuckerberg, Jeff Bezos, Elon Musk, and Google's Sundar Pichai--aligned behind President Trump during the Inauguration in January, many observers saw an allegiance based on corporate interests. The ultra-wealthy C.E.O.s were turning out to support a fellow-magnate, hoping perhaps for an era of deregulation, tax breaks, and anti-"woke" cultural shifts. The historian Janis Mimura saw something more ominous: a new, proactive union of industry and governmental power, wherein the state would drive aggressive industrial policy at the expense of liberal norms. In the second Trump Administration, a class of Silicon Valley leaders was insinuating itself into politics in a way that recalled one of Mimura's primary subjects of study: the élite bureaucrats who seized political power and drove Japan into the Second World War. "These are experts with a technological mind-set and background, often engineers, who now have a special role in the government," Mimura told me.

The industrialization of artificial intelligence – one of the 7 hot data topics for 2022 requires the implementation of MLOps. This approach includes some necessary steps, including a common platform and a feature store. To learn more about this approach, we offer you a how-to-guide for an iterative, but unavoidable transformation. After years, which were certainly fruitful in gaining experience, working on the development of PoC, organizations now aim to move into a new phase of maturity. And this phase aims in particular to design in an industrial way Data products with embedded artificial intelligence.

This year, the controversy about AI industrialization has become a hot topic. There are not only negative phenomena such as criticism of AI "research results are hard to break through in academia, and also difficult to commercialize in industry" from academia, AI scientists leaving the industry and returning to academia, but also positive encouragement from the successful listing of a number of AI unicorns.So, is there an opportunity for AI industrialization? And where are the opportunities? On these industry hot topics, Yuan Jinhui, the founder of OneFlow, launched a systematic elaboration in the QbitAI live. In previous years, society was crazy about AI. For example, there were discussions about the coming singularity, AI replacing humans, and fully automated driving by 2020.

Machine learning has been around for decades, but for much of that time, businesses were only deploying a few models and those required tedious, painstaking work done by PhDs and machine learning experts. Over the past couple of years, machine learning has grown significantly thanks to the advent of widely available, standardized, cloud-based machine learning platforms. Today, companies across every industry are deploying millions of machine learning models across multiple lines of business. Tax and financial software giant Intuit started with a machine learning model to help customers maximize tax deductions; today, machine learning touches nearly every part of their business. In the last year alone, Intuit has increased the number of models deployed across their platform by over 50 percent.

In this era of industrialization for Artificial Intelligence (AI), enterprises are scrambling to embed AI across a plethora of use cases in hopes of achieving higher productivity and enhanced experiences. However, as AI permeates through different functions of an enterprise, managing the entire charter gets tough. Working with multiple Machine Learning (ML) models in both pilot and production can lead to chaos, stretched timelines to market, and stale models. As a result, we see enterprises hamstrung to successfully scale AI enterprise-wide. To overcome the challenges enterprises face in their ML journeys and ensure successful industrialization of AI, enterprises need to shift from the current method of model management to a faster and more agile format.

China has overtaken the United States in total number of AI research citations, fewer AI startups are receiving funding, and Congress is talking about AI more than ever. Those are three major trends highlighted in the 2021 AI Index, an annual report released today by Stanford University. Now in its fourth year, the AI Index attempts to document advances in artificial intelligence, as well as the technology's impact on education, startups, and government policy. The report details progress in the performance of major subdomains of AI, like deep learning, image recognition, and object detection, as well as in areas like protein folding. The AI Index is compiled by the Stanford Institute for Human-Centered Artificial Intelligence and an 11-member steering committee, with contributors from Harvard University, OECD, the Partnership on AI, and SRI International.

After releasing what may well have been the most comprehensive report on the State of AI in 2019, Air Street Capital and RAAIS founder Nathan Benaich and AI angel investor and UCL IIPP visiting professor Ian Hogarth are back for more. In the State of AI Report 2020, Benaich and Hogarth outdid themselves. While the structure and themes of the report remain mostly intact, its size has grown by nearly 30%. This is a lot, especially considering their 2019 AI report was already a 136 slide long journey on all things AI. The State of AI Report 2020 is 177 slides long, and it covers technology breakthroughs and their capabilities, supply, demand, and concentration of talent working in the field, large platforms, financing, and areas of application for AI-driven innovation today and tomorrow, special sections on the politics of AI, and predictions for AI.

MLOps, short for machine learning operations, is the equivalent of DevOps for ML models: taking them from development to production, and managing …

Technological innovation plays a decisive role in the evolution of changes towards a new model that involves improving development, without leaving anyone behind, and with the focus on avoiding inequality and injustice, ensuring better protection of the environment. These are broadly the foundations of the 17 Sustainable Development Goals (SDGs) of the United Nations 2030 Agenda. Technology with its multiplier effect can accelerate the achievement of objectives and goals. There are four technologies (based on AI) that allow addressing the five basic elements on which the 2030 Agenda is structured: people, prosperity, planet, peace and alliances. The interconnection of the five pillars of the 2030 Agenda and with four technological blocks that pivot on the Internet of Things (IoT), Automation, the Analysis of large volumes of data (Big Data) and Advanced Robotics is essential so that the developed world that we know is in balance and the current imbalances are corrected.