"Before providing services to the public that use generative AI products, a security assessment shall be applied for through national internet regulatory departments," the draft law, released by the Cyberspace Administration of China, reads. The draft law -- dubbed "Administrative Measures for Generative Artificial Intelligence Services" -- aims to ensure "the healthy development and standardised application of generative AI technology", it read. AI generated content, it continued, must "reflect core socialist values, and must not contain content on subversion of state power". It must also not contain, among other things, "terrorist or extremist propaganda", "ethnic hatred" or "other content that may disrupt economic and social order." The Cyberspace Administration of China said it was seeking public input on the contents of the new regulations, which under Beijing's highly centralised political system are almost certain to become law. The fresh regulations come as a flurry of Chinese companies including Alibaba, JD.com, Netease and TikTok-parent Bytedance rush to develop services that can mimic human speech since San Francisco-based OpenAI launched ChatGPT in November, sparking a gold rush in the market.

Despite being viewed as a technology of the future, artificial intelligence (AI) has already impacted our daily lives in several ways. Right from the time we wake up, till we go to bed, AI is constantly a part of our lives in forms like voice assistants, online banking, OTT, face IDs among others. Shaping global standards for AI A number of standards covering significant AI issues are now being developed by the ISO/IEC committee for artificial intelligence under the working title ISO/IEC 42001 ISO/IEC DIS 42001 – Information technology -- Artificial intelligence -- Management system. The ISO/IEC 42001 standard, which is being developed by 50 countries, will be essential for improving AI governance and accountability globally. ISO/IEC standardisation brings together the opinions of all relevant stakeholder groups, including SMEs, academia, civil society, and many more.

Warning: Some readers may find some of the scenes described in this article disturbing. Kyiv, Ukraine – Ivan Ukraintsev, a stern-faced insurance broker turned director of a wartime charity providing crucial aid to Ukraine's military forces, is on a mission: to help Ukraine win the drone war. He is a polite but no-nonsense character, and he is here to talk about drones. "If we [Ukraine] had enough drones, we could end this war in two months," he says firmly. Ivan, who heads up the charity Starlife, had recently returned from overseeing a drone delivery to Bakhmut, a city in eastern Ukraine that has become the focal point for months of bloody battles between Ukrainian and Russian forces. Trench warfare, pockmarked and corpse-ridden swathes of no man's land, and constant artillery bombardments have drawn comparisons to battlefield conditions during World War I.

China has released a new draft regulation that it says is necessary to ensure the safe development of generative artificial intelligence (AI) technologies, such as ChatGPT. While it supports the innovative use of AI algorithms to improve user experience and access to information, the growth of such applications can lead to abuse. Emphasis should be placed on such tools and data resources to be used safely and reliably, said the Cyberspace Administration of China (CAC). Regulations were needed to drive a healthy and sustainable development of generative AI algorithms, said the government agency, as it published the draft laws on its website. Under the proposed rules, operators will be required to send their applications to regulators for "safety reviews" before offering the services to the public, according to a report by state-owned media Global Times.

Then-Google AI Research Scientist Timnit Gebru speaks during the TechCrunch Disrupt SF 2018 conference. Last Thursday POLITICO's Mark Scott, author of the Digital Bridge newsletter, interviewed the computer scientist and activist Timnit Gebru about a recent open letter from her Distributed AI Research Institute that argued -- contra the Future of Life Institute's high-profile letter calling for an "AI pause" -- that the major harms caused by AI are already here, and therefore "Regulatory efforts should focus on transparency, accountability and preventing exploitative labor practices." Mark asked her what she thinks regulators' role should be in this fast-moving landscape, and how society might take a more proactive approach to shaping AI before it simply shapes us. This conversation has been edited for length and clarity. Why is it important to increase the transparency and accountability for how AI systems are deployed, and how would it benefit people's understanding of how the technology works?

Taipei, Taiwan – After playing catchup to ChatGPT, China is racing to regulate the rapidly-advancing field of artificial intelligence (AI). Under draft regulations released this week, Chinese tech companies will need to register generative AI products with China's cyberspace agency and submit them to a security assessment before they can be released to the public. The regulations cover practically all aspects of generative AI, from how it is trained to how users interact with it, in an apparent bid by Beijing to control the at times unwieldy technology, the break-neck development of which has prompted warnings from tech leaders including Elon Musk and Apple co-founder Steve Wozniak. Under the rules unveiled by the Cyberspace Administration of China on Tuesday, tech companies will be responsible for the "legitimacy of the source of pre-training data" to ensure content reflects the "core value of socialism". Companies must ensure AI does not call for the "subversion of state power" or the overthrow of the ruling Chinese Communist Party (CCP), incite moves to "split the country" or "undermine national unity", produce content that is pornographic, or encourage violence, extremism, terrorism or discrimination.

Welcome to the latest issue of your guide to AI, an editorialized newsletter covering key developments in AI research, industry, geopolitics and startups during February 2023. We wrote an op-ed for Sifted on how generative AI will change the software landscape and commented for TIME's cover story on ChatGPT. On the politics side, we reviewed and recommended spinout policy reform in Tony Blair Institute for Global Change's paper A New National Purpose and were included in Politico's 20 people who matter in UK technology. Air Street was featured in Insider's list of top AI investors See some of you at London.AI on Thurs 9 March w/DeepMind, Adept, Palantir and Basecamp Research. Register for our one-day RAAIS conference on research and applied AI 23 June 2023 in London. We'll be hosting speakers from Meta AI, Cruise, Intercom, Genentech, Northvolt and more to come! FYI, you might have to read this issue in full online vs. in your inbox. As usual, we love hearing what you're up to and what's on your mind, just hit reply or forward to your friends:-) Building large-scale AI models requires enormous computing power, which has emerged as the soft power of our time.

In this paper, we propose a framework for fast trajectory planning for unmanned aerial vehicles (UAVs). Our framework is reformulated from an existing bilevel optimization, in which the lower-level problem solves for the optimal trajectory with a fixed time allocation, whereas the upper-level problem updates the time allocation using analytical gradients. The lower-level problem incorporates the safety-set constraints (in the form of inequality constraints) and is cast as a convex quadratic program (QP). Our formulation modifies the lower-level QP by excluding the inequality constraints for the safety sets, which significantly reduces the computation time. The safety-set constraints are moved to the upper-level problem, where the feasible waypoints are updated together with the time allocation using analytical gradients enabled by the OptNet. We validate our approach in simulations, where our method's computation time scales linearly with respect to the number of safety sets, in contrast to the state-of-the-art that scales exponentially.

Google AlphaGos win has significantly motivated and sped up machine learning (ML) research and development, which led to tremendous ML technical advances and wider adoptions in various domains (e.g., Finance, Health, Defense, and Education). These advances have resulted in numerous new concepts and technologies, which are too many for people to catch up to and even make them confused, especially for newcomers to the ML area. This paper is aimed to present a clear picture of the state-of-the-art of the existing ML technologies with a comprehensive survey. We lay out this survey by viewing ML as a MLOps (ML Operations) process, where the key concepts and activities are collected and elaborated with representative works and surveys. We hope that this paper can serve as a quick reference manual (a survey of surveys) for newcomers (e.g., researchers, practitioners) of ML to get an overview of the MLOps process, as well as a good understanding of the key technologies used in each step of the ML process, and know where to find more details.

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.