The United States Department of Commerce has proposed prohibiting key Chinese software and hardware in connected vehicles on American roads due to national security concerns, a move that would in effect bar Chinese cars and trucks from the US market. The planned regulation, proposed on Monday, would also force American and other major automakers in years ahead to remove key Chinese software and hardware from vehicles in the US. President Joe Biden's administration has raised concerns about data collection on US drivers and infrastructure by connected Chinese vehicles and potential foreign manipulation of vehicles connected to the internet and navigation systems. In February, the White House ordered an investigation. The proposed prohibitions would prevent testing of self-driving cars on US roads by Chinese automakers, extend to vehicle software and hardware produced by Russia, and could be extended to other US adversaries.

In this white paper, I present my community effort to automatically co-design cheaper, faster and more energy-efficient software and hardware for AI, ML and other popular workloads with the help of the Collective Mind framework (CM), virtualized MLOps, MLPerf benchmarks and reproducible optimization tournaments. I developed CM to modularize, automate and virtualize the tedious process of building, running, profiling and optimizing complex applications across rapidly evolving open-source and proprietary AI/ML models, datasets, software and hardware. I achieved that with the help of portable, reusable and technology-agnostic automation recipes (ResearchOps) for MLOps and DevOps (CM4MLOps) discovered in close collaboration with academia and industry when reproducing more than 150 research papers and organizing the 1st mass-scale community benchmarking of ML and AI systems using CM and MLPerf. I donated CM and CM4MLOps to MLCommons to help connect academia and industry to learn how to build and run AI and other emerging workloads in the most efficient and cost-effective way using a common and technology-agnostic automation, virtualization and reproducibility framework while unifying knowledge exchange, protecting everyone's intellectual property, enabling portable skills, and accelerating transfer of the state-of-the-art research to production. My long-term vision is to make AI accessible to everyone by making it a commodity automatically produced from the most suitable open-source and proprietary components from different vendors based on user demand, requirements and constraints such as cost, latency, throughput, accuracy, energy, size and other important characteristics.

However, over the last few years, the software news out of WWDC has been upstaged by hardware announcements. Rather than focusing on apps at recent WWDCs, Apple has used the gathering as an opportunity to unveil its M-series Apple Silicon chips, next-generation Macs, and most recently, the Apple Vision Pro mixed-reality headset. But this year, Apple pushed its hardware announcements out ahead of WWDC. In April, the company launched the M3-powered MacBook Air (13-inch and 15-inch). In May, it released an all-new M4 chip, along with the latest iPad Pro and iPad Air.

Artificial Intelligence (AI) has been bandied around for the last few decades or so and, I'm sure, most of us are still wondering: What exactly is this? So, will we be faced with an army of Terminator-like humanoids who will reign terror across the world? Will we witness "I, Robot"-like humanoids attending to our homecare needs – you know, washing, ironing, cooking and the like? Nah, I already have a wife that's dutifully doing that. Okay, stop – I know I shouldn't go there – just one small footnote though: My wife, Sarah, isn't remotely domesticated, although she does cook once in a while!

This is an early and tentative document, intended to roughly summarize a line of thinking and to spur discussion and action among relevant individuals, organizations and communities. Many particulars discussed here are expected to evolve as more and more of the concepts described here move to practical realization. This is a living, evolving body of ideas. The introduction of AI tools and agents into all sectors of the economy, from factory robots to highly specialized electronic scientific brains, and the transition from narrow AI (domain specific, at best weakly autonomous) toward Artificial General Intelligence (broadly intelligent and strongly autonomous), are likely to be the biggest story of the next few decades. The tremendous promise and peril of these developments, which are already well underway, have been much discussed in fictional, media and intellectual spheres.

Machine learning is gathering pace in China, with enterprises employing the technology to detect financial fraud, recommend products to consumers and streamline industrial operations, among various use cases. In a new report assessing key suppliers of machine learning platforms in China, IDC details its observations of the market and how major suppliers fare in supporting the needs of enterprises across industries. According to the analyst company, China's machine learning market – including hardware, software and services – was worth RMB1bn (US$139m) in 2018. But when applied in artificial intelligence (AI) applications, machine learning contributed RMB10bn to China's AI market last year. Chinese suppliers of machine learning platforms currently provide more than 30 classic machine learning algorithms, with support for mainstream frameworks such as TensorFlow, PyTorch and Caffe.

Artificial Intelligence (AI) has been bandied around for the last few decades or so and, I'm sure, most of us are still wondering: What exactly is this? So, will we be faced with an army of Terminator-like humanoids who will reign terror across the world? Will we witness "I, Robot"-like humanoids attending to our homecare needs – you know, washing, ironing, cooking and the like? Nah, I already have a wife that's dutifully doing that. Okay, stop – I know I shouldn't go there – just one small footnote though: My wife, Sarah, isn't remotely domesticated, although she does cook once in a while!

Advances in artificial intelligence (AI) software and hardware are giving rise to a multitude of smart devices that can recognize and react to sights, sounds, and other patterns--and do not require a persistent connection to the cloud. These smart devices, from robots to cameras to medical devices, could well unlock greater efficiency and effectiveness at organizations that adopt them. In some industries, smart machines may well help expand existing markets, threaten incumbents, and shift the way revenue and profits are apportioned among industry players. Rapid strides in technology and the growing investment in AI innovation signal how fast AI deployment is moving. Advances in software and hardware are propelling AI outside of the data center into devices and machines we use in our work and our everyday lives.

Navigating data increasingly requires artificial intelligence just to be able to organize that data.Kurt Cagle 2019 All things come to an end, especially economic cycles. People who have logged more than a couple of decades in information technology especially are attuned to it, because their jobs and interests both tend to be very forward facing - the inability of a software developer or information manager to read the future, at least in a general sense, usually means that they won't last long in the field. As the markets enter into the gyrations of this last December, with the Dow Jones Industrial Average now down 16% from the year's highs, the thought that the party would never end is now giving way to the notion that maybe it's time to grab the car keys and bid the hosts adieu, and those of us in IT are battening down the hatches in a serious way. I started writing these year end predictions way back in 2003, at a time when "blogging" was still considered a novel thing, and Google had just wrested the mantle of king of the search engines away from Alta Vista. Fifteen years later, with my then three year old baby girl now heading to college and my red hair and beard now gone mostly white, the landscape has changed, most of the big players have changed (who knew Microsoft would eventually end up migrating to Linux), and the buzzwords are now almost a different language, yet at the same time, the patterns that underlie tech remain very predictable. Business cycles, most economists have noticed, follow an eight to ten year pattern, usually with a bit of a wobble at the halfway point, and you can make a pretty compelling argument that there's a broader cycle that's double that, between eighteen and twenty years, where the economic crises oscillate between equity crashes (typically accompanied by commercial real estate disintegration) and mortgage (or residential real estate) collapses.

Humans are creatures of habit. Our brain creates neural pathways from repetitive actions and thoughts to make us more effective. This, in turn, can result in us acting on auto-pilot. And it's the reason why neurologically forming new habits, such as waking up earlier to do exercise or eating less, can often fail. The reason why most AI projects fail is because forming new habits or implementing new processes is hard. And it gets even harder if the barriers to using it and experimenting with it are too high to even bother with.