Prior work has investigated variations of prediction markets that preserve participants' (differential) privacy, which formed the basis of useful mechanisms for purchasing data for machine learning objectives. Such markets required potentially unlimited financial subsidy, however, making them impractical.

Prior work has investigated variations of prediction markets that preserve participants' (differential) privacy, which formed the basis of useful mechanisms for purchasing data for machine learning objectives. Such markets required potentially unlimited financial subsidy, however, making them impractical.

Cathie Wood is CEO, CIO and founder of ARK Invest, a closely watched investment fund known for its prescient high risk, high reward strategy (Wood was early on Bitcoin and Tesla) and radical transparency (she explains her decisions to buy and sell in a public newsletter). ARK has had a wild ride in the last two years. Its flagship innovation ETF was up 150% in 2020, gaining Wood celebrity status, but has been down 38% in the past 12 months. Wood spoke with TIME about her long- and short-term predictions for the business world and what she has learned from her fund's bumpy ride. This interview has been condensed and edited for clarity. Typically, we lean into it, and this time is no exception.

It seems the White House wants to ramp up America's Artificial Intelligence (AI) dominance. Earlier this month, the U.S. Office of Management and Budget released its "Guidance for Regulation of Artificial Intelligence Applications," for federal agencies to oversee AI's development in a way that protects innovation without making the public wary. The noble aims of these principles respond to the need for a coherent American vision for AI development--complete with transparency, public participation and interagency coordination. But the government is missing something key. For technological innovation to flourish, regulators have to be innovative too. A recent paper by Jack Clark and Gillian Hadfield at OpenAI proposes the idea of regulatory markets, wherein private competitive regulators would take on the role traditionally held by by legislators and government agencies.

A regulatory market approach would enable the dynamism needed for AI to flourish in a way consistent with safety and public trust. It seems the White House wants to ramp up America's artificial intelligence (AI) dominance. Earlier this month, the U.S. Office of Management and Budget released its "Guidance for Regulation of Artificial Intelligence Applications," for federal agencies to oversee AI's development in a way that protects innovation without making the public wary. The noble aims of these principles respond to the need for a coherent American vision for AI development--complete with transparency, public participation and interagency coordination. But the government is missing something key.

Prior work has investigated variations of prediction markets that preserve participants' (differential) privacy, which formed the basis of useful mechanisms for purchasing data for machine learning objectives. Such markets required potentially unlimited financial subsidy, however, making them impractical. In this work, we design an adaptively-growing prediction market with a bounded financial subsidy, while achieving privacy, incentives to produce accurate predictions, and precision in the sense that market prices are not heavily impacted by the added privacy-preserving noise. We briefly discuss how our mechanism can extend to the data-purchasing setting, and its relationship to traditional learning algorithms.

Prior work has investigated variations of prediction markets that preserve participants' (differential) privacy, which formed the basis of useful mechanisms for purchasing data for machine learning objectives. Such markets required potentially unlimited financial subsidy, however, making them impractical. In this work, we design an adaptively-growing prediction market with a bounded financial subsidy, while achieving privacy, incentives to produce accurate predictions, and precision in the sense that market prices are not heavily impacted by the added privacy-preserving noise. We briefly discuss how our mechanism can extend to the data-purchasing setting, and its relationship to traditional learning algorithms.

We dug into the private market bets made by major computer chip companies, including GPU makers. Our analysis encompasses the venture arms of NVIDIA, Intel, Samsung, AMD, and more. Recent developments in the semiconductor industry have been sending mixed signals. Stories about Moore's Law slowing have grown common, but analysts affirm that the latest crop of chips (specifically Intel's newest 10-nanometer technology) prove Moore's Law is still alive and well. Meanwhile, the vast application of graphics hardware in AI has propelled GPU (graphics processing unit) maker NVIDIA into tech juggernaut status: the company's shares were the best-performing stock over the past year.

We dug into the private market bets made by major computer chip companies, including GPU makers. Our analysis encompasses the venture arms of NVIDIA, Intel, Samsung, AMD, and more. Recent developments in the semiconductor industry have been sending mixed signals. Stories about Moore's Law slowing have grown common, but analysts affirm that the latest crop of chips (specifically Intel's newest 10-nanometer technology) prove Moore's Law is still alive and well. Meanwhile, the vast application of graphics hardware in AI has propelled GPU (graphics processing unit) maker NVIDIA into tech juggernaut status: the company's shares were the best-performing stock over the past year.

We dug into the private market bets made by major computer chip companies, including GPU makers. Our analysis encompasses the venture arms of NVIDIA, Intel, Samsung, AMD, and more. Recent developments in the semiconductor industry have been sending mixed signals. Stories about Moore's Law slowing have grown common, but analysts affirm that the latest crop of chips (specifically Intel's newest 10-nanometer technology) prove Moore's Law is still alive and well. Meanwhile, the vast application of graphics hardware in AI has propelled GPU (graphics processing unit) maker NVIDIA into tech juggernaut status: the company's shares were the best-performing stock over the past year.