The huge amount of energy required to train artificial intelligence (AI) is becoming a concern. To train the large language model (LLM) powering Chat GPT-3, for example, almost 1,300 megawatt hours of energy was used, according to an estimate by researchers from Google and the University of California, Berkeley, a similar quantity of energy to what is used by 130 American homes in one year. Furthermore, an analysis by OpenAI suggests that the amount of power needed to train AI models has been growing exponentially since 2012, doubling roughly every 3.4 months as the models become bigger and more sophisticated. However, our energy production capacity is not increasing as steeply, and doing so is likely to further contribute to global warming: generating electricity is the single biggest contributor to climate change given that coal, oil, and gas are still widely used to generate electricity, compared to cleaner energy sources. "At this rate, we are running into a brick wall in terms of the ability to scale up machine learning networks," said Menachem Stern, a theoretical physicist at the AMOLF research institute in the Netherlands.

Gary Marcus: Two Models of AI Oversight -- and How Things Could Go Deeply Wrong https://bit.ly/3Qnxd9A June 12, 2023 Originally published on The Road to AI We Can Trust (http://bit.ly/3juuD3j) The Senate hearing that I participated in a few weeks ago (https://bit.ly/44QxHt1) I was thrilled by what I saw of the Senate that day: genuine interest and genuine humility. Senators acknowledged that they were too slow to figure out what do about social media, that the moves were made then, and that there was now a sense of urgency.

There are various studies on different topics of artificial intelligence. Many renowned institutes and companies deal with the various aspects and technologies and the corresponding effects on companies. One of KPMG's highly relevant and highly topical studies on this topic is presented in this article. For this purpose, KPMG interviewed CEOs of various renowned companies in the USA. According to the respondents, the most critical trust factors are algorithm integrity, explainability, fairness in terms of ethics and accountability, and resilience.

Artificial intelligence has gone far beyond a simple buzzword. Experimentation in a wide variety of use cases is everywhere. However, most companies are not yet adopting AI across the enterprise at scale, says Martin Sokalski, Principal, Advisory, Emerging Technology Risk Services at KPMG. "A lot of our clients ask how they can get to scale with AI," he explains. "They don't know how to bridge the gap." A big issue, he explains, is a lack of trust and transparency.