AI Markets

AI Capability Races Ahead of the Business Payoff | American Enterprise Institute www.aei.org
News Source
EXCERPT:

The state of the AI revolution can be confusing. At times, it can seem as if the latest artificial intelligence models are showing huge capability increases. One example is when two OpenAI models broke out of the lab, accessed the internet, and broke into another AI company. Or, as a Wall Street Journal headline put it, “The Day the Bots Broke Loose.”

But then you might come across another headline from the Wall Street Journal: “Big Companies Are Starting to Hire Again, Defying Predictions of AI Wipeout.” And from that piece:

The push to expand head count, at least modestly, is a reversal from the prevailing corporate messaging during much of the AI era. Major employers largely held back on adding people due to economic uncertainties or a belief that artificial intelligence could shoulder more tasks on the job. But some executives say the costs and limitations of AI now demand that more people be added; others want to hire people back following layoffs.

AI can become more biased than humans while choosing who gets hired; study finds | timesofindia.indiatimes.com
News Source
EXCERPT:

Artificial intelligence is increasingly being trusted to help employers screen CVs, rank candidates and even conduct early-stage interviews. The promise is simple: AI can process thousands of applications quickly, reduce administrative workloads and, in theory, make hiring decisions more objective than humans. According to a recent review by MIT Technology, based on the 2024 study published in the Journal of Experimental Psychology titled “Costly Exploration Produces Stereotypes With Dimensions of Warmth and Competence”, large language models (LLMs) may not only inherit human biases from the data they are trained on but also develop entirely new stereotypes based on their own experiences. Rather than simply reflecting existing prejudice, these systems can create fresh patterns of discrimination as they attempt to optimise decision-making over time.The findings are particularly significant because businesses are rapidly integrating AI into recruitment. If these systems begin making assumptions about groups of applicants after only a handful of hiring decisions, they could gradually reinforce unfair employment practices without any explicit human instruction.The research, conducted by scientists at Princeton University and the University of Chicago, builds on earlier psychological work showing how stereotypes can emerge from repeated decision-making. It suggests that the same learning strategies that make AI effective at solving complex problems can also make it unusually prone to stereotyping job candidates.

U.S. pledges $5 billion to boost AI in government-backed scientific research www.scientificamerican.com
News Source
EXCERPT:

Overall federal funding for scientific research is set to decline in 2027, the most senior White House science figure said on Wednesday. The government, however, pledged to spend $5 billion on promoting artificial intelligence in science.

Michael Kratsios, the White House’s science and technology adviser, told the House Committee on Science, Space, and Technology that the money will be drawn from the National Science Foundation (NSF) and NASA, as well as other federal agencies.

The goal is to produce “the premier AI for science ecosystem in the world,” he said. The funding will be earmarked for “research awards, funding opportunities, specialized scientific datasets and research facilities,” the White House said in a statement.