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By the end of 2025, the world felt newly unsettled. Donald Trump's return to the White House reshaped the context for nearly every major story, sending tremors through conflict zones and alliances alike. Added to that was the rapid advance of artificial intelligence, inspiring both optimism and anxiety about where humanity was headed next. It was as if the global chessboard were being reset, with the post-World War II order giving way to something new. President Donald Trump pumps his fist as he delivers his inaugural address at his inauguration as the 47th president in the Rotunda at the Capitol in Washington on Jan. 20.

Just like Napster triggered a global, technological shift in the way music is consumed and distributed, we are now on the precipice of another major revolution certain to disrupt the music industry. Artificial intelligence, or "AI" as it is more commonly referred, has quickly emerged as a game changer across a myriad of industries and music is no exception. AI offers the promise of innovative opportunities and avenues for music creation, publishing, recording, synchronization, distribution, consumption and revenue generation. However, these opportunities also present significant, novel challenges for music rights holders and users alike--and the legal challenges have just begun. AI is not a new concept.

Conventional sheet metal manufacturing is highly inefficient for the low-volume production seen in the space industry. At Machina Labs, they developed a novel method of forming sheet metal using two robotic arms to bend the metal into different geometries. This method cuts down the time to produce large sheet metal parts from several months down to a few hours. Ed Mehr, Co-Founder and CEO of Machina Labs, explains this revolutionary manufacturing process. Ed Mehr is the co-founder and CEO of Machina Labs.

The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domains. One important problem class that has remained relatively elusive however is purposeful adaptive behavior. Currently there is a common perception that sequence models "lack the understanding of the cause and effect of their actions" leading them to draw incorrect inferences due to auto-suggestive delusions. In this report we explain where this mismatch originates, and show that it can be resolved by treating actions as causal interventions. Finally, we show that in supervised learning, one can teach a system to condition or intervene on data by training with factual and counterfactual error signals respectively.

The financial sector generates a lot of valuable data, from individual purchases to large transactions. Given the enormous treasure that all this information holds, expectations of the impact of artificial intelligence (AI) in the banking sector could not be higher. It is anticipated that the industry will save more than $1 trillion by 2030 due to recent developments in AI. Faced with these amazing opportunities, many banks are beginning to take action. But how can they make the most of artificial intelligence?

ONE THEORY HAS ARISEN in the decade since the subprime mortgage crisis: Machines may be better than humans at giving out home loans. A new Fannie Mae survey of mortgage lenders found that 40% of mortgage banks have deployed A.I.--using it to automate the document-heavy application process, detect fraud, and predict a borrower's likelihood of default. San Francisco–based Blend, for one, provides its online mortgage-application software to 114 lenders, including lending giant Wells Fargo, shaving at least a week off the approval process. Could it have prevented the mortgage meltdown? Maybe not entirely, but it might have lessened the severity as machines flagged warning signs sooner.

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