Connect with us

AI Research

Senator Cruz Unveils AI Framework and Regulatory Sandbox Bill

Published

on


On September 10, Senate Commerce, Science, and Transportation Committee Chair Ted Cruz (R-TX) released what he called a “light-touch” regulatory framework for federal AI legislation, outlining five pillars for advancing American AI leadership.  In parallel, Senator Cruz introduced the Strengthening AI Normalization and Diffusion by Oversight and eXperimentation (“SANDBOX”) Act (S. 2750), which would establish a federal AI regulatory sandbox program that would waive or modify federal agency regulations and guidance for AI developers and deployers.  Collectively, the AI framework and the SANDBOX Act mark the first congressional effort to implement the recommendations of AI Action Plan the Trump Administration released on July 23. 

  1. Light-Touch AI Regulatory Framework

Senator Cruz’s AI framework, titled “A Legislative Framework for American Leadership in Artificial Intelligence,” calls for the United States to “embrace its history of entrepreneurial freedom and technological innovation” by adopting AI legislation that promotes innovation while preventing “nefarious uses” of AI technology.  Echoing President Trump’s January 23 Executive Order on “Removing Barriers to American Leadership in Artificial Intelligence” and recommendations in the AI Action Plan, the AI framework sets out five pillars as a “starting point for discussion”:

  • Unleashing American Innovation and Long-Term Growth.  The AI framework recommends that Congress establish a federal AI regulatory sandbox program, provide access to federal datasets for AI training, and streamline AI infrastructure permitting.  This pillar mirrors the priorities of the AI Action Plan and President Trump’s July 23 Executive Order on “Accelerating Federal Permitting of Data Center Infrastructure.”
  • Protecting Free Speech in the Age of AI.  Consistent with President Trump’s July 23 Executive Order on “Preventing Woke AI in the Federal Government,” Senator Cruz called on Congress to “stop government censorship” of AI (“jawboning”) and address foreign censorship of Americans on AI platforms.  Additionally, while the AI Action Plan recommended revising the National Institute of Standards & Technology (“NIST”)’s AI Risk Management Framework to “eliminate references to misinformation, Diversity, Equity, and Inclusion, and climate change,” this pillar calls for reforming NIST’s “AI priorities and goals.”
  • Prevent a Patchwork of Burdensome AI Regulation.  Following a failed attempt by Congressional Republicans to enact a moratorium on the enforcement of state and local AI regulations in July, the AI Action Plan called on federal agencies to limit federal AI-related funding to states with burdensome AI regulatory regimes and on the FCC to review state AI laws that may be preempted under the Communications Act.  Similarly, the AI framework calls on Congress to enact federal standards to prevent burdensome state AI regulation, while also countering “excessive foreign regulation” of Americans.
  • Stop Nefarious Uses of AI Against Americans.  In a nod to bipartisan support for state digital replica protections – which ultimately doomed Congress’s state AI moratorium this summer – this pillar calls on Congress to protect Americans against digital impersonation scams and fraud.  Additionally, this pillar calls on Congress to expand the principles of the federal TAKE IT DOWN Act, signed into law in May, to safeguard American schoolchildren from nonconsensual intimate visual depictions.
  • Defend Human Value and Dignity.  This pillar appears to expand on the policy of U.S. “global AI dominance in order to promote human flourishing” established by President Trump’s January 23 Executive Order by calling on Congress to reinvigorate “bioethical considerations” in federal policy and to “oppose AI-driven eugenics and other threats.”
  1. SANDBOX Act

Consistent with recommendations in the AI Action Plan and AI Framework, the SANDBOX Act would direct the White House Office of Science & Technology Policy (“OSTP”) to establish and operate an “AI regulatory sandbox program” with the purpose of incentivizing AI innovation, the development of AI products and services, and the expansion of AI-related economic opportunities and jobs.  According to Senator Cruz’s press release, the SANDBOX Act marks a “first step” in implementing the AI Action Plan, which called for “regulatory sandboxes or AI Centers of Excellence around the country where researchers, startups, and established enterprises can rapidly deploy and test AI tools.”

Program Applications.  The AI regulatory sandbox program would allow U.S. companies and individuals, or the OSTP Director, to apply for a “waiver or modification” of one or more federal agency regulations in order to “test, experiment, or temporarily provide” AI products, AI services, or AI development methods.  Applications must include various categories of information, including:

  • Contact and business information,
  • A description of the AI product, service, or development method,
  • Specific regulation(s) that the applicant seeks to have waived or modified and why such waiver or modification is needed,
  • Consumer benefits, business operational efficiencies, economic opportunities, jobs, and innovation benefits of the AI product, service, or development method,
  • Reasonably foreseeable risks to health and safety, the economy, and consumers associated with the waiver or modification, and planned risk mitigations,
  • The requested time period for the waiver or modification, and
  • Each agency with jurisdiction over the AI product, service, or development method.

Agency Reviews and Approvals.  The bill would require OSTP to submit applications to federal agencies with jurisdiction over the AI product, service, or development method within 14 days.  In reviewing AI sandbox program applications, federal agencies would be required to solicit input from the private sector and technical experts on whether the applicant’s plan would benefit consumers, businesses, the economic, or AI innovation, and whether potential benefits outweigh health and safety, economic, or consumer risks.  Agencies would be required to approve or deny applications within 90 days, with a record documenting reasonably foreseeable risks, the mitigations and consumer protections that justify agency approval, or the reasons for agency denial.  Denied applicants would be authorized to appeal to OSTP for reconsideration.  Approved waivers or modifications would be granted for a term of two years, with up to four additional two-year terms if requested by the applicant and approved by OSTP. 

Participant Terms and Requirements.  Participants with approved waivers or modifications would be immune from federal criminal, civil, or agency enforcement of the waived or modified regulations, but would remain subject to private consumer rights of action.  Additionally, participants would be required to report incidents of harm to health and safety, economic damage, or unfair or deceptive trade practices to OSTP and federal agencies within 72 hours after the incident occurs, and to make various disclosures to consumers.  Participants would also be required to submit recurring reports to OSTP throughout the term of the waiver or modification, which must include the number of consumers affected, likely risks and mitigations, any unanticipated risks that arise during deployment, adverse incidents, and the benefits of the waiver or modification.

Congressional Review.  Finally, the SANDBOX Act would require the OSTP Director to submit to Congress any regulations that the Director recommends for amendment or repeal “as a result of persons being able to operate safely” without those regulations under the sandbox program.  The bill would establish a fast-track procedure for joint resolutions approving such recommendations, which, if enacted, would immediately repeal the regulations or adopt the amendments recommended by OSTP.

The SANDBOX Act’s regulatory sandbox program would sunset in 12 years unless renewed.  The introduction of the SANDBOX Act comes as states have pursued their own AI regulatory sandbox programs – including a sandbox program established under the Texas Responsible AI Governance Act (“TRAIGA”), enacted in June, and an “AI Learning Laboratory Program” established under Utah’s 2024 AI Policy Act.  The SANDBOX Act would require OSTP to share information these state AI sandbox programs if they are “similar or comparable” to the SANDBOX Act, in addition to coordinating reviews and accepting “joint applications” for participants with AI projects that would benefit from “both Federal and State regulatory relief.” 



Source link

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

AI Research

Databricks at a crossroads: Can its AI strategy prevail without Naveen Rao?

Published

on


“Databricks is in a tricky spot with Naveen Rao stepping back. He was not just a figurehead, but deeply involved in shaping their AI vision, particularly after MosaicML,” said Robert Kramer, principal analyst at Moor Insights & Strategy.

“Rao’s absence may slow the pace of new innovation slightly, at least until leadership stabilizes. Internal teams can keep projects on track, but vision-driven leaps, like identifying the ‘next MosaicML’, may be harder without someone like Rao at the helm,” Kramer added.

Rao became a part of Databricks in 2023 after the data lakehouse provider acquired MosaicML, a company Rao co-founded, for $1.3 billion. During his tenure, Rao was instrumental in leading research for many Databricks products, including Dolly, DBRX, and Agent Bricks.



Source link

Continue Reading

AI Research

NFL player props, odds: Week 2, 2025 NFL picks, SportsLine Machine Learning Model AI predictions, SGP

Published

on


The Under went 12-4 in Week 1, indicating that not only were there fewer points scored than expected, but there were also fewer yards gained. Backing the Under with NFL prop bets was likely profitable for the opening slate of games, but will that maintain with Week 2 NFL props? Interestingly though, four of the five highest-scoring games last week were the primetime games, so if that holds, then the Overs for this week’s night games could be attractive with Week 2 NFL player props.

There’s a Monday Night Football doubleheader featuring star pass catchers like Nico Collins, Mike Evans and Brock Bowers. The games also feature promising rookies such as Ashton Jeanty, Omarion Hampton and Emeka Egbuka. Prop lines are usually all over the place early in the season as sportsbooks attempt to establish a player’s potential, and you could take advantage of this with the right NFL picks. If you are looking for NFL prop bets or NFL parlays for Week 2, SportsLine has you covered with the top Week 2 player props from its Machine Learning Model AI.

Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop. 

Now, with the Week 2 NFL schedule quickly approaching, SportsLine’s Machine Learning Model AI has identified the top NFL props from the biggest Week 2 games.

Week 2 NFL props for Sunday’s main slate

After analyzing the NFL props from Sunday’s main slate and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Lions receiver Amon-Ra St. Brown goes Over 63.5 receiving yards (-114) versus the Bears at 1 p.m. ET. Detroit will host this contest, which is notable as St. Brown has averaged 114 receiving yards over his last six home games. He had at least 70 receiving yards in both matchups versus the Bears a year ago.

Chicago allowed 12 receivers to go Over 63.5 receiving yards last season as the Bears’ pass defense is adept at keeping opponents out of the endzone but not as good at preventing yardage. Chicago allowed the highest yards per attempt and second-highest yards per completion in 2024. While St. Brown had just 45 yards in the opener, the last time he was held under 50 receiving yards, he then had 193 yards the following week. The SportsLine Machine Learning Model projects 82.5 yards for St. Brown in a 4.5-star pick. See more Week 2 NFL props here.

Week 2 NFL props for Vikings vs. Falcons on Sunday Night Football

After analyzing Falcons vs. Vikings props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Falcons running back Bijan Robinson goes Over 65.5 rushing yards (-114). Robinson ran for 92 yards and a touchdown in Week 14 of last season versus Minnesota, despite the Vikings having the league’s No. 2 run defense a year ago. The SportsLine Machine Learning Model projects Robinson to have 81.8 yards on average in a 4.5-star prop pick. See more NFL props for Vikings vs. Falcons here

You can make NFL prop bets on Robinson, Justin Jefferson and others with the Underdog Fantasy promo code CBSSPORTS2. Pick at Underdog Fantasy and get $50 in bonus funds after making a $5 wager:

Week 2 NFL props for Buccaneers vs. Texans on Monday Night Football

After analyzing Texans vs. Buccaneers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Bucs quarterback Baker Mayfield goes Under 235.5 passing yards (-114). While Houston has questions regarding its offense, there’s little worry about the team’s pass defense. In 2024, Houston had the second-most interceptions, the fourth-most sacks and allowed the fourth-worst passer rating. Since the start of last year, and including the playoffs, the Texans have held opposing QBs under 235.5 yards in 13 of 20 games. The SportsLine Machine Learning Model forecasts Mayfield to finish with just 200.1 passing yards, making the Under a 4-star NFL prop. See more NFL props for Buccaneers vs. Texans here

You can also use the latest FanDuel promo code to get $300 in bonus bets instantly:

Week 2 NFL props for Chargers vs. Raiders on Monday Night Football

After analyzing Raiders vs. Chargers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Chargers quarterback Justin Herbert goes Under 254.5 passing yards (-114). The Raiders’ defense was underrated in preventing big passing plays a year ago as it ranked third in the NFL in average depth of target allowed. It forced QBs to dink and dunk their way down the field, which doesn’t lead to big passing yardages, and L.A. generally prefers to not throw the ball anyway. Just four teams attempted fewer passes last season than the Chargers, and with L.A. running for 156.5 yards versus Vegas last season, Herbert shouldn’t be overly active on Monday night. He’s forecasted to have 221.1 passing yards in a 4.5-star NFL prop bet. See more NFL props for Chargers vs. Raiders here

How to make Week 2 NFL prop picks

SportsLine’s Machine Learning Model has identified another star who sails past his total and has dozens of NFL props rated 4 stars or better. You need to see the Machine Learning Model analysis before making any Week 2 NFL prop bets.

Which NFL prop picks should you target for Week 2, and which quarterback has multiple 5-star rated picks? Visit SportsLine to see the latest NFL player props from SportsLine’s Machine Learning Model that uses cutting-edge artificial intelligence to make its projections.





Source link

Continue Reading

AI Research

In the News: Thomas Feeney on AI in Higher Education – Newsroom

Published

on


“I had an interesting experience over the summer teaching an AI ethics class. You know plagiarism would be an interesting question in an AI ethics class … They had permission to use AI for the first written assignment. And it was clear that many of them had just fed in the prompt, gotten back the paper and uploaded that. But rather than initiate a sort of disciplinary oppositional setting, I tried to show them, look, what you what you’ve produced is kind of generic … and this gave the students a chance to recognize that they weren’t there in their own work. This opened the floodgates,” Feeney said.

“I think the focus should be less on learning how to work with the interfaces we have right now and more on just graduate with a story about how you did something with AI that you couldn’t have done without it. And then, crucially, how you shared it with someone else,” he continued.



Source link

Continue Reading

Trending