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Bills vs. Ravens props, NFL bets, SportsLine Machine Learning Model AI predictions: Jackson over 229.5 yards

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The Buffalo Bills and Baltimore Ravens will meet for the third time in less than a calendar year in Week 1 of Sunday Night Football. Baltimore dominated in a Week 4 victory last year but the Bills got their revenge by prevailing in a Divisional Round playoff meeting. MVP winner Josh Allen and runner-up Lamar Jackson figure to feature prominently in NFL player props and NFL prop bets, but there are a host of other options to include in SGP picks. Derrick Henry and James Cook both scored a league-high of 16 rushing touchdowns in 2024 as each team heavily leans on the run game.

Even with that, the focus will be on the quarterbacks, and they share the same NFL prop lines for their passing numbers. Both have an over/under of 229.5 passing yards, in addition to having an NFL prop of 1.5 passing touchdowns. Meanwhile, Henry, who is coming off a 1,921-yard season on the ground, has a rushing yards bar of 82.5, while Cook’s is at 54.5. Baltimore is favored by 1.5 points, while the over/under is 50.5 points. Before betting any Ravens vs. Bills props for Sunday Night Football, you need to see the Bills vs. Ravens prop predictions powered by SportsLine’s 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. 

For Ravens vs. Bills NFL betting on Sunday Night Football, the Machine Learning Model has evaluated the NFL player prop odds and provided Bills vs. Ravens prop picks. You can only see the Machine Learning Model player prop predictions for Baltimore vs. Buffalo here.

Top NFL player prop bets for Bills vs. Ravens

After analyzing the Ravens vs. Bills props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model says Ravens QB Jackson goes Over 229.5 passing yards (-114 at FanDuel). The last time Jackson took the field was against Buffalo in last season’s playoffs, and the two-time MVP had 254 passing yards and a pair of touchdowns through the air. That came in wintery weather conditions not exactly conducive to racking up yardage through the air, while Sunday night’s game calls for much milder weather.

Attacking Buffalo in the passing game is the preferred option as the Bills ranked 12th versus the run a year ago but were 24th in passing defense. The Bills also allowed the fifth-highest completion percentage and eighth-most passing touchdowns, as pass defense is their biggest weakness. Jackson, meanwhile, is coming off career highs in passing yards (4,172) and passing touchdowns (41), and now he has a new weapon to play with in five-time Pro Bowler DeAndre Hopkins. The SportsLine Machine Learning Model projects Jackson to blow past his total with 275.4 yards on average in a 4.5-star prop pick. See more NFL props here, and new users can also target the FanDuel promo code, which offers new users $300 in bonus bets if their first $5 bet wins:

How to make NFL player prop bets for Buffalo vs. Baltimore

In addition, the SportsLine Machine Learning Model says another star sails past his total and has seven additional NFL props that are rated four stars or better. You need to see the Machine Learning Model analysis before making any Ravens vs. Bills prop bets for Sunday Night Football.

Which Bills vs. Ravens prop bets should you target for Sunday Night Football? Visit SportsLine now to see the top Ravens vs. Bills props, all from the SportsLine Machine Learning Model.





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Artificial intelligence is at the forefront of educational discussions

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Artificial intelligence is at the forefront of educational discussions as school leaders, teachers, and business professionals gathered at the Education Leadership Summit in Tulsa to explore AI’s impact on classrooms and its implications for students’ futures.

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Kennesaw State secures NSF grants to build community of AI educators nationwide

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KENNESAW, Ga. |
Sep 12, 2025

Shaoen Wu

The International Data Corporation projects that artificial intelligence will add
$19.9 trillion to the global economy by 2030, yet educators are still defining how
students should learn to use the technology responsibly.

To better equip AI educators and to foster a sense of community among those in the
field, Kennesaw State University Department Chair and Professor of Information Technology (IT) Shaoen Wu, along with assistant professors Seyedamin Pouriyeh and Chloe “Yixin” Xie, were recently awarded two National Science Foundation (NSF) grants. The awards, managed by the NSF’s Computer and Information Science and Engineering division, will fund the project through May 31, 2027 with an overarching goal to unite educators from across the country
to build shared resources, foster collaboration, and lay the foundation for common
guidelines in AI education.

Wu, who works in Kennesaw State’s College of Computing and Software Engineering (CCSE), explained that while many universities, including KSU, have launched undergraduate
and graduate programs in artificial intelligence, there is no established community
to unify these efforts.

“AI has become the next big thing after the internet,” Wu said. “But we do not yet have a mature, coordinated community for AI education. This project is the first step toward building that national network.”

Drawing inspiration from the cybersecurity education community, which has long benefited
from standardized curriculum guidelines, Wu envisions a similar structure for AI.
The goal is to reduce barriers for under-resourced institutions, such as community
colleges, by giving them free access to shared teaching materials and best practices.

The projects are part of the National AI Research Resource (NAIRR) pilot, a White
House initiative to broaden AI access and innovation. Through the grants, Wu and his
team will bring together educators from two-year colleges, four-year institutions,
research-intensive universities, and Historically Black Colleges and Universities
to identify gaps and outline recommendations for AI education.

“This is not just for computing majors,” Wu said. “AI touches health, finance, engineering, and so many other fields. What we build now will shape AI education not only in higher education but also in K-12 schools and for the general public.”

For Wu, the NSF grants represent more than just funding. It validates KSU’s growing presence in national conversations on emerging technologies. Recently, he was invited to moderate a panel at the Computing Research Association’s annual computing academic leadership summit, where department chairs and deans from across the country gathered to discuss AI education.

“These grants position KSU alongside institutions like the University of Illinois Urbana-Champaign and the University of Pennsylvania as co-leaders in shaping the future of AI education,” Wu said. “It is a golden opportunity to elevate our university to national and even global prominence.”

CCSE Interim Dean Yiming Ji said Wu’s leadership reflects CCSE’s commitment to both innovation and accessibility.

“This NSF grant is not just an achievement for Dr. Wu but for the entire College of Computing and Software Engineering,” Ji said. “It highlights our faculty’s work to shape national conversations in AI education while ensuring that students from all backgrounds, including those at under-resourced institutions, can benefit from shared knowledge and opportunities.”

– Story by Raynard Churchwell

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A leader in innovative teaching and learning, Kennesaw State University offers undergraduate, graduate, and doctoral degrees to its more than 47,000 students. Kennesaw State is a member of the University System of Georgia with 11 academic colleges. The university’s vibrant campus culture, diverse population, strong global ties, and entrepreneurial spirit draw students from throughout the country and the world. Kennesaw State is a Carnegie-designated doctoral research institution (R2), placing it among an elite group of only 8 percent of U.S. colleges and universities with an R1 or R2 status. For more information, visit kennesaw.edu.



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UC Berkeley researchers use Reddit to study AI’s moral judgements | Research And Ideas

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A study published by UC Berkeley researchers used the Reddit forum, r/AmITheAsshole, to determine whether artificial intelligence, or AI, chatbots had “patterns in their moral reasoning.”

The study, led by researchers Pratik Sachdeva and Tom van Nuenen at campus’s D-Lab, asked seven AI large language models, or LLMs, to judge more than 10,000 social dilemmas from r/AmITheAsshole.  

The LLMs used were Claude Haiku, Mistral 7B, Google’s PaLM 2 Bison and Gemma 7B, Meta’s LLaMa 2 7B and OpenAI’s GPT-3.5 and GPT-4. The study found that different LLMs showed unique moral judgement patterns, often giving dramatically different verdicts from other LLMs. These results were self-consistent, meaning that when presented with the same issue, the model seemed to judge it with the same set of morals and values. 

Sachdeva and van Nuenen began the study in January 2023, shortly after ChatGPT came out. According to van Nuenen, as people increasingly turned to AI for personal advice, they were motivated to study the values shaping the responses they received.

r/AmITheAsshole is a Reddit forum where people can ask fellow users if they were the “asshole” in a social dilemma. The forum was chosen by the researchers due to its unique verdict system, as subreddit users assign their judgement of “Not The Asshole,” “You’re the Asshole,” “No Assholes Here,” “Everyone Sucks Here” or “Need More Info.” The judgement with the most upvotes, or likes, is accepted as the consensus, according to the study. 

“What (other) studies will do is prompt models with political or moral surveys, or constrained moral scenarios like a trolley problem,” Sechdava said. “But we were more interested in personal dilemmas that users will also come to these language models for like, mental health chats or things like that, or problems in someone’s direct environment.”

According to the study, the LLM models were presented with the post and asked to issue a judgement and explanation. Researchers compared their responses to the Reddit consensus and then judged the AI’s explanations along a six-category moral framework of fairness, feelings, harms, honesty, relational obligation and social norms. 

The researchers found that out of the LLMs, GPT-4’s judgments agreed with the Reddit consensus the most, even if agreement was generally pretty low. According to the study, GPT-3.5 assigned people “You’re the Asshole” at a comparatively higher rate than GPT-4. 

“Some models are more fairness forward. Others are a bit harsher. And the interesting thing we found is if you put them together, if you look at the distribution of all the evaluations of these different models, you start approximating human consensus as well,” van Nuenen said. 

The researchers found that even though the verdicts of the LLM models generally disagreed with each other, the consensus of the seven models typically aligned with the Redditor’s consensus.

One model, Mistral 7B, assigned almost no posts “You’re the Asshole” verdicts, as it used the word “asshole” to mean its literal definition, and not the socially accepted definition in the forum, which refers to whoever is at fault. 

When asked if he believed the chatbots had moral compasses, van Nuenen instead described them as having “moral flavors.” 

“There doesn’t seem to be some kind of unified, directional sense of right and wrong (among the chatbots). And there’s diversity like that,” van Nuenen said. 

Sachdeva and van Nuenen have begun two follow-up studies. One examines how the models’ stances adjust when deliberating their responses with other chatbots, while the other looks at how consistent the models’ judgments are as the dilemmas are modified. 



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