AI Research

Bears vs. Vikings NFL props, odds, SportsLine Machine Learning Model AI prediction: Williams under 218.5 yards

Published

4 days ago

September 8, 2025

NFL Week 1 concludes with a Monday Night Football matchup at 8:15 p.m. ET between the Chicago Bears and Minnesota Vikings. J.J. McCarthy will make his regular season debut after missing last year due to injury, and he’ll see a member of his draft class on the other side of the field in Caleb Williams. NFL prop bettors will likely target the two young quarterbacks with NFL prop picks, in addition to proven playmakers like Justin Jefferson, D.J. Moore and Aaron Jones. Jefferson, who torched the Bears earlier in his career, has been contained in recent matchups which could influence MNF prop picks.

The two-time All-Pro has been held under 75 receiving yards in three straight games versus Chicago as Jefferson has an SNF prop total of 77.5 receiving yards. Both the Over and Under would return -112, per the latest NFL prop odds, as his early chemistry with McCarthy will be a focal point. You certainly want to end your Week 1 NFL betting on a winning note, so having the right NFL props advice is paramount. Before betting any Vikings vs. Bears props for Monday Night Football, you need to see the Bears vs. Vikings 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 Vikings vs. Bears NFL betting on Monday Night Football, the Machine Learning Model has evaluated the NFL player prop odds and provided Bears vs. Vikings prop picks. You can only see the Machine Learning Model player prop predictions for Minnesota vs. Chicago here.

Top NFL player prop bets for Bears vs. Vikings

After analyzing the Vikings vs. Bears props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model says Bears QB Williams goes Under 218.5 passing yards (-114 at FanDuel). Primetime games like what he’ll see on Sunday night weren’t too favorable to Williams as a rookie. He lost all three he played in, had one total passing score across them, was sacked an average of 5.3 times and, most relevant to this NFL prop, Williams failed to reach even 200 passing yards in any of the three.

One of those games came against the Vikings in Week 15 as Williams finished with just 191 yards through the air. Minnesota terrorized quarterbacks a year ago as it led the NFL with 24 defensive interceptions, held opposing QBs to the second-lowest passer rating (82.4) and racked up the fourth-most sacks (49). Given Minnesota’s prowess in defending the pass, and Williams’ primetime struggles, the SportsLine Machine Learning Model forecasts him to finish with just 174.8 passing yards, making Under 218.5 a 4.5-star NFL prop. 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 Chicago vs. Minnesota

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

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

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Artificial Intelligence Cheating | Nation

Published

1 hour ago

September 12, 2025

Jae C. Hong - AP

Artificial Intelligence Cheating | Nation | hjnews.com

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Artificial Intelligence in Healthcare Market : A Study of

Published

2 hours ago

September 12, 2025

The Editors

Global Artificial Intelligence in Healthcare Market size was valued at USD 27.07 Bn in 2024 and is expected to reach USD 347.28 Bn by 2032, at a CAGR of 37.57%

Artificial Intelligence (AI) in healthcare is reshaping the industry by enabling faster diagnosis, personalized treatment, and enhanced operational efficiency. AI-driven tools such as predictive analytics, natural language processing, and medical imaging analysis are empowering physicians with deeper insights and decision support, reducing human error and improving patient outcomes. Moreover, AI is revolutionizing drug discovery, clinical trial optimization, and remote patient monitoring, making healthcare more proactive and accessible in both developed and emerging markets.

The adoption of AI in healthcare is also being accelerated by the rising demand for telemedicine, wearable health devices, and real-time data-driven solutions. From virtual health assistants to robotic surgery, AI is driving innovation across patient care and hospital management. However, challenges such as data privacy, ethical considerations, and regulatory frameworks remain crucial in ensuring responsible deployment. As AI continues to integrate with IoT, cloud, and big data platforms, it is set to create a connected healthcare ecosystem that prioritizes precision medicine and patient-centric solutions.

Get a sample of the report https://www.maximizemarketresearch.com/request-sample/21261/

Major companies profiled in the market report include

BP Target Neutral . JPMorgan Chase & Co. . Gold Standard Carbon Clear . South Pole Group . 3Degrees . Shell. EcoAct.

Research objectives:

The latest research report has been formulated using industry-verified data. It provides a detailed understanding of the leading manufacturers and suppliers engaged in this market, their pricing analysis, product offerings, gross revenue, sales network & distribution channels, profit margins, and financial standing. The report’s insightful data is intended to enlighten the readers interested in this business sector about the lucrative growth opportunities in the Artificial Intelligence in Healthcare market.

Get access to the full description of the report @ https://www.maximizemarketresearch.com/market-report/global-artificial-intelligence-ai-healthcare-market/21261/

It has segmented the global Artificial Intelligence in Healthcare market

by Offering

Hardware

Software

Services

by Technology

Machine Learning

Natural Language Processing

Context-Aware Computing

Computer Vision

Key Objectives of the Global Artificial Intelligence in Healthcare Market Report:

The report conducts a comparative assessment of the leading market players participating in the globalArtificial Intelligence in Healthcare

The report marks the notable developments that have recently taken place in the Artificial Intelligence in Healthcare industry

It details on the strategic initiatives undertaken by the market competitors for business expansion.

It closely examines the micro- and macro-economic growth indicators, as well as the essential elements of theArtificial Intelligence in Healthcaremarket value chain.

The repot further jots down the major growth prospects for the emerging market players in the leading regions of the market

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Engineering, Procurement, and Construction Management (EPCM) Market https://www.maximizemarketresearch.com/market-report/engineering-procurement-and-construction-management-epcm-market/73131/

Global Turbomolecular Pumps Market

https://www.maximizemarketresearch.com/market-report/global-turbomolecular-pumps-market/20730/

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A Unified Model for Robot Interaction, Reasoning and Planning

Published

3 hours ago

September 12, 2025

Huang Fang, Mengxi Zhang, Heng Dong, Wei Li, Zixuan Wang, Qifeng Zhang, Xueyun Tian, Yucheng Hu, Hang Li

[Submitted on 1 Sep 2025 (v1), last revised 11 Sep 2025 (this version, v2)]

View a PDF of the paper titled Robix: A Unified Model for Robot Interaction, Reasoning and Planning, by Huang Fang and 8 other authors

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Abstract:We introduce Robix, a unified model that integrates robot reasoning, task planning, and natural language interaction within a single vision-language architecture. Acting as the high-level cognitive layer in a hierarchical robot system, Robix dynamically generates atomic commands for the low-level controller and verbal responses for human interaction, enabling robots to follow complex instructions, plan long-horizon tasks, and interact naturally with human within an end-to-end framework. Robix further introduces novel capabilities such as proactive dialogue, real-time interruption handling, and context-aware commonsense reasoning during task execution. At its core, Robix leverages chain-of-thought reasoning and adopts a three-stage training strategy: (1) continued pretraining to enhance foundational embodied reasoning abilities including 3D spatial understanding, visual grounding, and task-centric reasoning; (2) supervised finetuning to model human-robot interaction and task planning as a unified reasoning-action sequence; and (3) reinforcement learning to improve reasoning-action consistency and long-horizon task coherence. Extensive experiments demonstrate that Robix outperforms both open-source and commercial baselines (e.g., GPT-4o and Gemini 2.5 Pro) in interactive task execution, demonstrating strong generalization across diverse instruction types (e.g., open-ended, multi-stage, constrained, invalid, and interrupted) and various user-involved tasks such as table bussing, grocery shopping, and dietary filtering.