Did Texas use artificial intelligence to call fourth-down play against Florida? LeBron James doubles down with sneaky golf hip turns during national anthem after Trump's'total disgrace' dig Drake Maye's end-zone interception proves costly as Patriots fall 30-24 to Bears in primetime Rhamondre Stevenson's miraculous 48-yard touchdown stuns Bears as clock expires in first half Todd Benzinger, who caught the final out of the Reds' 1990 World Series sweep, dead at 63 Super Bowl champion Marshall Faulk says he is living with Parkinson's disease NBA legend Charles Barkley says he won't'burn every bridge' like Aaron Rodgers as he considers retirement Bears fans don Mike Ditka's famous mustache as franchise legend honored at first home game since death Jonathan Fahey on Ilhan Omar: 'This is a massive problem for her' Dan crushes Chris Ballard for the continued blundering of former 4th overall pick Anthony Richardson. Indianapolis Colts quarterback Anthony Richardson Sr. was charged with possession of marijuana this week after an encounter with law enforcement in Indiana earlier this month, court records revealed. Richardson, who was drafted by the Colts with the fourth overall pick in the 2023 NFL draft, is facing a criminal misdemeanor charge of possession following a traffic stop in Carmel, Indiana, on Oct. 12, court records show. Richardson was pulled over by officers with the Carmel Police Department after the black 2023 Dodge Charger he was driving had a license plate that did not match its expired registration, according to FOX59, which first reported the news.

Neanderthals bred with our human ancestors 100,000 years earlier than previously thought, according to a new study. Experts have discovered that a five–year–old child who lived 140,000 years ago had parents from both species. Their fossil – likely a female – was first unearthed 90 years ago in the Skhul Cave on Mount Carmel in what is now northern Israel. A team from Tel Aviv University and the French Centre for Scientific Research conducted a series of advanced tests on the remaining bones, including a CT scan of the skull. 'Genetic studies over the past decade have shown that these two groups exchanged genes,' said lead author Professor Israel Hershkovitz.

Ensuring the accuracy of responses provided by large language models (LLMs) is crucial, particularly in clinical settings where incorrect information may directly impact patient health. To address this challenge, we construct K-QA, a dataset containing 1,212 patient questions originating from real-world conversations held on K Health (an AI-driven clinical platform). We employ a panel of in-house physicians to answer and manually decompose a subset of K-QA into self-contained statements. Additionally, we formulate two NLI-based evaluation metrics approximating recall and precision: (1) comprehensiveness, measuring the percentage of essential clinical information in the generated answer and (2) hallucination rate, measuring the number of statements from the physician-curated response contradicted by the LLM answer. Finally, we use K-QA along with these metrics to evaluate several state-of-the-art models, as well as the effect of in-context learning and medically-oriented augmented retrieval schemes developed by the authors. Our findings indicate that in-context learning improves the comprehensiveness of the models, and augmented retrieval is effective in reducing hallucinations. We make K-QA available to to the community to spur research into medically accurate NLP applications.

As Artificial Intelligence (AI) technologies continue to gain traction in the modern-day world, they ultimately pose an immediate threat to current cybersecurity systems via exploitative methods. Prompt engineering is a relatively new field that explores various prompt designs that can hijack large language models (LLMs). If used by an unethical attacker, it can enable an AI system to offer malicious insights and code to them. In this paper, an enhanced intrusion detection system (IDS) that utilizes machine learning (ML) and hyperparameter tuning is explored, which can improve a model's performance in terms of accuracy and efficacy. Ultimately, this improved system can be used to combat the attacks made by unethical hackers. A standard IDS is solely configured with pre-configured rules and patterns; however, with the utilization of machine learning, implicit and different patterns can be generated through the models' hyperparameter settings and parameters. In addition, the IDS will be equipped with multiple datasets so that the accuracy of the models improves. We evaluate the performance of multiple ML models and their respective hyperparameter settings through various metrics to compare their results to other models and past research work. The results of the proposed multi-dataset integration method yielded an accuracy score of 99.9% when equipped with the XGBoost and random forest classifiers and RandomizedSearchCV hyperparameter technique.

With the steady advances in autonomous driving, advanced safety features using one or more sensors are highly desirable. In order to avoid collisions and unintended breaking maneuvers, it is crucial to detect potential road obstacles accurately. Although camera and LiDAR-based object detection have been studied in the literature [1, 2], it's only recently that interest in radar-based object detection using ML methods has begun, primarily because of its low cost, long-range detection capability, and robustness to poor weather conditions. Traditionally, automotive radar-based object detection is performed through peak detection using simple local thresholding methods such as the Constant False-Alarm Rate (CFAR) algorithm [3]. With the breakthroughs of ML in numerous applications [4, 5, 6, 7], radar-based object perception using ML has attracted attention [8, 9, 10, 11, 12, 13].

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

Most companies stick with their ERP system longer than people stay with their first spouse, which statistics estimate to be about eight years. Given the commitment involved, a move to an ERP cloud platform needs to be carefully planned, based not only on what your company needs today, but on a vision for the future. Brian Sommer, founder of technology advisory firm TechVentive in Carmel, Ind., pulls no punches when he talks about the problems with traditional ERP vendors' cloud offerings. He urges his clients to rethink their use of legacy ERP vendors because modern adaptations born in the cloud integrate AI and IoT capabilities and provide what businesses will need to compete in the years ahead. "Companies need to kick the tires on new vendors and think about how their competitors will use new technologies against their firm," he said.

CARMEL, Ind., March 19, 2018 (GLOBE NEWSWIRE) -- TradeRev, a digital platform that facilitates live, dealer-to-dealer vehicle auctions, announced they will unveil H, the company's newest suite of artificial intelligence capabilities at next week's National Auto Dealers Association (NADA) Show 2018 in Las Vegas. TradeRev is a business unit of global remarketing and technology solutions provider KAR Auction Services, Inc. (NYSE:KAR). H leverages data and technology from across the KAR platform and uses TradeRev's machine learning and proprietary algorithms to deliver clear, easy, actionable intelligence to dealers. At NADA, TradeRev will demo H's AI-driven automated condition report visualization tool and several recently released data and predictive analytics capabilities.