In less than a year-and-a-half, Frankie Johnson, a man incarcerated at the William E Donaldson prison outside Birmingham, Alabama, says he was stabbed around 20 times. In December of 2019, Johnson says, he was stabbed "at least nine times" in his housing unit. In March of 2020, an officer handcuffed him to a desk following a group therapy meeting, and left the unit, after which another prisoner came in and stabbed him five times. In November of the same year, Johnson says, he was handcuffed by an officer and brought to the prison yard, where another prisoner attacked him with an ice pick, stabbing him "five to six times", as two correctional officers looked on. According to Johnson, one of the officers had actually encouraged his attacker to carry out the assault in retaliation for a previous argument between Johnson and the officer.

He is best known for being CEO of one of the world's largest companies. But before Tim Cook took the reins at Apple, he started his career in a very surprising place. Speaking on the Table Manners podcast, Mr Cook revealed that he started working when he was just 11 years old. He says: 'A lot of [his upbringing] was centred on work and the belief that hard work was essential for everybody, regardless of your age. 'And so I started working when I was probably 11 or 12 on the paper route.'

In an era where large language models (LLMs) are increasingly integrated into a wide range of everyday applications, research into these models' behavior has surged. However, due to the novelty of the field, clear methodological guidelines are lacking. This raises concerns about the replicability and generalizability of insights gained from research on LLM behavior. In this study, we discuss the potential risk of a replication crisis and support our concerns with a series of replication experiments focused on prompt engineering techniques purported to influence reasoning abilities in LLMs. We tested GPT-3.5, GPT-4o, Gemini 1.5 Pro, Claude 3 Opus, Llama 3-8B, and Llama 3-70B, on the chain-of-thought, EmotionPrompting, ExpertPrompting, Sandbagging, as well as Re-Reading prompt engineering techniques, using manually double-checked subsets of reasoning benchmarks including CommonsenseQA, CRT, NumGLUE, ScienceQA, and StrategyQA. Our findings reveal a general lack of statistically significant differences across nearly all techniques tested, highlighting, among others, several methodological weaknesses in previous research. We propose a forward-looking approach that includes developing robust methodologies for evaluating LLMs, establishing sound benchmarks, and designing rigorous experimental frameworks to ensure accurate and reliable assessments of model outputs.

Accurate detection of inundated water extents during flooding events is crucial in emergency response decisions and aids in recovery efforts. Satellite Remote Sensing data provides a global framework for detecting flooding extents. Specifically, Sentinel-1 C-Band Synthetic Aperture Radar (SAR) imagery has proven to be useful in detecting water bodies due to low backscatter of water features in both co-polarized and cross-polarized SAR imagery. However, increased backscatter can be observed in certain flooded regions such as presence of infrastructure and trees - rendering simple methods such as pixel intensity thresholding and time-series differencing inadequate. Machine Learning techniques has been leveraged to precisely capture flood extents in flooded areas with bumps in backscatter but needs high amounts of labelled data to work desirably. Hence, we created a labeled known water body extent and flooded area extents during known flooding events covering about 36,000 sq. kilometers of regions within mainland U.S and Bangladesh. Further, We also leveraged citizen science by open-sourcing the dataset and hosting an open competition based on the dataset to rapidly prototype flood extent detection using community generated models. In this paper we present the information about the dataset, the data processing pipeline, a baseline model and the details about the competition, along with discussion on winning approaches. We believe the dataset adds to already existing datasets based on Sentinel-1C SAR data and leads to more robust modeling of flood extents. We also hope the results from the competition pushes the research in flood extent detection further.

Fortune ranked the University of Alabama at Birmingham's in-person master's degree in cybersecurity as the No. 1 program in the country. According to Fortune, there are nearly 770,000 cybersecurity job openings in the United States. "We are proud to be recognized for academic excellence by Fortune and named the nation's leading institution for graduate studies in cybersecurity," said UAB Provost and Senior Vice President for Academic Affairs Pam Benoit. "UAB's Department of Computer Science has created an outstanding collaborative master's degree program that prepares students to lead careers solving the world's most challenging cybersecurity problems." Fortune's first-ever ranking of in-person cybersecurity master's degree programs compared 14 programs across the United States in three components: Selectivity Score, Success Score and Demand Score.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Wondering where AI regulation stands in your state? Today, the Electronic Privacy Information Center (EPIC) released The State of State AI Policy, a roundup of AI-related bills at the state and local level that were passed, introduced or failed in the 2021-2022 legislative session (EPIC gave VentureBeat permission to reprint the full roundup below). Within the past year, according to the document (which was compiled by summer clerk Caroline Kraczon), states and localities have passed or introduced bills "regulating artificial intelligence or establishing commissions or task forces to seek transparency about the use of AI in their state or locality."

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Wondering where AI regulation stands in your state? Today, the Electronic Privacy Information Center (EPIC) released The State of State AI Policy, a roundup of AI-related bills at the state and local level that were passed, introduced or failed in the 2021-2022 legislative session. Within the past year, according to the document, states and localities have passed or introduced bills "regulating artificial intelligence or establishing commissions or task forces to seek transparency about the use of AI in their state or locality."

What Products are Right for Your Program? In addition, the event presents a unique opportunity for attendees to see drones from multiple vendors all in one place and experience the entire drone ecosystem. The event provides access to renowned public safety experts – as well as Adorama's own Director of Technical Specialists James Bushey. "We're excited to come back to Madison, Alabama for our second annual drone demo day in partnership with the Madison PD. We've expanded the event to two days, bringing together public safety UAS thought leaders as well as representatives from manufacturers like DJI, Autel, BRINC, Parrot, Yuneec, senseFly, Pix4D and more, showing public safety agencies all that UAS has to offer," says Adorama's CJ Smith.

LOS ANGELES/WASHINGTON, Nov 15 (Thomson Reuters Foundation) - When the sheriff in Suffolk County, New York, requested $700,000 from the U.S. government for an artificial intelligence system to eavesdrop on prison phone conversations, his office called it a key tool in fighting gang-related and violent crime. But the county jail ended up listening to calls involving a much wider range of subjects - scanning as many as 600,000 minutes per month, according to public records from the county obtained by the Thomson Reuters Foundation. Beginning in 2019, Suffolk County was an early pilot site for the Verus AI-scanning system sold by California-based LEO Technologies, which uses Amazon speech-to-text technology to transcribe phone calls flagged by key word searches. The company and law enforcement officials say it is a crucial tool to keep prisons and jails safe, and fight crime, but critics say such systems trample the privacy rights of prisoners and other people, like family members, on the outside. "The ability to surveil and listen at scale in this rapid way - it is incredibly scary and chilling," said Julie Mao, deputy director at Just Futures Law, an immigration legal group.

When the sheriff in Suffolk County, New York, requested $700,000 from the U.S. government for an artificial intelligence system to eavesdrop on prison phone conversations, his office called it a key tool in fighting gang-related and violent crime. But the county jail ended up listening to calls involving a much wider range of subjects -- scanning as many as 600,000 minutes per month, according to public records from the county obtained by the Thomson Reuters Foundation. Beginning in 2019, Suffolk County was an early pilot site for the Verus AI-scanning system sold by California-based LEO Technologies, which uses Amazon speech-to-text technology to transcribe phone calls flagged by keyword searches. The company and law enforcement officials say it is a crucial tool to keep prisons and jails safe, and to fight crime, but critics say such systems trample the privacy rights of prisoners and other people, like family members, on the outside. "T he ability to surveil and listen at scale in this rapid way -- it is incredibly scary and chilling," said Julie Mao, deputy director at Just Futures Law, an immigration legal group.