How The Pitt's AI Drama is Playing Out in Real Hospitals In Thursday's episode of The Pitt, the long-simmering tensions over the use of AI at the Pittsburgh Trauma Medical Center boiled over. In season two of the five-time Emmy winning medical drama, a new attending physician, Baran Al-Hashimi (Sepideh Moafi), is determined to improve efficiencies at the hospital. She tells her skeptical staff that new AI systems can cut down their time spent on charting by 80%, allowing them to spend more time both at the bedside and at home. But in episode six, doctors discover that the AI tool has made up false details about a patient and confused "urology" for "neurology." "AI's two percent error rate is still better than dictation," Al-Hashimi says, adding that it needs to be proofread for errors.

Jon Voight spoke to Fox News Digital while promoting his film "Reagan" and weighed in on the family drama between his daughter, Angelina Jolie, and her ex, Brad Pitt. A happily-ever-after with whom a woman assumed to be Hollywood hunk Brad Pitt quickly turned into a living nightmare. On Jan. 12, the French television channel TF1 aired an episode of its show "Sept à Huit," which told the story of a 53-year-old interior designer named Anne who revealed that she had lost 830,000 euros (approximately 850,000) in personal funds because she thought she was sending money to a cancer-ridden Pitt. Through falsified documents and images as well as artificial intelligence, Anne believed she was speaking to, and eventually in a relationship with, the 61-year-old actor. WHAT IS ARTIFICIAL INTELLIGENCE (AI)? A woman was conned into believing she was in a relationship with Brad Pitt after being contacted by someone claiming to be the actor on Instagram.

The neutrality thesis holds that technology cannot be laden with values. This long-standing view has faced critiques, but much of the argumentation against neutrality has focused on traditional, non-smart technologies like bridges and razors. In contrast, AI is a smart technology increasingly used in high-stakes domains like healthcare, finance, and policing, where its decisions can cause moral harm. In this paper, we argue that artificial intelligence, particularly artificial agents that autonomously make decisions to pursue their goals, challenge the neutrality thesis. Our central claim is that the computational models underlying artificial agents can integrate representations of moral values such as fairness, honesty and avoiding harm. We provide a conceptual framework discussing the neutrality thesis, values, and AI. Moreover, we examine two approaches to designing computational models of morality, artificial conscience and ethical prompting, and present empirical evidence from text-based game environments that artificial agents with such models exhibit more ethical behavior compared to agents without these models. The findings support that AI can embody moral values, which contradicts the claim that all technologies are necessarily value-neutral.

UK city-dwellers may have spotted a strangely shaped car cruising around their neighbourhood earlier this year. It looks just like a Google Street View vehicle, with a camera rig emerging from the back end to scan its environment – and like the Google cars, it, too, is scanning and photographing city streets. But these modified Teslas aren't just taking photos. They are kitted out with state-of-the-art sensors and scanners that enable them to report back on the exact dimensions, heat loss, materials, age and state of dilapidation of every building they drive past. Armed with this so-called built environment scanning system (BESS), the cars have been on the hunt to find out how leaky and run-down the UK's building stock really is.

Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional signals. While these methods are useful for exploring linear correlations among the isolated signals of those constructs (i.e., summary scores or fMRI signals), these classical frameworks fall short in providing insights into the comprehensive system-level dynamics underlying observable changes. Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs, and when applied through the lens of Finite State Automata (FSA), can provide a more integrated and intuitive framework for modeling and understanding the underlying controller (the prescription for how to respond to inputs) that fundamentally defines any system, as opposed to linearly correlating output signals produced by the controller. We present a simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that highlights FSA theory and is applicable for both behavioral analysis of questionnaire data and fMRI data. HMMs offer theoretic promise as they are computationally equivalent to the FSA, the control processor of a Turing Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the HMM model. It efficiently identifies the most likely sequence of hidden states. The vcHMM pipeline leverages this grammar to understand how behavior and neural activity relate to depression.

This paper provides an overview of the intricate relationship between social dynamics, technological advancements, and pioneering figures in the fields of cybernetics and artificial intelligence. It explores the impact of collaboration and interpersonal relationships among key scientists, such as McCulloch, Wiener, Pitts, and Rosenblatt, on the development of cybernetics and neural networks. It also discusses the contested attribution of credit for important innovations like the backpropagation algorithm and the potential consequences of unresolved debates within emerging scientific domains. It emphasizes how interpretive flexibility, public perception, and the influence of prominent figures can shape the trajectory of a new field. It highlights the role of funding, media attention, and alliances in determining the success and recognition of various research approaches. Additionally, it points out the missed opportunities for collaboration and integration between symbolic AI and neural network researchers, suggesting that a more unified approach may be possible in today's era without the historical baggage of past debates.

This tutorial is about the What do you Understand by Neural Network in Artificial Intelligence. We will try our best so that you understand this guide. I hope you like this blog What do you Understand by Neural Network in Artificial Intelligence. If your answer is yes then please do share after reading this. The term "artificial neural network" refers to a biologically inspired subset of artificial intelligence that is modeled after the brain.

In the summer of 2009, the Israeli neuroscientist Henry Markram strode onto the TED stage in Oxford, England, and made an immodest proposal: Within a decade, he said, he and his colleagues would build a complete simulation of the human brain inside a supercomputer. They'd already spent years mapping the cells in the neocortex, the supposed seat of thought and perception. "It's a bit like going and cataloging a piece of the rain forest," Markram explained. "How many trees does it have? What shapes are the trees?"

Rosenblatt led the design of a computer to implement this idea and tried to train it to recognize the differences between males and females in photos. "the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence."

Esports has become big business. Now, a video game app wants to make esports a "do-it-yourself" enterprise, allowing you to host your own online tournaments. A new tournament mode arrives today on Play One Up, an app launched a year ago to connect players in online matches on Xbox or PlayStation game systems, and let them wager on their skills. Play One Up tournament partners can create and host their own tournaments across games such as Fortnite, Madden NFL, NBA 2K or Call of Duty. Users can choose the game system, bracket size, cost to play and timing of the tournament.