We present DreamLLM-3D, a composite multimodal AI system behind an immersive art installation for dream re-experiencing. It enables automated dream content analysis for immersive dream-reliving, by integrating a Large Language Model (LLM) with text-to-3D Generative AI. The LLM processes voiced dream reports to identify key dream entities (characters and objects), social interaction, and dream sentiment. The extracted entities are visualized as dynamic 3D point clouds, with emotional data influencing the color and soundscapes of the virtual dream environment. Additionally, we propose an experiential AI-Dreamworker Hybrid paradigm. Our system and paradigm could potentially facilitate a more emotionally engaging dream-reliving experience, enhancing personal insights and creativity.

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

Advanced biological intelligence learns efficiently from an information-rich stream of stimulus information, even when feedback on behaviour quality is sparse or absent. Such learning exploits implicit assumptions about task domains. We refer to such learning as Domain-Adapted Learning (DAL). In contrast, AI learning algorithms rely on explicit externally provided measures of behaviour quality to acquire fit behaviour. This imposes an information bottleneck that precludes learning from diverse non-reward stimulus information, limiting learning efficiency. We consider the question of how biological evolution circumvents this bottleneck to produce DAL. We propose that species first evolve the ability to learn from reward signals, providing inefficient (bottlenecked) but broad adaptivity. From there, integration of non-reward information into the learning process can proceed via gradual accumulation of biases induced by such information on specific task domains. This scenario provides a biologically plausible pathway towards bottleneck-free, domain-adapted learning. Focusing on the second phase of this scenario, we set up a population of NNs with reward-driven learning modelled as Reinforcement Learning (A2C), and allow evolution to improve learning efficiency by integrating non-reward information into the learning process using a neuromodulatory update mechanism. On a navigation task in continuous 2D space, evolved DAL agents show a 300-fold increase in learning speed compared to pure RL agents. Evolution is found to eliminate reliance on reward information altogether, allowing DAL agents to learn from non-reward information exclusively, using local neuromodulation-based connection weight updates only.

Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge intervention framework to decouple the potential underlying factors of IFT, thereby enabling individual analysis of different factors. Surprisingly, our experiments reveal that attempting to learn additional world knowledge through IFT often struggles to yield positive impacts and can even lead to markedly negative effects. Further, we discover that maintaining internal knowledge consistency before and after IFT is a critical factor for achieving successful IFT. Our findings reveal the underlying mechanisms of IFT and provide robust support for some very recent and potential future works.

Powerful new artificial-intelligence software is already shaking up the travel industry, but it has a long way to go until it can plan a seamless trip. I want to hit a history museum and an amusement park -- and then I'd like 7 p.m. dinner reservations near the hotel at a restaurant with vegan options and a great wine list." But for now, travelers using ChatGPT -- the powerful new A.I. software that is already offering creative cocktail recipes and writing college papers -- may have to temper their expectations. Oded Battat, the general manager at Traveland, a travel agency in Bridgeport, Conn., asked ChatGPT for outings he might offer his clients going to Tuscany to see if it could help him with his work. He got a list of 14 activities, including winery tours and museum visits, with a stop for gelato in the town square of the medieval hill town San Gimignano.

I want to hit a history museum and an amusement park -- and then I'd like 7 p.m. dinner reservations near the hotel at a restaurant with vegan options and a great wine list." But for now, travelers using ChatGPT -- the powerful new A.I. software that is already offering creative cocktail recipes and writing college papers -- may have to temper their expectations. Oded Battat, the general manager at Traveland, a travel agency in Bridgeport, Conn., asked ChatGPT for outings he might offer his clients going to Tuscany to see if it could help him with his work. He got a list of 14 activities, including winery tours and museum visits, with a stop for gelato in the town square of the medieval hill town San Gimignano. "I knew of all these things," Mr. Battat said, but, he added, ChatGPT saved him the hassle of collecting all the information and delivered it in a format he was able to email to one of the clients.

January 22, 2018 in Culver City: A Tesla Model S hit the back of a fire truck parked at an accident in Culver City around 8:30 am on Interstate 405 using the cars Autopilot system. The Tesla, which was going 65mph, suffered'significant damage' and the firetruck was taken out of service for body work. May 30, 2018 in Laguna Beach: Authorities said a Tesla sedan in Autopilot mode crashed into a parked police cruiser in Laguna Beach. Laguna Beach Police Sgt. Jim Cota says the officer was not in the cruiser during the crash. He said the Tesla driver suffered minor injuries.

AI-powered plagiarism detection is gaining momentum. Utilizing natural language processing (NLP) technology boosted by machine learning algorithms looks like a smart approach, rather than using the traditional word-for-word match approach to detect plagiarism is what new companies such as Stamford, Connecticut-based Copyleaks are doing. This week, this company announced that it raised $6 million in Series A funding. The financing was led by the Israeli venture capital firm JAL Venture. Copyleaks said that it will use the capital raised "to expand its presence across industries, safeguard its intellectual property, and continue to provide cutting-edge AI solutions," according to a press release.

A Tesla Model 3 car in'Full Self-Driving' mode has been captured colliding with a bike lane barrier post, in a potential setback for Elon Musk's firm. The footage was captured during a drive in downtown San Jose, California, by a YouTuber who goes by the name AI Addict, and provides the first recorded evidence that the feature has been directly responsible for an accident. It shows the latest version of Tesla's self-driving software, Full Self-Driving (FSD) Beta version 10.10, veering the Model 3 into the bollard separating a bike lane. Even though the driver is hitting the brakes and furiously spins the steering wheel away from the obstacle, the AI-powered FSD system hits the bollard with a big thud. Worryingly, at other points in the video the Model 3 appears to run a red light and attempts to go down a railroad track and later a tram lane.

The Feinstein Institutes for Medical Research has spun out a startup whose artificial-intelligence device could help paralyzed patients regain the use of their hands. Earlier this month, the startup, Neuvotion Inc., announced a $1.1 million funding round from the Long Island Angel Network and the Good Shepherd Rehabilitation Network based in Allentown, Pennsylvania. The Darien, Connecticut, startup is in the process of transferring research developed in the laboratory of Chad Bouton, vice president of advanced engineering at the Feinstein Institutes, a unit of Northwell Health. Bouton also is founder of Neuvotion. The company's initial device, NeuStim, is worn as a patch on the patient's forearm and is being positioned for use in clinics and at home.