On a sunny day in San Francisco, along the city's waterfront, families dived into the wacky world of artificial intelligence inside the Exploratorium museum. Visitors made shadow puppets for AI to identify, used AI to generate songs, asked chatbots questions and faced off with AI in a game in which players tried to draw images that only humans would recognize. A giant robot hand moved around and people peered into a video game chip. They jotted down their hopes and worries about AI on cards displayed in the museum. Hope: AI will cure cancer.

Backstage at one of the largest artificial intelligence conferences in the world, Gov. Gavin Newsom listened to two leaders in the field debate opposite views of a high-profile bill on his desk to protect Californians from the technology. "Honestly, I take advantage of opportunities like this," Newsom said recounting the exchange later during an interview at the Salesforce conference in San Francisco in mid-September. "I just watched them, and I was like, 'Here we go. Should I sign it, or should I not?' Then'absolutely,' 'absolutely not' and back and forth." The scene offered a peek into Newsom's deliberations on regulating the tech industry, including an explosion of AI companies, and the forces seeking to influence him during bill-signing season at the state Capitol.

A controversial bill that seeks to protect Californians from artificial intelligence-driven catastrophes has caused uproar in the tech industry. This week, the legislation passed a key committee but with amendments to make it more palatable to Silicon Valley. SB 1047, from state Sen. Scott Wiener (D-San Francisco), is set to go to the state Assembly floor later this month. If it passes the Legislature, Gov. Gavin Newsom will have to decide whether to sign or veto the groundbreaking legislation. The bill's backers say it will create guardrails to prevent rapidly advancing AI models from causing disastrous incidents, such as shutting down the power grid without warning.

Programming LLM-based knowledge and task assistants that faithfully conform to developer-provided policies is challenging. These agents must retrieve and provide consistent, accurate, and relevant information to address user's queries and needs. Yet such agents generate unfounded responses ("hallucinate"). Traditional dialogue trees can only handle a limited number of conversation flows, making them inherently brittle. To this end, we present KITA - a programmable framework for creating task-oriented conversational agents that are designed to handle complex user interactions. Unlike LLMs, KITA provides reliable grounded responses, with controllable agent policies through its expressive specification, KITA Worksheet. In contrast to dialog trees, it is resilient to diverse user queries, helpful with knowledge sources, and offers ease of programming policies through its declarative paradigm. Through a real-user study involving 62 participants, we show that KITA beats the GPT-4 with function calling baseline by 26.1, 22.5, and 52.4 points on execution accuracy, dialogue act accuracy, and goal completion rate, respectively. We also release 22 real-user conversations with KITA manually corrected to ensure accuracy.

We introduce SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes, a hybrid question-answering (QA) pipeline that utilizes information from heterogeneous knowledge sources, including knowledge base, text, tables, and infoboxes. Our LLM-augmented approach achieves state-of-the-art performance on the Compmix dataset, the most comprehensive heterogeneous open-domain QA dataset, with 56.5% exact match (EM) rate. More importantly, manual analysis on a sample of the dataset suggests that SPAGHETTI is more than 90% accurate, indicating that EM is no longer suitable for assessing the capabilities of QA systems today.

Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors' opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term "opportunities" that are akin to "anticipation" in Plutchik's theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors' decision making.

Far from the palm trees of Miami or Austin's taco trucks, Catalin Voss has headquartered his literacy start-up between a cannabis club and pawn shop in the heart of the Mission District. Voss rents a nondescript office building in one of San Francisco's most vibrant neighborhoods as a home base for Ello, a company he co-founded in 2020 that uses speech recognition technology, powered by artificial intelligence, to help struggling students develop their reading skills. The office is within walking distance of his Noe Valley apartment and only steps away from some of the city's best taquerias and cocktail bars. And those are just a few of the perks he recited in explaining why he is headquartered in San Francisco. Voss is part of a sizable cohort of San Francisco loyalists -- old and new -- who say they are flummoxed by the "all is lost" narrative propagated by conservative media hosts and more recently a vocal contingent of tech leaders that includes billionaire entrepreneur-turned-agitator Elon Musk.

This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives. Ernest Rutherford once declared: "if your experiment Also, generative modelling offers a way to discern the needs statistics, you ought to have done a better experiment" most credible theory from various explanations for observational [1]. His remark reflects his belief in the significance data. This is achieved solely through the data, of well-controlled experiments and the need for experimental without any predetermined understanding of the potential designs that minimize uncertainties and sources of physical mechanisms operating within the studied system errors. However, while Rutherford's statement may have [18]. Therefore, the possibilities for using ML algorithms merit in his time, it no longer applies in the modern scientific in physics range from experiments to theoretical landscape.

San Francisco's eastbound 54 Felton line was heading up a narrow residential street when a white SUV coming the other way stopped in the middle of the road. It was a rainy Sunday evening last month, and the bus driver leaned up to the windshield and peered through the haze at the SUV's pulsing hazard lights before slumping back and exclaiming in surprise, "What the hell? The 54, brought to a halt by an autonomous vehicle belonging to Alphabet's Waymo, isn't the only bus that's run into trouble with San Francisco's growing crowd of driverless vehicles. Bus and train surveillance videos obtained by WIRED through public records requests show a litany of incidents since September in which anxiety and confusion stirred up by driverless cars has spilled onto the streets of the US city that has become the epicenter for testing them. A San Francisco public transit bus encounters a Waymo autonomous vehicle in its path on March 5. As the incidents stack up, the companies behind the autonomous ...

'San Fransicko' author Michael Shellenberger discusses the homeless crisis in California and how to solve it. A federal judge has issued a temporary ban on San Francisco clearing most homeless encampments amid an ongoing lawsuit against the city filed by advocacy groups seeking to stop police sweeps of homeless encampments. Last week, Magistrate Judge Donna M. Ryu in the U.S. District Court in Oakland questioned the tactics used by the city of San Francisco in its homeless encampment cleanups, suggesting that the city is not adhering to its own policies of providing shelter beds to individuals who are being asked to vacate a public area. In her decision, Ryu stated that the city did not offer shelter to homeless individuals before clearing encampments and confiscating their property. The judge also found the city's justification for taking enforcement actions to be "wholly unconvincing," stating that the defendants did not adequately dispute that they cleared people without first providing shelter.