Industrial projects rely heavily on lengthy, complex specification documents, making tedious manual extraction of structured information a major bottleneck. This paper introduces an innovative approach to automate this process, leveraging the capabilities of two cutting-edge AI models: Donut, a model that extracts information directly from scanned documents without OCR, and OpenAI GPT-3.5 Turbo, a robust large language model. The proposed methodology is initiated by acquiring the table of contents (ToCs) from construction specification documents and subsequently structuring the ToCs text into JSON data. Remarkable accuracy is achieved, with Donut reaching 85% and GPT-3.5 Turbo reaching 89% in effectively organizing the ToCs. This landmark achievement represents a significant leap forward in document indexing, demonstrating the immense potential of AI to automate information extraction tasks across diverse document types, boosting efficiency and liberating critical resources in various industries.

After suing OpenAI this month, alleging the company has become too closed, Elon Musk says he will release his "truth-seeking" answer to ChatGPT, the chatbot Grok, for anyone to download and use. "This week, @xAI will open source Grok," Musk wrote on his social media platform X today. That suggests his AI company, xAI, will release the full code of Grok and allow anyone to use or alter it. By contrast, OpenAI makes a version of ChatGPT and the language model behind it available to use for free but keeps its code private. Musk had previously said little about the business model for Grok or xAI, and the chatbot was made available only to Premium subscribers to X. Having accused his OpenAI cofounders of reneging on a promise to give away the company's artificial intelligence earlier this month, Musk may have felt he had to open source his own chatbot to show that he is committed to that vision.

Currently, the biggest shareholders include media company Advance Magazine Publishers, Chinese tech firm Tencent, US investment firm Fidelity, and Sam Altman, the chief executive of ChatGPT makers OpenAI.

The news: In the summer of 2021, OpenAI quietly shuttered its mulrobotics team, announcing that progress was being stifled by a lack of data necessary to train robots in how to move and reason using artificial intelligence. Now three of OpenAI's early research scientists say the startup they spun off in 2017, called Covariant, has solved that problem. They've unveiled a system that combines the reasoning skills of large language models with the physical dexterity of an advanced robot. How it works: The new model, called RFM-1, was trained on years of data collected from Covariant's small fleet of item-picking robots, as well as words and videos from the internet. Users can prompt the model using five different types of input: text, images, video, robot instructions, and measurements.

Workers at some of the world's leading AI companies harbor significant concerns about the safety of their work and the incentives driving their leadership, a report published on Monday claimed. The report, commissioned by the State Department and written by employees of the company Gladstone AI, makes several recommendations for how the U.S. should respond to what it argues are significant national security risks posed by advanced AI. Read More: Exclusive: U.S. Must Move'Decisively' To Avert'Extinction-Level' Threat from AI, Government-Commissioned Report Says The report's authors spoke with more than 200 experts for the report, including employees at OpenAI, Google DeepMind, Meta and Anthropic--leading AI labs that are all working towards "artificial general intelligence," a hypothetical technology that could perform most tasks at or above the level of a human. The authors shared excerpts of concerns that employees from some of these labs shared with them privately, without naming the individuals or the specific company that they work for. OpenAI, Google, Meta and Anthropic did not immediately respond to requests for comment. "We have served, through this project, as a de-facto clearing house for the concerns of frontier researchers who are not convinced that the default trajectory of their organizations would avoid catastrophic outcomes," Jeremie Harris, the CEO of Gladstone and one of the authors of the report, tells TIME. One individual at an unspecified AI lab shared worries with the report's authors that the lab has what the report characterized as a "lax approach to safety" stemming from a desire to not slow down the lab's work to build more powerful systems.

The new model, called RFM-1, was trained on years of data collected from Covariant's small fleet of item-picking robots that customers like Crate & Barrel and Bonprix use in warehouses around the world, as well as words and videos from the internet. In the coming months, the model will be released to Covariant customers. The company hopes the system will become more capable and efficient as it's deployed in the real world. In a demonstration I attended last week, Covariant cofounders Peter Chen and Pieter Abbeel showed me how users can prompt the model using five different types of input: text, images, video, robot instructions, and measurements. For example, show it an image of a bin filled with sports equipment, and tell it to pick up the pack of tennis balls. The robot can then grab the item, generate an image of what the bin will look like after the tennis balls are gone, or create a video showing a bird's-eye view of how the robot will look doing the task.

An influx of artificial intelligence (AI) startups is heating up the battle for technical talent in Europe, leaving companies like Google DeepMind to choose between paying big or losing out on the region's best minds. The runaway success of OpenAI's ChatGPT has energized investors, who have been pouring money into promising AI startups, eager to uncover the next overnight success. Riding the investment wave, a crop of foreign AI firms -- including Canada's Cohere and U.S.-based Anthropic and OpenAI -- opened offices in Europe last year, adding to pressure on tech companies already trying to attract and retain talent in the region.

A sensational story about the Israeli prime minister's "psychiatrist" has exploded online, but it was AI-generated, originating on one of hundreds of websites researchers warn are churning out tech-enabled fiction masquerading as news. Propaganda-spewing websites have typically relied on armies of writers, but generative artificial intelligence tools now offer a significantly cheaper and faster way to fabricate content that is often hard to decipher from authentic information. Hundreds of AI-powered sites mimicking news outlets have cropped up in recent months, fueling an explosion of false narratives -- about everything from war to politicians -- that researchers say is stoking alarm in a year of high-stake elections around the world.

Purpose This article presents a case for a next-generation grid monitoring and control system, leveraging recent advances in generative artificial intelligence (AI), machine learning, and statistical inference. Advancing beyond earlier generations of wide-area monitoring systems built upon supervisory control and data acquisition (SCADA) and synchrophasor technologies, we argue for a monitoring and control framework based on the streaming of continuous point-on-wave (CPOW) measurements with AI-powered data compression and fault detection. Methods and Results: The architecture of the proposed design originates from the Wiener-Kallianpur innovation representation of a random process that transforms causally a stationary random process into an innovation sequence with independent and identically distributed random variables. This work presents a generative AI approach that (i) learns an innovation autoencoder that extracts innovation sequence from CPOW time series, (ii) compresses the CPOW streaming data with innovation autoencoder and subband coding, and (iii) detects unknown faults and novel trends via nonparametric sequential hypothesis testing. Conclusion: This work argues that conventional monitoring using SCADA and phasor measurement unit (PMU) technologies is ill-suited for a future grid with deep penetration of inverter-based renewable generations and distributed energy resources. A monitoring system based on CPOW data streaming and AI data analytics should be the basic building blocks for situational awareness of a highly dynamic future grid.

The use of generative AI to create text descriptions from graphs has mostly focused on knowledge graphs, which connect concepts using facts. In this work we explore the capability of large pretrained language models to generate text from causal graphs, where salient concepts are represented as nodes and causality is represented via directed, typed edges. The causal reasoning encoded in these graphs can support applications as diverse as healthcare or marketing. Using two publicly available causal graph datasets, we empirically investigate the performance of four GPT-3 models under various settings. Our results indicate that while causal text descriptions improve with training data, compared to fact-based graphs, they are harder to generate under zero-shot settings. Results further suggest that users of generative AI can deploy future applications faster since similar performances are obtained when training a model with only a few examples as compared to fine-tuning via a large curated dataset.