Extending programming by example to more complex input domains is a very useful task, as it opens the range of possible real-world applications.

Machine learning (ML) has the potential to revolutionize various domains, but its adoption is often hindered by the disconnect between the needs of domain experts and translating these needs into robust and valid ML tools. Despite recent advances in LLM-based co-pilots to democratize ML for non-technical domain experts, these systems remain predominantly focused on model-centric aspects while overlooking critical data-centric challenges. This limitation is problematic in complex real-world settings where raw data often contains complex issues, such as missing values, label noise, and domain-specific nuances requiring tailored handling. To address this we introduce CliMB-DC, a human-guided, data-centric framework for LLM co-pilots that combines advanced data-centric tools with LLM-driven reasoning to enable robust, context-aware data processing. At its core, CliMB-DC introduces a novel, multi-agent reasoning system that combines a strategic coordinator for dynamic planning and adaptation with a specialized worker agent for precise execution. Domain expertise is then systematically incorporated to guide the reasoning process using a human-in-the-loop approach. To guide development, we formalize a taxonomy of key data-centric challenges that co-pilots must address. Thereafter, to address the dimensions of the taxonomy, we integrate state-of-the-art data-centric tools into an extensible, open-source architecture, facilitating the addition of new tools from the research community. Empirically, using real-world healthcare datasets we demonstrate CliMB-DC's ability to transform uncurated datasets into ML-ready formats, significantly outperforming existing co-pilot baselines for handling data-centric challenges. CliMB-DC promises to empower domain experts from diverse domains -- healthcare, finance, social sciences and more -- to actively participate in driving real-world impact using ML.

Additional Feedback: major points: lines 234-246: what is "fan in" and "fan out", this paragraph doesn't explain what it is? Does it relate to neural network architecture (shown in Figure 1)? I have no idea what "large fan-in" and "small fan-out" means. Is channeling a bottleneck in the state space? If so, where is this?

Check out all the on-demand sessions from the Intelligent Security Summit here. From its inception in 2015, OpenAI has always made it clear that its central goal is to build artificial general intelligence (AGI). Its stated mission is "to ensure that artificial general intelligence benefits all of humanity." This past Friday, OpenAI CEO Sam Altman authored a blog post titled "Planning for AGI and Beyond," which discussed how the company believes the world can prepare for AGI, both in the short and long term. Some found the blog post, which has a million "likes" on Twitter alone, "fascinating."

The debate surrounding the world's population and the collective plan of action to tackle overpopulation has its proponents and critics. Some believe we're definitely headed towards an overpopulation crisis, while others think we'll be experiencing a catastrophic population decrease. Granted, this debate can be an entirely separate story in itself, but let's take a look at one side of the coin first. With regard to overpopulation, AI expert and renowned behavioral psychologist Catriona Campbell believes that'Tamagotchi children' could pose a very real future for parents 50 years from now, outlined in her recently published book – AI by Design: A Plan for Living with Artificial Intelligence. A survey involving a slew of Nobel Laureates cites population rise and its link to environmental degradation as the biggest threat to humankind.

Uber Technologies Inc. inked a deal with Motional Inc. to offer driverless deliveries and rides, rekindling its vision of a self-driving taxi fleet nearly two years after it sold its autonomous vehicle division. The San Francisco-based company is partnering with Motional, which is an autonomous driving joint venture between Hyundai Motor Co. and Aptiv Plc. The 10-year deal will pair Motional's all-electric IONIQ 5 robotaxis with Uber's ride-hailing and delivery platform, the companies said in a statement Oct. 6. They did not disclose financial terms. "This agreement will be instrumental to the wide scale adoption of robotaxis," Motional Chief Executive Officer Karl Iagnemma said in a statement.

One True Holding Company d/b/a/ TrueML, a financial technology software company developing machine learning-driven products that revolutionize the debt collection process, today announced that Steve Carlson will join the company as president. He joins as TrueML readies to accelerate its growth, with plans to expand its industry leading software solution, doubling run rate to reach more consumers with a digital first experience. TrueML develops software including patented machine learning technology to create a digital-first debt collection process that aligns with consumer communication preferences. Formerly One True Holding Company, the financial technology company rebranded in July 2022 as TrueML to bring more focus to its machine learning software development, which is a key aspect of its overall growth strategy. As a mission-driven company, TrueML aims to bring solutions to the marketplace that redefine how creditors and consumers engage in debt collection.

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Silicon Valley CEOs usually focus on the positives when announcing their company's next big thing. In 2007, Apple's Steve Jobs lauded the first iPhone's "revolutionary user interface" and "breakthrough software." Google CEO Sundar Pichai took a different tack at his company's annual conference Wednesday when he announced a beta test of Google's "most advanced conversational AI yet." Pichai said the chatbot, known as LaMDA 2, can converse on any topic and had performed well in tests with Google employees. He announced a forthcoming app called AI Test Kitchen that will make the bot available for outsiders to try.