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The government shutdown is delaying another major economic report, leaving policymakers at the Federal Reserve with a cloudier picture even as the economy enters a challenging phase of stubbornly persistent inflation and a sharp slowdown in hiring.The Labor Department’s monthly inflation data was scheduled for release Wednesday, but late last week was postponed until Oct. 24. The department is recalling some employees to assemble the data, which was collected before the shutdown began. The figures are needed for the government to calculate the annual cost of living adjustment for tens of millions of recipients of benefit programs such as Social Security.The shutdown could make things worse for agencies like the Fed if it continues, because government agencies cannot collect the raw data that are then compiled into the monthly reports on jobs, inflation, and other economic trends. The September employment report, for example, which was due to be released Oct. 3 but was not issued because of the shutdown, was essentially completed before the government closed and could be released fairly quickly once the shutdown ends. But October data could be delayed much longer.Federal Reserve Chair Jerome Powell said Tuesday in remarks to the National Association for Business Economics that the central bank for now is looking at data from the private sector, such as payroll processor ADP, which issues its own monthly report on hiring by U.S. businesses, to gauge the economy. It is also relying on anecdotal reports from the hundreds of businesses that the regional Fed banks consult with.But while there are many firms that compile jobs-related data, there are fewer alternative sources of information to track inflation and growth, Powell added.“We’ll start to miss that data and particularly the October data,” Powell said. “If this goes on for a while, they won’t be collecting it. And it could become more challenging.”The Fed is already in a difficult spot, Powell has said, as it grapples with two policy goals that are nearly in conflict. It is tasked by Congress with seeking both maximum employment and stable prices.Right now, inflation remains above the Fed’s target of 2%, with the latest figures showing prices rose 2.9% compared with a year earlier, according to the Fed’s preferred measure. Typically, elevated inflation would lead the Fed to raise its key interest rate, or at least keep it elevated.Yet hiring has also weakened considerably, and the unemployment rate has ticked up to a still-low 4.3% in August from 4.2% in the previous month. When the Fed’s other goal of maximum employment is threatened, it usually responds with the opposite approach: Cutting rates to spur more borrowing and spending.On Tuesday, Powell noted those challenges and said, “There really isn’t a risk-free path.” Christopher Rugaber, AP Economics Writer
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Households that have bought Bens Original rice products will want to check their pantries right away. The brand, owned by food giant Mars, has issued a voluntary recall for select rice products. At issue is the possibility of small stones mixed in the rice, which could cause intestinal and other damage if consumed. Heres what you need to know about the Bens Original rice recall. Whats happened? On October 10, Bens Original announced a voluntary recall of some of its rice products. That recall notice was later published on the U.S. Food and Drug Administrations (FDA) website on October 14. The voluntary recall was initiated after Bens Original discovered that some of its rice products may contain small stones mixed within the rice. The recall notice states that these objects are small, naturally occurring stones originating from the rice farm. According to the recall notice, if the rice products do have small stones in them, the objects pose possible risk of oral or digestive tract injury if consumed. What products are being recalled? Bens Original says the recalled items only include a limited number of three select products. Whether the product is included in the recall depends on the batch codes and best by dates listed on the products packaging. The recalled products include: Ben’s Original Ready Rice Long Grain White Rice: batch codes 533ELGRV22 or 534ALGRV22 and Best By date of August 2026. Ben’s Original Ready Rice Whole Grain Brown Rice: batch codes 534AMGRV22, 534BMGRV22, or 534DMGRV22 and the best buy date of August 2026. Bens Original Ready Rice Long Grain & Wild Rice: batch codes 533BMGRV22, 533CLGRV22, or 533CMGRV22 and the best by date of August 2026. Where were the recalled products sold? According to the recall notice, the recalled products were sold at numerous stores across America. Depending on the specific recalled item, those stores may include: Amazon HEB Piggly Wiggly Target United Markets But the recalled products may have been sold at additional stores, the notice cautions. Impacted retailers are not limited to the ones mentioned in the recall notice as additional retailers may have purchased products distributed by Associated Grocers, C&S, and Dot Foods from August through September. Has anyone been harmed? Thankfully, the recall notice states that no reports of injury or illness in relation to the recalled products have been reported to date. However, as the recalled products have a long shelf life with a best by date of August 2026, it’s likely the recalled products are still sitting in many peoples cupboards. What should I do if I have the recalled products? The recall notice warns consumers who are in possession of the recalled products not to consume them. Instead, consumers should contact Bens Original Consumer Care to start the return process. The number consumers should call is 1-800-548-6253. Full details of the recall can be found on the recall notice here.
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There is an all-out global race for AI dominance. The largest and most powerful companies in the world are investing billions in unprecedented computing power. The most powerful countries are dedicating vast energy resources to assist them. And the race is centered on one idea: transformer-based architecture with large language models are the key to winning the AI race. What if they are wrong? What we call intelligence evolved in biological life over hundreds of millions of years starting with simple single-celled organisms like bacteria interacting with their environment. Life gradually developed into multi-cell organisms learning to seek what they needed and to avoid what could harm them. Ultimately humans emerged with highly complex brains, billions of neurons and exponentially more neural interactions designed to respond to their needs, interactions, and associations with each other and the world. Creating an artificial form of that likely involves more than cleverly generating language with tools trained on massive repositories of largely non-curated text and marketing it as intelligence. What if aggregating the vast collective so-called wisdom accumulated on the internet and statistically analyzing it with complex algorithms to mindlessly respond to human prompts is really just an unimaginably expensive and resource-intensive exercise in garbage-in-garbage-out? At best, it may be a clever chronicler of common wisdom. At worst, its an unprecedented and unnecessary waste of resources with potentially harmful consequences. Eerily foreshadowing a critique of current mainstream AI, Immanuel Kant famously wrote in his landmark work, A Critique of Pure Reason, “thoughts without content are empty, intuitions without concepts are blind.” Put another way, can eons of evolved intelligence be replicated and reduced to the worlds greatest parrot or the mother-of-all autocompletes? With all of the global power, hype, and resources behind this one approach, you may have the impression that it is the only viable way to create an artificial form of human intelligence. Fortunately, it is not. Incrementalism On the incrementalist end of the spectrum of AI research and development, there are approaches that seek to make more efficient use of resources such as grouping small language models (SLMs) with AI agents (https://www.fastcompany.com/91281577/autonomous-ai-agents-are-both-exciting-and-scary) to allow more focused, economical inquiries and responses. (See, Small Language Models are the Future of Agentic AI, Cornell University, https://arxiv.org/abs/2506.02153). The theory is simple: employ flexible, efficient AI agents (technology that can autonomously interact with the environment and perform tasks without human supervision) to access SLMs, smaller, more targeted, and less resource-intensive sets of data. The underlying theory is the same for SLMs and LLMsaggregating data and statistically modeling it to generate text or other data. SLMs are just a smaller and more efficient (but inherently more limited) way of doing this. This approach can incorporate additional technology to achieve greater accuracy such as retrieval augmented generation (RAG). RAG can access more targeted, verifiable, and critically, real-time information rather than simply relying on static (pretrained) data alone. A whole greater than the sum of its parts A more significant possible alternative to the LLM and GPT architecture that more closely simulates how we think is based on attempting to replicate evolutionary biology. One company pioneering such work is Softmax (named for a statistical function used in machine learning) led by a cofounder of Twitch, Emmett Shear, who briefly served as CEO of OpenAI. This approach is modeled on cellular biology and the idea that individual parts (cells) working (or in alignment) with each other can form a whole with greater coordinated functionality than the individual parts. A human being is made up of individual but synchronized cells that, on their own, dont function like us, but somehow cohere to allow us to think and function as human beings. In terms of building a computer model, AI agents are the equivalent of cells in this approach that in theory at least, can work together to form a greater functioning, learning entity. If the current domination of LLMs and GPT architecture continues and other innovative approaches fall (or are pushed) by the wayside, it wouldnt be the first time in the history of computing that commercial forces overrule potentially better alternatives (see Why bad ideas linger in software, Alan Kay, 2012, address to the Congress on the Future of Engineering Software). As Albert Einstein famously noted, if he had an hour to save the world, he would spend 55 minutes defining the problem and five minutes solving it. The massive entities pushing the current dominant approach to AI development have yet to define the problem they are trying to solve. LLMs and GPT have proven able to perform tasks that people find useful and they will likely continue to do so. The question is, what if anything, does that have to do with intelligence, human or otherwise?
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