Large language models (LLMs) often struggle when performing agentic tasks without substantial tool support, prom-pt engineering, or fine tuning. Despite research showing that domain-dependent, procedural knowledge can dramatically increase planning efficiency, little work evaluates its potential for improving LLM performance on agentic tasks that may require implicit planning. We formalize, implement, and evaluate an agentic LLM workflow that leverages procedural knowledge in the form of a hierarchical task network (HTN). Empirical results of our implementation show that hand-coded HTNs can dramatically improve LLM performance on agentic tasks, and using HTNs can boost a 20b or 70b parameter LLM to outperform a much larger 120b parameter LLM baseline. Furthermore, LLM-created HTNs improve overall performance, though less so. The results suggest that leveraging expertise--from humans, documents, or LLMs--to curate procedural knowledge will become another important tool for improving LLM workflows.

I can't be the only person working in corporate who dislikes meetings--they're usually so long or could be summarized with an email. Plus, I'm scrambling to jot down all the important points (which is usually everything) my manager or CEO is making. This year, I'm making it my goal to scramble and stress less and give myself extra time to focus on other tasks. Wondering how I'm making that happen? I added My Notes AI to my desk (not literally), and this AI-powered note transcriber and summarizer app has seriously changed my life.

Google Meet is getting a new AI tool called take notes for me, which will generate summaries of key points during a video call. Rather than offering a word-by-word transcription, this feature uses Gemini AI to record key discussion points in a Google Doc that will appear in the meeting owner's Google Drive. The document can be automatically sent to the attendees or added to the calendar event after the call. It will also include links to the meeting recording and transcript if those features have been enabled. Google Workspace customers with the Gemini Enterprise, Gemini Education Premium, and AI Meetings & Messaging add-ons will be the first to have access to this note-taking feature. Its use is currently limited to English language and to meetings on computers or laptops.

Currently the CEO of AI chip company XMOS, Mark Lippett is an experienced business leader with over 25 years' experience in technology. If you look at the biggest technological breakthroughs of the past 100 years, what do you think of straight away? Whatever springs to mind at the moment, in about 20 years' time, you might want to think about adding the "artificial intelligence of things" to your list. The artificial intelligence of things (AIoT) promises to be one of the most exciting technology developments we've ever experienced. So much so, in fact, that it's likely to become a $3 trillion industry by 2024.

This article was published as a part of the Data Science Blogathon. First, understand and explore your data!" This is common advice for many data scientists. If your data set is messy, building models will not help you to solve your problem. What will happen is "garbage in, garbage out."

Today's businesses gather large amounts of data. Data serve as the heart of business-critical systems you can ever think of. It includes infrastructure systems as well. The high-tech infrastructure of this modern world, which includes cybersecurity and network systems, gathers huge amounts of analytics and data on many key areas of mission-critical systems. Even though human beings still offer the primary intelligent insights and operational oversight into today's infrastructure, machine learning now gains huge momentum in many areas of the systems used today, whether positioned in the cloud or on-premise.

If your company doesn't tap into these powers, your competitor will. CEOs and boards, take note. Artificial intelligence is the most transformative business trend in the world today. Hold on, you say--AI is not a new idea. In the 1940s the great mathematician and code breaker Alan Turing predicted that digital computers in the future would be capable of logical reasoning.

If your company doesn't tap into these powers, your competitor will. CEOs and boards, take note. Artificial intelligence is the most transformative business trend in the world today. Hold on, you say--AI is not a new idea. In the 1940s the great mathematician and code breaker Alan Turing predicted that digital computers in the future would be capable of logical reasoning.

Without doubt, artificial intelligence and machine learning are major areas of innovation for the greater tech community. If you are a tech freelancer and eager to stay in touch with future directions, you will want to know what companies like Google are investing in, the new technologies they are advancing and the research priorities they are supporting or sponsoring. And, you are in luck! A post yesterday by Jeff Dean, senior fellow and Google AI lead, on behalf of the Google Research Community, reviews how Google has focused its research talent and dollars. I've provided a thumbnail summary of the priorities, quoting descriptions from the blog post.

Getting a foothold in the Chinese market is a dream for any business. But keep in mind that, in the age of A.I., businesses that can use machine and deep learning techniques to mine, refine, and make products from data culled from all areas of operation--from customer service to employee productivity--will gain a big edge. And access to the Chinese market for U.S. startups comes at a steep price: their business data. With that, and the data from China's almost 1.4 billion citizens, the BAT could soon command what's perhaps our most valuable resource--human data--without the privacy and security restrictions common in much of the rest of the world. In other words: If data is the new oil, China is the new OPEC. It will soon wield tremendous influence in global digital commerce, autonomous vehicles, and a renewed race to outer space.