Lifeboat Foundation

Your safety framework must include content filtering, output validation, rate limiting and detailed audit logging. I’ve found that implementing circuit breakers—automatic capability disablers triggered by anomalies—prevents small issues from becoming major incidents. For example, if an agent starts generating an unusual number of error responses, the system should automatically restrict its capabilities and alert the operations team.

Last year, I spoke to a tech company whose AI assistant became a victim of its own success. The system that flawlessly handled 1,000 daily requests crashed when usage jumped to 100,000 requests after a successful product launch. This taught us the importance of building for scale from day one. Even well-established companies like Netflix occasionally face challenges with scale, as seen during the recent live-streaming outages for the Jake Paul vs. Mike Tyson fight.

A production-ready architecture needs several key components working in harmony. The core engine should be modular, making updates and maintenance straightforward. Your integration layer should connect smoothly with enterprise systems through standardized APIs. Comprehensive monitoring helps you spot issues before they impact users, and robust memory management ensures consistent context handling across interactions.

In 1956, a group of pioneering minds gathered at Dartmouth College to define what we now call artificial intelligence (AI). Even in the early 1990s when colleagues and I were working for early-stage expert systems software companies, the notion that machines could mimic human intelligence was an audacious one. Today, AI drives businesses, automates processes, creates content, and personalizes experiences in every industry. It aids and abets more economic activity than we “ignorant savages” (as one of the founding fathers of AI, Marvin Minsky, referred to our coterie) could have ever imagined. Admittedly, the journey is still early—a journey that may take us from narrow AI to artificial general intelligence (AGI) and ultimately to artificial superintelligence (ASI).

As business and technology leaders, it’s crucial to understand what’s coming: where AI is headed, how far off AGI and ASI might be, and what opportunities and risks lie ahead. To ignore this evolution would be like a factory owner in 1900 dismissing electricity as a passing trend.

Let’s first take stock of where we are. Modern AI is narrow AI —technologies built to handle specific tasks. Whether it’s a large language model (LLM) chatbot responding to customers, algorithms optimizing supply chains, or systems predicting loan defaults, today’s AI excels at isolated functions.

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A powerful earthquake, possibly the strongest in years, has devastated the island nation of Vanuatu, killing at least 14 people. More than 200 are reported injured according to a post on X by Katie Greenwood, Fiji-based head of the Red Cross in the Pacific.

The 7.3-magnitude jolt rocked the region on Tuesday sending tremors through homes, businesses and critical infrastructure. Witnesses described buildings collapsing, roads blocked by landslides and hospitals stretched thin as reports of injuries — and unconfirmed casualties — surfaced.

Chris McHenry is Vice President of Product Management at Aviatrix.

Enterprise reliance on cloud computing is no longer a question of “if” but “how much” and “how secure.” The cloud has become the backbone of modern business, enabling rapid scaling, seamless integration and global reach.

However, as cloud adoption matures, so do its associated costs—driven significantly by the rise of artificial intelligence (AI) and the escalating energy demands of data centers. For instance, OpenAI recently revealed plans to increase its prices by 120% over the next five years, even after securing an industry-record $6.6 billion in funding.

Originally published on Towards AI.

One of the major challenges in using LLMs in business is that LLMs hallucinate. How can you entrust your clients to a chatbot that can go mad and tell them something inappropriate at any moment? Or how can you trust your corporate AI assistant if it makes things up randomly?

That’s a problem, especially given that an LLM can’t be fired or held accountable.

A small team of AI researchers at Adobe Inc., working with a colleague from Auburn University and another from Georgia Tech, has developed a small language model (SLM) that they claim can be run locally on a smart phone with no access to the cloud. The group has written a paper describing their new app, which they call SlimLM, and have posted it to the arXiv preprint server.

As LLM technology continues to mature, researchers across the globe continue to find new ways to improve it. In this new effort, the research team has found a way to cut the cord for a specific type of AI application—processing documents locally.

As LLMs such as ChatGPT become more popular, users have become more worried about privacy. And it is not just individuals—companies large and small have adopted AI applications that assist with a variety of business processes, some of which require a high degree of privacy.

Interim Intel co-CEO Michelle Johnston Holthaus announced that the first engineering samples of hardware manufactured with the company’s 18A semiconductor node have been delivered to customers. Her comments aim to reassure industry observers that Intel’s foundry business remains on track to compete with TSMC’s and Samsung’s 3nm and 2nm nodes starting next year.

At the Barclays Annual Global Technology Conference, Holthaus and co-CEO David Zinsner discussed Intel’s upcoming Panther Lake processors, which will debut the 18A process node upon their expected launch in the second half of 2025. Holthaus revealed that eight foundry customers have powered on ES0 (likely “Engineering Sample 0”) chips built on the 18A node, signaling significant progress compared to six months ago.

Intel released version 1.0 of the 18A process design kit in July, enabling customers to begin developing chips based on the node. In August, the company confirmed that internal samples of Panther Lake and Clearwater Forest processors, built on the 18A node, successfully powered on and booted Windows with satisfactory performance. The statements made at the Barclays event mark the first confirmation of 18A usage outside of Intel.

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What is time? Is it just a ticking clock, or is it something more profound?

Continue reading “What is Time? Stephen Wolfram’s Groundbreaking New Theory [Ep. 468]” »

AIs that generate answers to user queries could transform search, but only if someone can get the tech and the business model right.