#large-language-models

#large-language-models

For decades, public relations agencies controlled the gatekeeping process between brands and media. Companies relied on bloated retainers, traditional press kits, and personal connections to get coverage. But that model is fading fast. Today, anyone with an internet connection can leverage AI to create compelling press releases, distribute them on platforms like PRWeb, and pitch journalists directly-often faster, cheaper, and with more precision than legacy firms.

Paid media has always been about positioning-brands spending strategically to reach audiences where they live, scroll, and search. But the definition of "visibility" is shifting. Today, being seen is no longer limited to ad placements, keyword bidding, or social media impressions. Artificial intelligence has become the new filter through which information is discovered, recommended, and trusted. The rise of large language models (LLMs) like ChatGPT, Perplexity, and Claude, combined with real-time indexing from Google and Apple News, has changed how people interact with content.

Y Combinator startup Eloquent AI has raised $7.4 million in seed funding to provide AI-fueled customer service in the financial services industry. The startup says its AI product can help with complex, regulated workflows, such as onboarding new customers and unfreezing bank cards. Eloquent was cofounded by Tugce Bulut, who previously cofounded market research startup Streetbees. Bulut left the company two years ago. Last month it went into administration and laid off all staff.

AI mode is an advanced version of Google Search that uses large language models to summarise information from the web, so you can spend more time on Google than visiting websites. Google AI mode advanced analysis Source: BleepingComputer Google AI mode can answer complex answers, process images, summarize information on the web, create tables, graphs, charts, and even help you code.

He said it seemed like breakthroughs in AI would be exponential to the point where "it will just do research for us, so what do we do?" He said he spent a lot of time talking with students at the PhD level about how to organize themselves, even about what their role in the world would be going forward. It was "existential" and "surprising," he said. Then, he received another surprise: a student-led request for a change in testing.

With artificial intelligence integrating - or infiltrating - into every corner of our lives, some less-than-ethical mental health professionals have begun using it in secret, causing major trust issues for the vulnerable clients who pay them for their sensitivity and confidentiality. As MIT Technology Review reports, therapists have used OpenAI's ChatGPT and other large language models (LLMs) for everything from email and message responses to, in one particularly egregious case, suggesting questions to ask a patient mid-session.

Last month, at the 33rd annual DEF CON, the world's largest hacker convention in Las Vegas, Anthropic researcher Keane Lucas took the stage. A former U.S. Air Force captain with a Ph.D. in electrical and computer engineering from Carnegie Mellon, Lucas wasn't there to unveil flashy cybersecurity exploits. Instead, he showed how Claude, Anthropic's family of large language models, has quietly outperformed many human competitors in hacking contests - the kind used to train and test cybersecurity skills in a safe, legal environment.

"When we train our model, we're not training it to be an amazing conversationalist with you," Frosst said. "We're not training it to keep you interested and keep you engaged and occupied. We don't have like engagement metrics or things like that." The Canadian AI startup was founded in 2019 and focuses on building for other businesses, not for consumers. It competes with other foundational model providers such as OpenAI, Anthropic, and Mistral and counts Dell, SAP, and Salesforce among customers.

A phrase I've often clung to regarding artificial intelligence is one that is also cloaked in a bit of techno-mystery. And I bet you've heard it as part of the lexicon of technology and imagination: "emergent abilities." It's common to hear that large language models (LLMs) have these curious "emergent" behaviors that are often coupled with linguistic partners like scaling and complexity. And yes, I'm guilty too.

Not long ago, a storefront sign was the most important marketing channel a retailer had. If you didn't have one, customers didn't know you existed. Today, for mortgage companies, that sign has been replaced by something else entirely: search. But with borrowers now turning to ChatGPT and other AI tools instead of Google, even ranking on the first page may not mean you'll be found.

AI chatbots are widespread in many different sectors as they can provide natural and engaging interactions. We already know these models aren't good at protecting information.

LM Caches play a critical role in improving the efficiency and scalability of deploying large language models by caching and reusing previously computed results.

"The firm, based in San Francisco, California, detailed the system in a blog post and a technical description on 5 August. On some tasks, gpt-oss performs almost as well as the firm's most advanced models."

The environmental audit by Mistral reveals that the majority of CO2 emissions and water consumption arise during model training and inference, not from construction or end-user equipment.

The rise of agentic AI - autonomous systems that understand, plan, and act using large language models (LLMs) - is rapidly reshaping how developers and data professionals think about automation.