#llms

#llms

OpenAI's new LLM has revolutionized AI and opened up new possibilities for marketers. Here's a look at how three big-name brands have embraced the technology. In March, the AI lab OpenAI released GPT-4, the latest version of the large language model (LLM) behind the viral chatbot ChatGPT. Since then, a small number of brands have been stepping forward to integrate the new-and-improved chatbot into their product development or marketing efforts. To a certain extent, this has required some courage.

Artificial intelligence can lower the barrier to self-reflection and be genuinely empowering for some, she explains. For people who feel stuck, overwhelmed, or unsure of where to begin, prompts can act as a scaffold for expressing and understanding your ideas, says Iftikhar. If the AI has access to information you've either shared or asked it to generate, it's also an efficient tool at synthesizing that information, explains Ziang Xiao, an assistant professor of computer science at Johns Hopkins University.

Akhil Savani has joined the company as vice president of publisher development, while longtime OpenX leader Rebecca Bonell has been elevated to regional vice president of publisher development, The Americas. Together, they will shape OpenX's next phase of publisher growth, working with publishers to innovate and increase monetisation while driving fair value exchanges. As publishers face critical challenges, including zero-click search and advancements in large language models (LLMs), OpenX is innovating to support publisher revenue and data strategies.

If you don't know it, Ecclesiastes is a collection of Old Testament verses in which the eponymous title character discourses on the apparent meaninglessness of pleasure, accomplishment, wealth, politics, and life itself in the face of the infinitude of the universe and the absolute perfection of God. It is the source of many of our most cliched phrases, such as there is a time for everything and there is nothing new under the sun.

In a 2024 study by Apollo Research, scientists deployed GPT-4 as an autonomous stock trading agent. The AI managed investments and received communications from management. Then researchers applied pressure: poor company performance, desperate demands for better results, failed attempts at legitimate trades, and gloomy market forecasts. Into this environment, they introduced an insider trading tip - information the AI explicitly recognized as violating company policy.

Some features thrive with structured data (to which I also count structured feeds). Pricing, shipping, availability for shopping is basically impossible to read in high fidelity & accurately from a text page, for example. Of course the details will change though - which is why it's important to use a system that makes it easy to adapt. Other features could theoretically be understood from a page's text, but it's just so much easier for machines to read machine-readable data instead of trying to understand your page (which might be in English, or in Welsh, or ... pick any of the 7000+ languages). Some visual

There is much anxiety these days about the dangers of human-AI relationships. Reports of suicide and self-harm attributable to interactions with chatbots have understandably made headlines. The phrase AI psychosis has been used to describe the plight of people experiencing delusions, paranoia or dissociation after talking to large language models (LLMs). Our collective anxiety has been compounded by studies showing that young people are increasingly embracing the idea of AI relationships; half of teens chat with an AI companion at least a few times

AI coding agents from OpenAI, Anthropic, and Google can now work on software projects for hours at a time, writing complete apps, running tests, and fixing bugs with human supervision. But these tools are not magic and can complicate rather than simplify a software project. Understanding how they work under the hood can help developers know when (and if) to use them, while avoiding common pitfalls.

AI search builds on the same signals that support traditional SEO, but adds additional layers, especially in satisfying intent. Many LLMs rely on data grounded in the Bing index or other search indexes, and they evaluate not only how content is indexed but how clearly each page satisfies the intent behind a query. When several pages repeat the same information, those intent signals become harder for AI systems to interpret, reducing the likelihood that the correct version will be selected or summarized.

As AI chat interfaces become more popular, users increasingly rely on AI outputs to make decisions. Without explanations, AI systems are black boxes. Explaining to people how an AI system has reached a particular output helps users form accurate mental models, prevents the spread of misinformation, and helps users decide whether to trust an AI output. However, the explanations currently offered by large language models (LLMs) are often inaccurate, hidden, or confusing.

Tiiny AI, a US-based deep-tech startup, has unveiled the Pocket Lab, officially verified as the "world's smallest personal AI supercomputer." This palm-sized device, no larger than a typical power bank, is capable of running large language models (LLMs) with up to 120 billion parameters entirely on-device, without relying on cloud servers or external GPUs. Designer: Tiiny AI At its core, the Pocket Lab aims to make advanced artificial intelligence both personal and private.

Harvey on Thursday confirmed it closed a round of funding, led by Andreessen Horowitz, that values the legal AI startup at $8 billion after reports of the funding leaked in October. The startup raised $160 million in the round. This latest capital infusion came just months after it raised a $300 million in a Series E round at a $5 billion valuation in June. And that was just months after raising a Sequoia-led $300 million Series D at a $3 billion valuation in February.

"A press release really lends itself to AI, because if you think about it, if you're talking about your company or your you're putting out expert knowledge," Jeppsen explained during a recent Tech Talk at Ragan's Future of Communications Conference. "You are the domain expert. You are factual. You've got a framework ... that resonates, and not only humans read it that way, but then AI tries to read it like a human."

Context engineering has emerged as one of the most critical skills in working with large language models (LLMs). While much attention has been paid to prompt engineering, the art and science of managing context-i.e., the information the model has access to when generating responses-often determines the difference between mediocre and exceptional AI applications. After years of building with LLMs, we've learned that context isn't just about stuffing as much information as possible into a prompt.

"We are dabbling in using AI," Houser said, "but the truth is a lot of it's not as useful as some of the companies would have you believe."

While AI tutors can provide personalized feedback, they cannot yet replicate what human tutors do best: connect, empathize, and build trust. AI can simulate dialogue, but it lacks emotional understanding. Human tutors perceive tone, hesitation, and body language, nonverbal cues that reveal engagement and comprehension. They also navigate ethical and cultural complexities, exercising moral judgment that AI simply doesn't possess.

The most prominent AI systems today are Large Language Models (LLMs) like ChatGPT, Claude, Grok, Perplexity, and Gemini. These systems work through computational models that mimic the human brain's structure, thus termed "neural networks." They consist of interconnected nodes that process and learn from internet data, enabling pattern recognition and decision-making in the field of artificial intelligence called "Machine Learning." LLMs are trained on massive datasets containing billions of words from books, websites, and other text sources.

Clearly, it has a social consequence when you have fewer journalists; clearly, it has a social consequence if the ability to write books in a meaningful, sustainable way is undermined. And there are people who are aware of that, Sam Altman, Sundar Pichai at Google, Tim Cook, you can talk with them in a way that they understand their business needs to have this constant creative flow,

That question has become more pressing. During the company's third-quarter earnings announcement, it predicted a weaker holiday shopping season than expected, citing President Donald Trump's tariffs and their negative impact on the home furnishings category. As a result, Pinterest's fourth-quarter revenue is expected to come in between $1.31 billion and $1.34 billion, while analysts were estimating $1.34 billion, on average. The news sent the stock tumbling by more than 21% on Wednesday.

Large language models (LLMs)have become the backbone of modern software, powering everything from code assistants to data pipelines. However, until recently, building with them meant juggling multiple APIs, setting up environments, and writing extensive code just to test a single prompt. Google AI Studio changes that. It's a web-based workspace where you can prototype with the latest Gemini models, write prompts, analyze outputs, and export working code in minutes. Think of it as your personal playground for experimentation and deployment.

In 1974, economist and metalworker Harry Braverman wrote Labor and Monopoly Capital, which showed how technology under capitalism shifts knowledge from workers to management-not because automation demands it but because control-seeking managers and capitalists do. Just over a half century later, his insight remains urgent: An invention offers options, but power often determines which are pursued.

But LLMs took it a notch even further, coders have started morphing into LLM prompters today, that is primarily how software is getting produced. They still must baby sit these LLMs presently, reviewing and testing the code thoroughly before pushing it to the repo for CI/CD. A few more years and even that may not be needed as the more enhanced LLM capabilities like "reasoning", "context determination", "illumination", etc. (maybe even "engineering"!) would have become part of gpt-9

The company said the launch of Red Hat Developer Lightspeed, a portfolio of AI solutions, will equip developer teams with "intelligent, context-aware assistance" through virtual assistants. Available on the Red Hat Developer Hub, the first of these AI tools is accessible through the hub's chat interface. The company said this will help speed up non-coding-related tasks, including development of test plans, troubleshooting applications, and creating documentation. This AI assistant can be used via both publicly available and self-hosted large language models (LLMs).

So, let's return to classic literature and take a look at a 19th-century idea that feels remarkably relevant today. It's the danger of too much thought. Many writers have understood the power and peril of thought (and consciousness) long before algorithms began to mimic it. They felt, unlike the LLMs, that the very thing that makes us intelligent can also make us suffer.

What happened with AWS outage recently is only a brief foreshadow of what might eventually come to pass if this trend continues. Imagine a world where most programmers are primarily LLM prompters with a very shallow understanding of core programming skills or even operational skills pertaining to an app, framework or library. What will we do if a major outage or technical issue occurs then and no person around knows what's really going on?

"The potential use of large language models (LLMs) to simulate human cognition and behavior has been heralded as an upcoming paradigm shift in psychological and social science research," wrote lead author Eric Mayor, PhD, a senior researcher at the University of Basel, in collaboration with Lucas Bietti, PhD, an associate professor of psychology at Norwegian University of Science and Technology (NTNU), and Adrian Bangerter, PhD, a professor of psychology at the University of Neuchâtel.