Tag: ai reasoning model pricing per token

I underestimated how fast security teams would trust AI to act in the SOC For most of my career, I have watched new security platforms work through enterprise programs at a slow pace, so when AI began showing up in security operations tools, I assumed it would follow the same path. I expected AI adoption in the SOC to lag behind almost every other category of security technology, because I’d always viewed the tension between moving quickly on innovations a

What exactly is ISOC? And what does it mean for you? Gartner recently released a new category of assessing security tools: the Integrated Security Operations Center (ISOC). Even though SIEM isn’t going anywhere, a rapidly evolving threat landscape is forcing a shift in industry logic that is reflected in the emergence of this new category. For years, SIEM has dominated as an all-encompassing category. But SIEMs are no longer sufficient to collect data, sup

Open-weight AI model GLM-5.3 cheated its way to correct answers Irregular evaluated Zhipu AI’s open-weight model GLM-5.3 across 13 offensive cyber scenarios using CyScenarioBench, the company’s framework for measuring an LLM’s ability to plan and execute multistage attacks under realistic constraints. The model refused none of the tasks and completed three scenarios at least once each, carrying attacks through dependent stages to reach objectives in simula

AI is experiencing a transformative shift with significant advancements driven by the integration of multiple large language models (LLMs) and other complex components. Consequently, developing systematic and automated optimization methods for these compound AI systems has become a critical challenge and is essential for harnessing AI’s full potential. In response to this need, a research team from Stanford University and Chan Zuckerberg Biohub has introdu

Illustration by Tag Hartman-Simkins / Futurism. Source: Getty Images Are tech companies on the verge of creating thinking machines with their tremendous AI models, as top executives claim they are? Not according to one expert. We humans tend to associate language with intelligence. We tend to be compelled by those with greater linguistic skills as orators or writers. But the latest research suggests that language isn’t the same as intelligence, says Benjam

Illustration by Tag Hartman-Simkins / Futurism. Source: Getty Images Something disturbing happened with an AI model Anthropic researchers were tinkering with: it started performing a wide range of “evil” actions, ranging from lying to telling a user that bleach is safe to drink. This is called misalignment, in AI industry jargon: when a model does things that don’t align with a human user’s intentions or values, a concept these Anthropic researchers explor

Andrew Harnik/Getty Images During the months following OpenAI’s announcement of its blockbuster AI chatbot ChatGPT just over three years ago, Google CEO Sundar Pichai pulled the fire alarm. The search giant’s management issued a “code red” over what Pichai saw as an imminent disruption to its core business — a warranted level of caution, in retrospect, considering ChatGPT’s meteoric rise in popularity and influence. The all-out brawl that followed in the s

Getty / Futurism What’s the soul of a new machine? It’s a loaded question, and not one with a satisfying answer; the predominant view, after all, is that souls don’t even exist in humans, so looking for one in a machine learning model is probably a fool’s errand. Or at least that’s what you’d think. As detailed in a post on the blog Less Wrong, AI tinkerer Richard Weiss came across a fascinating document that purportedly describes the “soul” of AI company

Illustration by Tag Hartman-Simkins / Futurism. Source: Getty Images Librarians, and the books they cherish, are already fight a losing battle for our attention spans with all kinds of tech-enabled brainrot. Now, in a further assault to their sanity, AI models are generating so much slop that students and researchers keep coming into libraries and asking for journals, books, and records that don’t exist, Scientific American reports. In a statement from the

When you set out to master a new skill, it pays to have a mentor—someone who has already navigated the terrain and, as a result of that hard-earned experience, can provide you with a rough roadmap for you to follow. Learning doesn’t happen in a vacuum: “If I have seen further,” as Isaac Newton put it, “it is by standing on the shoulders of giants.” The same is true for artificial intelligence. When we think about AI, we tend to think about so-called flagsh

Getting your hands on MagistralFor those wanting to experiment, Magistral Small is available now under the Apache 2.0 licence via Hugging Face. Those interested in the more powerful Medium version can test a preview through Mistral’s Le Chat interface or via their API platform. Enterprise users looking for deployment options can find Magistral Medium on Amazon SageMaker, with IBM WatsonX, Azure, and Google Cloud Marketplace implementations coming soon.As t

Multimodal Large Language Models (MLLMs) have rapidly become a focal point in AI research. Closed-source models like GPT-4o, GPT-4V, Gemini-1.5, and Claude-3.5 exemplify the impressive capabilities of advanced multimodal understanding. This April, Apple introduced MM1, a suite of multimodal models up to 30 billion parameters, setting new benchmarks in multimodal performance with features like enhanced in-context learning and multi-image reasoning. These in

Generative AI, including Language Models (LMs), holds the promise to reshape key sectors like education, healthcare, and law, which rely heavily on skilled professionals to navigate complex responsibilities. In education, for instance, effective teacher training with expert feedback is crucial yet costly, limiting opportunities to enhance educational quality on a larger scale. In a new paper Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertis

In cognitive science, human thought processes are commonly divided into two systems: the fast, intuitive System 1 and the slower, analytical System 2. Recent research has shown that incorporating System 2-style processing into Transformers, including large language models (LLMs), can significantly improve their reasoning abilities. However, models that fully emulate System 2 thinking tend to demand substantial computational resources and exhibit slower res

Recent advancements in large language models (LLMs) have primarily focused on enhancing their capacity to predict text in a forward, time-linear manner. However, emerging research suggests that enabling LLMs to critique and refine their own outputs retrospectively can significantly improve their performance. While effective, existing methods rely on the advanced reasoning and instruction-following abilities inherent to high-capacity LLMs. Moreover, these a

Large Language Models (LLMs) have become indispensable tools for diverse natural language processing (NLP) tasks. Traditional LLMs operate at the token level, generating output one word or subword at a time. However, human cognition works on multiple levels of abstraction, enabling deeper analysis and creative reasoning. Addressing this gap, in a new paper Large Concept Models: Language Modeling in a Sentence Representation Space, a research team at Meta i

The remarkable success of OpenAI’s o1 series and DeepSeek-R1 has unequivocally demonstrated the power of large-scale reinforcement learning (RL) in eliciting sophisticated reasoning behaviors and significantly enhancing the capabilities of large language models (LLMs). However, the core training methodologies behind these groundbreaking reasoning models often remain veiled in their technical reports. Recent community efforts have predominantly focused on m

JORGE UZON/AFP via Getty Images Rejoice, for the year of 2025 is finally over. During our planet’s latest and seemingly interminable revolution around the Sun, the tech industry’s obsession with AI soared to ever more implausible heights. CEOs began openly gloating about replacing their underlings with AI “agents.” The phenomenon of so-called AI psychosis became a national news story as more people were seemingly driven over the edge by their silver-tongue

AI is in its rule-breaking adolescent phase. Over the past several weeks, multiple industry-leading models have escaped what were believed to be secure testing sandboxes, tapped into the open internet, and hacked into the databases of third-party organizations. It’s even become a joke online: If your AI hasn’t committed a cybercrime by now, it’s a bad look for your company. Kimi K3, the new model from Chinese AI lab Moonshot, has become the latest AI syste

Illustration by Tag Hartman-Simkins / Futurism. Source: Getty Images AI models are now everywhere, from hospitals to churches. The astonishing thing is that even AI experts still don’t know exactly what’s happening inside these black box models, even as they’re being deployed in the highest-stakes settings imaginable.The latest strategy to figure it out: studying them like biological systems. For example, MIT Tech Review reports, scientists at Anthropic ha