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Regal debuts Goal Oriented Agents, putting an actor and a director model on every call

Enterprise voice artificial intelligence company Regal Voice Inc. today added Goal Oriented Agents to its platform, enabling businesses using them to define what a call has to accomplish, and providing two AI models that split the work of getting there. The new agents are aimed at a problem Regal says shows up in calls such […] The post Regal debuts Goal Oriented Agents, putting an actor and a director model on every call appeared first on SiliconANGLE.

Edge Sound raises $2M to help physical AI make sense of audio

Edge Sound Research Inc. today announced it has raised $2 million in new funding to build audio infrastructure that isolates useful sounds and combines them with other sensor data to help physical AI systems understand what’s happening in real time. Stormbreaker Ventures led the round, bringing the company’s total funding to $9 million. When the […] The post Edge Sound raises $2M to help physical AI make sense of audio appeared first on SiliconANGLE.

Opera introduces a new web standard to let users deploy AI locally in their browser

The new standard, WebAI, is designed to work with a variety of AI models.

Nvidia backs Indian startup helping companies run AI models with fewer GPUs

Simplismart says its annualized revenue run rate has grown 14-fold in six months, with the U.S. accounting for more than half its revenue.

AI Agents Are Media’s New Favorite Audience

As agents go mainstream, some publishers are hoping that people will pay for content tailor-made for AI—not humans.

Cisco Partner Summit: Five insights that will define the channel in the AI era

For most of its 30-year history, Cisco Partner Summit has focused on how partners sell Cisco Systems Inc. technology. This year’s event in Vancouver was about something different: how partners make money in a world where the transaction is no longer the main event. Cisco entered the event with tremendous momentum, as its stock was […] The post Cisco Partner Summit: Five insights that will define the channel in the AI era appeared first on SiliconANGLE.

Conceptizer: Next-Token Prediction Is Not All You Need

arXiv:2610.33027v1 Announce Type: new Abstract: Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning. We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning. Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters. Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perp

Microsoft offers voluntary buyouts to 60,000 employees amid AI investments

Microsoft Corp. today issued voluntary buyout offers to about a quarter of its workforce. The approximately 60,000 workers affected by the move are located in the U.S., Canada, India and the UK. They reportedly all have a score of 70 or below on the company’s internal experience scale. That means the most senior employees impacted […] The post Microsoft offers voluntary buyouts to 60,000 employees amid AI investments appeared first on SiliconANGLE.

Build specialized multimodal models with LFM3

Build specialized multimodal models with LFM3

OpenAI announces three big science discoveries and a new way to work with academics

Less than a month after its mass release of hundreds of manuscripts that it said contained solutions to long-standing mathematics questions, OpenAI is back with three more "major" discoveries in math, computer science, and physics. What's different this time is the choice to have external experts that OpenAI describes as "shepherds" guiding the results in […]

Showing our work: a controlled experiment in context engineering

We ran one AI skill 80 times against four context sources. Engineered summaries cut cost 48.5% and run time 43.2%.

Microsoft offers buyouts to 60,000 employees

Microsoft is offering voluntary buyouts to up to 60,000 employees across the US, UK, Canada, and India. In a message to employees, Microsoft chief people officer Amy Coleman writes that this gives staff members "the opportunity to leave Microsoft with financial support and career planning resources" as the company looks to trim staff without another […]

Kevin Roose is AGI-pilled. Why aren’t you?

Today, I’m talking to Kevin Roose, journalist and author of the new book The AGI Chronicles, which details the recent and explosive transformation of the AI industry. Kevin was a longtime New York Times reporter and host of the popular podcast Hard Fork, but he and friend of the show Casey Newton just left the […]

Import AI 478: Math for alignment; ProgramBench; and Mirendil shows what RSI means

What is “night” to a machine?

SAGE Benchmark and GAR Framework Released: Advancing Multimodal Semantic Alignment Evaluation

On October 26, 2026, arXiv published the paper arXiv:2610.31838v1, introducing Sense-Aligned Grounding Evaluation (SAGE)—a unified 10-candidate image-selection benchmark—along with the training‑agnostic Gloss-Augmented Reasoning (GAR) framework, designed for cross‑noun, verb, and entity‑centered multimodal grounding evaluation.

Why it matters：This study is the first to unify three types of tasks—VWSD, VerSe, and WikiDiverse—under a term-centric grounded evaluation protocol, introducing a mechanism for identifying hard negative samples and context‑exchange diagnostics. Empirical results demonstrate that the synergistic effect of definition enhancement and constraint‑guided reasoning can significantly improve the Hits@1 metric (from 65.90 to 71.22), thereby providing a more fine-grained, interpretable new evaluation paradigm for multimodal semantic alignment.

Latent Coherence: Do Frontier Language Models' Probability Judgments Obey the Law of Total Probability?

arXiv:2610.31833v1 Announce Type: new Abstract: LawZero's Scientist AI trains a Predictor toward the Bayesian posterior, with an objective that penalizes inconsistent probabilities among latent explanations. We ask whether current frontier language models, with no such training, are already consistent in this sense zero-shot: do their probability judgments satisfy the law of total probability, $P(X)=\sum_z P(z)P(X|z)$, when the latent $z$ must be inferred from records in the context? In separate single-turn calls, models are asked for $P(X)$, for $P(z)$ over the latent's states, and for $P(X|z)$; a causal family applies the same test to $P(y|do(x))$ and the back-door adjustment. Every query has an exact ground-truth probability, and a subset of items is asked in three wordings to separate

Grok releases the first seven minutes of its AI-generated ‘Odyssey’

The footage looks okay, as long as you're not seeking artistry, inspiration, or human interest.

Google’s AI search feature deals a severe blow to the online publishing industry: traffic plummets, lawsuits abound, and compensation mechanisms are only just being explored.

In 2024, Google launched AI Overviews, and in May 2025 it rolled out AI Mode, causing a significant drop in search traffic from Google across several major media outlets—The New York Times saw its share of search traffic fall from 44% to 35.5% over three years. Among DCN members, 19 experienced traffic losses ranging from 1% to 25% in May–June 2025, with DMG Media reporting a peak decline of 89% in click-through rates. Publishers filed multiple lawsuits, but all were dismissed by U.S. judges; Google subsequently introduced an opt-out option, enhanced source attribution, and launched a news AI pilot involving 400 media organizations, along with a content‑paying test program.

Why it matters：The report systematically examines the structural impact of Google’s AI-powered search feature on the publishing ecosystem, covering a timeline (2024–2025), empirical data (from third-party institutions), responses from multiple stakeholders (publishers, regulators, courts, and nonprofit organizations), and recent countermeasures (opt-out mechanisms, source optimization, and licensing and paid-access pilots). The content is information-dense, timely, and policy-relevant.