Artificial intelligence

Artificial intelligence

Over the past decadeand especially in the last five yearsartificial intelligence has advanced at a breakneck pace. New AI applications appear daily. Nvidia, whose chips power this revolution, is now the world's most valuable company, and the seven giants known as Big Tech have become the economic engine of the United States. Some warn this boom could mirror the dot-com bubble, but few doubt the profound impact AI will have on human life in the decades ahead.

How many times have we heard executives tout AI as a thought partner, a collaborator, a productivity accelerator? Or as a catalyst for upskilling and repurposing people? And yet, we're already seeing evidence of this trend. In 2025 alone, UPS cut 48,000, Amazon eliminated 14,000 jobs, and Verizon announced plans to lay off 15,000 employees. The reason for all these cuts? While restructuring, the need for different skills, and business optimization are all frequently cited, increasingly AI is as well.

Last week, Google DeepMind made the Interactions API available as a public beta. The new API represents a fundamental change in how developers work with AI models: from stateless to a stateful architecture with server-side context management. With this move, Google is following the path that OpenAI embarked on in March 2025 with its Responses API. Over the past two years, developers have been working with generative AI via a so-called 'completion' model.

Founded in 2011, Chatterbox Labs focuses on AI security, transparency about AI activity, and quantitative risk analysis. The company's technology provides automated security and safety tests that generate risk metrics for enterprise implementations. This is an important piece of the puzzle in providing the necessary stability for the advance of AI. IDC predicts AI spending of $227 billion in the enterprise market by 2025, but scaling up pilots to production remains costly and complex.

Communication between vessels is one of the main challenges during the deployment of multi-domain, multi-vessel operations,

In January 2026, Wikipedia will turn 25 years old, a reminder that one of the internet's most‑visited sites began as an experiment many experts assumed would fail. It grew from a simple idea to more than 65 million articles in over 300 languages not by ignoring criticism, but by listening: adding quality controls, building policies, and keeping humans at the center of oversight and content creation.

So he scraped trip reports and psychological research on the effects of various psychoactive substances, wrote a batch of codes modules to hijack chatbot logic and get them to respond as if they are high or tipsy, then built a website to sell them. In October he launched Pharmaicy, a marketplace he's billing as the " Silk Road for AI agents" where cannabis, ketamine, cocaine, ayahuasca, and alcohol can be purchased in code form to make your chatbot trip.

Generating code using AI increases the number of issues that need to be reviewed and the severity of those issues. CodeRabbit, an AI-based code review platform, made that determination by looking at 470 open source pull requests for its State of AI vs Human Code Generation report. The report finds that AI-generated code contains significantly more defects of logic, maintainability, security, and performance than code created by people.

Under the agreement, Hut 8 will develop between 245 megawatts (MW) and up to 2,295 MW of AI-focused data center capacity in the United States, beginning with a flagship project at its River Bend campus in Louisiana. The partnership is structured across multiple tranches, creating a pathway to scale from an initial deployment to gigawatt-level infrastructure over time. The first phase centers on a 245 MW IT deployment at River Bend, supported by roughly 330 MW of utility power.

When the company reported Q3 earnings on Nov. 3, it beat on the top and bottom lines with EPS of 21 cents versus 17 cents expected, and revenue of $1.18 billion versus $1.09 billion expected. Palantir issued strong guidance, attributing growth to adoption of its AI software platform. Meanwhile, it announced that government sales - which have been essential to Palantir's rise - grew 52% from the same quarter a year ago.

The appointment comes at a time when web browsers are seeing a revitalization of sorts as AI changes how people use the internet. After more than a decade of dominating the market, incumbents like Firefox, Google Chrome and Apple's Safari are facing a fresh challenge from companies like Perplexity, Arc, OpenAI, and Opera, which are focused on baking in AI models and agents into their browsers to bring AI to users at the first point of contact with the internet: the web browser.

Google is testing extremely long and expandable search result snippet descriptions in the search results. These are also AI-generated snippets, which we've seen before, but not this long. This goes on for eight lines of text. This test was spotted by Brodie Clark who posted a video of it on X and on SERPAlerts - here is a GIF of that video:

In a post on X, the Gemini team this team announced that its app now gives local search results in a "rich, visual format," showing photos, tips for visiting, review summaries, ratings, and other important information from Google Maps. Also: Gemini vs. Copilot: I compared the AI tools on 7 everyday tasks, and there's a clear winner Previously, if you used Gemini to find this sort of information, you got mostly text results.

The Turing test, a long-established tool for measuring machine intelligence, gauges the point at which a text-generating machine can fool a human into thinking it's not a robot. ChatGPT passed that benchmark earlier this year, inaugurating a new technological era, though not necessarily one of superhuman intelligence. More recently, however, artificial intelligence passed another threshold, a kind of Turing test for the eye: the images and videos that A.I. can produce are now sometimes indistinguishable from real ones.

While I was working the other day organically, I opened up one of my favorite AI tools and had an unprompted thought to myself about how grateful I was to have access to this application. That realization then made me think about how many apps have now become an integral part of my everyday workflow, and at this point, many have made the list.

Alphabet's third-quarter results showed that this shift is underway. The company's total revenue in the period rose 16% year over year to $102.3 billion. But Google Cloud revenue jumped 34% to $15.2 billion, and the segment's operating income surged 85% to $3.6 billion. Additionally, Alphabet's cloud backlog climbed 46% year over year to $155 billion, reflecting multi-year commitments as customers reserve capacity for AI workloads.

OpenAI CEO Sam Altman is now maybe taking a page out of Elon Musk's and Trump's playbook, and posting a cheeky AI-generated photo of himself as a sexy calendar firefighter to Xitter. In what's apparently intended to show off the capabilities of Images 1.5, the new image-creation module in ChatGPT, OpenAI's Sam Altman posted the example below. The embedded version cuts off his head, but click through and you'll see it's Sam.

The DeepMind cofounder and CEO said in an episode of "Google DeepMind: The Podcast" published Tuesday that there are likely "bubbles" forming in today's AI funding frenzy, particularly among early-stage startups raising money at huge valuations. Some startups "basically haven't even got going yet," he said, yet are raising at "tens of billions of dollars valuations just out of the gate."

A decade ago, becoming an engineer who specialized in artificial intelligence meant having a Ph.D. and working in a research lab. Not anymore. The landscape has fundamentally shifted, and your goal to how to become a machine learning engineer is more achievable - and strategically vital - than ever.

Basware recently introduced InvoiceAI. This is an agentic addition to the AI that the company already uses to process invoices. A concrete example of the usefulness of InvoiceAI is that it not only identifies invoices without a PO number, but also interprets them (i.e., understands what they are for) and makes suggestions for the correct number based on previous invoices from the same party.

For many, enterprise AI adoption depends on the availability of high-quality open-weights models. Exposing sensitive customer data or hard-fought intellectual property to APIs so you can use closed models like ChatGPT is a non-starter. Outside of Chinese AI labs, the few open-weights models available today don't compare favorably to the proprietary models from the likes of OpenAI or Anthropic. This isn't just a problem for enterprise adoption; it's a roadblock to Nvidia's agentic AI vision that the GPU giant is keen to clear.

Last week at AWS re:Invent, amid many product announcements and cloud messages, AWS introduced AWS AI Factories. The press release emphasizes accelerating artificial intelligence development with Trainium, Nvidia GPUs, and reliable, secure infrastructure, all delivered with the ease, security, and sophistication you've come to expect from Amazon's cloud. If you're an enterprise leader with a budget and a mandate to "do more with AI," the announcement is likely to prompt C-suite inquiries about deploying your own factory.