Artificial intelligence

Artificial intelligence

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. The massive, rapid adoption of AI across industries - from personalized retail recommendations to automated factory floors - has created an insatiable demand for people who don't just build models, but who can integrate them into real products.

Elon Musk's xAI is purchasing a third building for the Colossus data center complex in Memphis, Texas. The total capacity will grow to nearly 2 gigawatts. The new building will be given the playful name "MACROHARDRR," according to Musk. The new building is located next to Colossus 2 near Southaven, Mississippi. xAI wants to convert it into a data center in 2025, Musk writes on X.

Marvell ( NASDAQ:MRVL) is one of the hidden gems that is about to get more spotlight. The custom AI chipmaker's stock is down by more than 20% year-to-date, but its revenue and earnings have soared during this time. This mismatch, combined with insatiable demand for AI chips, suggests Marvell is an underrated AI stock. Marvell is one of the companies that produces custom AI chips for big tech. These chips can't handle every task under the sun like Nvidia's GPUs, but they address specific workloads at a much lower price.

This rapid change is largely due to the efficacy of AI tools and how they've tripled productivity. Just a few years ago, we were debating whether tools like GitHub Copilot were even reliable enough for basic autocomplete. Fast forward to today, and AI isn't just generating components; it's scaffolding entire full-stack applications, leading many to wonder if it might truly "take our jobs" in the future.

Programming with AI is still in its infancy, and yet it has already redefined what programming will be about in the immediate future. As a programmer, you are going to interact with a frigging robot that writes code on your behalf. What was science fiction yesterday is now a new reality, and it raises many questions. I won't pretend to have any answers here, just assorted thoughts.

Retrieval-augmented generation (RAG) has quickly become the enterprise default for grounding generative AI in internal knowledge. It promises less hallucination, more accuracy, and a way to unlock value from decades of documents, policies, tickets, and institutional memory. Yet while nearly every enterprise can build a proof of concept, very few can run RAG reliably in production. This gap has nothing to do with model quality.

This post is Part 1 of a series. In my previous post, I discussed the conundrum we face regarding artificial intelligence (AI) today: On one hand, we're told to use it or get left behind; on the other, we're warned about the "cognitive diminishment" that can result from that very use. I suggested the solution wasn't an uncritical embrace, nor an outright rejection. Yes, we need to learn to use AI. But the dilemma of cognitive decay remains.

With its new Pax Silica Declaration, Washington has picked its most trusted partners in the AI sector: An array of close U.S. allies, including Australia, the U.K., and Israel. Yet despite deepening trade relations between the U.S. and ASEAN nations like Thailand, Malaysia and Vietnam, Singapore remains the agreement's only Southeast Asian signatory. That decision comes even as ASEAN nations like Malaysia are investing in their own AI industries, like semiconductors and data centers.

All the way back in 2019, an article in The Wall Street Journal warned readers that AI has learned to write fake news stories. One of the tools highlighted in the article was GPT-2, a precursor to what we now know as the game-changing tech that is ChatGPT. Fast forward to 2025, and we now have AI integrated directly within the Google Search experience. Now, when you look up something on Google Search, you will see an AI Overview at the top of the page.

A typical AI-powered tool leverages the "knowledge" gained from previously processed image libraries. The more vivid these databases and the more additional information they include - like detailed descriptions of objects in pictures or other tags that may simplify visual object recognition- the better the final result a tool delivers. The origin of processed databases and the legal safety of generated images are other critical issues regarding AI-powered tool usage.

Sam Altman reportedly issued an internal "code red" at OpenAI in December 2025 - an emergency directive to focus resources on improving ChatGPT whilst delaying initiatives like advertising or Pulse triggered by Google's launch of Gemini 3. The flagship product that triggered the current AI frenzy, deployed to hundreds of millions of users, needed an internal alarm bell. Not because it had fundamentally failed.

The deal is sure to turn heads too. Manus and its parent company Butterfly Effect are now based in Singapore but were founded in China - a country with a fraught relationship to the U.S tech industry - and maintain operations there. Facebook's parent company will reportedly pay more than $2 billion to acquire the startup, which it hopes will bolster its own lagging AI capabilities.

Neuro-symbolic Artificial Intelligence (NSAI) denotes a research paradigm and technological framework that synthesizes the capabilities of contemporary Machine Learning, most notably Deep Learning, with the representational and inferential strengths of symbolic AI. By integrating data-driven statistical learning with explicit knowledge structures and logical reasoning, NSAI seeks to overcome the limitations inherent in either approach when used in isolation. Symbolic: Logic, Ontologies. Neural Networks: Structure, Weights.

After the Cataclysm, the humans brought in robots to clear the rubble. It was why the robots had been constructed. They were sturdy enough to withstand any further tremors and falling debris, and they were strong enough to lift the shattered pieces of buildings. Twobit worked tirelessly, like their fellow robots. Solar panels kept them energized, and the engineers had developed circulatory systems to keep their joints lubricated by filtering elements from the air and remixing them, the peak of intelligent design.

The modern internet is less interested in demanding attention than in simply occupying it. Adavia Davis understands that better than perhaps anyone else. Since dropping out of Mississippi State University in 2020, the 22-year-old has built a thriving content-creation business out of what has come to be called "slop"- that high-volume, AI-generated background noise that thrives in the gaps of our focus. Davis' most successful videos aren't meant to be watched, shared, or even remembered. Often, Davis told Fortune, his viewers are asleep.