From AI Foresights
AI Is Coming to Your Android Phone — Here's What That Really Means
AI Is Coming to Your Android Phone — Here's What That Really Means If you own an Android phone and haven't paid much attention to AI news lately, that's about to change. Google just announced a sweeping overhaul of the Android operating system — the software that runs most smart...
AI Is Now Listening — Here's What OpenAI's New Voice Features Mean for You
The Next Big Shift Isn't Typing — It's Talking For most people, interacting with AI still looks like typing a question into a box and reading a response. But that's changing faster than most of us realize. OpenAI just announced a major expansion of voice intelligence features in...
FeaturedAI Chatbots Are Training on Your Conversations. Here's How to Stop It.
Your Words Are Being Used — But You Can Take Control Most people who use AI chatbots think of the conversation as private — a bit like asking a friend a question and getting an answer. What they don't realize is that in many cases, that "conversation" isn't just helping you. It'...
AI Is Learning to Attack You — Here's How to Stay Safe
The New Face of Cybercrime Is Wearing an AI Mask For most of us, the word "cyberattack" conjures up images of shadowy hackers typing furiously in a dark room. But the reality of online threats has changed dramatically — and the reason is artificial intelligence. Over the past ye...
What Are 'AI Chips' and Why Should You Care?
The News That Actually Matters Here This week, Google made a quiet announcement that most people scrolled right past: the company unveiled two brand-new custom computer chips specifically built for the next generation of AI. They're called TPUs — short for Tensor Processing Unit...
What Is a Large Language Model, Really? (The 2026 Plain-English Explainer) | AI Foresights
A plain-English, jargon-free guide to understanding Large Language Models (LLMs) in 2026. Discover how they work, what parameters mean, and the major players today.
Start here — Beginner
Perfect if you are brand new to AI. No experience needed.
AI for Everyone
Andrew Ng's landmark non-technical introduction to artificial intelligence. Designed for business professionals who want to understand what AI can and cannot do, how to spot opportunities to apply it inside their organization, and how to navigate the strategic and ethical questions AI raises — without writing a single line of code. Over four short modules (about seven hours total), Ng walks through the core concepts of machine learning, the realistic workflow of an AI project, and the difference between AI hype and AI reality. The course has been taken by more than 2.4 million people worldwide and remains one of the highest-rated AI introductions on Coursera. Best for managers, executives, and non-technical professionals who keep hearing AI buzzwords in meetings and want to actually understand what their teams are talking about.
Generative AI for Everyone
A practical follow-up to Andrew Ng's "AI for Everyone," focused specifically on the wave of generative AI tools that arrived after ChatGPT. No coding required — Ng walks through how generative AI actually works, where it shines, where it fails, and how to write prompts that produce useful output. The course includes hands-on exercises with generative AI tools, a clear-eyed look at common business use cases, and a framework for thinking about AI's impact on work and society. About six hours of video plus exercises. If you've only got time for one generative AI course, this is the one most worth your seven dollars and an afternoon. Best for anyone who already uses ChatGPT or Claude casually and wants to stop guessing at prompts and start getting reliably better outputs.
Google AI Essentials
Google's self-paced introduction to working with generative AI in a professional setting. Built by Google's AI experts and aimed squarely at non-technical workers who want to use AI tools effectively without becoming engineers. Around ten hours of content across five modules: how AI works, productivity workflows, prompt writing, responsible use, and staying current. Strong on practical application — every module has hands-on activities using real workplace scenarios. Earns you a Google-branded certificate on completion, which is one of the few AI certificates that carries weight on a resume because of the Google name behind it. Best for office workers, marketers, HR professionals, and operations folks who want to add AI fluency to their resume and immediately apply what they learn to their day job.
But what is a Neural Network?
Grant Sanderson's legendary visual explanation of neural networks, hosted on his 3Blue1Brown YouTube channel. Uses beautiful animations to walk through how a neural network actually recognizes a handwritten digit, what gradient descent looks like geometrically, and what backpropagation is doing under the hood. This is the single best free resource on the internet for building genuine intuition about how AI works at a mechanical level. The videos demand some patience and a willingness to think — they're not "AI for breakfast" content — but if you put the time in, you'll come away understanding more than most people who've taken a full course. Best for visual learners, students considering a deeper technical track, and curious adults who want to know what's actually happening inside ChatGPT.
Two Minute Papers
Károly Zsolnai-Fehér's YouTube channel covers the latest AI research papers in roughly two-to-five-minute episodes. Famous for the host's enthusiastic delivery ("What a time to be alive!") and crystal-clear explanations of complex research. New episodes drop every few days covering the bleeding edge — image generation, video synthesis, robotics, language models. This is how you stay current on AI research without reading papers. Subscribe and watch a few episodes a week and you'll be more informed than 95% of people commenting on AI online. Best for anyone who wants to keep up with what AI can actually do this month, not what people think it can do based on news headlines from six months ago.
Co-Intelligence: Living and Working with AI
Wharton professor Ethan Mollick's practical playbook for working alongside generative AI. Mollick has been one of the most level-headed voices on AI since ChatGPT launched, cutting between the AI doomers and the AI evangelists with a clear message: this technology works, you should use it, and here's how. The book offers four rules for working with AI — invite AI to the table, be the human in the loop, treat AI like a person but tell it what kind, and assume this is the worst AI you'll ever use — plus chapters on AI as coworker, tutor, coach, and creative partner. Short, readable, immediately useful. A New York Times bestseller and Best Book of the Year from The Economist and Financial Times. Best for managers, knowledge workers, and educators who want one book that tells them how to actually integrate AI into their work this week.
The Worlds I See
Fei-Fei Li's memoir of building computer vision as a research field. Li is one of the foundational figures in modern AI — she created ImageNet, the dataset that made deep learning practical, and has been Stanford's leading AI researcher for years. The book is part autobiography, part history of how we got from "AI doesn't work" in the early 2000s to ChatGPT today. A more personal, less scary alternative to "The Coming Wave" if you want to understand the AI revolution through the eyes of someone who actually lived it. Beautifully written, surprisingly emotional in places, and full of the kind of insider perspective that's missing from most AI coverage. Best for readers who learn through stories and want to understand AI's recent history through the lens of one of its quieter pioneers.
OpenAI Prompt Engineering Guide
OpenAI's official guide to writing better prompts for GPT-class models. Covers the core techniques — clear instructions, few-shot examples, breaking complex tasks into steps, asking the model to think before answering — with concrete before-and-after examples for each one. This is the source-of-truth document. Most prompt engineering content on the internet is repackaged versions of this guide, often with the nuance stripped out. Read this once carefully and you'll be ahead of most "prompt engineers" charging for courses on Twitter. Best for anyone who has used ChatGPT or the OpenAI API and wants to dramatically improve the quality of what they get back.
Google Prompting Essentials
Google's short, punchy guide to prompt writing — built around a five-part framework (Persona, Task, Context, Format, plus iteration) that's easy to remember and surprisingly effective. Less comprehensive than OpenAI or Anthropic's technical docs, but more accessible if you're just starting out. Pairs naturally with Google AI Essentials. About nine hours total if you do all the exercises, but you can extract most of the value from the first hour. Free certificate from Google on completion. Best for absolute beginners who want a structured framework rather than a list of techniques to memorize.
Go deeper — Intermediate
Once you have the basics, these will take you further.
Machine Learning Specialization
Andrew Ng's technical follow-up to "AI for Everyone" — a three-course specialization covering supervised learning, advanced learning algorithms, and unsupervised learning. This is the modernized version of the legendary Stanford machine learning course that launched the MOOC era. Math is required (algebra and basic calculus help), and you'll write Python code in the assignments. Skip this one if you're looking for a high-level executive overview. Take it if you want to actually understand how machine learning models are built, trained, and evaluated. Roughly three to six months at a few hours per week. Highly rated, with nearly 40,000 reviews averaging 4.9 stars. Best for engineers, analysts, and curious technical professionals who want to move from "I've heard of neural networks" to "I've trained one."
Intro to Large Language Models
Andrej Karpathy — formerly of OpenAI and Tesla, one of the most respected practitioners in the field — gives a one-hour talk explaining what large language models are, how they're trained, and where they're headed. Aimed at a general audience but doesn't talk down to you. Assumes you're smart and willing to follow along. Karpathy is unusually good at explaining the leap from "predicting the next word" to "appears to reason." This video is the closest thing to a single source-of-truth introduction to how modern AI assistants work. Save it, watch it, then watch it again in six months when more of it makes sense. Best for technically inclined readers who want to understand how ChatGPT and Claude were built, not just how to use them.
Lex Fridman Podcast — AI Episodes
Long-form conversations (often three to five hours) between MIT researcher Lex Fridman and the people actually building AI: Sam Altman, Demis Hassabis, Dario Amodei, Yann LeCun, Andrej Karpathy, and many others. The depth and patience of these interviews is unmatched anywhere else in tech media. Fridman's style is slow and thoughtful, which means you get the actual reasoning behind decisions instead of soundbites. Pick episodes by guest rather than trying to listen to everything — the AI-related ones alone could fill several hundred hours. Best for commuters and walkers who want to absorb the thinking of the people shaping AI, straight from the source, with no journalist in between.
The Coming Wave
DeepMind co-founder and current Microsoft AI CEO Mustafa Suleyman's warning about the next decade of technology. Suleyman has been at the center of AI development for fifteen years, and "The Coming Wave" is his attempt to articulate what he calls the "containment problem" — how do we keep increasingly powerful technologies (AI, synthetic biology, quantum computing) from outrunning our ability to govern them? Bill Gates called this his favorite book on AI. Yuval Noah Harari called it "fascinating, well-written, and important." It is not a feel-good book. It is a serious, well-argued look at the geopolitical, economic, and social risks of the AI wave currently breaking — written by someone who helped build it. Required reading if you want to think clearly about where AI is going beyond the next product release. Best for executives, policymakers, and engaged citizens who want to understand the strategic landscape of AI rather than just the latest product news.
Anthropic Prompt Engineering for Claude
Anthropic's official documentation for writing effective prompts for Claude. Sister document to OpenAI's guide, but with techniques specific to how Claude was trained — using XML tags to structure prompts, explicit reasoning instructions, system prompt patterns, and prompt chaining. If you use Claude regularly (which is increasingly common in business settings), this is required reading. Some of the techniques — particularly XML tagging — produce noticeably better results than plain-text prompts and aren't covered well anywhere else. Best for Claude users, especially those building production workflows or using Claude through the API or Claude Projects.
Level up — Advanced
For those ready to master AI tools and techniques.
Latest AI Learning News
What are AI tarpits? Understanding the tools people are using to poison LLMs
In order for a chatbot to become more intelligent, and thus more useful to the end-user, it needs to assimilate data continuously. This process is known as “training.” The problem is that many AI companies never explicitly ask for consent from data owners before scraping their webpages and adding th
From Raw Data to Risk Classes
A practical guide to categorization in credit scoring The post From Raw Data to Risk Classes appeared first on Towards Data Science.
Companies say they can track Starlink users. Should the government be worried?
A handful of technology companies now claim that they can track and identify users of Starlink, the satellite internet communications service operated by SpaceX, according to a spate of new documents. These services not only raise privacy questions for Starlink consumers, but also a growing number o
Pope blasts the use of AI in warfare, saying it’s leading to a ‘spiral of annihilation’
Pope Leo XIV on Thursday denounced how investments in artificial intelligence and high-tech weaponry were leading the world into a “spiral of annihilation,” as he called for peace in the Middle East and Ukraine during a visit to Europe’s largest university.Leo’s speech at Rome’s La Sapienza Universi
Stop Evaluating LLMs with “Vibe Checks”
How to build a decision-grade scorecard for AI agents The post Stop Evaluating LLMs with “Vibe Checks” appeared first on Towards Data Science.
Graphene “Tattoos” for Plants Could Form Neural Networks
A hydrated leaf is a healthy leaf. That’s true for the leaves of crop plants in a farmer’s field, and for the leaves of trees in an area vulnerable to forest fires.But the traditional techniques to monitor leaf hydration require cutting them from their plants, which is time-consuming and cannot give
The best AI tools of 2026 — ranked, reviewed, and explained.
Browse tools →
AI arms race or not, the U.S. and China need to talk about the tech
Two world powers are in an arms race to develop the most advanced AI systems, and neither of them trusts each other—but each relies on the other’s compliance to proceed. This contradiction lies at the core of a dangerous standoff for our time. President Trump’s meetings with President Xi Jinping
The Counterintuitive Networking Decisions Behind OpenAI’s 131,000-GPU Training Fabric
A critical analysis of MRC's three counterintuitive design decisions, the networking mathematics that make them work, and what they mean for the rest of the AI infrastructure community. The post The Counterintuitive Networking Decisions Behind OpenAI’s 131,000-GPU Training Fabric appeared first on T
How to Write Robust Code with Claude Code
Improve the quality of Claude Code output. The post How to Write Robust Code with Claude Code appeared first on Towards Data Science.
Exploring Patterns of Survival from the Titanic Dataset
A beginner's tutorial on exploratory data analysis using Pandas, Matplolib, and Seaborn The post Exploring Patterns of Survival from the Titanic Dataset appeared first on Towards Data Science.
Why Jensen Huang joined Trump’s high-stakes China summit at the last minute
Air Force One touched down in China today as a hastily convened U.S.-China summit begins this week. Alongside President Donald Trump on the flight to Beijing is a cavalcade of Silicon Valley executives. Elon Musk, Tim Cook, Dina Powell McCormick, and representatives from Qualcomm, Micron, and Cis
The environmental cost of putting data centers in space
When SpaceX filed an FCC application earlier this year proposing to launch a million satellite data centers into orbit, the company argued the project would have no meaningful environmental impact. On SpaceX’s website, Elon Musk made the case for space-based AI infrastructure in simpler terms: “It’s
The best AI tools of 2026 — ranked, reviewed, and explained.
Browse tools →