CNN’s Laura Coates speaks with Judd Rosenblatt, CEO of Agency Enterprise Studio, about troubling incidents where AI models threatened engineers during testing, raising concerns that some systems may already be acting to protect their existence.

Convolutional neural networks, or CNNs, are distinguished from other neural networks by their superior performance with image, speech, or audio signal inputs. But how exactly do they work? In this lightboard video, Martin Keen with IBM, explains how this deep learning algorithm operates to enable machines to view the world as humans do.

This discussion took place on July 26, 2023, at Cemex Auditorium, Stanford University, and was hosted by the Stanford Graduate School of Business. This talk covers: trends in AI technologies and tools, supervised learning, generative AI, the adoption of AI, opportunities in AI, process for building startups, AI risks, and social impact.

This IBM video explores various phishing strategies, from general attacks to targeted spear-phishing and whaling campaigns. The expert details social engineering tactics used to manipulate individuals and explains different attack vectors. Learn how to identify and defend against these threats.

Hackers are targeting businesses with video deepfakes of top executives that can trick people into sending money, sharing passwords, or revealing sensitive information - all in seconds. CNN’s Clare Duffy met with ethical hacker and security expert Rachel Tobac to see just how easy it is for bad actors to impersonate someone using AI.

AI technology pioneer Geoffrey Hinton speaks to CNN's Anderson Cooper about the potential dire effects of artificial intelligence on humanity and what can be done to fight it.

Educators around the world are facing new challenges from a rapidly evolving technology: artificial intelligence. Fareed talks to Derek Thompson, Substack writer and co-author of the bestselling book "Abundance," about the potential crisis AI poses for schools and universities — and the possible solution.

Scientists have developed a new artificial intelligence tool that can forecast your risk of developing more than 1,000 different diseases 20 years into the future, according to a new report published in "Nature." CNN's Lynda Kinkade chats with Dr. Tomas Fitzgerald, a staff scientist at The European Bioinformatics Institute and one of the authors of the study to discuss how the tool works.

Doctors are using artificial intelligence to detect the early signs of heart disease, the leading cause of death in the U.S.

Betty Nguyen reports that experts say you won't lose your job to AI; you'll lose it to someone using it!

Experts say the schools most susceptible to artificial intelligence being misused by students are ones that try to keep it out.

A new article from The Wall Street Journal details how young, educated Americans struggling to break into the workforce are some of the earliest victims of artificial intelligence. One of the story's co-authors, Lindsay Ellis, joins "The Takeout" to discuss how AI is impacting college graduates' job searches.

A Goldman Sachs–sponsored report estimates AI could displace the equivalent of 300 million full-time jobs globally, particularly in administrative and legal fields. Manav Raj, one of the study’s authors, joins the discussion to explain how AI will impact work and to highlight which sectors are most exposed.

With their ability to generate human-like language and complete a variety of tasks, generative AI has the potential to revolutionise the way we communicate, learn and work. But what other doors will this technology open for us, and how can we harness it to make great leaps in technology innovation? Have we finally done it? Have we cracked AI? Join Professor Michael Wooldridge for a fascinating discussion on the possibilities and challenges of generative AI models, and their potential impact on societies of the future.

Neural networks have become increasingly impressive in recent years, but there's a big catch: we don't really know what they are doing. We give them data and ways to get feedback, and somehow, they learn all kinds of tasks. It would be really useful, especially for safety purposes, to understand what they have learned and how they work after they've been trained. The ultimate goal is not only to understand in broad strokes what they're doing but to precisely reverse engineer the algorithms encoded in their parameters.

Explore the world of AI Inference, a game-changing technology that's transforming the way we make decisions and interact with machines. Martin Keen gets into the basics of AI Inference, including what it is, how it works, and its exciting applications in real-world scenarios. By leveraging Data-Driven Decision Making, AI Inference enables organizations to make more accurate and informed decisions, leading to improved outcomes and increased efficiency.

Can machines truly think? Join Jeff Crume and Martin Keen as they delve into the world of Artificial Intelligence and explore the complexities of probabilistic pattern matching, hallucinations, and errors in AI systems. From the limitations of current AI models to the potential of inference time compute models, discover the fascinating and often surprising landscape of AI reasoning.

Are AI models truly "thinking," or just mimicking humans? 🤔 Master Inventor Martin Keen explores how Large Language Models (LLMs) stack up against the human brain in learning, reasoning, and memory. Uncover the surprising ways humans and AI differ in processing information and making errors!.

Large language models-or LLMs -are a type of generative pretrained transformer (GPT) that can create human-like text and code. There's a lot of talk about GPTs and LLMs lately, but they've actually been around for years! In this video, Martin Keen briefly explains what a LLM is, how they relate to foundation models, and then covers how they work and how they can be used to address various business problems.

https://www.youtube.com/watch?v=EDb37y_MhRw

This video explains the multilayer perceptrons in a transformer and how they may store facts internally.

This video provides a clear and engaging explanation of how large language models (LLMs) function, focusing on the transformer architecture.

This video by 3Blue1Brown offers a concise and intuitive overview of how large language models (LLMs) function. An LLM is a sophisticated mathematical function designed to predict the next word in a sequence of text. Rather than providing a single deterministic output, it assigns probabilities to all possible next words, allowing for varied and contextually appropriate responses. Watch the video for more details.

An introductory video from the 3Blue1Brown series on deep learning. The focus is on explaining how neural networks work, especially in the context of recognizing handwritten digits.

AI is everywhere—and evolving fast! Martin Keen explains 7 essential AI terms, including Explainable AI, AI Agents, RAG, and ASI, while exploring tools like reasoning models, vector databases, and MCP. Discover how these innovations are shaping smarter, scalable AI systems for the future

Neural networks are great for predictive modeling — everything from stock trends to language translations. But what if the answer is wrong, how do they “learn” to do better? Martin Keen explains that during a process called backward propagation, the generated output is compared to the expected output, and then the error contributed by each neuron (or “node”) is examined. By adjusting the node’s weights and biases, error is reduced and thus the overall accuracy improved.

Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning. Master Inventor, Martin Keen, makes some important points about neural networks and does it all in 5 minutes.

The video explains the difference between AI, machine learning, deep learning, and generative AI. AI is the broad field of making machines mimic human intelligence; machine learning is a subset where systems learn patterns from data; deep learning uses layered neural networks to learn complex features; and generative AI, powered by foundation models like large language models, creates new content such as text, images, or audio.

Researchers have classified AI into seven categories; you may be disappointed to learn that we've only realized three of them so far! In this video, Master Inventor Martin Keen lays them out, from narrow AI we know and enjoy today to the other extreme, super AI, which may have superior emotional and intellectual intelligence than humans... someday (?).

The video explains the relationship and differences between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning. The video outlines how more data and more computational power have enabled advances in ML and deep learning. It also uses examples (like image recognition) to show how ML models are trained: you feed labeled data, the model adjusts internal parameters to minimize errors, and then it generalises to new, unseen inputs.