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Fox News
23-05-2025
- Fox News
5 AI terms you keep hearing and what they actually mean
Whether it's powering your phone's autocorrect or helping someone create a new recipe with a few words, artificial intelligence (AI) is everywhere right now. But if you're still nodding along when someone mentions "neural networks" or "generative AI," you're not alone. Today I am breaking down five buzzy AI terms that you've probably seen in headlines, group chats or app updates, minus the tech talk. Understanding these basics will help you talk AI with confidence, even if you're not a programmer. Stay tuned for more in this series as we dive deeper into privacy-related tech terms and other essential concepts, answering the top questions we get from readers like you. The big umbrella term Artificial Intelligence is a broad term for computer systems that can do tasks normally requiring human intelligence. That includes understanding language, recognizing images, making decisions and even learning from experience. You're using AI when: Think of AI as the category; everything else on this list is a branch of it. It's the foundation for all the smart tools we use today, from voice assistants to facial recognition. As AI continues to evolve, it has the power to make everyday tasks easier, faster and more personalized. But as it becomes more embedded in our lives, understanding the basics is key to using it wisely and protecting your digital autonomy. How AI learns patterns Machine Learning is a type of AI that learns from data instead of being explicitly programmed. It improves over time by finding patterns and making predictions. For example: You like action movies. You watch a few. Over time, the algorithm learns your preferences and recommends similar titles, even if you didn't say anything directly. Common uses of ML: ML is how AI "gets smarter" by itself, and it's a big part of how tech becomes more helpful and intuitive. From catching suspicious charges on your credit card to curating your favorite music, machine learning can make life more seamless and even safer. But as with any technology, it's important to stay aware of how your data is being used and who's doing the learning. The more we understand how it works, the better we can decide how and when to trust it. The tech that mimics your brain Neural Networks are a special kind of machine learning designed to mimic how the human brain works, at least loosely. They're made up of layers of "neurons" that process data and make decisions. They're particularly good at recognizing complex patterns, like identifying faces in photos or translating languages. Use cases include: If AI is the brain, neural networks are the brain cells doing the work. Neural networks are the part of AI that actually processes information. They're designed to mimic how human brains work, taking in data, learning patterns and making decisions. So, when AI recognizes a face, writes a sentence or makes a suggestion, it's neural networks making that happen behind the scenes. AI that creates, not just predicts Generative AI doesn't just analyze data, it creates new stuff: text, images, videos, code, music, even voices. It's trained on huge amounts of content and learns how to generate something new that mimics the original. You've seen it in action if you've used: It's like giving a machine a vibe and watching it invent something that fits. Generative AI is creative, fast and sometimes uncannily realistic, which is what makes it both exciting and a little unsettling. Think you can tell the difference?Be sure to play my game to guess which photo is AI and which one is real. It's harder than you think and a good reminder that as these tools get more advanced, staying alert and informed is more important than ever. The magic words that make AI work A prompt is the input you give to an AI system, usually a question, command or description. It's how you talk to tools like ChatGPT or image generators. The better your prompt, the better the result. Examples: Prompts are to AI what questions are to Google, but with more creativity and conversation. Unlike a search engine that simply points you to existing content, AI can generate entirely new ideas, images and text based on what you ask. It's more than a search box; it's a creative tool. Whether you're drafting a story, designing a logo or planning a vacation, learning how to prompt effectively lets you tap into AI's full potential as a collaborator, not just an information source. You don't need a computer science degree to understand AI, just a few solid definitions. From machine learning and neural networks to generative AI and prompts, these tools are no longer reserved for tech labs; they're becoming part of your everyday life. Whether it's helping you write an email, organize your photos or get dinner ideas based on what's in your fridge, AI is already working behind the scenes to make life a little easier (and sometimes a lot more interesting). Now that you've got the lingo down, you'll be better equipped to navigate the AI-powered world with confidence and curiosity. Want to go deeper? Interested in how AI can improve your daily routine or looking for creative prompt ideas to get the most out of tools like ChatGPT? Let us know by writing us at For more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Follow Kurt on his social channels: Answers to the most-asked CyberGuy questions: New from Kurt: Copyright 2025 All rights reserved.
Forbes
19-05-2025
- Entertainment
- Forbes
4 New Creative Directions For AI
AI and creativity getty We all know that AI is changing enterprise in dramatic ways right now. But there are so many vectors of this progress that it's hard to isolate some of the fundamental ideas about how this is going to work. I wanted to highlight a survey of ideas on new breakthrough research and applying neural network technologies. These come from a recent panel at an Imagination in Action event where innovators talked about pushing the envelope on what we can do with this technology as a whole. These insights, I think, are valuable as we look at the capabilities of what we have right now, and how companies can and will respond. To a large extent, past research in AI has been focused on working with text. Text was the first sort of data format that became the currency of LLMs. I would say that happened for a number of reasons, including that text words are easy to parse and separate into tokens. Also, text was the classical format of computing: it's easier to build systems to work with text or ASCII than to work through audio or video. In any case we're now exploring boundaries beyond text, and looking at how other data formats respond to AI analysis. One of these is audio. 'We certainly have text banks, but we haven't even scratched the surface on sound and decoding our voices,' said Joanna Pena-Bickley of Vibes AI, in explaining some of what her company is doing with wearables. 'Bringing these things together is really about breaking open an abundant imagination for creators. And if we can use our voices to be able to do that, I think that we actually stand a chance to actually (create) completely new experiences, sensual experiences.' It's great for auditory learners, and we may be able to do various kinds of diagnosis and problem-solving that we didn't before. Pena-Bickley explained how this could help with cognitive decline, or figuring out people's personal biological frequencies and how they 'vibe' together. In the world of gaming, too, scientists can apply different kinds of AI research to the players themselves. In other words, traditional gaming was focused on creating an entertainment experience, but as people play, they generate data that can be useful in building solutions beyond the gaming world. 'We are trying to create dynamic systems, systems that respond to players,' said Konstantina Yaneva, founder of Harvard Innovation Labs. 'So we map … cognitive science, game design, and then (use) AI-driven analytics to help map and then improve decision-making patterns in players. But this is also a very creative endeavor of how to collaborate with consumers of entertainment, and how to meet them where they are, and then to help them self-realize in some way.' Another area of pioneering is extended reality: in addition to AR and VR, XR is a technology that seems like it's due to have its day in enterprise. It's always been time consuming, difficult and hard to keep up in terms of your teaching or if you're doing research,' explained renowned multi-technology artist Rus Gant, who has some ties to MIT. 'So the idea is to use AI as a way to create content in near real time.' In addition, Gant talked about various aspects of applying AI, and in thinking out of the box, which could be interesting for anyone trying to meld AI with the humanities. Here's another part of how companies are using the agentic AI approach that's only developed in the last few years. Noting that agentic is now a big 'buzzword,' Armen Mkrtchyan of Flagship Pioneering talked about the practice of applying this concept to science. It basically has to do with understanding the patterns and strategies of nature, and applying them to the digital world. This starts with the idea of analyzing the human brain and comparing it to neural nets. But it can go far beyond that – nature has its own patterns, its own cohesion and its own structures. Mkrtchyan talked about how scientists can use that information to simulate things like proteins. He also mentioned that the company has a stealth project that will be revealed in time, that's based on this kind of engineering. Generally, he says it's possible to create a system that's sort of like a generative adversarial system (this is my reference, not his) where some AI agents come up with things, and others apply pressure to a decision-making process. 'About two years ago, we asked ourselves how we could try to mimic what nature does … nature is very good at creating new ideas, new things, and the way it does (this), it creates variability, creates optionality, and then applies selective pressure. That's how nature operates. And we thought at the time that we potentially AI agents, we could try to mimic the process … think of biology asking, can you create a specific peptide that binds with something else? Can we create an RNA molecule? Can it create a tRNA molecule?' Expanding on this, Mkrtchyan noted some of the possible outcomes, with an emphasis on a certain kind of collaboration. if we can say that intelligence consists of nature's intelligence, humans, intelligence, machines, intelligence, then can we leverage the power of all three of them to come up with new ideas, new concepts, and to drive them forward? I also wanted to include this idea from Gant about audiences. Our audiences are changing, and we should be cognizant of that. We have Generation X, the sort of 'bridge' generation to AI, and then we have AI-native generations who have never known a world without these technologies. 'There is a very distinct difference when you come down the line from the Boomers to Gen X to Millennials to Gen Z, Gen Alpha, these are different groups,' Gant said. 'They think differently. They absorb information differently. They're stimulated in different ways, as to what excites them and gets them motivated. And there's no one size fits all. And right now, I think there's a big danger, and in the AI world, particularly when you productize AI, to go for the largest number of customers, and sort of leave the margins alone, because there's no money there. I think… the students that I have (who are) most responsive are basically the Gen X and soon to be Gen Alpha, that they basically look at you and say, 'Why didn't you do this sooner? Why did you do it in a way that doesn't make any sense to me?' I think in a sort of multiplex way. I multitask, I take input in various ways … we don't know what they're going to do with this. Whether it's Millennials or Gen Z or Gen Alpha, they're going to do really interesting things, and that's why I'm interested in how we can work with the AI in its non-traditional role, the solutionization world, where it's thinking outside the box.' All of this is interesting information coming out of the April event, and reflects a survey of what companies can do now that they were not able to do in the past. Check out the video.
