🚀 The AI Revolution Nobody's Talking About: How Diffusion Models Could Change Everything

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In this eye-opening episode of Heliox: Where Evidence Meets Empathy, we dive deep into the technology that's silently reshaping AI as we know it: diffusion language models (DLLMs). While most people are fixated on ChatGPT's latest features, Inception Labs has been developing something far more revolutionary – Mercury, a family of AI models that could make current large language models obsolete.

Unlike traditional models that generate text one painful token at a time, DLLMs work like digital sculptors, simultaneously refining entire blocks of content. The result? AI that's 5-10x faster, dramatically smarter, and capable of correcting its own mistakes in real-time.

This isn't just another incremental tech improvement. Mercury represents a fundamental shift in how AI "thinks" – enabling more natural reasoning, reducing hallucinations, and potentially democratizing access to powerful AI through edge computing. But as with any transformative technology, Mercury's DLLMs raise profound questions about jobs, creative ownership, and societal impact.

Listen as we explore the promise and peril of what might be the most significant AI breakthrough since deep learning itself.

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Transcript

0:24

All right, so get this. Imagine an AI that can write like you and me, like in whole thoughts, and it can correct itself as it goes.- Whoa, that's wild.- That's the big idea behind diffusion language models. We call them DLLMs for short. And it's causing a huge buzz because they're super fast and smart.- Yeah, I've heard a lot about them.- Yeah, so today we're gonna do a deep dive into Mercury, which is Inception Lab's brand new family of these DLLMs.- Cool.- You sent over some excerpts from their website.- Yeah.- And let me tell you, some of their claims about speed and performance are pretty mind-blowing.- Yeah, and what I think is so fascinating about this is that this technology, it could change the game for anyone who uses AI.- Right.- Like from developers building cutting edge apps to everyday users like you and me.- Absolutely, okay, so let's unpack this a little bit.- Yeah.- Most of today's large language models, the ones that can like write and chat, are built on something called an autoregressive model.- Right. - What's that even mean?- So it means they generate text one tiny piece at a time, kind of like if you were typing out a sentence letter by letter.- Okay.- And while that works, it can be incredibly slow and expensive for the AI to do that, especially when you need it to do really complex tasks.- Yeah.- Like imagine writing a novel one letter at a time.- Oh my gosh.- It would take forever.- Yeah, you'd never finish.- Yeah.- So we need a totally new approach.- Totally.- And we've gotta make high quality AI that's accessible and actually useful.- Yeah.- So that's where these diffusion models come in. They're like a breath of fresh air for the AI world.- I like that.- Instead of like slowly going word by word, diffusion models work more like an artist sculpting a masterpiece.- Oh, that's a good way to put it.- Right, so they start with a jumbled mess.- Okay.- Like you know, a static filled image. And then step by step, they refine it, you know, chiseling away at the noise until this clear picture emerges.- I see.- And this course defined process, it's already taken the world by storm for image video and audio generation. Like think about the amazing things that are happening with AI image generators like Mid Journey.- Yeah, exactly.- But applying this to text now, that has been a real challenge.- I appreciate it.- And that's where Inception Labs says that their DLLMs like Mercury are really changing the game. They're not held back by that slow step by step approach. They can generate whole chunks of text at once.- Right.- And that leads to better reasoning and a more natural flow.- It's not just faster, it's smarter too, right?- Yeah.- They can actually correct their own mistakes as they go.- Whoa.- Which reduces those like weird moments where the AI spits out nonsensical stuff we call those hallucinations.- Yes.- Imagine an AI writer that can fact check itself in real time.- That's wild. Okay, so it's like having a built in editor, making sure everything is coherent and accurate.- Yeah.- But let's get down to like, you know, the brass tacks here.- Yeah.- How does that translate to real world applications?- So Inception Labs has released Mercury Coder, which is their first publicly available DLLM. And it's specifically for generating code.- Okay.- And this is where things get really interesting for developers.- Right, so they're claiming this speed boost of five to 10 times over current LLMs.- Yeah.- Hitting speeds of over 1000 tokens.- Wow.- You know, those chunks of text per second on those powerful Nvidia H100 chips.- Yeah, so that's a level of performance that used to require specialized and expensive AI chips.- Right.- And now it's achievable on more common hardware.- Wow.- It's like suddenly being able to run high end gaming software on your everyday laptop.- So this Mercury Coder is like a supercharged coding assistant for developers?- Exactly, and the best part is it's designed to seamlessly integrate with the tools and workflows that developers are already using.- Okay.- So no need to reinvent the wheel, just plug in this new powerful engine and watch things take off.- So it's like swapping out, you know, your old car engine for a brand new electric motor.- Exactly.- You get a massive performance boost without having to rebuild the whole car.- That's what DLLMs can do for your existing AI systems.- Okay, but speed is only part of the story, right?- Right.- Does this thing actually perform better than what's already out there?- Well, Inception Labs is making some bold claims.- Okay.- They're saying that Mercury Coder outperforms the competition on all those like industry standard coding benchmarks.- Yeah, and they provided some data to back it up.- They did.- And it's pretty impressive.- Yeah.- Their tables show Mercury Coder consistently scoring higher on accuracy across like a range of tests.- And get this, their smaller model, Mercury Coder Mini.- Okay.- It's apparently beating out big names like GPT-40 Mini and Gemini 1.5 Flash while being significantly faster.- That's a serious flex.- Right.- So it's not just theoretical, they're putting numbers behind these claims.- Yeah.- But what about real world use? Are people actually adopting this technology?- It's still early days, but Inception Labs is saying that some big players.- Okay.- In fields like customer support cogeneration and enterprise automation are already jumping on board.- Wow.- These are companies on the cutting edge of technology.- Yeah.- And they're choosing DLLMs over the old way of doing things.- So they're replacing their existing AI models with these DLLMs and seeing improvements in user experience and cost reduction.- Exactly.- That's a pretty compelling argument for the technology.- It is.- It sounds like DLLMs are allowing them to use larger, more capable AI models without sacrificing speed or breaking the bank.- Absolutely. And Inception Labs is making it easier for developers to embrace this shift.- Okay. How so?- So they offer an API, think of it as a plug and play interface for accessing their models in the cloud and even options for running them on your own hardware.- Okay.- It's like choosing between getting takeout or cooking a gourmet meal at home.- Right.- You have options.- So whether you want the convenience of the cloud or the control of having things on your own servers, they've got you covered.- Exactly.- And crucially, these models are compatible with existing hardware data sets and even those specialized processes that developers use to train and fine tune AI models.- Right, they're essentially saying you don't have to throw out your existing AI infrastructure.- Right.- Just upgrade it with our DLLMs.- It's like getting a software update.- Exactly.- For your AI making it faster, smarter, and more capable.- I like that analogy.- They're really betting big on DLLMs being the future of AI.- They are.- But this Mercury coder is just the tip of the iceberg. Inception Labs has hinted at a whole family of DLLMs that are in the pipeline.- Yeah, they've got a chat application model that's currently in closed beta testing.- Oh, wow.- Which suggests that they're aiming to take this technology beyond just coding.- That's exciting. We've all seen how chatbots have just exploded in popularity.- Totally.- Imagine if those were powered by DLLMs.- Right.- It would be smarter, less prone to those weird glitches.- Exactly. And Inception Labs has outlined some pretty incredible possibilities for the future DLLMs. Things that could change how we interact with AI on a fundamental level.- This is where things get really interesting. Let's take a break to process what we've learned so far and come back to explore these fascinating possibilities.- Sounds good.- Thank you for being curious and subscribing, following, liking, rating, and reviewing our podcast episodes. Your support really helps build a vibrant Heliox community. Back to Heliox, where evidence meets empathy.- Welcome back. It's clear that you're as captivated by this technology as I am. What makes Mercury and DLLMs so compelling is that they're not just like a minor improvement.- Yeah.- They're a whole new way of thinking about AI and language.- Yeah, it's like we've been puttering around in a horse-drawn carriage and someone just handed us the keys to a sports car.- Right.- We're not just talking about going faster, it's a completely different experience.- That's a great analogy. Yeah, this shift to diffusion models could be like a turning point in AI history. Like remember when deep learning came along and just revolutionized the field?- Oh yeah, it was huge.- DLLMs have the potential to be just as disruptive.- Wow.- They could change not just how we build AI, but what we can even accomplish with it. The possibilities are truly mind-boggling.- Okay, so let's dive into some of those possibilities. Inception Labs keeps mentioning, you know, DLMs being perfect for agentic applications.- Right.- Can you break that down for us? What does that actually mean in the real world?- So imagine an AI system that can act on its own to achieve a goal. It can take in information, make decisions, and take action.- So like a self-driving car?- Exactly.- Right.- Or a robot that can navigate a warehouse.- Yeah.- Those are examples of agents.- Okay, so how do DLLMs fit into all of this?- So let's say you're building an agent that needs to come up with like complex plans or strategies. Traditional LLMs, with their like step-by-step approach, they would really struggle with this. It'd be like trying to plan a cross-country road trip by writing out every single turn and stop along the way one word at a time.- That sounds like a nightmare.- Exactly. But DLLMs, with their ability to generate chunks of text simultaneously and refine their output on the fly, could be much more effective at this kind of planning.- So they could like map out the whole trip in broad strokes and then fill in the details and make adjustments as needed?- Precisely.- Like how we plan in real life?- Exactly like how we plan in real life. And this ability to think in a more holistic way could lead to agents that are far more sophisticated and adaptable. Agents that can handle unexpected detours, learn from their mistakes, and even come up with creative solutions that we might not have even considered.- It's almost like giving these AI agents the ability to think ahead, anticipate problems, and plan accordingly so, instead of just reacting, they're strategizing.- That's a great way to put it.- Yeah.- And the speed of DLLMs is critical here. If an agent needs to react quickly in a dynamic situation, it can't wait around for a traditional LLM to slowly churn out its next move.- Right, it's like in a fast-paced game, you need to make split-second decisions. A slow AI just wouldn't cut it.- Exactly. So DLLMs could be the key to AI agents that are not just intelligent, but also incredibly responsive and adaptable.- So this opens up a world of possibilities for fields like robotics, automation, even personalized education or healthcare, where AI agents could provide tailored experiences based on each individual's needs.- Absolutely.- That's amazing. But hold on a second. If these DLLM-powered agents are so adaptable, are we opening Pandora's box here? How do we make sure they don't become too independent and make decisions that we might not agree with?- That's a crucial question. It highlights the importance of responsible AI development.- Right.- We need to ensure that these agents are aligned with human values and operate within clearly defined boundaries.- So it's not just about making AI smarter, it's about making it safer and more trustworthy.- Absolutely.- Inception Labs also talked about DLLMs improving reasoning and reducing those hallucinations we see in AI.- Yeah.- These are big issues that have been plaguing language models for a while. How exactly do DLLMs tackle these challenges?- Remember how we talked about traditional LLMs being limited by their step-by-step approach?- Yeah.- It makes it really hard for them to think through complex problems effectively. It's like trying to solve a multi-step math problem by only looking at one number at a time.- You'd be stuck on that problem forever.- Exactly. But DLLMs can look at multiple parts of the problem simultaneously, which leads to more advanced reasoning skills.- Okay, so it's not just about generating words faster, it's about enabling the AI to think logically and connect the dots in a more sophisticated way.- Exactly.- And what about those hallucinations, those times when AI just makes stuff up or gets things completely wrong?- Yeah, the self-correction ability of DLLMs is key here.- Yeah.- Remember, they can constantly refine their output like they have a built-in fact checker.- Uh-huh, so it's not just spitting out random thoughts, it's evaluating its own responses and making adjustments to make sure they're accurate and consistent.- Exactly, it's like having an AI editor that catches all the errors before they even reach you.- That's incredible. Okay, so DLLMs might be able to better make fewer mistakes, that's huge, but what about control?- Yeah.- Inception Labs said DLLMs could give users more control over what the AI produces. What does that actually look like?- Well, with traditional LLMs, it's often a case of like, you get what you get. You give it an input, it generates an output, but you have very little say in how that output looks or sounds.- It's like asking someone to write a poem, but you can't tell them what style to use or what themes to explore.- Exactly.- You just have to hope for the best.- You just have to hope for the best, but DLLMs have the potential to give users much finer control over the entire process.- So you could specify things like the tone of the text, the level of formality, or even like the specific words that you want the AI to include.- Precisely, and because DLLMs can edit their own output and generate text in any order.- Okay.- You could even have the AI insert or modify specific sections after it's already generated a draft.- Oh, wow, that's incredible. Imagine being able to like tweak an AI-generated document to fit specific style guidelines or legal requirements, or what about having the AI create like different versions of a text tailored to different audiences?- Exactly.- Like a simplified version for children and then a more technical one for experts. That would be amazing.- Absolutely. This level of control could be transformative for fields like content creation, marketing, software development, where customization and hitting those specific requirements are critical.- It's like having an AI coworker that you can brainstorm with and refine ideas together.- Yeah.- This is more than just AI generating content. This is about human-AI collaboration.- That's a great way to put it. It's not about replacing human creativity, it's about amplifying it and opening up new avenues for expression.- That's really exciting. This is like a fundamental shift in how we interact with and utilize AI. This is no longer about AI doing things for us, it's about AI working with us.- Exactly, and one final area where DLLMs could be a game changer is edge computing.- Okay.- Perception Labs mentioned that DLLMs could be powerful enough to run on devices like phones and laptops.- Wow.- Why is this such a big deal?- Because most AI today relies on those giant data centers in the cloud. You need a constant internet connection, and even then things can be slow. Like imagine trying to play a video game that's streaming from a server across the world. Any lag or interruption just ruins the experience.- You hit the nail on the head, but DLLMs, with their efficiency and speed, have the potential to run directly on your devices.- Wow.- No cloud required.- So it's like taking all the power of those massive data centers and putting it right in your pocket.- That's the dream, and it's not just limited to personal assistants.- Right.- Think about using AI-powered tools for things like language translation, photo editing, even medical diagnosis, all without needing an internet connection.- Wow, that would be a game changer, especially for people in areas with limited internet access or where privacy is paramount.- Absolutely.- It could truly democratize access to powerful AI tools.- Absolutely, and it aligns with Inception Labs' vision of making AI accessible to everyone, regardless of their location or resources.- Okay, we've covered a lot of ground here, from those brainy agents to the power of edge computing. It's clear that DLLMs represent a huge late forward in AI, and I'm incredibly excited to see where this technology takes us. Inception Labs paints a very optimistic picture, though. Are there any potential downsides or limitations that we should be aware of? This is a powerful technology, and with great power comes great responsibility, right?- You're absolutely right to raise that point. It's crucial to approach any new technology with a balanced perspective, considering both its potential benefits and potential risks.- Okay, so let's take a moment to reflect on everything we've discussed so far when we return. Let's delve into some of the broader societal impacts of DLLMs and what they might mean for the future of AI.- Hey there, listeners. If you're enjoying today's episode, check out our previous episodes where we dive deep into fascinating topics in scientific research and more. Don't forget to tell your friends and family about Heliox, back to Heliox, where evidence meets empathy.- Welcome back to our deep dive on DLLMs. It's been wild exploring how this tech works and all the possibilities it can unlock.- Yeah, it really has, and I think it's clear that DLMs are like a huge step forward in AI.- For sure.- And as we move into this new era, I think it's really important to think about how this can change society.- Absolutely, because with any powerful new tool, there are always questions about, you know, how it's gonna shape the world, like, both for good and bad.- Exactly. One of the biggest things people always bring up with AI is how it could affect jobs.- Right, it's that classic fear of robots coming for our jobs.- Exactly, and while we shouldn't overblow that threat, I think we also can't ignore the fact that DLLMs will probably change the job landscape, you know, especially in fields that rely a lot on repetitive tasks or data processing.- Like customer service and data entry.- Exactly, even certain parts of software development, those could see some big changes.- But it's not just about jobs going away, right? It's also about how jobs will change and evolve.- All right, imagine a future where, like, every professional across every field, doctors, lawyers, teachers, everyone has access to these super capable AI assistants powered by DLLMs.- That would be wild.- These assistants could help them do research, analyze data, even create reports or presentations so humans could focus on more, like, creative, strategic, interpersonal stuff.- Yeah, so instead of replacing humans, AI becomes this powerful tool that helps us and lets us do what we're best at.- Exactly, I like that.- But that means we need to adapt, right? Like, our education and training programs need to catch up and prepare people for this new reality where working with AI is normal.- Absolutely, we need to give future generations the skills to work with AI, to understand what it's good at, what it's not good at, and how to use it in the right way.- So it's not just teaching people how to use AI, but also how to think about it in a smart way to ask the right questions and make sure it's being used ethically for everyone's benefit.- For sure.- And we need to make sure everyone has a chance to benefit, not just a select few.- Absolutely, that's where the whole issue of access and equity comes in.- Right.- If DLLMs become as big as Inception Labs thinks it's crucial that they don't become, like, a toy for the rich or just for big companies.- Right, we need to make sure that everyone has a chance to see how this technology can help them regardless of their background or how much money they make.- That means we've got to invest in developing DLLMs that are open source, creating ways for people to access it affordably, and promoting digital literacy programs all over the world.- So it's about making AI available to everyone and making sure it's a force for good, not something that makes things less equal.- I completely agree. It's a powerful tech that could reshape whole industries, but it comes with responsibility.- There's always that risk that something this powerful could be used in a bad way.- You're right, we have to watch out for that. Imagine how easy it would be to create super realistic fake news or propaganda with DLLMs or even deepfakes. They could ruin people's reputations or even cause violence.- And as this tech gets even better, it'll be even harder to know what's real and what's fake. That's kind of scary.- That's why it's so important to develop and use this stuff responsibly. We need clear rules for how to use DLLMs ethically and create strong ways to find and flag bad content. We also need to teach the public about the dangers of AI generated misinformation so people can be smarter about what they believe online.- Right, it's like teaching people to spot fake news in the age of DLLMs.- Exactly.- Okay, so we've talked about jobs equity, the potential for misuse. What about creativity? What happens to human ingenuity and originality in a world where AI can write good text, music, art, even code?- That's the question philosophers and artists have been debating forever as tech progresses. Right? - Right.- Will we become too dependent on AI for creative stuff and will that hurt human imagination or will these tools push us to explore new things in art to like collaborate with AI in ways we never thought possible?- It's hard to say, but it's an important conversation to have, especially as DLLMs and other AI that can create things become more advanced and part of our lives. If AI can write a symphony or a screenplay, what does that mean for human artists? What happens to originality? Does it even matter anymore?- Those are deep questions and there's no easy answers. I think the main thing is we don't avoid these conversations. We need to think hard about how AI might change human creativity. Maybe it'll change what we think of as art or originality or even what it means to be human and express ourselves.- As we wrap up our deep dive on DLLMs, it's clear that we're at the start of something new in AI. It could completely change industries, improve human abilities, even change how we think of ourselves as creative beings, but it's not a sure thing that it'll all be good. The future of DLLMs, like any powerful tech, depends on the choices we make now. It's up to developers, policy makers, and regular people like you to make sure it's used the right way ethically and to make everyone's lives better.- It's like we're at a fork in the road. One path leads to a future where AI helps and uplifts us and the other leads to a future where it makes our problems worse and the path we take depends on what we do right now.- We need to go into this new era with a sense of wonder, but also caution because it has so much potential, but also so many risks.- This has been an awesome journey exploring DLLMs. We've learned so much from how it works to the big picture stuff, and it's obvious that this tech is gonna keep shaping the world in huge ways.- And as we go forward, we need to stay informed, have open and honest talks about the future of AI, and work together to make sure it's a future we can all be proud of.- We hope this deep dive has helped you understand DLLMs better, made you more curious, and maybe even left you with more questions than answers because the future of AI isn't set in stone. We create it together, so stay curious, stay engaged, and let's shape this future in a smart way.