Speaker 1: 0:10

Ever feel like you're just drowning in all the news and stuff around large language models, llms.

Speaker 2: 0:16

Yeah, it's a lot.

Speaker 1: 0:17

It really is. So today we're going to try and throw you a lifeline. We're taking this fantastic collection of sources you sent in and we're diving deep, really deep, to pull out just the most important bits.

Speaker 2: 0:30

Exactly. Our mission really is to give you a shortcut. You know a way to get genuinely well informed about these large language models without wading through everything. We'll cut through the noise show you what they actually are, how top companies are using them like right now they're still kind of stumble and, maybe most importantly, where this is all heading. And yeah, there are some surprising facts in there too.

Speaker 1: 0:53

OK, let's unpack this then. I mean, for those of you already kind of following the LLM space, you know these aren't just fancy chatbots anymore, right, but maybe just to ground us for this deep dive, let's quickly remind ourselves of the fundamentals what exactly is an LLM? And well, how do they actually work?

Speaker 2: 1:11

Okay, yeah, good starting point. So, at their core, llms are basically general purpose AI text generators. They're the brains behind well pretty much every AI chatbot. You see writing tools, even those summarized search answers, popping up everywhere, and what makes them so powerful isn't just looking for keywords, like older search engines, it's their ability to try and understand your prompt, you know, and generate an answer that fits the context.

Speaker 1: 1:39

So it's more than just matching words.

Speaker 2: 1:40

Way more and that understanding, that magic ability people talk about it comes from just immense training. Right LLMs learn from these absolutely vast data sets. I mean, think, the entire public Internet.

Speaker 1: 1:53

Wow.

Speaker 2: 1:53

Pretty much every book, newspaper, magazine ever published, and even the stuff earlier AI models generated. It's almost unfathomable really.

Speaker 1: 2:01

Okay, so they digest all this information. Then what happens inside? How do they process it?

Speaker 2: 2:05

Well, internally they work by modeling these incredibly complex relationships between words, or sometimes even tiny fractions of words, called tokens.

Speaker 1: 2:14

Tokens right.

Speaker 2: 2:15

They use high dimensional vectors. It's complex math, basically mapping connections. Imagine this huge multilayered statistical model that predicts the next most likely word or token in a sequence. The structure is called a neural network. It's kind of inspired by the brain but, you know, on an industrial computer scale.

Speaker 1: 2:35

And when we hear about parameters, that relates to this network.

Speaker 2: 2:39

Exactly. When we talk about parameters, we're essentially talking about the number of these connections, these learned relationships within the network. Generally speaking, more parameters mean a more nuanced ability to understand and generate complex text, more layers, more nodes, more power.

Speaker 1: 2:55

Usually, it's amazing how fast it's evolved too. I mean, we started with just text right Text-only LLMs.

Speaker 2: 3:00

Yeah, feels like a lifetime ago almost.

Speaker 1: 3:03

But now we're really in the era of large multimodal models, llms. They can handle images, audio video, all alongside text.

Speaker 2: 3:13

Absolutely, and the really cutting-edge stuff now is reasoning models.

Speaker 1: 3:16

Right, tell us about those.

Speaker 2: 3:17

These don't just spit out an answer. They actually try to logically break down complex problems into steps. They show their work, so to speak. It's a big leap towards more complex thought processes.

Speaker 1: 3:30

That's fascinating. And there's one more step after the initial training, isn't there? Fine tuning?

Speaker 2: 3:34

Oh, definitely Crucial step. After that massive initial training on everything, LLMs go through more specific training, fine-tuning. This guides them towards generating responses that are actually safe, useful and on topic. It moves them beyond just mimicking the raw, sometimes messy, data they learned from initially.

Speaker 1: 3:56

Right, making them more helpful and less random.

Speaker 2: 3:59

Exactly Less random, more aligned with what we actually want them to do.

Speaker 1: 4:03

Okay, so now here's where it gets really compelling, I think, moving from the how to the what.

Speaker 2: 4:09

Yeah.

Speaker 1: 4:10

The real world application.

Speaker 2: 4:11

Yeah, this is where the rubber meets the road.

Speaker 1: 4:13

Your sources gave us like over 45 examples. We won't go through all of them, obviously, but we've picked out some of the most innovative ways top companies are using these models, like right now, and the common thread seems to be less about just replacing tasks and more about embedding these intelligent co-pilots that really change how people work.

Speaker 2: 4:33

That's a great way to put it co-pilots, Because LLMs are profoundly transforming operations across so many different sectors, they're definitely more than just a novelty.

Speaker 1: 4:42

But it's not easy, right?

Speaker 2: 4:43

Oh no, Turning that raw power, that potential into reliable production-grade systems. That's still a huge challenge.

Speaker 1: 4:55

But the ingenuity we're seeing is, well it, remarkable. So let's dive into some specifics. Maybe break it down by industry. How about e-commerce and retail first? What's happening there?

Speaker 2: 5:02

okay, yeah, so instacart, for example, they have an internal ai assistant. They call ava ava yeah, and their teams use it to help write, code review, code debug, even improve internal communications and build other internal tools. It's like an assistant for their tech teams.

Speaker 1: 5:17

Yeah.

Speaker 2: 5:18

And on the customer side, they're using generative AI to create really high quality images of food products for the grocers on their platform. They can write prompts, fine tune them and get great visuals much more efficiently.

Speaker 1: 5:28

Wow, okay, what else in retail?

Speaker 2: 5:30

Wayfair has something called Agent Copilot. It's a gen AI assistant for their actual human sales agents. It gives them live, contextually relevant suggestions for chat responses. So it's not replacing the agent, but it's giving them real-time help to serve customers better, Deeper insights faster.

Speaker 1: 5:48

That's a perfect example of augmentation, not just automation. What about Zillow?

Speaker 2: 5:57

Zillow's using LLMs in a really interesting way to detect discriminatory content in real estate listings, specifically looking for proxies for race or other historical biases. That shouldn't be there.

Speaker 1: 6:05

That's actually quite profound. Using AI to fight bias? Okay, and Walmart.

Speaker 2: 6:10

Walmart developed something called a product attribute extraction engine, or PAE. It uses LLMs to pull product attributes out of PDF files, text and images, which helps them onboard new items much faster and plan their assortment better.

Speaker 1: 6:23

Okay, so streamlining back-end processes too. Let's switch gears FinTech and banking. I'm more cautious, right, but are they using LLMs?

Speaker 2: 6:31

They are definitely and the impact is pretty significant. Grab, you know the big Southeast Asian super app.

Speaker 1: 6:38

Yeah.

Speaker 2: 6:38

They use LLMs for data governance classifying data, identifying sensitive info, assigning the right tags automatically at scale. That's huge for compliance.

Speaker 1: 6:48

Pretty cool yeah.

Speaker 2: 6:49

And then there's a platform called Digits. They use generative models to help accountants. The AI actually suggests questions about transactions for the accountant to ask their clients.

Speaker 1: 6:58

Really.

Speaker 2: 6:58

Yeah, they can send them right away or edit them first. It's like this intelligent assistant making sure nothing gets missed and those complex financial back and forths.

Speaker 1: 7:05

Streamlining communication, ensuring clarity Okay, makes sense. Now the tech sector itself. Obviously, they're all over this. What are some standouts?

Speaker 2: 7:14

Well, github Copilot is maybe the most famous example. Right, ai-powered code, suggestions, auto-completions it really feels like a coding partner for developers.

Speaker 1: 7:24

I hear about it constantly.

Speaker 2: 7:26

Salesforce introduced something called AI Summarist. It's a tool that summarizes Slack conversations for you Helps manage that information overload. Oh, I need that. Tell me about it. Dropbox added summarization and Q&A to file previews online, so you can get a quick summary of, say, a long video or ask questions about the content even across multiple files at once.

Speaker 1: 7:46

That's incredibly useful.

Speaker 2: 7:48

NVIDIA has a Gen AI app that looks at software vulnerabilities. It figures out if one likely exists and then generates a whole checklist of tasks to investigate it thoroughly.

Speaker 1: 7:58

Wow, proactive security.

Speaker 2: 7:59

And Microsoft uses LLMs to help manage cloud incidents, generating recommendations for root cause analysis, mitigation plans, making incident response faster and, hopefully, more effective. These aren't small things. They're becoming integral.

Speaker 1: 8:12

Definitely integral, almost invisible enhancements in some cases. What about delivery and mobility? Always about efficiency there.

Speaker 2: 8:18

Exactly. Uber's using LLMs for software testing. They have a system called Dragon Crawl.

Speaker 1: 8:23

Dragon Crawl.

Speaker 2: 8:24

Yep. It performs mobile app tests with sort of human-like intuition. They say Saves a ton of developer hours. They also built Genie. It's a generative AI on-call copilot Helps their engineers answer thousands of internal support questions that come up in Slack channels. Again, boosting that internal efficiency Makes sense.

Speaker 1: 8:43

DoorDash.

Speaker 2: 8:44

DoorDash is using LLMs to extract product details from raw merchant SKU data, Basically messy data. This helps them match customer searches to the right items much better.

Speaker 1: 8:56

Better search results Okay.

Speaker 2: 8:57

And, importantly, they also built an LLM-based support chatbot, but it uses a RAG system Retrieval, Augmented Generation.

Speaker 1: 9:04

Oh, rag, okay, you mentioned that acronym. Why is RA such a game changer for a chatbot?

Speaker 2: 9:10

Right, it's crucial because it means the chatbot isn't just relying on its initial massive training data, where it might just make stuff up. Hallucinate basically Exactly Hallucinate. Instead, the ArtRagus system makes the chatbot actively look up specific current information from a verified knowledge base before it generates the answer.

Speaker 1: 9:29

Oh, okay.

Speaker 2: 9:30

This dramatically cuts down the risk of those hallucinations we need to talk more about, makes the chatbot way more reliable and trustworthy.

Speaker 1: 9:37

Got it. That's a key distinction. Okay, last category for these examples social media and BDC apps. What's happening there?

Speaker 2: 9:45

So Duolingo, the language app they use LLMs to help their learning designers create exercises. The humans outline the themes, the goals and the model generates suitable content. Drills examples speeds up content creation massively.

Speaker 1: 9:59

Glover.

Speaker 2: 10:00

Roblox, the gaming platform. They use a custom multilingual model for real-time chat translation between like 16 different languages, any combination.

Speaker 1: 10:08

Wow, seamless global chat.

Speaker 2: 10:10

Yeah, really enabling that global community Playtika, a mobile game developer. They're saving a lot of art production time using AI to create art assets. Text to image, image to image, generating variations.

Speaker 1: 10:21

Streamlining creative work.

Speaker 2: 10:23

Exactly. And Yelp, they've upgraded their content moderation using LLNs to automatically detect inappropriate language in reviews, threats, harassment, hate speech much more effectively and at scale.

Speaker 1: 10:34

It's a tough job for humans alone. So just listening to all this, what does it mean for you? Listening right now, I mean? Think about how many of your everyday interactions online searches, ordering food, the apps you use are probably already touched by these models, often completely behind the scenes, right, you don't even realize it. They're just quietly reshaping our whole digital world.

Speaker 2: 10:56

They really are. But this is a big but. While these applications are incredibly exciting, it definitely raises an important question LLMs, for all their amazing power, are not perfect. Far from it, actually. Experts point to several core limitations, things like accuracy issues, capacity limits you know, token limits, right? Their reliance on the data they were trained on, which can be static, outdated and, of course, the big one, hallucinations.

Speaker 1: 11:23

Hallucinations Okay, probably the most talked about flaw. Let's define that. Yeah, what are they exactly?

Speaker 2: 11:28

So hallucinations are basically when an LLM generates content that's well irrelevant, completely made up or just inconsistent with the information it was given, and this poses a really critical risk. It can lead straight to misinformation, potentially even expose confidential data if it gets confused, and it fundamentally challenges the trust we can place in these models.

Speaker 1: 11:50

Yeah, it's not just a minor bug. It can have really serious consequences.

Speaker 2: 11:54

Absolutely and to really get a handle on it, it helps to break down the types. First you've got factuality hallucinations. Okay, factuality, this is where the LLM just generates something factually wrong. It can be factual inconsistency like it gets a known fact, wrong saying I don't know. Yuri Gagarin landed on the moon instead of Neil Armstrong Right, simple factual error, or it can be factual fabrication. This is where it just makes up a whole story, you know, creates a narrative with zero real world basis, like telling you about unicorns living in Atlantis.

Speaker 1: 12:23

Okay, making things up entirely, got it. What's the other main type?

Speaker 2: 12:27

The second main category is faithfulness hallucinations.

Speaker 1: 12:30

Faithfulness.

Speaker 2: 12:31

Yeah, these happen when the model produces stuff that's unfaithful to or inconsistent with the source material it was given, or maybe your specific instructions.

Speaker 1: 12:41

Ah examples.

Speaker 2: 12:42

So you could have instruction inconsistency, like you ask it for an answer translated into Spanish and it just gives it to you in English. It ignored your instruction, right. Then there's context inconsistency the output includes information that wasn't in the context you provided, or maybe it even contradicts it. Like you give it the text saying the Nile starts in the Great Lakes region and it spits out an answer saying it comes from mountains.

Speaker 1: 13:05

It's not sticking to the script, basically.

Speaker 2: 13:06

Exactly. And finally, logical inconsistency. The model might start out okay, maybe doing step-by-step reasoning for a math problem, but then it makes a logical error partway through, like it messes up the arithmetic.

Speaker 1: 13:19

Even if the steps looked right in initially. Okay, so that's the what, but why? Why do these hallucinations actually happen?

Speaker 2: 13:27

It's complicated. There isn't just one single cause, it's multifaceted. One big factor is training data issues.

Speaker 1: 13:34

Remember those vast data sets, yes, the whole internet books.

Speaker 2: 13:37

Exactly and that data is full of inaccuracies, biases, just plain weird stuff sometimes, and the LLM learns it all, the good and the bad. It can pick up and replicate those factual errors. There was that famous example with Google Bard of making a mistake about the James Webb telescope discoveries early on.

Speaker 1: 13:55

Right, learn from bad data. What else?

Speaker 2: 13:57

Then you have potential flaws in the architecture or training objectives. Maybe the model's internal design isn't quite optimal or the way it was trained the goals it was given accidentally encourages it to produce nonsensical or incorrect stuff sometimes.

Speaker 1: 14:11

The model itself is maybe flawed.

Speaker 2: 14:13

Could be. There are also inference stage challenges. This is about how the model generates the answer after training Things like defective decoding strategies or just the randomness built into how it picks the next word. If you turn up the temperature setting to make it more creative, you also increase the risk of it going off the rails and hallucinating.

Speaker 1: 14:33

A trade off between creativity and accuracy? Often yeah, and then there's prompt engineering.

Speaker 2: 14:35

How you ask the question and hallucinating. A trade-off between creativity and accuracy? Often, yeah, and then there's prompt engineering. How you ask the question matters a lot.

Speaker 1: 14:39

Right. Garbage in, garbage out.

Speaker 2: 14:41

Sort of If your prompt is vague or lacks enough context, the LLM might just guess or fill in the blanks incorrectly. It needs clear instructions. This relates to ambiguity handling too. If it doesn't have the info, it might just invent something to fill the gap.

Speaker 1: 14:54

Rather than saying I don't know.

Speaker 2: 14:56

Exactly. And one more thing Sometimes models are over-optimized for certain things, like maybe generating longer answers. This can lead them to become verbose, adding irrelevant details. And yeah, sometimes those details are hallucinations.

Speaker 1: 15:10

Wow, okay, so lots of potential causes. Yeah, and the real world impact you mentioned it could be severe.

Speaker 2: 15:16

Oh, definitely there was that legal case. Mata v Avianca, a lawyer in New York, used ChatGPT for legal research.

Speaker 1: 15:24

Uh-oh.

Speaker 2: 15:24

Yeah, and the chatbot just made up several case citations, completely fabricated them. The lawyer submitted them to a federal court.

Speaker 1: 15:31

Oh no.

Speaker 2: 15:32

And faced serious sanctions. It was a major embarrassment and a huge warning sign. Incidents like that really erode trust in AI right. They can have serious professional legal consequences and, bigger picture, they contribute to societal misinformation if people just blindly trust the output.

Speaker 1: 15:47

Underscores the need for verification for sure Human oversight.

Speaker 2: 15:51

Absolutely critical. These are tools, powerful tools, but they need to be used carefully and critically.

Speaker 1: 15:56

Okay, so that's the problem, but what's really encouraging, I think, is seeing how the AI community is actually tackling these challenges.

Speaker 2: 16:07

They're not just ignoring it. No, definitely not. There's a ton of work going into hallucination mitigation.

Speaker 1: 16:10

Okay, so what are some of those strategies?

Speaker 2: 16:12

Well, there's a range. Some methods involve human feedback loops, like having annotators score the level of hallucination and responses, or comparing the AI's output against known good answers baselines.

Speaker 1: 16:24

Right.

Speaker 2: 16:25

Product design plays a role too making it easy for users to edit the AI's output, providing structured inputs and outputs, building in feedback mechanisms.

Speaker 1: 16:33

Making the user part of the solution.

Speaker 2: 16:35

Exactly, and red teaming is huge. That's where teams of humans deliberately try to break the model, to find ways to make it hallucinate or give bad responses, so those flaws can be fixed.

Speaker 1: 16:45

Actively looking for weaknesses.

Speaker 2: 16:48

Yep. Then there are more technical approaches like methods to look at the model's internal confidence scores, the logit values to flag potential hallucinations before they're shown to the user. Then a validation step checks if it's actually wrong and if it is a mitigation strategy, tries to fix the error without introducing new problems. Some studies show these techniques can be really effective, like slashing hallucination rates on models like GPT 3.5 pretty dramatically.

Speaker 1: 17:13

That's promising. Any specific techniques gaining traction?

Speaker 2: 17:17

One notable approach is called knowledge graph-based retrofitting, or KGR, kgr, okay. This basically combines the LLM with a structured knowledge base like a database of facts. The system can autonomously check factual statements the LLM makes against the knowledge graph, validate them and even correct them if needed. This has shown really good results on factual question answering tests.

Speaker 1: 17:40

So grounding the LLM in verified facts makes sense. Okay, so that's fixing the problems. But looking ahead, beyond just mitigation, where are LLMs going? What are the big future trends? We should be watching?

Speaker 2: 17:52

Well, one clear trend is fact checking with real-time data integration.

Speaker 1: 17:57

Okay, like the RE idea, but maybe built in.

Speaker 2: 17:59

Kind of LLMs, will increasingly be able to access external up-to-the-minute sources and, importantly, provide citations and references for their answers so you can actually check where the information came from. Think Microsoft Co-Pilot pulling live data from Bing search results alongside GPT-4.

Speaker 1: 18:18

That would build a lot more trust.

Speaker 2: 18:20

Definitely. Another fascinating area is synthetic training data. Researchers are actually developing LLMs that can generate their own training data.

Speaker 1: 18:28

Wait, they train themselves.

Speaker 2: 18:30

Sort of they generate potential questions and answers, then maybe use another model or human feedback to curate the best ones and then fine-tune themselves on that high quality self-generated data. Google's been doing some interesting work here on self-improving models, Wow okay, what else?

Speaker 2: 18:44

a really big architectural shift is towards sparse expertise models, usually called mixture of experts or mo moe okay, that acronym too yeah, so instead of the entire massive neural network being active for every single calculation, an MOE model only activates specific subsets of its parameters, its experts, that are most relevant for the particular task or token being processed.

Speaker 1: 19:09

Ah more efficient.

Speaker 2: 19:10

Much more efficient. It leads to faster responses, potentially better performance on specific tasks and uses less computational power than trying to run the whole giant model all the time. Openai and others are heavily invested in this.

Speaker 1: 19:23

Okay, efficiency and specialization Makes sense.

Speaker 2: 19:26

We're also seeing naturally deep integration into enterprise workflows. Llms won't just be standalone chatbots. They'll be deeply woven into core business tools for customer service, HR, project management, decision support, Salesforce, Einstein Copilot is a good example of this direction.

Speaker 1: 19:43

Becoming part of the furniture almost.

Speaker 2: 19:45

Pretty much, and critically. A major leap forward is in reasoning models. We touched on this.

Speaker 1: 19:50

Right, the ones that show their work.

Speaker 2: 19:52

Exactly. This represents a shift from just you know, surface level fluency to deeper cognitive function. These models can plan, execute multi-step tasks, adapt if something goes wrong and provide logically sound step-by-step outputs. Using something like Anthropics' Claude 3.7 sonnet to refactor complex code is a prime example of this kind of reasoning power.

Speaker 1: 20:14

That feels like a step towards more general intelligence.

Speaker 2: 20:17

It's definitely a step in that direction. And finally, related to efficiency and accuracy, there's a huge investment in fine-tuned, domain-specific LLMs.

Speaker 1: 20:26

Ah, models trained just for one area.

Speaker 2: 20:28

Precisely. Companies are customizing models for specific industries or tasks. This leads to fewer hallucinations and much higher accuracy, because the model has deep knowledge of that specific domain. Think GitHub, copilot for coding, bloomberg GPT specifically for finance, google's Med, palm 2 for health care, chat law for legal stuff. Specialization is key.

Speaker 1: 20:49

Right, and that focus on specialized models brings us nicely to Lama 4 from Meta you mentioned. It's a prime example of these future directions.

Speaker 2: 20:56

Absolutely. Lama 4 isn't just like an incremental update. It really showcases several of these key trends and feels like a strategic move.

Speaker 1: 21:05

OK. So what makes Lama 4 so significant? What are the highlights?

Speaker 2: 21:08

Well, first off, it embraces that mixture of experts Moe architecture we just talked about.

Speaker 1: 21:13

Right the efficiency thing.

Speaker 2: 21:14

Exactly. That's a big deal for making these powerful models faster and cheaper to run. But maybe even more headline grabbing is the increased context length. Lama4 can reportedly process up to 10 million tokens 10 million.

Speaker 1: 21:29

That's huge compared to what we had before.

Speaker 2: 21:31

It's massive. Think about what that means. It can keep track of the conversation or the document over incredibly long stretches, analyzing entire books, lengthy legal documents, complex code bases. It can remember details from way, way back in the input.

Speaker 1: 21:45

That opens up a lot of possibilities.

Speaker 2: 21:47

Totally. And another key feature, native multimodal capabilities.

Speaker 1: 21:51

Ah, handling images too.

Speaker 2: 21:53

Exactly. It's designed from the ground up to process both text and image inputs together, so you can feed it documents with charts and images. Ask it questions about pictures, get it to generate image captions. It understands visual information alongside text.

Speaker 1: 22:07

Okay, so Moe massive context, multimodal. How's the actual performance?

Speaker 2: 22:12

Reports suggest it's state-of-the-art. It's apparently rivaling or in some cases, even surpassing top models like GBT-4 and CLAWD-3 on benchmarks for things like complex reasoning, coding ability and handling multiple languages. It's really pushing the boundaries.

Speaker 1: 22:29

And Meta did something interesting with Lama 4, didn't they? They released different versions.

Speaker 2: 22:32

They did, and that's really innovative too, showing how they're thinking about different use cases.

Speaker 1: 22:36

It's not one size fits all Okay, walk us through those variants.

Speaker 2: 22:39

So first there's Scout. This is the lightweight version, smaller, designed for speed and efficiency. Think mobile devices, edge computing applications where you need quick responses, low latency and don't want to drain the battery, smart assistance, maybe stuff on smartwatches, iot devices, real-time feedback systems.

Speaker 1: 22:58

Okay, the nimble one Next.

Speaker 2: 23:00

Next is Maverick. This is positioned as the balance performer, kind of the workhorse. Okay, it's built for versatility for production-grade AI in typical enterprise settings. It balances strength and speed. Good for demanding stuff like code generation or logical reasoning, but optimized for that low latency and high-throughput business's need.

Speaker 1: 23:20

The all-rounder and the last one.

Speaker 2: 23:22

The last one is behemoth, as the name suggests.

Speaker 1: 23:25

Yeah, the big one.

Speaker 2: 23:27

Exactly, this is the largest, most powerful variant, designed for maximum performance, deep understanding, handling really nuanced tasks Think high performance computing, massive enterprise AI deployments, cutting edge scientific research.

Speaker 1: 23:41

Like medical research or financial modeling.

Speaker 2: 23:43

Precisely Large scale analytics, complex simulations, even AI, safety research itself, where you need the most capable model possible.

Speaker 1: 23:51

So Scout Maverick Behemoth. It really highlights this trend towards specialized tools and it also underscores the importance of multimodal AI, which Lama 4 embraces. Why is this multimodal capability becoming such a critical trend?

Speaker 2: 24:05

Because the real world isn't just text. Right, we experience things through multiple senses. Multimodal AI tries to mimic that. It's about AI that can process and integrate information from different data types text, images, audio video, maybe even other sensors to get a much richer, more complete understanding of the situation.

Speaker 1: 24:25

And that leads to.

Speaker 2: 24:25

It leads to more robust and useful AI higher accuracy, less ambiguity. Think about image recognition. Adding text descriptions helps, or language translation. Maybe seeing the context helps choose the right word. It makes generative AI more powerful.

Speaker 1: 24:40

Can you give an example?

Speaker 2: 24:41

Sure, imagine a virtual assistant. If it can only hear your voice, that's one thing, but if it can also see visual cues, maybe you're pointing at something it gets much more context. Or a shopping chatbot that can look at a photo of your current glasses and make genuinely helpful sizing recommendations for new frames. That's multimodal in action.

Speaker 1: 25:00

Ah, okay, that makes it clear. So it's about combining data streams for better understanding.

Speaker 2: 25:05

Exactly. Engineers are working on tricky challenges around how to best represent these different data types, how to align them like matching words in a caption to parts of an image how to reason across them and how to generate multimodal outputs.

Speaker 1: 25:18

It's complex.

Speaker 2: 25:19

It is, but we're seeing trends towards unified models, models like GPT-4 with Vision or Google's Gemini that are designed to handle lots of data types in one architecture. Also, better cross-modal interaction, real-time processing vital for things like self-driving cars and using multimodal data itself to train even better models.

Speaker 1: 25:37

Okay, so bringing this all together, if we connect this power, this multi-modality, this reasoning to a specific area, one field seeing a massive impact is education.

Speaker 2: 25:47

How are LLMs really reshaping learning for students, for teachers?

Speaker 1: 25:51

Yeah, education is a fascinating space for LLMs. I mean AI and education isn't brand new, obviously, we've had basic tools for a while, but LLMs bring this whole new level of sophistication. We're seeing things like automated grading, not just for multiple choice, but for essays, short answers, even creative writing assignments. That frees up so much teacher time Huge time saver.

Speaker 2: 26:10

Huge. They also enable really tailored content generation. Need a quiz on photosynthesis for a specific reading level, or a lesson plan on Shakespeare, or just a clear explanation of a tricky math concept. Llms can generate that customized.

Speaker 1: 26:27

MARK MIRCHANDANI. Personalized Content on Demand MELANIE.

Speaker 2: 26:28

WARRICK, exactly, and maybe the most exciting part is AI-powered tutoring systems. These can provide genuine one-on-one help to students, answering questions, explaining things differently, adapting to their pace. It's like having a personal tutor available 247.

Speaker 1: 26:43

That could be revolutionary. So, for students, what are the big wins? Well, definitely personalized learning. These adaptive systems can analyze where a student is strong, where they're struggling, and adjust the materials and feedback in real time. It's tailored to their needs. For educators, as we said, it's about streamlining those admin tasks grading, answering common questions. It frees them up for the really important stuff Deeper interaction, mentoring, focusing on individual student needs.

Speaker 2: 27:10

More human connection time.

Speaker 1: 27:11

Exactly. They can also boost student engagement. Conversational AI agents can provide instant answers when a student gets stuck, keeping them motivated, and LLMs can generate interactive simulations, role-playing scenarios, engaging quizzes, making learning more active and, finally, just facilitating access. Students can get detailed explanations of anything, summarize dense textbooks, find curated online resources much more easily. It breaks down barriers to information.

Speaker 2: 27:40

It goes beyond the average student too, right. What about bridging educational divides?

Speaker 1: 27:45

Yes, absolutely. In special education, for instance, llm tools can offer highly customized learning programs. Text-to-speech, speech-to-text are obvious benefits, but also tools to help students with autism, say, practice social and communication skills in simulated real-time scenarios.

Speaker 2: 28:01

Wow that's powerful.

Speaker 1: 28:02

And think about overcoming language barriers. Multilingual LLMs can translate course materials instantly, offer real-time translation during live lessons, assist language learners with personalized grammar and vocabulary practice. It really helps ensure that language isn't the reason a student gets left behind.

Speaker 2: 28:17

Making education more equitable.

Speaker 1: 28:20

That's the goal. Of course, it's not all straightforward. There are big challenges, right, ethical considerations.

Speaker 2: 28:26

Danielle Pletka. Oh, definitely Top of the list is probably data privacy and student safety. You're dealing with sensitive information about minors. Strict adherence to privacy laws like GDPR is non-negotiable. Robust data security is paramount.

Speaker 1: 28:40

Absolutely critical.

Speaker 2: 28:41

And the other big point is emphasizing the irreplaceable role of educators. Ai is a tool, a powerful assistant maybe, but it's not a replacement for a human teacher. Right, teachers are the guides, the mentors. They foster critical thinking, empathy, creativity the human elements that AI can't replicate. Ai supports them, it doesn't replace them.

Speaker 1: 29:03

That's such an important distinction to keep making. So looking ahead in education.

Speaker 2: 29:07

We can probably expect even smarter adaptive learning systems, systems that really understand each student deeply, ai tools that help students collaborate better in real time. But schools need to prepare. They need to invest in the tech infrastructure, sure, but just as importantly, they need to provide really good professional development for teachers so they know how to use these AI tools effectively and ethically. It's an exciting future, but it requires careful planning and investment.

Speaker 1: 29:32

Definitely A complex road ahead, as you said yeah. So OK, we've covered a lot of ground today.

Speaker 2: 29:37

We really have.

Speaker 1: 29:38

From just defining LLMs what they are, how they work, to seeing this incredible range of real world uses e-commerce, tech delivery, social media and now education. We also dug into the tricky stuff the challenges, especially hallucinations, what they are, the different types, why they happen and the real impact they can have.

Speaker 2: 30:01

And, importantly, we looked at how the community is actively working to fix those problems the mitigation strategies, plus those really exciting future trends specialized models like LAMA4, the power of multimodal AI and that crucial move towards reasoning models.

Speaker 1: 30:15

It's evolving so fast. So maybe the final thought for everyone listening what does all this mean for your future interactions with technology? I mean, as LLMs keep getting smarter, faster, more integrated, how will you navigate that balance, the balance between their incredible power and that ongoing need for accuracy, for trust, for well responsible use?

Speaker 2: 30:37

It's a great question and, honestly, there's always more to learn well, responsible use. It's a great question and, honestly, there's always more to learn. Understanding these shifts, the capabilities and the limitations is going to be absolutely key to harnessing this technology well, effectively and responsibly in the years ahead.