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Google's Game-Changing AI Updates: What Business Leaders Need to Know
In a surprising move, Google recently made waves with an unexpected drop of AI updates, showcasing advancements that are poised to shift the landscape of large language models (LLMs) and robotics. As a business leader or decision-maker, understanding these developments and their implications could be pivotal for staying competitive. Here's a breakdown of what's new and what it means for your enterprise.
Revolutionizing with Gemini 2.0 Flash Thinking
Google's latest edition of Gemini 2.0 Flash Thinking brings significant enhancements that leverage reasoning models for improved performance. This update introduces file upload capabilities, better speed, and enhanced reasoning capabilities, all available for free in Google AI Studio. The standout feature, however, is the inline image generation, which utilizes Google's Imagine model. This allows businesses to create detailed content alongside high-quality AI-generated images, streamlining processes that traditionally required substantial time and resources.
Deep Research 2.0: A New Era of Insight
The overhaul of Google's Deep Research tool, now referred to as Deep Research 2.0, represents a major advancement in AI-facilitated research. Incorporating reasoning capabilities, it improves the planning and synthesis of information, making it a valuable asset for generating high-quality, nuanced reports. For businesses, this could mean more accurate market analyses and strategic insights with increased efficiency. Importantly, the update provides transparency in the reasoning process, which can be crucial for refining search outputs and reducing information inaccuracies.
Personalization and Privacy with Gemini's New Features
Another significant update includes personalized enhancements in Google's Gemini, which now integrates search histories to tailor responses. While some may harbor privacy concerns, these features provide businesses with more contextually relevant insights, aiding in decision-making processes. Users are equipped with transparent data usage controls, allowing them to manage linked data according to their privacy comfort level.
Gemma 3: Small Language Models Transforming the AI Frontier
Perhaps the most remarkable development is Google’s introduction of Gemma 3, a small language model that exhibits high performance despite its size. With open-source availability, businesses can leverage these models for specialized applications, running them locally on devices ranging from smartphones to workstations. Gemma 3 offers faster processing, enhanced security, and significant environmental benefits, representing a leap forward in making AI more accessible and efficient.
Robotics Integration: Gemini's Physical World Impact
Google has also set the stage for a new standard in robotics with Gemini Robotics. This model offers multimodal reasoning, allowing robots to understand natural language commands and interact seamlessly with environments and humans. While this may not seem immediately relevant to all businesses, the potential for robotics to transform operational efficiencies and consumer experiences is immense, marking a substantial opportunity for forward-thinking enterprises.
Notebook LM Enhancements: A Tool for Everyday Efficiency
Notebook LM, another powerful yet underutilized tool, receives a critical update with the introduction of Gemini 2.0 Flash Thinking. This tool is designed to provide insights grounded exclusively in user-supplied data, minimizing hallucinations and ensuring accuracy. The update includes improved citation capabilities and enhanced source customization, making it an invaluable tool for businesses looking to enhance productivity through AI-driven insights.
Our Takeaway
Google's unexpected release of these AI advancements marks a pivotal moment in the evolution of large language models and their application in business contexts. For decision-makers, embracing these tools could mean the difference between falling behind and leading the charge in your industry. By integrating Google's latest AI capabilities, businesses can capitalize on improved efficiency, security, and insight, driving growth and innovation in an increasingly competitive landscape.
Topics Covered in This Episode
- Google's Surprise AI Releases
- New Gemini 2.0 Features
- Google AI Studio Updates
- Deep Research Enhancements
- Gemini Personalization Mode
- Gemini Robotics for Physical Actions
- Notebook LM Updates with Gemini 2.0
- Gemma 3 Small Language Model
Podcast Transcript
Jordan Wilson [00:00:16]:
Out of nowhere, Google just said, hey. It's March. Let's bring some AI madness, because out of nowhere, Google just had a huge drop of new AI updates both to its Gemini models and even outside of that with some new small language models in robotics. Like, where did this all come from? So today, on, the show, we're gonna be looking at Google's surprise AI releases, what's new, and how it changes the LLM race. Alright. I'm excited for this one. I hope you are too. But what's going on y'all? My name is Jordan Wilson, and this is Everyday AI.
Jordan Wilson [00:01:02]:
Welcome. This thing is for you. It is your daily livestream podcast and free daily newsletter helping everyday people like you and me not just learn AI, but how we can actually leverage it to grow our companies and our careers. If that sounds like you, welcome. This is your new home. You're in the right place. The other home that you need to do is, well, our website, youreverydayai.com. Because, yeah, you can learn a lot from the podcast and the live streams and the great guests that we bring on, but how you actually leverage it, that's what happens in our newsletter.
Jordan Wilson [00:01:34]:
So make sure you go sign up at youreverydayai.com. Also on there, you can go listen for free to now, like, 470 some back episodes, from some of the leading experts in the world and myself, on whatever you want to learn about. Right? Whether it's it's marketing, communications, legal, ethics, it's all on our website sorted by category. Alright. If if you want the daily news, go check that in the newsletter. Sometimes we, you you know, spit that out right before, we start our podcast episode, but there's so many Google updates today. I'm like, I don't want this to accidentally turn into a, you know, fifty minute show. I know some of you all are, walking on the treadmill while listening to this live, so, I won't I won't keep you too long.
Jordan Wilson [00:02:20]:
Alright. Livestream audience, thank you for joining us. Love to see it. What questions do you have? I'll see if I have time to tackle them at the end. And have you tried any of these new updates? And if so, what do you think about them? You know, maybe if you have a good take, we'll, we'll put your take in our our our newsletter for today. So thanks for joining us. The YouTube YouTube crew coming strong. Yeah.
Jordan Wilson [00:02:43]:
So this is, if you didn't know, if you mainly just listened to the podcast, this is an unedited, unscripted, the realest thing in artificial intelligence. So, yeah, we do this live. So thanks to, livestream audience, doctor Harvey Castro, Christian, and the AI physician on YouTube. Love to see it. Michael, big bogey face, Sandra, Marie joining from LinkedIn with Douglas and Christopher and Denny and Brian. Thank you all for tuning in. Woozy. Good to see you.
Jordan Wilson [00:03:10]:
Alright. Let's get into Google's surprise AI releases. Y'all, this came from nowhere. Like, the the amount of new AI updates, that Google has released over the last, like, three days, it's like their Super Bowl. Right? Like so we all know back in December, that OpenAI and Google kinda had this back to back, you know, AI release fight. Right? Like, it was huge. It like, you know, OpenAI had their, twelve days of OpenAI and, you know, Google kind of out of nowhere, I think, came in and stole the show maybe. But, generally, you know when these AI updates are are coming.
Jordan Wilson [00:03:49]:
Right? The the company might say something. They might hold, an event, a release. Right? This is out of nowhere. I was not expecting this, and I think most people, you know, unless you worked at Google, were not expecting everything that Google, just updated over the past couple of days. And, you know, we we've been recapping them in our newsletter, and I was like, wait. Like, this is so much. Like, what is going on? All I know, especially if you are a fan of Google's models, if you use, their Google Workspace, you know, for your organization. Some huge things here.
Jordan Wilson [00:04:25]:
I'm not even gonna be able to cover it all because there's that much. Alright. Let's get, also, FYI, is anyone else gonna be at the GTC conference, with, at NVIDIA? So that's, I'm gonna be there next week, actually starting Sunday, but, I'll be there from the seventeenth to the nineteenth. But the conference goes through the twentieth. So, hey, if you are gonna be at the NVIDIA GTC conference, make sure to holler at me. So, I'm excited to be partnering with NVIDIA, to bring you all a lot of exclusive insights. We actually have something fun that we're working on with the NVIDIA inception program. Right? They have thousands of, some of the brightest, startups in the world in the NVIDIA inception program.
Jordan Wilson [00:05:09]:
So I'll have more details on that for you guys next week, but, I'm extremely excited. You know, and if you don't know, essentially, NVIDIA powers AI. Right? Like, across the world. Most of all the biggest companies are using NVIDIA's GPUs, to create their AI or create the AI that we all love and use. So, you know, it's gonna be some, exciting updates coming there. Alright. Here's what's new. So there is an updated version of Google's Gemini two point o flash thinking.
Jordan Wilson [00:05:40]:
Google's deep research got Gemini two point o flash thinking. Alright. So it got upgraded, and updated to the latest model that Google just released. Now we have Gemini with personalization updates, which some people might not like. I personally love it. Then we have Gemini two point o robotics. We have notebook l m updates and Gemma three, which I think is probably of all these things. Even though I'm not gonna be using, Gemma three a lot personally aside from some testing, I think Gemma three, which is, Google's kind of small language model, might be the biggest deal out of all of these.
Jordan Wilson [00:06:28]:
Right? And we'll get into that. I'm gonna cover that, at the end, but this is this is huge. This is huge. So, I'm excited to, talk about a lot of these also because Gemma is open source as well. So a lot to cover here. Alright. Let's get started. And, hey.
Jordan Wilson [00:06:52]:
Yeah. Live stream audience. Let me know. So big big bogey says Gemini will be the rapper, for all your Google products. Yeah. That's that's a good point. Douglas is saying Gemma three is interesting, but I'm seeing a decent amount of issues reported with users having issues of quality on Ollama solutions for local hosting. Yeah.
Jordan Wilson [00:07:12]:
I I I use Ollama, as well. I did download the, one of the smaller versions of of Gemma because I can't, do the 27 b version on on my computer, just yet. Yeah. Now Michael is saying, Google doesn't tease like OpenAI. They just deliver. Yeah. Obviously, Google, in the whole LLM race, they came out straight up stumbling. Right? They're I mean, they're transitioned from Bard to Gemini.
Jordan Wilson [00:07:38]:
They're, you know, they're, initial Gemini release snafu. Right? They came out with a marketing video essentially when they released Gemini, you know, in December, '23, I believe it was. And essentially, they showed all these capabilities that weren't possible. Right? So, Google came out, like, had the absolute worst rollout possible. I think they were extremely far behind until mid twenty twenty four, right around September. And then, you know, in '20, or or sorry, and then in December, like I just talked about, I think Google went from like, oh, okay. You know, they're they're they're number two, number three, right, probably, around September of twenty twenty four. Until that, I'm I'm like, alright.
Jordan Wilson [00:08:19]:
They're barely top three. But now it's one a one b with OpenAI and Google, and I think they're they're they're constantly, flip flopping, those spots. Alright. So let's talk about what's new in Gemini two point o flash thinking. Okay. So without going into too much detail, right, if you listen to the show, you know, but there's essentially now two kinds, are two very distinct flavors of large language models. Right? You have your, your kind of quote, unquote old school transformer GPT type models. Right? And that's your your normal Gemini two point o and Gemini two pro.
Jordan Wilson [00:08:56]:
And then you have your reasoning models, right? These are models, that kind of use, you know, chain of thought. They use this step by step thinking. They, kind of do a lot of the work that humans would do under the hood. So, they use more compute, you know, more inference, they take longer, but generally they give you much better results. So this is the flash two point o or sorry, Gemini two point o flash thinking, is the updated version of that. So couple things that are new. Number one, file upload, improved performance, better reasoning capabilities, improved speed. Also, you can try this for free in Google AI Studio.
Jordan Wilson [00:09:34]:
FYI, on Google AI Studio, it's free. You get to use everything. It's amazing. There's no data protection, FYI. Right? On the front end, if you are using, you you know, Google Gemini, as a paid user on the front end, there's great data protection. Right? It's enterprise grade data protection. Your data is not being shared with Google or anywhere else. Right? So if you're using, Gemini on the front end, Google AI Studio is more of a sandbox.
Jordan Wilson [00:10:02]:
Right? It's not necessarily something where you or your company would go to get work done, if that makes sense. It's it's more of a sandbox, but I know a lot of people are are using it as their main model, which I wouldn't necessarily, but you can. Right? But you so you can use this new Gemini two point o flash thinking, for free in Google's AI studio or it's available for paid users, on the front end of Gemini and paid paid users now get a 1,000,000 token context window. That's love to see it on a reasoning model. Right? I I haven't talked about this a lot yet, but, you know, I'd I'd say in, you know, mid twenty twenty three to, 2024, you know, all the the rage was around, you know, rag pipelines and, you know, so this retrieval augmented generation, you know, and I think the long context windows are gonna make rag a little less, important. Right? I I I still think, you know, rag definitely has its benefits, right, in terms of accuracy. But, you know, I don't know if, you know, a a year or two from now, we're gonna be as concerned or talking as much about RAG pipelines, just because these context windows. I mean, the fact that we have a million token context window right now, you know, that means you can dump in, hundreds of documents, you know, hundreds of pages worth of documents and, you know, for free, or or sorry, you know, for basic paid users, Google is gonna be able to, Google Gemini is gonna be able to remember it all in this new version of two point o flash thinking.
Jordan Wilson [00:11:35]:
Alright. But I think one of the biggest things, and this is what's on my screen now, that people are gonna be talking about is inline image generation. Right? Using Google's imagined model, and it's really good. So I just did this example. I was playing around with it a little bit, last night. So I said and this is inside Google AI Studio. This is just a simple prompt y'all. This is how I actually use models, but, it's easier for screenshots.
Jordan Wilson [00:12:03]:
Right? So I said write a long form blog post about why tourists should visit Chicago, make it very detailed, and create photos of the most historical stops. Right? So this is something that people might do. Right? So you might be writing, I don't know, a blog post for your company or updating something, you know, on your website that's super old and it needs visuals, but okay. So not only, and and again, I'm I'm, accessing, Gemini two point o flash thinking in this, instance through Google AI Studio and the output format you'll see you can set to images and text. Right? So this is amazing. So then look at this, this output. So it literally writes me a blog post about the top places to visit in Chicago, then it creates AI images of those historical sites in order. Right? Which is so good.
Jordan Wilson [00:13:00]:
It's so good. Right? This is one of those small features that when I used it, I was like, Right? Kind of at a loss loss of words, you know, just because this is something I used to do. Right? I've I've been, you know, obviously, I have a background in in marketing and content writing and as a journalist. Right? And this is stuff I had to do all the time, you know, spend a long time writing a, you know, a good blog post. You know, go find photos that you can legally use. Right? Sometimes this is a a process that would take a day or longer. Right? Gemini two point o Flash Thinking just did it in a couple of seconds in that a very a very high quality. Right? You can't see it maybe, too well on my screen here because I just have a screenshot, but these AI images were really good.
Jordan Wilson [00:13:54]:
Right? And it went along with the content. So think about what that means and can mean, for your business. Right? I I I mean, number one, there's no need for you anymore to have those, you know, old terrible stock photos. Like, sorry. You shouldn't. Right? But I mean, also, you shouldn't just copy and paste anything. You know, I have a large language model and slap it on your website. You know, we, the Internet is, sloppy enough as it is.
Jordan Wilson [00:14:20]:
Right? So you would probably want to do a little bit better job, take advantage of that context window, put in a bunch of, information about your company or, you know, your old blog post, maybe if you want it updated. Right. So you do wanna do some some good human in the in the loop work, before and during and after, and you probably wanna iterate. But, yeah, this would take a process that would literally take multiple hours, sometimes a day or more and get it down to, you know, maybe ten minutes. Right? And you're probably gonna have something better, especially if you spend the time, sharing the context in the, the information, about your company to improve this. And that and that's huge. And yes, right now, this is available in Google AI Studio. Yes.
Jordan Wilson [00:15:02]:
Google AI Studio doesn't have any data protection, but y'all anything you put out on the Internet, they all know anyways. Right? So that's why I'm saying, like, this is great for something like company blog posts. If if it's, you know, documents that you use internally that don't have sensitive or proprietary information, right, I would do it. Right? Yeah. Google's gonna use it for training, but if it's on the Internet anyways, guess what? Google and OpenAI and Microsoft and Meta and everyone else has already gobbled it up. Alright? So that's that's a big one. That's a big one. Alright.
Jordan Wilson [00:15:35]:
Next. In this one, even though I think Gemma three might be the biggest update, the one I'm most excited about might be, deep research. Alright? Because now we have kind of this deep research two point o because it got, Gemini two point o flash thinking, and the deep research product is completely different now. Alright. So the way that, in in in first, you know, everyone's like, oh, you know, who was first with this deep research? So, technically, Google was the first to release a deep research product. And then throughout, you know, January, February, March, everyone and their mama released a deep research product. Right? It's all the rage. Right? So, obviously, opening eyes, is still the best.
Jordan Wilson [00:16:23]:
We got deep research from perplexity. We got deep search, which is the same thing, from grok, which when it first came out, I'm like, this is terrible. It's actually improved a lot. The grok deep deep search is actually pretty good now. You you know, if you listen to the show, you know, I'm sometimes hard on, you know, Twitter slash x slash grok. The grok deep search is actually pretty good now. So Google's deep research was the first even though it if you go back and look at reports, OpenAI was reportedly working on this back in May. So, you know, they were the first company to be tied to this deep research, although Google was the first to market.
Jordan Wilson [00:17:00]:
Their first version of deep research worked completely differently. So I've only spent maybe about, an hour with this so far because it's brand new, but it works differently. So the first version of deep research essentially used cached versions of pages, and it just kind of ingested it all at once in one giant step. Right? Which is great. Right? There I think there's pros and cons to that approach. So the depth in detail, wasn't always there, but it did do a great job at very quickly, kind of synthesizing the information that humans would go out and wanna do anyways. Right? But it didn't take this kind of step by step thinking approach, which is what it does now. Right? Which is great because this is more of what, you know, grok and perplexity and and OpenAI's, deep research do is they take this reasoning approach.
Jordan Wilson [00:17:49]:
They go step by step because what happens, right, if they're looking at some of the most authoritative sources first. Right? So let's say, you know, you ask it about, you know, the latest updates. Right? This is just an easy one. The latest updates from Google. Right? If you'd ask the old version of Google's, deep research, it would just look at a cache of hundreds or maybe a thousand pages all at once. Right? Not knowing that maybe 95% of those sources might not make sense now because Google over the last three days has just released all of these new updates. Right? So now, it's gonna start in kind of a step by step fashion. So it's first gonna do some some high level research, and it's gonna find out, wait.
Jordan Wilson [00:18:30]:
Oh, Google just had a bunch of new updates. So, you know, if the user is asking about new updates from Google, we should then focus our, the the rest of the search just on those things. Right? So that's extremely, important in a huge, a huge update from Google. Right? So I haven't used this enough to test it to see if it's going to be on the level of, OpenAI's deep research. I did a whole show on that. So if you're very interested, you can go back and listen to it. Right? Perplexities was pretty bad. Hallucinations were were off the charts.
Jordan Wilson [00:19:06]:
The old Google deep research was pretty good. You know, had some hallucinations. GRACS, which we didn't compare at the time because it wasn't out. GRACS is actually pretty good. And then OpenAI was just in in a league of its own. So, I'll have to see, where the new Google, deep research kinda two point o with flash thinking, where it goes. But, it's great. And the thing I like as well is it's available, as a kind of like normal model, if that makes sense.
Jordan Wilson [00:19:37]:
So, you you know, in in LLMs, you have, like, kind of models and modes, and everyone works a little bit differently. Right? So previously, you would have to choose, deep research as a drop down in the menu. Right? So now if you're on a paid account, on Gemini's front end, so you're using Gemini as a chatbot. Right? So you can just now click. You can still do it as a, model, but then there is also a mode. So there's a new icon where you would chat that says deep research, which I like because then you can start another conversation in a different mode or a different model, and then you can click the deep research button and work within the context of that same window. So a big kind of, you know, sounds like a small thing, but it's actually pretty big. But so, couple of the things.
Jordan Wilson [00:20:24]:
Let's just go over the the the bullet points. Number one is there is free access to deep research. So it's it's it's very it's very limited. Right? I'll we'll double check-in the newsletter how many, queries you get. But even if you have a free account, you do get some, access to deep research. Like I said, it is definitely enhanced with the new Gemini two point o flash thinking. So, essentially it uses reasoning, that improves the planning, the searching, and its ability to synthesize, which ultimately gives you better, faster, deeper report generation. That's just I mean, the quality is gonna be much better.
Jordan Wilson [00:21:01]:
The other thing which is great is you can see the reasoning process. Right? So you can click and see where it's going. One of the biggest hacks which I think people don't do, which some some of these I talk around in the show, some of them I don't because they're my secrets. Right? Maybe I'll do a show on that one day. But, go always do a deep research twice. Never do it once. Right? Because you should go and look at its reasoning. Right? So you should go and look as an example.
Jordan Wilson [00:21:28]:
Oh, here's what, you you know, Gemini deep research did when I asked it about the newest Google AI. Right? Oh, I can see it started by, you know, searching, you know, just deep research or or just, you you know, Gemini two point o. Right? So maybe you can, or what you should be doing is you should be reading and learning by looking at that, the reasoning process. And then I always do this. I manually do this. I take notes. You you know, I save my original query. I see what went wrong, what went right, what could be improved.
Jordan Wilson [00:22:02]:
You know, I literally look at its research process and I'm like, yo, it maybe didn't start out right or, you know, it got a little sidetracked, you know, halfway through. Right? So much of, I think so much of the improvement or what's left to be desired from large language models isn't because the models aren't good. It's because the human's instructions aren't clear enough. Right? So that's why I always say take a second stab at anything, deep research. But I think, you know, if you're looking for immediate ROI, right, deep research products, everyone should be using them. Right? And and Google's, new one here, I think, you know, it was kind of in its in its own category because it was the first one, and it worked a little bit differently. And it's like, oh, okay. It's great.
Jordan Wilson [00:22:49]:
But compared to everything else, not that good. But, yeah, now it's, now it's instantly, back on the map. Alright. Next. And this one's gonna be a mixture for people. Alright? It's gonna be a mixture. You don't have to use it. But, Google, did just release a version of, Google Gemini, so a mode.
Jordan Wilson [00:23:11]:
So if you're a paid user, you can click the drop down and you should see this now. The other good thing I mean, bless bless up. Finally, some of these new models came to my workspace account. So you always heard me say like, oh, you know, I have a paid account, for my, you know, my personal Gmail address, and then I have a paid Gemini account, for my work. Right? So we, we use Google Workspace. Right? Which used to be called G Suite, and they've had, like, 30 other names for, you know, for whatever workspace is now, you know, in the past five years. Right? But I'm finally seeing some of these new modes, available in my paid workspace account, which is great because I'm like, what what good does all of this do? Yeah. Right? All this all these front end Gemini updates if I can't use them with my work data.
Jordan Wilson [00:24:02]:
Right? So now many of these updates are, available inside, my paid workspace account. So keep that in mind. You should probably go check for yourself, but not all of them. So as an example, the only one I don't have available in my, my paid workspace account is this one, the personalization. So that's only in my, my personal Gmail. So maybe they're they won't be rolling this out to Workspace accounts. But, if you have a paid, Google Gemini on your, you you know, on your personal Gmail, so that's just like at Gmail dot com. Right? You will have this, or you should have this personalization.
Jordan Wilson [00:24:41]:
So, it probably goes without saying, like, what this does, but it literally uses your Google search history, to improve your, and and to personalize your Gemini queries. Right? So, essentially now Gemini can connect with users search history, to improve the context and responses, and there's gonna be future integration, with Google Photos and YouTube. Alright. So, yeah, a lot of people are gonna look at this and be like, oh, this is a privacy, right, privacy issue. I don't want Gemini, you know, having my Google search history. I don't care. Take it. Right? I'm one of those people, and, like, I feel there's this this is a very polarizing issue.
Jordan Wilson [00:25:26]:
I don't care. Right? Meta, take my data. Even though I don't use Facebook or Instagram or WhatsApp. Right? But I use Llama. I use meta.ai. Right? Meta, take my data. Google, take my data. Microsoft, take my data.
Jordan Wilson [00:25:38]:
I don't care. Right? I would love to see more personalized ads. Right? I I like, I can't wait. Why why does my Google TV why does my Google TV alright. That's what I use for cable or whatever streaming. I don't know. But or my my YouTube TV. Gosh.
Jordan Wilson [00:25:53]:
Am I 90? What but why does my YouTube TV not have this feature? Right? I want my YouTube TV to just show me, like, ads for AI or or ads for, you know, I don't know. Like, I love North Carolina basketball and and Chicago stuff. Right? So I'm fine, and I'm looking forward to trying out, this new, personalized, version of Gemini. But the problem is, like I said, it's only available on my personal account. And for the most part, when I'm using Google, when I'm doing Google searches, I'm doing it under, under my, my workspace, my work account. So that doesn't have the feature right now. So I very rarely, use my personal Gmail for any searching. So this also does use, the, two point o, flash thinking model.
Jordan Wilson [00:26:42]:
Also users, so there are some, transparency tools. So users can view how Gemini uses their data sources including, past chats and search history. And privacy controls do allow users to disconnect, edit, or manage, linked data at any time. Also some other small things in there, Google Gems, was also updated and that's, now available for free users, which is pretty cool. I've never been a big fan of gems. I'm gonna have to go in and and relook. One of the biggest problems was, it didn't always do a great job at accurately grabbing the data out of my workspace account. So I'm gonna go in, you you know, I'll see if, you know, they didn't really talk a lot about the new go Google gems updates.
Jordan Wilson [00:27:26]:
So, if you don't know, right, so you have as an example, OpenAI has GPTs. Right? Essentially, you can create a small, specialized version of chat GPT, right, based on your own data, and you give it kind of custom instructions. That's what Google gems is. It took Google forever. Right? To, you know, they announced, Google Gems, and then it was, like, nine months before they were actually released. So I think they kinda missed the boat on that. Right? They're go to market kinda on that one stunk. It's improved.
Jordan Wilson [00:27:58]:
Right? Like I said, now Google, I love what they're doing now. No shiny announcements. They just come out of nowhere. Like I said, it's March. They choose madness. They bring all these updates to us. But, gems are available for free users, which is cool. So even if you're not a paid user, you can go use, Gems.
Jordan Wilson [00:28:19]:
And, you know, Gems are also available on workspace accounts as well. Although the data sharing, right, and being able to connect with your your Gmail, your YouTube, your calendar, not always, as robust or accurate in a workspace account, which I don't understand why. But on the personal account, it does it does a pretty good job. Alright. There's more y'all. Alright. This might not be for all of us, but I think the implications on this are pretty huge because Google, did announce, Gemini for the physical world or Gemini robotics based on Gemini two point o. Alright.
Jordan Wilson [00:28:56]:
So, essentially, Google's like, yeah. This is good. You know, Gemini two point o, here's a version of it for robots, and this is really gonna impact the physical world. Alright. So Gemini robotics integrates Gemini's two point o multimodal reasoning. Right? So text images, audio, and video with physical actions, which enables robots to understand natural language commands, adapt to changes in real time, and interact seamlessly with humans and environments. So it's pretty big. Right? Because this, according to my knowledge, this is the first publicly available.
Jordan Wilson [00:29:33]:
And when I say publicly available, it's commercially available. Right? But this is the first time one of the big AI labs. Right? So if you're saying like, okay. That's Microsoft, Google, OpenAI, Anthropic. You could throw Mistral in there, maybe Cohere. Right? As far as I know, this is the first time, a company is like, yes. Here is a model for robots. Right? Generally, a lot of these are proprietary, and they must be good because even as an example figure, which is one of the biggest, you know, probably three to four names in humanoid AI robots, They dropped OpenAI's model, and they are now using their own.
Jordan Wilson [00:30:09]:
I believe that's called Helix. So pretty big news here from Google. So, some of the features and updates. So it ex it it enhances, dexterity and manipulation. So robots can now with this new Gemini two point o robotics update can now perform complex multi step tasks, required for fine motor skills like folding, origami, packing lunch boxes, or handling delicate objects like coffee mugs, what's in my hand. So maybe in the future, I could have a, Google Gemini two point o robot as I'm doing this show. It can just pick up my coffee mug, and put it in my mouth so I don't have to take a break and I can keep typing and moving my mouse. Right? Couple other things.
Jordan Wilson [00:31:01]:
It has embodied reasoning. Alright. So that's, Gemini robotics ER. So it's a new model. So Gemini robotics ER adds advanced spatial understanding and coding capabilities, allowing rope, allowing robots to plan, detect, and interact with objects. So, very cool there. So, yeah, even though, you know, we don't go hard in the robotics paint here on the everyday AI show, This is gonna be something that impacts all of us. Right? Whether you know it or want it, doesn't matter.
Jordan Wilson [00:31:35]:
You know, if you listen to, our our twenty twenty five AI roadmaps and prediction series, which I suggest you all go back and listen to. Right? I said embodied AI, so not just humanoid, you you know, humanoid AI robots, but I said embodied AI in general, is gonna be a a huge, thing in 2025. I think 2024 was too early. But, I mean, here we go. Google is getting in the game. And the biggest thing is now, you know, people might be like, oh, okay. Does this mean Google's gonna, you know, have all these robots? No. Not necessarily.
Jordan Wilson [00:32:06]:
I think this is, a big step from Google to compete with NVIDIA for data. Right? So what I think many companies, are are are shifting toward, right, as they try to make AI more useful is they are shifting toward world models. Right? AI is great. Large language models are great. Generative AI is great. Right? But, ultimately, you know, as companies are in a foot race or a robot foot race, toward AGI, artificial general intelligence, ASI, which I don't necessarily want, but we're racing there anyways, artificial superintelligence. Right? You know, the big labs and the big AI companies now understand we need as much data as we can, with AI in the real world. Right? Essentially, right now, large language models are kinda confined to what we do as knowledge workers in front of a computer.
Jordan Wilson [00:32:58]:
Right? How we how we think in front of a computer, how we create content, how we synthesize information. Right? So, one of the biggest next, frontiers of AI in the real world is something like this. It's, you know, this data think of now all the data that Google is going to be able to, you know, have now. Right? And and reportedly, they're, working with groups like Boston Dynamics, Agility Robotics, which I think we might be bringing bringing you all in in in interview with agility. I think I'm gonna have to check my schedule for, NVIDIA GTC. Right? But now Google is gonna have all of this real world data, and that even makes the AI that we use today so much more useful. Yeah. It improves creative tools.
Jordan Wilson [00:33:48]:
Right? Like, you know, text to video. Right? Like, you know, Sora in in VO two. Right? Because as you get more of a better understanding of the actual physical world, that improves things like, you know, AI video generation. It improves obviously, you know, humanoid, robots, but it also just improves, how applicable and useful AI and generative AI is in the real world. Because right now, large language models, for the most part, don't understand how we interact with the physical world. So, actually, pretty big announcement even if you don't care about humanoids or anything like that. Alright. Aren't tack here from YouTube just said I want more robots.
Jordan Wilson [00:34:31]:
Hey. Big Bogey. I think I'm with Big Bogey here. He said, I'm not sure I want my nice clean, expensive robot washing dirty dishes. Yeah. That's that's a good point. But also what if it's Converse, like, on the flip side? What if your robot's, you know, super dirty and your dishes are super clean and expensive? You know? I don't know. Alright.
Jordan Wilson [00:34:52]:
Couple more things from Google. Yeah. I told y'all there was a lot. Another small one that Google kind of snuck in. I don't even know if there's a blog post for this necessarily. So this is just from, Josh Woodward. You know, he put out a a tweet on the Twitter machine. Also, side note, the notebook l m team, they've been crushing it.
Jordan Wilson [00:35:13]:
They've been crushing it. They've been putting out great updates. But, hey, notebook l m got that two point o love as well, which I think is really gonna change how we use notebook l m. If you listen to the show, this was, notebook l m was one of our top, AI tools or features of 2024, and it wasn't even close. I'm a huge notebook l m user. I use it every single day. It is, I think, one of, the most underrated still. It is one of the most underrated, underutilized, least talked about, most useful AI tools out there.
Jordan Wilson [00:35:48]:
Alright. So some new updates, from notebook l m. Well, the biggest one is now powered by new model. It's now powered by Gemini two point o thinking. That's huge. Right? Any thinking model, yes, it takes a little longer. I mean, but if you look at any benchmark, thinking models always outperform. They're always gonna give you better answers with more nuance, better understanding, higher accuracy, lower hallucination level.
Jordan Wilson [00:36:15]:
So the fact that notebook LM, which is grounded in your data. Right? So that means it's not like, you know, Gemini or or Chad GPT or Claude, right, where you can just go in and start asking questions. If you go in and start asking questions of notebook l m, it's like, yo, I don't know anything. You gotta give me data, and it only works with the data that you give it. Right? So if I upload a bunch of data, about, everyday AI and I say, hey. Explain how to make pancakes. Unless I've talked about that on the everyday AI show, notebook is like, yo. I don't know.
Jordan Wilson [00:36:45]:
Go figure that out yourself. I have like, I don't have that. Right? So it doesn't just make things up to try to be helpful, which is huge. Also side note, I've gotten real heavy into my pancake making game, making them from scratch the past couple of months. And I'm like, why why have I been making boxed pancakes for, you know, twenty five ish years. Right? From scratch pancakes. Gotta love it. Alright.
Jordan Wilson [00:37:09]:
Couple other things new in notebook l m. So now there's citations inside of your notes. Let me tell you what that means. So if you're using notebook l m, right, you you upload all your sources that can be YouTube videos, it can be Google Docs, copy and paste, you you know, certain URLs, although that doesn't always work well because a lot of them are blocked. Right? And then you can chat with notebook l m, and then you can save what's called notes. Right? But the downside, like, so in the chat window, everything is sourced. Right? So, let's say I upload 500 transcripts of everyday AI, and I'm like, yo. When did I talk about, you know, mid journey? And it's like, okay.
Jordan Wilson [00:37:50]:
Here in episode, you know, 320, you know, Rory Flynn came in and gave these five tips for Midjourney. Right? So if I save that for a note, and then I go back later and look at that note, the citations previously were not there. Okay? So now when you create new notes, it keeps those citations. So previously, you can only click, right, and go in and it will show you the source, right, which is huge. So, you know, unfortunately, there are some downsides previously to using these notes, inside notebook l m, but now this, citations carry over. That's big. And then also in audio overviews, you can customize the sources. So that's that's great as well.
Jordan Wilson [00:38:31]:
You know, I used to kinda do that manually. I would, you you know, get one big notebook with all my sources, then I would duplicate everything, delete sources. So now it's just better. So, you you know, those, the deep dive AI podcast with the two hosts, which are great. There's the interactive feature, which is not new, but it's still fantastic. So now you have a little bit more customization, with the sources for audio overviews. Douglas says, I like this from scratch pancake recipe from Alton Brown. Highly recommend.
Jordan Wilson [00:39:04]:
Hey, Douglas. What about the, the from scratch pancake recipe from Jordan Wilson? It's honestly, I just use ChatGPT, so I can't claim anything. Alright. Hey. Maybe maybe episode 500 should be Jordan's secrets, to box and boxed pancakes. Alright. We'll see. We'll see Angie.
Jordan Wilson [00:39:25]:
Alright. Last but not least, I kept you guys long enough. Gemma three, which I think even though most of us may not be using Gemma three. Right? I know a lot of us are. You know, we have some tinkerers in the house, but Gemma is, Google's small language model. It's open source, which means you can download it, you can fork it, you can fine tune. Right? You can do a lot of things with open source models. But generally, because you're working with them offline, you either, one, have to have an incredibly powerful computer, or two, you're working with a very small variation of this.
Jordan Wilson [00:40:04]:
So, JEMMA three, big news. Alright. So it is lightweight, high performance. So it does, it I'll say it's a state of the art, small language model now designed to run directly on devices. So ranging from phones to workstations. So here's the different model sizes. So it is one b, four b, 12 b, 27 b. Those are billions of parameters.
Jordan Wilson [00:40:29]:
So as an example, right, and I'll just, I'll just speak in generalities here. Alright. So let's say you have the newest smartphone. Alright. I'd say the newest smartphones could usually run about a four b model. Alright. So that's billions of parameters. Alright.
Jordan Wilson [00:40:46]:
So again, what's what's the like, why does this matter? Why are you talking about this, weirdo? So all the AI that we use right now for the most part goes to the cloud. Right? So number one means it's slower. Number two, it in theory means it's less secure. Although, I personally think and maybe this is because I'm I'm very lazy fair with with with data. Right? When you use if you have the paid version of ChatGPT, paid version of Gemini, paid version of Claude, paid version of Copilot, you have nothing to be concerned about when it comes to your data because you can turn off model training. You know, it doesn't show up randomly on the Internet. It doesn't go to like, I I I honestly don't understand why people don't under like, why people don't choose to educate themselves, on data privacy and protection when you're using a paid version of a large language model. Right? People think are like, oh, well, I would never put yeah.
Jordan Wilson [00:41:37]:
Yeah. We have an enterprise version of, you know, Copilot three sixty five or Gemini or whatever, but I would never put my data in there. It's like, oh, okay. What are you using for cloud storage? Oh, the same company. Guess what? It's the same data protection, which I don't understand anyways. Right. So these small language models like Gemma three are great because they're it's edge AI. It's it's local models.
Jordan Wilson [00:42:00]:
It's offline. Right? So you don't even need an Internet connection. You can download them. You can go in and fine tune them. You can create your own version of them or your company can create the own version of them. And then you can run them locally on device, which is huge. Number one, even though I know a lot of people don't care about this or think about this, it's better for the environment. That's one thing I think people are overlooking, when we talk about small language model.
Jordan Wilson [00:42:24]:
I think people talk about speed. It's faster. It's private. It's more private and it's more secure because you're not sending all of this information, you know, to the cloud. But, yo, how about the environment? Can can can we clap for small language models on device AI, edge AI? It is better for the environment y'all. I don't like I know I know people get all, like, up in a tizzy. Right? Like, oh, you know what? A chat GBT searches, you know, takes 10 times more, power or consumes 10 times more electricity or, you know, 10 times more than, you know, a traditional Google search, which I will offer the flip point. Yo.
Jordan Wilson [00:42:58]:
Like, I have to do 20 Google searches to get what I get from one chat GPT query. So is it does it consume more power? Absolutely. Are you doing fewer Google searches? Yes. Right? I I barely do a traditional Google search, anymore, which, gosh, Google dropped so much. I didn't even mention Google's new AI mode, that they just released. So, yeah, they they literally chose AI violence by releasing all this. So getting back to, you know, the small models. So So hopefully, you can kinda understand the difference.
Jordan Wilson [00:43:30]:
It it's it's better for the environment. It's safer. It's faster. But right now, you know, the biggest version, the best version, the '27 b, for the most part, no one can run that on a single PC. Right? If you're the the the IT administrator, you know, you know, and you you guys have some compute, right right, you have a server rack. Yeah. You can run a 27 b model. But for the most part, the the average person can't yet.
Jordan Wilson [00:43:57]:
Right? So I do think at this time in, like, you know, two years, even our phones will be able to run like a 27 b model. Right? Because the chips that we're all using, so the GPUs are improving, they're getting, faster and cheaper. The the the NPUs, the neural processing units, which are like AI chips, TPUs. Right? So all of these kind of AI chips, that we use to create and use AI, they're getting more capable. They're getting faster. They're getting smaller in physical size. So that's why, you know, things like this Gemma three are actually wildly, important, because I think, right, like I said, in in in probably two years, we're all gonna have the possibility or the option to run a state of the art large language model locally on our device. Alright.
Jordan Wilson [00:44:49]:
And let me show you kind of a graph here why I think this is kind of important. Right? This is the best small language model that has ever been released. Alright. So if we look at Elo scores, which I know I talk about a little bit, but an Elo score, if you go to the LM Arena, which I think is one of the most important things, we always look at benchmarks. I think benchmarks are are are great in in some regards, but they're also they can be a little deceiving, because companies can essentially overfit or overtrain their models to perform well on benchmarks, but then it's like humans hate them. Right? So Elo scores. So if you go to the LM Arena, it's I always say it's like the blind Pepsi taste test. You put in one prompt, you get two outputs, you have no clue, which models they are.
Jordan Wilson [00:45:33]:
You choose which output is better, and that gives you what's called an Elo score like chess. Right? So the higher the score, the better the model. And, Gemma three, I still can't comprehend. This is the 27,000,000,000 parameter version. It is a top 10 model on the EO scores, which is nuts. Right? Yeah. There's dozens and dozens of models, but it is a 27,000,000,000 parameter. So, as an example, DeepSeq v three is a 671,000,000,000 parameter model.
Jordan Wilson [00:46:10]:
Right? Everyone's going crazy. DeepSeq and yeah. By the way, they didn't tell the truth about their training and how much it costs. Right? I love this. Google put together a little chart. It said Nvidia h 100 GPUs required to to train these models. And they're like, nah, DeepSeek. You didn't do this for, you know, $5,000,000 in your backyard.
Jordan Wilson [00:46:29]:
You needed, like, a a huge cluster of GPUs. Right? Like, a lot of the reports said. Right? I I I think, DeepSeq was intentionally miscommunicated or undercommunicated. Right? And then everyone else in the world called them out and I'm like, yeah. That's not how it works. Anyways, GEMMA, three is a 27,000,000,000 parameter model. So it is significantly smaller than all of these other models, and it did a better it had a higher ELO score. So this is human preferences.
Jordan Wilson [00:46:57]:
Right? Humans preferred it over, as an example, DeepSeq v three. That is a 671,000,000,000 parameter model. So I'm not great at math, but that is 30 times larger. So humans preferred Gemma three, which is a downloadable 27,000,000,000 parameter model. They preferred the responses from Gemma three over DeepSeq v three, which is 20 times larger. Okay? We're not talking small multiples. It's 5% smaller, 20%. No.
Jordan Wilson [00:47:34]:
30 x. 30 x. Gemma three is one of the most, I think exciting, advancements that we've seen in small language models, potentially ever because I think this really changes not just the LLM race, but it changes what's possible because there's a lot of things, right, when we talk about, you you know, the future of of working with large language models locally, you you know, that we thought maybe were three, four, five years out. Nope. It's today. Right? Because as an example, yeah, 27,000,000,000 parameter. You gotta have you gotta have some juice to run that, but, I believe that would run on a single NVIDIA digits. Alright.
Jordan Wilson [00:48:19]:
So, NVIDIA digits is kind of a new, like, supercomputer, from NVIDIA. But for $3,000, you have a supercomputer in NVIDIA digits. And you can run now on one NVIDIA digits, which you can use as its own computer or you can hook it up to your existing computer. Right? For $3,000, you can run locally, JEMMA free, a state of the art small language model. It's gonna be fast. Humans prefer it over things like DeepSeq v three, llama three, o three, mini, mistrial large. Right? So it performs very, very well. So y'all, three years ago, if you would have said, hey.
Jordan Wilson [00:49:06]:
How much is it gonna cost in, 2025 to run a state of the art model that's, you know, one of the top 10 preferred models in the world on your own device, I would have said, you know, couple million dollars. And I think most people would have said a couple million dollars. You can do 3,000. This changes the race completely. So although I'm not gonna be using Gemma three every day, and I think a lot of our audience isn't gonna be using it as well. Right? Like I said, unless you're a developer, unless you are, you you know, someone making decisions, you know, on the IT side. You know, and if if you're more technical, you might be using Gemma, immediately. But, y'all, that is completely changing.
Jordan Wilson [00:49:51]:
I think it's, like, not just changing the LLM race. Like, we're on a different track now. Right? It's not just like, oh, we have, like, a a new, you know, oh, Gemma three is now leading the pack. They're leading the race. No. It's a new race. It's a new race. I mean, the just because a model that small performing that well in human preferences completely changes how we look at AI and its usefulness and how and where and why we use it.
Jordan Wilson [00:50:23]:
Alright, y'all. I hope this was helpful. If so, let someone know about it. Alright? Yeah. The Google surprised us all. I hope you were delightfully surprised with, you know, getting a little bit of value out of today's show. If you did, if you're listening on the podcast, appreciate it. Check out the show notes, please.
Jordan Wilson [00:50:44]:
You know, maybe hey. I always put my, our email. I put my, my LinkedIn. Reach out. Let me know. What's your what's your secret to pancakes? Or, let me know. What's your, you know, favorite release from Google, or do you not wanna use any of them? I I love hearing from y'all. My my responses are gonna be a little delayed since I'm gonna be at NVIDIA for a couple of days and probably be behind on some of my normal day to day work.
Jordan Wilson [00:51:07]:
So make sure you tune in. Next week, we're gonna have special shows. I'm gonna be reporting live at NVIDIA. I'll be talking with, NVIDIA leaders on what they announced at the keynote. I'm gonna be talking with other, partners. I think I have some, interviews lined up with some startups, with some leaders at enter enterprise tech companies, you know. So it's gonna be an exciting week, so make sure you tune in for that. Also, make sure you go, to youreverydayai.com.
Jordan Wilson [00:51:33]:
Sign up for the free daily newsletter. If this was helpful, yes, subscribe on the podcast. Leave us a rating and review. I'd appreciate that. If you're listening here live, on on the Twitter machine, on the LinkedIn machine, please repost this. You know? I I know, you know, I've been told that everyday AI is your cheat code. Right? But please, you know, that doesn't that doesn't pay the bills. Right? If if you keep this to yourself, share this with someone, you know, share this with your team.
Jordan Wilson [00:51:59]:
If you're giving a presentation, I'd love throw the podcast on your presentation. You know, that's what I wanna do. I wanna keep AI education free. I wanna keep it unbiased. I wanna keep it accessible. Right? And hopefully keep it, I don't know, a little fun. Maybe a little less boring than reading a bunch of research papers. Alright.
Jordan Wilson [00:52:17]:
Thank you all for tuning in. Hope to see you back later for more everyday AI. Thanks, y'all.
Midroll [00:52:25]:
And that's a wrap for today's edition of everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers, and we'll see you next time.