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- [00:01]
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- [00:24]
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- [01:00]
AI Industry Podcast Host
you want to get access to this episode and my next 30 episodes all AD free so there'll be no ads on them, go check out my podcast AI Chat. You can go search for that on Spotify or Apple. It's AI chat. I'm going to post all of these news episodes and I'm also posting interviews like I just interviewed the CEO of Cohere. They've raised over a billion dollars for their AI model, talking about what they're going to be spending the money on and the direction of the AI industry, along with all of this new stuff. So if you want to go check it out with no ads, free for free. It is AI chat. Anthropic's Mythos AI is finding Microsoft bugs faster than Microsoft's engineers can patch them. Google's Synth ID Watermark survived 300 edits, but fragmentation might actually make this completely useless. We'll get into that encore. AI just raised $30 million to turn sales call transcripts into voice agents. This is an interesting pipeline as far as the data goes. Writers right now are embracing typos, myself included, and all of the idiosyncrasies, basically to fight the AI writing that we're seeing everywhere. That has just become, you know, basically a plague on the entire Internet. Sierra is going to acquire Oasis Security for a billion dollars to police AI agents identities. If you have tasks that you do over and over again using AI tools, I'd love for you to check out The AI Box Builder platform. This is my own startup at AI Box AI and it allows you to link together multiple AI models and put prompts in and automate entire processes of repetitive tasks so you don't have to do them and, you know, put in your prompts and copy and paste between different documents or different AI models over and over again. And the cool thing is that all of all the different AI models we have over 80 on the platform work together. So you can have 11 labs creating audio, you can have Google VO3 creating video, you can use ChatGPT to generate images and you can use Claude for your writing, you can build out workflows and really cool tools all on the AI Box Builder platform. And it's linked in the description and it's only $8.99 a month. In addition, you can chat with all the different AI models in one place for that same price. Let's talk about what's going on with Anthropics Mythos. They have a bug hunting AI and it found 90 critical and 141 different important bugs in Microsoft SharePoint just in April. And this is basically the problem here is that it's finding these bugs faster than Microsoft's engineers can patch them. So Microsoft shipped to 600 fixes in on July 14, and their internal meetings show that the company was racing a May 31 deadline before hostile governments build equivalent tools. And I'll also say that this isn't the only thing, you know, like Microsoft, Microsoft SharePoint isn't the only thing it's been working on. It has found hundreds of Critical bugs in Microsoft 365Microsoft Teams, Microsoft Copilot, all, all of that since the beginning of the year. So I think they have about 300 moderate severity SharePoint flaws that are still queued for patching. So I mean, this is kind of a big deal. And on July of this month or the 14th of the month, Microsoft released patches for over 600 bugs. Only seven were low or moderate severity and one was already being exploited by hackers in the wild. So I mean, it literally found something that was actively being hacked. The Five Eyes Intelligence alliance warned in late June last month that the window for defenders to outpace AI equipped attackers would close within months. Internal Microsoft documents show that it might have already closed. Right, because we already have one exploit being used. The real danger, I think, is that Mythos can chain multiple small bugs into working exploits that humans miss. So Vin Nugent, who's a senior anthropic advisor and a former NSA AI chief, said that Microsoft's standard triage system, which is patch critical flaws first, low severity last, no longer works when AI can weaponize the bottom of the queue. So the race is definitely on and speed alone might not be enough. Right now we also have Google's Synth ID watermark. So basically this is an invisible watermark embedded on AI generated images, primarily from Google. But there's some other, a bunch of other companies have kind of all worked together to adopt this. Not everybody has. I think Grok, if I, if I'm wrong, remembering correctly, does not do this, but many others do. So when they were, when they did this, these AI generated images. When it had the synth ID watermark, apparently it actually survived 300 rounds of compression and resizing and real testing. And so and it still was able to detect that an image was AI generated. So people might say like, oh look, I'm going to, you know, take this AI generated images image, I'm going to compress it, I'm going to resize it, I'm going to zoom in and like do you try to edit it in all sorts of ways. But that watermark is still detectable, which is really cool. And that Google says proves that their tech is way more durable than they were expecting. I think there's still one big problem, and that's Google's detector can't read watermarks for OpenAI Runway or Nvidia. So I believe if I'm correct, they're working with Meta on this and maybe a couple other players. But even though they're using the exact same technology, OpenAI Runway, Nvidia, like the detector doesn't go cross platform, which is kind of weird. So synth ID is obviously really impressive. And with those 300 compressed cycles on both fully AI generated and AI edited images, it was only breaking after a 20% border crop was added on top. That was the only thing that was killing it. Google limits synth ID verification to about 10 image checks per day per user. And they throttle faster lookups on similar images to, to block attackers from reverse engineering their system. It's kind of like when you, when anthropic is complaining about everyone going and doing distillation attacks, right? They're like everyone's asking anthropic questions, getting the answers and training their AM models on it. Google's worried that people are going to use the verification, like, hey, is this, was this generated with Synth ID to reverse engineer how they're doing? I don't know if that's really a long term play because eventually, right, you're going to get enough data and fix figure it out. But for now there it's kind of a closely guarded secret. It feels kind of like it's going to be like these captchas. How captcha has had to evolve a lot, right? It used to just be like really simple letters and then it got more complicated and then it got turned into like identify all the bridges in this photo and you know, zoom this, you know, spin the 3D model till it lines up in the image. Like there's all these crazy things that have been invented for captchas. This is kind of what I feel like Synth idea is going to become. OpenAI Runway and Nvidia have each built their own versions of Watermarks but they all have a little bit different of implementation. That's what I was saying. Like it doesn't actually go cross platform. And so there's a bit of a fragmented landscape right now. And these detectors, they can only really detect their own watermarks, not everyone else's watermarks. The thing that I'm the most curious about, and I don't actually have a direct answer to this, that, that I would be curious though is if it's actually able to, you know, you could go to like Google generate an image maybe it looks like a great image to just go to an open source model, give it that image and say regenerate this. If it would kind of kill the synth ID on top and I would guess that it might actually kill it. So for that reason, I mean half the time I'm generating images, I'm like screenshotting another image or another graphic or something that I like out in the wild. And I'm like, hey, use this as like an inspiration and change it slightly. And so that's how I use like ChatGPT to generate images a lot of times. And I'm worried it's going to be the same thing which basically makes these image generating, synth ID things useless because for a majority of, you know, bad actors out there, they'll be smart enough to figure that out. And 99% of people that just use these AI image generators in a, you know, responsible or good way, well, it doesn't really matter. And so at the end of the day, yeah, I'm not sure if it, it's going to make a huge difference if I'm, you know, being honest. Sorry for the pessimism.
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Spinquest Advertiser
Okay.
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AI Industry Podcast Host
Encore AI has just raised $30 million to build voice agents. They're all going to be trained on real sales calls that have actually closed deals, which is fake. Fascinating. Right. Because we have all these data sets. I have a friend in particular and he's got a company and he's got a bunch of salespeople that help close deals for his cleaning company. And what's interesting is he uses go high level and every single call is recorded for the, the whole conversation. So he has transcripts of every single call and yeah, he knows which of those calls actually ended up closing. You can also, even if, if he wasn't tracking that bit of information in particular, you could usually just go read the transcript or have AI read the transcript and decide which ones were closed. But that is fascinating, right? These, it's like determining who the top sales reps are, which ones are actually closing, using those transcripts and training models to be more like those. So fine tuning models to actually be good at closing deals. This startup in particular, Encore AI, they are getting their call recordings and they're mining all of that to identify what the top performers say that move customers forward. And so, yeah, this is in my mind, really, really smart. The annual recurring revenue has grown more than 5x since they started. And since they did their seed round, which is about 18 months ago, they now serve over 40 enterprise customers. Most of those are financial institutions that they're servicing right now. This was started in 2022 as Esayit IO and then they rebranded to Encore later on. I think one of their advisors recommended it because Isayit is not, I know, very recognizable. In any case, they analyze different customer inter stage by stage to find revenue leaks and friction points and then they feed those insights into both AI agents and human employees for coaching. So what's interesting to me is it doesn't just grab the, just like the pure transcript and try to copy the whole thing, but it breaks it into stages. It's like, okay, we're in. You know, maybe it's like a, I don't know if they do cold calling, but let's say it's like cold calling. It's like you got to get the person to stay on the phone and it's like, then you got to get them to be interested in the product. Then you got to give them like a hook. Then you got to like, you know, get rid of any sort of issues they might have with your product. Then you got to sell them on the price, right? Like these are these different stages in the sale that you got to go through. And so it's breaking the transcripts down into all of those different pieces to, you know, identify it that way and kind of break it into chunks. I think this is so fascinating. Encore's edge over Salesforce and a bunch of other CRM giants is that the call recordings live outside of their platform. So financial services is, I think, one of the smartest areas that they could get into for this. Banks right now and also insurers already record every customer conversation for compliance. We've all heard it when we're on the phone. And it's like this call may be monitored for quality assurance purposes. Right. And so Encore right now is going to be able to plug into that infrastructure without forcing a renegotiation of how CRM data gets handled across every customer deployment. And if they're able to actually train it on like, let's say they go to Chase bank and they're like, hey, Chase bank, you already have all the transcripts. We're going to use your transcripts. So we understand how your top salespeople actually convert people, right? So and if they can just keep that data with just Chase bank and they're not like training off of that and giving it to, you know, bank of America or something else, that's really valuable because Chase bank gets, you know, the best their salespeople are able to do, and bank of America gets the best their salespeople are able to do. And they're not worried about that data or that, you know, confidential information leaking. I think this is incredible. And so when you have something like Salesforce, currently Salesforce and others like that are a little bit more centralized and they don't have those kind of out of the box, you know, custom client solutions. So very cool. Right now, writers and editors are deliberately breaking conventional style rules. We're all getting rid of EM dashes, We're all adding typos. Well, I mean, I actually just naturally have a lot of typos. So that's just me. My wife always rolls her eyes at me. But this is actually becoming something that's more and more popular. And it's kind of funny because in a weird way with this podcast, I've always kind of had that same, same mantra a lot of. When I first started this podcast, prior to this, I actually ran a bunch of 100% AI generated scripts and speaker podcasts for a different startup I had. And I was just doing it for, you know, lead gen or whatever. And they were kind of like educational episodes, evergreen content, not news like this is. But anyways, from the very beginning, I always knew that this was a possibility. And I always said, look, I got to have a real person myself, my real voice. On this podcast talking about. Because people are going to want to hear my, My background, my stories, my anecdotes, and even to the point where I was saying, like pauses and ums and buts and like little, I don't know, human things that you put in there. People want to hear that because they know that this isn't an AI talking to them. I, for one, I mean, even if the voice. Because the voices used to be bad and now they're actually really good. Like if you use 11 Labs v3, the voices are incredible. Sometimes I can't tell it's a. It's a real or it's just an AI. If, If I'm not careful and I've had calls, I've heard music. Like, I can detect it. But guys, I'm serious. Like, I'll get. I'll listen to a song. The other day I added it to my liked folder on YouTube music and realized probably like five listens in. I'm like, this is probably AI generated. So anyways, there's, there's. I think this happens more than we would like to admit. I mean, this stuff is getting a lot better. But my mantra has always been like, look, people are gonna want a real human. And this is what's happening with writers now. They're adding literal typos and kind of these messy first person stories to make all of their writing look more unmistakably human. It's not even just like looking human, but unmistakable. Like, we want people to know this is human. ChatGPT doesn't output text like this. It doesn't use all lowercase. So this is really interesting. One, One publication in particular said that they got a thousand pitches in May and almost all of them were AI generated pitchforks. Editorial head has banned AI from review submissions. That was starting in 2024, and I think that was like two years ago. But novelists are actively avoiding a lot of different things. And I mean, you'll see it on Twitter. It's kind of funnier in X or whatever, where everyone finds em dashes in old literature or they find just funny glitches that Claude always says, it's not this, it's that, right? Like all those funny things. And they'll screenshot them from old books and they're like, oh my gosh, I can't believe Jane Austen was using AI to write stuff. So anyways, there's a lot going on inside of. Inside of all of. Inside of the writing world trying to be more human. Sierra is a $12 billion data security company and they're buying Oasis Security for $1 billion to manage AI agent identities inside of companies. So right now basically every company is putting out all of these different agents to do work. But they need some tools to track who those agents are and what they have access to, what they're doing. This is a problem that I think a traditional security software would be tracking humans. But now that we have these agents, they also want to track that. And I think it's interesting because I've seen some tweets on X where people were like, they were screenshotting. I think it was chat GPT like 5.6 or whatever. And it was on cursor, they were using cursor and they said they were working on a project. They're like, okay, stop for now, finish this later. And it made a push after they told it to stop. Why'd you do that? And I was like, oh, I just like was doing the push anyways. These things happen where these agents, you know, especially if you give them a task, especially if it's more open ended, like, hey, just get XYZ thing done. They do it in their own way. And we don't always know what made the change, what made the edit, who did what. And so you can have some of these issues. Anyways, Oasis was founded in 2022. They raised $195 million. They specialize in securing non human identities and they do it through API, you know, API keys that are logging into corporate systems without any humans. There's. They just raised $600 million, a $12 million valuation and they're now making $150 million a year in revenue. But they're still not profitable. They've spent really aggressively. They have three acquisitions that they just did in quick, like really close together. This is their third acquisition in a few months, which is following them buying Rift and Genie Security. I think it's interesting when you have a company that's able to raise so much money, obviously they have a really good team, they have a really good, you know, visionary at the top. But when they make all these acquisitions, I mean, A, they're smart, but B, they, you know, they're growing so fast and they have so much money they have the bandwidth to do it and not to build it from scratch, which is interesting. There's a huge push right now to own the non human identity market before a lot of these bigger companies like Palo Alto Networks or Okta are kind of moving into this. And so that's why I think they're jumping on that other news that happened this week that I just want to cover the headlines. Google's DeepMind has just shipped Lyria 3.5 in Flow music with some better vocals and lyrics. I've talked about Lyria in the past, not been impressed with it. When it was first came out it was like only able to generate 30 second clips. Then it became, then it was able to do full songs and now I think they're getting better at vocals and lyrics. So I think this is going to be a big player challenging Yuio and Suno and stuff. When it first came out, it wasn't much to talk about, but I think Lyria is getting better. I still like Suno more because they have the entire AI studio and they have like music covers you can do and there's just so much to to do with Suno. But I am excited that the underlying model behind Google is getting better. Pentagram just raised $9 million to detect AI generated text. Apparently has 99% accuracy. It's interesting, you know, in light of what we were talking about with everyone using more, I don't know, typos or whatever you want to call it to seem less AI generated. So now we have these tools that are really good. It's interesting because OpenAI in the very early days came out, they had their own AI tool text detector and they actually shut the project down because they said it was too hard to detect AI generated text. Now I think we all kind of have AI generated text detection built into our brains now where we're understanding a lot of the phrases that are used, we're understanding the EM dashes, but those things the AI models are going to be able to get around very easily. I actually have a whole list of things that AI models frequently say that annoy me and, and whenever I get AI to write a podcast script or if I get it to go write an article or I go get it to write a, you know, blog posts or content or like basically any work I'm doing or even like, even websites, I very frequently will say like run through this whole list and remove every single M dash from the website. Every single time that we say X, Y or Z and I just get it to go like run this kind of audit on my content. Now eventually, hopefully the models will just get away from that. But it is interesting, all of this AI generated text detection stuff I think I like, I hope that it's good, but I'm always dubious of it because I feel like it's always this cat and mouse game. Okay, there's funny news, Martha Stewart has Co founded Hint, which is an AI assistant for homeowners and she has $10 million in funding for this project. So that's kind of interesting. If you haven't left a review on the podcast yet, it would help the show out a ton. I would super, super appreciate it. It helps more incredible people like yourself find the show. And so yeah, it's the number one thing that could help this show reach more a bigger audience. If you wouldn't mind. If you haven't already done it, it would mean the world to me. But thanks so much for tuning in. I hope you guys have an incredible day. Make sure to go check out AI box AI if you want to get 80 different AI models all on one platform and I'll see you in the next episode.
- [19:57]
Lady Luck (Spinquest Promoter)
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- [20:20]
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- [20:26]
AI Industry Podcast Host
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