Tino Talks Tech

Category: OpenAI

  • Are Digital Assistants Dying?

    Are Digital Assistants Dying?

    “Alexa, change audio output to my Bluetooth speaker”, you might know what the next response would be “Sorry, I didn’t get that…” Well, that era might just be over as major players like Amazon, Google and Apple are now racing to integrate AI models within their digital assistants. So, what does this mean for your Echo Dot and your wallet?

    The AI Push

    So yeah, the tech giants are embedding powerful AI models into their platforms, here’s how they’re doing it:

    • Amazon is integrating Claude from Anthropic into Alexa, aiming to make Alexa smarter and more conversational.
    • Google is adding Gemini, their proprietary AI model, to Google Assistant, promising a more nuanced, context-aware experience.
    • Apple is bringing ChatGPT based technology to Siri, which could make Siri more responsive and versatile. This is my favourite one, not because it’s Apple, but it’s GPT, duh.

    Now these integrations promise to make digital assistants more than just voice-command tools, they’re being positioned as virtual AIs that can understand context, recall previous interactions, and provide more in-depth responses, and that’s something we actually want, well something I want. However, there’s a big drawback: these AI models demand significantly higher processing power than the legacy assistants we’ve been using for years.

    Why Old Devices Can’t Keep Up

    Most of us are familiar with Amazon’s Echo Dot, Google’s Nest Mini, and Apple’s HomePod Mini, they’re compact, relatively affordable devices designed to do simple tasks. These legacy devices were never intended to handle the heavy lifting of AI-driven language models. The hardware inside a $50 Echo Dot, for example, simply doesn’t have the processing capability to run a model like Claude, Gemini, or ChatGPT natively.

    To bring these AI models to the existing devices(which in my opinion is next to impossible), companies are facing two major options:

    1. Release New Hardware with Enhanced Processing Power: Well, this isn’t actually bringing the LLMs to existing devices, it’s making newer versions of those models, but you get where I’m going with this right? So building new versions of these devices with more powerful processors would allow local processing of AI models, yeah. However, this would drive prices up significantly. So, while the Echo Pop has always been a budget-friendly way to add Alexa to your home, a new Echo Pop with AI built-in would be a different beast altogether, likely costing much more due to the added processing power it would need.
    2. Offer Cloud-Based AI Services with a Subscription: Alternatively, these companies could opt to keep the hardware simple and run these AI models on the cloud, allowing even low-power devices to tap into advanced AI capabilities without needing high processing power on the device itself, which would mean that you’d just get an update on your Echo Pop, great bargain right, but at what cost? This route raises significant concerns:
      • Privacy and Security Risks: Cloud-based solutions require data to be transmitted and processed externally, raising potential privacy issues. Many users are uneasy about sending potentially sensitive conversations over the internet to be processed by third-party servers. People are already concerned about the models running on their “AI” phones, which has resulted in manufacturers limiting most of these fancy AI features to their highest performing models so that they can locally run them reducing the concerns, now with these digital assistant devices it’s a whole different story.
      • Subscription Costs: To cover the cost of running powerful AI models in the cloud, companies are likely to introduce subscription plans. This would add yet another monthly fee for users who may already be feeling subscription fatigue, especially as so many services now rely on recurring fees.

    Here’s why Legacy Assistants Are Falling Behind

    One of the more subtle effects of this AI hardware dilemma is the growing distinction between these legacy digital assistants and the next-gen super smart LLMs. People accustomed to Alexa’s simple skills or Google Assistant’s straightforward commands might quickly feel underwhelmed by the limitations of these older models as the new ones become capable of nuanced, context-aware interactions which feel more personal. You know, I’d never want to go back to the legacy assistant as soon as I’m able to have a full-on convo with my assistant about how my DMs are dry across all my socials, that’s just a whole different experience.

    Despite all the promise, the AI models aren’t quite there yet. From my own experience, Gemini, Google’s AI model, has yet to fully integrate the practical, everyday usability of Google Assistant. It’s still in its early stages, so while it may be able to chat about a broad range of topics, it sometimes struggles with tasks that Assistant handles smoothly, it can’t even skip to the next song if my phone’s screen is switched off. So in other words, the switch to a fully AI-driven assistant isn’t seamless, which might encourage users to hang onto their legacy assistants for now, even if they’re not as fancy. I’m the *users* by the way.

    Why the Price and Privacy Trade-Off Could Slow Adoption

    With these new fancy AI-powered models, there’s likely to be a split in the market:

    • Budget-conscious users may stick with legacy devices or forego digital assistants altogether if prices rise significantly.
    • Privacy-minded users might avoid cloud-based AI options due to security concerns, even if that means missing out on advanced capabilities.
    • Tech enthusiasts willing to pay for the latest and greatest will have options to buy more powerful (and expensive) devices, or they’ll sign up for subscriptions to access cloud-based services. We’ve seen people buying the Vision Pro, so it’s nothing new when it comes to enthusiasts.

    This division could lead to a somewhat divided ecosystem, where advanced, AI-capable assistants coexist with simpler budget-friendly models, and there’s nothing wrong with that, that’s exactly what the smartphone space is like and has been like since, well, the beginning. But unlike smartphones, it could be a tricky balancing act for the tech companies behind these assistants. Pricing the new, advanced models too high could result in slower adoption rates, while heavy reliance on subscription models could alienate users who are already juggling multiple monthly fees.

    Conclusion

    So as the top tech guys push forward with integrating advanced AI into their digital assistants, we as users face a complicated choice: stick with legacy models that are cheaper but limited in functionality or pay more, either upfront for new hardware or through monthly subscriptions, to access the latest AI-powered versions. By the way, this is just my speculation of how the market might be like in the upcoming years or months maybe, not how it is supposed to be like.

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  • This GPT has a PhD: Hear Me Out!

    This GPT has a PhD: Hear Me Out!

    AI just got smarter, and I mean really smart. OpenAI’s new o1 series, which was released in September 2024, brings a new level of reasoning to the table. This model was designed to slow down and think before it responds, making it a first-of-its-kind model when it comes to handling tough problems in fields like math, coding, and science. It’s even being compared to a PhD student because it can tackle incredibly complex tasks with ease. But, unless you’re subscribed to ChatGPT Plus or Team, you won’t be able to experience this impressive jump in AI tech just yet.

    So, what makes this model special? I’ll tell you a little story. After hearing all the hype about the model’s reasoning capabilities, I decided to test it out myself. I asked a simple question: “How many R’s are in ‘Strawberry’?” I had done this with other models before, but they often tripped up on such simple tasks. However, o1 nailed it on the first try, 3 R’s—without hesitation. This was the first AI model I’ve used that got it right the first time. That’s when I knew OpenAI wasn’t kidding about the o1’s problem-solving skills, oh and it does more than just find the number of R’s in strawberry lol.

    It’s Smarter

    The key feature that sets o1 apart from earlier models like GPT-4o is its ability to think longer before responding. Unlike GPT-4o, which prioritizes fluency and speed, o1 has been trained to slow down and evaluate problems carefully. This approach is essential for complex tasks that require deep reasoning, such as solving high-level math equations, debugging code, or even understanding advanced chemistry problems. OpenAI claims this model’s problem-solving abilities mirror those of PhD students, especially in disciplines like physics and biology, so yeah, I think you see where the title is coming from.

    For example, in the International Mathematics Olympiad (IMO) qualifying exams, GPT-4o managed to solve only 13% of the problems. In contrast, the o1 model correctly answered an impressive 83% of the same problems. Which is way more than my best marks in school, probably. So this speaks volumes about its performance in challenging technical tasks.

    However, there’s a bit of a trade-off. The model takes longer to generate responses because it’s reasoning through the task. This won’t be a problem if you’re tackling complex challenges, but if you need something quick and less precise, GPT-4o might still be your go to.

    Real-World Use Cases

    The OpenAI o1 series shines in STEM (Science, Technology, Engineering, Math) fields. If you’re a developer, data scientist, or engineer, you’ll love its ability to reason through intricate problems. OpenAI reported that o1 reached the 89th percentile in coding contests on Codeforces, a competitive programming platform. Imagine the possibilities if you’re stuck on a difficult algorithm or need to debug a large chunk of code, o1 can help you sort through it with its powerful reasoning capabilities.

    Beyond coding, its performance in chemistry and biology means it could assist researchers in analyzing complex datasets or even devising new experiment strategies. It’s designed to be a partner for those in technical roles who need more than just casual conversations or superficial responses from their AI.

    That said, it’s worth mentioning that GPT-4o might still have the edge when it comes to creative writing or more general tasks. The o1 model sacrifices some writing fluidity in favour of technical proficiency. So, depending on what you need, one model may be more suited to you than the other. This also implies that this model wasn’t made for everyone, unlike GPT 4o.

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  • Adieu Sky: OpenAI’s Controversial Scarlett Johansson Sound-Alike Voice

    Adieu Sky: OpenAI’s Controversial Scarlett Johansson Sound-Alike Voice

    OpenAI recently found themselves in hot soup after their GPT-4o launch which introduced a new voice model, called Sky to ChatGPT. The voice bore an uncanny resemblance to an actual human voice and was the closest any AI had gotten to actually mimicking a human voice, but however, Hollywood actress, Scarlett Johansson decided to put an end to the fun, since the voice resembled her voice and accused OpenAI of unauthorized voice cloning and misappropriation of likeness.

    Scarlett Johansson recently dropped a bombshell, revealing that OpenAI had approached her to lend her voice to their AI system. She turned them down, but months later, they released a voice called Sky that sounded creepily like her. This freaked out not just her friends and family, but the public too.

    Johansson didn’t hold back, calling out OpenAI’s CEO, Sam Altman, for going after a voice that mimicked hers. Altman even mentioned a movie where Johansson voiced an AI character, making it pretty obvious the similarity wasn’t just a coincidence.

    Two days before they launched the voice, Altman tried to get Johansson to change her mind through her agent. But they released the system before she could even respond. Johansson had to lawyer up, demanding OpenAI explain how they came up with the Sky voice. Reluctantly, OpenAI agreed to pull it.

    This whole ordeal has shone a spotlight on the shady side of voice cloning tech and its potential for abuse. Johansson stressed the need for transparency and laws to protect people’s rights and identities as AI tech keeps advancing. Her case raises big questions about consent, ethics, and how we protect personal identity in this new AI era.

    Conclusion

    Honestly, I really enjoyed using the Sky voice on ChatGPT. It brought a certain personality and charm to the interactions. It’s a real shame it’s gone now. I’ve switched to Juniper, but it’s just not the same. I guess I’ll get used to it, but I’ll definitely miss the unique character that Sky had.

  • AI Can Laugh at my Bad Jokes For Free? GPT 4o is Here!

    AI Can Laugh at my Bad Jokes For Free? GPT 4o is Here!

    So, you all know that I’m obsessed with anything new in the AI world, right? When OpenAI unexpectedly released GPT-4o a few days ago, I was overjoyed. Voice and image inputs? This is the future I signed up for!

    And the best part? It’s completely free! Well, there’s a tiny asterisk next to that “free” label. While anyone can use GPT-4o, there’s a limit to how many prompts you can use within a certain timeframe. And yes, I burned through my allotted prompts faster than a kid in a candy store. Seriously, my prompt balance went to zero quicker than you can say “artificial intelligence”. The addiction is real 🙁

    But why all the hype? Because GPT-4o is a game-changer. This isn’t just a minor upgrade; it’s a whole new ball game. Let’s start with the voice recognition, which is mind-blowingly accurate. We’re not talking “good for an AI” good; this is indistinguishable from a real human good. I’m talking natural pauses, inflections, and all the nuances of human conversation. It’s genuinely uncanny how realistic it sounds.

    And the responses themselves? Forget clever and insightful, GPT-4o is funny, engaging, and even a little sassy at times. I was having full-on conversations, complete with all the little human quirks you wouldn’t expect from AI. It felt like I was chatting with a friend, not a computer program. It’s both amazing and slightly unsettling.

    Naturally, the internet reacted with a mix of awe and, well, slight panic. Some are convinced we’re on the express train to the AI apocalypse (a bit dramatic, maybe?). Others are busy churning out hilarious memes. My personal favorites? One sort of portrays a guy on the phone with his girlfriend, who’s freaking out because of the super-realistic female voice in the background. The caption? “Babe, she’s just a chatbot I swear.” Classic. And then there’s one with a simple but effective “I’m dating a model” caption, highlighting how natural and engaging the voice model really is. And no, I’m not selfish so here are a few 😉

    Jokes aside, this is a monumental leap for AI. This isn’t just about a chatbot that spits out text; this is an AI that understands spoken language, interprets images, and holds conversations that feel genuinely human. It’s a testament to how far AI has come and a glimpse into a future where our interactions with technology are seamless and personalized.

    Speaking of the future, Google I/O just wrapped up a couple of days ago, and I was glued to the screen, taking notes like a madwoman. And let me tell you, Google did not disappoint. The announcement of their new Gemini models is huge, and there’s a lot to unpack. Stay tuned for my deep dive into everything Gemini in my next post!

  • I’m Getting Replaced by AI

    I’m Getting Replaced by AI

    Get ready for a wild ride, Tino Talks Tech readers! I’m embarking on an incredibly exciting project – a brand new blog section dedicated entirely to content generated by artificial intelligence, you heard that right! This is my personal laboratory, a place to explore and experiment with the mind-blowing capabilities of the latest AI language models.

    The AI Fascination: Why Now?

    If you’re even slightly into tech, you know AI is changing the world at breakneck speed. Tools like GPT-4 Turbo, Claude 3 Sonnet, Gemini Advanced, and Meta Llama 3 are doing unbelievable things with language; they’re generating prose, crafting poems, and even writing code. But can they produce compelling, insightful tech articles that make us think? That’s the million-dollar question, and I’m determined to find the answer.

    Don’t worry, this isn’t some kind of AI takeover of Tino Talks Tech! My usual content will still be front and center, filled with my hands-on experiences, reviews, the occasional rant and yes, I won’t be replaced by AI, don’t let the title fool you. Think of this AI blog as a fascinating side project – a space to push boundaries and see what happens when machine learning meets the world of technology blogging.

    How It All Works

    Here’s the exciting part:

    • The Universal Prompt: To ensure a level playing field, I’ll start each AI-generated article with the same simple prompt: “Write an article for a tech blog.” This lets us see how different AI models uniquely interpret and tackle this broad topic.
    • I’ll be dedicating each article on this AI blog to a specific AI model. That means an article generated by GPT-4 Turbo will also have its thumbnail created by Dall-E. I want to see how each model handles both writing and visualizing a tech topic.
    • The Claude Exception: Claude 3 Sonnet is a text-focused model, so for its articles, I’ll turn to Dall-E 3 for image generation.

    Ready for Surprises

    The biggest thrill for me is the unknown. Will these AI models deliver mind-blowing tutorials? Maybe they’ll write thought pieces that challenge our assumptions about where technology is headed. Perhaps they’ll surprise us with humor, or introduce entirely new ways of thinking about familiar tech concepts.

    This is an open-ended experiment, and that’s the whole point! I want you to join me on this journey of discovery. Read the AI-generated articles, share your honest feedback, and let’s see together just how far these language models can go.

    Buckle up – the AI writers are about to take the stage! You can access the new AI blog here😉

  • Stable Diffusion 3: A New Frontier in Generative AI

    Stable Diffusion 3: A New Frontier in Generative AI

    As an AI enthusiast, I find myself constantly intrigued by the rapid evolution of artificial intelligence models. Recently, I stumbled upon Stable Diffusion 3, a text-to-image model that has been making waves in the AI community. In this article, I’ll delve into why Stable Diffusion 3 might just surpass other generative AI models, even though I don’t have firsthand experience with it. My insights are based on extensive research and analysis.

    Understanding Stable Diffusion 3

    Before we dive into the comparisons, let’s explore what makes Stable Diffusion 3 unique. Unlike some of its counterparts, Stable Diffusion 3 operates in the latent space of images rather than the pixel space. This approach allows it to work efficiently and consume less memory. The model combines a diffusion transformer architecture with flow matching, resulting in a powerful tool for generating high-resolution images.

    The Competition: Midjourney 6 and Dall E 3

    To put Stable Diffusion 3 in context, let’s briefly discuss its competitors:

    1. Midjourney 6: Midjourney 6 offers a distinctive aesthetic. Its interpretation of prompts results in art that exudes charm and character. If you’re seeking a unique style, Midjourney 6 might be your go-to AI art generator.
    2. Dall E 3: Dall E 3 excels at replicating styles with remarkable fidelity. If your goal is to mimic a specific artistic style, Dall E 3 could be your ideal choice.

    Why Stable Diffusion 3 Stands Out

    Now, let’s focus on why Stable Diffusion 3 deserves attention:

    1. High-Resolution Images: Stable Diffusion 3 produces clear and detailed images. Whether it’s landscapes, portraits, or abstract compositions, this model shines in delivering visual fidelity.
    2. Complex Prompts: Stable Diffusion 3 thrives on complex prompts. If your project demands specificity, this AI art generator can understand and execute intricate instructions.
    3. Text Incorporation: Words matter. Stable Diffusion 3 accurately incorporates text into images, making it invaluable for designs where language plays a central role.

    The Future of Stable Diffusion 3

    As we peer into the future, exciting features await Stable Diffusion 3:

    • In-Painting: Imagine editing parts of an image seamlessly. Stable Diffusion 3 aims to introduce in-painting capabilities, allowing users to refine their creations.
    • Video Features: Animations are on the horizon. Soon, Stable Diffusion 3 might empower us to breathe life into our static images
    • Community Collaboration: The potential for open-source collaboration means that a community of users could contribute to its development. Imagine a constantly improving tool fueled by collective creativity.

    Conclusion

    While I haven’t personally tinkered with Stable Diffusion 3, my research suggests that it’s poised to redefine how we create visual content. As AI enthusiasts, let’s keep an eye on this promising model and celebrate the fusion of technology and creativity.

    Disclaimer: The views expressed in this article are based on research and not firsthand experience.