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Gulf Business
18 hours ago
- Business
- Gulf Business
GPT-5 is here: Here's why the entire AI industry is watching
Image credit: Getty Images OpenAI's GPT models are the AI technology that powers the popular ChatGPT chatbot, and GPT-5 will be available to all 700 million ChatGPT users, OpenAI said. Read- The big question is whether the company that kicked off the generative AI frenzy will be capable of continuing to drive significant technological advancements that attract enterprise-level users to justify the enormous sums of money it is investing to fuel these developments. The release comes at a critical time for the AI industry. The world's biggest AI developers – Alphabet, Meta, Amazon and Microsoft, which backs OpenAI – have dramatically increased capital expenditures to pay for AI data centers, nourishing investor hopes for great returns. These four companies expect to spend nearly $400bn this fiscal year in total. OpenAI is now in early discussions to allow employees to cash out at a $500bn valuation, a huge step-up from its current $300bn valuation. Top AI researchers now command $100m signing bonuses. 'So far, business spending on AI has been pretty weak, while consumer spending on AI has been fairly robust because people love to chat with ChatGPT,' said economics writer Noah Smith. 'But the consumer spending on AI just isn't going to be nearly enough to justify all the money that is being spent on AI data centers.' OpenAI is emphasising GPT-5's enterprise prowess. In addition to software development, the company said GPT-5 excels in writing, health-related queries, and finance. 'GPT-5 is really the first time that I think one of our mainline models has felt like you can ask a legitimate expert, a PhD-level expert, anything,' OpenAI CEO Sam Altman said at a press briefing. 'One of the coolest things it can do is write you good instantaneous software. This idea of software on demand is going to be one of the defining features of the GPT-5 era.' In demos on Thursday, OpenAI showed how GPT-5 could be used to create entire working pieces of software based on written text prompts, commonly known as 'vibe coding.' One key measure of success is whether the step up from GPT-4 to GPT-5 is on par with the research lab's previous improvements. Two early reviewers told Reuters that while the new model impressed them with its ability to code and solve science and math problems, they believe the leap from the GPT-4 to GPT-5 was not as large as OpenAI's prior improvements. Even if the improvements are large, GPT-5 is not advanced enough to wholesale replace humans. Altman said that GPT-5 still lacks the ability to learn on its own, a key component to enabling AI to match human abilities. On his popular AI podcast, Dwarkesh Patel compared current AI to teaching a child to play a saxophone by reading notes from the last student. 'A student takes one attempt,' he said. 'The moment they make a mistake, you send them away and write detailed instructions about what went wrong. The next student reads your notes and tries to play Charlie Parker cold. When they fail, you refine the instructions for the next student. This just wouldn't work.' More thinking Nearly three years ago, ChatGPT introduced the world to generative AI, dazzling users with its ability to write humanlike prose and poetry, quickly becoming one of the fastest growing apps ever. In March 2023, OpenAI followed up ChatGPT with the release of GPT-4, a large language model that made huge leaps forward in intelligence. While GPT-3.5, an earlier version, received a bar exam score in the bottom 10 per cent, GPT-4 passed the simulated bar exam in the top 10 per cent. GPT-4's leap was based on more compute power and data, and the company was hoping that 'scaling up' in a similar way would consistently lead to improved AI models. But OpenAI ran into issues scaling up. One problem was the data wall the company ran into, and OpenAI's former chief scientist Ilya Sutskever said last year that while processing power was growing, the amount of data was not. He was referring to the fact that large language models are trained on massive datasets that scrape the entire internet, and AI labs have no other options for large troves of human-generated textual data. Apart from the lack of data, another problem was that 'training runs' for large models are more likely to have hardware-induced failures given how complicated the system is, and researchers may not know the eventual performance of the models until the end of the run, which can take months. At the same time, OpenAI discovered another route to smarter AI, called 'test-time compute,' a way to have the AI model spend more time compute power 'thinking' about each question, allowing it to solve challenging tasks such as math or complex operations that demand advanced reasoning and decision-making. GPT-5 acts as a router, meaning if a user asks GPT-5 a particularly hard problem, it will use test-time compute to answer the question. This is the first time the general public will have access to OpenAI's test-time compute technology, something that Altman said is important to the company's mission to build AI that benefits all of humanity. Altman believes the current investment in AI is still inadequate. 'We need to build a lot more infrastructure globally to have AI locally available in all these markets,' Altman said.
Fox News
a day ago
- Fox News
Forget SEO: How to get found by AI tools in 2025
Three years ago, I said Google was going the way of the dial-up modem. People called me crazy with a capital K. Well, I was spot on. We don't use the web the same way anymore. We're giving away a new iPhone. No purchase required. Enter now! Look at the numbers. ChatGPT now has over 180 million users and powers more than 800 million sessions each week. Google's own AI Overviews appear in over 60% of search results. One Pew study found that when those AI blurbs show up, only 8% of people bother to click through to a website. Ouch. SEO is dead. If you're still focused on keywords, backlinks, and trying to land on page one of Google, you're playing last year's game. You now need to make sure AI tools like ChatGPT, Perplexity, Claude, and Gemini find you when someone asks a question. You need to know about GEO (Generative Engine Optimization) and AIO (Artificial Intelligence Optimization). AI tools are trained on huge datasets: Reddit threads, Wikipedia entries, product reviews, how-to guides, forums, FAQs and even customer service transcripts. To get found in AI answers, you need to think less like a blogger and more like a helpful expert. Here's how: Let's use this article as an example of how you'd post it on your site. 1. Your meta title should be short, sharp, and targeted. Under 60 characters. Something like: "Forget SEO: How to Get Found by AI Tools in 2025" 2. Add a clear, benefit-driven description, under 160 characters, like: "SEO is dead. Learn how to optimize your content for ChatGPT, Perplexity, and Gemini to stay visible in the AI era." 3. Don't forget schema markup. Yes, Google still reads it, and so do other AI crawlers. It's behind-the-scenes code that tells machines, "This is an article," "This is a product review," or "Here's a list of FAQs." Need help? Just ask ChatGPT, "Can you generate a FAQ schema for my blog post?" and you'll get copy-paste code in seconds. There's no more gaming Google. No more squeezing 400 variations of the same keyword into a blog post. If your business, blog, or store isn't showing up in AI results, it's invisible. Now, if you found this helpful, send it to a friend. Let's help each other stay ten steps ahead of the tech curve. Award-winning host Kim Komando is your secret weapon for navigating tech. Copyright 2025, WestStar Multimedia Entertainment. All rights reserved.
Android Authority
2 days ago
- Android Authority
Free, offline ChatGPT on your phone? Technically possible, basically useless
Robert Triggs / Android Authority Another day, another large language model, but news that OpenAI has released its first open-weight models (gpt-oss) with Apache 2.0 licensing is a bigger deal than most. Finally, you can run a version of ChatGPT offline and for free, giving developers and us casual AI enthusiasts another powerful tool to try out. As usual, OpenAI makes some pretty big claims about gpt-oss's capabilities. The model can apparently outperform o4-mini and scores quite close to its o3 model — OpenAI's cost-efficient and most powerful reasoning models, respectively. However, that gpt-oss model comes in at a colossal 120 billion parameters, requiring some serious computing kit to run. For you and me, though, there's still a highly performant 20 billion parameter model available. Can you now run ChatGPT offline and for free? Well, it depends. In theory, the 20 billion parameter model will run on a modern laptop or PC, provided you have bountiful RAM and a powerful CPU or GPU to crunch the numbers. Qualcomm even claims it's excited about bringing gpt-oss to its compute platforms — think PC rather than mobile. Still, this does beg the question: Is it possible to now run ChatGPT entirely offline and on-device, for free, on a laptop or even your smartphone? Well, it's doable, but I wouldn't recommend it. What do you need to run gpt-oss? Edgar Cervantes / Android Authority Despite shrinking gpt-oss from 120 billion to 20 billion parameters for more general use, the official quantized model still weighs in at a hefty 12.2GB. OpenAI specifies VRAM requirements of 16GB for the 20B model and 80GB for the 120B model. You need a machine capable of holding the entire thing in memory at once to achieve reasonable performance, which puts you firmly into NVIDIA RTX 4080 territory for sufficient dedicated GPU memory — hardly something we all have access to. For PCs with a smaller GPU VRAM, you'll want 16GB of system RAM if you can split some of the model into GPU memory, and preferably a GPU capable of crunching FP4 precision data. For everything else, such as typical laptops and smartphones, 16GB is really cutting it fine as you need room for the OS and apps too. Based on my experience, 24GB RAM is required; my 7th Gen Surface Laptop, complete with a Snapdragon X processor and 16GB RAM, worked at an admittedly pretty decent 10 tokens per second, but barely held on even with every other application closed. Despite it's smaller size, gpt-oss 20b still needs plenty of RAM and a powerful GPU to run smoothly. Of course, with 24 GB RAM being ideal, the vast majority of smartphones cannot run it. Even AI leaders like the Pixel 9 Pro XL and Galaxy S25 Ultra top out at 16GB RAM, and not all of that's accessible. Thankfully, my ROG Phone 9 Pro has a colossal 24GB of RAM — enough to get me started. How to run gpt-oss on a phone Robert Triggs / Android Authority For my first attempt to run gpt-oss on my Android smartphone, I turned to the growing selection of LLM apps that let you run offline models, including PocketPal AI, LLaMA Chat, and LM Playground. However, these apps either didn't have the model available or couldn't successfully load the version downloaded manually, possibly because they're based on an older version of Instead, I booted up a Debian partition on the ROG and installed Ollama to handle loading and interacting with gpt-oss. If you want to follow the steps, I did the same with DeepSeek earlier in the year. The drawback is that performance isn't quite native, and there's no hardware acceleration, meaning you're reliant on the phone's CPU to do the heavy lifting. So, how well does gpt-oss run on a top-tier Android smartphone? Barely is the generous word I'd use. The ROG's Snapdragon 8 Elite might be powerful, but it's nowhere near my laptop's Snapdragon X, let alone a dedicated GPU for data crunching. gpt-oss can just about run on a phone, but it's barely usable. The token rate (the rate at which text is generated on screen) is barely passable and certainly slower than I can read. I'd estimate it's in the region of 2-3 tokens (about a word or so) per second. It's not entirely terrible for short requests, but it's agonising if you want to do anything more complex than say hello. Unfortunately, the token rate only gets worse as the size of your conversation increases, eventually taking several minutes to produce even a couple of paragraphs. Robert Triggs / Android Authority Obviously, mobile CPUs really aren't built for this type of work, and certainly not models approaching this size. The ROG is a nippy performer for my daily workloads, but it was maxed out here, causing seven of the eight CPU cores to run at 100% almost constantly, resulting in a rather uncomfortably hot handset after just a few minutes of chat. Clock speeds quickly throttled, causing token speeds to fall further. It's not great. With the model loaded, the phone's 24GB was stretched as well, with the OS, background apps, and additional memory required for the prompt and responses all vying for space. When I needed to flick in and out of apps, I could, but this brought already sluggish token generation to a virtual standstill. Another impressive model, but not for phones Calvin Wankhede / Android Authority Running gpt-oss on your smartphone is pretty much out of the question, even if you have a huge pool of RAM to load it up. External models aimed primarily at the developer community don't support mobile NPUs and GPUs. The only way around that obstacle is for developers to leverage proprietary SDKs like Qualcomm's AI SDK or Apple's Core ML, which won't happen for this sort of use case. Still, I was determined not to give up and tried gpt-oss on my aging PC, equipped with a GTX1070 and 24GB RAM. The results were definitely better, at around four to five tokens per second, but still slower than my Snapdragon X laptop running just on the CPU — yikes. In both cases, the 20b parameter version of gpt-oss certainly seems impressive (after waiting a while), thanks to its configurable chain of reasoning that lets the model 'think' for longer to help solve more complex problems. Compared to free options like Google's Gemini 2.5 Flash, gpt-oss is the more capable problem solver thanks to its use of chain-of-thought, much like DeepSeek R1, which is all the more impressive given it's free. However, it's still not as powerful as the mightier and more expensive cloud-based models — and certainly doesn't run anywhere near as fast on any consumer gadgets I own. Still, advanced reasoning in the palm of your hand, without the cost, security concerns, or network compromises of today's subscription models, is the AI future I think laptops and smartphones should truly aim for. There's clearly a long way to go, especially when it comes to mainstream hardware acceleration, but as models become both smarter and smaller, that future feels increasingly tangible. A few of my flagship smartphones have proven reasonably adept at running smaller 8 billion parameter models like Qwen 2.5 and Llama 3, with surprisingly quick and powerful results. If we ever see a similarly speedy version of gpt-oss, I'd be much more excited. Follow
Yahoo
2 days ago
- Yahoo
Instagram's Experimenting With a New Way to Highlight Your Interests
This story was originally published on Social Media Today. To receive daily news and insights, subscribe to our free daily Social Media Today newsletter. As it officially launches a range of new features designed to help users connect in the app, Instagram's also testing out a new option on this front, in the form of 'Picks,' which would enable users to highlight things that they're interested in via their inbox Notes. As you can see in these example screens, shared by app researcher Alessandro Paluzzi, Instagram's currently in the early stages of experimenting with Picks, which you would be able to access via an icon at the top of your inbox in the app. As Instagram explains in the second image above, Picks would enable you to highlight your interests, in order to 'find overlap with friends who are all about it too.' So, for example, you would be able to search for a topic within the Picks display, and then choose from specific subjects in the list. It could be another way to encourage more connection in the app, which aligns with a core function of Instagram, in bringing people together. Indeed, earlier this year, Instagram chief Adam Mosseri explained that the team is focused on maximizing both creativity and connection, with the latter set to include a range of new features to drive more user engagement. As per Mosseri: 'We're also going to look for more ways to make recommendations and consuming content more interactive and more social, and we're going to be exploring some new ways to connect with your friends on Instagram.' Picks, along with the new friend feed for Reels, the friend map, and re-posts, all align with expanded discovery, and facilitating connection between users via their shared interests. Will that work? I mean, Instagram has seen some success with features like inbox Notes, which provide another means to connect. And with fewer people posting original content, social platforms have become more interest-based, as opposed to expressive, so it makes sense that IG might be able to facilitate more connection around what people are interested in, as opposed to what they share. Meta's also presumably seeing some erosion in its user interest graphs, because with algorithms now showing you a constant stream of content that it thinks you'll like, you no longer need to like and follow in order to dictate such, as the systems are now much better at inferring interest based on your viewing activity. Direct indicators do still play a role, but users are growing more accustomed to automated refinement, which is likely having some impact on Meta's capacity to understand user interests for ad targeting and the like. So maybe, something like Picks works to provide more context, as well as expanded engagement activity. It seems like an interesting experiment either way. I've asked IG for more info on the test and will update this post if/when I get more info. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Geeky Gadgets
3 days ago
- Business
- Geeky Gadgets
OpenAI's New Open Models Overview : GPT-OSS 120B and 20B
What if the power of innovative AI wasn't locked behind proprietary walls but placed directly in the hands of developers, researchers, and innovators? OpenAI's latest release, GPT-OSS 120B and 20B, represents a bold step toward this vision. With their open-weight design and licensing under Apache 2.0, these models aim to bridge the gap between exclusivity and accessibility, offering developers the freedom to customize and deploy advanced AI systems without sacrificing performance. Whether you're running enterprise-grade cloud applications or experimenting on local hardware, these models promise to redefine what's possible in AI-driven development. Sam Witteveen explains the unique capabilities and trade-offs of the GPT-OSS models, from their scalable architecture to their new integration features. You'll discover how these tools empower developers to balance computational efficiency with task complexity, and why their open-weight framework could signal a paradigm shift in the AI landscape. But are they truly the providing widespread access to force they claim to be, or do their limitations—like restricted multilingual support and slower high-reasoning performance—temper their promise? Let's unpack the potential and challenges of these fantastic models, and what they mean for the future of AI innovation. OpenAI GPT-OSS Models Overview Key Features of GPT-OSS Models The GPT-OSS models are available in two configurations, each tailored to meet specific deployment needs: GPT-OSS 120B: This model is optimized for cloud environments and features 117 billion active parameters. It is well-suited for large-scale, enterprise-level applications that require robust computational power and scalability. This model is optimized for cloud environments and features 117 billion active parameters. It is well-suited for large-scale, enterprise-level applications that require robust computational power and scalability. GPT-OSS 20B: Designed for local deployment, this smaller model contains 3.6 billion active parameters and can operate on systems with as little as 16GB of RAM, making it accessible for developers with limited hardware resources. Both models use advanced training techniques, including reinforcement learning, supervised learning, and instruction tuning. These methods enhance their ability to perform complex reasoning and execute tasks effectively. Additionally, the models offer adjustable reasoning levels—low, medium, and high—allowing you to balance computational latency with task performance. For example, high reasoning levels improve accuracy in complex tasks but may result in slower response times, making them ideal for precision-critical applications. Licensing and Accessibility The GPT-OSS models are released under the Apache 2.0 license, granting you broad rights to use, modify, and redistribute them. However, while the models are labeled as 'open-weight,' they are not fully open source. OpenAI has not provided access to the training code or datasets, which limits the ability to reproduce the models independently. This approach reflects OpenAI's effort to enhance accessibility while safeguarding proprietary research and intellectual property. For developers, this licensing model offers significant flexibility. You can integrate the models into your projects, customize them to suit specific requirements, and even redistribute modified versions, all while adhering to the terms of the Apache 2.0 license. OpenAI GPT-OSS 120B & 20B Explained Watch this video on YouTube. Enhance your knowledge on OpenAI GPT Models by exploring a selection of articles and guides on the subject. Capabilities and Applications The GPT-OSS models are designed to support a wide range of advanced functionalities, making them versatile tools for developers. Key features include: Instruction Following: The models excel at following task-specific instructions, allowing you to build applications tailored to unique requirements. The models excel at following task-specific instructions, allowing you to build applications tailored to unique requirements. Tool and API Integration: Seamless integration with tools and APIs allows for enhanced functionality and streamlined workflows. Seamless integration with tools and APIs allows for enhanced functionality and streamlined workflows. Web Search Capabilities: These models can retrieve and process information from the web, expanding their utility in research and data analysis. These models can retrieve and process information from the web, expanding their utility in research and data analysis. Python Code Execution: The ability to execute Python code makes them valuable for automating tasks and performing complex computations. With a context length of up to 128,000 tokens, the models are particularly effective in tasks requiring extensive input processing. This includes document summarization, multi-turn conversations, and complex data analysis. Their architecture incorporates rotary positional embeddings and a mixture-of-experts framework, enhancing their reasoning and generalization capabilities. However, their current support is limited to English, which may restrict their use in multilingual contexts. Performance Insights Benchmark testing reveals that the GPT-OSS models perform competitively in reasoning and function-calling tasks. While they may not fully match the performance of proprietary OpenAI models in every area, they demonstrate strong capabilities in handling complex reasoning challenges. This makes them particularly valuable for applications in research, education, and enterprise solutions. However, there are trade-offs to consider. Higher reasoning levels improve accuracy but can lead to increased response times, which may not be ideal for real-time applications. For time-sensitive tasks, lower reasoning levels may offer a better balance between speed and performance. Understanding these trade-offs is essential for optimizing the models' use in your specific applications. Deployment Options The GPT-OSS models are designed to accommodate diverse deployment scenarios, offering flexibility for developers with varying needs: Local Deployment: The 20B model is optimized for local use and supports 4-bit quantization, allowing it to run efficiently on systems with limited resources. Tools like Triton can further enhance performance on compatible hardware, making it a practical choice for developers working with constrained computational environments. The 20B model is optimized for local use and supports 4-bit quantization, allowing it to run efficiently on systems with limited resources. Tools like Triton can further enhance performance on compatible hardware, making it a practical choice for developers working with constrained computational environments. Cloud Deployment: The 120B model is built for scalability and high performance, making it ideal for enterprise-level applications that demand robust computational power and seamless integration into cloud-based workflows. Both models integrate seamlessly with OpenAI's Harmony SDK and OpenRouter API, simplifying the process of incorporating them into existing systems. This ease of integration allows you to focus on building innovative applications without being bogged down by complex deployment challenges. Limitations to Consider Despite their strengths, the GPT-OSS models have several limitations that you should be aware of: Knowledge Cutoff: The models' training data only extends to mid-2024, which means they lack awareness of developments and events that have occurred since then. The models' training data only extends to mid-2024, which means they lack awareness of developments and events that have occurred since then. Language Support: Currently, the models support only English, which may limit their applicability in multilingual environments or for users requiring support for other languages. Currently, the models support only English, which may limit their applicability in multilingual environments or for users requiring support for other languages. Latency: Higher reasoning levels can result in slower response times, which may impact their suitability for time-sensitive applications. These limitations underscore the importance of carefully evaluating your specific use case to determine whether the GPT-OSS models align with your requirements. By understanding their capabilities and constraints, you can make informed decisions about how to best use these tools in your projects. Implications for the AI Community The release of GPT-OSS 120B and 20B marks a significant milestone in OpenAI's efforts to balance proprietary advancements with open contributions. By making these models accessible under an open-weight framework, OpenAI fosters innovation and competition within the AI community. For developers like you, this represents an opportunity to use innovative AI technologies while retaining control over deployment and customization. As other organizations consider adopting similar approaches, the release of these models could signal a broader shift toward more accessible AI development. Whether you are building applications for research, business, or personal use, the GPT-OSS models provide a powerful foundation to explore new possibilities in artificial intelligence. Media Credit: Sam Witteveen Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
