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PaliGemma, Google's latest vision language model (VLM), is a game-changer in the world of AI. Developed alongside other cutting-edge products at the 2024 Google I/O event, it boasts multimodal capabilities that outshine its predecessors. Unlike other VLMs like GPT-4o and Google Gemini, PaliGemma stands out with its robust object detection and segmentation abilities, thanks to its fusion of SigLIP and Gemma models. With 3 billion parameters, PaliGemma offers commercial usability and comes pre-trained on diverse datasets like WebLI and OpenImages. What sets PaliGemma apart is its flexibility. It's not just a ready-made solution; rather, it's a platform for customization. Through fine-tuning, users can tailor PaliGemma for specific tasks like image and video captioning, visual question answering, object detection, and segmentation. This adaptability opens up a world of possibilities, allowing for innovative applications in various domains. While benchmarking provides insights into its capabilities, the true potential of PaliGemma lies in its versatility through fine-tuning. Google's decision to release an open multimodal model with this level of customization is a significant breakthrough for AI. It empowers users to create bespoke models that cater to their unique needs, whether it's in the cloud or on edge devices like NVIDIA Jetsons. In essence, PaliGemma represents a new frontier in AI, where flexibility and performance converge to drive innovation. It's not just about what the model can do out of the box; it's about unlocking its full potential through fine-tuning, making it a powerful tool for tackling real-world challenges. Reference: https://lnkd.in/g2iHvb63

The Layers of Commoditization of Generative AI: Which Areas Would Accrue the Most Value? https://bit.ly/48Ksc10

The Layers of Commoditization of Generative AI: Which Areas Would Accrue the Most Value? https://is.gd/HkyTZZ #MachineLearning #Latest #ArtificialIntelligence

Which AI Stock to Bet On?

The Layers of Commoditization of Generative AI: Which Areas Would Accrue the Most Value? https://is.gd/HkyTZZ #MachineLearning #Latest #ArtificialIntelligence

AI News April 23 in English! Don't miss it 🤖 Meta has now released Llama 3 as open source, and it shows great potential in various LLM benchmarks, besides being free! Large companies complain about the costs of some projects with models that cost money becoming astronomical, so it's nice to have good free models as a contrast to this. Llama3 is the best in test among the open-source alternatives and even beats Claude, which costs money, in several tests. For example, the model is so fast that images are generated in real time. The next version will also be more multimodal, trained on 400 billion parameters. Humanoid robots are advancing at a rapid pace. Every week we see new progress, and the most viral news lately came from Boston Dynamics, showcasing their robot Atlas that can stand up like very few people. So the fine motor skills are here on many levels! If you haven't seen this yet, check out the AI news in this episode. Groq is rolling out its LPU-based (Language Processor Unit) AI chips, and CEO Jonathan Ross says they now have 75,000 developers working with this. If you're scaling a business today, you need the ability to switch between LLM models. Be able to use anything that is the latest! This is to use what is best and cheapest, and development is moving so fast. For example, Gemini now offers a 1 million context-window, which is very powerful in certain use cases. By the end of 2024, Meta, which makes Llama 3, for example, will have 650,000 H100s from Nvidia. By the end of 2024, Groq will have 100,000 LPU, which will be close to Meta in capacity (ratio 1:6) Next year, Groq will deploy 1.5 million LPUs, which will then correspond to about 9 million for competing solutions. Last year, Nvidia deployed 500,000 H500s So next year, Groq will have more inference and capacity for Gen AI than all others combined - including Nvidia If what Ross says is true, Groq will have just over 50% of all inference compute in the world for AI. If you're building something on GPT4 to increase the value of this model, then GPT5 will steamroll you according to Sam Altman, not because OpenAI doesn't like you but because they have this as a mission. He is amazed that only 5% of new AI companies see this coming. Sam Altman's advice is to build something that benefits from GPT 5 instead. They mention Klarna as a good example that will benefit from better models. MIT shows off physical intelligence, and with liquid networks, physical robots continue to learn even after deployment. You can run these on a mobile, computer, or new types of devices. In just a couple of hours, MIT can design software that controls physical products that handle text & image to robot, AND human to robot. In a few hours, we can now go from idea to physical AI machine. AI-Photo is converted to 3D, sliced then 3D printed. The printed parts are glued with motor and sensors. AI writes a bit of code, and voila, we have the bunny in the video! Ghostar Agency #GenAI #AI #GenerativeAI

The LLM behind generative AI requires processing massive amounts of data for training and faster transmission, which has presented lucrative business opportunities for manufacturers. President of ASUS Cloud and Taiwan Web Service Corporation (TWSC) Peter Wu highlighted the distinctions between traditional AI and generative AI by referring to the two as AI 1.0 and 2.0 respectively. Wu pointed out that AI 1.0 involves building specific models through supervised learning and requires project creation and labeling. This process needs to be repeated for different models and can only solve specific problems. On the other hand, the generative AI of AI 2.0 is different. Once the model is built, it can learn autonomously. From an application perspective, generative AI is also smarter and more versatile.

🚀 Exciting news in AI! 🚀 ⚡ The new Phi-3 model is redefining efficiency and power in language models. 📱 Small enough to fit on your smartphone, Phi-3 Mini packs a punch with capabilities that rival giants like Mixtral 8x7B and GPT-3.5. 🤯 Imagine carrying the power of advanced AI in your pocket! Phi-3 Mini's breakthrough lies in its innovative training on a scaled-up dataset, achieving top-notch performance while maintaining a tiny footprint. 🌍 This means sophisticated AI applications can now run locally on your device, reducing the need for cloud computing and enhancing privacy. 🔍 For a deep dive into the technical marvels of Phi-3, check out the full paper: "Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone" at https://lnkd.in/dFFTXDRe 💡 This is a game-changer for developers and businesses looking to integrate AI without the heavy lifting. With Phi-3, the future where "small is the new big" in AI is a reality!. #AI #MachineLearning #OpenSource #Phi3 #Innovation

It is very important that we invest in AI infrastructure: #AI #LLM https://lnkd.in/gzi3P_iX

The state of AI — Mid Aug 2023 The hype train is getting quieter and the hope is to keep the noise as low as possible. I’m mostly talking about “this will change everything” buddies. Behind the scene, a lot of things are cooking and the noise is louder than the ears can handle. Big tech doubling down their investment in AI where Google pitching news curators and music models, Apple getting their hands dirty with a lot of money on the table, Amazon claiming that most of their teams are experimenting or working on AI related features, Microsoft offering GPT for businesses, while OpenAI already working on GPT5 which rumored to be a multimodal. On the development side, we have HuggingFace partnering with cloud providers to make it as easy as it could be to deploy AI models for testing and production. Talking about Amazon’s SageMaker, Microsoft’s Azure, Nvidia’s Training Cluster. Hardware wise, Nvidia announces their super AI chip which is expected next year with triple the memory of H100 (that’s 240GB) — while AMD and Intel are promising to compete tightly with Nvidia when it comes to AI-ready chips. Research is not slowing down either, lots of papers with very promising discoveries and optimizations that may soon become part of our daily life. AI generated video is getting better and better, images and audio as well – giving individuals the ability to create acceptable production media. OpenSource is becoming a battlefield where big tech is trying to dominate – with Meta open source their models left and right, and China entering the field with their LLM models from the likes of Alibaba and Baidu. A happy marriage between some industries may fast forward a lot of the things, resulting in solutions for complex issues. Neurology, physics, chemistry, biology, and many more are integrating AI in their workflow – the results seem to be promising so far. Let’s not forget robotics where things started advancing rapidly thanks to LLMs. And just for those who thought that data is going to be the bottleneck for AI advances – sorry to break it to you, but synthetic data is becoming the go to. These are not my words, but Dario Amodei’s – the CEO of Anthropic (the company behind Claude) – in a recent interview. Last but not least, to make its adoption less frictionable, hidden AI may become the norm. Apple, for example, may incorporate a lot of functionalities without even mentioning the term “AI”. Many of us will use it on a daily basis, without realizing it’s an AI – everyone is happy and less freaking-out. #ai #artificialintelligence #aiforgood #aiforall #bigdata #tech #chatgpt #openai #microsoft #apple #amazon #huggingface #nvidia #amd #intel #anthropic #llm