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Google just released PaliGemma 2 Mix: new versatile instruction vision language models 🔥 > Three new models: 3B, 10B, 28B with res 224, 448 💙 > Can do vision language tasks with open-ended prompts, understand documents, and segment or detect anything 🤯

merve

88,374 subscribers

83,712 views • 1 year ago •via X (Twitter)

Science & Technology

Anya Rossi• Live Now

Private livecam show

11 Comments

merve1 year ago

All models and the demo are here 🤝 Read our blog to learn more 📚

merve1 year ago

@onuralpszr this might interest you!

Quantify Funds1 year ago

Watch this space for the reveal 👀 BIG NEWS: Our family of ETFs is expanding with 10 new single stock offerings. Read the pre-effective prospectus @ #STKd #QuantifyFunds #QuantifyChaos #Nasdaq #ETFs

ondevice1 year ago

love how, usually when we come across something from google, we usually see demos rather than just academic benchmarks thanks team! love this @DynamicWebPaige @OfficialLoganK @AarushSelvan

lambda1 year ago

openAI should take notes. Even google is open sourcing

merve1 year ago

google has been open sourcing since early times and for a long while actually

nonc3ai1 year ago

Great news, 3B should be small enough to run on a phone, right?

merve1 year ago

if you quantize, yes ☺️

Arpit Sharma1 year ago

AI that sees, understands, and segments. Impressive!

Matthew Rogers1 year ago

I want to scan my mail, lets go!

Furkan Gözükara1 year ago

Why res is still to low?

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1,180,562 views • 7 months ago

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Databricks

10,132 views • 11 months ago

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52,158 views • 11 months ago

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AK

66,472 views • 2 years ago

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Turing Post

62,362 views • 6 months ago

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60,243 views • 1 year ago

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162,842 views • 2 years ago

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Andrew Ng

151,198 views • 2 years ago

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AK

41,736 views • 1 year ago

Microsoft has just released an impressive update Copilot on Windows can now see and discuss anything on your screen in real time. Basically ChatGPT Advanced voice mode, but with vision and everywhere on your laptop. You don't need a subscription, it's 100% FREE: 1. Open Copilot 2. Click on the new vision icon 3. Choose the software to share 4. Start the conversation! Genuine game-changer to receive real-time guidance, understand anything on screen, learn faster and more. And the transcript is available right after your session ends.

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178,855 views • 1 year ago

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AI at Meta

163,313 views • 1 year ago

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ComfyUI

218,749 views • 2 months ago

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AI at Meta

310,120 views • 1 year ago

A new SEO tactic people are doing is they're making pages with “Reddit” in the URL slug, “Reddit” in the page title, “Reddit” in the H1… They are literally targeting the long searches that ChatGPT or other LLMs do which contain “Reddit.” Large language models will do searches that have the word “Reddit” in them. But the large language models won't search the site Reddit, they will just search Google for searches plus the word “Reddit.” So SEOs target these exact searches. Then of course, a bunch of Reddit results will come up, but their results come up as well, then they can influence the output from large language models. Crazy tactic, credit to the legendary SEO, David G Quaid (SEO), for sharing this.

A new SEO tactic people are doing is they're making pages with “Reddit” in the URL slug, “Reddit” in the page title, “Reddit” in the H1… They are literally targeting the long searches that ChatGPT or other LLMs do which contain “Reddit.” Large language models will do searches that have the word “Reddit” in them. But the large language models won't search the site Reddit, they will just search Google for searches plus the word “Reddit.” So SEOs target these exact searches. Then of course, a bunch of Reddit results will come up, but their results come up as well, then they can influence the output from large language models. Crazy tactic, credit to the legendary SEO, David G Quaid (SEO), for sharing this.

Edward Sturm

36,661 views • 25 days ago

We benchmarked leading multimodal foundation models (GPT-4o, Claude 3.5 Sonnet, Gemini, Llama, etc.) on standard computer vision tasks—from segmentation to surface normal estimation—using standard datasets like COCO and ImageNet. These models have made remarkable progress; however, it is unclear exactly where they stand in terms of understanding vision in detail. Especially when it comes to tasks beyond question-answering. How well do they understand an object's segments or geometry? Our analyses yield an assessment that is quantitatively and qualitatively detailed and is compatible with evaluations developed in the field of computer vision over the past decades. Observed trends: 🔹 The foundation models consistently underperform task-specific SOTA models across all tasks. However, they are respectable generalists, which is remarkable as they are presumably trained primarily on image-text-based tasks. 🔹 They perform semantic tasks notably better than geometric ones. 🔹 GPT-4o performs the best among non-reasoning models, getting the top position in 4 out of 6 tasks. 🔹 Reasoning models, e.g., o3, show improvements in geometric tasks. 🔹 The 'image generation' models, e.g., GPT-40 Image Generation, which have been natively trained multimodally, exhibit quirks. E.g., hallucinated objects, misalignment between the input and output, etc. 🔹 While the prompting techniques affect performance, better models exhibit less sensitivity to variations in prompts. We control for the variance introduced by the prompting methods in our experiments. 🌐 Detailed analyses, visualizations: ⌨️ code: 🧵 1/n

We benchmarked leading multimodal foundation models (GPT-4o, Claude 3.5 Sonnet, Gemini, Llama, etc.) on standard computer vision tasks—from segmentation to surface normal estimation—using standard datasets like COCO and ImageNet. These models have made remarkable progress; however, it is unclear exactly where they stand in terms of understanding vision in detail. Especially when it comes to tasks beyond question-answering. How well do they understand an object's segments or geometry? Our analyses yield an assessment that is quantitatively and qualitatively detailed and is compatible with evaluations developed in the field of computer vision over the past decades. Observed trends: 🔹 The foundation models consistently underperform task-specific SOTA models across all tasks. However, they are respectable generalists, which is remarkable as they are presumably trained primarily on image-text-based tasks. 🔹 They perform semantic tasks notably better than geometric ones. 🔹 GPT-4o performs the best among non-reasoning models, getting the top position in 4 out of 6 tasks. 🔹 Reasoning models, e.g., o3, show improvements in geometric tasks. 🔹 The 'image generation' models, e.g., GPT-40 Image Generation, which have been natively trained multimodally, exhibit quirks. E.g., hallucinated objects, misalignment between the input and output, etc. 🔹 While the prompting techniques affect performance, better models exhibit less sensitivity to variations in prompts. We control for the variance introduced by the prompting methods in our experiments. 🌐 Detailed analyses, visualizations: ⌨️ code: 🧵 1/n

Amir Zamir

73,074 views • 1 year ago

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Qwen

215,137 views • 1 year ago

A new robot policy just cleaned up a kitchen it had never seen before [watch what happens. paper included ⬇️] Pi-0.5 builds on top of Pi-0 and shows how smart co-training with diverse data can unlock real generalization in the home. It doesn’t just learn from one setup but adapts to many, including homes it’s never seen. What it does ✅ Handles new homes without training in them ✅ Follows complex language instructions ✅ Cleans, places dishes, handles spills ✅ Matches in-home training models using cross-embodiment and web data Robots that understand tasks and adapt to new spaces are finally within reach. More in the blog: Read the paper ⬇️ Physical Intelligence, co-founded by UC Berkeley professor Sergey Levine, is a robotics startup developing general-purpose AI foundation models that enable robots to perform a wide variety of real-world tasks with human-like adaptability, recently raising $400 million to advance this vision.

A new robot policy just cleaned up a kitchen it had never seen before [watch what happens. paper included ⬇️] Pi-0.5 builds on top of Pi-0 and shows how smart co-training with diverse data can unlock real generalization in the home. It doesn’t just learn from one setup but adapts to many, including homes it’s never seen. What it does ✅ Handles new homes without training in them ✅ Follows complex language instructions ✅ Cleans, places dishes, handles spills ✅ Matches in-home training models using cross-embodiment and web data Robots that understand tasks and adapt to new spaces are finally within reach. More in the blog: Read the paper ⬇️ Physical Intelligence, co-founded by UC Berkeley professor Sergey Levine, is a robotics startup developing general-purpose AI foundation models that enable robots to perform a wide variety of real-world tasks with human-like adaptability, recently raising $400 million to advance this vision.

Ilir Aliu - eu/acc

18,120 views • 1 year ago