Foundational Models Defining a New Era in Vision: A Survey and Outlook A comprehensive survey of visual foundation models. Covers emerging architectures(for vision+other modalities), training objectives, pre-training datasets, fine-tuning mechanisms, and the common prompting
MACHINE LEARNING
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LLM Text Adversarial Problems Easier Than Vision Recognition
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I do agree that trying to solve this within LLM will be hard. But the problem seems easier to solve in practice than vision, unless people come up with a way to make an adversarial string that reads like natural language
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Adversarial Examples in Vision vs Language Models Vulnerability
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Adversarial examples have been hard to solve in vision mainly because it was imperceptible to humans and added adv noise is hard to remove. Looking at the demo, it seems relatively easy to build a filter to remove adversarial suffix before feeding the query into LLM…
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Perplexity Labs Launches Web App for LLaMa 2
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Perplexity Labs https://
bit.ly/3K7P5RI
Web app for LLaMa 2!
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Llama 2: First Open-Source Model Matching OpenAI’s Caliber
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Llama 2 is the first open-source language model of the same caliber as OpenAI's models. You can run it on Replicate using JavaScript, Python, cURL, Go, Swift, or any other language. Here's a guide to get you started running Llama 2 with an API.
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Low-cost Catalyst Materials for Renewable Energy Discovery
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Low cost catalyst materials are an important means towards a renewable energy future. By making this demo available to the public, we hope to increase the pace of material discovery for low-cost catalyst materials and showcase the emerging generalizability of these models. pic.twitter.com/6TBscu8hl5
— AI at Meta (@AIatMeta) 27 juillet 2023Low cost catalyst materials are an important means towards a renewable energy future. By making this demo available to the public, we hope to increase the pace of material discovery for low-cost catalyst materials and showcase the emerging generalizability of these models.
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Open Catalyst Demo: 100M Surface-Adsorbate Combinations Analysis
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The Open Catalyst demo supports adsorption energy calculations for 11,427 catalyst materials and 86 adsorbates, which amounts to ~100M catalyst surface-adsorbate combinations — a scale impossible to explore without machine learning.
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Open Catalyst Demo Released for Accelerated Material Science Research
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Today we're releasing the Open Catalyst Demo to the public — this new service will allow researchers to accelerate work in material sciences by enabling them to simulate the reactivity of catalyst materials ~1000x faster than existing computational methods using AI.
— AI at Meta (@AIatMeta) 27 juillet 2023
Demo ⬇️Today we're releasing the Open Catalyst Demo to the public — this new service will allow researchers to accelerate work in material sciences by enabling them to simulate the reactivity of catalyst materials ~1000x faster than existing computational methods using AI. Demo
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Programmatic Custom Model Creation Now Available in Python SDK
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We are introducing programmatic custom model creation with our Python SDK. This empowers developers with a more flexible and efficient way to create custom models.
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How To Make AI Work In Your Organization
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How To Make AI Work In Your Organization
#AI #AIio #BigData #ML #NLU #Futureofwork @IIoT_World @ipfconline1 @jblefevre60 @JimMarous @insurtechtalk @KirkDBorne @kuriharan @LouisSerge http://
ow.ly/x0fu30swnpB