ESMFold Description
ESMFold demonstrates how AI can provide new tools for understanding the natural world. It is similar to the microscope which allowed us to see the world at a tiny scale and gave us a new understanding of the world. AI can help us see biology in a different way and understand the vastness of nature. AI research has largely focused on helping computers understand the world in a similar way to humans. The language of proteins is a language that is beyond human comprehension. Even the most powerful computational tools have failed to understand it. AI has the potential of opening up this language to our comprehension. AI can be studied in new domains like biology to gain a better understanding of artificial intelligence. Our research reveals connections across domains. Large language models that are behind machine translation, natural speech understanding, speech recognition, image generation, and machine translation are also able learn deep information about biology.
ESMFold Alternatives
Gemini 2.0
Gemini 2.0, an advanced AI model developed by Google is designed to offer groundbreaking capabilities for natural language understanding, reasoning and multimodal interaction. Gemini 2.0 builds on the success of Gemini's predecessor by integrating large language processing and enhanced problem-solving, decision-making, and interpretation abilities. This allows it to interpret and produce human-like responses more accurately and nuanced. Gemini 2.0, unlike traditional AI models, is trained to handle a variety of data types at once, including text, code, images, etc. This makes it a versatile tool that can be used in research, education, business and creative industries. Its core improvements are better contextual understanding, reduced biased, and a more effective architecture that ensures quicker, more reliable results. Gemini 2.0 is positioned to be a major step in the evolution AI, pushing the limits of human-computer interactions.
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GPT-3.5
GPT-3.5 is the next evolution to GPT 3 large language model, OpenAI.
GPT-3.5 models are able to understand and generate natural languages. There are four main models available with different power levels that can be used for different tasks. The main GPT-3.5 models can be used with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.
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Llama 3.3
Llama 3.3, the latest in the Llama language model series, was developed to push the limits of AI-powered communication and understanding. Llama 3.3, with its enhanced contextual reasoning, improved generation of language, and advanced fine tuning capabilities, is designed to deliver highly accurate responses across diverse applications. This version has a larger dataset for training, refined algorithms to improve nuanced understanding, and reduced biases as compared to previous versions. Llama 3.3 excels at tasks such as multilingual communication, technical explanations, creative writing and natural language understanding. It is an indispensable tool for researchers, developers and businesses. Its modular architecture enables customization in specialized domains and ensures performance at scale.
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Cohere
With just a few lines, you can integrate natural language understanding and generation into the product. The Cohere API allows you to access models that can read billions upon billions of pages and learn the meaning, sentiment, intent, and intent of every word we use. You can use the Cohere API for human-like text. Simply fill in a prompt or complete blanks. You can create code, write copy, summarize text, and much more. Calculate the likelihood of text, and retrieve representations from your model. You can filter text using the likelihood API based on selected criteria or categories. You can create your own downstream models for a variety of domain-specific natural languages tasks by using representations. The Cohere API is able to compute the similarity of pieces of text and make categorical predictions based on the likelihood of different text options. The model can see ideas through multiple lenses so it can identify abstract similarities between concepts as distinct from DNA and computers.
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Pricing
Pricing Starts At:
Free
Free Version:
Yes
Integrations
No Integrations at this time
Company Details
Company:
Meta
Year Founded:
2004
Headquarters:
United States
Website:
github.com/facebookresearch/esm
Media
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