AI Is Changing The Way We Work
Imagine a world where you can use AI to do your tasks faster, easier, and better. That’s the world that LLaVA, a new open-source AI system, is promising to create.
LLaVA is a powerful AI system that can understand and generate both text and images. It can do everything from translating languages to writing different kinds of creative content to answering your questions in an informative way. But what really sets LLaVA apart is its ability to understand and reason about the world around it.
For example, LLaVA can look at an image of a cat and tell you that it’s a cat. It can also tell you what the cat is doing, such as sleeping or playing. It can even tell you how the cat is feeling, such as happy or sad.
LLaVA’s ability to understand and generate both text and images makes it a powerful tool for a wide range of tasks. For example, LLaVA can be used to:
- Create new forms of art and entertainment
- Develop new educational tools
- Improve the efficiency of businesses and organizations
- Automate tasks that are currently done by humans
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And because LLaVA is open-source, anyone can use it to create new and innovative applications.
Here are just a few examples of how LLaVA could be used to make our lives easier and more productive:
- LLaVA could be used to create a new generation of virtual assistants that can understand and respond to our needs in a more natural way.
- LLaVA could be used to develop new tools for social media that can help us to connect with others and share our experiences in new and exciting ways.
- LLaVA could be used to create new educational tools that can tailor the learning experience to each individual student.
- LLaVA could be used to develop new tools for businesses and organizations that can help them to improve their efficiency and productivity.
The possibilities are endless.
LLaVA is still under development, but it has the potential to revolutionize the way we work and live. With its open-source nature and powerful capabilities, LLaVA is a tool that everyone should be excited about.
Both images were created in Dalle- 3 ( Microsoft image creator)
CHAT GPT -4
GPT-4 is a highly advanced AI, and it can actually be used to create other AI models. These new models can be even better than GPT-4 itself. Researchers, like Liu et al., used GPT-4 to make a special AI called LLaVA. LLaVA can understand both words and pictures together, making it possible to ask it questions about any image.
Since GPT-4 can’t see pictures but is great with text, the researchers sent it descriptions of images and had it create different things like questions, detailed descriptions, and answers based on those descriptions. Essentially, they gave GPT-4 a personality and role and had it make different kinds of information from the initial image descriptions.
Visual Instructions
The process of teaching an AI to understand and respond to visual instructions is quite simple. You take an image and the questions you’ve asked GPT-4 and use them to train a new model. This new model should be able to answer those questions without relying on the image captions that GPT-4 used. Instead, it needs to comprehend the image itself to provide accurate answers. To achieve this, we require an AI that can comprehend both images and text and blend its understanding of the question with the image for answering.
Rather than creating a new model from the ground up, we can leverage two already powerful models: one for language and another for vision. In the case of LLaVA, the researchers chose LLaMA as their foundational large language model, aiming to train it to understand both images and text simultaneously. LLaMA, developed by Meta, possesses exceptional text comprehension capabilities and offers the flexibility of being somewhat open-source. It means that researchers could adapt it to their specific task involving images through a process called fine-tuning, somewhich thing not feasible with GPT-4.
Since LLaMA primarily comprehends text, the researchers needed to translate their images into a format it could grasp. Luckily, there exists a powerful model known as CLIP, which has been trained on numerous image-caption pairs. CLIP specializes in converting images into a representation similar to text, using numerical matrices known as embeddings that resemble those of the corresponding image-caption pairs. This component of the model, responsible for translating image embeddings into text-like embeddings, is trained alongside the language model. Subsequently, you can merge your text instructions with this new text-like image representation and feed it to the LLaMA model, which is being trained to provide accurate answers.
Read the full article – www.louisbouchard.ai/llava
