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Gemini LLM, an increase in benefits, and risks.

 


Gemini LLM is being developed by Google Brain and Deepmind that was introduced at Google I/O 2023, and is expected to have a trillion parameters, like GPT-4. The project is using tens of thousands of Google's TPU AI chips for training, and could take months to complete. It may be introduced early next year.

Gemini is being trained on a massive dataset of text, audio, video, images, and other media. This will allow it to 'understand' and respond to a wider range of input than previous LLMs. It will also be able to use other tools and APIs, which will make it more versatile and powerful. It's clearly looking to compete with a future GPT-5, this time Google are looking to get ahead of the curve.

Training Gemini in a multimodal manner is significant because it allows the model to learn from a wider range of data. This should improve the model's accuracy and performance on a variety of tasks.

For example, if Gemini is trained on both text and images, it can learn to associate certain words with certain images. This can be helpful for tasks such as image captioning, where the model is given an image and asked to generate a text description of it.

Similarly, if Gemini is trained on both text and audio, it can learn to associate certain words with certain sounds. This can be helpful for tasks such as speech recognition, where the model is given an audio recording and asked to transcribe it into text.

By training Gemini in a multimodal manner, Google is able to create a model that is more powerful and versatile than previous models. This could lead to new and innovative applications for AI, such as virtual assistants that can understand and respond to both text and voice commands.

Benefits of training Gemini in a multimodal manner include:

  • Increased accuracy: Multimodal models can learn from a wider range of data, which can lead to increased accuracy on a variety of tasks.
  • Improved performance: Multimodal models can often perform tasks faster and more efficiently than unimodal models.
  • Increased versatility: Multimodal models can be used for a wider range of tasks, which can make them more valuable to businesses and consumers.

The risks of bias, misinformation, privacy, and security are more appropriate to a multimodal LLM than a current non-multimodal LLM because multimodal LLMs are trained on a wider range of data. This means that they are more likely to be exposed to biases and misinformation, and they are also more likely to collect and analyse personal data. Additionally, multimodal LLMs are more complex than non-multimodal LLMs, which makes them more susceptible to security vulnerabilities.

  • Bias: Multimodal LLMs are trained on massive datasets of data, which can contain biases. These biases can be reflected in the output of the model, leading to discrimination or other harmful outcomes. For example, a multimodal LLM that is trained on a dataset of news articles may be biased towards certain political viewpoints. This could lead to the LLM generating text that is biased towards those viewpoints.
  • Misinformation: Multimodal LLMs can be used to generate text that is misleading or false. This could be used to spread misinformation or propaganda. For example, a multimodal LLM could be used to generate fake news articles or social media posts. This could be used to influence public opinion or to damage the reputation of an individual or organization.
  • Privacy: Multimodal LLMs can be used to collect and analyze personal data. This data could be used to track people's movements, habits, and interests. For example, a multimodal LLM could be used to collect data from people's social media posts or from their online activity. This data could then be used to target people with advertising or to track their movements.
  • Security: Multimodal LLMs can be used to create malware or other malicious software. This software could be used to steal data, damage systems, or even cause physical harm. For example, a multimodal LLM could be used to create malware that can steal people's passwords or credit card numbers. This malware could then be used to commit identity theft or other crimes.
The questions remain of how aligned the next generation of LLMs will be. No doubt they will appear to be safer, we will not know though for some months after their release. The mass social experiment will continue, largely unabated.

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