PaLM-E (AI Robot) Can See and Understands Language!

Google's New AI Model PaLM-E Explained

Good morning fellow AI enthusiast! This week's iteration focuses on Google's new model: PaLM-E!

Imagine what happens when you merge both text and image models. You get an AI able to understand images and text, which means it understands pretty much anything. What can you do with that? At first sight, not much since it can just understand things, but what if you also combine those with something that can move in the world like a trained robot? You get PaLM-E! We hope you enjoy it.

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2️⃣ Google’s New AI Robot Can See and Understands Language! (PaLM-E)

PaLM-E, Google’s most recent publication, is what they call an embodied multimodal language model. What does this mean? It means that it is a model that can understand various types of data, such as text and images from the ViT and PaLM models we mentioned, and is able to turn these insights into actions from a robotics hand! Learn more in the video or article...

3️⃣ AI Ethics with Auxane

Large language models have become increasingly prevalent in real-world scenarios, ranging from chatbots like Siri and Alexa to language translation services like Google Translate and complex tasks such as sequential robotic manipulation planning, visual question answering, and captioning for social media platforms like Instagram and TikTok, enabling better accessibility for individuals with hearing impairments.

While these models offer tremendous potential for improving efficiency, accessibility, and knowledge in various fields, there are also several ethical considerations that must be addressed to ensure they are used responsibly. We will dive into those questions here!

One significant ethical concern is the potential for bias and unfairness in large language models. These models are trained on vast amounts of data, and if that data is not diverse and representative of different communities and cultures, the resulting models can perpetuate and amplify existing biases. For example, a language model trained on data predominantly from white, middle-class individuals may struggle to recognize and respond appropriately to language and concepts commonly used in communities of colour or those with different socio-economic backgrounds. To ensure that large language models are fair and unbiased, it is essential to prioritise diversity in the data used to train them. 

Privacy and security are also critical ethical considerations when it comes to large language models. These models require access to vast amounts of data to train effectively, and that data can include sensitive information about individuals, such as their personal details, medical histories, and financial information. If this data is not collected ethically or handled securely, it can put individuals' privacy and security at risk. It is crucial to ensure that the data used to train large language models is collected ethically and handled in a secure and responsible manner. Here, the state of the art regarding data - and cyber - security is the best answer out there, accompanied by data minimisation (take only the data you need) for example. 

Another ethical concern with large language models is accountability and transparency. These models can be challenging to interpret, which makes it difficult to hold anyone accountable for their decisions. This lack of transparency can erode trust in these models and lead to scepticism about their use. It is essential to ensure that the decisions made by large language models are explainable enough to be audited, and to maintain transparency and accountability. A way to get there could be to develop visual tools explaining how a model gets to their decision. Even if some ‘black box’ components might still be in there, a minimal understanding by all the users is recommendable. 

Despite these ethical considerations, there are also several opportunities associated with large language models, as always with technologies!

One significant opportunity is improved efficiency. Large language models can automate many tasks, freeing up time and resources for more complex and creative activities. For example, chatbots can provide customer service support, freeing up human agents to tackle more complex customer inquiries. Another example is how large language models can automate certain routine administrative tasks, such as filling out forms or processing paperwork, thus improving efficiency and accuracy regarding bureaucracy (and let’s be honest, who wouldn’t love to have those tasks made easier for them!). 

Large language models also offer enhanced accessibility for individuals with disabilities. For example, speech recognition and text-to-speech technology can help individuals with hearing or speech impairments to communicate more easily. Similarly, language translation services can help individuals who speak different languages to communicate and access information that would otherwise be inaccessible. For instance, translation services can be used in medical settings to improve communication between physicians and patients who speak different languages, or simply when on a trip and discovering a new culture by speaking with locals!

In conclusion, while large language models offer tremendous potential, it's essential to address ethical considerations associated with their use, such as bias and unfairness, privacy and security, and accountability and transparency. By prioritising these concerns, we can ensure that these models are used responsibly, equitably, and effectively to improve our world, and isn’t that the goal of implementing such technologies after all?! 

I wish you all a great week! - Auxane Boch (iuvenal research consultant, TUM IEAI research associate).

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Thank you for reading, and we wish you a fantastic week! Be sure to have enough rest and sleep!

We will see you next week with another amazing paper!

Louis