The bias, ethics, and responsible AI dilemma There are fundamental issues with AI that still need to be resolved, such as biases in the models themselves. To make these issues even more complicated, there are differences in societal biases worldwide. The most extreme forms of bias, such as violence and discrimination, are already being addressed, but usually only after the model has been built, which is not an ideal situation. As a global community, we should be striving to improve AI by incorporating ethical considerations into the AI development process itself.
undertaking, there is a lot of potential for a solution like this in rural healthcare, where access to doctors is limited.
Video analysis is also seeing some interesting developments. Using surveillance as the North Star of vision problems, developers are considering whether it’s possible to link and analyze footage from multiple camera feeds. So, to provide a very simplistic example, if the police need to find out who stole a blue bag while wearing a red shirt, could they search through all the footage to find the perpetrator? While it’s not immediately possible — and ethical issues around privacy still need to be addressed — breakthroughs in this sector are imminent.
It’s challenging to determine how much bias should be removed from a model, and what kind of bias should be addressed. Should a model be completely unbiased, or should it reflect the biases present in society? These are complex questions with no simple answers.
To generalize or specialize — that is the question.
At Fractal, we prioritize responsible and unbiased development. We have established a specialized AI ethics team composed of data scientists, data engineers, lawyers, and designers to ensure ethical considerations are considered when building our models. We have already started incorporating privacy-preserving features into our solutions — especially in the field of machine vision. Imminent disruptions The field of AI is moving quickly, and we are likely to see many process innovations and fundamental breakthroughs in the near future. Life sciences, healthcare, and insurance are particularly exciting areas for these kinds of develop- ments right now. Developers are exploring the possibility of using GPT-like models, such as Bio GPT, designed to break down specialist silos and create a “brain” that knows most medical information. Although this is a huge
Another area of development is generalizable and efficient learning models. There are currently different research directions, one of which is developing small models that can perform specific tasks effectively without needing a large model that does everything. Another avenue is exploring multi-agent learning, where multiple models work together and learn from each other. Finally, progress is also being made in multimodal language models, where multiple modes of communication are incorporated into a single generative model — for example, adding machine vision to GPT-4. The next big breakthrough is potentially multimodality in vision, which is still a work in progress. At a minimum, we are several months away from seeing it.
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