Open Alternatives and Corporate Accountability in AI

Open Alternatives and Corporate Accountability in AI

Author: Johannes Margraf, ChemistryViews

The intersection of chemistry and artificial intelligence (AI) is a fascinating area that attracts a lot of attention in both research and industry. We talked to people working in the field about the potential of AI to revolutionize chemical research, but also concerns, (current) limitations, and ethical implications for chemical applications. We also asked for ideas to try or experiment with, as well as useful articles and videos for beginners and advanced users.

Professor Johannes Margraf, University of Bayreuth, Germany, works in the fields of theoretical chemistry and machine learning.


What fascinates you about AI?

AI and machine learning (ML) continue to surprise me, both in my field (theoretical chemistry) and in general. It feels like we constantly have cases where something that we considered impossible a few years ago suddenly works.


Is there anything we should fear?

I don’t think we should fear AI or any technology as such. However, I do worry about having an oligopoly of a few profit-oriented companies dominating the field, and about irresponsible uses and unsafe products based on AI/ML. It’s very important that truly open and transparent alternatives are developed and that the companies are held accountable if their products are unsafe or biased.


Do you have something for our readers to try out or experiment with?

In the spirit of the previous question, I would encourage people to actually build and train models themselves rather than just using ChatGPT. This would help to increase their understanding of this technology and inspire them to build their own, open models and tools.

A great starting point is this old blog post from Andrej Kaparthy: The Unreasonable Effectiveness of Recurrent Neural Networks


Thank you very much for the insights.

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