Mar 24, 2022 11:52 PM
Who’s afraid of Kathleen Stock? (cancel culture in academic philosophy)
https://rajasmusingsconfusings.blogspot.com/2022/03/
If philosophers refuse to use books simply because they include philosophical views that we dislike or disagree with, then we would be failing to live up to our professional standards and contributing to dismantling the field itself.
RELATED: AI suggested 40,000 new possible chemical weapons in just six hours
Bioethics: AI misused for de novo design of biochemical weapons
https://www.nature.com/articles/s42256-022-00465-9
EXCERPT: Our proof of concept thus highlights how a nonhuman autonomous creator of a deadly chemical weapon is entirely feasible.
Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community. Although some domain expertise in chemistry or toxicology is still required to generate toxic substances or biological agents that can cause significant harm, when these fields intersect with machine learning models, where all you need is the ability to code and to understand the output of the models themselves, they dramatically lower technical thresholds. Open-source machine learning software is the primary route for learning and creating new models like ours, and toxicity datasets9 that provide a baseline model for predictions for a range of targets related to human health are readily available.
Our proof of concept was focused on VX-like compounds, but it is equally applicable to other toxic small molecules with similar or different mechanisms, with minimal adjustments to our protocol. Retrosynthesis software tools are also improving in parallel, allowing new synthesis routes to be investigated for known and unknown molecules. It is therefore entirely possible that novel routes can be predicted for chemical warfare agents, circumventing national and international lists of watched or controlled precursor chemicals for known synthesis routes.
The reality is that this is not science fiction. We are but one very small company in a universe of many hundreds of companies using AI software for drug discovery and de novo design. How many of them have even considered repurposing, or misuse, possibilities?
Most will work on small molecules, and many of the companies are very well funded and likely using the global chemistry network to make their AI-designed molecules. How many people have the know-how to find the pockets of chemical space that can be filled with molecules predicted to be orders of magnitude more toxic than VX? We do not currently have answers to these questions.
There has not previously been significant discussion in the scientific community about this dual-use concern around the application of AI for de novo molecule design, at least not publicly. Discussion of societal impacts of AI has principally focused on aspects such as safety, privacy, discrimination and potential criminal misuse10, but not on national and international security.
When we think of drug discovery, we normally do not consider technology misuse potential. We are not trained to consider it, and it is not even required for machine learning research, but we can now share our experience with other companies and individuals.
AI generative machine learning tools are equally applicable to larger molecules (peptides, macrolactones, etc.) and to other industries, such as consumer products and agrochemicals, that also have interests in designing and making new molecules with specific physicochemical and biological properties. This greatly increases the breadth of the potential audience that should be paying attention to these concerns.
For us, the genie is out of the medicine bottle when it comes to repurposing our machine learning. We must now ask: what are the implications? (MORE - missing details)
https://rajasmusingsconfusings.blogspot.com/2022/03/
If philosophers refuse to use books simply because they include philosophical views that we dislike or disagree with, then we would be failing to live up to our professional standards and contributing to dismantling the field itself.
RELATED: AI suggested 40,000 new possible chemical weapons in just six hours
Bioethics: AI misused for de novo design of biochemical weapons
https://www.nature.com/articles/s42256-022-00465-9
EXCERPT: Our proof of concept thus highlights how a nonhuman autonomous creator of a deadly chemical weapon is entirely feasible.
Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community. Although some domain expertise in chemistry or toxicology is still required to generate toxic substances or biological agents that can cause significant harm, when these fields intersect with machine learning models, where all you need is the ability to code and to understand the output of the models themselves, they dramatically lower technical thresholds. Open-source machine learning software is the primary route for learning and creating new models like ours, and toxicity datasets9 that provide a baseline model for predictions for a range of targets related to human health are readily available.
Our proof of concept was focused on VX-like compounds, but it is equally applicable to other toxic small molecules with similar or different mechanisms, with minimal adjustments to our protocol. Retrosynthesis software tools are also improving in parallel, allowing new synthesis routes to be investigated for known and unknown molecules. It is therefore entirely possible that novel routes can be predicted for chemical warfare agents, circumventing national and international lists of watched or controlled precursor chemicals for known synthesis routes.
The reality is that this is not science fiction. We are but one very small company in a universe of many hundreds of companies using AI software for drug discovery and de novo design. How many of them have even considered repurposing, or misuse, possibilities?
Most will work on small molecules, and many of the companies are very well funded and likely using the global chemistry network to make their AI-designed molecules. How many people have the know-how to find the pockets of chemical space that can be filled with molecules predicted to be orders of magnitude more toxic than VX? We do not currently have answers to these questions.
There has not previously been significant discussion in the scientific community about this dual-use concern around the application of AI for de novo molecule design, at least not publicly. Discussion of societal impacts of AI has principally focused on aspects such as safety, privacy, discrimination and potential criminal misuse10, but not on national and international security.
When we think of drug discovery, we normally do not consider technology misuse potential. We are not trained to consider it, and it is not even required for machine learning research, but we can now share our experience with other companies and individuals.
AI generative machine learning tools are equally applicable to larger molecules (peptides, macrolactones, etc.) and to other industries, such as consumer products and agrochemicals, that also have interests in designing and making new molecules with specific physicochemical and biological properties. This greatly increases the breadth of the potential audience that should be paying attention to these concerns.
For us, the genie is out of the medicine bottle when it comes to repurposing our machine learning. We must now ask: what are the implications? (MORE - missing details)