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Hello readers,
Welcome to the AI For All newsletter! Today, we’ll be looking at how AI is poised to revolutionize drug production (in a good way!), the delicate process of using AI in financial risk assessment, and AI news from around the web!
AI in Action: Speeding up drug manufacturing
Welcome to the AI in Action, we feature a practical application for AI tech!
This week, many AI headlines have been dominated by Studio Ghibli-style images and memes, a suddenly popular usecase for OpenAI’s newly-launched image feature in GPT-4o that is stressing the company’s infrastructure. But of course not all AI applications are so frivolous. This month, AI startup ReactWise has been making the rounds explaining how it’s poised to revolutionize drug manufacturing.
By using an AI-driven "copilot," ReactWise expedites the painstaking process of chemical production optimization—crucial for scaling up drug materials for clinical trials, TechCrunch explains. Traditionally a labor-intensive, trial-and-error effort, the AI approach accelerates this essential step by as much as 30 times, significantly reducing the time required to pinpoint optimal manufacturing methods.
The startup's system is built on extensive lab-generated datasets. With thousands of chemical reactions already analyzed, the models can predict efficient manufacturing methods, potentially skipping over multiple rounds of costly experiments and making it quicker and cheaper to bring new medicines to market.
🔥 Rapid Fire
Bill Gates says AI will replace doctors, teachers within 10 years
ChatGPT’s viral image-generation AI is ‘melting’ OpenAI’s GPUs
A new AI test is outwitting OpenAI, Google models, among others
Microsoft pulls back from more data center leases in US and Europe
Alibaba launches new open-source AI model for ‘cost-effective AI agents’
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📖 What We’re Reading
“Machine learning algorithms assess risk using enormous datasets, discovering tendencies that might not be immediately obvious to human analysts. By evaluating significant historical data, AI algorithms predict the possibility of a borrower defaulting on a loan. This automation reduces human involvement and is supposed to promote efficiency, minimize errors, and improve risk assessment.
However, AI-based lending systems are not immune from bias despite these advantages. These algorithms inherit prejudices buried in past data, potentially leading to discriminatory lending practices contributing to economic and social disparity.”