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Greg Donald 🤘
@gregdonald
- 40 Followers
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- 115 Posts
Author. Software maker. Provocateur. RC flyer. Bibliophile. Runner. Gamer. String bender. Metalhead🤘.
@tracyspcy I'm old enough to remember seeing designers be let go for refusing to let go of their <font> tags and learn CSS.
It's not much different from anything else I've experienced in three decades of software engineering: assimilate or be left behind.
@tracyspcy My recent software engineering job interviews have included using AI agents to generate code, skills with prompt creation, vetting generated code, etc.
@DrMikeWatts Fast. Friendly. Accurate. Pick two!
I couldn't care less if they are friendly, especially if they are accurate and fast.
@manchuck I agree with most of this. I would never let an AI Agent near my inbox or credit card or production database.
@manchuck Machine learning models are only black boxes for those who don't seek the knowledge.
https://christophm.github.io/interpretable-ml-book/
https://www.mdpi.com/1422-0067/25/15/8044
https://www.medrxiv.org/content/10.1101/2024.08.10.24311796v1.full.pdf
https://academic.oup.com/bib/article/26/4/bbaf409/8235626
https://pmc.ncbi.nlm.nih.gov/articles/PMC12547363/
https://ascopubs.org/doi/10.1200/PO-24-00908
You seem to be focused on all the inadequacies of LLMs, while discounting all of machine learning.
@manchuck Cancer research is not limited to just reinforcement learning like you describe.
@manchuck Natural language processing accelerates research by scanning millions of research papers to uncover drug interactions that would take humans decades to find. By optimizing clinical trials and predicting which patients will respond to specific immunotherapies, AI transforms once-terminal diagnoses into manageable or curable conditions.
@manchuck Beyond diagnostics, unsupervised learning identifies the specific genetic mutations that make a tumor unique. This enables the creation of personalized mRNA vaccines tailored to an individual patient's immune system.
@manchuck "AI will never cure cancer" ignores how machine learning is already the primary engine driving modern oncology. AI functions as a force multiplier across every stage of research and treatment processes. Supervised learning models can analyze imaging with much higher consistency than human eyes. This statistically improves early detection rates, and that is currently the most effective way to achieve a clinical cure.