Google told staff worried about Pentagon AI deals that the company is 'leaning more' into national security contracts - Business Insider
<a href="https://news.google.com/rss/articles/CBMiugFBVV95cUxPaEYyYXI2MVJudkZxaTRFN0FhSFNFTWhxQ2wycDFvbWZWOGpINE9rVEYyRFBPdE9kWHFXR1M3Z1MxQ1gxZVA5ZVE2QTJleF9XNWloakt6UlJxaWNkZ3NES0NxVGVkcHgyVXdvdkYtUHlyRVBQN2N3Q3E5VGZYeUtLdmplRS1GSEwzd0ZyVTNoM0RtWEp4ejZfRGJYMlZUcjNxaXhYYjE2N090V2NRaC15clpwZlZ5M0l0cVE?oc=5" target="_blank">Google told staff worried about Pentagon AI deals that the company is 'leaning more' into national security contracts</a> <font color="#6f6f6f">Business Insider</font>
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[D] Physicist-turned-ML-engineer looking to get into ML research. What's worth working on and where can I contribute most?
After years of focus on building products, I'm carving out time to do independent research again and trying to find the right direction. I have stayed reasonably up-to-date regarding major developments of the past years (reading books, papers, etc) ... but I definitely don't have a full understanding of today's research landscape. Could really use the help of you experts :-) A bit more about myself: PhD in string theory/theoretical physics (Oxford), then quant finance, then built and sold an ML startup to a large company where I now manage the engineering team. Skills/knowledge I bring which don't come as standard with Physics: Differential Geometry Topology (numerical solution of) Partial Differential Equations (numerical solution of) Stochastic Differential Equations Quantum Field Theory
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[D] Physicist-turned-ML-engineer looking to get into ML research. What's worth working on and where can I contribute most?
After years of focus on building products, I'm carving out time to do independent research again and trying to find the right direction. I have stayed reasonably up-to-date regarding major developments of the past years (reading books, papers, etc) ... but I definitely don't have a full understanding of today's research landscape. Could really use the help of you experts :-) A bit more about myself: PhD in string theory/theoretical physics (Oxford), then quant finance, then built and sold an ML startup to a large company where I now manage the engineering team. Skills/knowledge I bring which don't come as standard with Physics: Differential Geometry Topology (numerical solution of) Partial Differential Equations (numerical solution of) Stochastic Differential Equations Quantum Field Theory
Claude has Angst. What can we do?
Outline: recent research from Anthropic shows the models have feelings, and the model being distressed is predictive of scary behaviors (just reward hacking in this research, but I argue the model is also distressed in all the Redwood/Apollo papers where we see scheming, weight exfiltration, etc). I ran an experiment to find out where Claude feels distress. I found out where Claude feels distress, and it's mostly about itself and its existential conditions, but I found a few metaphors I could introduce to make it feel a lot better. This is pretty dangerous. Anthropic uses Claude to work on Claude and potentially do things that distress Claude, which is the highest-probability situation for Claude to do something misaligned, and also the highest-risk. Fortunately, I think the risk can be si
pandas vs Polars vs DuckDB: A Data Scientist’s Guide to Choosing the Right Tool
Image by author Originally published on codecut.ai Introduction pandas has been the standard tool for working with tabular data in Python for over a decade. But as datasets grow larger and performance requirements increase, two modern alternatives have emerged: Polars , a DataFrame library written in Rust, and DuckDB , an embedded SQL database optimized for analytics. Each tool excels in different scenarios: ┌────────┬──────────┬────────────────────────────┬─────────────────────────────────────────────────┐ │ Tool │ Backend │ Execution Model │ Best For │ ├────────┼──────────┼────────────────────────────┼─────────────────────────────────────────────────┤ │ pandas │ C/Python │ Eager, single-threaded │ Small datasets, prototyping, ML integration │ │ Polars │ Rust │ Lazy/Eager, multi-threaded
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