johncwheeler

I hear what you are saying man, and to greater and lesser degrees most all of those things are real effects or potential effects in the blast radius of AI.

They are exactly that, though - growing pains. They have to be, because this applied math/tech is not going back in the box. No significant scientific advancement does. And rarely does a major significant scientific advancement realize its potential in the first generation, or without real side effects and social backlash.

The printing press, Darwin’s Evolution, germ theory (sanitization), Turing’s foundational mathematical work (demonized due to his biological research on homosexuality), the electric loom (where we unfortunately invented a whole ass word for the detractors: “Luddites”). Each of these advancements, and many more, met disagreement and (often violent) blowback. This was almost always due to the social implications, instead of the actual science or technology. The plow and irrigation, iron smelting, the sawmill, the cotton gin, the locomoti

johncwheeler

AI's Impact Across Fields: Real Examples

Materials Science

Google DeepMind's GNoME (Graph Networks for Materials Exploration), published in Nature in November 2023, predicted 2.2 million new crystal structures, roughly 380,000 of which appear stable — about an order-of-magnitude expansion of known stable inorganic crystals.

In parallel, Berkeley Lab's A-Lab autonomously synthesized 41 novel target compounds in 17 days using ML-guided recipe planning.

Microsoft Research's MatterGen (2024) extended this to diffusion-based generative design of materials with targeted properties.

johncwheeler

Materials science is evolving due to AI tools.

Google DeepMind’s GNoME (Graph Networks for Materials Exploration) used deep learning to discover 2.2 million new crystals, including 380,000 stable materials that could advance technologies like superconductors, batteries, and electronics. This output equates to roughly 800 years of prior knowledge. Other examples include Microsoft’s MatterGen for generative inorganic materials design and AI-driven autonomous labs (e.g., A-Lab) that accelerate synthesis and testing, alongside tools like physics-informed neural networks (PINNs) for property prediction.

Energy technology is evolving due to AI tools.

AI optimizes solar forecasting, predictive maintenance, battery management (e.g., charge-discharge cycles via reinforcement learning), and grid integration for solar + storage systems. Companies like BluWave-ai use AI for real-time energy orchestration, while tools manage large-scale battery storage for safety, arbitrage, and efficiency. AI also supports des

johncwheeler

"""Test for presence of files indicating infection by Keyednap malware."""
import subprocess
from blessings import Terminal


trigger_paths = [
    "/Applications/Transmission.app/Contents/Resources/License.rtf",
    "/Volumes/Transmission/Transmission.app/Contents/Resources/License.rtf",
    "$HOME/Library/Application Support/com.apple.iCloud.sync.daemon/icloudsyncd",
    "$HOME/Library/Application Support/com.apple.iCloud.sync.daemon/process.id",

johncwheeler

"""
  - By naming the placeholders in the string, I give them meaningful readability.
  - I am not required to pass parameters in the same order that they appear in the 
    string, making it easier to reflow language without reorganizing parameters.
  - I am responsible for making sure that difference is converted from a number to a string.
    Nearly every data type in python has some sort of string representation.
    Passing a value to the built-in function str() returns that representation.
"""

sibling = "brother"