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What is a quantum computer good for? Absolutely nothing — yet

To this day, we have yet to see a quantum computer conclusively perform a single useful task. Existing machines are simply too small and error-ridden to solve commercially relevant problems. That hasn't stopped Donald Trump's science adviser from promising a "quantum computer powerful enough for scientific discovery by 2028" and Trump from issuing a new […]

2026-07-01 原文 →
AI 资讯

Orthogonal: The Word That Taught Me to Cut Things Apart

The second word a professor told me to carry for life. It took me years — and a lot of vectors — to start understanding it. A look back — long before any of the tools we argue about now. The same professor — Sang Lyul Min — handed us these words one at a time in lecture. After trade-off , two more stuck with me. But before the second word itself, here are the two pieces of news he brought to class around then. The internet barely existed; information moved through journals, magazines, and word of mouth. Looking back, it's a little amazing how much still got through. When a chess machine started winning The first breakthrough I remember: computers had finally started playing chess on roughly even terms with the world's best. Deep Blue beat Kasparov around 1996, so the machines he was describing came just before — names like Deep Thought, ChessMachine, Socrates II. He told us, deadpan, that one human competitor's head had "physically burst" from the strain — and we groaned, "Come on, Professor, that's a bit much." We live on the far side of AlphaGo now, so it's easy to forget how much we shrugged at all this back then. I was a decent amateur — a 1-dan at Go, hopeless at janggi (Korean chess) against any program — and I still remember the hollow, slightly bitter feeling the AlphaGo era left even in someone who only ever played for fun. A full-body scan The second: in the US, death-row inmates had consented to the first dense full-body image scans. That was the news that taught me — embarrassingly late — that this kind of computing could reach all the way into medicine. Computers, it turned out, showed up in the strangest places. orthogonal Back to the words. The second one, the professor said, would run through my whole career: orthogonal . The Korean rendering — 직교하는, "at right angles" — was, naturally, a word I'd never heard. The plain-language version was "unrelated, independent." It came back hard years later, when I had to take vectors seriously — first in linear

2026-06-30 原文 →
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Rocket Lab is buying Iridium’s satellite network for $8 billion to take on SpaceX

Rocket Lab, the space company best known for its small satellite launcher Electron, has announced plans to acquire Iridium Communications for $8 billion. The deal will combine Rocket Lab's launch services and spacecraft manufacturing with Iridium's satellite-based communications network, putting it in a better position to challenge SpaceX. Iridium offers communications services to over 2.5 […]

2026-06-29 原文 →
AI 资讯

The war against ‘woke’ could end US science as we know it

A sneaky rule change has the potential to blow up scientific research in the United States. But there's still time to fight it. On May 29th, the Office of Management and Budget (OMB) issued a 412-page proposal to revise federal financial assistance. The language is a combination of distinctly Trumpian attacks on "woke" policies and […]

2026-06-29 原文 →
开发者

How I Explored a US Health Dataset with Python — EDA + Hypothesis Testing

I recently completed an exploratory data analysis project on the NHANES (National Health and Nutrition Examination Survey) dataset from Kaggle. It's a real-world health survey collected by the CDC covering body measurements, lifestyle habits, and demographic data from thousands of US adults. In this article I'll walk you through exactly what I did — from loading and cleaning the data all the way to running statistical tests — and share what I found along the way. The Dataset The dataset has 5,735 rows and 28 columns , but for this project I focused on 8 columns that were relevant to the questions I wanted to answer: Column Description smoking Has the person smoked at least 100 cigarettes? gender Male or Female age Age in years education Highest level of education weight Weight in kg height Height in cm bmi Body Mass Index Step 1 — Loading and Selecting Columns import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns db = pd . read_csv ( ' NHANES.csv ' ) data = db . loc [:, ( ' SEQN ' , ' SMQ020 ' , ' RIAGENDR ' , ' RIDAGEYR ' , ' DMDEDUC2 ' , ' BMXWT ' , ' BMXHT ' , ' BMXBMI ' )] data = data . rename ( columns = { ' SEQN ' : ' id ' , ' SMQ020 ' : ' smoking ' , ' RIAGENDR ' : ' gender ' , ' RIDAGEYR ' : ' age ' , ' DMDEDUC2 ' : ' education ' , ' BMXWT ' : ' weight ' , ' BMXHT ' : ' height ' , ' BMXBMI ' : ' bmi ' }) One thing worth knowing about NHANES: all the columns come in as numeric codes. 1 means Male, 2 means Female. 1 means the person smoked, 2 means they didn't. You have to map these to readable labels before doing any analysis, otherwise your charts are meaningless. Step 2 — Cleaning the Data Drop the ID column and remove nulls data . drop ( ' id ' , axis = 1 , inplace = True ) data . dropna ( inplace = True ) This brought us from 5,735 rows down to 5,406 — about 6% lost, which is acceptable. Remove outliers using the IQR method The IQR (Interquartile Range) method flags values that fall too far outside the middle 50% of

2026-06-29 原文 →
AI 资讯

China claims the world’s fastest supercomputer

Despite trade restrictions, China has reclaimed the title of the world's fastest supercomputer for the first time since 2018. LineShine has pushed El Capitan out of number one on the TOP500 ranking. That's despite strict limits on what high-powered computing components can be sold to China by US firms, which dominate the list, with America […]

2026-06-29 原文 →