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Amazon has enough satellites to launch its Starlink competitor

Amazon says it now has enough satellites operating in low-Earth orbit to light up its Starlink internet competitor. With last night's launch, Amazon Leo has 396 satellites deployed, which is "enough to support continuous service across initial latitudes," according to Chris Weber, VP heading up business and product for Amazon Leo. That puts the company […]

2026-07-02 原文 →
AI 资讯

Logistic Regression (Supervised Family)

1. The Problem It Solves Logistic Regression is used when the outcome is a category rather than a number . Most commonly, it's used for binary classification , where the answer is either Yes or No , True or False , or 1 or 0 . Typical business problems include: Will a customer churn? Is this transaction fraudulent? Will a customer click an ad? Will a loan default? Is an email spam? Will a machine fail in the next 24 hours? Unlike Linear Regression, we're not trying to predict a continuous value. Instead, we're predicting the probability that an event belongs to a particular class. For example: A customer may have an 82% probability of churning . The business can then decide whether that probability is high enough to trigger an intervention. 2. Core Intuition Imagine you're trying to predict whether a customer will cancel their subscription. Suppose the only feature you have is how many times they opened your app this month. If you use a straight line like Linear Regression, the predictions quickly become unrealistic. A very active customer might end up with a -20% chance of churn . A completely inactive customer could end up with 140% . Probabilities obviously can't work like that. To fix this, Logistic Regression takes the linear equation and passes it through a mathematical function called the Sigmoid Function . Instead of producing a straight line, it creates an S-shaped curve . No matter how large or small the input becomes, the output always stays between 0 and 1 . That makes it perfect for probability estimation. 3. The Mathematical Model The model first calculates a linear score. Instead of using that score directly, it passes it through the Sigmoid function. Where: z = linear score p̂ = predicted probability The final output is always between 0 and 1 . For example: 0.08 → Very unlikely 0.32 → Low risk 0.65 → Moderate risk 0.94 → Very high probability Businesses can then choose a decision threshold. For example: Probability ≥ 0.50 → Predict Churn Probability

2026-07-02 原文 →
AI 资讯

Elon Musk denies a report about SpaceX’s AI phone prototype

Elon Musk says a report about a SpaceX AI phone prototype is "utterly false." The report, published on Wednesday by The Wall Street Journal, says SpaceX showed off a "handset-like prototype" to some investors before launching its record-breaking initial public offering in June. The device was "slimmer than an iPhone," and they were told it […]

2026-07-02 原文 →
开发者

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 →