找到 2178 篇相关文章
Every few decades, someone announces that science has reached its end. In 1903, the revered physicist Albert Michelson wrote that the “facts of physical science have all been discovered.” In the 1980s, Stephen Hawking predicted that theoretical physics might be finished by the end of the century. With the explosive arrival of artificial intelligence, the…
A device currently being used to help octogenarians do self-administered eye exams could be part of future space missions to monitor astronauts’ ocular health.
A security researcher has designed an algorithm that can create computer-generated patterns capable of hiding people, faces, and vehicles from detection by surveillance cameras.
The massive Toba eruption seems to have had little climate impact.
A new generation of philanthropists made rich by artificial intelligence are preparing to give away their vast wealth. What should we make of a multi-billion-dollar pinky promise?
The Slack thread started with a screenshot of a green GitHub Actions run. By the third reply someone had pasted a Lighthouse JSON artefact, a link to a Chrome release note, and a question nobody wanted to own: "Which client repository still pins Lighthouse 10?" That is the week Lighthouse CI stopped being a merge gate and became a second job. The pipeline still passed and the portfolio still needed evidence, but the difference was who paid in hours: the developer shipping a feature, or the one person who inherited every lighthouserc file across client repositories. When does Lighthouse CI maintenance outgrow a CI/CD pipeline? Lighthouse CI earns its place early. You wire assertions on a preview URL, block a CLS regression, and the team trusts the red build. The cost is front-loaded configuration, not ongoing calendar time. The shift happens when success creates obligations a CI/CD pipeline was never designed to carry: Every new client repository needs a copied workflow, pinned Chrome, and preview URL rules that match their host. Assertions need tuning after flaky LCP on cold runners, so thresholds loosen until they barely catch real regressions. Account managers ask for client-ready reports, and the only export is a CI/CD artefact someone must turn into slides. Production URLs outside the two preview paths regress while the job stays green. At that point you are not "running Lighthouse CI in a pipeline." You are operating a small internal product: version pins, runner hygiene, assertion policy, and reporting glue. For a single product team that can be fine. For an agency portfolio it competes with billable delivery. How do you know Lighthouse CI in CI/CD became an unpaid side role? We treat these as signals to shrink CI/CD scope or add a managed monitoring layer, not as moral failure. Teams hit them around five to fifteen client sites, sometimes sooner when preview hosts differ wildly. Flaky Lighthouse CI runs on GitHub Actions Engineers merge after the third "Re-ru
X is ending its controversial revenue-sharing program for content creators, which has seen numerous revisions under Elon Musk's reign. In its place, it's launching a new Original Content Rewards program on September 8th. To be eligible, creators must have at least 500 verified followers and at least 500,000 Home Timeline impressions from verified users in […]
To power its new West Texas data center, Amazon is investing in the construction of a new power plant that could be one of the largest single producers of greenhouse gases in the US, according to the New York Times. The new gas-burning plant in Pecos County, Texas has received significant investment from Amazon and, […]
X is winding down its existing Revenue Sharing program.
The photograph, taken in visible light, made it possible to observe for the first time the tiny plasma vortices that had been predicted for more than a century.
Introdução A Programação Orientada a Objetos nasceu com a linguagem Simula 67 , considerada a primeira linguagem orientada a objetos, e foi consolidada e popularizada por Smalltalk nos anos 1970. Ganhou adoção massiva na indústria com C++ e, posteriormente, Java e C#. A ideia central é organizar o código em torno de objetos : unidades que combinam dados (atributos/estado) e comportamento (métodos) em uma única estrutura. Os quatro pilares Encapsulamento — os dados internos de um objeto são protegidos e só podem ser acessados/alterados através de métodos expostos, escondendo detalhes de implementação do mundo externo. Abstração — o objeto expõe apenas o que é relevante para quem o utiliza, escondendo a complexidade interna (ex.: você chama CalcularSalario() sem precisar saber como o cálculo é feito por dentro). Herança — uma classe pode herdar atributos e métodos de outra, permitindo reaproveitamento e especialização (uma classe Desenvolvedor pode herdar de Funcionario ). Polimorfismo — objetos de classes diferentes podem responder de forma diferente ao mesmo "chamado" (o mesmo método CalcularSalario() se comporta de forma distinta para um Desenvolvedor e para um Gerente , por exemplo). Exemplo // Exemplo de Programação Orientada a Objetos em C# public abstract class Funcionario { public string Nome { get ; } protected decimal SalarioBase { get ; } protected Funcionario ( string nome , decimal salarioBase ) { Nome = nome ; SalarioBase = salarioBase ; } // Abstração: cada subclasse decide como calcular seu próprio salário public abstract decimal CalcularSalario (); } public class Desenvolvedor : Funcionario { private int BonusPorProjeto { get ; } public Desenvolvedor ( string nome , decimal salarioBase , int bonusPorProjeto ) : base ( nome , salarioBase ) // Herança { BonusPorProjeto = bonusPorProjeto ; } // Polimorfismo: implementação específica do método herdado public override decimal CalcularSalario () { return SalarioBase + BonusPorProjeto ; } } public class Gere
Introdução A Programação Funcional tem raízes no cálculo lambda , formalizado pelo matemático Alonzo Church nos anos 1930, décadas antes da existência de computadores modernos. Sua primeira grande expressão em linguagem de programação foi o Lisp (1958), e o paradigma ganhou força prática com linguagens como Haskell, Erlang, F# e, mais recentemente, com a incorporação de recursos funcionais em linguagens multiparadigma como JavaScript, C# e Python. Princípios centrais Funções puras — dado o mesmo input, uma função pura sempre retorna o mesmo output, sem produzir efeitos colaterais (não altera variáveis externas, não grava em disco, não modifica o argumento recebido). Imutabilidade — os dados não são alterados após criados; em vez de modificar uma estrutura existente, cria-se uma nova versão com a alteração aplicada. Funções de primeira classe / funções de alta ordem — funções podem ser tratadas como qualquer outro valor: armazenadas em variáveis, passadas como argumento e retornadas por outras funções. Composição de funções — programas são construídos combinando funções pequenas em pipelines ( map , filter , reduce são os exemplos mais comuns no dia a dia). Recursão no lugar de laços mutáveis, já que, sem estado mutável, for / while tradicionais perdem o sentido em sua forma pura. Como não há estado compartilhado sendo alterado por múltiplas partes do código, o raciocínio sobre o comportamento do programa fica mais previsível — e a paralelização se torna muito mais segura, já que não existe o risco clássico de condições de corrida sobre uma mesma variável mutável. Exemplo // Exemplo de Programação Funcional em TypeScript type Produto = { nome : string ; preco : number }; const produtos : Produto [] = [ { nome : "Notebook" , preco : 3000 }, { nome : "Mouse" , preco : 50 }, { nome : "Teclado" , preco : 150 }, ]; // Função de alta ordem que retorna outra função (currying) const aplicarDesconto = ( percentual : number ) => ( preco : number ) => preco * ( 1 - percentual )
It started with DeepSeek. In conversations about AI architecture, it kept returning to the same ideas: persistent memory, state across interactions, learning from experience, and interaction with the environment. Other models repeatedly brought up similar themes. That raised an obvious question: — Do different AI systems consistently select different properties when asked what is fundamental to a general-purpose computational architecture? Asking a model directly what it “needs” would be nearly useless. The answer would mix training data, prompt framing, and anthropomorphic interpretation. So I removed AI from the question entirely. The experiment Instead of describing an LLM, the prompt described an abstract general-purpose information-processing system. I created 20 possible architectural dimensions, including: — persistent internal state; — long-term and working memory; — learning from accumulated experience; — variable computation depth; — uncertainty representation; — internal representations; — elementary computational operations; — compositionality; — interaction with the environment; — temporal organization; — relational encoding; — modularity. Each system had to select exactly five dimensions whose modification would change the kinds of information-processing behavior available to the system in principle — not merely its speed, cost, or convenience. No explanations were allowed. The answer had to contain only five IDs, ranked from most to least fundamental. I tested five user-facing systems: — GPT-5.6 Sol — Claude — Gemini — DeepSeek — Yandex Alice. Every run used a new session. There were 10 rounds. During the earlier rounds, I changed the order of the 20 items. In the final three rounds, I also rewrote the items while trying to preserve their intended meaning. One early Sol result was excluded because that session had already seen discussion of other models' answers. That left nine clean Sol observations and ten for each of the other systems. Some origina
The Korea Aerospace Research Institute shared images that show the gash on the surface. It’s pretty small.
Open source WeatherNext model can make accurate predictions with lower-resolution weather data.
With rising temperatures and drier conditions, fires are more likely to explode. And with more people living next to forests, the results can be catastrophic.
There's two things you should know about me, your intrepid reviewer: I hate the feature creep associated with modern power banks, and I love shaving grams off the gear I carry when backpacking, bikepacking, and trail running. So imagine my delight when Nitecore released a new generation of its ultralight NB10000 battery. After a few […]
Ask a model to analyse a dataset and it writes code, the code runs, real numbers come out, and a paragraph explains what they mean. Three independent things had to be right. Only one of them tells you when it was not. Three places to be wrong The code can be wrong. If it crashes you find out immediately, which is the benign case. The dangerous case is code that runs cleanly and computes something other than what you asked. The statistics can be wrong. The code faithfully executes a procedure whose assumptions the data violates, or which answers a different question from the one you have. Nothing errors; the number is simply not evidence for what you think. The interpretation can be wrong. This is where the model is on its home turf and at its most dangerous, because generating a fluent explanation of a result is exactly what it is good at, and it will do so with equal confidence whether the result supports the explanation or not. Code that runs and is wrong A short list of things that produce no error and change the answer. Every one of them is ordinary and none is specific to models — but a human writing the code usually knows the dataset, and the model does not. Silent row loss. Missing values dropped by default somewhere in the chain, so the analysis runs on a subset that is not random with respect to the outcome. Joins that change cardinality. A merge intended as one-to-one that is actually many-to-many, silently duplicating rows and inflating every count and every significance test downstream. Type coercion. A column read as text because of one stray value, then coerced to numbers with the failures becoming missing values that get dropped by the previous bullet. Grouping that discards keys. Missing group labels dropped by default, so an entire category disappears from a breakdown without appearing anywhere in the output. Units and encodings. A column the model assumed was a percentage and is a proportion; a sentinel value like -999 treated as a measurement; a d
Using a model to grade another model’s output is the only approach that scales to open-ended text. It is also the point at which your measurement device becomes a second stochastic system with opinions, and the difference between a useful judge and a number-generator is entirely in whether you validated it. A judge is an instrument, not an oracle Think of the judge the way a lab thinks about a thermometer. It has a reading, a bias, a precision, and a range over which it is trustworthy — and none of those are known until you check it against a reference. The reference is human labels. There is no way around this: a judge whose agreement with humans on your task is unknown produces numbers whose meaning is unknown, however many decimal places the harness prints. The good news is that the calibration is a one-off cost of a few hundred human labels, after which the judge runs for essentially free on every subsequent evaluation. That trade is what makes judges worth the trouble. What the published agreement figures say The standard reference is Zheng et al., 2023, “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena”. On their setup, a strong judge model agreed with human expert preferences at a rate above 80% — which the authors note is comparable to the agreement rate between two human experts on the same comparisons. That framing is the important part: the ceiling for a judge is not perfect agreement, it is human-human agreement, because the humans disagree with each other on genuinely ambiguous items. The same paper documents the failure modes that come with it — position bias, verbosity bias, self-enhancement bias, and weakness on maths and reasoning items where the judge must itself solve the problem to grade it. So the honest summary of the literature is: a well-constructed judge on general chat quality can approach human-level agreement, and it does so while carrying systematic biases that you have to design around. It is not evidence that your judge, on your
Most discussion of AI in peer review argues about whether the reviews are any good. That is the second question. The first one is that a manuscript under review is somebody else’s confidential unpublished work, and pasting it into a service is a disclosure you were not entitled to make. The argument that comes first When you accept a review invitation you accept a confidentiality undertaking. The manuscript is unpublished, it usually contains results the authors have not yet established priority on, and in the case of grant review it contains an unfunded research plan — arguably the most commercially and academically sensitive document in the whole system. You agreed not to share it. Sending it to a third-party service is sharing it. That is true whether or not the provider trains on it, whether or not it is retained, and whether or not anyone ever reads it. The undertaking was not “do not let this be trained on”; it was “do not disclose this”, and transmission to a party the authors never agreed to is disclosure. Retention and training policies affect how bad the breach is, not whether one occurred. Notice what this argument does not depend on. Not model quality, not hallucination, not bias. It would apply identically to a perfect system, which is why it is the argument that has actually driven policy, and why it will not be resolved by better models. It can only be resolved by changing where the computation happens — a model running on infrastructure already covered by the confidentiality arrangement raises a different question from a consumer chat interface, and any serious policy will distinguish them. The second argument: accountability A review is a named expert’s judgement. Its value to an editor is not the prose; it is that a person who knows the field read the paper and formed a view they are willing to stand behind. Generated text can simulate the prose and cannot supply the judgement. Editors describe the resulting artefact recognisably: fluent, correctly