标签：#ci

Imagine working as a Data Analyst in a healthcare Non-Governmental Organization (NGO) implementing HIV and AIDS programmes across several communities. The organization has limited resources. There may not be enough funding, healthcare workers, testing kits, transport, outreach teams, or community programmes to serve every community at the same intensity. This creates an important question: How can we use data and machine learning to direct limited programme resources to communities with the greatest need? This is where Machine Learning (ML) can become valuable. Rather than distributing resources equally across all communities, an NGO can use historical programme data to identify communities experiencing greater HIV-related service gaps or higher levels of need. Resources can then be prioritized based on evidence. What Is Machine Learning? Machine Learning is a branch of Artificial Intelligence that enables computers to learn patterns from data and use those patterns to make predictions or support decisions. Instead of manually creating rules for every situation, you provide the algorithm with historical data and allow it to identify relationships within that data. For example, the NGO could have this information about different communities: Community HIV Testing Coverage ART Coverage Missed Appointments Outreach Activities Community A 85% 90% 5% High Community B 52% 61% 25% Low Community C 70% 75% 15% Medium Community D 40% 55% 32% Low Looking at this data, Community D appears to have greater programme gaps than Community A. However, in a real programme, the decision should not be based on one indicator alone. Machine learning can analyse many variables simultaneously to identify communities that may require greater attention. Why Resource Allocation Matters in HIV Programmes HIV programmes operate in environments where resources are often limited. An NGO may have: A limited number of community health workers A fixed outreach budget Limited HIV testing supplies Limi

Recent findings provide one of the most detailed pictures to date of the genetic architecture of schizophrenia, opening up new avenues for research into the disorder.

Context: I'm a data scientist/analyst (in Python and R) learning development from scratch. Inevitably, I am learning these through the lens of what I already know. If you have a similar background and are a beginner developer, I hope these analogies help! Any comments, especially if you spot any misunderstanding, are appreciated. Commenting is caring <3 Motivation: I was building a mock data layer for a fitness social app — simulating what happens when users fetch new posts from a feed. The function needs to return mock posts after a delay, simulating a real network request. Working Code: `function fakeFetchPosts() { return new Promise((resolve) => { setTimeout(() => { resolve(posts); }, 2000); }); } async function main() { console.log("Fetching..."); const fetchedPosts = await fakeFetchPosts(); console.log("Fetched posts:", fetchedPosts); } main(); console.log("Sync code ran");` What do you expect to see as an output? I first confused the logic with blocking. For example, in webscraping, something like time.sleep() or Selenium's WebDriverWait(driver, 10).until(EC.presence_of_element_located(...)) . In this case, output will be Fetching..., Fetched posts: ..., then Sync code ran. However, the output gives Fetching..., Sync code ran, and then Fetched posts. In the former, the whole script (single thread) pauses and does nothing else until the wait ends or the condition is met. The latter is different in that the rest of your program keeps running during the wait, and thus the output where Sync code ran is printed first before the fetchedPosts. By the way, posts are arrays. const posts = [{ author: "j1wonkim", text: "Testing Physical", likes: 100, }, {author: "onewc0218", text: "Love love", likes: 55, }, {author: "gakbca", text: "You are good", likes: 10, } ];

Trump falsely claimed the MMR vaccine is "quite lethal" and linked shots to autism.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last week, I headed 30 miles south of San Francisco to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI…

Bluesky has added a new feature that lets you hide reposts from a specific person in your feeds. The tool might be helpful if you want to stop seeing reposts from that one person who clogs up your feed with reposts of things you don't care about. If you follow me, I may be that […]

Platforms like Meta, TikTok, Snapchat, and Google are facing a long road of litigation.

Creators who want to start earning on the platform will need at least 8,000 qualified watch hours over the past year or 20 million qualified Shorts views in the last 90 days.

Three semantic layers, three architectures, three very different bills. All three will define what a metric means. None of them proves an AI agent is allowed to run it. Quick orientation dbt Semantic Layer Cube AtScale Core idea Metrics as version-controlled code Headless API in front of metrics OLAP-style aggregate acceleration Strongest when You want engineering discipline Many apps consume the same numbers Heavy, stable aggregate workloads Modelling Hand-authored YAML Hand-authored data model Hand-authored cubes Cost driver Plan tier + query volume Pre-aggregation builds + compute Quote-based licence + compute Governance Upstream, in the warehouse In front of the API On the cube Each is competent at what it was built for. If your consumers are dashboards and analysts, any of the three will serve you. The question none of them answers An agent doesn't arrive with a metric name. It arrives with an intent in English and has to work out which entities, which grain, which joins, and whether it's entitled to any of it. That exposes two gaps every one of these shares: Undefined intent has no answer. Coverage is whatever someone remembered to model. Business questions don't respect that boundary. Authorisation is checked around the query, not inside it. A filter applied after execution means the data already moved. What to actually evaluate on Ignore feature matrices and score these five: Answer a question nobody modelled, on your schema Show why one join path was chosen over two others Same question, two users with different entitlements — show both SQL statements Ask something ambiguous. Refusal or guess? Reproduce a number from six months ago with the definitions then in force Most evaluations stop at 1. Numbers 3 and 5 are the ones that decide whether the thing ships in a regulated business. The full breakdown — architecture-by-architecture comparison, cost profiles, and the migration implications of each — is here: 👉 dbt Semantic Layer vs Cube vs AtScale: Choosing a

fMRI scans show happiness, fear, anger, and sadness have distinct brain activity patterns in doggy brains.

Platforms are finally recognizing that people don’t want to consume AI slop. A growing number of sites and apps now have tools and policies to flag, label, and ban AI-generated content.

As research and AI-assisted papers surge, volunteer reviewers struggle to keep up.

The approach is like a Costco for heat pumps, and it’s helping make installations more accessible as heat waves worsen.

Solar energy, at any time of day or night - that's the dream of space mirror projects. Futurists have been imagining satellite mirrors that could reflect the sun's light onto the Earth's surface for over a century. Russian scientists experimented with the concept in the '90s with the Znamya project. The idea is to put […]

An AI interview is increasingly the first step of a hiring process. Since there’s no human on the other end, candidates are scheduling them whenever—even deep into the night.

Most businesses don't buy disconnected systems on purpose. They arrive one tool at a time. You start with a spreadsheet. You add a CRM because the spreadsheet stopped scaling. Accounting software because the taxman insisted. A shipping portal because you started sending parcels. Each choice was sensible on its own day. Then one morning you look up and realize that none of these tools talk to each other — and the thing holding them together is a person. Someone reads a number off one screen and types it into another. That person is your integration. And that's a cost you're paying every day without ever seeing it on an invoice. The Cost You Don't Get Billed For Here's the trap. The cost of a disconnected system doesn't show up anywhere you look. It's not a subscription. It's not a line item. It's hidden inside a job title. Someone on your team spends the first hour of every day taking yesterday's orders and copying them, one at a time, from the checkout into the accounting system, then into the CRM, then into the shipping portal. It feels like work, because it is. Nobody questions it, because it's always been done that way. But watch what actually happens as you grow. At ten orders a day, that copying is an hour. At thirty, it's most of a morning. At sixty, it's a full-time role whose entire purpose is retyping data that already exists somewhere else. You never made a decision to create that role. It grew on its own, one order at a time, and now it sets a limit: you can only take as many orders as one person can hand-transcribe before they burn out or start making mistakes. That's the real shape of the cost. It isn't the minutes. It's the ceiling. When a human is your integration, your growth is capped at the throughput of that human — and you'll experience the cap not as a cost, but as a vague sense that "we can't handle more right now." Where the Errors Live The second cost is quieter and more expensive: the mistakes. Anything a person copies by hand between two sy

Two-thirds of an octopus’s neurons are in its arms—each operating independently—including the one it uses to have sex.

Kennedy Torkura discusses practical GenAI red teaming techniques to safeguard LLMs and knowledge bases against security threats like data poisoning and LLMjacking on AWS. He explains how engineering leaders and architects can bridge traditional cloud security with MITRE ATLAS frameworks to proactively identify vulnerabilities, implement guardrails, and secure production AI applications. By Kennedy Torkura

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new…