In contrast to previous work on this learning model, we consider scenarios in which the family of groups is too large to explicitly enumerate, and hence we seek algorithms that only access groups via an optimization oracle.

Player and Nature, where at each stage both pick actions based on the contexts.

Often, the needs and visual abilities differ between the annotator group and the end user group. Generating detailed diagram descriptions for blind and low-vision (BLV) users is one such challenging domain. Sighted annotators could describe visuals with ease, but existing studies have shown that direct generations by them are costly, bias-prone, and somewhat lacking by BLV standards. In this study, we ask sighted individuals to assess -- rather than produce -- diagram descriptions generated by vision-language models (VLM) that have been guided with latent supervision via a multi-pass inference. The sighted assessments prove effective and useful to professional educators who are themselves BLV and teach visually impaired learners. We release Sightation, a collection of diagram description datasets spanning 5k diagrams and 137k samples for completion, preference, retrieval, question answering, and reasoning training purposes and demonstrate their fine-tuning potential in various downstream tasks.

Digital listening is becoming the most powerful new scientific tool for observing and preserving our natural environment. From the Arctic to the Amazon, scientists are covering the globe with networks of digital microphones. Citizen scientists are using open source, DIY devices like the AudioMoth--a handheld device not much larger than a credit card--to listen in on nature's sounds. These devices detect sounds inaudible to humans: from low-frequency infrasounds made by elephants and whales to high-frequency ultrasounds made by mice, bats, and even plants. In 2023, our newfound listening powers will allow us to exponentially accelerate environmental monitoring, measure the health of ecosystems, track the sonic signatures of climate change, reveal the existence of entirely new species, and even rediscover species once thought to be extinct.

We used machine learning and trained a convolutional neural network to navigate along a bat's route using visual information that would have been …

Live app here, just try to insult it! "The Conversation AI team, a research initiative founded by Jigsaw and Google (both a part of Alphabet) are working on tools to help improve online conversation. One area of focus is the study of negative online behaviors, like toxic comments (i.e. So far they've built a range of publicly available models served through the Perspective API, including toxicity. But the current models still make errors, and they don't allow users to select which types of toxicity they're interested in finding (e.g. "Have you been online lately, it is pretty toxic" Andrew Marantz It is not unknown the limitations from current AI: well-known limitations are called "shallowness", name from "The Shallowness of Google Translate". I have a rich set of discussions on my book "Computational Thinking": feel free to grab and copy and come to me for discussions. Current best AI systems cannot understand human subtlety. They are "shallow": see just the obvious. This is known on translations, and other areas. "You are NOT a whore" -, insult, obscene, toxicity (wrong) Without the negative, it works as wanted. The issue is: we know as human that the negative can even be a compliment! Even though, I would know recommend it! "Mr Pires, what you said is one of the most insanely idiotic things I ever heard.

This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. Over millions of years of evolution, nature has worked out solutions to many problems. Humans have arrived late in the day and pinched them. For example, Velcro was invented after a Swiss engineer marveled at the burdock burrs that got stuck to his dog's fur; the idea for robotic arms came from the motion and gripping ability of elephant trunks, and the front of Japan's bullet trains were redesigned to mimic a kingfisher's streamlined beak, reducing the sonic boom they made exiting tunnels. There are different types of mimicry, the most straightforward is the simple idea of copying something that exists in nature. Buildings are an obvious example, as outlined by research published in Nature.

In many regions of the world, sparse data on key economic outcomes inhibit the development, targeting and evaluation of public policy1,2. We demonstrate how advancements in satellite imagery and machine learning (ML) can help ameliorate these data and inference challenges. In the context of an expansion of the electrical grid across Uganda, we show how a combination of satellite imagery and computer vision can be used to develop local-level livelihood measurements appropriate for inferring the causal impact of electricity access on livelihoods. We then show how ML-based inference techniques deliver more reliable estimates of the causal impact of electrification than traditional alternatives when applied to these data. We estimate that grid access improves village-level asset wealth in rural Uganda by up to 0.15 standard deviations, more than doubling the growth rate during our study period relative to untreated areas. Our results provide country-scale evidence on the impact of grid-based infrastructure investment and our methods provide a low-cost, generalizable approach to future policy evaluation in data-sparse environments. Advancements in satellite imagery and machine learning can be used to infer the causal impact of electricity access on livelihoods, providing a low-cost, generalizable approach to evaluating public policy in data-spare environments.

Locked down in London at the height of the pandemic, bombarded with scary news, I'd felt my connection with nature starting to fray. The daily hour we were allowed to walk in the park for exercise became for me (and many others) a lifeline. And for these walks, I took my phone--not to chat, but to learn. Despite being a well-traveled wildlife filmmaker, I was shamefully clueless about the names and habits of many of the species native to my homeland. Soon a tree-identifying app introduced me to the flora I'd been strolling past.

"Chemists like me have been working on trying to turn molecules into machines for about 25 years now," says Leigh, an organic chemist from the University of Manchester in the United Kingdom. You're building on all those that went before you." In 1936, English mathematician Alan Turing imagined an autonomous machine capable of carrying out any precisely coded algorithm. The hypothetical machine would read a strip of tape dotted with symbols that, when interpreted sequentially, would instruct the machine to act. It might transcribe, translate, or compute--turning code into a message, or a math problem into an answer.