Category: AI News & Commentary

  • Heat Magnets Everywhere

    A new dataset of 31 million rooftops finds nearly all of them absorb most of the sun that hits them — on every continent.

    More than 4,500 years ago, the Pyramids of Giza didn’t have the sandy color they do today. They were sheathed in polished white Tura limestone, bright enough to be seen for far distances, reflecting the desert sun like massive mirrors. The Egyptians called the Great Pyramid Ikhet — the “Glorious Light.” It must have been a staggering sight.

    In a way, they were the first cool roofs. What made them shine is what scientists now call albedo: the amount of sunlight a surface bounces back instead of soaking up. The builders were likrly not thinking about heat, and with millions of tons of stone the pyramids soaked up plenty of it anyway.

    Today, most roofs do the opposite. Dark surfaces, rusted iron sheets, asphalt tiles dominate rooftops across much of the Global South, absorbing most of the sun’s energy and radiating it back down as heat. Cities like Ibadan, Nigeria, or Kumasi, Ghana are full of these brown-roofed neighborhoods, and the roofs make the whole area hotter. The health toll is real, especially for older people and infants: broken sleep, no relief from the heat, a higher chance of getting sick.

    City planners have known the fix for a long time. Paint a roof white, or coat it with reflective material, and heat absorption drops sharply. Studies have measured roof surface temperatures falling 15°C to 30°C and indoor temperatures 2°C to 5°C. For someone without air conditioning, which is most people in the Global South, that’s the difference between an OK night and a brutal one.

    But how do you find the worst roofs at the scale of a city, or a country? What pulled me down this rabbit hole was a paper and dataset from research colleagues and their collaborators that takes on exactly that question. For decades, scientists faced a trade-off: free public satellite imagery is too blurry to pick out the small, old, densely packed buildings in informal settlements — the very places that need cooling most — while the commercial imagery sharp enough to see them is expensive and patchy. The paper’s move is to fuse the two: the complete global coverage of the free data, the detail of the commercial data. It’s a similar super-resolution idea my team used on Open Buildings, an earlier project I was involved with that mapped building and road footprints across Africa but pointed at a new target. Instead of a building’s outline, it recovers the building’s thermal identity, turning blurry 10-meter blocks into rooftop-level albedo estimates as sharp as a drone flyover.

    Now the data.

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    Globla state of urban rooftop solar albedo and cool roo adotoption (sampled cities) Highresolution satelite measurement across 31M rooftops in 67 urban agglomerations

    London, one of the wealthiest cities on the planet, in a country that pioneered the industrial revolution, sits at a median roof reflectivity of 0.10 out of 1.0. More than 95% of its rooftops are classified as “very dark”.

    According to roof tops sampled in this dataset, London has only 3 cool roofs in total: two industrial buildings, and one backyard storage unit in Eastern London ( yes that tiny white rooftop in the middle below).

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    The only non industrial cool roof in London according to sampled roof tops (Google Maps)

    (Worth flagging: these are models estimates with margin of errors, a roof could’ve been repainted or sits under the cover of a tree, still just a few vs millions tells a story of its own)

    Even within Africa, the range is wide. Surprisingly to me, northern Nigerian cities like Kano have naturally higher reflectivity. Light-colored metal roofing, plus dust that settles in arid climates happens to help them reflect more light. Possibly the one case where dust is actually useful.

    Lagos is a coastal megacity with high humidity. Cooler during the rainy season but the rest of the year you feel the heat. It’s a daily physical tax on people who live in the densest, least served parts of the city, who are also least likely to have air conditioning or even constant electricity. Lagos has 6 cool roofs out of 955,543 buildings sampled. Another surprise here, two of them are mega churches in Victoria Island, one of the wealthiest districts in the city.

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    Two “Cool roofs” in Lagos VI (blue dots)

    Kisumu, Kenya, a hot city right on Lake Victoria has 2 out of 147,252 sampled.

    Across more than 4.5 million buildings scanned across African cities, over 90% of rooftops are absorbing at least three-quarters of incoming sunlight. Heat magnets, everywhere.

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    African Cities Cool Roof Spatial Infographic

    This sounds like a low-hanging fruit for policy in Africa. Ahmedabad, India proves it. Out of the 67 cities across 14 countries in this dataset, Ahmedabad stands out because of deliberate policy: a cool-roofs program launched in 2017 has measurably shifted the city’s reflectivity compared to Mumbai and Kolkata, which have no such program and absorb vastly more heat.

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    Indian Cities Cool Roof Spatial Infographic

    Looking at the whole dataset, every continent falls below the reflectivity threshold needed to meaningfully reduce the urban heat island effect. Nobody is doing well. But the consequences of not doing well aren’t evenly distributed and Ahmedabad shows that a city can decide to change this rather than just live with it.

    Since looking into this research, I’ve caught myself looking at rooftops differently on my street, from a plane window, wherever. You start noticing which houses are probably the hottest.

    Congrats to everyone who made this significant research possible. (See paper for full author credits.)

    Note: Infographics were generated via AntiGravity, Google’s agentic development platform.

    Paper

    Blog

    Heat Resilience Page (Dataset Downloads)

    Demo

  • Depth > Breadth

    How do you get hired in 2026? The rules have changed in the past few months.

    In a world where anyone can go from idea to working app rapidly, “what you built” matters far less than “how deeply you understand it.” I just read Sakana AI’s unofficial guide on research hiring, and while it targets researchers, it’s spot on and the lessons apply to every field navigating the post-AI shift. I highly recommend it.

    Here is the new hiring meta:
    1. The Interview is a Debate, Not a Presentation. No one is going to be impressed with volume (e.g. code, papers). Interviewers are easily bored and they have seen the standard solutions dozens of times. Instead, turn the interview into an interesting discussion. If this doesn’t come naturally to you, treat it as a skill to practice with friends, and even an AI. Be ready to articulate why you made specific choices and defend them against alternatives. What were your “best” failures and what did you learn from them?

    2. Depth > Breadth. It is easy in 2026 to be creative with AI tools. To stand out, you can’t just tweak existing paradigms. Find a “good rabbit hole.” Interviewers want to see you think deeply about small interesting things that have potential rather than throw shallow experiments at the wall.

    3. AI Accelerates, You Steer. The expectation is now that you will use AI tools to be more productive. But there is a catch: You must understand every line the AI produces. Because AI creates high-level abstractions, you need to also review your basics more than ever. Do you actually remember how an Adam Optimizer works?

    4. Be Your Own Harshest Critic. Before you walk in, critique your work like a skeptical reviewer. Know your limitations explicitly. If you don’t know an answer, just say so, then reason through it. Skilled interviewers can smell BS instantly, it’s a huge turn off.

    The differentiator in 2026 is clarity of thought and a deep level of understanding.

    https://pub.sakana.ai/Unofficial_Guide

  • Melissa path predictions by GDM WeatherLab

    This was sobering forecast for 𝐇𝐮𝐫𝐫𝐢𝐜𝐚𝐧𝐞 𝐌𝐞𝐥𝐢𝐬𝐬𝐚 on Google DeepMind’s experimental Weather Lab. (Seen on Sunday 26th, 2025)

    The AI model’s ensemble predicted with high confidence the devastating track: CAT 4 landfall in 𝐉𝐚𝐦𝐚𝐢𝐜𝐚 (around Tuesday, Oct 28th) [Turned out it was a CAT 5], followed by a CAT 3 impact on Cuba (Wednesday, Oct 29th).

    References:

    ECMWF Tracking: https://charts.ecmwf.int/products/cyclone/overview/product?base_time=202510260000&product=tc_strike_probability&unique_id=13L_MELISSA_2025

    Google DeepMind AI Model: https://deepmind.google.com/science/go/NDyUM0uxIfrnXnAR 

  • First Images from ESA Biomass Satellite

    Absolutely stunning images of Gabon and Tchad from the European Space Agency’s (ESA) Biomass satellite.

    The first image shows the Ivindo River in Gabon, stretching from the DRC border all the way to Makoukou in the Ogooué-Ivindo province. This region is known for its dense forests. Typically, when we look at forests from above, all we see are the treetops. However, Biomass uses a special kind of radar, called P-band radar, which has the ability to penetrate through the forest canopy to reveal the terrain below. This means it can measure all the woody material—the trunks, branches, and stems—offering a much more complete picture than ever before.

    The second image features the Tibesti Mountains in northern Chad, and it looks like something straight out of space. Here, the radar demonstrates its ability to see up to five meters beneath dry sand. This opens up fascinating possibilities for mapping and studying hidden features in deserts, such as ancient riverbeds and lakes that have long been buried. Such insights are incredibly valuable for understanding Earth’s past climates and even for locating vital water sources in arid regions.

    It’s an exciting time as our ability to collect information about Earth continues to advance, especially with progress in remote sensing and Artificial Intelligence (AI). The rise of geospatial AI, in particular, is opening up fascinating new avenues for understanding our planet and opening new fields of research.

    If you’re a student considering a career in understanding Earth through technology, leveraging AI. In my opinion, this field presents some interesting opportunities. You can explore more about the amazing Biomass mission on the official ESA website:

    https://www.esa.int/Applications/Observing_the_Earth/FutureEO/Biomass/Biomass_satellite_returns_striking_first_images_of_forests_and_more

    Image credit: ESA

  • Small Language Models: Notes from the past couple of weeks 🤖🤯

    The past few days have brought interesting developments in small language models that could expand mobile computing and low-resource environment applications.

    Here’s what caught my attention:

    • Microsoft’s Phi was made fully open source (MIT license) and has been improved by Unsloth AI. 🚀🔓 Blog: https://unsloth.ai/blog/phi4

    • Kyutai Labs based in Paris 🇫🇷 introduced Helium-1 Preview, a 2B-parameter multilingual base LLM designed for edge and mobile devices.

    Model: https://huggingface.co/kyutai/helium-1-preview-2b

    Blog: https://kyutai.org/2025/01/13/helium.html

    • OpenBMB from China 🇨🇳, released MiniCPM-o 2.6, an 8B-parameter multimodal model that matches the capabilities of several larger models. Model: https://huggingface.co/openbmb/MiniCPM-o-2_6

    • Moondream2 added gaze 👀 detection functionality with intestesting application for human-computer interaction and market research applications.

    Blog: https://moondream.ai/blog/announcing-gaze-detection

    • OuteTTS, a series of small Text-To-Speech model variants expanded to support 6 languages and punctuation for more natural sounding speech synthesis. 🗣️

    Model: https://huggingface.co/OuteAI/OuteTTS-0.3-1B

    These developments suggest continued progress in making language models more efficient and accessible and we’re likely to see more of this in 2025.

    Note: Views on this post are my own opinion.

  • Singapore Launches MERaLiON: Speech Recognition and Multimodal LLM for Multilingual Applications

    A public institution in Singapore 🇸🇬 🚀 , as part of a national effort to advance AI capabilities , has just released a speech recognition model and a multimodal large language model (LLM) tailored to Singapore’s multilingual landscape.

    The MERaLiON-SpeechEncoder is a speech foundation model designed to support downstream speech applications. Researchers built this model from scratch 🤯 and trained it on a massive dataset of speech data, including English and data from the National Speech Corpus. This corpus includes English spoken in Singapore, as well as Singlish. To handle this vast amount of data, they used supercomputers in both Europe and Singapore.

    The MERaLiON-AudioLLM is a multimodal LLM that can process both speech and text inputs and is specifically designed for Singapore’s multilingual and multicultural landscape. This first release was created by combining a fine-tuned version of the MERaLiON-Whisper encoder, based on OpenAI’s Whisper-large-v2 model, with the SEA-LION V3 text decoder. SEA-LION V3 is a localized LLM developed by AI Singapore based on Google’s Gemma 2 9B model.

    This is really impressive 🔥 and I hope it can inspire other communities around the world!

    Learn more:
    MERaLiON-SpeechEncoder research paper: https://arxiv.org/abs/2412.11538?hl=en-US
    MERaLiON-AudioLLM research paper: https://arxiv.org/abs/2412.09818
    Models:
    https://huggingface.co/MERaLiON/MERaLiON-SpeechEncoder-v1
    https://huggingface.co/MERaLiON/MERaLiON-AudioLLM-Whisper-SEA-LION