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Spanish private equity fund BeAble Capital has invested €174,000 in Ecogranular, a spin-off of the University of Granada, whose technology aims to transfer wastewater treatment, reducing the water footprint and operating expenses and raising the quality of our waters. One of the biggest challenges that cities face is to optimally manage wastewater treatment, minimising energy expenditure while reducing its impact on the environment.  Ecogranular has developed a new technology that, for the first time, allows granular biomass to be used continuously, which will greatly facilitate its implementation in water purification stations. The tech minimises the loss of biomass inside the…

Our approach to analyzing and mitigating future risks posed by advanced AI modelsGoogle DeepMind has consistently pushed the boundaries of AI, developing models that have transformed our understanding of what's possible. We believe that AI technology on the horizon will provide society with invaluable tools to help tackle critical global challenges, such as climate change, drug discovery, and economic productivity. At the same time, we recognize that as we continue to advance the frontier of AI capabilities, these breakthroughs may eventually come with new risks beyond those posed by present-day models.Today, we are introducing our Frontier Safety Framework - a…

Mozilla has officially announced a roadmap that outlines some important features which will be added to Firefox. Notable additions will include support for Tab Groups, Vertical Tabs, and a better Profile Management system. Vertical Tabs are coming to Firefox Mozilla's Tweet poked fun at itself, saying that it heard users who had been asking for a vertical tab bar in the browser. The feature is already available in the Larch channel of Firefox, which Martin tested last month. Vertical tabs aren't new, Brave browser has the feature, as do Vivaldi, and Microsoft Edge. Firefox users have relied on extensions like…

Yesterday, the AI startup Anthropic published a paper detailing the successful interpretation of the inner workings of a large language model (LLM). LLMs are notoriously opaque — their size, complexity, and numeric representation of human language have hitherto defied explanation — so it’s impossible to understand why inputs lead to outputs. Anthropic used a technique called dictionary learning, leveraging a sparse encoder to isolate specific concepts within its Claude 3 Sonnet model. The technique allowed them to extract millions of features, including specific entities like the Golden Gate Bridge as well as more abstract ideas such as gender bias. They…

I’m excited to share Generational’s inaugural growth & late-stage company briefing with a deep dive on Scale AI, blending analytical rigor with feature writing. Disclaimer: I have a financial interest in Scale. Don’t take this as investment advice.In this deep dive, you’ll learn insights from conversations with Scale’s customers, ex-employees, and competitors. I could do this thanks to Tegus, which centralizes expert calls into a single platform. Nothing beats primary research when it comes to understanding a company. If you’re curious about Tegus, try them out with this link.Scale AI accelerates the development of AI applications through services and software.…

You may think of the places that remain uncharted by maps as very distant or rural areas. In reality, thriving, bustling communities of millions are not captured in any data source.  In fact, places all around the world have yet to be fully mapped, which is a huge problem. With the increase in climate-related disasters, this lack of data poses serious risks to the most vulnerable populations, especially in the global south. Gaps in data mean gaps in service delivery and gaps in disaster response. Here’s how you can join 600,000 volunteers worldwide and help communities put their citizens, roads,…

If you’ve ran off-the-shelf evals for your tasks, you may have found that most don’t work. They barely correlate with application-specific performance and aren’t discriminative enough to use in production. As a result, we could spend weeks and still not have evals that reliably measure how we’re doing on our tasks. To save us some time, I’m sharing some evals I’ve found useful. The goal is to spend less time figuring out evals so we can spend more time shipping to users. We’ll focus on simple, common tasks like classification/extraction, summarization, and translation. (Although classification evals are basic, having a…

from deephaven import ui, agg, empty_tablefrom deephaven.stream.table_publisher import table_publisherfrom deephaven.stream import blink_to_append_onlyfrom deephaven.plot import express as dxfrom deephaven import updateby as ubyfrom deephaven import dtypes as dhtstocks = dx.data.stocks().reverse()def set_bol_properties(fig): fig.update_layout(showlegend=False) fig.update_traces(fill="tonexty", fillcolor='rgba(255,165,0,0.08)')@ui.componentdef line_plot( filtered_source, exchange, window_size, bol_bands): window_size_key = { "5 seconds": ("priceAvg5s", "priceStd5s"), "30 seconds": ("priceAvg30s", "priceStd30s"), "1 minute": ("priceAvg1m", "priceStd1m"), "5 minutes": ("priceAvg5m", "priceStd5m")} bol_bands_key = {"None": None, "80%": 1.282, "90%": 1.645, "95%": 1.960, "99%": 2.576} base_plot = ui.use_memo(lambda: ( dx.line(filtered_source, x="timestamp", y="price", by="exchange" if exchange == "All" else None, unsafe_update_figure=lambda fig: fig.update_traces(opacity=0.4)) ), [filtered_source, exchange]) window_size_avg_key_col = window_size_key[window_size][0] window_size_std_key_col = window_size_key[window_size][1] avg_plot = ui.use_memo(lambda: dx.line(filtered_source, x="timestamp",…