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A suite of technologies are helping taxonomists speed up species identification. Listen to more stories on the Noa app. Across a Swiss meadow and into its forested edges, the drone dragged a jumbo-size cotton swab from a 13-foot tether. Along its path, the moistened swab collected scraps of life: some combination of sloughed skin and hair; mucus, saliva, and blood splatters; pollen flecks and fungal spores. Later, biologists used a sequencer about the size of a phone to stream the landscape's DNA into code, revealing dozens upon dozens of species, some endangered, some invasive.

Obtaining the solution of constrained optimization problems as a function of parameters is very important in a multitude of applications, such as control and planning. Solving such parametric optimization problems in real time can present significant challenges, particularly when it is necessary to obtain highly accurate solutions or batches of solutions. To solve these challenges, we propose a learning-based iterative solver for constrained optimization which can obtain very fast and accurate solutions by customizing the solver to a specific parametric optimization problem. For a given set of parameters of the constrained optimization problem, we propose a first step with a neural network predictor that outputs primal-dual solutions of a reasonable degree of accuracy. This primal-dual solution is then improved to a very high degree of accuracy in a second step by a learned iterative solver in the form of a neural network. A novel loss function based on the Karush-Kuhn-Tucker conditions of optimality is introduced, enabling fully self-supervised training of both neural networks without the necessity of prior sampling of optimizer solutions. The evaluation of a variety of quadratic and nonlinear parametric test problems demonstrates that the predictor alone is already competitive with recent self-supervised schemes for approximating optimal solutions. The second step of our proposed learning-based iterative constrained optimizer achieves solutions with orders of magnitude better accuracy than other learning-based approaches, while being faster to evaluate than state-of-the-art solvers and natively allowing for GPU parallelization.

Reverse engineering deep ReLU networks is a critical problem in understanding the complex behavior and interpretability of neural networks. In this research, we present a novel method for reconstructing deep ReLU networks by leveraging convex optimization techniques and a sampling-based approach. Our method begins by sampling points in the input space and querying the black box model to obtain the corresponding hyperplanes. We then define a convex optimization problem with carefully chosen constraints and conditions to guarantee its convexity. The objective function is designed to minimize the discrepancy between the reconstructed network's output and the target model's output, subject to the constraints. We employ gradient descent to optimize the objective function, incorporating L1 or L2 regularization as needed to encourage sparse or smooth solutions. Our research contributes to the growing body of work on reverse engineering deep ReLU networks and paves the way for new advancements in neural network interpretability and security.

A neural network consisting of piecewise affine building blocks, such as fully-connected layers and ReLU activations, is itself a piecewise affine function supported on a polyhedral complex. This complex has been previously studied to characterize theoretical properties of neural networks, but, in practice, extracting it remains a challenge due to its high combinatorial complexity. A natural idea described in previous works is to subdivide the regions via intersections with hyperplanes induced by each neuron. However, we argue that this view leads to computational redundancy. Instead of regions, we propose to subdivide edges, leading to a novel method for polyhedral complex extraction. A key to this are sign-vectors, which encode the combinatorial structure of the complex. Our approach allows to use standard tensor operations on a GPU, taking seconds for millions of cells on a consumer grade machine. Motivated by the growing interest in neural shape representation, we use the speed and differentiability of our method to optimize geometric properties of the complex. The code is available at https://github.com/arturs-berzins/relu_edge_subdivision .

I've just published a story about the first attempt to calculate the broader emissions of one of the most popular AI products right now--large language models--and how it could help nudge the tech sector to do more to clean up its act. AI startup Hugging Face calculated the emissions of its large language model BLOOM, and its researchers found that the training process emitted 25 metric tons of carbon. However, those emissions doubled when they took the wider hardware and infrastructure costs of running the model into account. They published their work in a paper posted on arXiv that's yet to be peer reviewed. The finding in itself isn't hugely surprising, and BLOOM is way "cleaner" than large language models like OpenAI's GPT-3 and Meta's OPT, because it was trained on a French supercomputer powered by nuclear energy. Instead, the significance of this work is that it points to a better way to calculate AI models' climate impact, by going beyond just the training to the way they're used in the real world.

You are free to share this article under the Attribution 4.0 International license. Will artificial intelligence be a help or a hindrance in the response to climate change? In the journal Nature Climate Change, a team of experts in AI, climate change, and public policy present a framework for understanding the complex and multifaceted relationship of AI with greenhouse gas emissions and suggest ways to better align AI with climate change goals. "AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," says coauthor David Rolnick, assistant professor of computer science at McGill University and a core academic member of Mila – Quebec AI Institute. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." "Climate change should be a key consideration when developing and assessing AI technologies," says Lynn Kaack, assistant professor of computer science and public policy at the Hertie School, and lead author of the report.

As the world fights climate change, will the increasingly widespread use of artificial intelligence (AI) be a help or a hindrance? In a paper published this week in Nature Climate Change, a team of experts in AI, climate change, and public policy present a framework for understanding the complex and multifaceted relationship of AI with greenhouse gas emissions, and suggest ways to better align AI with climate change goals. "AI affects the climate in many ways, both positive and negative, and most of these effects are poorly quantified," said David Rolnick, Assistant Professor of Computer Science at McGill University and a Core Academic Member of Mila – Quebec AI Institute, who co-authored the paper. "For example, AI is being used to track and reduce deforestation, but AI-based advertising systems are likely making climate change worse by increasing the amount that people buy." The paper divides the impacts of AI on greenhouse gas emissions into three categories: 1) Impacts from the computational energy and hardware used to develop, train, and run AI algorithms, 2) immediate impacts caused by the applications of AI - such as optimizing energy use in buildings (which decreases emissions) or accelerating fossil fuel exploration (which increases emissions), and 3) system-level impacts caused by the ways in which AI applications affect behaviour patterns and society more broadly, such as via advertising systems and self-driving cars.

Climate change is the biggest challenge facing the planet. It will need every solution possible, including technology like artificial intelligence (AI). Seeing a chance to help the cause, some of the biggest names in AI and machine learning--a discipline within the field--recently published a paper called "Tackling Climate Change with Machine Learning." The paper, which was discussed at a workshop during a major AI conference in June, was a "call to arms" to bring researchers together, said David Rolnick, a University of Pennsylvania postdoctoral fellow and one of the authors. "It's surprising how many problems machine learning can meaningfully contribute to," says Rolnick, who also helped organize the June workshop. The paper offers up 13 areas where machine learning can be deployed, including energy production, CO2 removal, education, solar geoengineering, and finance.

As more and more industries take on artificial intelligence to solve some of their biggest challenges, can machines help us understand and fix climate change issues? So your phone recognises your face, and your bank can block any transaction unlike your spending habits. And your online supermarket nudges you with their vegan products just because you've bought that oat milk once, while your online movie platform keeps throwing B-movies at you after you watched that soap opera last month. A growing number of our devices and services are relying on artificial intelligence (AI), a technology that continues to branch out and pop up in more and more areas of our lives. Scientists, entrepreneurs, and governments are leveraging AI to explore solutions for some of society's biggest challenges.