Breakthroughs, discoveries, and DIY tips sent every weekday. Amidst the flurry of AI announcements and product reveals from Google in recent months, you might have missed one of the most useful AI-powered apps in the whole collection: NotebookLM (that LM stands for Language Model). Perhaps NotebookLM has gone largely under the radar because it was originally launched as more of an academic research tool when it first appeared back in 2023. Its user interface lacks some of the slickness and accessibility of Google Gemini, and it's not quite as obvious how you're supposed to use it, or what it can do. However, NotebookLM is gradually becoming better known amongst consumers, with official apps for Android and iOS now available, alongside the web app.

Apple's new HomePod will have a display, and it might arrive later this year. This is according to Bloomberg's Mark Gurman (via 9to5Mac), who claims that the device will launch "by the end of this year," though he admits that the timing is uncertain. The new device will be a smart home speaker with an added display (a 7-inch one, previous rumors claim), that should one day become a centerpiece of Apple's smart home tech. Other features, per previous reports, includes support for Apple Intelligence, a camera, smart home controls, and a rechargeable battery. While the device will have a built-in speaker, Apple will likely position it as a smart hub instead of just a home speaker.

A UFO researcher has an ominous message for humanity as governments around the world begin releasing more information about alleged contact with extraterrestrials. Dr Julia Mossbridge is a cognitive neuroscientist and a researcher of unidentified aerial phenomena (UAP) - the new term for UFOs and alien sightings. After scientists in Colombia recovered a mysterious, sphere-shaped object that many now believe is a piece of UFO technology, Mossbridge said the world is moving into an era which may soon have to deal with the knowledge that aliens exist. 'We are entering a time when we are starting to recognize as humans we don't have the control that we thought we had over everything,' Dr Mossbridge told Fox News. However, Mossbridge, who studies how humans think and also attended the May 1 congressional hearing on UAPs, said the impending disclosure of alien life could throw the worldview of many people into chaos.

A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements. We propose an approach for extracting low-dimensional dynamics from multiple, sequential recordings. Our algorithm scales to data comprising millions of observed dimensions, making it possible to access dynamics distributed across large populations or multiple brain areas. Building on subspace-identification approaches for dynamical systems, we perform parameter estimation by minimizing a moment-matching objective using a scalable stochastic gradient descent algorithm: The model is optimized to predict temporal covariations across neurons and across time.

DR-submodular continuous functions are important objectives with wide real-world applications spanning MAP inference in determinantal point processes (DPPs), and mean-field inference for probabilistic submodular models, amongst others. DR-submodularity captures a subclass of non-convex functions that enables both exact minimization and approximate maximization in polynomial time. In this work we study the problem of maximizing non-monotone DR-submodular continuous functions under general down-closed convex constraints. We start by investigating geometric properties that underlie such objectives, e.g., a strong relation between (approximately) stationary points and global optimum is proved. These properties are then used to devise two optimization algorithms with provable guarantees.

We study implicit regularization when optimizing an underdetermined quadratic objective over a matrix X with gradient descent on a factorization of X. We conjecture and provide empirical and theoretical evidence that with small enough step sizes and initialization close enough to the origin, gradient descent on a full dimensional factorization converges to the minimum nuclear norm solution.

If you're in the market for a new device, you can't really beat the MacBook Air. This sleek and powerful Apple computer is easy to bring anywhere, and right now you can get one for just 199.97-- 800 off the regular price--through July 20. It's hard to find the perfect laptop--they're often packed with serious perks but not very lightweight, or they're slim and portable but lacking in performance. The MacBook Air strikes the perfect balance--it packs a 1.8GHz Intel Core i5 processor and 8GB of RAM in a sleek 2.96-pound design. You can answer emails, stream content, or browse the web on a generous 13.3-inch widescreen display equipped with Intel HD Graphics 6000.

Neural Information Processing Systems http://nips.cc/

Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and wellchosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, are not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple "filter normalization" method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.

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