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It is extraordinary enough to see the world's top central banker make an unscheduled video statement on social media. My first thought upon seeing the post from the Federal Reserve chair Jerome Powell was: Is this an AI deepfake? That sense did not go away as I listened to what were indeed the real words of the world's most important financial official. The background here is a long-running spat between President Trump and the man responsible for setting interest rates in the US and indirectly much of the rest of the world. In theory, this has officially been about the cost of a renovation project at the Federal Reserve, the US equivalent of the Bank of England.

Many analysts are predicting an election-year upturn, but they aren't accounting for the President's ability to cause more chaos. In a primetime address from the Oval Office last week, Donald Trump said, "We are poised for an economic boom the likes of which the world has never seen." This was the sort of bloviating that has convinced many voters he's hopelessly out of touch, but it did raise the question of how the economy is likely to perform in 2026, a midterm-election year. Given the data fog that the government shutdown created, the old joke applies more than ever: it's difficult to make predictions, especially about the future. But some things seem reasonably clear.

We report the discovery that binary encoding allows neural networks to extrapolate periodic functions beyond their training bounds. We introduce Normalized Base-2 Encoding (NB2E) as a method for encoding continuous numerical values and demonstrate that, using this input encoding, vanilla multi-layer perceptrons (MLP) successfully extrapolate diverse periodic signals without prior knowledge of their functional form. Internal activation analysis reveals that NB2E induces bit-phase representations, enabling MLPs to learn and extrapolate signal structure independently of position.

Algorithm 4 gives pseudocode for K-BFGS/K-BFGS(L), which is implemented in the experiments. In this section, we prove the convergence of Algorithm 5, a variant of K-BFGS(L). To accomplish this, we prove Lemmas 1-3, which in addition to Assumptions AS.1-2, ensure that all of the assumptions in Theorem 2.8 in [ Algorithm 6 SQN method for nonconvex stochastic optimization.Require: Given θ ( k 1) ( k 1) ( k 1) ( k 1) Hence, Theorem 2.8 of [41] applies to Algorithm 5, proving Theorem 2. Thus, we propose the following heuristic based on Powell's damped-BFGS approach In Powell's damping on H (see e.g. This is used in lines 2 and 3 of the DD (Algorithm 3). Our double damping (Algorithm 3) is a two-step damping procedure, where the first step (i.e.

There are growing fears of an imminent stock market crash – one that will transform from a dip to a dive when euphoric headlines about the wonders of artificial intelligence begin to wane. Shares in US tech stocks have fallen in recent weeks and the prospect is that a flood of negative numbers will become the norm before the month is out. It could be 2000 all over again, and just like the bursting of the dotcom bubble it may be ugly, with investors junking businesses that once looked good on paper but now resemble a huge liability. Jerome Powell, the Federal Reserve chair, is one of the policymakers tasked with keeping the wolf from the door. Speaking on Friday at the annual Jackson Hole gathering of central bank governors in Wyoming, he tried to calm nerves.

We introduce a novel approach to variational Quantum algorithms (VQA) via continuous bandits. VQA are a class of hybrid Quantum-classical algorithms where the parameters of Quantum circuits are optimized by classical algorithms. Previous work has used zero and first order gradient based methods, however such algorithms suffer from the barren plateau (BP) problem where gradients and loss differences are exponentially small. We introduce an approach using bandits methods which combine global exploration with local exploitation. We show how VQA can be formulated as a best arm identification problem in a continuous space of arms with Lipschitz smoothness. While regret minimization has been addressed in this setting, existing methods for pure exploration only cover discrete spaces. We give the first results for pure exploration in a continuous setting and derive a fixed-confidence, information-theoretic, instance specific lower bound. Under certain assumptions on the expected payoff, we derive a simple algorithm, which is near-optimal with respect to our lower bound. Finally, we apply our continuous bandit algorithm to two VQA schemes: a PQC and a QAOA quantum circuit, showing that we significantly outperform the previously known state of the art methods (which used gradient based methods).

Taipei, Taiwan – Generative artificial intelligence (AI) has already brought about a "healthcare revolution" and is set to transform everything from pharmaceutical research to patient diagnostics and post-operative treatment, a top executive at chip giant Nvidia has said. Kimberly Powell, vice president of healthcare at Nvidia, said on Wednesday while it is still "early days", healthcare will probably be more affected by AI than any other area of life. "Healthcare is probably the most impactful utility of generative AI that there will be," Powell said during Nvidia's AI Summit, held on the sidelines of the Computex expo in Taipei. Powell said AI is already making its mark in the field of developing and testing new drugs, which can take up to 15 years and cost up to 2bn under current timeframes. "We care about fast and fast means in this industry, that we'll be able to do more, and we know that drug discovery is essentially an infinite problem. You're looking at a chemical space and 10 to the 60th power potential chemical compounds," Powell said.

Epidemiological compartmental models are useful for understanding infectious disease propagation and directing public health policy decisions. Calibration of these models is an important step in offering accurate forecasts of disease dynamics and the effectiveness of interventions. In this study, we present an overview of calibrating strategies that can be employed, including several optimization methods and reinforcement learning (RL). We discuss the benefits and drawbacks of these methods and highlight relevant practical conclusions from our experiments. Optimization methods iteratively adjust the parameters of the model until the model output matches the available data, whereas RL uses trial and error to learn the optimal set of parameters by maximizing a reward signal. Finally, we discuss how the calibration of parameters of epidemiological compartmental models is an emerging field that has the potential to improve the accuracy of disease modeling and public health decision-making. Further research is needed to validate the effectiveness and scalability of these approaches in different epidemiological contexts. All codes and resources are available on https://github.com/Nikunj-Gupta/