Our natural gradient approach enables application of parallel filtering andsmoothing, further reducing thetemporal spancomplexitytobelogarithmic inthe number oftime steps.

During World War I, Allied navies started implementing shocking, cubist-inspired "dazzle" paint jobs on ships. The now-iconic geometric designs were intended to throw off the visual perception of German U-boats crews and prevent them from accurately targeting ships with torpedoes. Conventional wisdom claims the bizarre camouflage pattern worked and helped turn the tide of Great War naval battles. But new research reevaluating one of the only rigorous studies testing that hypothesis suggests those conclusions were probably overblown. Researchers now claim another phenomena known as the "horizon effect" may have actually done more to throw off submarine gunners than the wacky aesthetic.

The current winning recipe for automatic summarization is using proprietary large-scale language models (LLMs) such as ChatGPT as is, or imitation learning from them as teacher models. While increasingly ubiquitous dependence on such large-scale language models is convenient, there remains an important question of whether small-scale models could have achieved competitive results, if we were to seek an alternative learning method -- that allows for a more cost-efficient, controllable, yet powerful summarizer. We present InfoSumm, a novel framework to distill a powerful summarizer based on the information-theoretic objective for summarization, without relying on either the LLM's capability or human-written references. To achieve this, we first propose a novel formulation of the desiderata of summarization (saliency, faithfulness and brevity) through the lens of mutual information between the original document and the summary. Based on this formulation, we start off from Pythia-2.8B as the teacher model, which is not yet capable of summarization, then self-train the model to optimize for the information-centric measures of ideal summaries. Distilling from the improved teacher, we arrive at a compact but powerful summarizer with only 568M parameters that performs competitively against ChatGPT, without ever relying on ChatGPT's capabilities. Extensive analysis demonstrates that our approach outperforms in-domain supervised models in human evaluation, let alone state-of-the-art unsupervised methods, and wins over ChatGPT in controllable summarization.

The artificial-intelligence model that powers ChatGPT can create superficially plausible scientific data sets.Credit: Mateusz Slodkowski/SOPA Images/LightRocket via Getty Researchers have used the technology behind the artificial intelligence (AI) chatbot ChatGPT to create a fake clinical-trial data set to support an unverified scientific claim. In a paper published in JAMA Ophthalmology on 9 November1, the authors used GPT-4 -- the latest version of the large language model on which ChatGPT runs -- paired with Advanced Data Analysis (ADA), a model that incorporates the programming language Python and can perform statistical analysis and create data visualizations. The AI-generated data compared the outcomes of two surgical procedures and indicated -- wrongly -- that one treatment is better than the other. "Our aim was to highlight that, in a few minutes, you can create a data set that is not supported by real original data, and it is also opposite or in the other direction compared to the evidence that are available," says study co-author Giuseppe Giannaccare, an eye surgeon at the University of Cagliari in Italy. The ability of AI to fabricate convincing data adds to concern among researchers and journal editors about research integrity.

Biochemical reaction networks are an amalgamation of reactions where each reaction represents the interaction of different species. Generally, these networks exhibit a multi-scale behavior caused by the high variability in reaction rates and abundances of species. The so-called jump-diffusion approximation is a valuable tool in the modeling of such systems. The approximation is constructed by partitioning the reaction network into a fast and slow subgroup of fast and slow reactions, respectively. This enables the modeling of the dynamics using a Langevin equation for the fast group, while a Markov jump process model is kept for the dynamics of the slow group. Most often biochemical processes are poorly characterized in terms of parameters and population states. As a result of this, methods for estimating hidden quantities are of significant interest. In this paper, we develop a tractable Bayesian inference algorithm based on Markov chain Monte Carlo. The presented blocked Gibbs particle smoothing algorithm utilizes a sequential Monte Carlo method to estimate the latent states and performs distinct Gibbs steps for the parameters of a biochemical reaction network, by exploiting a jump-diffusion approximation model. The presented blocked Gibbs sampler is based on the two distinct steps of state inference and parameter inference. We estimate states via a continuous-time forward-filtering backward-smoothing procedure in the state inference step. By utilizing bootstrap particle filtering within a backward-smoothing procedure, we sample a smoothing trajectory. For estimating the hidden parameters, we utilize a separate Markov chain Monte Carlo sampler within the Gibbs sampler that uses the path-wise continuous-time representation of the reaction counters. Finally, the algorithm is numerically evaluated for a partially observed multi-scale birth-death process example.

Founded in 1991, India-headquartered HCL Technologies is a global technology company that helps enterprises reimagine their businesses for the digital age. The company specializes in key areas, including digital, IoT, cloud, automation, cybersecurity, and analytics, amongst others. With the company increasingly having a presence in the RegTech space, how does it see the sector changing? How is RegTech changing compliance? According to Daryl Wilkinson – Senior Executive, Strategic Initiatives, Financial Services UK&I at HCL Technologies, "I think you can look at this through two lenses. First, there appears to be a consensus that the global RegTech market is expected to achieve $30bn by 2027 – so that alone is changing the compliance market –new investment is disrupting incumbent models and is changing the way regulators engage with businesses. The second lens is cost; financial services rely heavily on legacy technology – RegTech's nature is to find that niche to solve those problems at a much lower cost than the banks and insurers might otherwise do themselves."

We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterior linearisation (PL) as extensions of Newton's method for optimising the parameters of a Bayesian posterior distribution. This viewpoint explicitly casts inference algorithms under the framework of numerical optimisation. We show that common approximations to Newton's method from the optimisation literature, namely Gauss-Newton and quasi-Newton methods (e.g., the BFGS algorithm), are still valid under this'Bayes-Newton' framework. This leads to a suite of novel algorithms which are guaranteed to result in positive semi-definite covariance matrices, unlike standard VI and EP. Our unifying viewpoint provides new insights into the connections between various inference schemes. All the presented methods apply to any model with a Gaussian prior and non-conjugate likelihood, which we demonstrate with (sparse) Gaussian processes and state space models. Keywords: Approximate Bayesian inference, optimisation, variational inference, expectation propagation, Gaussian processes.

Researchers at the Massachusetts Institute of Technology (MIT) have used machine learning to reduce the processing power needed to render convincing holographic images, making it possible to generate them in near-real time on consumer-level computer hardware. Such a method could pave the way to portable virtual-reality systems that use holography instead of stereoscopic displays. Stereo imagery can present the illusion of three-dimensionality, but users often complain of dizziness and fatigue after long periods of use because there is a mismatch between where the brain expects to focus and the flat focal plane of the two images. Switching to holographic image generation overcomes this problem; it uses interference in the patterns of many light beams to construct visible shapes in free space that present the brain with images it can more readily accept as three-dimensional (3D) objects. "Holography in its extreme version produces a full optical reproduction of the image of the object. There should be no difference between the image of the object and the object itself," says Tim Wilkinson, a professor of electrical engineering at Jesus College of the U.K.'s University of Cambridge.

The Seinfeld idiom, "worlds are colliding," is probably the best description of work in the age of Corona. Pre-pandemic, it was easy to departmentalize one's professional life from one's home existence. Clearly, my dishpan hands have hindered my writing schedule. Thank goodness for the robots in my life, scrubbing and vacuuming my floors; if only they could power themselves with the crumbs they suck up. The World Bank estimates that 3.5 million tons of solid waste is produced by humans everyday, with America accounting for more than 250 million tons a year or over 4 pounds of trash per citizen.

Wilkinson recently had a close call when a shark trailed him, only inches away. Wilkinson recently had a close call when a shark trailed him, only inches away. The surfer had no idea a shark was trailing him. Near Sharpes Beach in Australia, professional surfer Matt Wilkinson was paddling on his board on Wednesday. Unbeknownst to him, a shark quickly surfaced and began stalking the surfing world champion, at one point only inches away.