Manifold learning flows are a class of generative modelling techniques that assume a low-dimensional manifold description of the data. The embedding of such a manifold into the high-dimensional space of the data is achieved via learnable invertible transformations. Therefore, once the manifold is properly aligned via a reconstruction loss, the probability density is tractable on the manifold and maximum likelihood can be used to optimize the network parameters. Naturally, the lower-dimensional representation of the data requires an injective-mapping. Recent approaches were able to enforce that the density aligns with the modelled manifold, while efficiently calculating the density volume-change term when embedding to the higher-dimensional space.

This work builds off the manifold-constrained Gaussian process inference (MAGI) method for Bayesian parameter inference and trajectory reconstruction of ODE-based dynamical systems, focusing primarily on sparse and noisy data conditions. First, we introduce Pilot MAGI (pMAGI), a novel methodological upgrade on the base MAGI method that confers significantly-improved numerical stability, parameter inference, and trajectory reconstruction. Second, we demonstrate, for the first time to our knowledge, how one can combine MAGI-based methods with dynamical systems theory to provide probabilistic classifications of whether a system is stable or chaotic. Third, we demonstrate how pMAGI performs favorably in many settings against much more computationally-expensive and overparameterized methods. Fourth, we introduce Pilot MAGI Sequential Prediction (PMSP), a novel method building upon pMAGI that allows one to predict the trajectory of ODE-based dynamical systems multiple time steps into the future, given only sparse and noisy observations. We show that PMSP can output accurate future predictions even on chaotic dynamical systems and significantly outperform PINN-based methods. Overall, we contribute to the literature two novel methods, pMAGI and PMSP, that serve as Bayesian, uncertainty-quantified competitors to the Physics-Informed Neural Network.

Code generation benchmarks such as HumanEval are widely adopted to evaluate LLMs' capabilities. However, after consolidating the latest 24 benchmarks, we noticed three significant imbalances. First, imbalanced programming language. 95.8% of benchmarks involve Python, while only 5 benchmarks involve Java. Second, imbalanced code granularity. Function-/statement-level benchmarks account for over 83.3% of benchmarks. Only a mere handful extends to class-/project-levels, and all are limited to Python. Third, lacking advanced features. Existing benchmarks primarily assess basic coding skills, while overlooking advanced Object-Oriented Programming (OOP) features (i.e., encapsulation, inheritance, and polymorphism). To fill these gaps, we propose JavaBench, a project-level Java benchmark that exercises OOP features. It comprises four Java projects with 389 methods in 106 Java classes. The test coverage is up to 92%, and JavaBench is attested by 282 undergraduate students, reaching a 90.93/100 average score (i.e., pass rate against the test suite), ensuring the quality of documentation, code skeleton, and tests. To better evaluate LLM's capability against JavaBench, we introduce a systematic evaluation design covering three context settings and five synthesis strategies at two granularities using three hierarchical metrics. Our extensive experiment yields several interesting findings. First, we noticed that regarding project-level Java programming, LLMs are far behind undergraduate students (no project can be correctly completed by any studied LLMs, and at most 41.17% Pass@5 in a more relaxed evaluation). Second, using method signature as prompt context may strike an ideal balance for project-level code generation. JavaBench is publicly available at https://github.com/java-bench/JavaBench.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! More often than not, when organizations deploy applications across hybrid and multicloud environments, they use the open-source Kubernetes container orchestration system. Kubernetes itself helps to schedule and manage distributed virtual compute resources and isn't optimized by default for any one particular type of workload, that's where projects like Kubeflow come into play. For organizations looking to run machine learning (ML) in the cloud, a group of companies including Google, Red Hat and Cisco helped to found the Kubeflow open-source project in 2017.

We have established a new data science practice at Canonical. The team will innovate in the open source data science technology stack, deliver advanced business analytics, support product roadmap decisions for Canonical through actionable insights, and lead by example in setting and publicly advocating for industry standards in open source data science. The team will have both Data Scientists and Data Engineers, apply here if you are most excited about the Data Engineer role! As a Data Engineer at Canonical you will act as a technical expert in an exciting field at the intersection of data engineering, data science, and machine learning technologies, with particular emphasis on the open source ecosystem of Canonical and Ubuntu. You will drive the organisation, instrumentation, ingestion, and transformation of data from a wide range of sources in the company.

This is an exciting opportunity for an experienced software engineer passionate about open source software, Linux, Kubernetes, and MLOps. In this role, you'll be building Charmed Kubeflow - an MLOps product suite composed of open source Python operators orchestrated by Juju. Your work will bring Kubeflow to a wide range of users and compute platforms - from embedded devices to public cloud environments and GPU-accelerated bare-metal. This role requires a skilled Python software developer with solid Kubernetes experience. You'll need to be driven by the idea of writing Charmed Operators.

Rob Gibbon, Product Manager at Canonical, and Gabriel Aguiar Noury, Robotics Product Manager at Canonical, the publisher of Ubuntu discuss their predictions and AI/ML & IoT trends in 2022. Whilst the AI/ML adoption trend accelerates, shadow IT environments and ungoverned cloud run costs will increasingly become an unacceptable, untenable marker of bad business. Organizations have become savvy, and discerning buyers are increasingly looking to move cost-sensitive, high run-rate applications back on-premise as effective private cloud options gain currency. "The IoT market is in a defining stage. People have adopted more and more IoT devices and connected them to the Internet. However, they've also downloaded apps onto their phones to control these devices, without even reading the terms and conditions. They've also been providing passwords and more sensitive data without understanding where they will be stored and how they will be protected. And even more importantly, they're using devices without checking if they are getting security updates. The Morris worm was the first computer worm that gained significant mainstream media attention after it infested millions of computers and paralyzed the Internet for several days. It was because of this scandal that the US took cybersecurity risks seriously. And now, just like in 1988, people are not thinking enough about security risks, so it is up to the IoT companies themselves to take control of the situation. In 2022, we predict that more and more governments will start demanding that IoT manufacturers declare how long IoT devices will keep receiving security maintenance to their customers up-front. The UK is one of the first countries that start working on such regulations, conscious of the interconnected risk that IoT devices bring. The global IoT market is expected to reach a value of $1386 billion USD by 2026 (up from $761 billion USD in 2020). Either the industry and governments start taking security risks seriously, or another Morris worm will force the industry to change."

The companies are collaborating to bring enterprise-grade Linux to the world of adaptive SoCs to accelerate the development of new software-defined devices across all IoT verticals. The goal is to ensure a smooth experience from prototyping on evaluation and starter kits to production-grade SOMs, reducing development costs and time. The past decade saw huge growth in the demand for fully configurable adaptive computing devices, integrating the traditional hardware programmability and flexibility of an FPGA with the software programmability of embedded processors. Xilinx addresses this market need with the Zynq UltraScale MPSoC family of products widely adopted across various industry verticals, including the industrial, vision, and healthcare markets. Now, Xilinx and Canonical are working together to enable Ubuntu on select Xilinx Zynq UltraScale MPSoC-based platforms to bring the reliable and proven Ubuntu OS experience.

New York, New York--(Newsfile Corp. - March 31, 2020) - Dell EMC, a leading provider of full-stack solutions for data science teams, and Comet, the industry-leading meta machine learning experimentation platform, announced a collaboration with a reference architecture for data science teams looking to harness the power of the Dell EMC infrastructure in tandem with Comet's meta machine learning platform. With Dell EMC PowerEdge reference architectures, organizations can deploy artificial intelligence workload-optimized rack systems approximately 6-12 months faster than it would have taken to design the correct configurations and deploy the solution. Organizations can now rely on architectures that are tested and validated by our Dell engineers and know that services are available when and where you need them. "Orchestrating and managing the stack for enterprise data science teams is a huge pain point for many of our customers," said Gideon Mendels, Co-founder/CEO, Comet. "Dell EMC's Kubeflow and Kubernetes solutions are best-in-class and an excellent choice for any data science team looking to build a robust and scalable ML platform."

The key concepts in Machine Learning How AI applications and their development are reshaping company's IT How enterprises are applying devops practices to their ML infrastructure and workflows How Canonical's AI / ML portfolio from Ubuntu to Charmed Kubernetes and and how to get started quickly with your project How AI applications and their development are reshaping company's IT How Canonical's AI / ML portfolio from Ubuntu to Charmed Kubernetes and and how to get started quickly with your project What is an AI model? How do you train it? How do you develop / improve it? How do you execute it? What is an AI model?