The sun is getting a close-up as NASA releases historic new photographs. Talk about "to boldly go." A new space toilet is making its way to the crew of the International Space Station aboard a Cygnus spacecraft. "Its features improve on current space toilet operations and help NASA prepare for future missions, including those to the Moon and Mars," explained NASA, in a statement. "The Universal Waste Management System (UWMS) demonstrates a compact toilet and the Urine Transfer System that further automates waste management and storage."

A global virtual conference aimed at promoting the use of artificial intelligence (AI) and machine learning (ML) for the development of Odisha and advancement of Odia language in the digital era is being organized on 4th October Sunday, on the virtual meeting platform Zoom. The conference, called Odias in ML Conference, is being organized by a group of Odias, also called Odias in ML, with a shared interest in AI and ML. The conference also aims to showcase career and entrepreneurship opportunities in AI and ML for Odias across the world. This first-of-its-kind conference will see participation of a multitude of stakeholders including technologists, researchers, academicians, business executives, entrepreneurs, policymakers, linguists, language activists, media persons and community leaders, all with a commitment to AI and machine learning. The speakers, all Odias based across three continents, will come together to brainstorm how the opportunities created by these emerging technologies can be leveraged effectively to propel the next phase of growth for Odisha and Odias.

A Data Scientist spends most of his time inside a Jupyter Notebook exploring the data and drafting ideas. Usually, when we try to version our work, we end up with a bunch of duplicated ipynb files, assuming different naming schemes. Can we have something that automatically snapshots our work, before and after every step in an ML pipeline? Moreover, can we get started using it without a ton of configuration needed? Just open a Notebook, do our thing and be sure that everything else will take care of itself.

Fifty million artificial neurons--a number roughly equivalent to the brain of a small mammal--were delivered from Portland, Oregon-based Intel Corp. to Sandia National Laboratories last month, said Sandia project leader Craig Vineyard. The neurons will be assembled to advance a relatively new kind of computing, called neuromorphic, based on the principles of the human brain. Its artificial components pass information in a manner similar to the action of living neurons, electrically pulsing only when a synapse in a complex circuit has absorbed enough charge to produce an electrical spike. "With a neuromorphic computer of this scale," Vineyard said, "we have a new tool to understand how brain-based computers are able to do impressive feats that we cannot currently do with ordinary computers." Improved algorithms and computer circuitry can create wider applications for neuromorphic computers, said Vineyard. Sandia manager of cognitive and emerging computing John Wagner said, "This very large neural computer will let us test how brain-inspired processors use information at increasingly realistic scales as they come to actually approximate the processing power of brains.

As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.

This paper introduces new parameterizations of equilibrium neural networks, i.e. networks defined by implicit equations. This model class includes standard multilayer and residual networks as special cases. The new parameterization admits a Lipschitz bound during training via unconstrained optimization: no projections or barrier functions are required. Lipschitz bounds are a common proxy for robustness and appear in many generalization bounds. Furthermore, compared to previous works we show well-posedness (existence of solutions) under less restrictive conditions on the network weights and more natural assumptions on the activation functions: that they are monotone and slope restricted. These results are proved by establishing novel connections with convex optimization, operator splitting on non-Euclidean spaces, and contracting neural ODEs. In image classification experiments we show that the Lipschitz bounds are very accurate and improve robustness to adversarial attacks.

Applying differentiable programming techniques and machine learning algorithms to foreign programs requires developers to either rewrite their code in a machine learning framework, or otherwise provide derivatives of the foreign code. This paper presents Enzyme, a high-performance automatic differentiation (AD) compiler plugin for the LLVM compiler framework capable of synthesizing gradients of statically analyzable programs expressed in the LLVM intermediate representation (IR). Enzyme synthesizes gradients for programs written in any language whose compiler targets LLVM IR including C, C++, Fortran, Julia, Rust, Swift, MLIR, etc., thereby providing native AD capabilities in these languages. Unlike traditional source-to-source and operator-overloading tools, Enzyme performs AD on optimized IR. On a machine-learning focused benchmark suite including Microsoft's ADBench, AD on optimized IR achieves a geometric mean speedup of 4.5x over AD on IR before optimization allowing Enzyme to achieve state-of-the-art performance. Packaging Enzyme for PyTorch and TensorFlow provides convenient access to gradients of foreign code with state-of-the art performance, enabling foreign code to be directly incorporated into existing machine learning workflows.

Ambiguity is inherent to open-domain question answering; especially when exploring new topics, it can be difficult to ask questions that have a single, unambiguous answer. In this paper, we introduce AmbigQA, a new open-domain question answering task which involves finding every plausible answer, and then rewriting the question for each one to resolve the ambiguity. To study this task, we construct AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous, with diverse sources of ambiguity such as event and entity references. We also present strong baseline models for AmbigQA which we show benefit from weakly supervised learning that incorporates NQ-open, strongly suggesting our new task and data will support significant future research effort. Our data and baselines are available at https://nlp.cs.washington.edu/ambigqa.

Here's What You Need To Remember: Russia, along with China, has continued to explore ways of utilizing machine learning and artificial intelligence (AI) in weapons platforms--a move that the Pentagon has considered dangerous, as AI may not be able to properly separate civilians from targets in hostile zones. Even as Russia continues to overcome production issues with its Su-57 stealth fighter, Moscow reportedly has its eyes set on a sixth-generation fighter jet, which could be developed under the MiG-Sukhoi joint brand. Such a fighter could build on the best features of the MiG-21, which has become the most-produced supersonic jet in aviation history; and the very capable MiG-35, as well as the Su-57. "Possibly, this will be so: the fighter produced by the MiG-Sukhoi," Rostec Aviation Cluster Industrial Director Anatoly Serdyukov told Tass on Tuesday. "But so far, all the work is at the stage of discussions and it is early to speak about details."

Artificial intelligence (AI) and deep learning models can help advance research on neural degeneration, showing its capabilities in identifying and categorizing its forms on a model organism. Using the organism Caenorhabditis elegans or the roundworm – a 1-millimeter near-transparent nematode – researchers used deep learning to conduct a quantitative image-based analysis of neural degeneration patterns observed in the PVD neuron of the organism. Researchers from North Carolina State University have detailed their work in the journal BMC Biology, September 23. The worms were found alive last week in a biological container that was among the debris from the Space Shuttle Columbia recovered in East Texas. The worms are descendants of those that were part of an experiment that flew on Columbia's last mission before the spacecraft broke up on reentry February 1, killing all seven astronauts.