The paper presents an approach to building a parameter server for distribute ML systems that presents a view to each client where parameters have a bounded degree of staleness. Using a combination of caches, the client interface guarantees that all updates to the parameter array occurring after a fixed deadline (the current clock/iteration/tick minus a fixed delay) are visible along with more recent updates if possible. Thus the interface presents a view of parameters that integrates most updates along with best-effort service for more recent updates. It is shown that this simple semantic preserves the theoretical guarantees of cyclic delay methods while being significantly faster in practice. Empirical analysis on several problems with multiple cluster configurations show that the advantage is due to a combination of increased efficiency (over BSP) and optimization progress per update (over Asynchronous).

A personal trainer called Siri has been forced to change her name to stop iPhones pinging every time someone says her name. Siri Price, 26, from Edinburgh, has put up with sharing her name with Apple's personal assistant for over a decade but reached her limit with the company's latest update. With the release of iOS 17 two weeks ago, iPhone users must simply say'Siri' to activate the hands-free assistant, when previously they had to say'Hey Siri'. The recent update means that human Siri - who was just 14 years old when the technology was first released - has been inundated with phones pinging when anyone tries to speak to her. The fitness coach said she has been left'fuming' at the constant noise.

Soldiers controlling a robot dog with their mind as they patrol a dusty road and sweep an delipidated building may sound like science fiction, but it is the scene in a real world demonstration. The Australian Army has perfected mind-controlling abilities with eight sensors neatly packed inside a helmet that work in tandem with a Microsoft HoloLens. The innovation features an AI-decoder that translates a soldier's brain signals into explainable instructions that are sent to the robotic quadruped, allowing humans to stay focused on their surroundings. A new video shows military personal conducting a simulated patrol clearance using the robot dog, which was instructed to sweep a facility using what it read from a person's brain waves - and with 94 percent accuracy. The system was developed by the University of Technology Sydney that first unveiled the innovation last year, but recently published a new paper detailing the work. 'The user used our augmented brain–robot interface (aBRI) platform to control the robot systems,' reads the paper published by American Chemical Society on March 16.

Abstract: Contrastive learning has become a prominent ingredient in learning representations from unlabeled data. However, existing methods primarily consider pairwise relations. This paper proposes a new approach towards self-supervised contrastive learning based on Group Ordering Constraints (GroCo). Building on the recent success of differentiable sorting algorithms, group ordering constraints enforce that the distances of all positive samples (a positive group) are smaller than the distances of all negative images (a negative group); thus, enforcing positive samples to gather around an anchor. This leads to a more holistic optimization of the local neighborhoods.

Abstract: Contrastive learning has become a prominent ingredient in learning representations from unlabeled data. However, existing methods primarily consider pairwise relations. This paper proposes a new approach towards self-supervised contrastive learning based on Group Ordering Constraints (GroCo). Building on the recent success of differentiable sorting algorithms, group ordering constraints enforce that the distances of all positive samples (a positive group) are smaller than the distances of all negative images (a negative group); thus, enforcing positive samples to gather around an anchor. This leads to a more holistic optimization of the local neighborhoods.

The European Parliament's Legal Affairs (JURI) Committee, one of the 20 standing committees made up of a number of Members of the European Parliament, recently held a session discussing the EU Artificial Intelligence Act ("AI Act"). Here, we highlight key'thinking points' discussed to give an indication of where the AI Act may change from its current draft. The session was short, so potential answers will be the subject of further debate. For the background on the European Commission's proposed AI Act, see our articles "Artificial intelligence - EU Commission publishes proposed regulations" and "EU Artificial Intelligence Act - what has happened so far and what to expect next". AI has the potential to bring many benefits to users and wider society.

Andrew Beam, PhD is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, with secondary appointments in the Department of Biomedical Informatics at Harvard Medical School and the Department of Newborn Medicine at Brigham and Women's Hospital. His research develops and applies machine-learning methods to extract meaningful insights from clinical and biological datasets, and he is the recipient of a Pioneer Award from the Robert Wood Johnson Foundation for his work on medical artificial intelligence. Previously he was a Senior Fellow at Flagship Pioneering and the founding head of machine learning at Generate Biosciences, Inc., a Flagship-backed venture that seeks to use machine learning to improve our ability to engineer proteins.

This GitHub repository may be a bit out of date, having not been updated at all in the past 5 months, but given its wealth of quality links to other deep learning repositories I thought it was relevant enough to point out. Trending Deep Learning is a collection of, well, trending deep learning GitHub repos "sorted by the number of stars gained on a specific day." Mahmoud Badry maintians the collection (or did), and also prepared the companion collection repo Top Deep Learning (note the swapping of "trending" for "top"). Here's a list of top 100 deep learning Github trending repositories sorted by the number of stars gained on a specific day. Repositories with 50000 stars or more are excluded.

There is definitely an established machine learning ecosystem, or, perhaps more accurately, a small set of established machine learning ecosystems. For research it would seem that the undisputed champion of machine learning ecosystems is centered on Python and its many libraries which support the data preparation and subsequent machine learning process itself, whether it be via scikit-learn, one of the many deep learning libraries available, or home-spun and highly specialized tools for achieving the same goals. This says nothing of the great support tools that grow up around the edges of the ecosystem, some of which become polished and useful enough to carve out their own eventual niche. As those in industry would be the first to let me know, Python is not the only option. There are Java-based tools (Deeplearning4j, Weka), those integrated with Apache Spark and/or Hadoop (MLlib, Mahout), C solutions (TensorFlow is written in C, as are many others in the Python ecosystem), and even those for Clojure, F#, Rust, and a whole host of other languages, environments, and ecosystems.

There is definitely an established machine learning ecosystem, or, perhaps more accurately, a small set of established machine learning ecosystems. For research it would seem that the undisputed champion of machine learning ecosystems is centered on Python and its many libraries which support the data preparation and subsequent machine learning process itself, whether it be via scikit-learn, one of the many deep learning libraries available, or home-spun and highly specialized tools for achieving the same goals. This says nothing of the great support tools that grow up around the edges of the ecosystem, some of which become polished and useful enough to carve out their own eventual niche. As those in industry would be the first to let me know, Python is not the only option. There are Java-based tools (Deeplearning4j, Weka), those integrated with Apache Spark and/or Hadoop (MLlib, Mahout), C solutions (TensorFlow is written in C, as are many others in the Python ecosystem), and even those for Clojure, F#, Rust, and a whole host of other languages, environments, and ecosystems.