Prior work introduced a gradient descent trained expert system that conceptually combines the learning capabilities of neural networks with the understandability and defensible logic of an expert system. This system was shown to be able to learn patterns from data and to perform decision-making at levels rivaling those reported by neural network systems. The principal limitation of the approach, though, was the necessity for the manual development of a rule-fact network (which is then trained using backpropagation). This paper proposes a technique for overcoming this significant limitation, as compared to neural networks. Specifically, this paper proposes the use of larger and denser-than-application need rule-fact networks which are trained, pruned, manually reviewed and then re-trained for use. Multiple types of networks are evaluated under multiple operating conditions and these results are presented and assessed. Based on these individual experimental condition assessments, the proposed technique is evaluated. The data presented shows that error rates as low as 3.9% (mean, 1.2% median) can be obtained, demonstrating the efficacy of this technique for many applications.

In this week's geeked-out edition of the Good News Roundup, Ukraine's jaw-dropping battlefield victories with HIMARS are documented using OSINT, South Africa implements AI technology to track dangerous locust swarms, biologists and naturalists overwhelmingly agree that gay sex is normal throughout the animal kingdom, and BirdNet proves reliable at crowdsourcing the task of identifying wild birds by their songs. In wholesome news for sci fi/space fantasy fans everywhere, Ukraine's president Zelensky continues attending technology trade shows through holograms in which he promises that Ukraine will defeat the Empire. Ukrainians are also using 3d imaging technology to preserve the cultural heritage of their country from looters and bombs, storing their data in a digital archive that will support restoration work when the invaders have been defeated. And in good news for new Ukrainian parents, the non-profit Embrace Global is making headlines for using innovative technology to provide incubators for babies in Ukraine at a tiny fraction of their usual cost. You can see their TED talk by entrepreneur Jane Chen here.

Dr. Reinhard Klette, Fellow of the Royal Society of New Zealand, is a Professor at the Auckland University of Technology (AUT). His numerous publications include the books "Computer Vision for Driver Assistance" (co-authored by Mahdi Rezaei), "Multi-target Tracking" (co-authored by Junli Tao), "Concise Computer Vision", "Euclidean Shortest Paths" (co-authored by Fajie Li), "Panoramic Imaging" (co-authored by Fay Huang and Karsten Scheibe), "Digital Geometry" (co-authored by the late Azriel Rosenfeld), "Computer Vision - Three-Dimensional Data from Images" (co-authored by Karsten Schluens and Andreas Koschan), "The Handbook of Image Processing Operators" (co-authored by the late Piero Zamperoni), and "Fast Algorithms and their Implementation on Specialized Parallel Computers" (co-authored by Jozef Miklosko, Marian Vajtersic, and Imre Vrto)

The Artificial Intelligence as a Service market research report provides a competitive edge to stakeholders by tracking the past and present industr

The largest prostate cancer biopsy dataset – involving over 95,000 images – has been created by researchers in Sweden to ensure AI can be trained to diagnose and grade prostate cancer for real world clinical applications. The researchers will call today, at the European Association of Urology annual congress (EAU22), for large-scale clinical trials of artificial intelligence (AI) algorithms and greater global coordination to ensure that AI enhanced diagnostics, prognostication, and treatment selection can help save lives. There is a shortage of pathologists around the world, both generalists and those specialised in urology. AI can help in detecting prostate cancer at an early stage, but because of the vast differences in the way clinics prepare samples, scan images and in the diverse patient populations they serve, many algorithms do not have universal application. The team, from Karolinska Institutet, worked with colleagues from Radboud University Medical Center in the Netherlands, University of Turku in Finland and Google Health in the US to run an AI competition involving nearly 1,300 developers from around the world.

Automated audio captioning aims to describe audio data with captions using natural language. Existing methods often employ an encoder-decoder structure, where the attention-based decoder (e.g., Transformer decoder) is widely used and achieves state-of-the-art performance. Although this method effectively captures global information within audio data via the self-attention mechanism, it may ignore the event with short time duration, due to its limitation in capturing local information in an audio signal, leading to inaccurate prediction of captions. To address this issue, we propose a method using the pretrained audio neural networks (PANNs) as the encoder and local information assisted attention-free Transformer (LocalAFT) as the decoder. The novelty of our method is in the proposal of the LocalAFT decoder, which allows local information within an audio signal to be captured while retaining the global information. This enables the events of different duration, including short duration, to be captured for more precise caption generation. Experiments show that our method outperforms the state-of-the-art methods in Task 6 of the DCASE 2021 Challenge with the standard attention-based decoder for caption generation.

In recent years, there has been a surge in building and adopting machine learning (ML) tools. The use of GPUs to accelerate increasingly compute-intensive models has been a prominent trend. To increase user access, the Accelerated WEKA project provides an accessible entry point for using GPUs in well-known WEKA algorithms by integrating open-source RAPIDS libraries. In this post, you will be introduced to Accelerated WEKA and learn how to leverage GPU-accelerated algorithms with a graphical user interface (GUI) using WEKA software. This Java open-source alternative is suitable for beginners looking for a variety of ML algorithms from different environments or packages.

This editorial refers to'Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England', by N.R. Hill et al., https://doi.org/10.1093/ehjdh/ztac009. To see things in the seed, that is genius. Undiagnosed atrial fibrillation (AF) is an important cause of stroke.1 AF screening may enable prompt detection of AF and initiation of oral anticoagulation (OAC) to prevent stroke.2 The 2007 SAFE trial reported a roughly 50% increase in AF diagnosis with screening individuals aged 65 years using electrocardiography (ECG) with or without pulse palpation,3 resulting in a Class I recommendation from the European Society of Cardiology4 and the Cardiac Society of Australia and New Zealand5 for AF screening using ECG among individuals aged 65 years. However, more recent studies suggest that mass screening may not be effective.6,7

Quantum algorithms based on variational approaches are one of the most promising methods to construct quantum solutions and have found a myriad of applications in the last few years. Despite the adaptability and simplicity, their scalability and the selection of suitable ans\"atzs remain key challenges. In this work, we report an algorithmic framework based on nested Monte-Carlo Tree Search (MCTS) coupled with the combinatorial multi-armed bandit (CMAB) model for the automated design of quantum circuits. Through numerical experiments, we demonstrated our algorithm applied to various kinds of problems, including the ground energy problem in quantum chemistry, quantum optimisation on a graph, solving systems of linear equations, and finding encoding circuit for quantum error detection codes. Compared to the existing approaches, the results indicate that our circuit design algorithm can explore larger search spaces and optimise quantum circuits for larger systems, showing both versatility and scalability.

We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. This popular game has an enormous game tree on the order of $10^{535}$ nodes, i.e., $10^{175}$ times larger than that of Go. It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes). Decisions in Stratego are made over a large number of discrete actions with no obvious link between action and outcome. Episodes are long, with often hundreds of moves before a player wins, and situations in Stratego can not easily be broken down into manageably-sized sub-problems as in poker. For these reasons, Stratego has been a grand challenge for the field of AI for decades, and existing AI methods barely reach an amateur level of play. DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego via self-play. The Regularised Nash Dynamics (R-NaD) algorithm, a key component of DeepNash, converges to an approximate Nash equilibrium, instead of 'cycling' around it, by directly modifying the underlying multi-agent learning dynamics. DeepNash beats existing state-of-the-art AI methods in Stratego and achieved a yearly (2022) and all-time top-3 rank on the Gravon games platform, competing with human expert players.