As the demand for energy-efficient, sustainable and smart technology is rapidly increasing, IBM has developed a new technology, considered the world's first energy-efficient chips for AI inference and training. The chip is built with 7-nanometer technology. A team of researchers proposed a hardware accelerator that supports a range of model types that can achieve leading power efficiency on all the models in a paper presented at the 2021 International Solid-State Conference. AI accelerators are specialized hardware that is specifically designed to speed up AI applications, especially neural networks, deep learning, and machine learning. They are multicore and mainly focus on low-precision arithmetic or in-memory computing.

GPT-3 has created a lot of buzz since its release a few months ago. The system can generate (almost) plausible conversations with the likes of Nietzsche, write op eds for The Guardian and was even used successfully to post undercover comments on Reddit for a week. But even with GPT-3, AI is still stuck in Uncanny Valley. GPT-3 output feels like it was written by a human at first glance, but it isn't quite. On closer inspection, it lacks substance and coherence.

DALL-E from Open AI can generate images from text captions. It is very similar to GPT-3 and Image GPT and produces amazing results.

In recent years, artificial intelligence has been attracting increasing attention, money and talent. But much of the hype is the result of myths and misconceptions being peddled by people outside of the field. For many years, the field was growing incrementally, with existing approaches performing around 1-2 percent better each year on standard benchmarks. But there was a real breakthrough in 2012, when computer scientist Geoffrey Hinton and his colleagues at the University of Toronto showed that their "deep learning" algorithms could beat state-of-the-art computer vision algorithms by a margin of 10.8 percentage points on the ImageNet Challenge (a benchmark dataset). At the same time, AI researchers were benefiting from ever-more powerful tools, including cost-effective cloud computing, fast and cheap number-crunching hardware (GPUs), seamless data sharing through the internet, and advances in high-quality open-source software.

To address this challenge, two recent works approach the problem from different perspectives. Tramer et al. (2020) Reliable evaluation of adversarial defenses is a outlines an approach for manually crafting adaptive attacks challenging task, currently limited to an expert that exploit the weak points of each defense. Here, a domain who manually crafts attacks that exploit the defense's expert starts with an existing attack, such as PGD (Madry inner workings, or to approaches based et al., 2018) (denoted as - in Figure 1), and adapts it based on on ensemble of fixed attacks, none of which may knowledge of the defense's inner workings. Common modifications be effective for the specific defense at hand. Our include: (i) tuning attack parameters (e.g., number key observation is that custom attacks are composed of steps), (ii) replacing network components to simplify the from a set of reusable building blocks, attack (e.g., removing randomization or non-differentiable such as fine-tuning relevant attack parameters, network components), and (iii) replacing the loss function optimized transformations, and custom loss functions.

Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format. However, in order to achieve a superior coding performance, many state-of-the-art block-based compression standards such as High Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are designed primarily for YUV 4:2:0 format, where U and V components are subsampled by considering the human visual system. This paper investigates various DLEC designs to support YUV 4:2:0 format by comparing their performance against the main profiles of HEVC and VVC standards under a common evaluation framework. Moreover, a new transform network architecture is proposed to improve the efficiency of coding YUV 4:2:0 data. The experimental results on YUV 4:2:0 datasets show that the proposed architecture significantly outperforms naive extensions of existing architectures designed for RGB format and achieves about 10% average BD-rate improvement over the intra-frame coding in HEVC.

Human brain has been used as an inspiration for building autonomous agents, but it is not obvious what level of computational description of the brain one should use. This has led to overly opinionated symbolic approaches and overly unstructured connectionist approaches. We propose that using homeostasis as the computational description provides a good compromise. Similar to how physiological homeostasis is the regulation of certain homeostatic variables, cognition can be interpreted as the regulation of certain 'cognitive homeostatic variables'. We present an outline of a Cognitive Homeostatic Agent, built as a hierarchy of physiological and cognitive homeostatic subsystems and describe structures and processes to guide future exploration. We expect this to be a fruitful line of investigation towards building sophisticated artificial agents that can act flexibly in complex environments, and produce behaviors indicating planning, thinking and feelings.

Non-invasive and cost effective in nature, the echocardiogram allows for a comprehensive assessment of the cardiac musculature and valves. Despite progressive improvements over the decades, the rich temporally resolved data in echocardiography videos remain underutilized. Human reads of echocardiograms reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. Furthermore, all modern echocardiography artificial intelligence (AI) systems are similarly limited by design - automating measurements of the same reductionist metrics rather than utilizing the wealth of data embedded within each echo study. This underutilization is most evident in situations where clinical decision making is guided by subjective assessments of disease acuity, and tools that predict disease onset within clinically actionable timeframes are unavailable. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such clinical example. To address this, we developed a novel video AI system trained to predict post-operative right ventricular failure (RV failure), using the full spatiotemporal density of information from pre-operative echocardiography scans. We achieve an AUC of 0.729, specificity of 52% at 80% sensitivity and 46% sensitivity at 80% specificity. Furthermore, we show that our ML system significantly outperforms a team of human experts tasked with predicting RV failure on independent clinical evaluation. Finally, the methods we describe are generalizable to any cardiac clinical decision support application where treatment or patient selection is guided by qualitative echocardiography assessments.

Due to limited computational cost and energy consumption, most neural network models deployed in mobile devices are tiny. However, tiny neural networks are commonly very vulnerable to attacks. Current research has proved that larger model size can improve robustness, but little research focuses on how to enhance the robustness of tiny neural networks. Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources. To this end, we propose a multi-objective oneshot network architecture search (NAS) algorithm to obtain the best trade-off networks in terms of the adversarial accuracy, the clean accuracy and the model size. Specifically, we design a novel search space based on new tiny blocks and channels to balance model size and adversarial performance. Moreover, since the supernet significantly affects the performance of subnets in our NAS algorithm, we reveal the insights into how the supernet helps to obtain the best subnet under white-box adversarial attacks. Concretely, we explore a new adversarial training paradigm by analyzing the adversarial transferability, the width of the supernet and the difference between training the subnets from scratch and fine-tuning. Finally, we make a statistical analysis for the layer-wise combination of certain blocks and channels on the first non-dominated front, which can serve as a guideline to design tiny neural network architectures for the resilience of adversarial perturbations.

Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance this understanding, but it requires a lot of labelled data. This paper introduces a new benchmark dataset of semantic urban meshes, a novel semi-automatic annotation framework, and an open-source annotation tool for 3D meshes. In particular, our dataset covers about 4 km2 in Helsinki (Finland), with six classes, and we estimate that we save about 600 hours of labelling work using our annotation framework, which includes initial segmentation and interactive refinement. Furthermore, we compare the performance of several representative 3D semantic segmentation methods on our annotated dataset. The results show our initial segmentation outperforms other methods and achieves an overall accuracy of 93.0% and mIoU of 66.2% with less training time compared to other deep learning methods. We also evaluate the effect of the input training data, which shows that our method only requires about 7% (which covers about 0.23 km2) to approach robust and adequate results whereas KPConv needs at least 33% (which covers about 1.0 km2).