Today, I'm extremely happy to announce that Amazon SageMaker Debugger can now profile machine learning models, making it much easier to identify and fix training issues caused by hardware resource usage. Despite its impressive performance on a wide range of business problems, machine learning (ML) remains a bit of a mysterious topic. Getting things right is an alchemy of science, craftsmanship (some would say wizardry), and sometimes luck. In particular, model training is a complex process whose outcome depends on the quality of your dataset, your algorithm, its parameters, and the infrastructure you're training on. As ML models become ever larger and more complex (I'm looking at you, deep learning), one growing issue is the amount of infrastructure required to train them.

The artificial intelligence (AI) renaissance is largely due to advances in deep learning, a type of machine learning with architectural elements inspired by the biological brain. However, unlike the energy-efficient human brain, the process of training large scale deep neural networks is enormously energy-intensive, requiring colossal amounts of computing memory and power. In a world's first, IBM Research reveals at this week's NeurIPS conference an unprecedented 4-bit AI training system that may help reduce machine learning's heavy carbon footprint. "Training AI models has become extremely expensive and generates a massive carbon footprint. IBM Research over the last five years has introduced a number of key techniques to address these challenges and dramatically improve how we train neural network models," said Kailash Gopalakrishnan, IBM Fellow and Senior Manager, Accelerator Architectures and Machine Learning, IBM Research.

The development of machine learning methods based on deep learning boosted the field of artificial intelligence towards unprecedented achievements and application in several fields. Such prominent results were made in parallel with the first successful demonstrations of fault tolerant hardware for quantum information processing. To which extent deep learning can take advantage of the existence of a hardware based on qubits behaving as a universal quantum computer is an open question under investigation. Here I review the convergence between the two fields towards implementation of advanced quantum algorithms, including quantum deep learning.

The idea of artificial general intelligence as we know it today starts with a dot-com blowout on Broadway. Twenty years ago--before Shane Legg clicked with neuroscience postgrad Demis Hassabis over a shared fascination with intelligence; before the pair hooked up with Hassabis's childhood friend Mustafa Suleyman, a progressive activist, to spin that fascination into a company called DeepMind; before Google bought that company for more than half a billion dollars four years later--Legg worked at a startup in New York called Webmind, set up by AI researcher Ben Goertzel. Today the two men represent two very different branches of the future of artificial intelligence, but their roots reach back to common ground. Even for the heady days of the dot-com bubble, Webmind's goals were ambitious. Goertzel wanted to create a digital baby brain and release it onto the internet, where he believed it would grow up to become fully self-aware and far smarter than humans.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Deep learning has come a long way since the days it could only recognize hand-written characters on checks and envelopes. Today, deep neural networks have become a key component of many computer vision applications, from photo and video editors to medical software and self-driving cars. Roughly fashioned after the structure of the brain, neural networks have come closer to seeing the world as we humans do. But they still have a long way to go and make mistakes in situations that humans would never err.

IMAGE: Time-lapse gradient light interference microscopy, or GLIM, left, and phase imaging with computational specificity imaged over seven days. Researchers at the University of Illinois Urbana Champaign have developed a new technique that combines label-free imaging with artificial intelligence to visualize unlabeled live cells over a prolonged time. This technique has potential applications in studying cell viability and pathology. The study "Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments" was published in Nature Communications. "Our lab specializes in label-free imaging, which allows us to visualize cells without using toxic chemicals," said Gabriel Popescu, a professor of electrical and computer engineering and the director of the Quantitative Light Imaging Laboratory at the Beckman Institute for Advanced Science and Technology.

I made a quick video on the basics of DeepMind's AlphaFold 2, one of the biggest accomplishments in the history of AI and life sciences. Hope the video is useful.

This paper proposes a multi-sensor based approach to detect, track, and localize a quadcopter unmanned aerial vehicle (UAV). Specifically, a pipeline is developed to process monocular RGB and thermal video (captured from a fixed platform) to detect and track the UAV in our FoV. Subsequently, a 2D planar lidar is used to allow conversion of pixel data to actual distance measurements, and thereby enable localization of the UAV in global coordinates. The monocular data is processed through a deep learning-based object detection method that computes an initial bounding box for the UAV. The thermal data is processed through a thresholding and Kalman filter approach to detect and track the bounding box. Training and testing data are prepared by combining a set of original experiments conducted in a motion capture environment and publicly available UAV image data. The new pipeline compares favorably to existing methods and demonstrates promising tracking and localization capacity of sample experiments.

ABSTRACT While benefiting people's daily life in so many ways, smartphones and their location-based services are generating massive mobile device location data that has great potential to help us understand travel demand patterns and make transportation planning for the future. While recent studies have analyzed human travel behavior using such new data sources, limited research has been done to extract multimodal travel demand patterns out of them. This paper presents a datadriven analytical framework to bridge the gap. To be able to successfully detect travel modes using the passively collected location information, we conduct a smartphone-based GPS survey to collect ground truth observations. Then a jointly trained single-layer model and deep neural network for travel mode imputation is developed. Being "wide" and "deep" at the same time, this model combines the advantages of both types of models. The framework also incorporates the multimodal transportation network in order to evaluate the closeness of trip routes to the nearby rail, metro, highway and bus lines and therefore enhance the imputation accuracy. To showcase the applications of the introduced framework in answering real-world planning needs, a separate mobile device location data is processed through trip end identification and attribute generation, in a way that the travel mode imputation can be directly applied. The estimated multimodal travel demand patterns are then validated against typical household travel surveys in the same Washington D.C. and Baltimore Metropolitan Regions. BACKGROUND Thanks to the rapidly evolving smartphone industry and mobile computing technology, mobile device location data has never been so readily available before. According to the Pew Research Center, the United States has around 223 million smartphone users in 2017 (Mobile Fact Sheet). More than three-quarters of Americans (77%) now own a smartphone, with lower-income Americans and senior citizens above the age of 50 exhibiting a sharp uptick in ownership over the past years. These devices are generating a massive amount of location data continuously through the widespread use of location-based service (LBS) via Wi-Fi hotspots, cellular towers, Global Positioning System (GPS)-based technologies, and GPSenabled applications on these smartphone devices. This ubiquitous LBS data provides an opportunity to innovatively and accurately observe individuals' travel behavior and model the overall travel demand patterns for a region, a state, and even an entire country.

Depth (disparity) estimation from 4D Light Field (LF) images has been a research topic for the last couple of years. Most studies have focused on depth estimation from static 4D LF images while not considering temporal information, i.e., LF videos. This paper proposes an end-to-end neural network architecture for depth estimation from 4D LF videos. This study also constructs a medium-scale synthetic 4D LF video dataset that can be used for training deep learning-based methods. Experimental results using synthetic and real-world 4D LF videos show that temporal information contributes to the improvement of depth estimation accuracy in noisy regions.