AI Research News Updates: Issue 10 (Jan 26-Feb 1, 2022)
Asif Razzaq
AI Research Editor | CEO @ Marktechpost | 1 Million Monthly Readers and 56k+ ML Subreddit
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Please find below some of the latest AI/ML research news from last week.
A New AI Research Propose ‘UniFormer’ (Unified transFormer) to Unify Convolution and Self-Attention for Visual Recognition
For visual recognition, representation learning is a crucial research area. Essentially, researchers are confronted with two separate issues in visual data, such as photographs and movies. On the one hand, there is a great deal of local redundancy; for example, visual material in a particular region (space, time, or space-time) is often comparable. Inefficient computation is frequently introduced by such localization. Global reliance, on the other hand, is complex; for example, objectives in various regions have dynamic relationships. Such long-distance communication frequently results in inefficient learning.
Latest Research Based On AI Building AI Models
Artificial intelligence (AI) is primarily a math problem. We finally have enough data and processing capacity to take full advantage of deep neural networks, a type of AI that learns to discover patterns in data, when they began to surpass standard algorithms 10 years ago.?
Today’s neural networks are even more data and processing power-hungry. Training them necessitates fine-tuning the values of millions, if not billions, of parameters that describe these networks and represent the strength of artificial neuron connections. The goal is to discover almost perfect values for them, known as optimization, but training the networks to get there is difficult.
TensorFlow Team Introduce BlazePose GHUM Posture Estimation Model and Selfie Segmentation For Body Segmentation Using MediaPipe and TensorFlow.js
Image segmentation is a method used in computer vision to group pixels in an image into semantic areas, which is typically used to locate objects and boundaries. Body segmentation models do the same thing for a person and their twenty-four body parts. This technology can be used for a variety of purposes, including augmented reality, picture editing, and creative effects on photographs and movies, to name a few.
The TensorFlow team has recently released two new highly optimized body segmentation models that are accurate and quick as part of their improved body segmentation and posture APIs in TensorFlow.js.
Kedro, A Python Machine Learning Pipeline Framework Developed By McKinsey, Has Been Donated To The Linux Foundation
According to the Linux Foundation , McKinsey’s?QuantumBlack ?will offer?Kedro , a machine learning pipeline tool, to the open-source community. This non-profit organization provides a vendor-independent center for open source initiatives. The Linux Foundation will maintain Kedro within its umbrella organization, the Linux Foundation AI & Data (LF AI & Data), created in 2018 to encourage AI innovation by fostering technical initiatives, developer communities, and enterprises.
CMU’s Latest Machine Learning Research Analyzes and Improves Spectral Normalization In GANs
GANs (generative adversarial networks) are cutting-edge deep generative models that are best known for producing high-resolution, photorealistic photographs. The goal of GANs is to generate random samples from a target data distribution with only a small set of training examples available. This is accomplished by learning two functions: a generator G that maps random input noise to a generated sample, and a discriminator D that attempts to categorize input samples as accurate (i.e., from the training dataset) or fake (i.e., not from the training dataset) (i.e., produced by the generator).
Meta AI Builds A Massive AI Research Supercomputer To Advance The Field Of Machine Learning And Data Science?Research
In recent years, Meta has made a significant amount of contribution in the field of self-supervised learning and transformer-based models. Many of these AI models have revolutionized various domains such as computer vision, natural language processing, automatic speech recognition, etc. Adding improvements and training such models with a massive number of parameters for utilizing the vast amount of data that is made available daily will require new secure and reliable AI supercomputers capable of bringing down the training time by a critical factor.
Cornell And NTT Researchers Introduces Deep Physical Neural Networks To Train Physical Systems To Perform Machine Learning Computations Using Backpropagation
Deep-learning models have become commonplace in all fields of research and engineering. However, their energy requirements are limiting their ability to scale. Synergistic hardware has contributed to the widespread use of deep neural networks (DNNs). DNN ‘accelerators,’ generally based on direct mathematical isomorphism between the hardware physics and the mathematical processes in DNNs, have been inspired by the developing DNN energy problem.
Multi-Modal Deep Learning For Behavior Understanding And Indoor Scene Recognition
Recognizing an indoor environment is not difficult for humans, but training an artificial intelligence (AI) system to distinguish various settings is. Indoor scene identification is a rapidly developing discipline with enormous potential in behavior analysis, robot localization, and geriatric monitoring, to name a few. AI systems are trained to recognize spaces solely through photos, and identifying a space alone through objects almost always goes wrong.
Exclusive Talk with Naveed Ahmed Janvekar: Senior Data Scientist at Amazon
Naveed Ahmed Janvekar is a Senior Data Scientist working at Amazon in the United States. He works on solving fraud and abuse problems on the platform that impacts millions of customers of Amazon in the US and other parts of the world using Machine Learning and deep learning. He has 7+ years of expertise in the Machine Learning space which includes classification algorithms, clustering algorithms, graph modeling, BERT to name a few. He is using Machine Learning and Deep Learning to solve multi-faceted problems. He has a Master’s degree in Information Science from The University of Texas at Dallas where he graduated top of his class and was awarded as a scholar of high distinction and inducted in the prestigious International Honor Society Beta Gamma Sigma. He has a Bachelor’s of Engineering in Electronics and Communications from India. He has worked with other influential firms such as Fidelity Investments and KPMG. In his current role, he is researching identifying novel fraud and abuse vectors on ECommerce platforms and using Active Learning to improve Machine Learning model performance.
This SF Based Startup is Working on Cloud Video and AI for Colonoscopies and Other GI Endoscopy
In the United States, around?75 million ?endoscopic medical procedures are performed each year. They all include a physician making a diagnostic or performing surgery while seeing a real-time video feed from inside their patient. The actual video feed used by the physician is not captured in most of these procedures. Earlier, doctors had to rely on DVDs and external hard drives to record and store video footage from procedures.
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Virgo Surgical Video Solutions ?addresses this issue by providing a platform for gathering and organizing endoscopic video data. A medical practitioner can automate endoscopy video capture without the workflow hassle and video highlights and instrument detection for efficient analysis. Virgo’s device can be externally plugged into the digital video output on a hospital server, and it helps record and save the video on the cloud with the push of a button. Virgo’s vision is to create automation and AI technologies for endoscopy in order to improve patient outcomes and clinical workflows in healthcare. It is the leading endoscopic cloud video capture, administration, and artificial intelligence analysis platform. A variety of academic, integrated, and private practice healthcare providers use the Virgo platform to improve patient care through video-based research and training endeavors.
Meta AI Introduces ‘Data2vec’ : A Self-Supervised Algorithm That Works For Speech, Computer Vision, and NLP
Many critical recent developments in AI have been enabled by self-supervised learning. Machines learn by directly observing the world rather than being explicitly instructed through labeled images, text, audio, and other data sources.
However, whereas people appear to learn in similar ways regardless of how they obtain information — whether, through sight or hearing, self-supervised learning algorithms currently learn from images, speech, text, and other modalities in quite different ways.
Researchers Introduce A Novel Human-Like Driving And Decision Making Framework Designed For Autonomous Vehicles (AVs)
In the last years, we assisted to a great hype around autonomous vehicles (AVs). However, if we want to see streets where AVs and human drivers live side by side, AVs must be able to dive into our transportation system. For this reason, human-like driving and decision-making processes represent a hot topic in the AVs research field.?
Misunderstandings between AVs and human drivers are the cause of accidents that have been taken into account by the Nanyang Technological University of Singapore, which designed a novel human-like driving and decision-making framework for AVs. Since?lane change?is one of the most frequent types of car accidents, this research paper mainly studied human-like lane-change decision-making for AVs. The architecture of the proposed framework is depicted in the following figure.
Google AI Propose An Machine Learning (ML) Based Audio Separation Approach That Can Identify Birdsongs For Better Species Classification
Birds are identifiable not only by their appearance but also by their songs. We can appreciate many things around us if we listen carefully to our surroundings. Ecologists use birds to study food networks and forest health. If a forest has many woodpeckers, it is reasonable to assume much deadwood. Birds use songs and calls to communicate and mark their territory. As a result, identifying them by ear is the most efficient method.ARUs were designed to address this problem.?
Researchers Introduce ‘SimpleBits’: An Information-Reduction Strategy That Learns To Synthesize Simplified Inputs For Neural Network Understanding
The practice of identifying and labeling groups of pixels or vectors inside an image based on specific rules is known as image classification. One or more spectral or textural properties can be used to create the classification law.
Scientists believe that a deeper knowledge of how deep networks learn information can lead to new scientific discoveries, help us better comprehend the distinctions between human and model behavior, and serve as valuable auditing tools. According to studies, it is essential to understand how neural network-based image classifiers would react to increasingly simple inputs. For this, a clear measure of input simplicity, an optimization goal that correlates with simplification, and a framework to incorporate such a goal into training and inference are required.?
Meta AI Research Proposes ‘OMNIVORE’: A Single Vision (Computer Vision) Model For Many Different Visual Modalities
Computer vision research encompasses a wide range of visual modalities, including images, videos, and depth perception. In general, the computer vision model treats each of these modalities separately to extract the most useful characteristics from their differences. While these modality-specific models outperform people on their specialized tasks, they lack the flexibility of a human-like visual system—the capacity to work across modalities.?
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About:?
Asif Razzaq:? Asif Razzaq is an AI Journalist and Cofounder of Marktechpost, LLC. He is a visionary, entrepreneur, and engineer who aspires to use the power of Artificial Intelligence for good.
Email: A[email protected]
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2 年Thank you for this article Asif Razzaq. Have you heard about WAICF - World AI Cannes Festival? Could be probably of interest for you!