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Today, corporations like Google, Facebook and Microsoft have been dominating tools and deep learning frameworks that AI researchers use globally. Many of their open-source libraries are now gaining popularity on GitHub, which is helping budding AI developers across the world build flexible and scalable machine learning models.

From conversational chatbot, self-driving cars to the weather forecast and recommendation systems, AI developers are experimenting with various neural network architectures, hyperparameters, and other features to fit the hardware constraints of edge platforms. The possibilities are endless. Some of the popular deep learning frameworks include Googles TensorFlow and Facebooks Caffe2, PyTorch, Torchcraft AI and Hydra, etc.

According to Statista, AI business operations global revenue is expected to touch $10.8 billion by 2023, and the natural language processing (NLP) market size globally is expected to reach $43.3 billion by 2025. With the rise of AI adoption across businesses, the need for open-source libraries and architecture will only increase in the coming months.

Advancing in artificial intelligence, Facebook AI Research (FAIR) at present is leading the AI race with the launch of state of the art technology tools, libraries and frameworks to bolster machine learning and AI applications across the globe.

Source: Analytics India Magazine

Here are some of the latest open-source tools, libraries and architecture developed by Facebook:

PyTorch is the most widely used deep learning framework, besides Caffe2 and Hydra, which helps researchers build flexible machine learning models.

PyTorch provides a Python package for high-level features like tensor computation (NumPy) with strong GPU acceleration and TorchScript for an easy transition between eager mode and graph mode. Its latest release provides graph-based execution, distributed training, mobile deployment and more.

Flashlight is an open-source machine learning library that lets users execute AI/ML applications using C++ API. Since it supports research in C++, Flashlight does not need external figures or bindings to perform tasks such as threading, memory mapping, or interoperating with low-level hardware. Thus, making the integration of code fast, direct and straightforward.

Opacus is an open-source high-speed library for training PyTorch models with differential privacy (DP). The library is claimed to be more scalable than existing methods. It supports training with minimal code changes and has little impact on training performance. It also allows the researchers to track the privacy budget expended at any given moment.

PyTorch3D is a highly modular and optimised library that offers efficient, reusable components for 3D computer vision research with the PyTorch framework. It is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data. As a result, the library can be implemented using PyTorch tensors, handle mini-batches of heterogeneous data, and utilise GPUs for acceleration.

Detectron2 is a next-generation library that provides detection and segmentation algorithms. It is a fusion of Detectron and maskrcnn-benchmark. Currently, it supports several computer vision research work and applications. Detection can be used on Mask R-CNN, RetinaNet, Faster R-CNN, RPN, TensorMask as well.

Detectron is an open-source software architecture that implements object detection algorithms like Mask R-CNN. The software has been written in Python and powered by the Caffe2 deep learning framework.

Detectron has enabled various research project at Facebook, including Feature pyramid networks for object detection, Mask R-CNN, non-local neural networks, detecting and recognising human-object interactions, learning to segment everything, data distillation: towards Omni-supervised learning, focal loss for dense object detection, DensePose: dense human pose estimation in the wild, and others.

Prophet is an open-source architecture released by Facebooks core data science team. It is a procedure for forecasting time series data based on an additive model where non-linear trends fit yearly, weekly, and daily seasonality, plus holiday effects. The model works best with time-series data, which has several seasons of historical data such as weather records, economic indicators and patient health evolution metrics.

The code is available on CRAN and PyPI.

Classy Vision is a new end-to-end PyTorch-based framework for large-scale training of image and video classification models. Unlike other computer vision (CV) libraries, Classy Vision claims to offer flexibility for researchers.

Typically, most CV libraries lead to duplicative efforts and require users to migrate research between frameworks and relearn the minutiae of efficient distributed training and data loading. On the other hand, Facebooks PyTorch-based CV framework claimed to offer a better solution for training at scale and deploying to production.

BoTorch is a library for Bayesian optimization built on the PyTorch framework. Bayesian optimization is a sequence design strategy for machines that do not assume any functional forms.

BoTorch seamlessly provides a modular and easily extensible interface for composing Bayesian optimization primitives such as probabilistic models, acquisition functions and optimizers and others. In addition to this, it also enables seamless integration with deep or convolutional architectures in PyTorch.

FastText is an open-source library for efficient text classification and representation learning. It works on standard and generic hardware. Machine learning models can be further reduced on mobile devices as well.

TC is a fully-functional C++ library that automatically synthesises high-performance machine learning kernels using Halide, ISL, NVRTC or LLVM. The library can be easily integrated with Caffe2 and PyTorch and has been designed to be highly portable and machine-learning framework agnostic. Also, it requires a simple tensor library with memory allocation, offloading, and synchronisation capabilities.

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All The Machine Learning Libraries Open-Sourced By Facebook Ever - Analytics India Magazine

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Front Cell Dev Biol. 2021 Apr 15;9:630355. doi: 10.3389/fcell.2021.630355. eCollection 2021.

ABSTRACT

Bone-related malignancies, such as osteosarcoma, Ewings sarcoma, multiple myeloma, and cancer bone metastases have similar histological context, but they are distinct in origin and biological behavior. We hypothesize that a distinct immune infiltrative microenvironment exists in these four most common malignant bone-associated tumors and can be used for tumor diagnosis and patient prognosis. After sample cleaning, data integration, and batch effect removal, we used 22 publicly available datasets to draw out the tumor immune microenvironment using the ssGSEA algorithm. The diagnostic model was developed using the random forest. Further statistical analysis of the immune microenvironment and clinical data of patients with osteosarcoma and Ewings sarcoma was carried out. The results suggested significant differences in the microenvironment of bone-related tumors, and the diagnostic accuracy of the model was higher than 97%. Also, high infiltration of multiple immune cells in Ewings sarcoma was suggestive of poor patient prognosis. Meanwhile, increased infiltration of macrophages and B cells suggested a better prognosis for patients with osteosarcoma, and effector memory CD8 T cells and type 2 T helper cells correlated with patients chemotherapy responsiveness and tumor metastasis. Our study revealed that the random forest diagnostic model based on immune infiltration can accurately perform the differential diagnosis of bone-related malignancies. The immune microenvironment of osteosarcoma and Ewings sarcoma has an important impact on patient prognosis. Suppressing the highly inflammatory environment of Ewings sarcoma and promoting macrophage and B cell infiltration may have good potential to be a novel adjuvant treatment option for osteosarcoma and Ewings sarcoma.

PMID:33937231 | PMC:PMC8082117 | DOI:10.3389/fcell.2021.630355

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Application of Immune Infiltration Signature and Machine Learning Model in the Differential Diagnosis and Prognosis of Bone-Related Malignancies -...

Artificial Intelligence is one of the greatest achievements in the tech world and while machine learning was only limited to reading still frames up until now and is quite efficient in it, the next step being taken in the advancement of machine learning and artificial intelligence is identifying individual objects within video in order to open up new considerations in brand placement, visual effects, accessibility features and more.

The first and successful step taken towards making AI identify individual objects within video was done by Google. Google had been working towards accomplishing this feature for some time now and after a lot of efforts it has now introduced new advances in its YouTube option which includes being able to tag products in that are present in video clips and provide direct links to shop for those products.

This simply means that companies now can tag their products in YouTube videos no matter at what timing it is being displayed, it can tag its product at that specific time. Along with this it will also provide direct shopping options, facilitating broader ecommerce opportunities in the app.

After the successful introduction of this feature in YouTube, Facebook is taking the next step and introducing a similar feature on its platform and the company claims that their feature will be much better at singling out individual objects within video frames.

Facebook explained that they have collaborated with researchers at Inria with whom they have developed a new method called DINO. This method will be used to train Vision Transformers (ViT) with no supervision. The company has claimed that besides setting a new state of the art among self-supervised methods, this approach leads to a remarkable result that is unique to this combination of AI techniques. Facebook further said that their model can discover and segment objects in an image or a video with absolutely no supervision and without being given a segmentation-targeted objective and all this will make this process effectively automated.

Hence that is why the company claims that their feature is the best of the best.

Facebook is still working towards this feature and once it is launched we cannot wait to see if it out does YouTubes similar feature or not. However, we know that both YouTube and Facebook have always delivered their best and therefore we are sure that they will deliver the best this time as well.

Read next:According to the exec, over 60 percent Instagram users are connected to Facebook Messenger

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YouTube Introduces Machine Learning Individual Object Recognition on Videos and Facebook Follows Next - Digital Information World

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The consequences of hearing loss are many, including social isolation1-3 and depression.10-13 The failure of hearing aids to either prevent or improve depression may stem from a complex relationship between hearing loss, depression, and the social dynamism of hearing loss that we have yet to fully understand. To this end, there are several opportunities to explore the relationship between hearing loss and depression. Moreover, there may be utility in identifying depression in patients with hearing loss at the point of care considering the established link between hearing loss and depression.

Shutterstock/Photographee.eu, technology. Hearing loss, mental health.

Using machine learning to predict depression scores (adapted from Crowson et. al., 2020). Technology. Hearing loss, mental health.

The primary objective of our work was to use a predictive approach using machine learning and audiometric data to determine if these data accurately predict patient-reported depression. We hypothesized that an advanced machine learning model may be useful for identifying depression in patients with audiometric data. We also sought to determine if the addition of other clinical and demographic variables combined with the audiometric data would produce further gains in model accuracy.

In our study, we developed a supervised machine learning model using National Health and Nutrition Examination Survey (NHANES) data, composed of subjective and objective audiometric variables and several other health determinant predictors, to predict scores on a validated depression scalethe Patient Health Questionnaire (PHQ-9). The NHANES is a cross-sectional public health survey designed to assess the health and nutrition status of individuals in the United States. NHANES data has been used in numerous research investigations to incorporate the interplay between determinants of health and specific medical conditions.

In a sample of participants from a survey cycle of the NHANES database, our supervised machine learning approach accurately predicted depression scale scores using audiometric and health determinant predictors. The model's most influential audiometric predictors of higher scores on the depression scale were functional dimensions and not objective audiometric testing variables. Among the most influential predictors, half were related to the social dynamics of hearing loss. The remaining predictors associated with depression in hearing loss were related to noise exposure, tinnitus, and objective audiometric testing. When expanding to include predictors ranging from demographics to other medical and health status content, a social context of hearing loss ranked in the top five most influential.

A strong association between social isolation and hearing loss has been demonstrated in adults.1-3 The observation that hearing loss leads to social isolation is intuitive given that hearing loss leads to impaired efficiency of communication. The connection between social isolation and depression is a natural extension as humans are social creatures.

If hearing care professionals treat hearing loss with conventional hearing aid devices, would it be reasonable to expect social isolation and associated depression to improve? Unfortunately, the relationship appears more complex. Prior work has shown that hearing aids do not result in consistent improvement in social isolation11,14 or depression.10-13 Perhaps the disconnect might be explained by noting that hearing amplification exclusively does not address hearing performance in real-life social situations. Basic sound amplification can and does help individuals with hearing loss. More advanced hearing aids incorporate signal processing technologies to better isolate the relevant sounds in noisy environments. Perhaps future research involving hearing aids with enhanced signal processing technology may lead to further insights into the utility of hearing aids to directly augment the social dynamics of hearing loss.

In summary, we found the NHANES dataset is useful for training machine learning models to accurately predict depression scale scores from audiometric data. As many of the variables collected in the NHANES data are the same clinical data we extract from our patients in real-life clinical encounters, such a predictive model could be useful in predicting depression scale scores at the point of care. We found the most influential audiometric predictors of higher scores on the depression scale were functional dimensions of hearing loss and not simply objective audiometric data like thresholds and word recognition scores. Among these influential functional dimensions, our model indicated the specific effect of hearing loss on social relations was particularly powerful. This is an interesting finding, as prior investigations into the effect of hearing aids on depression and social isolation have failed to show a consistent benefit. Simply giving a patient a hearing aid is not a guarantee that social isolation or depression will improve. Thus, our model output puts forth a new hypothesis that simply amplifying sound alone, in general, fails to address or improve the social dynamism of hearing loss. For today's hearing care clinicians, we suggest that recognizing that social aspects of hearing loss may carry more influence on the development or maintenance of depression than previously thought. Moreover, we may need to reimagine our aural rehabilitation strategies to include specific interventions to optimize social dynamics.

Originally posted here:
Predicting Depression from Hearing Loss Using Artificial... : The Hearing Journal - LWW Journals