"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
To create an effective machine learning and deep learning model, you need more data, a way to clean the data and perform feature engineering on it. It is also a way to train models on your data in a reasonable amount of time. After that, you need a way to install your models, surveil them for drift over time, and retrain them as required. If you have invested in compute resources and accelerators such as GPUs, you can do all of that on-premises. However, you may find that if your resources are adequate, they are also inactive much of the time.
In recent years, researchers have been developing machine learning algorithms for an increasingly wide range of purposes. This includes algorithms that can be applied in healthcare settings, for instance helping clinicians to diagnose specific diseases or neuropsychiatric disorders or monitor the health of patients over time. Researchers at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have recently carried out a study investigating the possibility of using deep reinforcement learning to control the levels of unconsciousness of patients who require anesthesia for a medical procedure. Their paper, set to be published in the proceedings of the 2020 International Conference on Artificial Intelligence in Medicine, was voted the best paper presented at the conference. "Our lab has made significant progress in understanding how anesthetic medications affect neural activity and now has a multidisciplinary team studying how to accurately determine anesthetic doses from neural recordings," Gabriel Schamberg, one of the researchers who carried out the study, told TechXplore.
The genetic algorithm (GA) is a biologically-inspired optimization algorithm. It has in recent years gained importance, as it's simple while also solving complex problems like travel route optimization, training machine learning algorithms, working with single and multi-objective problems, game playing, and more. Deep neural networks are inspired by the idea of how the biological brain works. It's a universal function approximator, which is capable of simulating any function, and is now used to solve the most complex problems in machine learning. What's more, they're able to work with all types of data (images, audio, video, and text).
Every once in a while, a machine learning framework or library changes the landscape of the field. In this article, we'll quickly understand the concept of object detection and then dive straight into DETR and what it brings to the table. In Computer Vision, object detection is a task where we want our model to distinguish the foreground objects from the background and predict the locations and the categories for the objects present in the image. Current deep learning approaches attempt to solve the task of object detection either as a classification problem or as a regression problem or both. For example, in the RCNN algorithm, several regions of interest are identified from the input image.
Doron Adler and Justin Pinkney, two software engineers, recently released a "Toonification translation" AI model that turns real faces into flawless cartoon representations. And while the toonification tool, "Toonify," was originally available to the public, it became too popular to sustain cheaply. But some people managed to Toonify a ton of celebrities before the tool was pulled, and all the animations are stellar. After much training of neural networks @Norod78 and I have put together a website where anyone can #toonify themselves using deep learning!https://t.co/OQ23p30isC In a series of blog posts, which come via Gizmodo, Pinkney outlines how he and Adler created Toonify.
To build a machine learning model dataset is one of the main parts. Before we start with any algorithm we need to have a proper understanding of the data. These machine learning datasets are basically used for research purposes. Most of the datasets are homogeneous in nature. We use a dataset to train and evaluate our model and it plays a very vital role in the whole process. If our dataset is structured, less noisy, and properly cleaned then our model will give good accuracy on the evaluation time. Imagenet dataset is made by the group of researchers and the images in the dataset organized according to the WordNet hierarchy. This dataset can be used for machine learning purposes and computer vision research fields as well.
Tiny robots that can transport individual neurons and connect them to form active neural circuits could help us study brain disorders such as Alzheimer's disease. The robots, which were developed by Hongsoo Choi at the Daegu Gyeongbuk Institute of Science and Technology in South Korea and his colleagues, are 300 micrometres long and 95 micrometre wide. They are made from a polymer coated with nickel and titanium and their movement can be controlled with external magnetic fields.
Summary: Since BERT NLP models were first introduced by Google in 2018 they have become the go-to choice. New evidence however shows that LSTM models may widely outperform BERT meaning you may need to evaluate both approaches for your NLP project. Over the last year or two, if you needed to bring in an NLP project quickly and with SOTA (state of the art) performance, increasingly you reached for a pretrained BERT module as the starting point. Recently however there is growing evidence that BERT may not always give the best performance. In their recently released arXiv paper, Victor Makarenkov and Lior Rokach of Ben-Gurion University share the results of their controlled experiment contrasting transfer-based BERT models with from scratch LSTM models.
"According to the filing, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks." Looks like Google won't be stopping its infamous patenting spree anytime soon. Earlier this month, Google filed a patent for capsule networks. Turing award recipient and Google researcher Geoff Hinton was named amongst the list of inventors in the filing. According to the patent filed, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks for traditional computer vision applications. Capsule networks are aimed at alleviating the extra dimensionality which surfaces with a convolutional neural network.
Convolutional Neural Networks (CNNs) are considered as game-changers in the field of computer vision, particularly after AlexNet in 2012. And the good news is CNNs are not restricted to images only. They are everywhere now, ranging from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes almost inevitable in all the fields of Data Science. Even most of the Recurrent Neural Networks rely on CNNs these days.