A new deep learning model has been created by a team of researchers at Massachusetts General Hospital (MGH). Early evidence shows that the system is more accurate at predicting a woman's risk of developing breast cancer than current risk assessment tools. Using deep learning to recognize imaging biomarkers in mammograms presents an opportunity to detect the early warning signs of breast cancer in women who may not be considered at-risk, allowing intervention strategies to begin early, potentially improving prognosis. Deep learning, a subset of machine learning, has already made big waves in the field of medicine and healthcare. It uses artificial neural networks to solve highly complex problems that are out of the realm of capabilities for traditional computers to solve.

This project's aim, is to explore the world of Natural Language Processing (NLP) by building what is known as a Sentiment Analysis Model. A sentiment analysis model is a model that analyses a given piece of text and predicts whether this piece of text expresses positive or negative sentiment. To this end, we will be using the sentiment140 dataset containing data collected from twitter. An impressive feature of this dataset is that it is perfectly balanced (i.e., the number of examples in each class is equal). Our approach was unique because our training data was automatically created, as opposed to having humans manual annotate tweets.

BEGIN ARTICLE PREVIEW: In recent years, computer vision has taken a leap to advance deep learning applications by performing vision tasks like image labelling, classification and facial recognition. END ARTICLE PREVIEW READ MORE FROM SOURCE ARTICLE

Recently, the combination of RL with deep learning has led to impressive results, such as agents that can learn how to play boardgames like Go and chess, the full spectrum of Atari games, as well as more modern, difficult video games like Dota and StarCraft II.

Artificial intelligence has been the buzzword in 2020 and with the benefits of this technology evident around us; AI has had its own share of controversies. From algorithms¹ unfairly discriminating women in hiring and students complaining about unrealistic grades, there is no doubt that AI has evolved in 2020 and as 2021 beckons, it is time to take stock of what the year has been. With GPT3, deepfakes, and facial recognition making headlines in 2020, there are many arguments surrounding privacy and regulations. Clearview AI provides organizations, predominantly law enforcement agencies, with a database that is able to match images of faces with over three billion other facial pictures scraped from social media sites. The company has recently been hit with a series of reprisals from social media platforms, who have taken a hostile stance in response to Clearview AI's operations.

Thanks to breakthroughs in natural language processing (NLP), machines can generate increasingly sophisticated representations of words. Every year, research groups release more and more powerful language models -- like the recently announced GPT-3, M2M 100, and MT-5 -- that are able to write complex essays or translate text into multiple languages with better accuracy than previous iterations. However, since machine learning algorithms are what they eat (in other words, they function based on the training data they ingest), they inevitably end up picking up on human biases that exist in language data itself. This summer, GPT-3 researchers discovered inherent biases within the model's results related to gender, race, and religion. Gender biases included the relationship between gender and occupation, as well as gendered descriptive words.

Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time–frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.

You may have heard about "DeepMind" in the past, and if you haven't, now you will. To this day, DeepMind has acquired a number of achievements since it was founded, but it is most notable for AlphaGo, an AI program that beat some of the best professional Go players in history including Ke Jie. DeepMind's AlphaFold 2 can now identify a protein's three-dimensional structures from its amino-acid sequence to the width of an atom. To give some context, AlphaFold2 competed with over 100 research groups worldwide in a competition known as the Critical Assessment of Protein Structure Prediction, or CASP. The goal was exactly what AlphaFold 2 achieved, to be able to predict a protein's structure from its amino-acid sequence.

Being able to rapidly respond to the changing scenes and traffic situations by generating feasible local paths is of pivotal importance for car autonomy. We propose to train a deep neural network (DNN) to plan feasible and nearly-optimal paths for kinematically constrained vehicles in small constant time. Our DNN model is trained using a novel weakly supervised approach and a gradient-based policy search. On real and simulated scenes and a large set of local planning problems, we demonstrate that our approach outperforms the existing planners with respect to the number of successfully completed tasks. While the path generation time is about 40 ms, the generated paths are smooth and comparable to those obtained from conventional path planners.

For hourly PM2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this study, a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and deep temporal convolutional neural network (DeepTCN) is developed to predict PM2.5 concentration, by modelling the data patterns of historical pollutant concentrations data, meteorological data, and discrete time variables' data. Taking PM2.5 concentration of Beijing as the sample, experimental results showed that the forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the time series model, artificial neural network, and the popular deep learning models. The new model has improved the capability to model the PM2.5-related factor data patterns, and can be used as a promising tool for forecasting PM2.5 concentrations.