In response, the government takes some scripted measures like shutting down schools on occasion and enforcing the infamous Odd-Even scheme, forcing half the cities cars off the streets. Pollution in Delhi has several causes: seasonal stubble burning in neighboring states, vehicular emission, as well as smoke from power plants and brick kilns dotting the national capital region. Fighting pollution needs a multi-pronged approach -- government policies alone are not enough, they need to be coupled with action on the ground. Let's take brick kilns as an example. Before we can address the pollution caused by them, we need to know exactly how many such kilns are there and their location, whether they are increasing in number or decreasing, and how many are adopting technology to reduce emission, as mandated by the law. Satellite imagery coupled with deep learning can answer these questions, increase accountability and drive results on the ground.

Something you'll hear a lot is that the increasing availability of computing resources has paved the way for important advances in artificial intelligence. With access to powerful cloud computing platforms, AI researchers have been able to train larger neural networks in shorter timespans. This has enabled AI to make inroads in many fields such as computer vision, speech recognition, and natural language processing. But what you'll hear less is the darker implications of the current direction of AI research. Currently, advances in AI are mostly tied to scaling deep learning models and creating neural networks with more layers and parameters.

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia.

The cryptocurrency industry has a reputation for being volatile, unpredictable, and ever-changing. Predicting the way that the market would move could easily give an advantage to the everyday investor, and one data scientist believes that he's figured it out. Abinhav Sagar of the prestigious Vellore Institute of Technology recently stated that it is possible to use a Long Short-Term Memory (LSTM) neural network to predict these prices with real-world accuracy. Sagar published a blog about this exact method on December 2nd, showing the four steps he can take with the technology to create predictions in a "relatively unpredictable" market. The demonstration started with a comment from Sagar that the application of this machine learning tech has been relatively limited in the cryptocurrency sector, even though it has had some success in the stock market.

A data scientist at India's prestigious Vellore Institute of Technology has outlined a method for how to purportedly predict crypto prices in real-time using a Long Short-Term Memory (LSTM) neural network. In a blog post published on Dec. 2, researcher Abinhav Sagar demonstrated a four-step process for how to use machine learning technology to forecast prices in a sector he purported is "relatively unpredictable" as compared with traditional markets. Sagar prefaced his demonstration by noting that while machine learning has achieved some success in predicting stock market prices, its application in the cryptocurrency field has been restricted. In support of this claim, he argued that cryptocurrency prices fluctuate in accordance with fast-paced technological developments, as well as economic, security and political factors. Sagar's four-step proposed method involves 1) collecting real-time cryptocurrency data; 2) preparing the data for neural network training; 3) testing the prediction using the LSTM neural network; 4) visualizing the results of the prediction.

When the promising British artificial intelligence start-up DeepMind was bought by Google in 2014 for a reported £400m, the deal came with strict conditions meant to ease fears that its world-leading technology would be exploited by the US tech giant. The company would set up an independent ethics board to preside over its development of "artificial general intelligence", a system that mimics the way humans think. None of DeepMind's health data would be associated with Google accounts or services. And its leaders and headquarters would remain in London, where the company was founded. Critics say that those pledges have been eroded since then.

James Loy has more than five years, expert experience in data science in the finance and healthcare industries. He has worked with the largest bank in Singapore to drive innovation and improve customer loyalty through predictive analytics. He has also experience in the healthcare sector, where he applied data analytics to improve decision-making in hospitals. He has a master's degree in computer science from Georgia Tech, with a specialization in machine learning. His research interest includes deep learning and applied machine learning, as well as developing computer-vision-based AI agents for automation in industry.

"I really do think [nbdev] is a huge step forward for programming environments": Chris Lattner, inventor of Swift, LLVM, and Swift Playgrounds. It is a Python programming environment called nbdev, which allows you to create complete python packages, including tests and a rich documentation system, all in Jupyter Notebooks. We've already written a large programming library (fastai v2) using nbdev, as well as a range of smaller projects. Nbdev is a system for something that we call exploratory programming. Exploratory programming is based on the observation that most of us spend most of our time as coders exploring and experimenting.

BERT is one of the most popular algorithms in the NLP spectrum known for producing state-of-the-art results in a variety of language modeling tasks. Built on top of transformers and seq-to-sequence models, the Bidirectional Encoder Representations from Transformers is a very powerful NLP model that has outperformed many. The state-of-the-art results that it produces on a variety of language-specific tasks are enough to show that it is indeed a big deal. The results come from its underlying architecture which uses breakthrough techniques such as seq2seq (sequence-to-sequence) models and transformers. The seq2seq model is a network that converts a given sequence of words into a different sequence and is capable of relating the words that seem more important.

Sophisticated machine learning applications require not only enormous amounts of training data, but powerful computer hardware on which to train. An analysis conducted by San Francisco research firm OpenAI found that since 2012, the amount of compute used in the largest training runs has been increasing exponentially with a 3.4-month doubling time, and that it's grown by more than 300,000 times over that same time period. The trend spurred the development of supercomputers like the U.S. Department of Energy's Sierra and Summit, which leverage dedicated accelerator chips to speed up AI computation. Now, IBM's Hardware Center, in collaboration with New York State, SUNY Polytechnic Institute, and other members of IBM's AI Hardware Center, has delivered a new machine for the Department of Computer Science at Rensselaer Polytechnic Institute (RPI) that's optimized for state-of-the-art machine learning workloads. It's dubbed Artificial Intelligence Multiprocessing Optimized System, or AiMOS (in honor of Rensselaer cofounder Amos Eaton), and it will principally tackle projects in biology, chemistry, the humanities, and related domains underway at the new IBM Research AI Hardware Center on the SUNY campus in Albany.