"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).
But how? We'll try and explain. Historically, Crows Crows Crows has written 37 newsletters to their community, which, along with Webster's dictionary, they have fed into the neural network model. This they have termed the "base data". The customisation element comes with what they call "variable input data", which comes from the questionnaire required to generate a newsletter. Like alchemy, the variable input data transforms the base data into pure gold, ie. a fully customised, fully unique newsletter.
COVID-19 pandemic has affected the entire world. Many people lost their jobs, kids stay at home, and the economic crisis is disastrous. The question of "how will the world be after COVID-19" is of high interest. Many futurists predict a different world, where we should rethink public spaces and believes that the memory of the COVID-19 lockdown will remain for a long time (Del Bello, 2020). This information presents a sad situation, where the COVID-19 continues to spreads with tragic death cases.
MIT researchers claim that deep learning neural networks need better uncertainty analysis to reduce errors. "Deep evidential regression" reduces uncertainty after only one pass on a network, greatly reducing time and memory. This could help mitigate problems in medical diagnoses, autonomous driving, and much more.
Research in the field of traditional computing systems is slowing down, with new types of computing moving to the forefront now. A team of engineers from Pennsylvania State University (Penn State) in the U.S. has been working on creating a type of computing based on our brain's neural networks' systems all while using the brain's analog nature. The team has discovered that graphene-based memory resistors show promise for this new computing form. Their findings were recently published in Nature Communications. "We have powerful computers, no doubt about that, the problem is you have to store the memory in one place and do the computing somewhere else," said Saptarshi Das, the team leader and Penn State assistant professor of engineering science and mechanics.
Machine Learning (ML) is a well-known innovation that nearly everyone knows about. A study uncovers that 77% of devices that we presently use are utilizing ML. From a social event of SMART devices over Netflix proposition through products like Amazon's Alexa, and Google Home, artificial intelligence services are proclaiming cutting-edge innovative solutions for organizations and regular day to day existences. The year 2021 is ready to observe some significant ML and AI trends that would maybe reshape our economic, social, and industrial workings. As of now, the AI-ML industry is developing at a quick rate and gives sufficient advancement scope to companies to bring the vital change. According to Gartner, around 37% of all companies reviewed are utilizing some type of ML in their business and it is anticipated that around 80% of modern advances will be founded on AI and ML by 2022.
As demands for AI applications grow, we've seen a lot of effort put by companies to build their Machine Learning Engineering (MLE) tools tailored for their needs. There are just so many challenges faced by industries in regards to having a well-designed environment for their Machine Learning (ML) lifecycle: building, deploying, and managing ML models in production. This post will cover two papers, explaining MLE practices from two of the leading tech companies: Google and Microsoft. Adding a little bit of context, this article is part of a graduate-level course at Columbia University: COMS6998 Practical Deep Learning System Performance taught by Prof. Parijat Dube who also works at IBM New York as Research Staff Member. The first section will present a paper from Google and will touch on the building part of an ML lifecycle.
This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. If I wanted to learn deep learning with Python again, I would probably start with PyTorch, an open-source library developed by Facebook's AI Research Lab that is powerful, easy to learn, and very versatile. When it comes to training material, however, PyTorch lags behind TensorFlow, Google's flagship deep learning library. There are fewer books on PyTorch than TensorFlow, and even fewer online courses. Among them is Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann, three engineers who have contributed to the project and have extensive experience developing deep learning solutions.
America is facing a health care crisis primarily due to its aging population. Physician shortages have come to the forefront recently, as many hospitals are overwhelmed due to the COVID-19 pandemic. In truth, our looming physician shortage is a generation in the making, as baby boomer doctors retire in droves. This is all occurring as lifespans are increasing--hence, there are fewer doctors to treat more patients. Exacerbating the problem is that medical schools are not churning out medical students fast enough due to capacity constraints, and it takes 12 to 15 years to train a doctor. Today, more than half of active physicians are older than 55, and by the year 2032, the Association of American Medical Colleges projects a shortfall of 122,000 doctors in the United States.
Playform AI launched the limited-time exhibition this week with work from four artists, who include established names in the fine art word, new media specialists and a pseudonymous Instagram creator. The pieces range in price from about $60 to $1,500 and include wall prints, jigsaw puzzles, framed video screens and masks. The shop also features an augmented reality tool that allows prospective buyers to project artworks into their home before purchase. The startup is one of a handful of companies to venture into the AI art exhibition space as new advances in machine learning research have spawned a small but growing community of creatives, artists and technologists attempting to harness the power of AI in art. The key piece of a technology involved is a form of neural network called a generative adversarial network (GAN), which brands have already experimented with using for everything from deepfaked commercials to product design.
Neural networks are great at learning trends in both large and small data sets. However, data scientists have to be aware of the dangers of overfitting, which are more evident in projects where small data sets are used. Overfitting is when an algorithm is trained and modeled to fit a set of data points too closely so that it does not generalize well to new data points. Often, overfitting machine learning models have very high accuracy on the data sets they are trained on, but as a data scientist, the goal is usually to predict new data points as precisely as possible. To make sure that the model is evaluated based on how good it is to predict new data points, and not how well it is modeled to the current ones, it is common to split the datasets into one training set and one test set (and sometimes a validation set).