So far in 2020's epic battle of nature vs. man vs. technology, man is not winning any "Best in Show" contests. So, needless to say, when a colleague sent me an article about the GPT-3 language generator released last week by the nonprofit artificial intelligence research company OpenAI, I was intrigued. The article is a blog post authored by Argentinian software engineer Manuel Araoz in which he describes his experiments with GPT-3 on the bitcointalk.org When he plugged some sample text into the model -- a few sentences from an existing forum post, for example -- GPT-3 generated an original body of text which mimicked the sentence structure, grammar and other subtleties of the sample… and with incredible results. GPT-3's predicted sentences read like they were written by a human.

Sometimes it's tempting to think of every technological advancement as the brave first step on new shores, a fresh chance to shape the future rationally. In reality, every new tool enters the same old world with its same unresolved issues. In a moment where society is collectively reckoning with just how deep the roots of racism reach, a new paper from researchers at DeepMind -- the AI lab and sister company to Google -- and the University of Oxford presents a vision to "decolonize" artificial intelligence. The aim is to keep society's ugly prejudices from being reproduced and amplified by today's powerful machine learning systems. The paper, published this month in the journal Philosophy & Technology, has at heart the idea that you have to understand historical context to understand why technology can be biased.

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions ( 1024 1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods.

Dublin, July 28, 2020 (GLOBE NEWSWIRE) -- The "Global Video Surveillance Market: Focus on Ecosystem, Application (Infrastructure, Commercial Residential, Industrial, Institutional, Others), and Region - Analysis and Forecast, 2020-2025" report has been added to ResearchAndMarkets.com's offering. The video surveillance industry analysis projects the market to grow at a significant CAGR of 10.06% on the basis of value during the forecast period from 2020 to 2025. Asia-Pacific region dominated the global video surveillance with a share of 58.12% in 2019. The constantly expanding infrastructure in China and India has been a significant driver in promoting the growth of video surveillance in these countries. The decline in the costs of the overall CCTV-based security system packages due to the reduction in the prices of video cameras has hiked the price competitiveness of these surveillance systems, especially in the Chinese, Indian, and South Korean markets.

Within the next decade, artificial intelligence is likely to play a significant role in our everyday lives. For any aspiring developer, learning how to code smart software is a good move. These skills are highly valued in tech, finance, sales, marketing, and many other sectors. The Hacker News recently partnered with professional trainers to offer their popular artificial intelligence online training programs at hugely discounted prices. The "Essential AI & Machine Learning Certification Training Bundle," the program aims to help you explore the technology, with four hands-on video courses working towards certification: Artificial Intelligence (AI) and Machine Learning (ML) Foundation -- Explore the Field of AI & ML and Develop Your Expertise in Neural Network & Deep Architectures Data Visualization with Python and Matplotlib -- Arrange Critical & Meaningful Data Using Python as a Data Visualization Tool Computer Vision -- Explore the World of Visual Data Recognition & Analysis and Understand the Processes Used for Today's Applications Natural Language Processing -- Understand NLP Processes & Identify NLP Tasks in Your Day-to-Day Work Though all these 4 training courses cost a total of $656 when subscribed through the trainer's website, you can now pick up the same for just $39.99 (at 93% Discount) at The Hacker News store.

Writing an all-encompassing book on Python machine learning is difficult, given how expansive the field is. But reviewing one is not an easy feat either, especially when it's a highly acclaimed title such as Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition. The book is a best-seller on Amazon, and the author, Aurélien Géron, is arguably one of the most talented writers on Python machine learning. And after reading Hands-on Machine Learning, I must say that Geron does not disappoint, and the second edition is an excellent resource for Python machine learning. Geron has managed to cover more topics than you'll find in most other general books on Python machine learning, including a comprehensive section on deep learning.

A team lead by researchers in the Pritzker School of Molecular Engineering (PME) at the University of Chicago reports that it has developed an artificial intelligence-led process that uses big data to design new proteins that could have implications across the healthcare, agriculture, and energy sectors. By developing machine-learning models that can review protein information culled from genome databases, the scientists say they found relatively simple design rules for building artificial proteins. When the team constructed these artificial proteins in the lab, they discovered that they performed chemistries so well that they rivaled those found in nature. "We have all wondered how a simple process like evolution can lead to such a high-performance material as a protein," said Rama Ranganathan, PhD, Joseph Regenstein Professor in the Department of Biochemistry and Molecular Biology, Pritzker Molecular Engineering, and the College. "We found that genome data contains enormous amounts of information about the basic rules of protein structure and function, and now we've been able to bottle nature's rules to create proteins ourselves."

Sequence classification plays an important role in metagenomics studies. We assess the deep neural network approach for fungal sequence classification as it has emerged as a successful paradigm for big data classification and clustering. Two deep learning-based classifiers, a convolutional neural network (CNN) and a deep belief network (DBN) were trained using our recently released barcode datasets. Experimental results show that CNN outperformed the traditional BLAST classification and the most accurate machine learning based Ribosomal Database Project (RDP) classifier on datasets that had many of the labels present in the training datasets. When classifying an independent dataset namely the “Top 50 Most Wanted Fungi”, CNN and DBN assigned less sequences than BLAST. However, they could assign much more sequences than the RDP classifier. In terms of efficiency, it took the machine learning classifiers up to two seconds to classify a test dataset while it was 53 s for BLAST. The result of the current study will enable us to speed up the taxonomic assignments for the fungal barcode sequences generated at our institute as ~ 70% of them still need to be validated for public release. In addition, it will help to quickly provide a taxonomic profile for metagenomics samples.

Let's get started by introducing the dataset. I will be using the very famous Iris flower dataset that contains 4 different measurements (sepal length, sepal width, petal length, petal width) of the following 3 species of flowers. The goal is to accurately identify the species using the 4 measurements for each flower. Note that nowadays it's relatively easy to use a model (Convolutional Neural Networks) that learns directly from the images but I will leave that topic for the next lesson. The Iris flower dataset can be easily downloaded from sklearn datasets as shown in the code below.

I studied in depth how to be successful in my PhD applications and it paid off: I got admitted to Stanford, University of Washington, UCL, CMU, and NYU. This blog post is a mish-mash of how to proceed in your PhD applications from A to Z. It discusses what is important and what is not. It discusses application materials like the statement of purpose (SoP) and how to make sense of these application materials. There are some excellent sources out there on this topic and it is worth stopping for a second and understand what this blog post will give you and what other sources can give you. This blog post is mainly focused on PhD applications for deep learning and related fields like natural language processing, computer vision, reinforcement learning, and other sub-fields of deep learning. This blog post assumes that you already have a relatively strong profile, meaning you probably have already one or multiple publications under your belt and you worked with more than one person on research. This blog post is designed to help you optimize your chance for success for top programs.