In 2018, OpenAI found that the amount of computational power used to train the largest AI models had doubled every 3.4 months since 2012. The San Francisco-based for-profit AI research lab has now added new data to its analysis. This shows how the post-2012 doubling compares with the historic doubling time since the beginning of the field. From 1959 to 2012, the amount of power used doubled every two years, tracking Moore's Law. This means the resources used today are doubling at a rate seven times faster than before.

Imagine a computer that can read your handwriting (even if it's as bad as mine). Or one that can read a tiny street sign in a grainy picture you snapped on your phone. Or better yet, one that can do this and immediately translate the results into 100 different languages. In the last few years, all these things suddenly became possible. This is the power of modern, Deep Learning driven Optical Character Recognition (OCR). OCR is the process of using machine vision, letter recognition and other techniques to automatically extract text from an image.

Imagine a computer that can read your handwriting (even if it's as bad as mine). Or one that can read a tiny street sign in a grainy picture you snapped on your phone. Or better yet, one that can do this and immediately translate the results into 100 different languages. In the last few years, all these things suddenly became possible. This is the power of modern, Deep Learning driven Optical Character Recognition (OCR). OCR is the process of using machine vision, letter recognition and other techniques to automatically extract text from an image.

Over the last couple of years, powerful deep learning methods have emerged to build industrial scale natural language understanding applications. The first wave of deep learning models employed pre-trained word embeddings (word2vec or GloVe) to initialize the first layer of a neural network followed by a task specific model trained using labelled data. The next wave of deep learning architectures (ELMo, ULMFiT, BERT) showed how to learn contextual word embeddings from massive amounts of unlabelled text data and then transfer this information to a wide variety of downstream tasks such as sentiment analysis, question answering etc. with limited amounts of labelled data. This approach is quite relevant for industrial settings where obtaining large amounts of labelled data is expensive. In this hands on tutorial, we will cover the important concepts behind recent developments such as word embeddings, sequence to sequence models, attention mechanism, contextual word embeddings, transfer learning and probing embeddings.

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in this domain. A large number of skin lesion datasets are available publicly, and researchers have developed AI solutions, particularly deep learning algorithms, to distinguish malignant skin lesions from benign lesions in different image modalities such as dermoscopic, clinical, and histopathology images. Despite the various claims of AI systems achieving higher accuracy than dermatologists in the classification of different skin lesions, these AI systems are still in the very early stages of clinical application in terms of being ready to aid clinicians in the diagnosis of skin cancers. In this review, we discuss advancements in the digital image-based AI solutions for the diagnosis of skin cancer, along with some challenges and future opportunities to improve these AI systems to support dermatologists and enhance their ability to diagnose skin cancer.

With less than a week to go, the excitement and anticipation are building up for industry's largest cloud computing conference - AWS re:Invent. As an analyst, I have been attempting to predict the announcements from re:Invent (2018, 2017) with decent accuracy. But with each passing year, it's becoming increasingly tough to predict the year-end news from Vegas. Amazon is venturing into new areas that are least expected by the analysts, customers, and its competitors. AWS Ground Station is an example of how creative the teams at Amazon can get in conceiving new products and services.

And sometimes the past defines us. What route we've walked through and what choices we made along the way. They are curved as our history and tells us what kind of person we were and where we're headed. This is also applicable to data. They can have their past, and that history can be used for predicting what's coming next, the future. As we have a fortune teller, likewise data, which is called the sequence models.

As part of the CleanTechnica series on the use of machine learning in advancing our low-carbon future, it would be remiss to not point out the carbon debt. However, it's not as bad as was reported earlier this year, in my estimation. Let's talk about the study itself, and the assumptions it made. The paper that made some headlines was Energy and Policy Considerations for Deep Learning in NLP by Strubell, Ganesh, and McCallum of the University of Massachusetts Amherst, and it was published in June of 2019. Strubell and McCallum are part of the team that built a state-of-the-art natural language processing model, LISA.

Deep learning, embedded vision, hyperspectral/multispectral imaging, 3D imaging, computational imaging, and polarization imaging have emerged as some of the most popular machine vision technologies today. Featured in the November/December issue, the results of a first-of-its-kind market survey highlights how much these technologies are used, where, how, and by whom. A roundtable discussion on December 4 featuring three top experts in machine vision today (David Dechow, Daniel Lau, and Perry West) will provide a forum for questions on these topics, how they might be using them, and how they may improve machine vision systems today. Submit your question ahead of time by contacting editor Jimmy Carroll at jcarroll@endeavorb2b.com, and register for the webcast here.