Artificial intelligence has become an intricate part of our everyday lives. We encounter it consciously and subconsciously -- at the grocery store, when we call customer service, and even in our homes and cars. With an increasing reliance on a technology designed to constantly collect our data – one that is programmed to be "smarter" than the human brain – are we leaving ourselves open to significant issues such as data breaches or information misuse in the future? How can we mitigate the potential challenges posed by artificial intelligence in healthcare and other industries? The emergence of artificial intelligence in healthcare has brought about countless opportunities for improved patient care outcomes, machine learning-assisted care, and deep learning technological advancements. Although there is no question that artificial intelligence brings added value to the healthcare industry, we also must pause to evaluate the potential challenges that technology-driven patient care poses to patients, providers and healthcare organizations.

Today WekaIO announced that The Business Intelligence Group has named Weka a winner in its Artificial Intelligence Excellence Awards program. The Weka File System (WekaFS), Weka's flagship product that is uniquely built to solve big problems, delivers the industry's best performance at any scale. WekaFS has a clean sheet design that handles the demands of new emerging and converging workloads, including artificial intelligence (AI) and machine/deep learning (ML/DL), high-performance data analytics (HPDA), and high-performance computing (HPC). The file system can deliver 80 GB/sec of bandwidth to a single GPU server, scale to Exabytes in a single namespace, and support an entire pipeline for edge-to-core-to-cloud workflows. The system also delivers operational agility with versioning, explainability, and reproducibility along with governance and compliance with in-line encryption and data protection.

Despite attempts to contain COVID-19 spread, as of March 26 more than 530,000 people had been infected worldwide and the number of new cases continues to grow at an alarming rate. AI tools have already joined the fight, guiding UAVs to automatically disinfect public areas, tracking disease spread vectors, diagnosing patients, etc. A new project from researchers with the UN Global Pulse Data Science Team, the World Health Organization and the Mila – Quebec AI Institute looks at current studies and programs that are using AI to tackle the COVID-19 crisis and suggests some promising future research directions. The team categorizes the AI applications in three areas; medical, which includes individual patient diagnosis and treatment; molecular, comprising drug discovery-related research; and societal. Most clinical applications of AI during the COVID-19 pandemic response have been in medical imaging diagnosis, amid growing interest in using medical imaging for screening and diagnosis.

There's a common refrain among the chronically disappointed, it goes a little something like this: "if this is the future, where is my jetpack?" Juxtaposing this longing for a retro-future against the wonder-world of ubiquitous computing, programmable cells, and renascent space exploration can make the gripe sound out-of-sorts on a cursory examination. For some people this misplaced nostalgic futurism can be remarkably persistent.

Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.

Spending on artificial intelligence in Asia Pacific is set to soar over the next five years as the demand for deep learning ramps up. By 2024, the APAC region is estimated to account for about 30 per cent of the global AI platform revenue at approximately U.S.$97.5 billion, according to research firm GlobalData. But that figure is expected to increase with businesses and the rising number of start-ups specialising in the technology and advancement in the space supporting higher computational capabilities. Sunil Kumar Verma, lead ICT analyst at GlobalData, said the APAC market is already deploying deep learning-based AI technology for offline automation, safety and security for businesses and assets. "In addition, AI hardware optimisation with increased computing speed on small devices will result in the cost reduction and drive deep learning adoption across the region," Verma said.

To understand how to accomplish a specific task in TensorFlow, you can refer to the TensorFlow tutorials. Keras is one of the most popular and open-source neural network libraries for Python. Initially designed by a Google engineer for ONEIROS, short for Open-Ended Neuro Electronic Intelligent Robot Operating System, Keras was soon supported in TensorFlow's core library making it accessible on top of TensorFlow.

Automated analysis of Chest X-ray images to diagnose various pathologies will help in overcoming the costly, time consuming and prone to error from manual analysis of them, especially using deep learning based approaches. One of such recent efforts in this direction is Classification of Common Thorax which combines the advantages of CNN based feature extraction and problem transformation methods in multi-label classification task. So this is one of the key areas where deep learning based solution has already made an impact and has the potential to come up with even a better and well improved performance. For this session, I am going to discuss about the problem at hand, the data-set, several approaches that has been explored and that worked quite well so far in this research. Also I am going to mention about the potential use case and the real world impact of such a real world healthcare application that can save millions of lives by early and effective detection.

Automated analysis of CT scan images using AI solutions to diagnose abnormalities will help in overcoming the costly, time consuming and prone to error from manual analysis. Deep Learning has proved to be quite efficient to mimic human cognitive abilities (and even exceed that in many cases), especially with unstructured data. DL algorithms can detect, localize and quantify a growing list of brain pathologies including intra-cerebral bleeds and their subtypes, infarcts, mass effect, midline shift, and cranial fractures. So, with advanced DL algorithms, analysis of radiographic data can be easily achieved and this can accelerate early detection of certain critical medical conditions, powered by AI. As mentioned, Deep Learning algorithms for computer vision use cases has been extremely successful for classification and localization related problems.

Yann Lecun, in his talk, introduced the "cake analogy" to illustrate the importance of self-supervised learning. Though the analogy is debated(ref: Deep Learning for Robotics(Slide 96), Pieter Abbeel), we have seen the impact of self-supervised learning in the Natural Language Processing field where recent developments (Word2Vec, Glove, ELMO, BERT) have embraced self-supervision and achieved state of the art results. "If intelligence is a cake, the bulk of the cake is self-supervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL)." Curious to know how self-supervised learning has been applied in the computer vision field, I read up on existing literature on self-supervised learning applied to computer vision through a recent survey paper by Jing et. This post is my attempt to provide an intuitive visual summary of the patterns of problem formulation in self-supervised learning.