Every package you'll see is free and open source software. Thank you to all the folks who create, support, and maintain these projects! If you're interested in learning about contributing fixes to open source projects, here's a good guide. And If you're interested in the foundations that support these projects, I wrote an overview here. Pandas is a workhorse to help you understand and manipulate your data.

Full Stack Deep Learning covers the full lifecycle of an AI application, from ideation through deployment but it does not cover theory or model fitting.

The tool is the outcome of a project named'Digital Control Centre for COVID-19' by health innovation body, EIT Health, which was initiated in April 2020. Since then, the tool has undergone development and validation, and has shown early success in the stratification and personalisation of treatment for patients with serious COVID-19, leading to improved treatment responses and a 50% reduction in mortality rates. The study has been published in Clinical Infectious Diseases. The main cause of death for patients with COVID-19 is respiratory failure, however, many of these patients can be effectively treated if adequate care is provided at the right timepoint. Researchers at Hospital Clinic Barcelona-IDIBAPS created the Artificial Intelligence solution capable of analysing, in real time, more than a trillion anonymised data points of COVID-19 patients, identifying clinical patterns and suggesting personalised treatments.

AI could also have a transformative effect on clinical decision-making through the utilisation of the huge levels of genomic, biomarker, phenotype, behavioural, biographical and clinical data that is generated across the health system. Bayer and Merck & Co provide a perfect example of this. They have developed an AI software system to support clinical decision-making of chronic thromboembolic pulmonary hypertension (CTEPH) – a rare form of pulmonary hypertension. The software helps differentiate patients from those suffering with similar symptoms that are actually a result of asthma and chronic obstructive pulmonary disease (COPD), and therefore diagnose CTEPH more reliably and efficiently. The CTEPH Pattern Recognition Artificial Intelligence obtained FDA Breakthrough Device Designation in December 2018.

This post provides an overview of chest CT scan machine learning organized by clinical goal, data representation, task, and model. A chest CT scan is a grayscale 3-dimensional medical image that depicts the chest, including the heart and lungs. CT scans are used for the diagnosis and monitoring of many different conditions including cancer, fractures, and infections. The clinical goal refers to the medical abnormality that is the focus of the study. Many CT machine learning papers focus on lung nodules.

We often hear in the news about this thing called "machine learning" and how computers are "learning" to perform certain tasks. From the examples we see, it almost seems like magic when a computer creates perfect landscapes from thin air or makes a painting talk. But what is often overlooked, and what we want to cover in this tutorial, is that machine learning can be used in video game creation as well. In other words, we can use machine learning to make better and more interesting video games by training our AIs to perform certain tasks automatically with machine learning algorithms. This tutorial will show you how we can use Unity ML agents to make an AI target and find a game object. More specifically, we'll be looking at how to customize the training process to create an AI with a very specific proficiency in this task. Through this, you will get to see just how much potential machine learning has when it comes to making AI for video games. So, without further ado, let's get started and learn how to code powerful AIs with the power of Unity and machine learning combined!

Prostate biopsy with cancer probability (blue is low, red is high). This case was originally diagnosed as benign but changed to cancer upon further review. The AI accurately detected cancer in this tricky case. A study published today (July 27, 2020) in The Lancet Digital Health by UPMC and University of Pittsburgh researchers demonstrates the highest accuracy to date in recognizing and characterizing prostate cancer using an artificial intelligence (AI) program. "Humans are good at recognizing anomalies, but they have their own biases or past experience," said senior author Rajiv Dhir, M.D., M.B.A., chief pathologist and vice chair of pathology at UPMC Shadyside and professor of biomedical informatics at Pitt. "Machines are detached from the whole story. To train the AI to recognize prostate cancer, Dhir and his colleagues provided images from more than a million parts of stained tissue slides taken from patient biopsies. Each image was labeled by expert pathologists to teach the AI how to discriminate between healthy and abnormal tissue. The algorithm was then tested on a separate set of 1,600 slides taken from 100 consecutive patients seen at UPMC for suspected prostate cancer. During testing, the AI demonstrated 98% sensitivity and 97% specificity at detecting prostate cancer -- significantly higher than previously reported for algorithms working from tissue slides. Also, this is the first algorithm to extend beyond cancer detection, reporting high performance for tumor grading, sizing, and invasion of the surrounding nerves. These all are clinically important features required as part of the pathology report. AI also flagged six slides that were not noted by the expert pathologists. But Dhir explained that this doesn't necessarily mean that the machine is superior to humans. For example, in the course of evaluating these cases, the pathologist could have simply seen enough evidence of malignancy elsewhere in that patient's samples to recommend treatment. For less experienced pathologists, though, the algorithm could act as a failsafe to catch cases that might otherwise be missed. "Algorithms like this are especially useful in lesions that are atypical," Dhir said. "A nonspecialized person may not be able to make the correct assessment.

Every feature of intelligence or learning aspects in principle can be so precisely described that a machine can seamlessly simulate it. John McCarthy, who is the Father of Artificial Intelligence, was a pioneer in the fields of AI. He not only is credited to be the founder of AI, but also one who coined the term Artificial Intelligence. In 1955, John McCarthy coined the term Artificial Intelligence, which he proposed in the famous Dartmouth conference in 1956. This conference attended by 10-computer scientists, saw McCarthy explore ways in which machines can learn and reason like humans.

Machines have gotten smaller and more efficient over the years. However, the majority of these microscopic-scale machines have limited capabilities due to restrictive movements -- something which the scientists have been working to rectify. The most extensive use case of this kind of technology could be seen in the Healthcare sector. I have recently talked about the extended role of nanotechnology in the future of Healthcare. Taking inspiration from the Japanese art of Origami, researchers at the University of Michigan have taken this approach to create more agile micro machines to be used in diverse fields like medical equipment and infrastructure sensing.

In Deloitte's third edition of the "State of AI in the Enterprise" survey, conducted between October and December 2019, the authors suggest that businesses are now entering an age of Pervasive AI, where its use is becoming more and more widespread. In fact, 74% of the businesses surveyed think that AI will be fully integrated into all aspects of their business in the next three years, and 64% say it enables them to gain a competitive edge. As AI becomes more pervasive, Deloitte's survey claims that we are now moving from the "early adopter" phase of AI's use, to the "early majority" phase, where many more businesses are starting to invest in AI and are increasingly convinced of its benefits. The businesses surveyed were split into three types of AI adopter: starters (27%), skilled (47%) and seasoned (26%). So how do different adopters use AI, and what are their reasons for integrating it into their business operations?