Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. With such history comes a set of terminology that has a specific way in which it is applied. However, when two distinct fields with overlapping terminology start to collaborate, miscommunication and misunderstandings can occur. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical AI contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. Then the data sets used for AI evaluation are classified, namely random splitting, cross-validation, temporal, geographic, internal, and external sets. The accurate and standardized description of these data sets is crucial for demonstrating the robustness and generalizability of AI applications in medicine. This review clarifies existing literature to provide a comprehensive understanding of these classifications and their implications in AI evaluation. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion. Among these solutions are the use of standardized terminology such as 'training set,' 'validation (or tuning) set,' and 'test set,' and explicit definition of data set splitting terminologies in each medical AI research publication. This review aspires to enhance the precision of communication in medical AI, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.

The discipline of AI most often mentioned these days is deep learning (DL) along with its many incarnations implemented with deep neural networks. DL also is a rapidly accelerating area of research with papers being published at a fast clip by research teams from around the globe. I enjoy keeping a pulse on deep learning research and so far in 2021 research innovations have propagated at a quick pace. In this article, we'll take a brief tour of my top picks for deep learning research (in no particular order) of papers that I found to be particularly compelling. I'm pretty attached to this leading-edge research. I'm known to carry a thick folder of recent research papers around in my backpack and consume all the great developments when I have a spare moment.

THERE'S AN APOCRYPHAL story about how NVIDIA pivoted from games and graphics hardware to dominate AI chips – and it involves cats. Back in 2010, Bill Dally, now chief scientist at NVIDIA, was having breakfast with a former colleague from Stanford University, the computer scientist Andrew Ng, who was working on a project with Google. "He was trying to find cats on the internet – he didn't put it that way, but that's what he was doing," Dally says. Ng was working at the Google X lab on a project to build a neural network that could learn on its own. The neural network was shown ten million YouTube videos and learned how to pick out human faces, bodies and cats – but to do so accurately, the system required thousands of CPUs (central processing units), the workhorse processors that power computers.

Click here to learn more about Gilad David Maayan. Deep learning is the basis for many complex computing tasks, including natural language processing (NLP), computer vision, one-to-one personalized marketing, and big data analysis. Deep learning algorithms are based on neural networks, which commonly have millions of parameters that need to be calculated numerous times in order to train the model. Training a neural network is very computationally intensive, and because these computations can very easily be parallelized, they call for a new approach to hardware. Graphical processing units (GPUs), originally designed for the gaming industry, have a large number of processing cores and very large on-board RAM (compared to traditional CPUs).

The digitisation of society means we are amassing data at an unprecedented rate. Healthcare is no exception, with IBM estimating approximately one million gigabytes accruing over an average person's lifetime and the overall volume of global healthcare data doubling every few years.1 To make sense of these big data, clinicians are increasingly collaborating with computer scientists and other allied disciplines to make use of artificial intelligence (AI) techniques that can help detect signal from noise.2 A recent forecast has placed the value of the healthcare AI market as growing from $2bn (£1.5bn; €1.8bn) in 2018 to $36bn by 2025, with a 50% compound annual growth rate.3 Deep learning is a subset of AI which is formally defined as "computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction."4 In practice, the main distinguishing feature between convolutional neural networks (CNNs) in deep learning and traditional machine learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition; they do not require domain expertise to structure the data and design feature extractors.5

Rendering a simple shape into a proper object with geometry, texture, and other material properties is a painstakingly long process; however, with AI, researchers can now do this rendering ten times faster than the real-time. A machine learning model is trained on images that are closer to the target. When it is presented with a shape and matching properties, it would recommend a photorealistic image. This opened a whole new field altogether -- differentiable programming. Traditional rendering engines are not differentiable, so they can't be incorporated into deep learning pipelines.

The intersection between artificial intelligence(AI) and human cognition is one of the most fascinating areas of research in the modern technology space. Deep learning is constantly trying to emulate mechanisms of the human brain in order to improve the capabilities of AI agents. Many of those mechanisms are centered around how humans learn and build knowledge. A recent research paper from DeepMind is proposing a method that emulates the progressive nature of human learning in deep learning model. DeepMind calls this technique progressive neural networks.

Word embeddings have revolutionized the world of natural language processing(NLP). Conceptually, word embeddings are language modeling methods that map phrases or words in a sentence to vectors and numbers. One of the first steps in any NLP application is to determine what type of word embedding algorithm is going to be used. Typically, NLP models resort to pretrained word embedding algorithm such as Word2Vec, Glove or FastText. While that approach is relatively simple, it also results highly inefficient as is near to impossible to determine what word embedding will perform better as the NLP model evolves.

Deep learning is hot right now. Applications such as voice recognition, facial recognition, language translation, medical diagnostics, self-driving vehicles, and even the detection of credit fraud, are becoming more and more woven into the fabric of modern life. Because of such successes, and the opportunities they open up for further extensions of the technology, deep learning is currently one of the most active fields in computer science research, and progress has been rapid. In this article we'll take a brief look at several of the latest trends in deep learning research. Perhaps the area of deep learning research that has received the most public notice in recent years relates to the advent of driverless cars and trucks.

Adaptability is one of the key cognitive abilities that defined us as humans. Even as babies, we can intuitively shift between similar tasks even if we don't have prior training on them. This contrasts with the traditional train-and-test approach of most artificial intelligence(AI) systems which require an agent to go through massive amounts of training before it can master a specific task. By definition, train-and-test systems are not very adaptable and, consequently, they are not very applicable to scenarios that operate in real word environments. Improving the adaptability of AI systems has been one of the core areas of research of an increasingly popular discipline known as meta-learning that focuses on improving the learning abilities of AI agents.