Worldwide, many millions of people die suddenly and unexpectedly each year, either with or without a prior history of cardiovascular disease. Such events are sparse (once in a lifetime), many victims will not have had prior investigations for cardiac disease and many different definitions of sudden death exist. Accordingly, sudden death is hard to predict. This analysis used NHS Electronic Health Records (EHRs) for people aged $\geq$50 years living in the Greater Glasgow and Clyde (GG\&C) region in 2010 (n = 380,000) to try to overcome these challenges. We investigated whether medical history, blood tests, prescription of medicines, and hospitalisations might, in combination, predict a heightened risk of sudden death. We compared the performance of models trained to predict either sudden death or all-cause mortality. We built six models for each outcome of interest: three taken from state-of-the-art research (BEHRT, Deepr and Deep Patient), and three of our own creation. We trained these using two different data representations: a language-based representation, and a sparse temporal matrix. We used global interpretability to understand the most important features of each model, and compare how much agreement there was amongst models using Rank Biased Overlap. It is challenging to account for correlated variables without increasing the complexity of the interpretability technique. We overcame this by clustering features into groups and comparing the most important groups for each model. We found the agreement between models to be much higher when accounting for correlated variables. Our analysis emphasises the challenge of predicting sudden death and emphasises the need for better understanding and interpretation of machine learning models applied to healthcare applications.

The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries. But this won't happen--or shouldn't happen--unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur--and it's inevitable they will. That's one reason Nvidia's car is still experimental.

The car's underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries. But this won't happen--or shouldn't happen--unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur--and it's inevitable they will. That's one reason Nvidia's car is still experimental.

Deep learning, a subset of machine learning and artificial intelligence (AI), has been there since a while, but became an overnight "sensation" when in 2016, Google's AI program, a robot player beat human grandmaster Lee Seedol in the famed game of AlphaGo . Since then, deep learning training and learning methods became widely acknowledged for "humanizing" machines. Many of the advanced automation capabilities now found in enterprise AI platforms are due to the rapid growth of ML and deep learning technologies, as researchers predict deep learning to provide formidable momentum for the adoption and growth of AI, even though most of these experiments are in their infancy. By definition, deep learning is a powerful tool for enterprises looking to gain actionable insights and enable automated responses to a flood of data, especially unstructured data, from all kinds of devices, Internet of Things (IoT), social media and – of course – from corporate data systems. From that perspective deep learning works incredibly well with unstructured data, such as images, sound, time-series of events and so on.

Arguably, one of the biggest debates happening in data science in 2019 is the need for AI explainability. The ability to interpret machine learning models is turning out to be a defining factor for the acceptance of statistical models for driving business decisions. Enterprise stakeholders are demanding transparency in how and why these algorithms are making specific predictions. A firm understanding of any inherent bias in machine learning keeps boiling up to the top of requirements for data science teams. As a result, many top vendors in the big data ecosystem are launching new tools to take a stab at resolving the challenge of opening the AI "black box." Some organizations have taken the plunge into AI even with the realization that their algorithm's decisions can't be explained.

One way could be to identify some of the most critical parameters to look for in any AI solution, and to rate/label them on a standard scale. Few such parameters are discussed below. Perhaps the community and policymakers can crystallize these further, and add to the list. Decision trees, Random forest, Gradient boosting, Monte Carlo, to name a few. The use of any one of these (say, Regression) in a solution can technically qualify it as AI-enabled, but it would not be very accurate or useful for a user. This has led to disillusionment among early AI users, while also giving rise to plethora of solutions and companies calling themselves AI.

Is the world too chaotic for any technology to control? Is technology revealing that things are even more chaotic and uncontrollable than first thought? Artificial intelligence, machine learning and related technologies may be underscoring a realization Albert Einstein had many decades ago: "The more I learn, the more I realize how much I don't know." When it comes to employing the latest analytics in enterprises and beyond, even the best technology -- predictive algorithms, artificial intelligence -- can't explain, and even reveal, the complexity and interactions that shape events and trends. That's the word from Harvard University's David Weinberger who explains, in his latest book, how AI, big data, science and the internet are all revealing a fundamental truth: things are more complex and unpredictable than we've allowed ourselves to see.

Researchers at New York's Ichan [sic] School of Medicine ran a deep-learning experiment to see if it could train a system to predict cancer. The school, based within Mount Sinai Hospital, had obtained access to the data for 700,000 patients, and the data set included hundreds of different variables. Called Deep Patient, the system used advanced techniques to spot new patterns in data that didn't entirely make sense to the researchers but turned out to be very good at finding patients in the earliest stages of many diseases, including liver cancer. Somewhat mysteriously, it could also predict the warning signs of psychiatric disorders like schizophrenia. But even the researchers who built the system didn't know how it was making decisions.

In the absence of codified humanistic values within the big tech giants, personal experiences and ideals are driving decision-making. This is particularly dangerous when it comes to AI, because students, professors, researchers, employees, and managers are making millions of decisions every day, from seemingly insignificant (what database to use) to profound (who gets killed if an autonomous vehicle needs to crash). Artificial intelligence might be inspired by our human brains, but humans and AI make decisions and choices differently. Princeton professor Daniel Kahneman and Hebrew University of Jerusalem professor Amos Tversky spent years studying the human mind and how we make decisions, ultimately discovering that we have two systems of thinking: one that uses logic to analyze problems, and one that is automatic, fast, and nearly imperceptible to us. Kahneman describes this dual system in his award-winning book Thinking, Fast and Slow.

Artificial intelligence, or AI, is an umbrella term for machine learning and deep learning. It is where a machine takes in information from its surroundings and, from that, makes the most optimal decision appropriate to the situation. In machine learning, a machine can take a dataset, analyze it, and make a decision or prediction based on what it has learned. Deep learning is a more complex version of this, where there are several layers of process features and each layer takes some information. These are both based on neural networks, which are algorithms acting similarly to the human brain in that they take an input and provide an output based on what they have learned. However, the algorithm does not need to have the more cognitive problem-solving abilities of machine learning and deep learning to be considered an AI, it just needs to lead to the most ideal solution.