Article | March 30, 2020
Quantum Mechanics created their chapter in the history of the early 20th Century. With its regular binary computing twin going out of style, quantum mechanics led quantum computing to be the new belle of the ball! While the memory used in a classical computer encodes binary ‘bits’ – one and zero, quantum computers use qubits (quantum bits). And Qubit is not confined to a two-state solution, but can also exist in superposition i.e., qubits can be employed at 0, 1 and both 1 and 0 at the same time.
Article | March 9, 2021
For many, 2021 has brought hope that they can cautiously start to prepare for a world after Covid. That includes living with the possibility of future pandemics, and starting to reflect on what has been learned from such a brutal shared experience. One of the areas that has come into its own during Covid has been artificial intelligence (AI), a technology that helped bring the pandemic under control, and allow life to continue through lockdowns and other disruptions.
Plenty has been written about how AI has supported many aspects of life at work and home during Covid, from videoconferencing to online food ordering. But the role of AI in preventing Covid causing even more havoc is not necessarily as widely known. Perhaps even more importantly, little has been said about the role AI is likely to play in preparing for, responding to and even preventing future pandemics.
From what we saw in 2020, AI will help prevent global outbreaks of new diseases in three ways: prediction, diagnosis and treatment.
Predicting pandemics is all about tracking data that could be possible early signs that a new disease is spreading in a disturbing way. The kind of data we’re talking about includes public health information about symptoms presenting to hospitals and doctors around the world. There is already plenty of this captured in healthcare systems globally, and is consolidated into datasets such as the Johns Hopkins reports that many of us are familiar with from news briefings.
Firms like Bluedot and Metabiota are part of a growing number of organisations which use AI to track both publicly available and private data and make relevant predictions about public health threats. Both of these received attention in 2020 by reporting the appearance of Covid before it had been officially acknowledged. Boston Children’s Hospital is an example of a healthcare institution doing something similar with their Healthmap resource.
In addition to conventional healthcare data, AI is uniquely able to make use of informal data sources such as social media, news aggregators and discussion forums. This is because of AI techniques such as natural language processing and sentiment analysis. Firms such as Stratifyd use AI to do this in other business settings such as marketing, but also talk publicly about the use of their platform to predict and prevent pandemics. This is an example of so-called augmented intelligence, where AI is used to guide people to noteworthy data patterns, but stops short of deciding what it means, leaving that to human judgement.
Another important part of preventing a pandemic is keeping track of the transmission of disease through populations and geographies. A significant issue in 2020 was difficulty tracing people who had come into contact with infection. There was some success using mobile phones for this, and AI was critical in generating useful knowledge from mobile phone data.
The emphasis of Covid tracing apps in 2020 was keeping track of how the disease had already spread, but future developments are likely to be about predicting future spread patterns from such data. Prediction is a strength of AI, and the principles used to great effect in weather forecasting are similar to those used to model likely pandemic spread.
To prevent future pandemics, it won’t be enough to predict when a disease is spreading rapidly. To make the most of this knowledge, it’s necessary to diagnose and treat cases. One of the greatest early challenges with Covid was the lack of speedy, reliable tests.
For future pandemics, AI is likely to be used to create such tests more quickly than was the case in 2020. Creating a useful test involves modelling a disease’s response to different testing reagents, finding right balance between speed, convenience and accuracy. AI modelling simulates in a computer how individual cells respond to different stimuli, and could be used to perform virtual testing of many different types of test to accelerate how quickly the most promising ones reach laboratory and field trials.
In 2020 there were also several novel uses of AI to diagnose Covid, but there were few national and global mechanisms to deploy these at scale. One example was the use of AI imaging, diagnosing Covid by analysing chest x-rays for features specific to Covid. This would have been especially valuable in places that didn’t have access to lab testing equipment. Another example was using AI to analyse the sound of coughs to identify unique characteristics of a Covid cough.
AI research to systematically investigate innovative diagnosis techniques such as these should result in better planning for alternatives to laboratory testing. Faster and wider rollout of this kind of diagnosis would help control spread of a future disease during the critical period waiting for other tests to be developed or shared. This would be another contribution of AI to preventing a localised outbreak becoming a pandemic.
Historically, vaccination has proven to be an effective tool for dealing with pandemics, and was the long term solution to Covid for most countries. AI was used to accelerate development of Covid vaccines, helping cut the development time from years or decades to months. In principle, the use of AI was similar to that described above for developing diagnostic tests.
Different drug development teams used AI in different ways, but they all relied on mathematical modelling of how the Covid virus would respond to many forms of treatment at a microscopic level.
Much of the vaccine research and modelling focused on the “spike” proteins that allow Covid to attack human cells and enter the body. These are also found in other viruses, and were already the subject of research before the 2020 pandemic. That research allowed scientists to quickly develop AI models to represent the spikes, and simulate the effects of different possible treatments. This was crucial in trialling thousands of possible treatments in computer models, pinpointing the most likely successes for further investigation.
This kind of mathematical simulation using AI continued during drug development, and moved substantial amounts of work from the laboratory to the computer.
This modelling also allowed the impact of Covid mutations on vaccines to be assessed quickly. It is why scientists were reasonably confident of developing variants of vaccines for new Covid mutations in days and weeks rather than months.
As a result of the global effort to develop Covid vaccines, the body of data and knowledge about virus behaviour has grown substantially. This means it should be possible to understand new pathogens even more rapidly than Covid, potentially in hours or days rather than weeks.
AI has also helped create new ways of approaching vaccine development, for example the use of pre-prepared generic vaccines designed to treat viruses from the same family as Covid. Modifying one of these to the specific features of a new virus is much faster than starting from scratch, and AI may even have already simulated exactly such a variation.
AI has been involved in many parts of the fight against Covid, and we now have a much better idea than in 2020 of how to predict, diagnose and treat pandemics, especially similar viruses to Covid. So we can be cautiously optimistic that vaccine development for any future Covid-like viruses will be possible before it becomes a pandemic. Perhaps a trickier question is how well we will be able to respond if the next pandemic is from a virus that is nothing like Covid.
Was Rahman is an expert in the ethics of artificial intelligence, the CEO of AI Prescience and the author of AI and Machine Learning. See more at www.wasrahman.com
Article | February 28, 2020
An enormous amount of data is generated daily through various medium and amid this their storage becomes a great concern for organizations. Currently, two significant styles of data storage capacities are available Cloud and Data Centre.The main difference between the cloud vs. data centre is that a data centre refers to on-premise hardware while the cloud refers to off-premise computing. The cloud stores the data in the public cloud, while a data centre stores the data on company’s own hardware. Many businesses are turning to the cloud. In fact, Gartner, Inc. predicted that the worldwide public cloud services market has grown to 17.5 percent in 2019 to total US$214.3 billion. For many businesses, utilizing the cloud makes sense. While, in many other cases, having an in-house data centre is a better option. Often, maintaining an in-house data centre is expensive, but it can be beneficial to be in total control of computing environment.
Article | March 17, 2020
Technology is driving change in every industry and region around the world and insurance is no different. The financial services sector is a good example of how digitally disruptive technologies such as artificial intelligence, Big Data and mobile-first banking experiences have paved the way for innovative fintechs.The insurance industry is no different. According to a report by Accenture titled The Rise of Insurtech: How Young Startups and a Mature Industry Can Bring Out the Best in One Another, for example, there is a growing recognition that the insurance industry will ultimately see the greatest benefit and the highest levels of disruption - from this global upsurge in innovation”.