Big Data’s Moment in the Cloud Has Been Acknowledged

| January 29, 2016

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We were delighted to see the announcement of the latest version of Cloudera Director, and a corresponding write up on Curt Monash’s DBMS2 blog. The industry’s movement toward cloud-optimized features, such as support for Spot Instances and dynamic creation and termination of clusters, validates the direction that we’ve set for our company and product.

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iNovex Information Systems, Inc

GOT WICKED PROBLEMS? We innovate across the capabilities spectrum to deliver agile, breakthrough solutions. SOLVED. Our Agile methodology reduces the risks inherent in disruptive technology. We maintain an Independent Research and Development (IR&D) capability and lab using heterogeneous hardware in our OpenStack iLab that emulates the reference architectures of our customers to enable our engineers to conduct in-house research and training in Semantic SOA, big data and cloud-based applications.

OTHER ARTICLES

Will We Be Able to Use AI to Prevent Further Pandemics?

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. Prediction 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. Diagnosis 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. Treatment 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

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Value Vs Cost: 3 Core Components to Evaluate a Data and Analytics Solution

Article | March 9, 2021

All business functions whether it is finance, marketing, procurement, or others find using data and analytics to drive success an imperative for today. They want to make informed decisions and be able to predict trends that are based on trusted data and insights from the business, operations, and customers. The criticality of delivering these capabilities was emphasised in a recent report, “The Importance of Unified Data and Analytics, Why and How Preintegrated Data and Analytics Solutions Drive Busines Success,” from Forrester Consulting. For approximately two-thirds of the global data warehouse and analytics strategy decision-makers surveyed in the research, their key data and analytics priorities are:

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How can we democratize machine learning on IoT devices

Article | March 9, 2021

TinyML, as a concept, concerns the running of ML inference on Ultra Low-Power (ULP 1mW) microcontrollers found on IoT devices. Yet today, various challenges still limit the effective execution of TinyML in the embedded IoT world. As both a concept and community, it is still under development.Here at Ericsson, the focus of our TinyML as-a-Service (TinyMLaaS) activity is to democratize TinyML, enabling manufacturers to start their AI businesses using TinyML, which runs on 8, 16 and 32 bit microcontrollers.Our goal is to make the execution of ML tasks possible and easy in a specific class of devices. These devices are characterized by very constrained hardware and software resources such as sensor and actuator nodes based on these microcontrollers.Below, we present how we can bind the as-a-service model to TinyML. We will provide a high-level technical overview of our concept and introduce the design requirements and building blocks which characterize this emerging paradigm.

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Choosing External Data Sources: 4 Characteristics to Look For

Article | March 9, 2021

Decision-makers at consumer brands are finally realizing the full transformative potential of external data - but they’re also realizing how difficult it is to source. Forrester reports that 87% of decision-makers in data and analytics have implemented or are planning initiatives to source more external data. And those initiatives are growing outside of the IT team; 29% of those surveyed say that IT has primary ownership of data sourcing, down from 37% in 2016. To support these projects, organizations are increasingly turning to a new specialist: the data hunter, who identifies and vets external data sources. It’s a lot of work to build external data-focused teams, and many leaders are realizing that external data is difficult to scale as the source list grows. Perhaps that’s why 66% of those decision-makers surveyed by Forrester report that they’re using or planning to use external service providers for data, analytics, and insights.

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Spotlight

iNovex Information Systems, Inc

GOT WICKED PROBLEMS? We innovate across the capabilities spectrum to deliver agile, breakthrough solutions. SOLVED. Our Agile methodology reduces the risks inherent in disruptive technology. We maintain an Independent Research and Development (IR&D) capability and lab using heterogeneous hardware in our OpenStack iLab that emulates the reference architectures of our customers to enable our engineers to conduct in-house research and training in Semantic SOA, big data and cloud-based applications.

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