If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Technology is reshaping most activities humans do today. Concepts like Smart Farming have gained prominence as newer methods for crop and farm management are on the rise. It is making farming an efficient and profitable activity. Going by the estimates, there will be a 15% increase in the demand for agricultural products in the coming decade. Using tech solutions to cope up is an ideal way forward.
For the first time, researchers from the London School of Hygiene & Tropical Medicine, Mercator Research Institute on Global Commons and Climate Change and the University of Leeds deploy machine learning algorithms to scan evidence on climate change and health across the world. Funded by the Foreign, Commonwealth and Development Office, they used machine learning to map the global published evidence on climate change, weather and health from 2013 to 2020 and produce an online interactive results platform. The approach identified the effects on health of air quality and heat to be the most frequently studied in an evidence base dominated by studies from high-income countries and China. There is currently very limited evidence from low- and middle-income countries that suffer most from the health consequences of climate change. Evidence on the impact of climate change on mental health and on maternal and child health is extremely limited.
In this special guest feature, Stuart Gillen, Senior Manager at Kalypso, offers a few ways manufacturing organizations can leverage predictive maintenance to identify potential issues, reduce the occurrence and length of unplanned downtime, and get the most value from assets and budgets. Stuart is a proven leader passionate about AI and able to successfully work through the hype to provide clients actual implementation in IoT and Machine Learning projects which provide true business value and positive ROI. His areas of specialty include IoT architectures, platforms, and technologies. With testimonial success applying leading innovation capabilities, Stuart has a unique perspective on how clients can enhance their creative aptitude and maximize their return on innovation investments.. As manufacturers become increasingly connected, their systems, machines, sensors and other devices are generating a wealth of new data, and given the sheer volume of data generated, that isn't easily analyzed.
An AI identifies a person while they are walking on the street. As the AI market expands and AI use cases permeate every industry, every once in a while I hear the question - when will the AI singularity occur? For those who are not familiar with this term - the AI singularity refers to an event where the AIs in our lives either become self aware, or reach an ability for continuous improvement so powerful that it will evolve beyond our control. While this is a reasonable concern in the future, I argue that there are much more pressing concerns in the present - in particular that AI has reached a Tipping Point. A tipping point is a state where a technology grows and permeates our lives very rapidly, building upon itself.
Before I go any further it's probably worth establishing what a Deepfake is and isn't. A technique by which a digital image or video can be superimposed onto another, which maintains the appearance of an unedited image or video. The term is often misinterpreted, and that's potentially as a result of definitions like this. The concept of manipulating images and video in this way is certainly not a new concept. Visual effects artists working on Hollywood films back in the '90s would probably describe parts of their job as something very similar to this.
Using drones and artificial intelligence to monitor large colonies of seabirds can be as effective as traditional on-the-ground methods, while reducing costs, labor and the risk of human error, a new study finds. Scientists at Duke University and the Wildlife Conservation Society (WCS) used a deep-learning algorithm--a form of artificial intelligence--to analyze more than 10,000 drone images of mixed colonies of seabirds in the Falkland Islands off Argentina's coast. The Falklands, also known as the Malvinas, are home to the world's largest colonies of black-browed albatrosses (Thalassarche melanophris) and second-largest colonies of southern rockhopper penguins (Eudyptes c. chrysocome). Hundreds of thousands of birds breed on the islands in densely interspersed groups. The deep-learning algorithm correctly identified and counted the albatrosses with 97% accuracy and the penguins with 87%.
As we gain access to a greater depth and range of health-related information about individuals, three questions arise: (1) Can we build better models to predict individual-level risk of ill health? (2) How much data do we need to effectively predict ill health? (3) Are new methods required to process the added complexity that new forms of data bring? The aim of the study is to apply a machine learning approach to identify the relative contribution of personal, social, health-related, biomarker and genetic data as predictors of future health in individuals. Using longitudinal data from 6830 individuals in the UK from Understanding Society (2010-12 to 2015-17), the study compares the predictive performance of five types of measures: personal (e.g. age, sex), social (e.g. occupation, education), health-related (e.g. body weight, grip strength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotide polymorphisms (SNPs). The predicted outcome variable was limiting long-term illness one and five years from baseline. Two machine learning approaches were used to build predictive models: deep learning via neural networks and XGBoost (gradient boosting decision trees). Model fit was compared to traditional logistic regression models. Results found that health-related measures had the strongest prediction of future health status, with genetic data performing poorly. Machine learning models only offered marginal improvements in model accuracy when compared to logistic regression models, but also performed well on other metrics e.g. neural networks were best on AUC and XGBoost on precision. The study suggests that increasing complexity of data and methods does not necessarily translate to improved understanding of the determinants of health or performance of predictive models of ill health.
Electrical batteries are increasingly crucial in a variety of applications, from integration of intermittent energy sources with demand, to unlocking carbon-free power for the transportation sector through electric vehicles (EVs), trains and ships, to a host of advanced electronics and robotic applications. A key challenge however is that batteries degrade quickly with operating conditions. It is currently difficult to estimate battery health without interrupting the operation of the battery or without going through a lengthy procedure of charge-discharge that requires specialized equipment. In work recently published by Nature Machine Intelligence, researchers from the Smart Systems Group at Heriot-Watt University in Edinburgh, UK working together with researchers from the CALCE group at the University of Maryland in the US developed a new method to estimate battery health irrespective of operating conditions and battery design or chemistry, by feeding artificial intelligence (AI) algorithms with the raw battery voltage and current operational data. Darius Roman, the Ph.D. student that designed the AI framework said: "To date, the progress of data-driven models for battery degradation relies on the development of algorithms that carry out inference faster. Whilst researchers often spend a considerable amount of time on model or algorithm development, very few people take the time to understand the engineering context in which the algorithms are applied. By contrast, our work is built from the ground up. We first understand battery degradation through collaborations with the CALCE group at the University of Maryland, where in-house degradation testing of batteries was carried out. We then concentrate on the data, where we engineer features that capture battery degradation, we select the most important features and only then we deploy the AI techniques to estimate battery health."
Looking ahead, I'm excited about this month's launch of our new hardware and software platform called Microsoft Azure Percept. It's an entire platform that aims to simplify the ways customers can enable AI capabilities at the edge. It seamlessly integrates with other Azure services, including Azure Machine Learning, Azure Live Video Analytics, and Azure Cognitive Services, to help provide detailed insight. The platform also provides detailed vision and audio insights in real time.