Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and tradeoffs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.

They want more intelligent applications for significant use cases such as real-time fraud prediction, a better customer experience, or faster, more accurate analysis of medical images. The problem facing most organisations is they store data in different forms and locations, each of which may belong to a business unit or department. Making this data usable by advanced applications is demanding. Before the advent of the new paradigm – the smart data fabric – the approach would have been to create a data lake or warehouse, using the relatively low cost of storage and compute. The organisation also likely then using time-consuming ETL processes to normalise the data. This approach, which is still in widespread use, has had its victories but creates a centralised repository that leaves data difficult to analyse and often fails to provide consistent or fast answers to business questions.

Hospitals in Victoria's South West, including public health agencies under the South West Alliance of Rural Health and Barwon Health in Geelong, are set to roll out a data platform capable of real-time analysis using AI, machine learning, as well as business and clinical intelligence. The health organisations will be deploying the IRIS for Health platform by global tech provider InterSystems. The data platform, according to InterSystems's website, is specifically engineered to extract value from healthcare data. It is a standards-based platform that is able to read and write Health Level 7's Fast Healthcare Interoperability Resources (HL7 FHIR) for developing healthcare applications. It is also capable of ingesting, processing and storing transaction data "at high rates" while simultaneously processing high volume analytic workloads involving historical and real-time data. While the health providers have interconnected systems, including clinical and patient administration systems, specialist healthcare applications and data analytics solutions, they don't have a single data repository supporting real-time data analysis.

Demand for these offerings is so high that businesses that are unable to deliver them, due to a lack of agility, are likely to become less meaningful to consumers and ultimately fall to the wayside. As organisations attempt to respond to consumers' changing requirements, artificial intelligence (AI) has been shown to be effective in helping them to provide the goods and services their customers desire. However, this technology is equally giving consumers themselves access to streamlined online tools which empower them to tailor products and services to their own personal preferences on demand. For instance, when booking a holiday, online platforms now allow consumers to build it from scratch themselves by sourcing different options for everything from flights and hotels, to car rentals and activities. While great for holidaymakers, this trend threatens the traditional package holiday and providers of those.

While healthcare has lagged behind other industries in the deployment of artificial intelligence (AI) and many other advanced technologies, the COVID-19 pandemic is proving to be the mother of invention when it comes to technological innovation. Machine learning -- a key part of AI where computer algorithms automatically improve through experience -- has been called upon to leverage healthcare data to help deal with many of the challenges COVID-19 has presented. Public health systems have turned to machine learning to complement their contact tracing and other efforts to control the disease and track outbreaks. Private healthcare operators have embraced machine learning to remain competitive when faced with a drop in demand for elective surgery or, in many countries, a reluctance or inability to visit hospitals or clinics. The pace of AI and machine learning adoption is also accelerating in hospitals.

In an increasingly digital world filled with chatbots, tap-and-go payments and "buy now, pay later" credit lines, hyper-personalisation is the new frontier on top of a new frontier in financial services. Hyper-personalisation enables financial services organisations to leverage the huge volumes of customer data they have in their systems efficiently and effectively to make more specific and more relevant product recommendations, such as an increase of a credit limit at the point of sale, or a list of previous interactions pushed to the chatbot, allowing it to pick up where the last left off. It does so by analysing the data available to it through the power of analytics, artificial intelligence (AI) and machine learning. It offers immense growth opportunities for all financial services providers if they can cater to small and specific groups. Hyper-personalisation can foster loyalty in an era in which loyalty has declined, and it pushes the next generation of consumers and investors towards those financial services which can be agile in what they offer.

When it comes to leveraging technologies like machine learning and artificial intelligence to enhance processes and improve business operations, many financial services firms know what they want but, to steal a line, "just don't know how to go about getting it." One of the keynote presentations at the upcoming FinovateWest Digital conference in November is designed specifically to address this problem. Jeff Fried, Director of Product Management for InterSystems, will provide a address titled The 7 Steps to Using Machine Learning to Improve Your Business that will give stakeholders key insights into the steps they can take to get their machine learning- and AI-based projects underway. "Continued advancements in ML and AI have huge potential in many domains," he wrote in a blog post titled Maximize Today's Downtime to Train ML Models for Tomorrow in August. "The key is to surface low-risk, high reward business solutions to ensure your organization continues to thrive, while also weathering the effects of an economic downturn."

In fact, more than three-quarters of UK businesses take a fortnight to complete this multi-step process, meaning by the time they get to the data it's outdated. This is a problem that is in dire need of rectification, particularly as in these uncertain times, businesses need quick access to data and insights in order to adapt to rapidly changing situations. Therefore, eliminating lengthy and time-consuming processes to be able to adopt a data-first approach to business is essential. The following three steps can help businesses in this endeavour, allowing them to put the processes and tools in place to turn data into actionable insights and deliver value back to not only their business, but also their customers. For many businesses, the focus tends to be on overarching organisational strategies as opposed to their top business priorities.

This video introduces the basic concepts of machine learning and some of its potential uses and benefits. You will also find a brief overview of IntegratedML in InterSystems IRIS Data Platform and see how you can implement machine learning in your applications directly from the SQL environment of InterSystems IRIS. If you would like to explore a wider range of topics related to this video, please check out the IntegratedML Resource Guide: https://learning.intersystems.com/cou... More about Machine Learning on InterSystems Developers: https://community.intersystems.com/ta... Ready to try InterSystems IRIS?

The coronavirus was declared a global emergency by the World Health Organization on January 30. AI is being employed extensively to track the spread of the new deadly virus, for now dubbed the 2019-novel coronavirus (2019-nCoV). Receiving fair attention as a result is BlueDot, a venture-backed startup that has built an AI platform to process billions of pieces of data, such as from world air travel, to identify outbreaks. BlueDot issued its first alert on Dec. 31, ahead of the US Centers for Disease Control and Prevention, which issued its own warning on Jan. 6, according to an account in Forbes. BlueDot was founded by Kamran Khan, an infectious disease physician and professor of Medicine and Public Health at the University of Toronto.