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Soldiers from the 20th Armoured Infantry Brigade used an AI engine which provides information on the surrounding environment and terrain.

Through the development of significant automation and smart analytics, the engine is able to rapidly cut through masses of complex data. Providing efficient information regarding the environment and terrain, it enables the Army to plan its appropriate activity and outputs.

The deployment was a first of its kind for the Army. It built on close collaboration between the MOD and industry partners that developed AI specifically designed for the way the Army is trained to operate.

The lessons this has provided are considerable, not just in terms of our support to deployed forces, but more broadly in how we inform Defences digital transformation agenda and the best practices we must adopt to integrate and exploit leading-edge technologies.

This AI capability, which can be hosted in the cloud or operate in independent mode, saved significant time and effort, providing soldiers with instant planning support and enhancing command and control processes.

Announced by the Prime Minister last November, Defence has received an increase in funding of over 24 billion across the next four years, focusing on the ability to adapt to meet future threats. Further outlined in the Defence Command Paper, the MOD intends to invest 6.6billion over the next four years in defence research and development, focusing on emerging technologies in artificial intelligence, AI-enabled autonomous systems, cyber, space and directed energy systems.

This was a fantastic opportunity to use a new and innovative piece of technology in a deployed environment. The kit was shown to outperform our expectations and has clear applications for improving our level of analysis and speed at which we conduct our planning. Im greatly looking forward to further opportunities to work with this.

In future, the UK armed forces will increasingly use AI to predict adversaries behaviour, perform reconnaissance and relay real-time intelligence from the battlefield.

During the annual large-scale NATO exercise, soldiers from France, Denmark, Belgium, Estonia and the UK used the technology whilst carrying out live-fire drills.

Operation Cabrit is the British Armys deployment to Estonia where British troops are leading a multinational battlegroup as part of the enhanced Forward Presence.

Artificial Intelligence has already been incorporated in a number of key military initiatives, including the Future Combat Air System, and is the focus of several innovative funding programmes through the Defence and Security Accelerator.

Read more here:
Artificial Intelligence used on Army operation for the first time - GOV.UK

Some of the biggest challenges of the next generation of astronomy lie in studying all the data. To take on the challenges, astronomers are turning to machine learning and artificial intelligence (AI) to build new tools to rapidly search for the next big breakthroughs.

A research by Ashley Spindler from the department of Astrophysics, University of Hertfordshire, has thrown light on this, as reported by news agency PTI.

Here are the four ways in which AI is helping astronomers

1. Planet hunting: There are a few ways to find a planet but the most successful has been by studying transits. When an exoplanet passes in front of its parent star, it blocks some of the light which the humans can see.

By observing many orbits of an exoplanet, astronomers build a picture of the dips in the light, which they can use to identify the planets properties, such as its mass, size and distance from its star.

AI's time-series analysis techniques, which analyse data as a sequential sequence with time have been combined with a type of AI to successfully identify the signals of exoplanets with up to 96 per cent accuracy.

2. Gravitational waves: Time-series models arent just great for finding exoplanets, they are also perfect for finding the signals of the most catastrophic events in the universe.

When these dense bodies fall inwards, they send out ripples in space-time that can be detected by measuring faint signals here on Earth. Gravitational wave detector collaborations - Ligo and Virgo - have identified the signals of dozens of these events, all with the help of machine learning.

By training models on simulated data of black hole mergers, the teams at Ligo and Virgo can identify potential events within moments of them happening and send out alerts to astronomers around the world to turn their telescopes in the right direction.

3. The changing sky: When the Vera Rubin Observatory, currently being built in Chile, comes online, it will survey the entire night sky every night - collecting over 80 terabytes of images in one go - to see how the stars and galaxies in the universe vary with time. One terabyte is 8,000,000,000,000 bits.

Over the course of the planned operations, the Legacy Survey of Space and Time being undertaken by Rubin will collect and process hundreds of petabytes of data. To put it in context, 100 petabytes is about the space it takes to store every photo on Facebook, or about 700 years of full high-definition video.

4. Gravitational lenses: One celestial phenomenon that excites many astronomers is strong gravitational lenses. This is what happens when two galaxies line up along our line of sight and the closest galaxys gravity acts as a lens and magnifies the more distant object, creating rings, crosses and double images.

In 2018, astronomers from around the world took part in the Strong Gravitational Lens Finding Challenge where they competed to see who could make the best algorithm for finding these lenses automatically.

The winner of this challenge used a model called a convolutional neural network, which learns to break down images using different filters until it can classify them as containing a lens or not.

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Here's how artificial intelligence helping astronomers learn about the universe - Hindustan Times

The use of artificial intelligence in business is showing signs of acceleration. Nearly three-quarters of companies are now using AI (31%) or are exploring the use of AI (43%), according to IBM's "2021 Global AI Adoption Index."

IT professionals responding to the IBM survey cited changing business needs in the wake of the pandemic as a driving factor in the adoption of AI at their companies. Indeed, 43% said their companies have accelerated AI rollouts as a result of the pandemic.

Advances in AI tools have made artificial intelligence more accessible for companies, according to survey respondents. They listed data security, process automation and customer care as top areas where their companies were applying AI. Natural language processing (NLP) is at the forefront of AI adoption, the report found: Over half of businesses are using applications with NLP.

Business leaders, IT managers, executive advisors, analysts and AI experts interviewed for this article said they're not surprised by the expansion of AI in the enterprise. AI can significantly lower costs, increase efficiency and boost productivity as well as create avenues into new products, services and markets, they said.

Here are nine top applications of artificial intelligence in business and the benefits that AI brings. This is followed by a section on industry-specific AI use cases.

One of the most common enterprise use cases for AI centers around customer experience, service and support.

"The uses for AI that are really first and foremost in organizations are customer-facing types of things," said Seth Earley, author of The AI-Powered Enterprise and founder and CEO of Earley Information Science.

Chatbots, for example, use both machine learning algorithms and NLP to understand customer requests and respond appropriately. And they do that faster than human workers can and at lower costs.

AI also powers recommendation functions, which use customer data and predictive analytics to suggest products that customers are most likely to need or want and therefore buy.

Intelligent systems can help employees better serve customers, too, drawing on analytics similar to the ones used in chatbots and recommendation engines to give workers suggestions as they tend to customers.

"The system can propose next-best actions, how to take discussions with the customer further and how to present a certain targeted option," explained Alex Linden, an analyst and research vice president with Gartner who specializes in data science, machine learning and advanced algorithms.

Online search providers, online retailers and other internet entities use intelligent systems to understand users and their buying patterns, so they can select advertisements for the specific products that they're most likely to want or need.

"Every advertisement [on the internet] is placed by machines, and it's designed to optimize click-through rates," Linden said.

AI also helps businesses deliver targeted marketing in the real world, too. Some organizations have started combining intelligent technologies, including facial recognition and geospatial software along with analytics, using the technologies to first identify customers and then promote products, services or sales designed to match their personal preferences.

Organizations across industries are using AI to improve management of their supply chains. They're using machine learning algorithms to forecast what will be needed when as well as the optimal time to move supplies.

In this use case, AI helps business leaders create more efficient, cost-effective supply chains by minimizing and even possibly eliminating overstocking and the risk of running short on in-demand products.

Gartner, the tech research and advisory firm, predicted that 50% of supply chain organizations will invest in applications that support AI and advanced analytics capabilities between 2020 and 2024.

As developers of business process applications build AI-enabled capabilities into their software products, AI is becoming embedded across the enterprise.

"There is AI in all the functions that support the business, like human resources, finance and legal," said Beena Ammanath, executive director of Deloitte AI Institute. "The [software] itself is using AI, and the team members may be using the tool and might not even know that AI is being used in a way that's enabling their function."

AI, for example, can handle many customer requests; it can route customer calls not just to available workers but to those best suited to handle the specific needs.

Meanwhile, retailers are using AI for intelligent store design, optimized product selection and in-store activities monitoring. Some are using AI to monitor inventory on shelves in various ways, including for the freshness of perishable goods.

AI is also impacting IT operations. For example, some intelligence software applications identify anomalies that indicate hacking activities and ransomware attacks, while other AI-infused solutions offer self-healing capabilities for infrastructure problems.

AI is being used by a multitude of industries to improve safety.

Construction companies, utilities, farms, mining interests and other entities working on-site in outside locales or in spacious geographical areas are gathering data from endpoint devices such as cameras, thermometers, motion detectors and weather sensors. Organizations can then feed that data into intelligent systems that identify problematic behaviors, dangerous conditions or business opportunities and can then make recommendations or even take preventative or corrective actions.

Other industries are making similar use of AI-enabled software applications to monitor safety conditions. For example, manufacturers are using AI software and computer vision to monitor workers' behaviors to ensure they're following safety protocols.

Similarly, organizations of all kinds can use AI to process data gathered from on-site IoT ecosystems to monitor facilities or workers. In such cases, the intelligent systems watch for and alert companies to hazardous conditions -- such as distracted driving in delivery trucks.

Manufacturers have been using machine vision, a form of AI, for decades. However, they're now advancing such uses by adding quality control software with deep learning capabilities to improve the speed and accuracy of their quality control functions while keeping costs in check.

These systems are delivering a more precise, and ever-improving, quality assurance function, as deep learning models create their own rules to determine what defines quality.

Businesses are also using AI for contextual understanding. Linden pointed to the insurance industry's use of monitoring technologies to offer safe driving discounts as a case in point. AI is used in processing data about driving behavior to predict whether it is low or high risk. For example, driving 65 miles per hour is safe on a highway but not through an urban neighborhood; intelligence is needed to understand and report when and where fast driving is acceptable or not.

"Classifying the risk is to some extent AI," Linden explained.

AI is used in a similar manner in the emerging area of usage-based prices, he said. Turning again to the insurance industry as an example, he said providers could use AI to customize rates beyond the typical parameters of annual mileage and place of registration by understanding when, how and where -- perhaps even down to street level -- a vehicle is being driven

Optimization is another use case for AI that stretches across industries and business functions. AI-based business applications can use algorithms and modeling to turn data into actionable insights on how organizations can optimize a range of functions and business processes -- from worker schedules to production product pricing.

AI's potential impact on education is significant, with many organizations already using or exploring intelligence software to improve how people learn.

"There are so many ways that AI can be used to make learning better," Ammanath said, noting that use of AI in this space is still in its early stages. "This is the one area we will definitely see evolve over the next couple of years."

Ammanath said intelligent tools can be used to customize educational plans to each student's unique learning needs and understanding levels. Businesses, too, can benefit from AI-infused training software to upskill workers.

Although many AI applications span industry sectors, other use cases are specific to individual industry needs. Here are some examples:

See the rest here:
9 top applications of artificial intelligence in business - TechTarget

Healthcare organizations continue to make great strides in their use of artificial intelligence applications to improve patient care. But for AI systems to produce high-quality algorithms, they need large, diverse data sets.

Compiling these data sets can be a challenge because of regulatory and ethical obligations that restrict access to patient data. These obligations can lead chief medical informatics officers to adopt policies that forbid healthcare data from leaving an organization.

Reluctance to compile large data sets, driven by the increasing risks of financial penalties and reputational damage, clashes with the rapidly growing interest in creating and deploying medical AI models within healthcare. New solutions are needed that enable the training of AI models while also protecting patient privacy.

The amount of training data in medical imaging, especially publicly available data, is a fraction of what is available in other fields. The shortage of curated and representative data sets is one of the largest impediments to developing meaningful AI solutions for medical imaging, and the protection of patient privacy adds to the difficulty.

Recently, companies such as NVIDIA and Google have created software tools to enable data-distributed techniques for training AI. One example is federated learning, which works by deploying AI models to each participating institution in a discrete group (or federation). Models are then trained individually at each institution through exposure to local data. During training, models are periodically sent to a central federated server, where they are aggregated together. The aggregated model is then redistributed to each institution for further training. This is the key step in preserving privacy, as the models themselves consist only of parameters that have been tuned to data, not the protected data itself.

Over time, this process allows AI models to receive the benefit of knowledge learned at every institution within the federation. Once training is complete, a single aggregated model is produced that has been, indirectly, trained on data from all institutions in the federation.

MORE FROM HEALTHTECH:Whats next for AI in healthcare?

In a study published this year, our team in the UCLA Computational Diagnostics Lab investigated using a federated learning architecture to train a deep-learning AI model to locate and delineate the prostate within MRIs using data from different institutions. We found that federated learning produced an AI model that worked better on data from the participating institutions and on data from different institutions compared with models trained on one participating institutions data alone.

To understand the enthusiasm for federated learning, consider if organizations in a federation were smartphone users who had agreed to allow an algorithm to analyze the images on their phones, potentially allowing model training with distributed compute and data from a vast user group. From this perspective, one can imagine analogous scenarios in medicine in which patients can opt in to federations for compensation. This could speed up innovation within the medical AI space.

Successful medical AI algorithm development requires exposure to a large quantity of data that is representative of patients across the globe. Our findings demonstrate an alternative to the financial, legal and ethical complexities this has posed: Institutions can team up into federations and develop innovative, valuable medical AI models that can perform just as well as those developed through the creation of massive, siloed data sets, with less risk to privacy.

See more here:
How AI Learning Can Protect Patient Privacy and Still Offer Valuable Research - HealthTech Magazine

Cancer is the leading cause of death around the world and a key barrier in increasing life expectancy in almost every country. The World Health Organization estimates, between 2000-2019, cancer was the first or second leading cause of death before the age of 70 in 112 of 183 countries and ranks third or fourth in a further 23 countries.

For both sexes combined, one-half of all cases and 58.3% of cancer deaths were estimated to occur in Asia in 2020, where 59.5% of the global population resides. It is this part of the world which faces composite challenges in terms of cancer care: failure to translate policy and planning into action; resource constraints in terms of infrastructure and human resources; gaps in service availability; lack of spending on healthcare etc.

Emerging technologies are the fulcrum we need to bridge the healthcare divide in the continuum of care for cancer. Artificial intelligence ( AI) has emerged to be this game changer. AI-guided clinical care has the potential to play an important role in reducing health disparities, particularly in low-resource settings. Integration of AI technology in cancer care can improve the accuracy and speed of diagnosis, aid clinical decision-making, and lead to better health outcomes.

AI can play a key role in improving cancer screening, aid in the genomic characterization of tumours, accelerate drug discovery and improve cancer surveillance. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused on bringing AI technology to clinics safely and ethically.

Keeping in mind the alacrity of the diseases burden, the Centre for Fourth Industrial Revolution of the World Economic Forum India, has initiated a project Fourth Industrial Revolution for Sustainable Transformation (FIRST) of Cancer Care. The Indian Council of Medical research has projected that by 2025 India is expected to see a rise of 12% in the number of cancer cases, adding another 1.56 million to the disease burden.

The World Economic Forum was the first to draw the worlds attention to the Fourth Industrial Revolution, the current period of unprecedented change driven by rapid technological advances. Policies, norms and regulations have not been able to keep up with the pace of innovation, creating a growing need to fill this gap.

The Forum established the Centre for the Fourth Industrial Revolution Network in 2017 to ensure that new and emerging technologies will helpnot harmhumanity in the future. Headquartered in San Francisco, the network launched centres in China, India and Japan in 2018 and is rapidly establishing locally-run Affiliate Centres in many countries around the world.

The global network is working closely with partners from government, business, academia and civil society to co-design and pilot agile frameworks for governing new and emerging technologies, including artificial intelligence (AI), autonomous vehicles, blockchain, data policy, digital trade, drones, internet of things (IoT), precision medicine and environmental innovations.

Learn more about the groundbreaking work that the Centre for the Fourth Industrial Revolution Network is doing to prepare us for the future.

Want to help us shape the Fourth Industrial Revolution? Contact us to find out how you can become a member or partner.

The FIRST cancer care project focusses on leveraging emerging technologies like AI, internet of things (IoT) and blockchain, which can help provide accessible, affordable and quality healthcare in India. The strategy is being formulated by partners across government, clinicians, IT solution providers, academia and civil society organizations. Microsoft has been a key partner of the Forum, and this article highlights how the IT giant is using technology to face the cancer head on.

Microsoft is just one example which is changing the face of cancer care, likewise we see many start-ups which are coming forward to leverage this technology. As time progresses, we will see that by using an AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions.

Written by

Keren Priyadarshini, Regional Business Lead, Worldwide Health, Microsoft Asia

Ruma Bhargava, Project Lead, Fourth Industrial Revolution for Health, India, World Economic Forum, C4IR India

The views expressed in this article are those of the author alone and not the World Economic Forum.

Excerpt from:
These AI projects are improving cancer screening and outcomes - World Economic Forum