Mediaboss Marketing

Astronomy is all about data. The universe is getting bigger and so too is the amount of information we have about it. But some of the biggest challenges of the next generation of astronomy lie in just how were going to study all the data were collecting.

To take on these challenges, astronomers are turning to machine learning and artificial intelligence (AI) to build new tools to rapidly search for the next big breakthroughs. Here are four ways AI is helping astronomers.

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 we 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. Nasas Kepler space telescope employed this technique to great success by watching thousands of stars at once, keeping an eye out for the telltale dips caused by planets.

Humans are pretty good at seeing these dips, but its a skill that takes time to develop. With more missions devoted to finding new exoplanets, such as Nasas (Transiting Exoplanet Survey Satellite), humans just cant keep up. This is where AI comes in.

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% accuracy.

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 mergers between black holes and neutron stars.

When these incredibly 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.

Read more: What happens when black holes collide with the most dense stars in the universe

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.

You wont be able to just log onto the servers and download that data, and even if you did, you wouldnt be able to find what youre looking for.

Machine learning techniques will be used to search these next-generation surveys and highlight the important data. For example, one algorithm might be searching the images for rare events such as supernovae dramatic explosions at the end of a stars life and another might be on the lookout for quasars. By training computers to recognise the signals of particular astronomical phenomena, the team will be able to get the right data to the right people.

As we collect more and more data on the universe, we sometimes even have to curate and throw away data that isnt useful. So how can we find the rarest objects in these swathes of data?

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.

Finding these lenses is like finding a needle in a haystack a haystack the size of the observable universe. Its a search thats only going to get harder as we collect more and more images of galaxies.

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. Surprisingly, these models were even better than people, finding subtle differences in the images that we humans have trouble noticing.

Over the next decade, using new instruments like the Vera Rubin Observatory, astronomers will collect petabytes of data, thats thousands of terabytes. As we peer deeper into the universe, astronomers research will increasingly rely on machine-learning techniques.

Continued here:
Four ways artificial intelligence is helping us learn about the universe - The Conversation UK

Using AI driven data analysis to peel back the noise and find the actual shape of the Universe. Credit: The Institute of Statistical Mathematics

Japanese astronomers have developed a new artificial intelligence (AI) technique to remove noise in astronomical data due to random variations in galaxy shapes. After extensive training and testing on large mock data created by supercomputer simulations, they then applied this new tool to actual data from Japans Subaru Telescope and found that the mass distribution derived from using this method is consistent with the currently accepted models of the Universe. This is a powerful new tool for analyzing big data from current and planned astronomy surveys.

Wide area survey data can be used to study the large-scale structure of the Universe through measurements of gravitational lensing patterns. In gravitational lensing, the gravity of a foreground object, like a cluster of galaxies, can distort the image of a background object, such as a more distant galaxy. Some examples of gravitational lensing are obvious, such as the Eye of Horus. The large-scale structure, consisting mostly of mysterious dark matter, can distort the shapes of distant galaxies as well, but the expected lensing effect is subtle. Averaging over many galaxies in an area is required to create a map of foreground dark matter distributions.

But this technique of looking at many galaxy images runs into a problem; some galaxies are just innately a little funny looking. It is difficult to distinguish between a galaxy image distorted by gravitational lensing and a galaxy that is actually distorted. This is referred to as shape noise and is one of the limiting factors in research studying the large-scale structure of the Universe.

To compensate for shape noise, a team of Japanese astronomers first used ATERUI II, the worlds most powerful supercomputer dedicated to astronomy, to generate 25,000 mock galaxy catalogs based on real data from the Subaru Telescope. They then added realist noise to these perfectly known artificial data sets, and trained an AI to statistically recover the lensing dark matter from the mock data.

After training, the AI was able to recover previously unobservable fine details, helping to improve our understanding of the cosmic dark matter. Then using this AI on real data covering 21 square degrees of the sky, the team found a distribution of foreground mass consistent with the standard cosmological model.

This research shows the benefits of combining different types of research: observations, simulations, and AI data analysis. comments Masato Shirasaki, the leader of the team, In this era of big data, we need to step across traditional boundaries between specialties and use all available tools to understand the data. If we can do this, it will open new fields in astronomy and other sciences.

Reference: Noise reduction for weak lensing mass mapping: an application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data by Masato Shirasaki, Kana Moriwaki, Taira Oogi, Naoki Yoshida, Shiro Ikeda and Takahiro Nishimichi, 9 April 2021, Monthly Notices of the Royal Astronomical Society.DOI: 10.1093/mnras/stab982

View post:
Astronomers Use Artificial Intelligence to Reveal the Actual Shape of the Universe - SciTechDaily

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.

Go here to read the rest:
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