LONDON, Sept. 27, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the high energy lasers market, the emergence of artificial intelligence is gaining popularity among the high energy lasers industry trends. Many companies operating in the market are focused on developing AI-based products to get a competitive advantage.

For instance, in April 2022, the US Navy successfully tested the Layered Laser Defense (LLD), a laser weapon designed and developed by Lockheed Martin, a US-based aerospace, arms, defense, information security, and technology company. This is the Layered Laser Defense (LLD). It can use a high-power laser to counter unmanned aerial systems and fast-attack boats, as well as track inbound air threats, support combat identification, and conduct battle damage assessments of engaged targets. With specialized optics for viewing a target and directing laser beams for maximum effect, as well as artificial intelligence to improve tracking and aiming, LLD is compact and powerful yet more efficient than previous systems.

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The global high energy lasers market size is expected to grow from $10.42 billion in 2021 to $12.05 billion in 2022 at a compound annual growth rate (CAGR) of 15.7%. The global high energy lasers market growth is expected to reach $19.97 billion in 2026 at a CAGR of 13.5%.

The rising demand for laser weapon systems in defense is expected to propel the growth of the high-energy laser market going forward. Laser weapons systems are described as systems that solve recognized capability gaps against asymmetric threats (UAS [unmanned aerial systems], small boats, and ISR sensors). Laser weapon systems are highly necessary in the Navy and Air Force across the world to prevent airborne threats like missiles and drones, as lasers are effective against missiles and are being used as the first line of defense. For instance, in 2019, Northrop Grumman Corporation, a US-based aerospace and defense company, and the United States Air Force Research Laboratory (AFRL), inked a contract to develop an advanced laser system. The laser system has been used to defend US fighter jets against enemy missiles. Apart from that, in 2021, the DRDO (Defense Research and Development Organization), an India-based government agency aimed at the production of a high-power laser weapon, is eyeing a budget of $100 million from the ministry of defense. As a result, the market, which was valued at $7.4 billion in 2020, is expected to reach $14.7 billion by 2026. Therefore, rising demand for laser weapon systems in defense is driving the growth of the high energy market.

Major players in the high energy lasers market are SICK AG, Petasense Inc, Allegro MicroSystems, Inc., Robert Bosch GmbH, NXP Semiconductors NV, Infineon Technologies AG, Sensoronix, Inc., TE Connectivity, Inc., SPECTEC, Sensor Solutions Corporation, ABB, Rockwell Automation Inc., STMicroelectronics, Pepperl+Fuchs, and Schneider Electric.

The global high energy lasers market is segmented by product type into gas laser, fiber laser, solid state laser, excime laser, and by application into cutting, welding, and drilling, military and defence, and communications.

North America was the largest region in the high energy lasers market in 2021. Asia-Pacific is expected to be the fastest-growing region in the high energy lasers market during the forecast period. The regions covered in the global high energy lasers market research report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.

High Energy Lasers Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide high energy lasers market overviews, analyze and forecast market size and growth for the whole market, high energy lasers market segments and geographies, high energy lasers market trends, high energy lasers market drivers, high energy lasers market restraints, high energy lasers market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.

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The Emergence Of Artificial Intelligence Is A Key Trend In The High Energy Lasers Market As Per The Business Research Company's High Energy Lasers...

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Chip Market by Chip Type, by Application, by Architecture, by Processing Type, by End User - Global Opportunity Analysis and Industry Forecast, 2022-2030" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence (AI) Chip Market size was valued to USD 20.77 billion in 2021, and it will elevate to USD 304.09 billion by 2030, with a CAGR of 29.9% from 2022-2030.

Artificial Intelligence Chips are special silicon chips, programmed for machine learning. AI Chips can process vast amount of data, identify the underlying patterns, interpret the trends and utilize the feed to achieve specific goals. AI Chips are multi-functioning and can proficiently manage multiple operations at a time.

The demand for AI Chip is consistently raising due to the increasing adoption of artificial intelligence in almost every industry, in areas such as voice recognition, object detection, medical or military simulation, intelligent routing, and autonomous driving among others. Adoption of artificial intelligence not only reduces the cost of operations, but increases the efficiency or the response time and minimizes the risk to human life at various levels in specific industry verticals.

Market Dynamics and Trends

With the technological advancements the market is switching towards smart devices, smart homes, and smart cities, resulting to a tremendous elevation in the Artificial Intelligence (AI) Chip market. Furthermore, increased investments in AI start-ups along with emergence of quantum computers, are expected to elevate the market growth in future.

Other factors like wide application of AI technology and increase in robotics, catalyze to promote the market growth. However, high development cost and lack of skilled work-force tend to impede the growth of artificial intelligence (AI) chip market over the forecast period.

Moreover, the massive upsurge in research and development, increased use of autonomous robotic at various industry verticals, and high-tech product launches shall create new market-opportunities, fueling-up the growth-rate of artificial intelligence (AI) chip market, over the forecast period.

Companies Mentioned

Key Market Segments:

Global Artificial Intelligence (AI) Chip market - By Chip Type

Global Artificial Intelligence (AI) Chip market - By Application

Global Artificial Intelligence (AI) Chip market - By Architecture

Global Artificial Intelligence (AI) Chip market - By Processing Type

Global Artificial Intelligence (AI) Chip market - By End User

Global Artificial Intelligence (AI) Chip market - By Geography

For more information about this report visit https://www.researchandmarkets.com/r/508z6

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Global Artificial Intelligence Chip Market Report to 2030 - Increasing Focus on Human-Aware AI Systems and Development of Smarter Robots Presents...

The modern invention of drones and other high-tech products powered by AI technology can be great for some but an absolute nuisance for others. And firms getting harmed by them can now breathe a sigh of relief thanks to new EU Draft rules.

As reported by Reuters, its going to be much easier to get the compensation firms to feel they deserve from damage done through these devices.

By Wednesday, well be witnessing a few directives come our way via the European Commission. This is being done to mark the growing rage of AI-powered technology and to better provide the necessary guidance needed across 27 different nations of the EU.

Those trapped or victimized may put their cases forward in terms of damage done to their lives, health, privacy, and their property. This might be due to reasons like faults carried out by providers during the recruitment process, as delineated in the draft rules.

The regulations are outlining the huge amount of proof seen on victims by putting forward a term called presumption of causality. This means victims will only be required to put forward a reason that shows how a manufacturer failed to stay in line with certain rules and cause harm. Then, this would be linked to AI technology in the case.

Under the right to gain access to the cases evidence, all victims may request the court to force companies to give more information about high-risk AI systems. This way, they can better identify who is responsible and what really went wrong.

In the same way, well be seeing the executive provide updates to its directives linked to Product Liability. This sets the tone for all products that are defective and arise from smart technology to pharmaceuticals and even machines.

These new changes are going to provide users with the right to sue whenever a software update possesses the likelihood of harm to their devices or even when producers arent able to amend security gaps.

However, those having dangerous products that are non-EU based wont have this right under the new system of compensation.

For now, the new directive needs to be issued a green light from various countries in the European Union before it can turn into law.

But just the thought of being compensated for the harm done through such technology has many companies excited.

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Companies Suffering Harm From Drones And Artificial Intelligence Products Can Now Sue Under EU Draft Rules - Digital Information World

The COVID-19 pandemic has not only changed how we interact but also how we do business. At the start of 2020, almost all sectors were forced to operate from home, a scale unimaginable in the 21st century. Naturally, it forced companies to think out of the box and come up with ways for talent retention and acquisition. There have been a lot of talks about incorporating AI into the Human Resource Department (HRD) to look for the ideal candidate and to check on employees' well-being.

While that reality is still a dream, some are looking for ways to upgrade their Human Resource department, especially in this hybrid work environment.

To learn more about the future of talent acquisition and retention, India Today Tech spoke to Deepti Varma, Vice President, People and Experience Technology (PXT) at Amazon Stores India and EM. The senior Amazon executive also spoke about the role of technology in providing a better employee experience.

During the conversation, Varma even clarified that Amazon brands its HR department as PXT (People Experience and Technology Solutions). That's because the company does a "hypothesis across talent acquisition, talent retention, talent development" and then looks at data to find the best solution.

In 2022, how much relevance do you think traditional CVs and online profiles possess during a hiring process?

At Amazon, more than just the skills, we believe that we need to focus on whether the person is culturally fitting into the company. So, even before we start trying to look at skill, we first check whether the person would be able to align themselves to the culture. So, the traditional CV can do only a little justice with what the person would have to offer. I think there is a lot that we gather through a very intensive interview process that we have, and we kind of rely on that a lot.

Can you elaborate a bit on this process? Are you relying on technology or some kind of tool?

We use a lot of AI and have quite a lot of automated tools that we use in order to hire. In fact, as we speak, in the US, we are experimenting with 'no see hire', which means that without actually asking the person to come for technical roles, can we look at hiring people based on some of the simulations that we have made, and can we, try to reduce the dependencies on a recruiter. So we are trying to do a lot of these things, in fact, because the number of people that we hire is so many, we don't believe in a traditional way of scheduling interviews. All we use a tool to do that [hire] where the candidate as well as the person who's interviewing, their schedule gets matched and there is a schedule that gets popped up on its own.

Do you think a maximum dependency on tech like AI is ideal for this segment of business?

I am going to give my view and, it can be very different from what industry experts feel.

The short answer to your question is that both algorithms, as well as human decisions, can be flawed. It cannot be either or it cannot be too much usage of technology or too much usage of human intervention that would help us to scale in the future. But I feel technology certainly would be a great enabler for us to focus on the things that can be simplified in order for us to scale talent acquisition in the future.

And I'll give you a very simple example of that. Today, when you look at an Amazon.in website, no one goes around training you, this is how you need to show up at Amazon. So, the way we have used the technology is, that it should be simple and scalable. It should be intuitive, and you don't need to get trained on it. The way we want to use technology is that it should be simple, scalable, should have a good candidate experience because, for us, we always work backwards from the customer's need, but it should be something that should be an enabler rather than being something that we solely depend on.

Can you elaborate on some tools and techniques that Amazon uses for acquisition, data retention, and development?

I would start with something that we actually experimented during COVID time. We launched something called Alexa onboarding. We used Alexa to onboard our employees. And Alexa would answer a lot of questions. Because people wanted to understand about Amazon's culture, people wanted to understand about how should they navigate the ways in first 30 days. In fact, we got an Economic Times award for the experiment.

Then, during COVID times, we were hiring from campus, and we were hiring quite a lot. We thought, are we missing out on women-talent, so we launched something called as 'Amazon Wow', where we were trying to hire through technology. And this is a technology portal that we started where women, students from all across India, engineering college could apply.

There is one more thing which is very close to my heart, which I am sure you would have heard about is 'Amazon Connections'. And what Connection basically does is that as soon as anyone across the globe open their laptop early in the morning, the first thing - it just pops a question onto your screen to understand how you are feeling. Amazon believes that you should be checking how the employees feeling throughout the year. So, every day there is one question that gets asked to you on different aspects that are important.

No technology is perfect. How do you think Amazon can then evolve these tools that it is using?

If a technology can be used to proactively like if machine learning can be used to proactively assess if the person is having stress or may be going towards a situation where they could experience stress and you can proactively, just take care of that employee that would be more meaningful. And I don't think we've reached that stage. But

The vision is such that we would want to use technology to proactively predict what's coming. And how do we help the employee in order to handle that situation? So that is something we haven't yet been able to crack. But that is something that is one of the visions that we have that we should see. There are other places where we feel we could use technology more. This is just one example that I wanted to share.

(Conversation with Deepti Varma has been edited for clarity)

--- ENDS ---

Read more:
Can artificial intelligence replace HR department? Amazon on future of hiring and talent retention - India Today

By Richard Johnstone on 28/09/2022 | Updated on 28/09/2022

Image by AkshayaPatra Foundation from Pixabay

The United Nations education agency has called on the Indian government to take a host of actions to boost public trust and understanding in the use of artificial intelligence in education.

In its annual report on thestate of education in the worlds largest democracy, The United Nations Educational, Scientific and Cultural Organization (UNESCO) focused on the potential role for AI in schools. Indias 2020 National Education Policy has called for increased technical knowledge at all levels of education, including the integration of AI to promote quality and skill-based education.

UNESCO said that the use of AI had endured various misconceptions, and highlighted that AI could have a number of positive uses in the Indian education system. These included using intelligent tutoring systems to conduct assessments, track learning progress and provide regular individual feedback, and the development of smart schools and universities to deliver quality content to remote locations using AI techniques like facial and speech recognition alongside augmented and virtual reality.

However, its recommendations called for an increased focus on improving trust in artificial intelligence. In particular, the review called on the government to consider the ethics of artificial intelligence in education as an utmost priority as well as rapidly providing an overall regulatory framework for artificial intelligence in education.

Read more: Artificial intelligence in the public sector: an engine for innovation in government if we get it right

Other recommendations in the report included a need to improve public trust in artificial intelligence, with options including more work by government to expand AI literacy efforts, and attempting to correct algorithmic biases and the resulting discrimination.

The recommendations come as the Indian government aims to improve its digitisation in a host of areas through its Digital India programme, which is intended to transform the country into a digitally empowered society and knowledge economy.

Eric Falt, the director of UNESCOs New Delhi office, said that while improving the quality of education and the learning outcomes of students are the utmost priorities of all countries, India has made significant strides in its education system.

Strong indicators point to the countrys notable efforts to enhance learning outcomes, including by using artificial intelligence-powered education technology, he said.

Read more: Canada to create top official to police artificial intelligence under new data law

Dr. Rajendra Kumar, the additional secretary in Indias Ministry of Electronics and Information Technology, said the ministry was really pleased to note that UNESCO has dedicated the theme of its 2022 State of the Education Report for India to artificial intelligence in education, adding: We believe that this report is timely, as it can contribute towards transforming India into a digitally empowered society and knowledge economy and make Digital India a reality.

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Indian government must address AI ethics in education 'as utmost priority' - Global Government Forum

There has never been a better time to be a CIO. In todays challenging macro-economic environment with high inflation, constrained supply chains, talent shortages and hybrid work among other challenges the technology strategy drives the business strategy. Hence, the role of the CIO is essential to every organisations success.

With companies facing multiple challenges in todays economy, its crucial they uphold customer loyalty at a time when so many other uncertainties threaten business growth. CIOs will need to make the most of emerging technologies in the enterprise, such as artificial intelligence (AI), to maintain loyalty and enhance the customer journey especially when consumer expectations remain high.

One focus area is utilising AI to enable account teams to make more informed decisions. Customer-facing staff have traditionally relied on past experiences and instincts to help manage expectations. However, connected AI models can make predictions to power decision-making capabilities internally, and these powerful insights deliver value for clients and employees alike. For instance, a decision-assist approach uses AI to give staff recommendations on how to optimise each customers user experience.

Lets say a business hires a new account executive in the sales team and they manage some of the key accounts. At any point in time, there are hundreds of actions they could perform on the customer to change the trajectory of the relationship. For example, they could fix an implementation issue, suggest tactics to increase product usage, encourage the client to take a training course, or fix a support issue.

An AI system will be the repository of things that happened in the past, things happening right now, and what could happenin the future

By creating an AI system, a company can subsequently amass the customers marketing, sales, success, product, and support interactions with their business. These interactions are organised, connected and enriched in the sales system. Companies can then run various action scenarios for every customer to figure out the best actions to yield a better relationship. It learns from the past successful actions based on the results and prioritises it for the future. In this way, the system learns from the best and scales everyone to be the best.

An AI system will learn from real-time feedback on the recommendations and the actions taken towards each customer. It will be the repository of things that happened in the past, things happening right now, and what could happen in the future.

A growing number of companies are realising that AI can also help reduce customer churn, a significant risk for any business. Preventable churn is when a company can intervene when the risk is known in advance and issues can be avoided. By analysing why certain client relationships have broken down and contrasting that with successful customer journeys, businesses can better understand the reasons for churn.

These reasons include product usage, customer experience, feedback, engagement and training data. When a new customer exhibits these risk factors, an AI model can assign the churn a risk score, which is computed for all the clients. Those with the highest risk scores are then used for intervention actions based on the specific risk factors to mitigate the churn.

This complements ERP as every action a company takes to improve its customer experience is an investment. Many of these actions consume resources and must be planned accordingly. AI-driven actions are not only creating positive value for clients but also increasing loyalty and driving business revenue. This shows how important technology is to a companys business strategy and proves theres never been a better time to be a CIO.

Chris Bedi is chief digital information officer, ServiceNow

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Artificial intelligence will revolutionise the customer journey ERP Today - ERP Today

Opinion: AI can now help write essays that appear highly similar, if not indistinguishable, from that of a human author

By Eamon Costello and Mark Brown, DCU

Artificial intelligence-powered automatic essay writing technologies have taken a huge leap forward and are becoming widely available. In many cases, AI can help write essays that appear highly similar, if not indistinguishable, from that of a human author.

As this piece was being typed, Google AI helpfully suggested words to auto-complete these sentences. This speeds up writing but other tools and services, such as those based on the GPT-3 technology from the OpenAI group, can do much more than simple sentence completion: they can write the whole thing from scratch from start to finish.

AIs can generate surprising comprehensible text

By simply taking a prompt of a sentence or phrase from a human author, AIs can generate surprising comprehensible text. When the prompt "when an AI wrote my essay" was typed into a freely available online AI text generator to see what it would produce, the AI wrote: the following

"And don't forget", the professor said, "AIs are not allowed to write your essay or help you write your essay in any way" I groaned and rolled my eyes, thinking that I knew what he was going to say. I had never been good at writing essays, and I knew that an AI would only make it worse. But when I sat down to start it, I found myself in the middle of a beautiful essay. The words flowed easily from my fingers, and I was suddenly inspired. I had never written so well, and I was surprised that the AI had written it for me. I finished the essay and handed it in, feeling proud of myself. I didn't know what the professor would say, but I was sure that I had done a better job than if I had written it myself.

One can see how a student could generate text in this way, perhaps generating several texts, seeing which one suited best and then lightly editing for clarity. AI can also take large bodies of text as input and attempt to summarize them.

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From RT Radio 1's Today with Claire Byrne, Brian O'Connell reports on student essay mills

A student could prompt an AI to write several paragraphs and could then select which paragraphs seemed most well written and coherent. Finally, they could feed these paragraphs back to the AI asking it to summarise them. This could form the basis of a summary/conclusion section of an essay.

Using AI in this way requires particular skills. Just as correct spelling is becoming less critical with the ability of autocorrect, perhaps essay writing will evolve similarly. It may be that writers in the future engage in the higher level activity of orchestrating a composition, while AI does the heavy lifting of producing the actual sentences.

If using a spell checker isn't seen as cheating, will the use of AI for essay writing then be accepted as the new normal? Perhaps future students will use AIs to write their essays, while professors deploy AIs to check their authenticity.

Of course AIs can grade essays too, but does this mean that teachers will have less work?

Of course AIs can grade essays too, but does this mean that teachers will have less work? The jury is still out on this question: one major review of the research on AI in education found a conspicuous absence of reference to actual teachers. One scenario is a teacherless future where students are accelerated through courses of study by advanced robo-Profs.

A contrasting future has been foreseen by AI education expert Peirre Dillenbourg. He has predicted that we will have more teachers in the future, not less. He foresees teachers working in teams to oversee and design learning scenarios using multiple AIs dedicated to specific educational tasks.

That is the future taken care of but what about the present? Universities worldwide currently invest heavily in anti-plagiarism and academic integrity technologies. Many of these systems have been termed 'data-extractive', in that they often rely on extracting and mining large bodies of student work. At their worst these expensive systems can create climates of fear., where students feel they are being policed by big brother or sister.

With all of the fuss about AI, it is worth remembering that people are always at the heart of education

AI essay writing may be seen as just another chapter in the long history of so-called "essay mills", services that students can use to commission and buy their homework from. Will AI make these services redundant in the future? What constitutes cheating and breaches of academic integrity in the world of AI? After all, irrespective of how we define cheating, who loses if the student does not fully engage in their own learning?

Something that educators can do is to have conversations with students about their learning and especially their assessment. A guiding principle should be that a student will always want to do the work themselves given the right conditions. This is the opposite of a starting principle that says: every student is a potential cheater.

Assessment mixes that are not completely dependent on traditional essays can allow students to express themselves in a variety of ways. Do we try to tame AI to protect old ways of learning or should we embrace its potential and reimagine our assessment practices to reflect the modern reality of living in the 21st century? One creative educator had his students purposefully use and evaluate AI essay writers as part of their assignment.

With all of the fuss about AI, it is worth remembering that people are always at the heart of education. Student and teacher workloads should be key considerations in the design of assessment. Giving each other space to build trusting environments in which to teach and learn will require much human ingenuity, care and intelligence.

Dr Eamon Costello is an Associate Professor of Digital Learning at the DCU Institute of Education. Professor Mark Brown is Chair of Digital Learning and Director of the National Institute for Digital Learning at DCU.

The views expressed here are those of the author and do not represent or reflect the views of RT

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Will artificial intelligence be able to write my college essay? - RTE.ie

CLEVELAND, Ohio The city of Cleveland will work with Case Western Reserve University and Cleveland State University on a solution for illegal dumping thats powered by artificial intelligence.

The end product will ideally provide new city-owned technology that Cleveland could use to identify people responsible for dumping, according to Roy Fernando, chief innovation and technology officer under Mayor Justin Bibb, who has promised to use technology to improve city services.

Cleveland City Council on Monday approved legislation allowing students and faculty, who are part of the two universities Internet of Things Collaborative, to commence work. It was one of two initiatives approved this week that is intended to bring smart technology to city devices and operations.

Students and staff will use smart cameras to develop and test an AI model designed to identify illegal dumping. Such work would be performed in a controlled-environment, likely on-campus, where students will walk into the field of view of a monitor and leave an item behind, Fernando said.

Once the model has been tweaked and perfected, it would be able to identify that person as having illegally dumped the item on the ground, Fernando said.

Then, the city intends to deploy smart cameras outfitted with the new technology on two corridors known for being dumping hotspots. One would be deployed on the citys East Side, and one on the West Side, Fernando said.

Once someone dumps an item and the AI model detects it, it would automatically alert authorities, so they could investigate and potentially ticket whoevers responsible.

If the test projects are successful, the technology could then be scaled-up for use elsewhere in Cleveland. The technology could also serve as a guide, of sorts, for creating different smart-city solutions for other problems, Fernando said.

Ward 3s Councilman Kerry McCormack, who has long advocated for Cleveland to begin using smart-city technology, praised the idea during a Monday committee hearing. He identified illegal dumping as one of the citys largest problems.

Ward 14s Councilwoman Jasmin Santana, who said illegal dumping has been a big concern in alleyways in her neighborhood, was a bit skeptical. We [already] know the hotspots for illegal dumping. Thats not the question, Santana said. [The issue is] capacity within the illegal dumping task force, and cameras.

The second smart-city initiative approved by Council on Monday was a no-cost partnership with Honeywell, a manufacturing and technology company, to develop a smart city roadmap that could be used to guide Clevelands future use of technology in delivering city services.

Cleveland was one of five cities selected for the partnership by Accelerator for America, which is a coalition of U.S. mayors that seeks and shares innovative solutions for problems commonly faced by municipalities.

Technology advancements identified by Honeywell could relate to any number of city services or needs. Examples mentioned by Fernando and McCormack include uses for transportation, sustainability, smart buildings, smart sensors embedded in roads or other infrastructure, meter-reading for utilities, making traffic lights more efficient, or monitoring air quality or waste collection.

Over a two- or three-month period, Honeywell will interview leaders of several city departments about challenges they routinely face. Honeywell will then present findings about how to address those challenges with smart technology, Fernando said.

Bibb intends to use those findings and recommendations to apply for federal grants that would be used to pay for the needed technology upgrades, he said.

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Cleveland wants to use artificial intelligence to fight illegal dumping - cleveland.com

SAN FRANCISCO, Sept. 20, 2022 /PRNewswire/ --The global AI in retail market size is anticipated to reach USD 40.74 billion by 2030, expanding at a CAGR of 23.9% from 2022 to 2030, according to a new study by Grand View Research, Inc. The rising prominence of advanced technologies, such as chatbots and voice recognition programs, has furthered the growth potential. Moreover, the emerging online retail sales, increasing focus of retailers on improving customers' shopping experience, rising reliance on digital marketing, and growing investments in AI, accompanied by supportive government regulations, are the crucial factors contributing to the progress of the industry worldwide.

Key Industry Insights & Findings from the report:

Read 145 page full market research report for more Insights, "AI In Retail Market Size, Share & Trends Analysis Report By Component, By Technology (Chatbots, Natural Language Processing), By Sales Channel, By Application, By Region, And Segment Forecasts, 2022 - 2030", published by Grand View Research.

AI In Retail Market Growth & Trends

AI algorithms play a pivotal role in assessing a considerable amount of data collated from consumers' online behavior. Moreover, AI-driven image and video analytics have become trendier to help filter out and classify images and less important visual content, expediting investments across developed and developing economies.

Adopting AI in retail helps provide better outcomes and engage customers in the virtual world, which is expected to increase demand for AI in retail in the coming years. For instance, in January 2021, Google LLC launched Product Discovery Solutions for Retail. This product is a suite of services created to improve retailers' e-commerce potential and aid them in delivering personalized consumer experiences.

The image and video analytics segment is poised to gain a significant share in the AI in retail market during the assessment period, partly due to the growing prominence of in-store promotional strategies and image search. To illustrate, eBay uses AI to streamline image searches, enhance buyer-seller trust, and boost shipping and delivery times. Meanwhile, Amazon prioritizes AI to expand visual search and facial recognition, among others.

Stakeholders anticipate the virtual assistant segment to contribute significantly to the global market. The trend is mainly attributed to the growing prominence of voice-powered search queries and personalized shopping experiences. Prominently, intelligent virtual assistants have reshaped the industry dynamics, helping retailers handle customer queries seamlessly.

Major players in the market are consistently investing in advanced technologies and introducing customer targeting and tailored solutions to stay ahead of the competition. For instance, in August 2020, Kenco, a company that provides logistics services based in the U.S., launched DaVinci AI to create predictive insights, boosting supply chain and prescriptive actions.

AI In Retail Market Segmentation

Grand View Research has segmented the global AI in retail market based on component, technology, sales channel, application, and region:

AI In Retail Market - Component Outlook (Revenue, USD Million, 2017 - 2030)

AI In Retail Market - Technology Outlook (Revenue, USD Million, 2017 - 2030)

AI In Retail Market - Sales Channel Outlook (Revenue, USD Million, 2017 - 2030)

AI In Retail Market - Application Outlook (Revenue, USD Million, 2017 - 2030)

AI In Retail Market - Regional Outlook (Revenue, USD Million, 2017 - 2030)

List of Key Players in the AI In Retail Market

Check out more related studies published by Grand View Research:

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Newswise (New York, NY September 20, 2022) Mount Sinai researchers have used novel artificial intelligence methods to examine structural and cellular features of human brain tissues to help determine the causes of Alzheimers disease and other related disorders. The research team found that studying the causes of cognitive impairment by using an unbiased AI-based methodas opposed to traditional markers such as amyloid plaquesrevealed unexpected microscopic abnormalities that can predict the presence of cognitive impairment. These findings were published in the journal Acta Neuropathologica Communications on September 20.

AI represents an entirely new paradigm for studying dementia and will have a transformative effect on research into complex brain diseases, especially Alzheimers disease, said co-corresponding author John Crary, MD, PhD, Professor of Pathology, Molecular and Cell-Based Medicine, Neuroscience, and Artificial Intelligence and Human Health, at the Icahn School of Medicine at Mount Sinai. The deep learning approach was applied to the prediction of cognitive impairment, a challenging problem for which no current human-performed histopathologic diagnostic tool exists.

The Mount Sinai team identified and analyzed the underlying architecture and cellular features of two regions in the brain, the medial temporal lobe and frontal cortex. In an effort to improve the standard of postmortem brain assessment to identify signs of diseases, the researchers used a weakly supervised deep learning algorithm to examine slide images of human brain autopsy tissues from a group of more than 700 elderly donors to predict the presence or absence of cognitive impairment. The weakly supervised deep learning approach is able to handle noisy, limited, or imprecise sources to provide signals for labeling large amounts of training data in a supervised learning setting. This deep learning model was used to pinpoint a reduction in Luxol fast blue staining, which is used to quantify the amount of myelin, the protective layer around brain nerves. The machine learning models identified a signal for cognitive impairment that was associated with decreasing amounts of myelin staining; scattered in a non-uniform pattern across the tissue; and focused in the white matter, which affects learning and brain functions. The two sets of models trained and used by the researchers were able to predict the presence of cognitive impairment with an accuracy that was better than random guessing.

In their analysis, the researchers believe the diminished staining intensity in particular areas of the brain identified by AI may serve as a scalable platform to evaluate the presence of brain impairment in other associated diseases. The methodology lays the groundwork for future studies, which could include deploying larger scale artificial intelligence models as well as further dissection of the algorithms to increase their predictive accuracy and reliability. The team said, ultimately, the goal of this neuropathologic research program is to develop better tools for diagnosis and treatment of people suffering from Alzheimers disease and related disorders.

Leveraging AI allows us to look at exponentially more disease relevant features, a powerful approach when applied to a complex system like the human brain, said co-corresponding author Kurt W. Farrell, PhD, Assistant Professor of Pathology, Molecular and Cell-Based Medicine, Neuroscience, and Artificial Intelligence and Human Health, at Icahn Mount Sinai. It is critical to perform further interpretability research in the areas of neuropathology and artificial intelligence, so that advances in deep learning can be translated to improve diagnostic and treatment approaches for Alzheimers disease and related disorders in a safe and effective manner.

Lead author Andrew McKenzie, MD, PhD, Co-Chief Resident for Research in the Department of Psychiatry at Icahn Mount Sinai, added: Interpretation analysis was able to identify some, but not all, of the signals that the artificial intelligence models used to make predictions about cognitive impairment. As a result, additional challenges remain for deploying and interpreting these powerful deep learning models in the neuropathology domain.

Researchers from the University of Texas Health Science Center in San Antonio, Texas, Newcastle University in Tyne, United Kingdom, Boston University School of Medicine in Boston, and UT Southwestern Medical Center in Dallas also contributed to this research. The study was supported by funding from the National Institute of Neurological Disorders and Stroke, the National Institute on Aging, and the Tau Consortium by the Rainwater Charitable Foundation.

About the Mount Sinai Health System

Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 43,000 employees working across eight hospitals, over 400 outpatient practices, nearly 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it.

Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients medical and emotional needs at the center of all treatment. The Health System includes approximately 7,300 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News & World Report's Best Hospitals, receiving high Honor Roll status, and are highly ranked: No. 1 in Geriatrics and top 20 in Cardiology/Heart Surgery, Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News & World Reports Best Childrens Hospitals ranks Mount Sinai Kravis Children's Hospital among the countrys best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is one of three medical schools that have earned distinction by multiple indicators: It is consistently ranked in the top 20 by U.S. News & World Reports Best Medical Schools, aligned with a U.S. News & World Report Honor Roll Hospital, and top 20 in the nation for National Institutes of Health funding and top 5 in the nation for numerous basic and clinical research areas. Newsweeks Worlds Best Smart Hospitals ranks The Mount Sinai Hospital as No. 1 in New York City and in the top five globally, and Mount Sinai Morningside in the top 30 globally; Newsweek also ranks The Mount Sinai Hospital highly in 11 specialties in Worlds Best Specialized Hospitals, and in Americas Best Physical Rehabilitation Centers.

For more information, visit https://www.mountsinai.org or find Mount Sinai on Facebook, Twitter and YouTube.

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Mount Sinai Researchers Use Artificial Intelligence to Uncover the Cellular Origins of Alzheimers Disease and Other Cognitive Disorders - Newswise