Category Archives: Artificial Intelligence

About 35 percent of healthcare organizations plan to leverage artificial intelligence within two years and more than half intend to do so within five.

Thats according to the Healthcare IT News and HIMSS Analytics HIT Market Indicator: Artificial Intelligence.

[Also:AI, machine learning will shatter Moore's Law in rapid-fire pace of innovation]

If you look at those with plans to leverage AI in some way, shape or form, were going to see significant growth, said Brendan FitzGerald, director of research at HIMSS Analytics.

While only 4.7 percent of the 85 survey respondents are already using AI technologies, the future looks promising. Indeed, 10.6 percent plan to adopt them within 12 months, 23.5 percent indicated they will within two years and another 24.7 percent in three to five years.

Participants ranked population health, clinical decision support, patient diagnosis and precision medicine, respectively, as the top places AI will have the most substantial initial impact.

Other areas include hospital and physician workflow, security, revenue cycle and drug discovery.

As with most emerging technologies, of course, there are barriers to adopting AI. Chief among those is the feeling that AI technologies are still in early stages of development and a difficulty understanding the immediate opportunities.

[Also:More than half of hospitals hit with ransomware in last 12 months]

There are a number of technologies that require considerable fine-tuning for the healthcare space and I dont think AI is any different, FitzGerald said.

FitzGerald cautioned that vendors will be trying to keep pace with the AI gold rush the way many did with population health and telemedicine to show theyre on top of the latest and greatest technologies in healthcare and ahead of where people are going, even some who are not actually AI companies.

[Also:Hospitals rank population health, value-based care, patient experience as top strategic drivers of precision medicine]

Once the industry has a handle on how to implement AI at the point of care, Fitzgerald said, the ability for AI to present clear and present opportunities may escalate adoption.

Healthcare IT News and HIMSS Analytics are collaborating on HIT Market Indicator studies to highlight hot button issues across the healthcare IT landscape. This survey comprised responses from CEOs, CIOs, CFOs, COOs, chief medical information officers, chief clinical officers, as well as professionals working at the IT-director level and above.

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Half of hospitals to adopt artificial intelligence within 5 years - Healthcare IT News

Researchers highlight the 'dark side' ofAI and question whether humanity can ever truly understand its most advanced creations

WHAT sets humansapart from machines is the speed at which we can learn from our surroundings.

But scientists have successfully trained computers to use artificial intelligence to learn from experience and one day they could be smarter than their creators.

Now scientists have admitted they are already baffled by the mechanical brains they have built, raising the prospect that we could lose control of them altogether.

Computers are already performing incredible feats such as driving cars and predicting diseases but their makers say they arent entirely in control of their creations.

This could have catastrophic consequences for civilisation, tech experts have warned.

Take thestrange driverless car which appeared on the streets of New Jersey, US, last year.

It differed fromGoogle, Tesla or Ubers autonomous vehicles, which follow the rules set by tech developers to react to scenarios while on the road.

This car couldmake its own decisions after watching how humans learnt how to drive.

And itscreators, researchers at chip making company Nvidia (who supply some of the biggest car makers with supercomputer chips), said they werent 100 per cent sure how it did so, MIT Technology Review reported.

Its mysterious mind could be a sign of dark times to come, sceptics fear.

The cars underlying technology, dubbed deep learning, is a powerful tool for solving problems.

It helps us tag our friends on Facebook, provides assistance on our smartphones using Siri, Cortana or Google.

Deep learning has helpedcomputers get better at recognising objects than a person.

The military is pouring millions into the technology so it can be used to steer ships, control drones and destroy targets.

And theres hope it will be able to diagnose deadly diseases, make traders billionaires by reading the stock market and totally transform the world we live in.

But if we dont make sure creators have a full understanding of how it works, were in deep trouble, scientists claim.

Scientists have been training computers how to learn, like humans, since the 1970s.

But recent advances in data storage mean that the process has sped up exponentially in recent years.

Interest in the field hit a peak when Google paid hundreds of millions to buy a British deep learning company in 2015.

Coined machine learning or a neural network, deep learning is effectively training a computer so it can figure out natural language and instructions.

Its fed information and is then quizzed on it, so it can learn, similarly to a child in the early years at at school.

If they cant figure out how the algorithms (the formulaswhich keep computers performing the tasks we ask them to do) work, they wont be able to predict when they fail.

Tommi Jaakkola, a professor at MIT who works on applications of machine learning warns: If you had a very small neural network [deep learning algorithm], you might be able to understand it.

But once it becomes very large, and it has thousands of units per layer and maybe hundreds of layers, then it becomes quite un-understandable.

That means a driverless car, like Nvidias, could soar headfirst into a tree and we would have no idea why it decided to do so.

Just imagine if artificial intelligence was given control of the stock market or military systems.

Another computer was also tasked with analysing patient records to predict disease.

Joel Dudley, who led the project at New Yorks Mount Sinai Hospital, said the machine was inexplicably good at recognising schizophrenia but no-one knew why.

We can build these models, but we dont know how they work, he said.

Several big technology firms have been asked to be more transparent about how they create and apply deep learning.

This includes Google, which said it would create an AI ethics boardbut has kept mysteriously quiet about its existence.

A top Britishastronomer recently warnedthat humans will be wiped outby robots who will take over the earth in a matter of centuries.

He claims aliens could already be remnants of a human-like civilisation which have evolved into artificially intelligent machines.

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Humanity is already losing control of artificial intelligence and it ... - The Sun

SAN FRANCISCO, April 11, 2017 /PRNewswire/ -- Package holiday specialists TravelBird have announced that starting April, 2017, they will partner with DigitalGenius to provide Artificial Intelligence (AI) for customer service.

TravelBird receives 900,000 incoming traveler questions per year, answered in 11 countries by email, phone, social media and SMS. To manage an increasing volume of queries while increasing customer satisfaction, TravelBird looked to new computer technology to do what it does best: taking charge of repetitive tasks like tagging and answering frequently asked questions while learning to predict answers based on historical data. These procedures enable travel and booking associates to spend more time deeply engaging with customers for a more rewarding experience on both ends.

DigitalGenius is providing TravelBird booking associates with an AI assistant to cut down on repetitive daily tasks, creating a layer of machine intelligence to enhance the quality and productivity of customer service experiences. The DigitalGenius Human+AI Platform is trained on historical customer service logs and provides AI-powered macro suggestions, automation of ticket tagging, auto-triaging, as well as automation of responses. This combination of human and machine intelligence helps customer service teams support increasing volumes faster, while unlocking more time for complex cases and meaningful conversations with customers.

TravelBird anticipates the use of AI systems will improve post-contact customer satisfaction and increase productivity while creating a more satisfying work experience for travel and booking associates.

"At TravelBird, we believe that every traveler deserves a wonderful, memorable holiday, and that sometimes, people are in need of guidance on the road," says Symen Jansma, CEO and Founder of TravelBird. "We want to better serve our travelers every day, which means quick response times for traveler queries and personalized service. With our partnership with DigitalGenius, we let computers do what they do best and leave the work involving real human connection to our experts."

Rapid growth of TravelBird's business, has led to a massive increase in message volume. Instead of replacing customer service agents with bots or moving them to a more cost effective location, they have chosen to implement DigitalGenius AI in conjunction with Zendesk as a way to make their existing team more efficient while maintaining and improving the quality of the customer service experience.

"We are thrilled to see our AI products used inside customer service centers to empower professionals working in this space, and unlocking valuable time to conduct a much higher level of service," says Mikhail Naumov, Co-founder & CSO at DigitalGenius. "TravelBird is a fantastic company with an admirable commitment to serving its customers and travelers."

To measure progress, an analytics tool will measure the number of predictions and the percentage of accuracy of the AI models. In the future, TravelBird expects AI to be widely used in all large companies to meet increasing customer service demands.

About TravelBird: TravelBird brings you the best holiday experiences, both for short breaks and international get-aways. They make a personalized selection of offers to bring you the perfect travel experience. Their aim is to make travelers discover something new through various holiday proposals, whilst their range of choices will also help you immerse into new cultures. For more information, please visit: https://travelbird.nl

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About DigitalGenius: DigitalGenius brings practical applications of artificial intelligence into the customer service operations of growing companies. Its Human+AI Customer Service Platform combines the best of human & machine intelligence, enabling companies to live up to and exceed rising customer expectations. At its core are deep-learning algorithms, which are trained on historical customer service logs and integrated directly into the contact center's existing software. Once enabled, the platform automates and increases the quality and efficiency of customer service conversations across text-based communication channels like email, chat, social media and mobile messaging. For more information, please visit http://www.digitalgenius.com

To view the original version on PR Newswire, visit:http://www.prnewswire.com/news-releases/how-artificial-intelligence-will-improve-traveler-experience-with-travelbird-300438403.html

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How Artificial Intelligence will Improve Traveler Experience with TravelBird - Yahoo Finance

Artificial intelligence (AI) is having a huge impact on businesses in a variety of industries, including high tech. ServiceMax, a company that provides cloud-based field service software to help organizations manage contracts, scheduling, and parts, is a case in point.

"We're constantly exploring different options and paths to incorporate artificial intelligence and machine learning into our product offering," said Indresh Satyanarayana, chief architect at ServiceMax, which was acquired by GE Digital in November 2016 to enhance is Industrial Internet of Things (IIoT) capabilities in field service.

"For the field service industry in particular, AI holds interesting implications for changing how we operate our businesses," Satyanarayana said. "We already see the practical applicability in many different areas of AI -- from enabling natural language processing in our mobile applications to realizing autonomous field work by enabling AI-based decision making."

In servicing equipment and keeping it running right, AI has the ability to directly impact how field technicians complete jobs and managers optimize work processes, Satyanarayana said.

The creation of a product that leverages AI and machine learning faces a host of challenges, however. Understanding how humans and machines can work together is never going to be easy, Satyanarayana said.

"AI and machine learning concepts operate at a fundamental level in product and feature design, and can sometimes lead to false positives if the data being fed into it isn't robust," Satyanarayana said. "We can't treat those concepts as peripheral utilities or one-trick ponies. Instead, we must take a holistic view of how we can enable them in our products."

In addition, AI and machine learning take a somewhat tangential approach compared with conventional software design and development. "The mindset shift has to happen sooner," he said.

AI has been gaining buzz for many months, but before jumping in headfirst into it, companies need to consider a few different areas of their business and whether AI will work for them, Satyanarayana said.

"The success of AI counts on the quality of the data that's fed through its system, but the magnitude, depth, and heterogeneity of it gets multiplied in the context of field service," Satyanarayana said. "No two businesses execute their processes in the same way -- especially in field service -- so it's important to make sure that your data is understood in a particular context and the insights derived from it can cater to both the process and the business."

Furthermore, individuals with different roles come and go through the flow of AI-supported programs, whether that's technicians in the field with mobile devices, back office users, dispatchers, or others. "Even machines are connected to enterprise systems" via the Internet of Things (IoT), Satyanarayana said. "It's difficult to make the engagement across all of these moving parts optimized based on continuous feedback mechanisms."

Finally, because decisions are made on account of data, making sure the decisions made by AI are accurate is crucial. "It impacts the evolution of field service organizations, whether that's how a field tech is dispatched or whether a larger business process is deployed," Satyanarayana said.

"These decisions must be made by an intelligent system, even if it's artificial. So, while enterprise data is valuable, it's tough to make computers process data the same way that people do. It takes a major mindset change that isn't always straightforward for organizations."

VIDEO: DeepMind and healthcare: How is the NHS using AI?

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Artificial intelligence: Lessons from the field | ZDNet - ZDNet

At a demonstration of Amazon Web Services' new artificial intelligence image recognition tool last week, the deep learning analysis calculated with near certainty that a photo of speaker Glenn Gore depicted a potted plant.

It is very clever, it can do some amazing things but it needs a lot of hand holding still. AI is almost like a toddler. They can do some pretty cool things, sometimes they can cause a fair bit of trouble, said AWS chief architect in his day two keynote at the companys summit in Sydney.

Where the toddler analogy falls short, however, is that a parent can make a reasonable guess as to, say, what led to their child drawing all over the walls, and ask them why. Thats not so easy with AI.

Artificial intelligence in its application of deep learning neural networks, complex algorithms and probabilistic graphical models has become a black box according to a growing number of researchers.

And they want an explanation.

Opening the black box

You dont really know why a system made a decision. AI cannot tell you that reason today. It cannot tell you why, says Aki Ohashi, director of business development at PARC (Palo Alto Research Center). Its a black box. It gives you an answer and thats it, you take it or leave it.

For AI to be confidently rolled out by industry and government, he says, the technologies will require greater transparency, and explain their decision making process to users.

You need to have the system accountable, he told the AIIA Navigating Digital Government Summit in Canberra on Wednesday. You cant blame the technology. They have to be more transparent about the decisions that are made. Its not just saying well thats what the system told me.

PARC has been working with the Defense Advanced Research Projects Agency, an agency of the U.S. Department of Defense on what is being called Explainable Artificial Intelligence, or XAI.

The research is working towards new machine-learning systems that will have the ability to explain their rationale, characterise their strengths and weaknesses, and convey an understanding of how they will behave in the future. Importantly they will also translate models into understandable and useful explanations for end users.

In current models nodes arbitrarily decide how they make decisions, in image recognition using miniscule dots or shadows.

They focus on whatever they want. The things they focus on are not things that tend to be intuitive to humans, Ohashi says.

One way to do change this, being explored by PARC, is to restrict the way nodes in a neural network consider things to concepts like colour and shapes and textures.

The AI then starts thinking about things from a perspective which is logically understandable to humans,

Others are working towards the same goal. While humans are surprisingly good at explaining their decisions, said researchers at University of California, Berkeley and the Max Planck Institute for Informatics in Germany in a recent paper, deep learning models frequently remain opaque.

They are seeking to build deep models that can justify their decisions, something which comes naturally to humans.

Their December paper Attentive Explanations: Justifying Decisions and Pointing to the Evidence, primarily focused on image recognition, makes a significant step towards AI that can provide natural language justifications of decisions and point to the evidence.

Smart, dumb or evil?

Being able to explain its decision-making is necessary for AI to be fully embraced and trusted by industry, Ohashi says. You wouldn't put a toddler in charge of business decisions.

If you use AI for financial purposes and it starts building up a portfolio of stocks which are completely against the market. How does a human being evaluate whether its something that made sense and the AI is really really smart or if its actually making a mistake? Ohashi says.

There have been some early moves into XAI among enterprises. In December Capital One Financial Corp told the Wall Street Journal that it was employing in-house experts to study explainable AI as a means of guarding against potential ethical and regulatory breaches.

UK start-up Weave, which is now focused on XAI solutions has been the target of takeover talks in Silicon Valley, reports the Financial Times.

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'Explainable Artificial Intelligence': Cracking open the black box of AI - Computerworld Australia

This article originally appeared on the Motley Fool.

The science of deep learning, a sub-discipline ofartificial intelligence(AI), is only a recent development in the grand scheme of things, but during its short existence, it has been producing some impressive technological achievements. Advances in image recognition, language understandingand translation have led to the development ofvirtual assistants,smart home speakersandgainsincybersecurity, and they are leading the charge towardautonomous driving. Now, companies have found a way to use those AI smarts to fight cancer.

Deep learninginvolves the construction of artificial neural networks, using software and complex algorithms to recreate the capacity of the human brain to learn. These learning computers have a particular knack for sifting through vast amounts of data and recognizing patterns, getting smarter as they go. The first breakthrough involved feeding a system thousands of pictures of cats until the program was able to recognize a cat on its own.

This ability to identify patterns has led to a significant breakthrough in the area of breast cancer research. Last month, in a paper titledDetecting Cancer Metastases on Gigapixel Pathology Images,Google announced that it had created a neural network that could analyze medical images and identify tumors with a greater degree of accuracy than human pathologists. The study revealed that the company, using itsGoogLeNet AI, reviewed thousands of medical images supplied by a Dutch university and was able to identify malignant tumors in breast cancer images with an 89 percentaccuracy rate, compared to 73 percentfor its human counterparts. In ablog, Google researchers explained:

Pathologists are responsible for reviewing all the biological tissues visible on a slide. However, there can be many slides per patient, each of which is 10+ gigapixels when digitized at 40X magnification. Imagine having to go through a thousand 10 megapixel (MP) photos, and having to be responsible for every pixel. Needless to say, this is a lot of data to cover, and often time is limited.

Cancer cells are seen on a large screen connected to a microscope at the CeBit computer fair in Hanover, Germany, March, 6, 2012. Reuters

This technology has the potential to provide initial screenings, allowing doctors to review only those images that have been flagged as potentially cancerous. The system still requires improvement, as it generated a number of false positivesidentifying cancerous cells where none were present. So, while AI won't be replacing pathologists anytime soon, these algorithms could be used to pre-screen images and not only reduce the workload on doctors, but also serve parts of the world where pathologists are in short supply.

Google isn't the only one testing AI for this purpose. Nearly a year ago, International Business Machines Corporationpartnered with numerous health systems, imaging technology companies, and academic medical centers to trainWatson AI, its cognitive computer, to read medical images. This more broadly based collaboration is focusing on cancer, cardiovascular disease, eye healthand diabetes. The company announced last month that it had expanded that initiative to 24 organizations worldwide, which will provide the vital input necessary to train the system. IBM also announced the first fruits of this endeavor, an application for the detection of one type of cardiovascular disease involving a narrowing of the heart's aortic valve. It plans to expand Watson's efforts to a variety of additional cardiovascular conditions in the near future.

Watson is already seeing success in a number ofmedical applications. In its most impressive results in the field to date, Watsonreviewedthe medical records of 1,000 cancer patients and was able to develop a treatment plan that concurred with oncologists' recommendations with 99 percentaccuracy. The AI was also able to provide additional recommendations in about 30 percentof cases that had been missed by doctors, thanks to its ability to review eventhe most recent medical research.

Neither company provides specific information regarding how these AI-related medical breakthroughs would contribute to the overall business, and any contribution would represent only a minuscule part of each company's total revenue at this juncture. These are still extremely early developments, but they illustrate the vast potential of AI in the not-too-distant future.

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Artificial Intelligence's Potential to Transform Medical Screening - Newsweek

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Canada Tries to Turn Its AI Ideas Into Dollars New York Times The Mars Discovery District in Toronto is one of the world's largest innovation hubs. After years of losing its artificial intelligence scientists and start-ups to Silicon Valley, Canada is focusing on keeping its A.I. leaders in the country. Credit ... and more »

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Canada Tries to Turn Its AI Ideas Into Dollars - New York Times

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Artificial intelligence and drones 'future of policing' - BBC News BBC News Artificial intelligence and drones will be key policing tools in the future amid budget and job cuts, Gwent Police's chief constable has said. Jeff Farrar said he ... and more »

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Artificial intelligence and drones 'future of policing' - BBC News - BBC News

Discover how artificial intelligence is shaping eCommerce today. The age of AI and machine learning is upon us. Don't let your business get left behind.

Artificial intelligence will take over. But its not going to be an apocalyptic scenario, unless the latest U.S. military developments actually come up with a mind of their own.

AI will have control over our everyday lives, but only because we want it to. For some people, Siri or Cortana already play this role, as AI assistants.

A perfect example of AI automation is the US stock market that sees around around 70 percent of trades being done by automated algorithms. Gartner predicts that by 2020 over 80 percent of all customer interactions will be handled by AI. We can already see automation taking over with services like Amazon Go, being advertised as an AI-based shopping experience.

But how will AI change the landscape of online shopping and other forms of online commerce? How can business owners leverage this sprawling AI ecosystem to their advantage? Lets find out.

With services like CamFind, people can already leverage the power of artificial intelligence to facilitate their shopping. Its the perfect mix of augmented reality and AI that has the potential to transform how businesses do their marketing, address user experience issues and creates revenue streams. Given that, according to some studies, over 50 percent of young shoppers are interested in VR and AR products, AI will see even more implementations like that.

RankBrain is Googles own take on artificial intelligence -- an AI-based search algorithm that has a lot of practical implications for businesses. Algorithms like these will eventually remove any possibility of gaming search engines to get traffic and increase sales.

They should focus the majority of their eCommerce website development efforts on better user experience and quality content. After all, this is what will matter to AI and companies that create machine-learning products. Google, for example, likes to stress the importance of user experience. With more information, instant product discovery and the growing pace of online shopping, the average attention span of an online user has decreased by 30 percent over the last 15 years.

Now you have even less time to capture users, with exactly the right products that they were looking for and in a convenient way. You can also use content that might give your brand an opportunity to build a relationship with the user. Its pretty obvious from these developments that SEO and other technical marketing tools will be neglected by artificial intelligence and app-based shopping assistants. And this takes us to our next point.

Although theres the browsing versus buying gap when it comes to mobile users (only 16 percent of eCommerce dollars are spent via mobile), AI-based technologies are already here to close it. This is a huge window of opportunity for businesses. We can already see big companies pioneer the machine-learning way in hopes of getting a competitive advantage.

Macys teamed up with IBMs Watson to simplify shopping for mobile users. Theres a growing number of various shopping assistance apps, like Mona or AI apps by famous brands, like "My Starbucks Barista." All of these products have a single goal to make mobile the default shopping domainand close that revenue gap, where desktop shoppers are at the top.

Companies that really want to exploit this trend have to include mobile app development or mobile UX efforts as parts of their growth strategy.

Providing proper customer care is one of the most important aspects of todays business. For example, 73 percent of customers tend to like brands specifically for their support. And since customers prefer human interactions for a quality customer care experience, a growing business might find this specific chunk of their expenditures to be taxing on their bottom line. But theres no getting away from this important part of running a business -- its six times cheaper to keep a customer than to bring a new one. And if a business wants to keep customers, adequate customer support is crucial.

Luckily, with the latest advances in AI and machine-learning, customer care is getting cheaper every day. Conversational chat bots are very popular right now. Companies like DigitalGenius merge real customer care departments with AI-based solutions.

These products greatly extend the reach of any business and its ability to communicate. Customer care becomes more effortless. This means that companies can discover additional growth and marketing opportunities. Theres no excuse for eCommerce businesses that dont have a proper customer care process in place.

Machine-learning tools have greatly simplified modeling and analysis for various business niches. For example, companies like BigML and DataRobot present amazing advances in the world of data science and automated machine learning.

Although these kinds of technologies seem to be more suited for FinTech industry players, like loan and car insurance companies, theres a window of opportunity for eCommerce businesses that are ready to fully embrace these new technologies.

AI is perfect for handling customer data, predicting visitors and their behaviors, analyzing purchasing patterns and doing all kinds of other manipulations with big sets of data.

As machine-learning tools are becoming more prevalent, eCommerce businesses find it easier to implement automation and AI solutions for their specific product or marketing needs. This is the next big thing in eCommerce that will change how businesses address planning and development.

As if businesses didnt have enough on their hands, competitors arent going to wait for anybody. Thats why there is already a plenty of services that handle various elements of competitive analysis with the help of artificial intelligence. Price scraping, dynamic pricing patterns and many other intelligence nomenclatures are now handled by companies like Clavis, Indix or Quicklizard.

It is projected that AI will be responsible for an economic impact of up to $33 trillion in annual growth and cost reduction. Companies that fail to get on board and efficiently utilize machine-learning tools are going to get left behind in terms of revenue and expansion.

In general, AI and machine-learning platforms open a myriad of opportunities for eCommerce businesses. The biggest problem, at this point, is cost/benefit rationalization for many of these practices and products. Companies that adopt automated data science and AI tools early on may suffer due to increased costs and imperfections in many of the products offered in this niche. At the same time, companies that refuse to innovate may soon end up on the curb and stagnate.

Photo credit: Shutterstock /Willyam Bradberry

Maria Marinina

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How Artificial Intelligence Is Reshaping ECommerce - Business.com

By: IANS | Published:April 9, 2017 4:20 pm

Most customers are confused about the use of artificial intelligence (AI) and are, therefore, reluctant to embrace this new technology, a study said on Friday.Released by US-based software firm Pegasystems, it revealed that these fears are often eased once the users gain firsthand AI experience which ironically many enjoy without even realising it.

Our study suggests the recent hype is causing some confusion and fear among consumers, who may not really understand how its already being used and helping them every day, said Don Schuerman, Vice President (Product Marketing) Pegasystems.The study that involved 6,000 customers in six countries found that consumers were hesitant to fully embrace AI devices and services.

Only 36 per cent are comfortable with businesses using AI to engage with them. Almost 72 per cent express some sort of fear about AI, the study found. Twenty four per cent of respondents even worried about robots taking over the world.

Also Read:Scientists develop AI which defeated professional poker players

Interestingly, 34 per cent of respondents said they had directly experienced AI but when asked about the technologies they used it was revealed that 84 per cent actually used at least one AI-powered service or device. Seventy two per cent respondents confidently claimed they understood AI but very few could correctly define it.

Though AI has been around for more than 30 years, it has now evolved to the point that businesses can engage with each individual consumer on a real-time, one-to-one basis, Schuerman said.Businesses need to focus on using AI to develop applications that provide real value for customers to improve their experiences rather than overhyping the technology itself, the findings suggested.

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Consumers confused about artificial intelligence, claims study - The Indian Express