Mediaboss Marketing

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

MORE FROM HEALTHTECH:Whats next for AI in healthcare?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Written by

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

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

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

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

Artificial Intelligence (AI) holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment, and use, according to new WHO guidance published today.

The report, Ethics and governance of artificial intelligence for health, is the result of 2 years of consultations held by a panel of international experts appointed by WHO.

Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm, said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. This important new report provides a valuable guide for countries on how to maximize the benefits of AI, while minimizing its risks and avoiding its pitfalls.

Artificial intelligence can be, and in some wealthy countries is already being used to improve the speed and accuracy of diagnosis and screening for diseases; to assist with clinical care; strengthen health research and drug development, and support diverse public health interventions, such as disease surveillance, outbreak response, and health systems management.

AI could also empower patients to take greater control of their own health care and better understand their evolving needs. It could also enable resource-poor countries and rural communities, where patients often have restricted access to health-care workers or medical professionals, to bridge gaps in access to health services.

However, WHOs new report cautions against overestimating the benefits of AI for health, especially when this occurs at the expense of core investments and strategies required to achieve universal health coverage.

It also points out that opportunities are linked to challenges and risks, including unethical collection and use of health data; biases encoded in algorithms, and risks of AI to patient safety, cybersecurity, and the environment.

For example, while private and public sector investment in the development and deployment of AI is critical, the unregulated use of AI could subordinate the rights and interests of patients and communities to the powerful commercial interests of technology companies or the interests of governments in surveillance and social control.

The report also emphasizes that systems trained primarily on data collected from individuals in high-income countries may not perform well for individuals in low- and middle-income settings.

AI systems should therefore be carefully designed to reflect the diversity of socio-economic and health-care settings. They should be accompanied by training in digital skills, community engagement and awareness-raising, especially for millions of healthcare workers who will require digital literacy or retraining if their roles and functions are automated, and who must contend with machines that could challenge the decision-making and autonomy of providers and patients.

Ultimately, guided by existing laws and human rights obligations, and new laws and policies that enshrine ethical principles, governments, providers, and designers must work together to address ethics and human rights concerns at every stage of an AI technologys design, development, and deployment.

To limit the risks and maximize the opportunities intrinsic to the use of AI for health, WHO provides the following principles as the basis for AI regulation and governance:

Protecting human autonomy: In the context of health care, this means that humans should remain in control of health-care systems and medical decisions; privacy and confidentiality should be protected, and patients must give valid informed consent through appropriate legal frameworks for data protection.

Promoting human well-being and safety and the public interest. The designers of AI technologies should satisfy regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications. Measures of quality control in practice and quality improvement in the use of AI must be available.

Ensuring transparency, explainability and intelligibility. Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology. Such information must be easily accessible and facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used.

Fostering responsibility and accountability. Although AI technologies perform specific tasks, it is the responsibility of stakeholders to ensure that they are used under appropriate conditions and by appropriately trained people. Effective mechanisms should be available for questioning and for redress for individuals and groups that are adversely affected by decisions based on algorithms.

Ensuring inclusiveness and equity. Inclusiveness requires that AI for health be designed to encourage the widest possible equitable use and access, irrespective of age, sex, gender, income, race, ethnicity, sexual orientation, ability or other characteristics protected under human rights codes.

Promoting AI that is responsive and sustainable. Designers, developers and users should continuously and transparently assess AI applications during actual use to determine whether AI responds adequately and appropriately to expectations and requirements. AI systems should also be designed to minimize their environmental consequences and increase energy efficiency. Governments and companies should address anticipated disruptions in the workplace, including training for health-care workers to adapt to the use of AI systems, and potential job losses due to use of automated systems.

These principles will guide future WHO work to support efforts to ensure that the full potential of AI for healthcare and public health will be used for the benefits of all.

Continue reading here:
WHO issues first global report on Artificial Intelligence (AI) in health and six guiding principles for its design and use - World Health Organization

Dublin, June 28, 2021 (GLOBE NEWSWIRE) -- The "Technology Themes 2021 - Tracking Development of 3 Key Trends, 5G, Quantum Computing and Artificial Intelligence (AI)" report has been added to ResearchAndMarkets.com's offering.

5G, Quantum Computing and AI have been discussed at length for a number of years and the hype that surrounds them can lessen the understanding of real world impacts these themes are already having. This report aims to track their present position, explain their potential benefits and see where any issues have arisen. 2021 promises to be a significant year for all three industries.

Key Highlights

5G consists of the fifth generation of cellular technology and is built to enable faster mobile data speeds than previous 4G LTE and earlier technologies, providing the potential for revenue growth and lower customer churn. 5G will allow networks to be virtually sliced to provide a range of different service characteristics for different use cases. For example, ultra-reliable and low-latency communications (URLLC) will support use cases including virtual reality (VR) and augmented reality (AR), automated and remotely operated robotics, and many others. Meanwhile, massive machine-type communications (mMTC) will eventually support millions, and eventually billions, of sensors and meters that can provide value through data analytics and automation in use cases such as agriculture, healthcare, and public safety.

While widespread enterprise use remains years away, the hype around quantum computing (QC) continued to build in 2020, and with good reason. More people than ever are getting to grips with QC, with many companies now offering quantum cloud experiences for beginners and developers alike. Two separate organizations, Google and the University of Science and Technology of China (USTC), have claimed the quantum advantage, but practical use cases need to be proven in 2021 for the field to continue to attract investment.

AI is ubiquitous today. It can be found everywhere, from wearable tech to automated home devices, smart cities, cars, offices, and more. The technology is embedded in a range of systems, making it challenging to identify revenue explicitly generated by AI. GlobalData forecasts that the market for AI platforms will reach $52bn in 2024, up from $29bn in 2019. AI is one of the most hyped technologies, with reality often falling short of vendors' world-altering promises.

Scope

Story continues

Learn how AI is developing

See how quantum computing is becoming more mainstream

See how 5G is now ubiquitious

Understand what the major technology themes are and how they are developing

Reasons to Buy

What are the major themes developing in the technology sector?

How developed is the 5G network now?

Is quantum computing having a major impact?

What sectors has AI now developed into?

Key Topics Covered:

Executive Summary

5G is the fifth generation of cellular technology

Mobile network operator deployments are patchy

Mobile 5G subscription evolution

Mobile 5G revenue evolution

5G capabilities and use cases

Ultra-reliable and low latency communications (URLLC)

Massive machine-type communications (mMTC)

Quantum Computing

Artificial Intelligence

Appendix

For more information about this report visit https://www.researchandmarkets.com/r/7qp3yw

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

Go here to see the original:
5G, Quantum Computing and Artificial Intelligence (AI) Technology Development Trends Report 2021 - Yahoo Finance