The potentials of Artificial Intelligence have extended its reach across several platforms. Even social media platforms like Facebook and LinkedIn are using AI to make sense of the pool of human data. Around 3 billion people use social media across various countries creating data in volumes one can only imagine. And this is where AI is introduced to harness the true worth of voluminous data.

In this era of social media, whether it is about reaching out to new clients or nurturing existing business or personal relationships, one can easily cling onto a site like Facebook, Instagram, or LinkedIn. Through such platforms, people can become an integral part of far off society and community.

In terms of business, effective social media management helps enhance a brand’s strength and increase social media conversations. It is an efficient way of engaging millions of social media users. AI helps analyze voluminous data to identify trending topics, hashtags, and patterns to understand user behavior.

Such innovative algorithms can keep a check on millions of unstructured user comments or data to understand crisis situations or trends to provide a personalized experience. With effective segmentation, technology can help organizations provide content based on online activity and demographics. Many social networking sites have acquired AI businesses to move to the next level.

In the contemporary market, a variety of companies that offer online marketing services are exploring new ways to use social media with AI as well. They have started using AI to identify new demographics to target based on previous conversions. These AI tools rely on predictive analytics algorithms, which can extrapolate information on all known users in a given social network.

AI can also recognize images and help identify consumer behavior patterns. AI-empowered software recognition tools can help gather actionable insights to understand the shift in user patterns through millions of images posted on social media.

With the sheer amount of images being posted every minute, it would be very difficult for a person to notice an opportunity like but with AI the task can be carried out efficiently.

At Facebook, where there are more than two billion users, it is using artificial intelligence to flag posts automatically that show expressions of suicidal thoughts for human moderators to review. Moreover, the social media platform can enhance its program to allow human moderators to review 20 times more suicidal posts, and Facebook is sending its suicide prevention materials to twice as many people.

Over, Microsoft owned professional networking site LinkedIn, AI’s subset machine learning is being used for almost all its products. With almost seven million open candidates, LinkedIn offers the biggest pool of candidates for a recruiter to contact and connect with the highest response rate. LinkedIn uses algorithms with the capability to predict users who are possibly the best fit for the role. Using AI algorithms, LinkedIn can highlight candidates who are most likely to respond or seeking new opportunities.

Furthermore, Twitter recently launched an update to its service using AI that crops an image using face detection or creating a thumbnail from an entire image. With neural networks, Twitter can decipher which part of the image user would be interested in.

As we can see through social media platforms AI is gaining prominence. While AI can never replace human interaction, it can assist them in making things more productive and efficient.

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Microsoft recently described the ways in which its artificial intelligence (AI) and machine learning offerings, as well as its Azure cloud, are being used by researchers and other public health groups responding to the ongoing coronavirus (COVID-19) pandemic.

In one collaboration announced Friday, Adaptive Biotechnologies Corp. and Microsoft are using Azure to map the immune system’s response to threats, including COVID-19. Adaptive is using Azure’s machine learning capabilities to sift through data on the body’s T-cell receptor sequences, which get generated in response to antigens in the blood. The machine learning process is used to refine a “map” of those sequences, which is done by “matching trillions of T cells to the diseases they recognize,” per Adaptive’s fact sheet (PDF download).

Leveraging Microsoft’s hyperscale machine learning capabilities and the Azure cloud platform, the accuracy of the immune response signature will be continuously improved and updated online in real time as more trial samples are sequenced from the study,” the announcement stated.

The aim of the effort is to more accurately detect diseases via blood tests. The partnership between Microsoft and Adaptive on the T-cell antigen map isn’t new, but dates back to 2018. It’s already led to a proof-of-concept for identifying Lyme disease. Adaptive plans to apply for clinical trials with the U.S. Food and Drug Administration sometime this year.

Adaptive is promising to share T-cell response signatures from COVID-19 with other researchers via a soon-to-come “open data access portal.”

“These data will be made freely available to any researcher, public health official or organization around the world via an open data access portal,” the announcement stated regarding the COVID-19 data.

Adaptive plans to use de-identified blood samples from individuals diagnosed with COVID-19 to bolster its data samples, and is seeking additional contributors. It’s working with LabCorp on the blood collection effort. Adaptive also is currently collaborating with the Providence health group, whose hospital in Seattle “treated the first U.S. COVID-19 patient.”

COVID-19 Assessment Bot
In addition to these efforts, Microsoft announced on Friday that it is supporting the U.S. Centers for Disease Control and Prevention’s (CDC’s) newly released COVID-19 assessment bot, which is “powered by Microsoft Azure.” The bot, which asks users health questions to assess possible COVID-19 infections, is based on Microsoft’s Healthcare Bot service and uses artificial intelligence in the screening process.

“Microsoft’s Healthcare Bot service is one solution that uses artificial intelligence (AI) to help the CDC and other frontline organizations respond to these inquiries, freeing up doctors, nurses, administrators and other healthcare professionals to provide critical care to those who need it,” the announcement explained.

Hospital systems are already using Microsoft’s bot to screen COVID-19. They include Seattle-based Providence, Virginia Mason Health System in the Pacific Northwest and Novant Health in the Southeast. Customized versions of the bot are currently handling “more than 1 million messages per day.”

Microsoft is helping other organizations build their own health bots by providing four “COVID-29 response templates.” Two of the templates are based on the CDC’s protocols. Organizations can modify the templates, if wanted.

COVID-19 Portal
The CDC reports U.S. cases of COVID-19 at this page, which includes a U.S. “heat map” illustration of the disease’s distribution. Cases have been reported in all 50 states.

Another site showing COVID-19 distribution around the world is nCoV2019.live. The site was put together by Avi Schiffmann, a high school junior from Mercer Island near Seattle, according to a report by the Democracy Now! news program. Schiffmann used Web scraping to pull the data together from various government sites.

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CC seems to be focusing more on future technologies under the new leadership of Ag. UCC Executive Director Irene Kaggwa Sewankambo . A lot has been said about the likelihood of machines replacing humans and she believes in the Fourth Industrial Revolution, Artificial Intelligence (AI) should complement and enhance rather than replace human capabilities.

Eng. Kaggwa Sewankambo told the 2nd Annual Higher Education Conference organised by the National Council for Higher Education (NCHE) at Hotel Africana on 16 March 2020 that ” You will always require a human touch to take you from where the machine stops.”

Speaking on the topic, “Leveraging Artificial Intelligence to Achieve Sustainable Development Goal 4,” which seeks to promote quality and inclusive education, the Ag. UCC ED said AI must not be seen as an attempt to replace the teacher as human interaction and collaboration between teachers and learners remains critical in the learning process. “AI completes, not substitutes the teaching process,” she said.

The conference, whose stated aim was to “explore ways and means of adequately preparing the Human Capital in Uganda for the 4IR”, was understandably dominated by debate on the Fourth Industrial Revolution (4IR) and how Artificial Intelligence can be harnessed to improve learning.

Describing AI as “the making of intelligent machines that are able to mimic human intelligence while handling tasks that normally a human would perform,” Ms Kaggwa Sewankambo said these technologies present opportunities to empower teaching and teachers, improve learning, and help in education management.

“AI provides support in educational and pedagogical responsibilities, e.g. more efficient, motivational, personalised (to abilities and needs) and contextualised support for students instead of on the ‘one size fits all’ approach,” she explained.

The UCC ED added that AI could also be used in time-consuming tasks such as exam preparation and grading of students so that teachers have more time for innovation and other relevant duties.

She further spoke of AI’s potential to promote lifelong formal and informal learning, as well as personalised, flexible learning and use of assistive technology for vulnerable groups such as older people and Persons with Special Needs (PSNs).

However, Ms Kaggwa Sewankambo was quick to point out that to reap AI benefits, the AI design must inculcate ethics, privacy and security. She added that a balance between data privacy, data ownership and data availability is paramount.

Underscoring the need for good quality data, the Ag. UCC ED argued that formulation of the tasks for AI to work with, collection of the data, and design of algorithms must all be ethical to avoid a “garbage in garbage out” scenario.

On leveraging AI to attain SDG 4, she said it must not be lost on AI users that its rationale is to complement and enhance, rather than to replace human capacities in order to create optimum learning environments.

She said development and use of AI must be “human-controlled and centred on people – ethical, non-discriminatory, equitable, transparent and auditable.”

Opened by the Minister of State for Higher Education J.C. Muyingo and addressed by the Permanent Secretary, Ministry of ICT and National Guidance, Vincent Bagiire, among others, the conference was attended by mostly leaders of higher institutions of learning, researchers, as well as private and public sector institutions associated with education.

Various speakers painted the picture of the future with AI in full flow, including Dr Andrew Katumba of Makerere University who amused the audience with his illustration of a kettle connected to the Internet in his presentation on the Internet of Things (IoT). 

Despite science and technology dominating the day’s discussion, several presenters, including Ms Kaggwa Sewankambo, and Makerere University Vice Chancellor Barnabas Nawangwe pointed out that the Fourth Industrial Revolution needs humanities just as it needs scientists if its transformative potential is to be realised.

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Roger Magoulas recently sat down with Edward Jezierski, reinforcement learning AI principal program manager at Microsoft, to talk about reinforcement learning (RL). They discuss why RL’s role in AI is so important, challenges of applying RL in a business environment, and how to approach ethical and responsible use questions.

Here are some highlights from their conversation:

Reinforcement learning is different than simply trying to detect something in an image or extract something from a data set, Jezierski explains— it’s about making decisions. “That entails a whole set of concepts that are about exploring the unknown,” he says. “You have the notion of exploring versus exploiting, which is do the tried and true versus trying something new. You bring in high-level concepts like the notion of curiosity—how much should you buy as you try new things? The notion of creativity—how crazy are the things you’re willing to try out? Reinforcement learning is a science that studies how these things come together in a learning system. (00:18)

The biggest challenge for businesses, Jezierski says, is correctly identifying and defining goals, and deciding how to measure success. For example, is it the click you’re after or something a bit deeper? This honest, clarifying conversation is key, he says. “This is why we’re focused first on the applied use of services because it can be very abstract otherwise. It’s like, ‘Oh, I’ve got to make decisions. I get rewards, and I’m going to explore—how do I look at my own business problem through that light?’ A lot of people get tripped up in that. So we’ll try to say, ‘Look, we’re going to draw a smaller box. We’re going to say we want to define personalization using RL as ‘choose the right thing’ for my menu in a context and tell us how well it went.’ That’s not the universe of possibility, but 90% of people can frame a part of their problem that way. If we can design a small box where people in it can have guaranteed results and we can tell you whether you fit in the box or not, that’s a great way to get people started with RL.” (3:24)

Ethics and responsible use are essential facets of reinforcement learning, Jezierski notes. Guidelines in this area aren’t necessarily addressing bad actors, but are aiming to help those unaware of the consequences of what they’re doing become more aware and to help those who are aware of the consequences and have good intentions to have more backing. Asking the right questions, Jezierski explains, is the difficult part. “In reinforcement learning, you get very specific questions about ethics and personalization—like, where is it reasonable to apply reinforcement learning? Where is it consequential to explore or exploit? Should insurance policies be personalized in a webpage using reinforcement learning, and what are the attributes that should drive that? Or is an algorithm trying to find out better ways that are not goaled toward the purpose of insurance, which is a long-term financial pool of risk and social safety net. Is it even ethical to apply to that sort of scenario?” It’s important, Jezierski says, to make these types of conversations non-taboo in team environments, to empower anyone on the team to hit the brakes to address a potential issue. “If you have an ethical or responsible use concern, you can stop the process and it’s up to everybody else to justify why it should restart. It’s not up to you to justify why you stopped it. We take it very seriously because in the real world, these decisions will have consequences.” (9:40)

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We live in a fabulous era of technological achievements. Practically something new, something better emerges every day. But can we catch up? The recent speed of innovation outpaces the capacity of many individuals and teams to adapt. That is why the playing field for business is changing.

Over the past several years things like big data, transformation, digitalisation, artificial intelligence and robotic process automation (RPA) have turned from fantasy into reality. These trends have influenced the future of many business sectors, including the managed services industry.

Once the preserve of books and movies, artificial intelligence (AI) is becoming a reality in today’s world. It is already here in fact – all around us. From big data to our personal devices, from workplaces and manufacturing automation to intelligent home systems and even healthcare – powerful processing units and next-level software algorithms are making our lives easier in an unprecedented ways and pace.

And here comes the question: Should we be afraid of AI?

Artificial intelligence is by definition the simulation of human intelligence processes by machines, especially computer systems. AI systems can learn (acquire information and rules for using it), make reasonable decisions (i.e. they “know” how to use rules to reach conclusions) and self-correct.

On the other hand, “artificial general intelligence” (AGI) systems are those that really disturb the public. These are hypothetical machines that will exhibit behaviour, skills and flexibility as humans do. They will possess generalised human cognitive abilities, creativity, self-consciousness and will be able to find a solution when presented with an unfamiliar task, without human intervention. Futurists are afraid of the so-called “singularity” event – a hypothesis that the invention of artificial superintelligence will abruptly trigger fast self-improvement cycles, resulting in the creation of a system far surpassing the capabilities of human intellect and civilisation. The big problem is that an algorithm could not possibly have emotions and empathy and being superior to humanity will be posing a potential grave threat to the existence of human race in general.

Sounds scary? Naturally, except it does not exist. It is just a theory. There are only couple of dozen organisations in the world involved in research on AGI. Nobody could claim that they are somewhere close to creating an AGI machine. And even if in decades or centuries there happens to be progress in that field, it will be an object of strong regulation.

How about RPA? Do we even need automation?

It is vital for each and every business to find means to grow, improve efficiency levels and be flexible when developing and providing products and services. Among the top 10 challenges for developing companies in 2019 are the integration of new technologies, innovation and customer service improvement. Users have never been more informed and have never had higher requirements when being supported by a company.

RPA is among the fastest-growing industries worldwide due to the solution it provides to some of the major problems faced by large businesses. The process automation via software eliminates the need for some frequently repetitive tasks to be handled manually, thus allowing employees to focus on activities that have higher value and importance. Gartner reports that by the end of 2020 around 40 per cent of large businesses will have integrated RPA software which will be optimizing the workflow within the company. In some industries the percentage of companies that have already implemented RPA is even higher.

Each day innovators in the business create new products, methods and ideas. By aiming to provide services with higher quality and optimisation of the business process, for instance, the Modis team looked for opportunities for higher productivity and effectiveness. The Modis business is related to delivering managed services and solutions, IT service desk and data centre support, support and maintenance of end-user devices, development of applications and solutions for outsourcing of business processes. It turns out that RPA has the potential to satisfy many of the needs of such types of business. That is why in 2017 the Modis Bulgaria team launched the Modis Innovation and Automation (MIA) Lab – a project aiming at developing innovative solutions for automation of tasks, performed by employees.

In response to global trends to meet increasingly complex client needs and maximize efficiency Modis Bulgaria invested in tools, systems and resources, and launched a series of innovation and automation projects and initiatives to further improve its capacity to transform and optimise service and cost of delivery.

The MIA Lab has become one vertical centre of excellence that provides automation solutions across the company in the fields of automation, analytics and workforce management.

The objective is to focus on creating efficiencies in our solutions leveraging latest technologies, by automating basics and repetitive tasks and allow employees to focus on the more interesting and rewarding aspects of their jobs.

With the establishment of the MIA Lab we have put the main focus on removing repetitive work by utilising RPA. The purpose of the lab is to solve critical business problems, create new kinds of smart solutions and add value, using advanced innovative technologies.

The MIA Lab team have managed to complete 18 projects removing repetitive work from service desk personnel, improving the speed and accuracy of transactions as well as service level agreements. By using the latest technologies, we have managed to provide a better customer experience and upskill the service desk personnel, enabling them to focus on added value activities.

What is the future of the IT managed service?

We are living in the era of the so-called Fourth Industrial Revolution, that will see an unprecedented change in the way humans live and work. Many professions will disappear, but that doesn’t actually mean that people will be unemployed. In fact, the same thing has happened in the past. In the early 19th century the Luddites, a group of radical English textile workers, were afraid that the time they spent learning the skills of their craft would go to waste because of the introduction of machines. They even destroyed machinery as a form of protest. Actually, towards the end of the century, it became increasingly apparent that technological progress was benefiting all sections of society, including the working class.

Ideally, implementing machine learning, AI and RPA will create an environment which will enhance the human experience for both the organisation and the employee. Increased efficiencies will allocate more resources for the highest-value interactions. Increasing speed and better quality will come without sacrificing meaningful communication and relationships. That could possibly be the right balance leading to the best possible outcome.

Managed services organisations will need to significantly increase their functional and technical capabilities in these IT segments to better manage their client’s problems and requirements. Transforming its workforce with intensive training and assessments will be instrumental.

The post AI and RPA: Threat or opportunity for the IT managed services industry? appeared first on Artificial Intelligence.

Deep learning can help scholars restore ancient Greek texts. Specifically, researchers at University of Oxford (Thea Sommerschield and Professor Jonathan Prag) and DeepMind (Yannis Assael) built Pythia, training a neural network to guess missing words or characters from Greek inscriptions.

These were on surfaces including stone, ceramic and metal. They were between 1500 and 2600 years old. New Scientist reported that AI beat humans in deciphering damaged tablets.

“In a head-to-head test, where the AI attempted to fill the gaps in 2949 damaged inscriptions, human experts made 30 percent more mistakes than the AI. Whereas the experts took 2 hours to get through 50 inscriptions, Pythia gave its guesses for the entire cohort in seconds.”

Starting out, the authors knew that restoring text was a time-consuming tasks—-even for expert epigraphists. They set out to evaluate the difficulty of the restoration task at hand—and thereby judge the impact of our work—with the help of two doctoral students with epigraphical expertise. The scholars were allowed to use the training set to search for “parallels.”
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Gege Li wrote on Friday in New Scientist. The AI seems to be better than humans at filling in missing words, but this is no Team A versus Team B competition. Rather, the AI technique, said Li, “may be most useful as a collaborative tool, where researchers use it to narrow down the options.”

Many ancient insicriptionshave become eroded or damaged over the centuries. The authors said that “Only a small minority of surviving inscriptions are fully legible and complete.”

With segments of text lost, how could one try to fill in the blanks of missing words? As Li said, it would mean looking at the rest of the inscription and looking at other similar texts.

Consider New Scientist‘s report on what the AI, dubbed Pythia, was able to do: (1) Pythia learned to recognise patterns in 35,000 relics, with over 3 million words. (2) Patterns it picks up on include the context in which different words appear, the grammar, and also the shape and layout of inscriptions.

The accomplishment is reflected in the title of their paper which now up on arXiv: “Restoring ancient text using deep learning: a case study on Greek epigraphy.”

To aid the epigraphist, Pythia doesn’t just give the scholar a single prediction. Rather, it returns multiple predictions as well as the level of confidence for each result.

“Specifically, we provide a set of the Top 20 predictions decoded using beam search.” With 20 suggestions to fill the gap, it is up to the person to select the best one. “It’s all about how we can help the experts,” said Assael. To be sure, their position is that Pythia can serve as an assistive method in digital epigraphy.

Encylopaedia Brittanica: Epigraphy is “the study of written matter recorded on hard or durable material. The authors similarly provided a definition. They stated that “Epigraphy is the study of documents, ‘inscriptions’, written on a durable surface (stone, ceramic, metal) by individuals, groups and institutions of the past.”

The team talked about Pythia’s future potential, and they pointed out that it is the combination of machine learning and epigraphy that has the potential to impact meaningfully the study of inscribed textual cultures.

“By open-sourcing PYTHIA, and PHI-ML’s processing pipeline, we hope to aid future research and inspire further interdisciplinary work.”

Why their research matters: Pythia, they wrote, is “the first ancient text restoration model that recovers missing characters from a damaged text input using deep neural networks.” The authors believe that Pythia “sets the state-of-the-art in ancient text restoration.”

Faculty of Classics at the University of Oxford site similarly commented on Pythia’s strengths. “The architecture works at both the character- and word-level, thereby effectively handling long-term context information, and dealing efficiently with incomplete word representations. This makes it applicable to all disciplines dealing with ancient texts (philology, papyrology, codicology) and applies to any language (ancient or modern).”

The Faculty of Classics at the University of Oxford said that an online Python notebook, Pythia, and PHI-ML’s processing pipeline have been open sourced on GitHub.

With origins in London in 2010, DeepMind, meanwhile, is in the frontlines of artificial intelligence research.

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Artificial intelligence, including machine learning, presents exciting opportunities to transform the health and life sciences spaces. It offers tantalizing prospects for swifter, more accurate clinical decision making and amplified R&D capabilities. However, open issues around regulation and clinical relevance remain, causing both technology developers and potential investors to grapple with how to overcome today’s barriers to adoption, compliance, and implementation.

Here are key obstacles to consider and how to handle them:

Developing regulatory frameworks. Over the past few years, the U.S. Food and Drug Administration (FDA) has been taking incremental steps to update its regulatory framework to keep up with the rapidly advancing digital health market. In 2017, the FDA released its Digital Health Innovation Action Plan to offer clarity about the agency’s role in advancing safe and effective digital health technologies, and addressing key provisions of the 21st Century Cures Act.

The FDA has also been enrolling select software-as-a-medical-device (SaMD) developers in its Digital Health Software Precertification (Pre-Cert) Pilot Program. The goal of the Pre-Cert pilot is to help the FDA determine the key metrics and performance indicators required for product precertification, while also identifying ways to make the approval process easier for developers and help advance healthcare innovation.

Most recently, the FDA released in September its “Policy for Device Software Functions and Mobile Medical Applications” — a series of guidance documents that describe how the agency plans to regulate software that aids in clinical decision support (CDS), including software that utilizes machine-learning-based algorithms.

In a related statement from the FDA, Amy Abernethy, its principal deputy commissioner, explained that the agency plans to focus regulatory oversight on “higher-risk software functions,” such as those used for more serious or critical health circumstances. This also includes software that utilizes machine learning-based algorithms, where users might not readily understand the program’s “logic and inputs” without further explanation.

An example of CDS software that would fall under the FDA’s “higher-risk” oversight category would be one that identifies a patient at risk for a potentially serious medical condition — such as a postoperative cardiovascular event — but does not explain why the software made that identification.

Achieving FDA approval. To account for the shifting FDA oversight and approval processes, software developers must carefully think through how to best design and roll out their product so it’s well positioned for FDA approval, especially if the software falls under the agency’s “higher risk” category.

One factor that must be considered is the fact that AI-powered therapeutic or diagnostic tools, by nature, will continue to evolve. For example, it is reasonable to expect that a software product will be updated and change over time (e.g., security updates, adding new features or functionalities, updating an algorithm, etc.). But given the product has technically changed, its FDA approval status could be put at risk after each update or new iteration.

In this case, planning to take a version-based approach to the FDA approval process might be in the developer’s best interest. In this approach, a new version of software is created each time the software’s internal ML algorithm(s) is trained by a new set of data, with each new version being subjected to independent FDA approval.

Although cumbersome, this approach sidesteps FDA concerns about approving software products that functionally change post-FDA approval. These strategic development considerations are crucial for solutions providers to consider.

Similarly, investors must also have a clear understanding of a company’s product development plans and intended approach for continual FDA approval as this can provide clear differentiation over other competitors in the same space. Clinicians will be hard pressed to adopt technologies that haven’t been validated by the FDA, so investors need to be sure the companies they are considering supporting have a clear product development roadmap — including an approach to FDA approvals as software products themselves and regulatory guidelines continue to develop.

AI is a black box. Besides current regulatory ambiguity, another key issue that poses challenges to the adoption of AI applications in the clinical setting is their black-box nature and the resulting trust issues.

One challenge is tracking: If a negative outcome occurs, can an AI application’s decision-making process be tracked and assessed — for example, can users identify the training data and/or machine learning (ML) paradigm that led to the AI application’s specific action?. To put it more simply, can the root cause of the negative outcome be identified within the technology so that it can be prevented in the future?

From reclassifying the training data to redesigning the ML algorithms that “learn” from the training data, the discovery process is complex — and could even result in the application being removed from the marketplace.

Another concern raised about the black-box aspect of AI systems is that someone, either on purpose or by mistake, could feed incorrect data into the system, causing erroneous conclusions (e.g., misdiagnosis, incorrect treatment recommendations). Luckily, detection algorithms designed to identify doctored or incorrect inputs could reduce, if not eliminate, this risk.

A bigger challenge posed by AI systems’ black box nature is that physicians are reluctant to trust (due in part to malpractice-liability risk) — and therefore adopt — something that they don’t fully understand. For example, there are a number of emerging AI imaging diagnostic companies with FDA-approved AI software tools that can assist clinicians in diagnosing and treating conditions such as strokes, diabetic retinopathy, intracranial hemorrhaging, and cancer.

However, clinical adoption of these AI tools has been slow. One reason is physician certification bodies such as the American College of Radiology (ACR) have only recently started releasing formalized use cases for how AI software tools can be reliably used. Patients are also likely to have trust issues with AI-powered technologies. While they may accept the reality that human errors can occur, they have very little tolerance of machine error.

While efforts to help open up the black box are underway, AI’s most useful role in the clinical setting during this early period of adoption may be to help providers make better decisions rather than replacing them in the decision-making process. Most physicians may not trust a black box, but they will use it as a support system if they remain the final arbiter.

To gain physicians’ trust, AI-software developers will have to clearly demonstrate that when the solutions are integrated into the clinical decision-making process, they help the clinical team do a better job. The tools must also be simple and easy to use. Applying AI in lower-stakes tasks initially, such as billing and coding (e.g., diagnostics, AI-assisted treatments), should also help increase trust over time.

At the industry level, there needs to be a concerted effort to publish more formalized use cases that support AI’s benefits. Software developers and investors should be working with professional associations such as the ACR to publish more use cases and develop more frameworks to spur industry adoption and get more credibility.

Lower hurdles in life sciences. While AI’s application in the clinical care setting still faces many challenges, the barriers to adoption are lower for specific life sciences use cases. For instance, ML is an exceptional tool for matching patients to clinical trials and for drug discovery and identifying effective therapies.

But whether it’s in a life sciences capacity or the clinical care setting, the fact remains that many stakeholders stand to be impacted by AI’s proliferation in health care and life sciences. Obstacles certainly exist for AI’s wider adoption — from regulatory uncertainties to the lack of trust to the dearth of validated use cases. But the opportunities the technology presents to change the standard of care, improve efficiencies, and help clinicians make more informed decisions is worth the effort to overcome them.

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There is a vast number of products sold online through various outlets all over the world. distinguishing, matching and cross-checking product for functions like worth comparison becomes a challenge as there aren’t any international distinctive identifiers. There are several things wherever accurately distinguishing a product match is crucial. as an example, stores might want to check competition costs for the constant product they’ll supply. Similarly, customers could use comparison tools to urge the most effective deals.

To improve the consumer experience, e.g., by allowing for easily comparing offers by different vendors, approaches for product integration on the Web are needed. we present an approach that leverages neural language models and deep learning techniques in combination with standard classification approaches for product matching and categorization. In our approach, we tend to use structured product information as oversight for coaching feature extraction models able to extract attribute-value pairs from matter product descriptions. To minimize the need for lots of data for supervision, we use neural language models to produce word embeddings from large quantities of publicly available product data marked up with Microdata, that boost the performance of the feature extraction model, therefore resulting in higher product matching and categorization performances.

Leveraging multiple data sources

Identifying an identical product is additionally necessary to construct the ultimate item page. product may be represented in terms of their options like the whole, color, size, etc. so as to create it easier for sellers to aboard their things, most product options don’t seem to be obligatory for sellers to supply. As a result, we discover that completely different sellers could give different options in their product feed. By utilizing totally different sources of knowledge for a constant product, we will increase the coverage of product specifications on the item page.

AI Implementations in the E-Commerce Value Chain

Product Searching: Product looking is one in every of the foremost oftentimes used and necessary options for e-commerce platforms. Customers are able to notice product matching their interests through keywords, wherever product matching depends on informatics technologies; and visual “search by image,” that leverages pc vision. E-commerce platforms conjointly utilize reinforcement learning technologies to optimize their ranking algorithms and deliver higher search results.

personalized Product Recommendation: additionally to looking, e-commerce platforms conjointly use machine learning and informatics techniques to have interaction customers and build personalized product recommendations supported their searching trends and browsing history.

Dynamic Pricing: several e-commerce platforms use dynamic valuation tools steam-powered by massive information and machine learning algorithms to create time period worth changes or predict future costs supported to provide and demand projections.

Fraud Risk Management: E-commerce retailers utilize machine learning technologies to spot potential deceitful MasterCard transactions to forestall and manage risks in real-time and guarantee secure online payments.

Limitations of AI Application in E-commerce

Cold start Problem: because of information insufficiency, retailers in operation a replacement business on an e-commerce platform might not be able to benefit of advanced AI-based options like recommendation system and a dynamic valuation that think about massive information and analytics.

Algorithm Scalability Issue: Reinforcement learning technology will encounter performance bottlenecks on e-commerce platforms. Algorithms typically struggle with scaling issues and may face challenges effectively and expeditiously ransacking through terribly giant call areas.

Long Tail Effect: E-commerce recommendation algorithms could gift only a little variety of the foremost common things to customers, and fail to suggest rare “long-tail” product that might be a lot of appealing to niche customers, in and of itself product will as an example lack spare ratings information.

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Unless you’ve been saving your credit card rewards for a specific purpose, such as paying down your existing debt or purchase airline tickets for a vacation, sifting through the credit card reward options can be overwhelming.

Some major credit card providers, however, are using artificial intelligence to make it easier to not just choose your rewards, but offer the rewards consumers want the most. In this drive for hyper-personalization of our rewards, artificial intelligence banking is benefiting all parties involved.

According to a recent study, 33 percent of customers who abandoned a business relationship did so due to lack of personalization.

But artificial intelligence in banking is changing that.

Deployed by major banks including HSBC and Bank of America, AI and predictive analytics make it easier for banks and loyalty program issuers to determine what rewards consumers will want at any given time and offer those rewards, along with incentives to use their card for future, similar rewards.

What is AI?

If you haven’t heard the term used before, artificial intelligence is when computer systems or machines are able to perform tasks that normally require human intelligence. These skills include speech recognition, as in the case of the Amazon Alexa virtual assistant or Apple’s Siri, decision-making, and visual perception, such as facial recognition.

Futurist Andrew Ng wrote in the Harvard Business Review, “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”

These tasks primarily involve pattern recognition and comparisons of data. Sophisticated marketing software uses AI algorithms for ad retargeting on social media platforms. The software can recognize, from the thousands of users who saw an ad or visited a website, those who are most likely to click an offer.

So where do these skills AI offers come into play in banking and designing better credit card rewards programs?

How AI banking could change what’s in your wallet

Just as social media marketers use ad retargeting to deliver more relevant ads to users, banks and credit card companies can design rewards programs based not just on basic demographic information, but on your past redemption activity and buying behavior.

The banks are already collecting this information – it’s available within your online accounts and on your statements. But it would take countless hours for human beings to sift through the data, find patterns, and derive logical conclusions from those patterns.

AI makes that kind of hyper-personalization possible, so you get more relevant rewards that you’re more likely to use, delivered proactively – before you even realize you want to redeem the rewards you’ve accrued.

You could have two choices when redeeming rewards in the future:

• Sift through pages of offers to analyze how you can get the most bang for your buck with rewards you’ll use or;
• Receive relevant rewards suggestions with the best return, delivered directly to your computer screen or mobile device.

Which would you choose?

Hyper-personalization: the future (and present) 

AI needs lots of data to work effectively. Fortunately, the majority of consumers are willing to share personal information if it means improved services or products, according to a digital banking report sponsored by Personetics, a digital banking solutions provider.

HSBC has already rolled out a pilot program that uses AI to deliver more relevant rewards and seeing redemption rates of 70 percent based on the AI-generated recommendations. To determine the best rewards, the software analyzes the user’s purchasing and redemption history.

But the potential of an AI card goes far beyond the typical or expected predictions. While a human being might assume that someone who redeemed their rewards for a flight to Orlando in the past might do so again, AI can aggregate all their past spending habits and recommend other offers that could be an even better fit.

And that’s only a fraction of the data programs could use to make rewards recommendations.

Tapping into location tracking on your phone, your AI card could deliver promotions via text with location-specific offers, from tickets for tourist attractions in a city you are visiting to gift cards for stores within a shopping center.

Best credit cards and banks for AI banking

Several of the major banks are already using AI banking for hyper-personalization and improved customer service.

HSBC makes great strides with AI program

HSBC is on the cutting edge, putting together a Client Intelligence Utility with 10 petabytes of corporate and institutional data from 1.6 million clients.

For reference, there are 1 million gigabytes of data in one petabyte. A single petabyte of storage can hold 13.3 years of HD video, and the entire written works of all time would take up 50 petabytes, according to this Gizmodo infographic.

HSBC’s program, so far, is experimental, but it’s safe to assume that the company’s top tier rewards cards, like the HSBC Cash Rewards Mastercard® credit card, will soon employ AI to make the best rewards recommendations, if they aren’t already.

In fact, more targeted rewards choices and hyper-personalization could help make up for the card’s relatively modest 1.5% unlimited rewards. If HSBC impresses users with proactive, useful rewards suggestions that make redemption easy and provide more value, it could keep customers using the card long after the first-year introductory rewards have been exhausted.

American Express® embraces AI

American Express is known for its high-end rewards and its travel concierge service. The charge card and credit card provider is now incorporating AI into its travel services with the purchase of Mezi, an AI-powered virtual assistant and chatbot that provides services normally offered by personal shoppers and travel agents.

Right now, Mezi’s services are offered as a perk to American Express cardholders through an downloadable smartphone app, AskAmex. Many Amex offers, such as rewards redemption and 2X rewards offered through AmexTravel, are not available through AskAmex, yet. But it’s not a stretch to think that integration between the programs could be the next step.

For now, cardmembers can “AskAmex” for the convenience of travel suggestions based on voice queries, and then book through AmexTravel using their American Express® Gold Card to maximize their points.

Let Bank of America’s erica guide you

First, there was Siri. Then, Alexa. Bank of America’s counterpart to the American Express AI chatbot is named erica, and “she” already has more than 1 million users.

Right now, the capabilities include searching for transactions, transferring money, or checking account balances. For instance, let’s say you make a purchase at Walmart using your Bank of America debit card and you want to return the item without a receipt. To expedite the return process, erica can find the transaction for you.

This virtual banking assistant was introduced shortly before Bank of America revamped its top-tier Bank of America Cash Rewards credit card. While erica currently doesn’t integrate with Bank of America rewards programs, it would be a logical next step.

The newly revamped Bank of America Cash Rewards® credit card enables users to choose their own bonus categories to earn 3X cash back on up to $2,500 in combined choice categories. While this benefits card users, giving them more flexibility to maximize their rewards, it also enables Bank of America to collect data on customer preferences and create more tailored redemption programs using AI software.

In the future, it’s possible that Bank of America’s cash rewards programs will also integrate with erica, enabling the AI to suggest relevant rewards.

Voice and AI will learn and improve

While voice recognition isn’t necessary for a successful AI card, it helps. Bank of America reports that more people are using erica through tap and gesture functions, with voice and text being used equally after that.

Voice recognition is still not perfect, and there will be learning on both sides, by people and the machines, before we can have seamless spoken conversations. But the more voice is used by today’s AI-powered virtual assistants, the better erica, Mezi, Siri, Alexa, and all the rest will get at understanding us.

Artificial intelligence banking in the future

In general, the more data AI-powered software can gather, the more effective it will be. Credit card issuers, with a world of consumer data at their fingertips, stand in a strong position to provide their customers with the best options for their lifestyles.

Banks are already using AI to make banking easier and help consumers make the best choices to get out of debt.

Hyper-personalization, more relevant rewards and even better choices for bonus points are the future, and the present, of AI-powered credit cards.

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Technological advances like artificial intelligence, machine learning and big data are impacting the power sector as well, making it imperative for producers to re-skill resources, a senior official of Tata PowerBSE -0.71 % said.

“With technological disruptions like artificial intelligence, machine learning, big data, augmented reality etc. that are impacting the power sector as well, it has become imperative to re-train and re-skill resources for emerging careers where the demand is more than the supply,” Tata Power Chief Human Resource Officer Jayant Kumar said.

Addressing the seventh Power HR Round Table organised by the University of Petroleum & Energy Studies (UPES), Kumar said, “Curiosity, courage and comfort with technology are the traits of a future digital worker.”

CEOs and HR heads of leading Indian companies in the power sector such as Tata Power, Power GridBSE 0.30 %, GMR Energy, Adani Green Energy, and DB Power deliberated on various challenges faced by the sector today and possible solutions, UPES said in a statement.
“Power distribution companies (Discoms), due to their nature of work, have access to huge consumer data and they can use this data to foray into other allied businesses just the way cab aggregators are delivering food or e-commerce companies providing video-on-demand services,” Ashis Basu, CEO (Corporate), GMR Energy, suggested.
UPES has entered into an MoU with NITI Aayog to assess national energy issues like implications of high penetration of renewables on the grid, Utpal Ghosh, President & CEO of UPES, said.

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