Category Archives: Artificial Intelligence

Proximity Lab, a leading UX research, strategy, and design agency, released an in-depth research report that studies the impact of artificial intelligence (AI) on digital product design. The report, "Up & Rising: How AI is Transforming Product Design & Creativity," is free and available for download at Proximity Lab's website.

PORTSMOUTH, N.H. (PRWEB) January 17, 2020

Proximity Lab, a leading UX research, strategy, and design agency, released an in-depth research report that studies the impact of artificial intelligence (AI) on digital product design. The report provides highlights from interviews with AI experts at leading product development companies including Adobe, Alarm.com, Eagle Genomics, MIT Media Lab, Nuance, and Salesforce and summarizes the results of a survey of over 100 digital product designers and knowledge workers.

Key areas of exploration include:

Anthony Finbow, CEO of Eagle Genomics and an expert in AI and ML, was part of the research. "The team at Proximity Lab focused on finding the answer to a critical issue for anyone building products: how to foster the right relationship between AI and the people using it. If you can get these interactions right, you have the opportunity to develop an entirely new conversation that can lead to insights and knowledge that neither a person or machine alone would have uncovered."

The report is divided into the following sections:

The report, "Up & Rising: How AI is Transforming Product Design & Creativity," is free and available for download at Proximity Lab's website.

About Proximity Lab

Proximity Lab is an award-winning interaction design studio with deep experience in research, product strategy and UX design. We are designers, creators and thinkers who bring diverse backgrounds together to develop a common vision to create products that emphasize clarity, simplicity and value. Our team has been helping enterprise software leaders and innovators rethink and reimagine software experiences for over 10 years. Proximity Lab is headquartered in Portsmouth, NH with offices in San Francisco, CA.

Proximity Lab on LinkedIn

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Proximity Lab Releases Research-focused Report on the Impact of Artificial Intelligence (AI) on Product Design: Features Insights from AI Experts and...

The new decade opened with some intriguing news: The journal Nature reported that artificial intelligence was better at identifying breast cancers on mammograms than radiologists. Researchers at Google Health teamed up with academic medical centers in the United States and Britain to train an AI system using tens of thousands of mammograms.

But even the best artificial intelligence system cant fix the uncertainties of early cancer diagnosis.

To understand why, it helps to have a sense of how AI systems learn. In this case, the system was trained with images labeled as either cancer or not cancer. From them, it learned to deduce features such as shape, density and edges that are associated with the cancer label.

Thus, the process is dependent on starting with data that are correctly labeled. In the AI mammography study, the initial diagnoses were determined by a pathologist who examined biopsy specimens under a microscope after an abnormal mammogram. In other words, the pathologist determined whether the mammogram showed cancer.

Unfortunately, this pathologic standard is problematic. Over the last 20 years there has been a growing recognition that screening mammography has led to substantial overdiagnosis the detection of abnormalities that meet the pathological definition of cancer, yet wont ever cause symptoms or death.

Furthermore, pathologists can disagree about who has breast cancer even when presented with the same biopsy specimens under the microscope. The problem is far less for large, obvious cancers far greater for small (even microscopic), early-stage cancers. Thats because there is a gray area between cancer and not cancer. This has important implications for AI technology used for cancer screening.

AI systems will undoubtedly be able to consistently find subtle abnormalities on mammograms, which will lead to more biopsies. This will require pathologists to make judgments on subtler irregularities that may be consistent with cancer under the microscope, but may not represent disease destined to cause symptoms or death. In other words, reliance on pathologists for the ground truth could lead to an increase in cancer overdiagnosis.

The problem is not confined to breast cancer. Overdiagnosis and disagreement over what constitutes cancer are also problems relevant to melanoma, prostate and thyroid cancer. AI systems are already being developed for screening skin moles for melanoma and are likely to be employed in other cancers as well.

In a piece for the New England Journal of Medicine last month, we proposed a better way of deploying AI in cancer detection. Why not make use of the information contained in pathological disagreement? We suggested that each biopsy used in training AI systems be evaluated by a diverse panel of pathologists and labeled with three distinct categories: unanimous agreement of cancer, unanimous agreement of not cancer, and disagreement as to the presence of cancer. This intermediate category of disagreement would not only help researchers understand the natural history of cancer, but could also be used by clinicians and patients to investigate less invasive treatment for cancers in the gray area.

The problem of observer disagreement is not confined to pathologists; it also exists with radiologists reading mammograms. Thats the problem AI is trying to solve. Yet, while the notion of disagreement may be unsettling, disagreement also provides important information: Patients diagnosed with an early-stage cancer should be more optimistic about their prognoses if there were some disagreement about whether cancer was present, rather than all pathologists agreeing it was obviously cancer.

Artificial intelligence cant resolve the ambiguities surrounding early cancer diagnosis, but it can help illuminate them. And illuminating these gray areas is the first step in helping patients and their doctors respond wisely to them. We believe that training AI to recognize an intermediate category would be an important advance in the development of this technology.

Adewole S. Adamson is a dermatologist and assistant professor of medicine at Dell Medical School at the University of Texas at Austin. H. Gilbert Welch is a senior researcher in the Center for Surgery and Public Health at Brigham and Womens Hospital in Boston and author of Should I Be Tested for Cancer? Maybe Not and Heres Why. They wrote this piece for the Los Angeles Times.

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Adamson, Welch: Using artificial intelligence to diagnose cancer could mean unnecessary treatments - St. Paul Pioneer Press

Current rapid growth in artificial intelligence (AI) is fuelled predominantly by the rediscovery of deep learning. When it first surfaced 15 years ago, the concept was met with unfavourable conditions and largely abandoned in the aftermath. Nowadays, deep learning is back, circumstances are right and the field flourishes.

The described third boom in artificial AI and subsequent tightening technological and economic competition sent ripples through various aspects of the social realm, including policymaking. Many countries began working on national AI strategies, including current leaders China and U.S. and Japan also followed suit.

However, what distinguishes Japan from other countries is a consistent concept underlying all introduced regulation the vision of Society 5.0. This new form of society is saturated with AI-related technology, which not only improves the lives of its members, but also creates new aspects and new values. Individual needs are met in a timely and proportionate manner allowing for fulfilled and contented lives. With society 5.0 as a targeted outcome, the recently updated AI Strategy 2019 contains a wide spectrum of necessary actions comprising both facilitating the development of AI and utilising it for the advancement of industry and society.

The strength of the strategy lies in its emphasis on the practical application of AI, Japanese policymakers correctly recognised the versatility of AI and its potential to pervade and transform any given field, employing it to improve and lower costs of various processes, perform tasks beyond human capabilities and gain insight where human-operated analysis didnt reach.

The strategy aims not only to improve situation on a national level, targeting five designated priority areas (manufacturing, transportation and logistics, health and medical care, agriculture and disaster response) but also globally by helping solve major societal problems like ageing society or labour shortage, diversification of energy sources, GHG reduction or more efficient waste management, which lines up perfectly with achieving Sustainable Development Goals. According to the strategy, the solutions developed in Japan would then be made available for the world and if realised successfully, it might just be the advantage that Japan needs to win over its competitors.

Still, reaching the targets mentioned above requires major efforts in at least three vital areas: R&D, data and human resources.

At the moment, Japan along with the rest of the world struggles with a lack of educated professionals capable of handling AI-related technologies. The short-term relief could be brought by encouraging more women to participate in the job market and attracting skilled resources form overseas. In the long-term perspective, Japan is preparing for major educational system reform, introducing AI into curricula and making it obligatory part of the university entrance exam, creating a learning inducive environment for students (sufficient network infrastructure and access to communication devices) and facilitating lifelong learning for the existing workforce.

Recognising data as the sine qua non for the development of AI, the Japanese Government is making considerable effort to facilitate data circulation while maintaining its quality. Notable examples include The Basic Act on the Advancement of Utilising Public and Private Sector Data and The Act on the Protection of Personal Information. It is also worth mentioning, that all the practical applications of AI planned in the new AI Strategy are designed as two-way data flows one direction is technology and data deployment into the industry area and the other is the data gathered from users feeding further development of AI. To organise and structure the exchange, the Japanese Government plans to construct a data linkage infrastructure.

Finally, a lot of effort is also directed at the R&D area, with establishing a network of centres of excellence as the first step. In the search for disruptive innovation, the Japanese Government initiated many R&D programmes, the last of which Moonshot R&D positively surprises with scale and forwardness and also offers many opportunities for international collaboration for European partners.

Japan certainly seems determined to embrace and take advantage of AIs potential in transforming its own society and reinforcing its global position. It would, therefore, be beneficial for European companies to closely follow Japans next movements, as the two have a lot in common. With a shared approach to technology and related values (confirmed recently by the European Unions (EU) welcoming Japans Social Principles of Human-Centric AI) and common rivals, Japan and EU are natural allies and its high time to capitalise on cooperation potential.

For more information about AI policy in Japan, see the Analysis of opportunities for EU SMEs in Japans Data Economy and Artificial Intelligence in connection with Robotics report available on the EU-Japan Centre for Industrial Cooperation website.

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The rapid growth in artificial intelligence in Japan - Open Access Government

Artificial intelligence is supposed to free the hiring process from prejudices and biases. We can have a totally neutral system that evaluates candidates and selects the best possible one, regardless of race, gender, or any other characteristic.

It sounds fantastic, but it's been an abysmal failure in that matter. Artificial intelligence is only as good as the programmers, who, of course, are actual humans with flaws. Amazon, which, of course, has gobs of money to pour into development, had to scrap its A.I. recruiting process because the bot didn't like women.

HireVue faces pressure from rights groups over its hiring systems, which, according to TheWashington Post,

use video interviews to analyze hundreds of thousands of data points related to a person's speaking voice, word selection and facial movements. The system then creates a computer-generated estimate of the candidates' skills and behaviors, including their "willingness to learn" and "personal stability."

This model of gaming the system has been in place for as long as people have applied for jobs. There are thousands of articles on the internet that tell you how to answer standard interview questions ("Where do you see yourself in five years?") or extol the virtues of a firm handshake. This is really no different than the training these consultants give. Except, instead of trying to convince a human, you're trying to convince a machine.

And that makes this training so much more valuable. I can tell you "firm handshakes are important!" and then you interview with someone who prefers the dead-fish version of shaking hands and my advice harms instead of helps. Butif two companies use the same software, the information from these consultants will help you shine regardless of who the hiring manager is.

That's the goal, of course, to take the human biases out of interviews. But the biases still exist in A.I.--it's just that every job requires you to overcome the same preferences. Which means it will be easier to beat the system. Once the consultants figure out what the algorithms want, they can train you to respond the right way.

While it potentially levels the playing field, people who can afford training will do better in the interviews. Interviewers already discriminate on class, so this doesn't solve that problem at all.

Can artificial intelligence potentially make hiring better? Probably. But, as these consultants understand--anytime there is a system, there is a way to beat it. While humans are fallible, at least we all know they are. Artificial intelligence allows you to think the process is bias-free, but it's not. It just makes for consistent bias.

Published on: Jan 15, 2020

The opinions expressed here by Inc.com columnists are their own, not those of Inc.com.

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Companies Use Artificial Intelligence to Help With Hiring. Korean Consultants Teach You How to Beat It - Inc.

Enterprise Artificial Intelligence (AI) Market by Deployment Type (Cloud and On-Premise), Technology (Machine Learning, Natural Language Processing, Image Processing, and Speech Recognition), Organization Size (Large Enterprises and Small & Medium Enterprises), and Industry Vertical (Media & Advertising, BFSI, IT & Telecom, Retail, Healthcare, Automotive & Transportation, and Others): Global Opportunity Analysis And Industry Forecast, 2019-2026

New York, Jan. 17, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Enterprise Artificial Intelligence Market by Deployment Type, Technology, Organization Size, and Industry Vertical : Global Opportunity Analysis And Industry Forecast, 2019-2026" - https://www.reportlinker.com/p05828825/?utm_source=GNW

Artificial intelligence has been one of the fastest-growing technologies in recent years. AI is associated to human intelligence with similar characteristics, such as language understanding, reasoning, learning, problem solving, and others. Manufacturers in the market witness enormous underlying intellectual challenges in the development and revision of such technology. AI is positioned at the core of the next-gen software technologies in the market. Companies, such as Google, IBM, Microsoft, and other leading players, have actively implemented AI as a crucial part of their technologies. The increase in number of innovative start-ups and advancements in technology have led to rise in investment in artificial intelligence technologies. Moreover, escalating demand for analyzing and interpreting large amount of data boosts the requirement of artificial intelligence industry solutions. Moreover, development of more reliable cloud computing infrastructures and improvements in dynamic artificial intelligence solutions have a strong impact on the growth potential of the AI market. However, lack of trained and experienced staff hinders the growth of the enterprise Artificial Intelligence (AI) market. Furthermore, increase in adoption of AI in developing economies, such as China, and India are expected to provide major opportunities for the market growth in the upcoming years. Also, on-going developments in smart virtual assistants and robots are anticipated to be opportunistic for the growth of the enterprise artificial intelligence (AI) market. The global enterprise artificial intelligence (AI) market is segmented on the basis of deployment type, technology, organization size, industry vertical, and region. Based on deployment type, the market is bifurcated into cloud and on-premise. Based on technology, the market is divided into machine learning, natural language processing, image processing, and speech recognition. Based on organization size, the market is classified into large enterprises and small & medium enterprises. Depending on industry vertical, the market is segmented into media & advertising, BFSI, IT & telecom, retail, healthcare, automotive & transportation, and others. Based on region, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA. The report includes the profiles of key players operating in the market analysis. These include Alphabet Inc. (Google Inc.), Apple Inc., Amazon Web Services, Inc., International Business Machines Corporation, IPsoft Inc., MicroStrategy Incorporated, NVIDIA Corporation, SAP, Verint, and Wipro Limited.

KEY BENEFITS The report provides an in-depth analysis of the global enterprise artificial intelligence (AI) market trends, key driving factors, and potential areas for product investments. Key players are analyzed with respect to their primary offerings, recent investments, and future development strategies. Porters five forces analysis illustrates the potency of buyers and suppliers operating in the industry. The quantitative analysis of the global enterprise artificial intelligence (AI) market share from 2018 to 2026 is provided to determine the market potential.

KEY MARKET SEGMENTS

BY DEPLOYMENT TYPE Cloud On-premise

BY TECHNOLOGY Machine Learning Natural Language Processing Image Processing Speech Recognition

BY ORGANIZATION SIZE Large Enterprises Small & Medium Enterprises

BY INDUSTRY VERTICAL Media & Advertising BFSI IT & Telecom Retail Healthcare Automotive & Transportation Others

BY REGION North America o U.S. o Canada

Europe o UK o Germany o France o Russia o Rest of Europe

Asia-Pacific o China o Japan o India o Australia o Rest of Asia-Pacific

LAMEA o Latin America o Middle East o Africa

KEY MARKET PLAYERS PROFILED IN THE REPORT Alphabet Inc. (Google Inc.) Apple Inc. Amazon Web Services, Inc. International Business Machines Corporation IPsoft Inc. MicroStrategy Incorporated NVIDIA Corporation SAP Verint Wipro Limited

Read the full report: https://www.reportlinker.com/p05828825/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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Enterprise Artificial Intelligence Market by Deployment Type, Technology, Organization Size, and Industry Vertical : Global Opportunity Analysis And...

As access to healthcare professionals (HCPs) declines, the challenges facing sales representatives continue to increase: less time with HCPs, Sunshine Act restrictions, and integration of practices into larger health systems. It can be daunting.

Once, influence was based on interactions between reps and HCPs more than just about anything else. But today, influence is spread across a variety of touch points, many digital, which can be accessed by an HCP at any time and place. To reinforce their value, sales reps are expected to have deep knowledge of the market and their customers, so that they can tailor their interactions to the unique needs of each.

How can todays rep succeed? Its all about data.

Data gathered judiciously, digested accurately, analysed rapidly, and used wisely makes the sales force more efficient and productive. This concept is nothing new: it dates back to the beginnings of CRM in the 20th century.

But todays digital world offers new possibilities, enabling connections and predictions that yesterdays rep never even dreamed of.

What if reps could anticipate relevance?

By combining the best in industry expertise, brand strategy, CRM technology, and artificial intelligence (AI) and machine learning, reps can have the tools to make anticipated relevance possible.

At a recent Digital Health Coalition Midwest Summit, Intouch demonstrated examples of what this could look like for a brand, using their AI assistant, EVA, which is short for embedded virtual assistant.

How does it work?

EVA connects with Veeva to access a reps calendar of appointments to obtain information about where they need to go and who they need to see. Combined with marketing segmentation, EVA tells a sales rep the segmentation of todays calls. Data further informs the conversation with helpful facts like script-writing history, marketing plan, prior messages presented, and online activity, giving our rep a prediction of what their next best actions should be. These suggestions can be offered through the voice assistant, or sent by text or email for later reference, and can power the flow of the in-office detail. After the call, EVA can help a rep record a call quickly and easily in the CRM system.

An AI-powered ecosystem makes sure no pertinent data goes to waste. Whether its an email open, a website visit, a rep conversation, a script, or any other activity, the rep can quickly and easily understand what their HCP cares about and what information will be most helpful to their practice.

By anticipating relevance, the rep can provide an HCP with information thats useful to them, in the format, time, and place that helps them most. And EVA is able to use the most relevant assets efficiently and minimise the burden of administrative tasks. Time is used wisely on both sides, making it possible for the right information to help patients that much sooner.

Want to learn more about AI and modern pharma marketing? Download Intouchs comprehensive ebook.

Interested in learning how AI can work for your reps? Reach out to the Intouch team today.

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Artificial intelligence and the future of rep visits - - pharmaphorum

The reality of artificial intelligences role in media buying may be turning out very different from the dream.

Automation and AI could be used, so the theory went, for forecasting, analyzing data and ultimately improving campaign performance, so that marketers could change and reallocate budgets quickly. But despite advancements being made, AIs adoption for media buying is ending up with slightly different use cases.

The AI is there, said Jason Harrison, CEO of North America for WPPs Essence. Youre just not seeing it in the ways you would have expected.

The expectation was that AIs use for media buying otherwise known as automated decision-making would enable machines to tap data about specific audiences so as to create automated campaigns across different digital channels. And this would enable agencies to stop worrying about the minute details of media planning and buying so they could instead spend more time on strategic work and insight delivery for their clients. But so far, that hasnt been the case, as AI has led media buyers, as previously reported by Digiday, to spend added time on campaign reporting and the more difficult aspects of the job.

And AIs role in media buying hasnt been nearly as sexy as pitched: While media agencies have been able to use AI to automate campaigns (mostly for paid search advertising), it has been delivering better targeted audiences for the same amount of money as marketers previously spent and clarifying the gaps in a media plan rather than handling all the minute details.

AIs use for media buying hasnt lived up to the dream for a few reasons. For one, the effects of AIs use on media buying are generally still found in a biddable, programmatic environment where marketers have come to expect automation. And agencies, marketers and platform providers are all still testing the best ways to use AI for media buying. Without a set of standards in place, it difficult to compare marketers use of AI for media buying.

Outside of the current state of programmatic, adoption of AI isnt widespread in a systemic and systematic way across the industry, wrote William Restrepo, svp of business intelligence for Publicis Media, in an email. Different agencies and different vendors (and vendor types) are still in a trial-and-error phase determining what works and doesnt work for them.

At the same time, Facebook and Google have made advancements in the AI media buying capabilities for their platforms, making it appealing for marketers to use those platforms AI rather than continuing to build out their own.

An exclusive, inside look at whats actually happening in the video industry, including original reporting, analysis of important stories and interviews with interesting executives and other newsmakers.

At least, that has been case for Orangetheory Fitness. Just slightly more than two years ago, the high-end gym chainlaunched its own AI platform, enabling the company to slice its cost per lead from $20 to $8 and end up with better leads. With those results, the chain quadrupled its media spend and focused most of those dollars on AI. But although the company once was bullish about its own AI platform, Orangetheory has since pulled the plug on it, opting instead to have its internal teams and its media agency, the Tombras Group, manage more of its media buying and planning decisions.

We were heavily relying our digital media efforts on AI two years ago, said Tammie DeGrasse-Cabrera, the global marketing director for Orangetheory Fitness. Weve shifted back and really made sure the humans on our team, [those on] our media agency, are really doing that for us. Were also using AI thats already being developed in media platforms like Facebook and Google and connecting that and marrying that to the art and science of media buying, she added.

Orangetheory Fitnesss AI journey might be a microcosm of what midlevel marketers have been experiencing when using AI for media buying. Now that Facebooks and Googles AI for media buying has become more advanced, relying on those platforms has become attractive for marketers rather than spending significant resources on building out their own. Thats especially true at this point since the promise that AI would making the job of media buying simpler has not come to fruition.

But thats not the case for larger marketers with the resources to build their own AI solution, according to media executives; they said that major marketers are still vying for custom solutions that tap AI for media buying across a variety of platforms.

We rely on automation, but we dont set it and forget it, said Doug Rozen, chief media officer for 360i, who noted that his company has made significant advancements in use of AI for media buying over the last six months although work still remains. Its the human and the robot working together almost like sometimes the automation is taking a blunt object to something thats more nuanced than just applying the overall algorithmic automation to it.

When it comes to AIs use for media buying, the complexity of whats being accomplished is at times difficult to convey to marketers. And for someone not using the platforms each day, its easy to miss the ways that AI is already changing media buying in a biddable environment. Publishers and platform providers have done a good job of externalizing the technology and interfaces to make it easy for someone to place a media buy and enter constraints, Harrison said. It becomes akin to indicating this is how much Im willing to pay; these are my bid thresholds; this is the outcome I expect; heres how much I have to spend and hitting go, he said.

Added Harrison: Behind the scenes, the work of those platforms has gotten a lot smarter; and the return, the value that advertisers get for that money, is a lot more because in theory its being targeted to the right people; its more precise and all of that is powered by AI decisioning. He said, Its not fair to [say] it hasnt gone anywhere. It has. Youre just not as explicitly seeing it.

As Luke Lambert, OMD USAs head of programmatic advertising, observed, What were really seeing is not the change in output that we were always dreaming about, that we thought AI would produce for us. He added, Instead, its taught us that theres a better way of doing things before we even give the AI a dollar to spend, which is a positive thing. Its a good thing to find process efficiencies. We just expected them to be on the other side.

Even though AIs use for media buying has not to date delivered quite what was expected, media executives are still bullish on its potential and the need for marketers to enlist it. Weve really only scratched the surface, Harrison said. The more complexity you have in the media ecosystem with the number of players, platforms and opportunities, [and] the more complexity you have in the content ecosystem, with options in the way people consume and see content, the harder it is to train machines to anticipate where the next best impression should go. He added, Thats really the challenge now to build AI that can accommodate and contemplate all of that complexity.

And despite the challenges associated with contending with all that complexity, he said, the need for AI to help media buyers manage those players, platforms and channels is clear. Humans are not going to be able to do that decision game much longer and arguably were doing it at a suboptimal way today, Harrison said. The sooner we build AI to do that job the better marketers outcomes will be.

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Dont set it and forget it: Artificial intelligences role in media buying is taking shape - Digiday

Artificial Intelligence in BFSI Market by Offerings (Hardware, Software, and Services), Solution (Chatbots, Fraud Detection & Prevention, Anti-Money Laundering, Customer Relationship Management, Data Analytics & Prediction, and Others), Technology (Deep Learning, Querying Method, Natural Language Processing, and Context Aware Processing) : Global Opportunity Analysis and Industry Forecast, 20192026

New York, Jan. 16, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence in BFSI Market by Offerings, Solution, Technology : Global Opportunity Analysis and Industry Forecast, 20192026" - https://www.reportlinker.com/p05836997/?utm_source=GNW

The global artificial intelligence (AI) in BFSI market was valued at $17,765.2 million in 2018 and is expected to reach $247,366.7 million by 2026, registering a CAGR of 38.0% from 2019 to 2026. Artificial intelligence is the recreation of human intelligence that perform tasks like humans. In financial institutions and other major finance industries, AI has become a core adaption and is expected to change the overall scenario of product and service offerings. For instance, insurance companies are improving risk models to maintain customer loyalty and client satisfaction with the help of advanced AI technological platforms.

Various fraud detection, risk mitigation, back-end office works with thousands of people processing customer requests are improved with the help of AI enabled technologies such as chatbots, machine learning, and other such technologies, which boosts the growth of the market. In addition, the reduction in the tendency of human errors by automation of backend processes and enhancement in proactive customer experience is expected to drive the growth of the AI in the BFSI market. However, rise in security concerns, inadequacy of trust while issuing customer data, and higher cost for implementation of AI technologies is expected to restrain the market growth. New entrants like FinTech (Financial Technology) with advance features in the market, new initiatives in government regulations, and existing traditional banking system provides lucrative opportunities for the market growth.

The global artificial intelligence (AI) in BFSI market is segmented on the basis of offerings, solution, technology, and region. On the basis of offerings, it is segmented into hardware, software, and services. By service providers, it is segmented into chatbots, fraud detection & prevention, anti-money laundering, customer relationship management, data analytics & prediction, and others. By technology, it is classified into deep learning, querying method, natural language processing, and context aware processing. Region wise, the market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

KEY BENEFITS FOR STAKEHOLDERS ? The study provides an in-depth analysis of the global artificial intelligence (AI) in BFSI market along with the current trends and future estimations to elucidate the imminent investment pockets. ? Comprehensive analysis of the factors that drive and restrict the market growth is provided in the report. ? Comprehensive quantitative analysis of the industry from 2019 to 2026 is provided to enable the stakeholders to capitalize on the prevailing market opportunities. ? Extensive analysis of the key segments of the industry helps in understanding the offerings, solution, and technology across the globe. ? Key market players and their strategies have been analyzed to understand the competitive outlook of the market.

KEY MARKET SEGMENTS By Offerings o Hardware o Software o Services By Solution o Chatbots o Fraud Detection & Prevention o Anti-Money Laundering o Customer Relationship Management o Data Analytics & Prediction o Others By Technology o Deep Learning o Querying Method o Natural Language Processing o Context Aware Processing By Region o North America U.S. Canada Mexico o Europe o UK o Germany o France o Rest of Europe o Asia-Pacific o Japan o India o China o Australia o Rest of Asia-Pacific o LAMEA o Middle East o Latin America o Africa

KEY PLAYERS PROFILED Alphabet Inc. (Google) Baidu, Inc. Inbenta Technologies, Inc. Intel Corporation International Business Machines Corporation (IBM) Microsoft Corporation Oracle Corporation Palantir Technologies Inc. SAP SE salesforce.com, inc.

The other players in the market include (profiles not included in the report) the following: Lexalytics Inc. Digital Reasoning Inc. Interaction LLC, Inc. Ipsoft Inc. Zest FinanceRead the full report: https://www.reportlinker.com/p05836997/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

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The global artificial intelligence (AI) in BFSI market was valued at $17,765.2 million in 2018 and is expected to reach $247,366.7 million by 2026,...

The IoT global market revenue will reach approximately USD 1.1 trillion by 2025, predicts IDC.

IDC also says that the global IoT connections will rise with a 17 percent Compound Annual Growth Rate (CAGR) from 7 billion to 25 billion between the years 2017 to 2025.

Back in 2018, Sophia a humanoid robot performed a duet with Jimmy Fallon at his show. This performance left the audience awestruck. The entire world was spellbound of how Sophia (humanoid robot) could showcase human emotion while performing the song.

David Hanson, an American roboticist, who is the founder and Chief Executive Officer of Hanson Robotics, not only invented AI that could mirror human intelligence but also enabled it to show human emotion.

A major breakthrough in the technology world.

Well, this is just a teaser of whats coming for us.

While were here sitting in an era where science fiction is a popular genre of books has already engulfed how AI will be seen at the forefront were already living in the future thats beyond AI.

With leaps and bounds in the tech industry, AI holds a lot more in the technology world. Combining it with IoT has only further enhanced the usage of both the technologies. While IoT enables connecting two or more sensors, platforms, objects or networks to enable data transmission for several applications, AI offers the capability of analyzing the most critical information easily providing valuable insights and making highly informed decisions.

This simply means that smart AI experts will get the opportunity to bring in new IoT-enabled solutions to life.

IoT is described as the network of physical objects. For instance, these can be things that can be embedded with technologies, software or sensors that further helps in connecting or the exchange of data with other devices or systems via the internet or vice versa.

Now, these devices could be a simple ordinary household object or even sophisticated industrial tools.

There are over 8.3 billion IoT devices connected today. The growth further projects to grow to 10 billion by next year (2020) and 22 billion in 2025.

If youre wondering how AI is being used with IoT, here are a few perfect examples for you to understand.

Created by Alibaba Cloud, ET City Brain is an AI platform solution which helps in optimizing the usage of public urban resources. This has been successfully implemented in Hangzhou, China that led to a decrease in traffic by 15 percent.

The ET City Brain not only helped detect road accidents and illegal parking but also helps ambulances reach their destinations by changing the traffic signals.

Youve probably heard of the classroom monitoring system. Although this has raised certain controversy, a high school in Hangzhou, China is already making use of this system.

This camera scans the room once per 30 seconds. The algorithm is then able to determine the emotions of the student (sad, happy, angry or bored, etc.) along with their behavior such as writing, reading or raising their hand.

According to the Vice-principal of the school, its said that the system is managed locally and the behavior is focused on the entire class and not a single individual.

The data gathered is through cameras and the next step which is the image recognition step is done at the local servers.

The Tesla autopilot system enables GPS, sonars, cameras and forward-looking radars, in combination with specialized hardware, through which data can be fully utilized and coupled into Neural Network Architectures. This works like a self-enclosed system that gathers information from the sensors and further uses the Neural Network model that determines the next change in the movement of the car.

Dearth for talent and lack of expertise in the IoT market reveals the upsurge for AI professionals AI specialists and AI engineers.

AI and IoT are inseparable. The entire idea of artificial intelligence is to capture more actionable data from IoT devices.

The Internet of Things is already disrupting various industries impacting human lives in several ways.

Precisely, AI is more about making machines put in intelligent behavior. Whilst, the function of IoT is to make these machines connect. The reciprocal behavior of both these technologies manifests itself in forms that cannot be comprehended.

Thus, AI experts will play a significant role in the unprecedented growth of the IoT era.

Although the disruption of these technologies will not happen overnight, its already indubitably arriving at a much faster pace than expected.

Artificial Intelligence and IoT cannot be ignored anymore!

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Revolutionizing IoT Through AI: Why Theyre Perfect Together - IoT For All

Artificial intelligence has revolutionised every piece of technology it has touched. However, this augmentation for better or worse has also brought up a lot of confusion. With more and more AI application coming up in different fields, specifically in automation like Cognitive Automation, the conditions associated with it give the impression that the technology is artificially intelligent and seems to dilute the real meaning behind it. This poses a more significant problem as what qualifies as a mere application of AI can be called artificial intelligence.

When we talk about automation and AI, there is a lot of buzz around cognitive automation as it uses technology to mimic human behaviour and precisely the reason why some people call it as cognitive automation artificial intelligence.

Artificial Intelligence Vs Cognitive Automation

If one had to define artificial intelligence regarding computing, then it can be defined as the area of computer science that focuses on the creating intelligent machines that work and interact like humans with each other or with living beings. Some activities include speech recognition, learning, among others. When it comes to AI creating intelligent machines that work like humans is what one has to keep in mind from the definition. The creation process depicts the intelligence part of the device.

For example, AI in healthcare has had many applications over the years. Now, if a doctor wants to take the help of an AI, then during a particular procedure, intelligence comes into play when AI suggests which course of action to choose based on its analysis.

Intelligence, especially artificial intelligence, requires a lot of information to carry out its analysis about a process.

On the other hand, cognitive automation mimics quantitative human judgement or augments human intelligence. In short, cognitive automation imitates human thinking. If you look at the technologies in cognitive automation like natural language processing, image processing and contextual analysis all are more profound concepts of perceptions and judgements and are heavily influenced by AI.

If one looks at the cognitive applications, it becomes evident that the automation happens via hardcoded human-generated rules or through dense inputs.

According to Franois Chollet, creator of the neural network library, Keras, Automation is, at best, robustly handling known unknowns over known tasks, which is already incredibly difficult and resource-intensive in the real world whether engineering or data.

Therefore, when it comes to automation, it can only work if it is made aware of the unknowns. Working with the unknown entirely on itself will only result in the failure when it comes to automation. For instance, in the healthcare sector, doctors do take the help of AI for deciding the course of action based on the suggestions made by the intelligent system. However, when it comes to automation, this technology is only here to enhance the doctors practice and not independently run any analysis.

Cognitive automation learns through different unstructured data and connects to creating tags, annotations and other metadata. Cognitive automation tries to find similarities between items to specific processes. It seeks to identify the mentioned items in the process and then searches for similar ones in order to connect them.

To carry out a process by an automation system requires data. And, once enough information has been provided during the automation process, there is no requirement for humans to build an additional model to carry out the analysis further. As the new data set is provided, the automation makes more connections with the old one, which allows the cognitive automation systems to keep learning without any supervision and can continuously adjust to the new information.

Whereas for AI it carries out its analysis after been given a different data set at the expense of a massive amount of information which has been fed to the system. This information/data is more than the required data for cognitive automation.

In the current scenario, when one reads about the cognitive applications, the process and its workings might be similar to artificial intelligence, and thus creating confusion between the two. This happens because ultimately, cognitive automation is an application of artificial intelligence itself, which is just a little less intelligent. Cognitive automation doesnt deal with the unknowns of a process or the real-world problems, and it can only work through them if there is data fed to it in.

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It's 2020 Stop Confusing Cognitive Automation With Artificial Intelligence - Analytics India Magazine