Category Archives: Artificial Intelligence
The costs and benefits of artificial intelligence – The Japan Times
New York The robots are no longer coming; they are here. The COVID-19 pandemic is hastening the spread of artificial intelligence, but few have fully considered the short- and long-run consequences.
In thinking about AI, it is natural to start from the perspective of welfare economics productivity and distribution. What are the economic effects of robots that can replicate human labor? Such concerns are not new. In the 19th century, many feared that new mechanical and industrial innovations would replace workers. The same concerns are being echoed today.
Consider a model of a national economy in which labor performed by robots matches that performed by humans. The total volume of labor robotic and human will reflect the number of human workers, H, plus the number of robots, R. Here, the robots are additive they add to the labor force rather than multiplying human productivity. To complete the model in the simplest way, suppose the economy has just one sector, and that aggregate output is produced by capital and total labor, human and robotic. This output provides for the countrys consumption, with the rest going toward investment, thus increasing the capital stock.
What is the initial economic impact when these additive robots arrive? Elementary economics shows that an increase in total labor relative to initial capital a drop in the capital-labor ratio causes wages to drop and profits to rise.
There are three points to add. First, the results would be magnified if the additive robots were created from refashioned capital goods. That would yield the same increase in total labor, with a commensurate reduction in the capital stock, but the drop in the wage rate and the increase in the rate of profit would be greater.
Second, nothing would change if we adopted the Austrian Schools two-sector framework in which labor produces the capital good and the capital good produces the consumer good. The arrival of robots still would decrease the capital-labor ratio, as it did in the one-sector scenario.
Third, there is a striking parallel between the models additive robots and newly arrived immigrants in their impact on native workers. By pushing down the capital-labor ratio, immigrants, too, initially cause wages to drop and profits to rise. But it should be noted that with the rate of profit elevated, the rate of investment will rise. Owing to the law of diminishing returns, that additional investment will drive down the profit rate until it has fallen back to normal. At this point, the capital-labor ratio will be back to where it was before the robots arrived, and the wage rate will be pulled back up.
To be sure, the general public tends to assume that robotization (and automation generally) leads to a permanent disappearance of jobs, and thus to the immiseration of the working class. But such fears are exaggerated. The two models described above abstract from the familiar technological progress that drives up productivity and wages, making it reasonable to anticipate that the global economy will sustain some level of growth in labor productivity and compensation per worker.
True, sustained robotization would leave wages on a lower path than they otherwise would have taken, which would create social and political problems. It may prove desirable, as Bill Gates once suggested, to levy taxes on income from robot labor, just as countries levy taxes on income from human labor. This idea deserves careful consideration. But fears of prolonged robotization appear unrealistic. If robotic labor increased at a non-vanishing pace, it would run into limits of space, atmosphere, and so on.
Moreover, AI has brought not just additive robots but also multiplicative robots that enhance workers productivity. Some multiplicative robots enable people to work faster or more effectively (as in AI-assisted surgery), while others help people complete tasks they otherwise could not perform.
The arrival of multiplicative robots need not lead to a lengthy recession of aggregate employment and wages. Yet, like additive robots, they have their downsides. Many AI applications are not entirely safe. The obvious example is self-driving cars, which can (and have) run into pedestrians or other cars. But, of course, so do human drivers.
A society is not wrong, in principle, to deploy robots that are prone to occasional mistakes, just as we tolerate airplane pilots who are not perfect. We must judge costs and benefits. For efficiency, people ought to have the right to sue robots owners for damages. Inevitably, a society will feel uncomfortable with new methods that introduce uncertainty.
From the perspective of ethics, the interface with AI involves imperfect and asymmetric information. As Wendy Hall of the University of Southampton says, amplifying Nicholas Beale, We cant just rely on AI systems to act ethically because their objectives seem ethically neutral.
Indeed, some new devices can cause serious harm. Implantable chips for cognitive enhancement, for example, can cause irreversible tissue damage in the brain. The question, then, is whether laws and procedures can be instituted to protect people from a reasonable degree of harm. Barring that, many are calling on Silicon Valley companies to establish their own ethics committees.
All of this reminds me of the criticism leveled at innovations throughout the history of free-market capitalism. One such critique, the book Gemeinschaft und Gesellschaft by the sociologist Ferdinand Tonnies, ultimately became influential in Germany in the 1920s and led to the corporatism arising there and in Italy in the interwar period thus bringing an end to the market economy in those countries.
Clearly, how we address the problems raised by AI will be highly consequential. But they are not yet present on a wide scale, and they are not the main cause of the dissatisfaction and resulting polarization that have gripped the West.
Edmund S. Phelps, the 2006 Nobel laureate in economics and director of the Center on Capitalism and Society at Columbia University, is author of Mass Flourishing and co-author of Dynamism. 2020, Project Syndicate
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The costs and benefits of artificial intelligence - The Japan Times
Analysis Covid 19: Artificial Intelligence in Healthcare Market Scenario 2020 Current Trends, Size, Share and Future Opportunities by 2026 – The…
Impact Analysis of Covid-19
The complete version of the Report will include the impact of the COVID-19, and anticipated change on the future outlook of the industry, by taking into the account the political, economic, social, and technological parameters.
Artificial Intelligence in Healthcare Market Research Study provides detailed information about the key factors influencing the growth of the industry which include drivers, restraints, opportunities, and industry-specific challenges, strategically profile key players and comprehensively analyze their market share and core competencies. This report includes analytical assessment of the prime challenges faced by the Artificial Intelligence in Healthcare industry currently and in the coming years, which helps Market participants in understanding the problems they may face while operating in this market over a longer period of time.
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These research report also provides an overall analysis of the market share, size, segmentation, revenue forecasts and geographic regions of the Artificial Intelligence in Healthcare Market along with industry-leading players are studied with respect to their company profile, product portfolio, capacity, price, cost, and revenue. The research report also provides detail analysis on the Artificial Intelligence in Healthcare market current applications and comparative analysis with more focused on the pros and cons of Artificial Intelligence in Healthcare and competitive analysis of major companies.
Major Players Operating in this market include IBM Corporation, Google, Inc., NVIDIA Corporation, Microsoft Corporation, iCarbonX, Next IT Corp., CloudMex Inc., Carescore, Atomwise Inc., Zephyr Health Inc., Deep Genomics Inc., Medtronic Plc., Koninkiljke Philips N.V., and Oncora Medical, Inc.
The key players are highly focusing on innovation in production technologies to improve efficiency and shelf life. The best long-term growth opportunities for this sector can be captured by ensuring ongoing process improvements and financial flexibility to invest in optimal strategies. Company profile section of players includes its basic information like legal name, website, headquarters, its market position, historical background, and top 5 closest competitors by Market capitalization/revenue along with contact information. Each player/ manufacturer revenue figures, growth rate, and the gross profit margin is provided in easy to understand tabular format for past 5 years and a separate section on recent development like mergers, acquisition or any new product/service launch, etc.
In the end, the report makes some important proposals for a new project of Artificial Intelligence in Healthcare Industry before evaluating its feasibility. Overall, the report provides an in-depth insight into the global market covering all important parameters.
Artificial Intelligence in Healthcare Driver Artificial Intelligence in Healthcare Challenge Artificial Intelligence in Healthcare Trend
The report includes chapters which deeply display the following deliverable about the industry:
Research Objective and Assumption
Market Overview Report Description, Executive Summary, and Coherent Opportunity Map (COM)
Market Dynamics, Regulations, and Trends Analysis Market Dynamics, Regulatory Scenario, Industry Trend, Mergerand Acquisitions, New system Launch/Approvals, Value Chain Analysis, Porters Analysis, and PEST Analysis
Global Artificial Intelligence in Healthcare Market, By Regions
Artificial Intelligence in Healthcare Market Competition by Manufacturers including Production, Share, Revenue, Average Price, Manufacturing Base Distribution, Sales Area, and Product Type.
Manufacturers Profiles/Analysis including Company Basic Information, Manufacturing Base, and Its Competitors.
Artificial Intelligence in Healthcare Market Manufacturing Cost Analysis including Key Raw Materials and Key Suppliers of Raw Materials.
Industrial Chain, Sourcing Strategy and Downstream Buyers including Upstream Raw Materials Sourcing and Downstream Buyers
Marketing Strategy Analysis, Distributors/Traders including Marketing Channel, Market Positioning, and Distributors/Traders List.
Market Effect Factors Analysis including Technology Progress/Risk, Consumer Needs/Customer Preference Change, and Economic/Political Environmental Change.
Artificial Intelligence in Healthcare Market Forecast including Production, Consumption, Import, and Export Forecast by Type, Applications, and Region.
Research Findings and Conclusion
Why This Report is Useful? It helps:
1. The report will include the qualitative and quantitative analysis with Artificial Intelligence in Healthcare market estimation and compound annual growth rate (CAGR) between 2020 and 2026
2. Assess the Artificial Intelligence in Healthcare production processes, major issues, and solutions to mitigate the development risk.
3. Comprehensive analysis of market dynamics including factors and opportunities of the global Artificial Intelligence in Healthcare Market will be provided in the report
4. Insights from this report will allow marketers and management authorities of companies to make informed decisions with respect to their future product launch, technology upgrades, market expansion, and marketing tactics.
In this study, the years considered to estimate the market size of 2020-2026 Artificial Intelligence in Healthcare Market are as follows:
History Year: 2016-2018Base Year: 2018Estimated Year: 2019Forecast Year 2020 to 2026
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Analysis Covid 19: Artificial Intelligence in Healthcare Market Scenario 2020 Current Trends, Size, Share and Future Opportunities by 2026 - The...
Artificial Intelligence can improve CT screening to identify patients infected with the Coronavirus – EdexLive
Image for representational purpose only
Researchers are developing a new technique using Artificial Intelligence (AI) that would improve CT screening to more quickly identify patients infected with COVID-19.
The new technique will reduce the burden on the radiologists tasked with screening each image, according to a research team from the University of Notre Dame in the US. Testing challenges have led to an influx of patients hospitalised with COVID-19 requiring CT scans which have revealed visual signs of the disease, including ground-glass opacities, a condition that consists of abnormal lesions, presenting as a haziness on images of the lungs.
"Most patients with coronavirus show signs of COVID-related pneumonia on a chest CT but with a large number of suspected cases, radiologists are working overtime to screen them all," said study lead author Yiyu Shi from the Notre Dame. "We have shown that we can use deep learning -- a field of AI -- to identify those signs, drastically speeding up the screening process and reducing the burden on radiologists," Yiyu added.
The research team is working to identify the visual features of Coronavirus-related pneumonia through analysis of 3D data from CT scans. The team is working to combine the analysis software with off-the-shelf hardware for a light-weight mobile device that can be easily and immediately integrated into clinics around the country.
The challenge, Shi said, is that 3D CT scans are so large, it's nearly impossible to detect specific features and extract them efficiently and accurately on plug-and-play mobile devices.
"We're developing a novel method inspired by Independent Component Analysis, using a statistical architecture to break each image into smaller segments, which will allow deep neural networks to target COVID-related features within large 3D images," Shi wrote.
The research team is collaborating with radiologists at Guangdong Provincial People's Hospital in China and the University of Pittsburgh Medical Centre, where a large number of CT images from COVID-19 pneumonia are being made available. The team hopes to have development completed by the end of the year.
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Artificial Intelligence can improve CT screening to identify patients infected with the Coronavirus - EdexLive
Lost your job due to coronavirus? Artificial intelligence could be your best friend in finding a new one – The Conversation US
Millions of Americans are unemployed and looking for work. Hiring continues, but theres far more demand for jobs than supply.
As scholars of human resources and management, we believe artificial intelligence could be a boon for job seekers who need an edge in a tight labor market like todays.
Whats more, our research suggests it can make the whole process of finding and changing jobs much less painful, more effective and potentially more lucrative.
Over the last three years, weve intensely studied the role of AI in recruiting. This research shows that job candidates are positively inclined to use AI in the recruiting process and find it more convenient than traditional analog approaches.
Although companies have been using AI in hiring for a few years, job applicants have only recently begun to discover the power of artificial intelligence to help them in their search.
In the old days, if you wanted to see what jobs were out there, you had to go on a job board like Monster.com, type in some keywords, and then get back hundreds or even thousands of open positions, depending on the keywords you used. Sorting through them all was a pain.
Today, with AI and companies like Eightfold, Skillroads and Fortay, it is less about job search and more about matchmaking. You answer a few questions about your capabilities and preferences and provide a link to your LinkedIn or other profiles. AI systems that have already logged not just open jobs but also analyzed the companies behind the openings based on things like reputation, culture and performance then produce match reports showing the best fits for you in terms of job and company.
Typically, there is an overall match score expressed as a percentage from 0% to 100% for each job. In many cases the report will even tell you which skills or capabilities you lack or have not included and how much their inclusion would increase your match score. The intent is to help you spend your time on opportunities that are more likely to result in your getting hired and being happy with the job and company after the hire.
Usually, when you look for a job, you apply to lots of openings and companies at the same time. That means two choices: save time by sending each one a mostly generic resume, with minor tweaks for each, or take the time and effort to adjust and tailor your resume to better fit specific jobs.
Today, AI tools can help customize your resume and cover letter for you. They can tell you what capabilities you might want to add to your resume, show how such additions would influence your chances of being hired and even rewrite your resume to better fit a specific job or company. They can also analyze you, the job and the company and craft a customized cover letter.
While researchers have not yet systemically examined the quality of human- versus AI-crafted cover letters, the AI-generated samples weve reviewed are difficult to distinguish from the ones weve seen MBA graduates write for themselves over the last 30 years as professors. Try it for yourself.
Granted, for lots of lower-level jobs, cover letters are relics of the past. But for higher-level jobs, they are still used as an important screening mechanism.
Negotiations over compensation are another thorny issue in the job search.
Traditionally, applicants have been at a distinct informational disadvantage, making it harder to negotiate for the salary they may deserve based on what others earn for similar work. Now AI-enabled reports from PayScale.com, Salary.com, LinkedIn Salary and others provide salary and total compensation reports tailored to job title, education, experience, location and other factors. The data comes from company reported numbers, government statistics and self-reported compensation.
For self-reported data, the best sites conduct statistical tests to ensure the validity and accuracy of the data. This is only possible with large databases and serious number crunching abilities. PayScale.com, for example, has over 54 million respondents in its database and surveys more than 150,000 people per month to keep its reports up-to-date and its database growing.
Although no academics have yet tested if these reports result in better compensation packages than in the old days, research has long established that negotiating in general gets candidates better compensation offers, and that more information in that process is better than less.
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Use of these tools is growing, especially among young people.
A survey we conducted in 2018 found that half of employed workers aged 18 to 36 said that they were likely or highly likely to use AI tools in the job search and application process. And 64% of these respondents felt that AI-enabled tools were more convenient.
Most of the research on the use of AI in the hiring process including our own has focused on recruitment, however, and the use of the technology is expected to double over the next two years. Weve found it to be effective for companies, so it seems logical that it can be very useful for job candidates as well. In fact, at least US$2 billion in investments are fueling human resources startups aimed at using AI to help job candidates, according to our analysis of Crunchbase business data.
While more research is needed to determine exactly how effective these AI-enabled tools actually are, Americans who lost their jobs due to the coronavirus could use all the help they can get.
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Lost your job due to coronavirus? Artificial intelligence could be your best friend in finding a new one - The Conversation US
Evil AI: These are the 20 most dangerous crimes that artificial intelligence will create – ZDNet
From targeted phishing campaigns to new stalking methods: there are plenty of ways that artificial intelligence could be used to cause harm if it fell into the wrong hands. A team of researchers decided to rank the potential criminal applications that AI will have in the next 15 years, starting with those we should worry the most about. At the top of the list of most serious threats? Deepfakes.
By using fake audio and video to impersonate another person, the technology can cause various types of harms, said the researchers. The threats range from discrediting public figures to influence public opinion, to extorting funds by impersonating someone's child or relatives over a video call.
The ranking was put together after scientists from University College London (UCL) compiled a list of 20 AI-enabled crimes based on academic papers, news and popular culture, and got a few dozen experts to discuss the severity of each threat during a two-day seminar.
SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)
The participants were asked to rank the list in order of concern, based on four criteria: the harm it could cause, the potential for criminal profit or gain, how easy the crime could be carried out and how difficult it would be to stop.
Although deepfakes might in principle sound less worrying than, say, killer robots, the technology is capable of causing a lot of harm very easily, and is hard to detect and stop. Relative to other AI-enabled tools, therefore, the experts established that deepfakes are the most serious threat out there.
There are already examples of fake content undermining democracy in some countries: in the US, for example, a doctored video of House Speaker Nancy Pelosi in which she appeared inebriated picked up more than 2.5 million views on Facebook last year.
UK organization Future Advocacy similarly used AI to create a fake video during the 2019 general election, which showed Boris Johnson and Jeremy Corbyn endorsing each other for prime minister. Although the video was not malicious, it stressed the potential of deepfakes to impact national politics.
The UCL researchers said that as deepfakes get more sophisticated and credible, they will only get harder to defeat. While some algorithms are already successfully identifying deepfakes online, there are many uncontrolled routes for modified material to spread. Eventually, warned the researchers, this will lead to widespread distrust of audio and visual content.
Five other applications of AI also made it to the "highly worrying" category. With autonomous cars just around the corner, driverless vehicles were identified as a realistic delivery mechanism for explosives, or even as weapons of terror in their own right. Equally achievable is the use of AI to author fake news: the technology already exists, stressed the report, and the societal impact of propaganda shouldn't be under-estimated.
Also keeping AI experts up at night are applications that will be so pervasive that defeating them will be near impossible. This is the case for AI-infused phishing attacks, for example, which will be perpetrated via crafty messages that will be impossible to distinguish from reality. Another example is large-scale blackmail, enabled by AI's potential to harvest large personal datasets and information from social media.
Finally, participants pointed to the multiplication of AI systems used for key applications like public safety or financial transactions and to the many opportunities for attack they represent. Disrupting such AI-controlled systems, for criminal or terror motives, could result in widespread power failures, breakdown of food logistics, and overall country-wide chaos.
UCL's researchers labelled some of the other crimes that could be perpetrated with the help of AI as only "moderately concerning". Among them feature the sale of fraudulent "snake-oil" AI for popular services like lie detection or security screening, or increasingly sophisticated learning-based cyberattacks, in which AI could easily probe the weaknesses of many systems.
Several of the crimes cited could arguably be seen as a reason for high concern. For example, the misuse of military robots, or the deliberate manipulation of databases to introduce bias, were both cited as only moderately worrying.
The researchers argued, however, that such applications seem too difficult to push at scale in current times, or could be easily managed, and therefore do not represent as imminent a danger.
SEE: AI, machine learning to dominate CXO agenda over next 5 years
At the bottom of the threat hierarchy, the researchers listed some "low-concern" applications the petty crime of AI, if you may. On top of fake reviews or fake art, the report also mentions burglar bots, small devices that could sneak into homes through letterboxes or cat flaps to relay information to a third party.
Burglar bots might sound creepy, but they could be easily defeated in fact, they could pretty much be stopped by a letterbox cage and they couldn't scale. As such, the researchers don't expect that they will cause huge trouble anytime soon. The real danger, according to the report, lies in criminal applications of AI that could be easily shared and repeated once they are developed.
UCL's Matthew Caldwell, first author of the report, said: "Unlike many traditional crimes, crimes in the digital realm can be easily shared, repeated, and even sold, allowing criminal techniques to be marketed and for crime to be provided as a service. This means criminals may be able to outsource the more challenging aspects of their AI-based crime."
The marketisation of AI-enabled crime, therefore, might be just around the corner. Caldwell and his team anticipate the advent of "Crime as a Service" (CaaS), which would work hand-in-hand with Denial of Service (DoS) attacks.
And some of these crimes will have deeper ramifications than others. Here is the complete ranking of AI-enabled crimes to look out for, as compiled by UCL's researchers:
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Evil AI: These are the 20 most dangerous crimes that artificial intelligence will create - ZDNet
3 Important Ways Artificial Intelligence Will Transform Your Business And Turbocharge Success – Forbes
From the smallest local business to the largest global players, I believe every organization must embrace the AI revolution, and identify how AI (artificial intelligence) will make the biggest difference to their business.
3 Important Ways Artificial Intelligence Will Transform Your Business And Turbocharge Success
But before you can develop a robust AI strategy in which you work out how best to use AI to drive business success you first need to understand whats possible with AI. To put it another way, how are other companies using AI to drive success?
Broadly speaking, organizations are using AI in three main ways:
Creating more intelligent products
Offering a more intelligent service
Improving internal business processes
Lets briefly look at each area in turn.
Creating more intelligent products
Thanks to the Internet of Things, a whole host of everyday products are getting smarter. What started with smartphones has now grown to include smart TVs, smartwatches, smart speakers, and smart home thermostats plus a range of more eyebrow-raising "smart" products such as smart nappies, smart yoga mats, smart office chairs, and smart toilets.
Generally, these smart products are designed to make customers lives easier and remove those annoying bugbears from everyday life. For example, you can now get digital insoles that slip into your running shoes and gather data (using pressure sensors) about your running style. An accompanying app will give you real-time analysis of your running performance and technique, thereby helping you avoid injuries and become a better runner.
Offering a more intelligent service
Instead of the traditional approach of selling a product or service as a one-off transaction, more and more businesses are transitioning to a servitization model, in which the product or service is delivered as an ongoing subscription. Netflix is a prime example of this model in action. For a less obvious example, how about the Dollar Shave Club, which will deliver razor blades and grooming products to your door on a regular basis. Or Stich Fix, a personalized styling service that delivers clothes to your door based on your personal style, size, and budget.
Intelligent services like this are reliant on data and AI. Businesses like Netflix have access to a wealth of valuable customer data data that helps the company provide a more thoughtful service, based on what it knows the customer really wants (whether its movies, clothes, grooming products or whatever).
Improving internal business processes
In theory, AI could be worked into pretty much any aspect of a business: manufacturing, HR, marketing, sales, supply chain and logistics, customer services, quality control, IT, finance and more.
From automated machinery and vehicles to customer service chatbots and algorithms that detect customer fraud, AI solutions and technologies are being incorporated into all sorts of business functions in order to maximize efficiency, save money and improve business performance.
So, which area should you focus on products, services, or business processes?
Every business is different, and how you decide to use AI may differ wildly from even your closest competitor. For AI to truly add value in your business, it must be aligned with your companys key strategic goals which means you need to be clear on what it is you're trying to achieve before you can identify how AI can help you get there.
That said, its well worth considering all three areas: products, services and business processes. Sure, one of the areas is likely to be more of a priority than the others, and that priority will depend on your companys strategic goals. But you shouldnt ignore the potential of the other AI uses.
For example, a product-based business might be tempted to skip over the potential for intelligent services, while a service-based company could easily think smart products arent relevant to its business model. Both might think AI-driven business processes are beyond their capabilities at this point in time.
But the most successful, most talked-about companies on the planet are those that deploy AI across all three areas. Take Apple as an example. Apple built its reputation on making and selling iconic products like the iPad. Yet, nowadays, Apple services (including Apple Music and Apple TV) generate more revenue than iPad sales. The company has transitioned from purely a product company to a service provider, with its iconic products supporting intelligent services. And you can be certain that Apple uses AI and data to enhance its internal processes.
In this way, AI can throw up surprising additions and improvements to your business model or even lead you to an entirely new business model that you never previously considered. It can lead you from products to services, or vice versa. And it can throw up exciting opportunities to enhance the way you operate.
Thats why I recommend looking at products, services, and business processes when working out your AI priorities. You may ultimately decide that optimizing your internal processes (for example, automating your manufacturing) is several years away, and thats fine. The important thing is to consider all the AI opportunities, so that you can properly prioritize what you want to achieve and develop an AI strategy that works for your business.
AI is going to impact businesses of all shapes and sizes, across all industries. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.
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3 Important Ways Artificial Intelligence Will Transform Your Business And Turbocharge Success - Forbes
Experts Are Divided Over Future Of Artificial Intelligence But Agree On Its Growing Impact – Outlook India
As humans, we love contrast. It is no wonder that experts, while defining the impact of Artificial Intelligence (AI) on the future of humankind, are at two ends of the spectrum. One is a happy scenario of human beings and artificially intelligent machines coexisting in perfect harmony. Another is an Orwellian dystopia of AI dominance over human intelligence and civilization. While there may be disagreements about the future, everyone agrees on the impact and growing ubiquity of AI.
Let us look at the potential impact of AI on our society. Algorithms have been generally successful in predicting almost all the weather calamities (except, of course, earthquakes) with reasonable accuracies. Since we started using AI, global death share from natural disasters since 2010 has reduced from 0.47% of all world deaths to 0.02% in 2017. AI worked wonders in healthcare by increasing the accuracy and timeliness of disease detection. Using a combination of big data and machine learning algorithms, we can predict machine part failures better. Stability of electricity grids, metal productions and commodity prices are predicted with astonishing precisions.
Enterprises were quick to jump on the AI bandwagon. Big four tech companies are seen to have made most of it. In the midst of the pandemic, global news media on June 9 reported an all-time high share prices for these companieswith a combined market capitalisation of almost $5 trillion. These companies are changing the way we live, do business and relax. We navigate lot more smoothly now with maps on our phone and do not need a translator to understand another language. We have super-efficient digital assistants to manage our schedules intelligently and can buy essential items from our phones.
Consumer packed goods companies have started using big data and machine learning to determine which of the retail stores should get what commodity and at what price. Many of the manufacturing organisations worldwide have started using predictive analytics to analyse their planning efficiency. Using AI techniques, logistics and transportation companies have started planning significant route optimisation, reducing cost and delivering faster to ports. Banks, stock markets and insurance companies use data, machine learning techniques and natural language processing techniques to provide the precise stocks and other financial products recommendations to their customers. Transformative aspects of AI seem to be going beyond delivering powerful use cases and outcomes. It seems to be changing the model of business itself. Organisations are no longer getting measured by the number of employees, assets and real estate they hold. Classic adage of David killing a Goliath is not a fable anymore. AI seems to have the potential to take a powerful business opportunity, analyse a lot of data with powerful algorithms and present the outcome through multiple channels to bring transformation right at the doorsteps (or screens) of consumers. Possibly, that is where it is getting a bit worrisome.
In his 2018 best seller Factfulness, Dr. Hans Rosling points out five global risks that should worry the human race. He could not have been more prophetic. First of them was global pandemic; others being financial collapse, World War III, climate change and extreme poverty. In the middle of a significant disruption, AI is seen to present a real disturbing proposition. Can the enterprise bring in more automation to replace the severely depleted job markets? Can the potential of AI create a situation where powerful corporations and states with the power of algorithm, processing capability of big data get into a position of more unassailable lead - where they have absolute power and society? Would we be left with the intent and resources to focus on most important challenge in post COVID world - more people than ever in the state of extreme hunger?
German philosopher Arthur Schopenhauer wrote, Talent hits a target that no one else can hit, Genius hits the target no one else can see. Human geniuses have their limited time to shape the future as clock ticks on.
(The author is partner, Deloitte India. Views expressed are personal.)
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Experts Are Divided Over Future Of Artificial Intelligence But Agree On Its Growing Impact - Outlook India
COVID-19 Impacts: Artificial Intelligence-as-a-Service (AIaaS) Market Will Accelerate at a CAGR of Over 48% Through 2020-2024|Growing Adoption of…
LONDON--(BUSINESS WIRE)--Technavio has been monitoring the artificial intelligence-as-a-service (AIaaS) market and it is poised to grow by USD 15.14 billion during 2020-2024, progressing at a CAGR of over 48% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.
Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Please Request Free Sample Report on COVID-19 Impact
Frequently Asked Questions-
The market is concentrated, and the degree of concentration will accelerate during the forecast period. Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.
The growing adoption of cloud-based solutions has been instrumental in driving the growth of the market.
Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Segmentation
Artificial Intelligence-as-a-Service (AIaaS) Market is segmented as below:
To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR41175
Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Scope
Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. Our artificial intelligence-as-a-service (AIaaS) market report covers the following areas:
This study identifies the increasing adoption of AI in predictive analysis as one of the prime reasons driving the artificial intelligence-as-a-service (AIaaS) market growth during the next few years.
Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Vendor Analysis
We provide a detailed analysis of vendors operating in the artificial intelligence-as-a-service (AIaaS) market, including some of the vendors such as Alphabet Inc., Amazon.com Inc., Apple Inc., Intel Corp., International Business Machines Corp., Microsoft Corp., Oracle Corp., Salesforce.com Inc., SAP SE, and SAS Institute Inc. Backed with competitive intelligence and benchmarking, our research reports on the artificial intelligence-as-a-service (AIaaS) market are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.
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Artificial Intelligence-as-a-Service (AIaaS) Market 2020-2024: Key Highlights
Table of Contents:
Executive Summary
Market Landscape
Market Sizing
Five Forces Analysis
Market Segmentation by End-user
Customer Landscape
Geographic Landscape
Drivers, Challenges, and Trends
Vendor Landscape
Vendor Analysis
Appendix
About Us
Technavio is a leading global technology research and advisory company. Their research and analysis focuses on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.
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COVID-19 Impacts: Artificial Intelligence-as-a-Service (AIaaS) Market Will Accelerate at a CAGR of Over 48% Through 2020-2024|Growing Adoption of...
AI can speed up the search for new treatments here’s how – World Economic Forum
The sudden appearance and rapid spread of COVID-19 took governments and society by surprise. As they dusted off pandemic response plans and geared up to fight the virus, it became clear that we needed to turbo-charge R&D efforts and find better ways to hunt down promising treatments for emerging diseases.
Artificial intelligence (AI) has proven a powerful tool in this fight.
In a pandemic, speed is of the essence. Although scientists managed to sequence the genetic code of the new coronavirus and produce diagnostic tests in record time, developing drugs and vaccines against the virus remains a long haul.
AI has the power to accelerate the process by reasoning across all available biomedical data and information in a systematic search for existing approved medicines a vital step in helping patients while the world waits for a vaccine.
Machines excel in handling data in fast-changing circumstances, which means machine learning systems can be harnessed to work as tireless and unbiased super-researchers.
This is not just theory. In late January, using its proprietary platform of AI models and algorithms to search through the scientific literature, researchers at BenevolentAI in London identified an established, once-daily arthritis pill as a potential treatment for COVID-19. The findings were published in two papers in The Lancet and The Lancet Infectious Diseases, in line with our commitment under the Wellcome Trust pledge to share our coronavirus-related research rapidly and openly.
BenevolentAI's COVID-19 timeline
Image: BenevolentAI
The discovery followed a computer-driven hunt for drug candidates with both antiviral and anti-inflammatory properties, since in severe cases of COVID-19 it is the bodys overactive immune response that can cause significant and sometimes fatal damage.
The drug, baricitinib, is currently marketed by Eli Lilly to treat rheumatoid arthritis. Now, thanks to AI, it is being tested against COVID-19 in a major randomised-controlled trial in collaboration with the U.S. National Institute for Allergies and Infectious Diseases (NIAID) in combination with remdesivir, an antiviral drug from Gilead Sciences that recently won emergency-use approval for COVID-19. Eli Lilly has now commenced its own independent trial of baricitinib as a therapy for COVID-19 in South America, Europe and Asia.
The BenevolentAI knowledge graph found that baricitinib might help treat COVID-19.
Image: BenevolentAI
The system used to identify baricitinib was not actually set up to find new uses of existing medicines, but rather to discover and develop new drugs a sign of the potential for AI to uncover novel insights and relationships across an unlimited number of biological entities. In a crisis like COVID-19, it clearly makes sense to hunt through already approved drugs that can be ready for large-scale clinical trials until vaccines are approved and readily available in the global supply chain.
BenevolentAIs vision is to dramatically improve pharmaceutical R&D productivity across the board and to expand the drug discovery universe by making predictions in novel areas of biology. Currently, around half of late-stage clinical trials fail due to ineffective drug targets, resulting in only 15% of drugs advancing from mid-stage Phase 2 testing to approval.
Using a knowledge graph composed of chemical, biological and medical research and information, the companys AI machine learning models and algorithms can identify potential drug leads currently unknown in medical science and far faster than humans. While such systems will never replace scientists and clinicians, they can save both time and money. And the agnostic approach adopted by machine learning means such platforms can generate leads that may have been overlooked by traditional research.
The endeavour has already led to an in-house project on amyotrophic lateral sclerosis (ALS), ulcerative colitis, atopic dermatitis and programmes with partners on progressive kidney and lung diseases, as well as hard-to-treat cancers like glioblastoma.
The ability of machines to solve complex biological puzzles more rapidly than human experts has prompted increased investment in AI drug discovery by a growing number of large pharmaceutical companies.
And AI is also being harnessed in other areas of medicine, such as the analysis of medical images. This encompasses long-standing work on cancer scans and much more recent efforts to use computer power to identify COVID-19 from chest X-rays, including the open-access COVID-Net neural network.
The application of precision medicine to save and improve lives relies on good-quality, easily-accessible data on everything from our DNA to lifestyle and environmental factors. The opposite to a one-size-fits-all healthcare system, it has vast, untapped potential to transform the treatment and prediction of rare diseasesand disease in general.
But there is no global governance framework for such data and no common data portal. This is a problem that contributes to the premature deaths of hundreds of millions of rare-disease patients worldwide.
The World Economic Forums Breaking Barriers to Health Data Governance initiative is focused on creating, testing and growing a framework to support effective and responsible access across borders to sensitive health data for the treatment and diagnosis of rare diseases.
The data will be shared via a federated data system: a decentralized approach that allows different institutions to access each others data without that data ever leaving the organization it originated from. This is done via an application programming interface and strikes a balance between simply pooling data (posing security concerns) and limiting access completely.
The project is a collaboration between entities in the UK (Genomics England), Australia (Australian Genomics Health Alliance), Canada (Genomics4RD), and the US (Intermountain Healthcare).
Clearly, COVID-19 has been a wake-up call for the world. It seems this outbreak may be part of an increasingly frequent pattern of epidemics, fuelled by our hyper-connected modern world. As a result, medical experts are braced for more previously unknown Disease X threats in the years ahead as viruses jump from animals to humans and jet around the world.
Technology has helped create a world in which pathogens like COVID-19, SARS and Zika can spread. But technology, in the form of AI, can also provide us with the weapons to fight back.
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AI can speed up the search for new treatments here's how - World Economic Forum
Job interviews: Recruiters are using artificial intelligence to analyse what you say to find the right hire – TechRepublic
Harqen's AI platform analyses language to determine a candidate's suitability for a role, potentially making it less prone to bias than video-based recruitment technology.
Artificial-intelligence-based hiring tools are already transforming the recruitment process by allowing businesses to vastly speed up the time it takes to identify top talent. With algorithms able to scour applications databases in the fraction of a time it would take a human hiring manager, AI-assisted hiring has the potential not only to give precious time back to businesses, but also draw in candidates from wider and more diverse talent pools.
AI-assisted hiring is also posited as a potential solution for reducing human bias whether subconscious or otherwise in the hiring process.
SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)
US company Harqen has been offering hiring technologies to some of the world's biggest companies for years, partnering with the likes of Walmart, FedEx and American Airlines to streamline and improve their hiring processes. Originating as an on-demand interviewing provider, the company has now expanded into AI with a new platform that it says offers a more dependable and bias-free means of matching employers with employees.
The solution, simply called the Harqen Machine Learning Platform, analyses candidate's answers to interview questions and assesses the type of words and language used in their responses. According to Harqen, this allows it to put together a profile of psychological traits that can be used to help determine a candidate's suitability for a role.
Combined with a resume analysis, which provides a more straightforward determiner of whether a candidate's professional and educational background fits with the requirements of the job, Harqen says its machine-learning platform is capable of making the same hiring decision as human recruiters 95% of the time. In one campaign that assessed approximately 3,500 job applications with "a very large US diagnostic firm" in early 2020, Harqen's machine-learning platform successfully predicted 2,193 of the candidate applications that were accepted, and 1,292 that were declined.
Key to Harqen's offering is what the company's chief technology officer Mark Unak describes as the platform's linguistic analysis, which can identify word clusters that are specific to certain job types but also offers a personality analysis based on the so-called "big five" traits, also known as the OCEAN model (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism), which can help hiring managers determine a candidate's enthusiasm for the position.
"We have a dictionary of terms where most positive words are ranked as a +5 and most negative words are ranked as a -5, so we can determine how enthusiastic you are in the answers that you're giving," Unak tells TechRepublic.
"We can also use a linguistic analysis to analyse the grammar," he adds, noting that about 60% of our vocabulary consists of just 80 words. Those are the pronouns, the propositions, the articles and the intransigent verbs. "The remaining 10,000 words in the English language fill in that 40%. By the analysis of how you use that, we can get a psychological trait analysis."
Harqen's machine-learning tool analyses word clusters to help determine candidates' personality traits, such as enthusiasm.
Source: Harqen.ai
According to Unak, using a machine-learning system that determines a candidate's suitability based on linguistic analysis is a more accurate and impartial method than those that rely on facial-scanning or vocal-inflection algorithms. Such machine-learning techniques within hiring are on the rise and are increasingly being adopted by major companies around the world.
"That's kind of problematic," says Unak. "Not everybody expresses emotions in the same way, with the same facial expressions, and not everybody expresses the same emotion that's expected. Different cultures and different races might have different problems in expressing those facial expressions and having the computer recognise them."
SEE:Diversity and Inclusion policy (TechRepublic Premium)
By only analysing the linguistic content that has been transcribed from recorded interviews, Harqen's algorithm never factors in appearance, facial expressions, or other self-reported personality traits that could be unreliable. Unak says the company will also retrain its models on a regular basis as new data comes in, which will help ensure that algorithms don't get stuck in their old ways if candidates begin giving new answers to questions that are equally relevant.
"If our customer evolves and they start to hire people who are either more diverse, or come up with different answers to the questions that are just as relevant, our models will pick that up," Unak adds.
Diversity whether based on gender, race, age or otherwise has been show to play a significant role in the success or failure of workplace productivity and collaboration. Whether AI-based hiring tools can help here remains to be seen, and ultimately depends on whether they can be implemented in a fair and impartial way.
Beyond diversity, Harqen is exploring how its machine-learning tool could help businesses get the best return on investment form their hiring choices. The magic word here is delayed gratification: the ability to accurately identify employees who can resist the temptation for immediate rewards and instead persevere for an even greater payoff in the future.
"It's grit, it's persistence, it's the ability to imagine a future and it's the ability to develop and execute a plan to get there," says Unak. "Isn't that what hope and delayed gratification mean? I hope for a better future, I can imagine it, my hope is realistic and that there's a plan or a way to get there, and I'm going to work towards it."
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Job interviews: Recruiters are using artificial intelligence to analyse what you say to find the right hire - TechRepublic