Category Archives: Artificial Intelligence
Some countries in the Middle East are using artificial intelligence to fight the coronavirus pandemic – CNBC
View of an empty street amid the COVID-19 pandemic in Doha, capital of Qatar, on April 13, 2020.
Nikku | Xinhua News Agency via Getty
Countries in the Gulf Cooperation Council are stepping up their use of artificial intelligence tools to halt the spread of the coronavirus pandemic.
Governments throughout the GCC a group of countries in the Middle East that includesBahrain, Saudi Arabia, Qatar, Oman, Kuwait and the United Arab Emirates have enacted some of world's strictest measures, including suspending passenger flights and imposing curfews on citizens to put brakes on the number of new cases of Covid-19 that currently totalover 2 million (2,064,115)globally, according to Johns Hopkins University data.
But countries aren't restricting their efforts to simply imploring their residents to stay locked in and shutting down all but the most essential of businesses.
They are increasingly deploying sophisticated technology to ensure that movement is limited and social distancing is in place through the use of speed cameras, drones and robots.
By applying location-based contact tracing, governments can monitor those who have tested positive for coronavirus, and try to limit their exposure to the population.
AI's ability to crunch large amounts of data has allowed governments worldwide to collect information to try and stop the pandemic. Contact-tracing has allowedHong Kong, China and Singapore to monitor cases.
While governments and companies grapple with what could be a controversial violation ofprivacy issues, many countries have found it to be the key to lifting lockdown measures.
In Bahrain, an application called 'BeAware' allows residents to track proximity to someone with Covid-19. The application uses location data to alert individuals in the event they approach an active case.
"BeAware registration is mandatory for those in quarantine, while non-quarantined cases may choose to register," Mohammed Ali AlQaed, chief executive of Information & eGovernment Authority in Bahrain told CNBC.
Bahrain has reported1,671cases according to Hopkins data, and was one of the first to begin easing restrictions, allowing some stores and malls to reopen.
AI can also help businesses work more efficiently throughout the pandemic.
Majed M. Al Tahan, co-founder & MD of Danube Online told CNBC the Saudi-based hypermarket and supermarket chain is using AI to minimize delivery time.
Using 'aisle-mapping' technology, packers can locate items in an online customer's order, which are tracked around stores using an app.
Saudi Arabia extended its curfew indefinitely on Sunday and the country remains in total lockdown. Saudi Arabia has reported the highest number of cases in the GCC5,862 on Thursday,according to Hopkins data.
A Qatari Government communications spokesperson told CNBC the government is working with the Qatar Computing Research Institute on a diagnostic monitoring app,connected to a ministry of health database that uses computing and geolocation services to help diagnose and track Covid-19 cases. According to Hopkins data,Qatar has reported3,711cases of coronavirus to date.
In the United Arab Emirates, the government is using AI to limit the movement of Dubai residents, the UAE's most densely-populated emirate and home to 3.3 million people.
Dubai police are monitoring permits required by residents leaving their homes in the region's business hub.
Dubai Police use a program called 'Oyoon' which, through a network of cameras in the city uses facial, voice and license plate recognition. The information is fed through a large database and the computer can cross-reference and analyze the data to determine, in this instance, if aresidentis employedin a vital sector or in possession of a valid permit.
The United Arab Emirates has reported5,365cases of coronavirus, according to Hopkins.
UAE-based healthcare startupNabta Healthwill use AI to provide risk and symptom assessments for Covid-19. Co-founder Sophie Smith told CNBC that advanced technologies such as AI, applied machine learning and blockchain could help alleviate the effects of future pandemics.
"When the dust settles, people will look at this pandemic and say 'we are only as strong as our lowest common denominator, and that's people with underlying health conditions,'"Smith said.Nabta Health uses AI to diagnose those very conditions.
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Some countries in the Middle East are using artificial intelligence to fight the coronavirus pandemic - CNBC
Artificial Intelligence developed to monitor social distancing on construction sites – The Architect’s Newspaper
With most Americans complying with nationwide stay-at-home orders enacted to reduce the spread of the novel coronavirus, a handful of states have nonetheless permitted construction sites to continue operations on essential projects. Site safety inspectors have therefore been left with the difficult task of ensuring that the workers they oversee are practicing all safety protocols as advised by the Center for Disease Control (CDC) and the Occupational Safety and Health Administration (OSHA), that include maintaining a distance of six feet apart from one another, wearing face coverings over their noses and mouths during work hours, and minimizing interactions when picking up or delivering equipment or materials.
On April 6, the artificial intelligence (AI) company Smartvid.io unveiled Vinnie, a new feature for its interface that will be able to monitor construction workers level of compliance with the advised social distancing protocols as a virtual safety inspector. The big thing with construction continuing to go on, Josh Kanner, CEO and founder of Smartvid.io, told Engineering News Record, is weve got some projects where the client is paying for extra labor on site to monitor people [for social distancing] and separate them.While Smartvid.io has provided AI technology for construction sites for over three years, the pandemic presented an unexpected set of challenges that required quick advancements.According to the companys website, Vinnie has been trained to findand counta number of indicators of project risk in the areas of safety, productivity and quality that include worker proximity and their use of personal protective equipment. Safety inspectors can either watch the footage in real-time or from recorded photos and videos, allowing their surveillance to be carried out beyond typical working hours.
For construction workers who may be concerned about any potential breaches of privacy afforded by the updated surveillance technology, Smartvid.io has made clear that there is no facial recognition and never will be, and that Vinnie has been certified to be compliant with the strict privacy requirements specified by the European GDPR standard.
Zoom is cracking down on virtual sex parties with artificial intelligence – Dazed
Now that were a month into lockdown, youve probably spent a considerable amount of your social life (read: all) on video messaging platforms. While its admittedly a great way to stay connected with friends when youre most likely cooped up in a cramped London flatshare, or enjoying a second wave of teenage angst at your parents house, its also led to some pretty raunchy gatherings: introducing the virtual sex party.
According to Rolling Stone, Zoom the popular teleconferencing app has become an unlikely gathering place for COVID-19 era millennials wanting to partake in play parties (AKA virtual chats where you can jerk off in the company of other socially-distanced people).
In short, Zooms not happy about it, and its using machine learning to identify accounts in violation of its policies, which strictly prohibit displays of nudity, violence, pornography, sexuality explicit material, or criminal activity.
We encourage users to report suspected violations of our policies, and we use a mix of tools, including machine learning, to proactively identify accounts that may be in violation, a spokesperson for Zoom told Rolling Stone.
While the platform hasnt specified what sort of machine-learning tools its using, or how the technology alerts the platform to pornographic content, a spokesperson said that itll take a number of actions against those caught in the act.
Meanwhile, rival video platform Houseparty is offering $1 millionfor info on an alleged smear campaign, which claims users have been getting their accounts hacked and personal information stolen. Basically, the internets reverted into the Wild West, and we love it.
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Zoom is cracking down on virtual sex parties with artificial intelligence - Dazed
Artificial Intelligence (AI)
Early diagnosis of Alzheimers disease (AD) using analysis of brain networks
AD-related neurological degeneration begins long before the appearance of clinical symptoms. Information provided by functional MRI (fMRI) neuroimaging data, which can detect changes in brain tissue during the early phases of AD, holds potential for early detection and treatment. The researchers are combining the ability of fMRI to detect subtle brain changes with the ability of machine learning to analyze multiple brain changes over time. This approach aims to improve early detection of AD, as well as other neurological disorders including schizophrenia, autism, and multiple sclerosis.
NIBIB-funded researchers are building machine learning models to better manage blood glucose levels by using data obtained from wearable sensors. New portable sensing technologies provide continuous measurements that include heart rate, skin conductance, temperature, and body movements. The data will be used to train an artificial intelligence network to help predict changes in blood glucose levels before they occur. Anticipating and preventing blood glucose control problems will enhance patient safety and reduce costly complications.
This project aims to develop an advanced image scanning system with high detection sensitivity and specificity for colon cancers. The researchers will develop deep neural networks that can analyze a wider field on the radiographic images obtained during surgery. The wider scans will include the suspected lesion areas and more surrounding tissue. The neural networks will compare patient images with images of past diagnosed cases. The system is expected to outperform current computer-aided systems in the diagnosis of colorectal lesions. Broad adoption could advance the prevention and early diagnosis of cancer.
Smart, cyber-physically assistive clothing (CPAC) is being developed in an effort to reduce the high prevalence of low back pain. Forces on back muscles and discs that occur during daily tasks are major risk factors for back pain and injury. The researchers are gathering a public data set of more than 500 movements measured from each subject to inform a machine learning algorithm. The information will be used to develop assistive clothing that can detect unsafe conditions and intervene to protect low back health. The long-term vision is to create smart clothing that can monitor lumbar loading; train safe movement patterns; directly assist wearers to reduce incidence of low back pain;and reduce costs related to health care expenses and missed work.
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Artificial Intelligence (AI)
Artificial Intelligence That Can Evolve on Its Own Is Being Tested by Google Scientists – Newsweek
Computer scientists working for a high-tech division of Google are testing how machine learning algorithms can be created from scratch, then evolve naturally, based on simple math.
Experts behind Google's AutoML suite of artificial intelligence tools have now showcased fresh research which suggests the existing software could potentially be updated to "automatically discover" completely unknown algorithms while also reducing human bias during the data input process.
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According to ScienceMag, the software, known as AutoML-Zero, resembles the process of evolution, with code improving every generation with little human interaction.
Machine learning tools are "trained" to find patterns in vast amounts of data while automating such processes and constantly being refined based on past experience.
But researchers say this comes with drawbacks that AutoML-Zero aims to fix. Namely, the introduction of bias.
"Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML," their team's paper states. "Innovation is also limited by having fewer options: you cannot discover what you cannot search for."
The analysis, which was published last month on arXiv, is titled "Evolving Machine Learning Algorithms From Scratch" and is credited to a team working for Google Brain division.
"The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms," Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.
As noted by ScienceMag, AutoML-Zero is designed to create a population of 100 "candidate algorithms" by combining basic random math, then testing the results on simple tasks such as image differentiation. The best performing algorithms then "evolve" by randomly changing their code.
The resultswhich will be variants of the most successful algorithmsthen get added to the general population, as older and less successful algorithms get left behind, and the process continues to repeat. The network grows significantly, in turn giving the system more natural algorithms to work with.
Haran Jackson, the chief technology officer (CTO) at Techspert, who has a PhD in Computing from the University of Cambridge, told Newsweek that AutoML tools are typically used to "identify and extract" the most useful features from datasetsand this approach is a welcome development.
"As exciting as AutoML is, it is restricted to finding top-performing algorithms out of the, admittedly large, assortment of algorithms that we already know of," he said.
"There is a sense amongst many members of the community that the most impressive feats of artificial intelligence will only be achieved with the invention of new algorithms that are fundamentally different to those that we as a species have so far devised.
"This is what makes the aforementioned paper so interesting. It presents a method by which we can automatically construct and test completely novel machine learning algorithms."
Jackson, too, said the approach taken was similar to the facts of evolution first proposed by Charles Darwin, noting how the Google team was able to induce "mutations" into the set of algorithms.
"The mutated algorithms that did a better job of solving real-world problems were kept alive, with the poorly-performing ones being discarded," he elaborated.
"This was done repeatedly, until a set of high-performing algorithms was found. One intriguing aspect of the study is that this process 'rediscovered' some of the neural network algorithms that we already know and use. It's extremely exciting to see if it can turn up any algorithms that we haven't even thought of yet, the impact of which to our daily lives may be enormous." Google has been contacted for comment.
The development of AutoML was previously praised by Alphabet's CEO Sundar Pichai, who said it had been used to improve an algorithm that could detect the spread of breast cancer to adjacent lymph nodes. "It's inspiring to see how AI is starting to bear fruit," he wrote in a 2018 blog post.
The Google Brain team members who collaborated on the paper said the concepts in the most recent research were a solid starting point, but stressed that the project is far from over.
"Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent... multiplicative interactions. These results are promising, but there is still much work to be done," the scientists' preprint paper noted.
Walsh told Newsweek: "The developers of AutoML-Zero believe they have produced a system that has the ability to output algorithms human developers may never have thought of.
"According to the developers, due to its lack of human intervention AutoML-Zero has the potential to produce algorithms that are more free from human biases. This theoretically could result in cutting-edge algorithms that businesses could rely on to improve their efficiency.
"However, it is worth bearing in mind that for the time being the AI is still proof of concept and it will be some time before it is able to output the complex kinds of algorithms currently in use. On the other hand, the research [demonstrates how] the future of AI may be algorithms produced by other machines."
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Artificial Intelligence That Can Evolve on Its Own Is Being Tested by Google Scientists - Newsweek
Should I Stay or Should I Go? Artificial Intelligence (And The Clash) has the Answer to Your Employee Access Dilemma. – Security Boulevard
What happens when employees have access to data, apps or services that they shouldnt? Best case scenario: they might know the salaries of all their colleagues and company execs. Worst case scenario: malicious actors exploit that access and extract sensitive business data, causing millions of dollars in damage and irreparable harm to brand reputation.
In past blogs, I wrote how security starts with protecting users and that by verifying the user we greatly reduce the attack surface from all humans to just those you actually trust (aka your employees). I also wrote that we want to make sure every device is being used in a secure manner. In other words, by validating every device, we reduce the attack surface even more by limiting the devices that gain access from billions of computers, phones, or tablets to just the select few in the users possession.
Verifying users and validating devices represent steps one and two on the road to Zero Trust. But while this combination drastically improves security posture, more layers are necessary to guarantee risks of fraudulent access are no more. Just because a person is who they say they are and are using a trusted device doesnt mean that they should have broad access rights beyond what they need to do their job. Whether by accident or malicious intent, insiders can still misuse their access or share access with people whom they shouldnt.
To stop this from happening, you need to vastly reduce the risk associated with the access rights each user has. We do this by limiting user access (even to verified users and validated devices) to only those apps and resources that they need to do their job, and to only when they specifically need to do it. This is step number three that completes the trinity of a Zero Trust security approach: Verify every user, validate their devices, and intelligently limit their access.
Companies typically grant access to necessary apps and resources as they onboard employees. When an employee moves on, either up the ranks or out the door, we tend to forget about those original grants. Were all guilty of this. For example, Im now head of marketing at Idaptive, so I shouldnt have access to our product source code the same way I did back when I was a product manager. The accumulation of access to data, apps, and services creates serious risks. Instead, we must tailor that access to just what a person needs for the job they perform today and automatically remove that access when they leave.
Thats easier said than done for IT teams (and sometimes HR) who historically had to manually provision and deprovision users or at least manually write the rules for role-based access control programs. Someone had to tell IT that an employees role had changed, and then IT would have to figure out how that relates to the access that they should or shouldnt have. We often refer to this process as lifecycle management, and provisioning is just one piece of this mammoth responsibility that enterprise teams are tasked with managing.
The role of lifecycle management in the Zero Trust model is critically important because it determines who has which rights on which systems and applications. You can ensure that a user only has access to what he needs to do his job, create reliable reports, and audit those rights at any given time.
IT staff knows that accounts are difficult to manage because:
Some form of automation and automatic deprovisioning is required. Combining self-service, workflow, and provisioning automation can ensure that users only receive the access they need, help them be productive quickly, and automatically remove their access as their roles change or when they leave the company.
Even if you dont have hands-on experience with lifecycle management, its not hard to see how this spreadsheet-style or swivel chair provisioning access can snowball into something both time-consuming and error-prone leading to an accumulation of access over time. And when employees have access to things they shouldnt, attackers know that a simple phishing attempt is all it takes to gain insider access and wreak havoc on business systems.
If youre saying right now there has to be a secure, more efficient and maybe even automated way to do this, youd be right. The answer lies within a Zero Trust approach powered by Next-Gen Access identity technology.
With Provisioning and Lifecycle Management you can enable users to request access to applications from the app catalog of pre-integrated applications, provide specific users the ability to approve or reject these access requests, and automatically create, update, and deactivate accounts based on roles in your user directory. Provisioning enables users to be productive on day one with the appropriate access, authorization, and client configuration across their devices.
Lifecycle Management should also seamlessly import identities from your preferred HR system or application, including Workday, UltiPro, BambooHR, or SuccessFactors, and provision them (typically) to Active Directory. This enables you to unify your provisioning and HR workflows and have an HR-driven primary system of record for user data across all your applications.
By way of example, with Active Directory (AD) synchronization for Microsoft Office 365, you can keep your AD accounts and Office 365 accounts in sync and automatically provision and deprovision user accounts, groups, and group memberships to simplify Office 365 license management.
Lifecycle Management not only can save IT teams a great deal of time and frustration, but it can ultimately save companies from crippling data breaches. Such is the power of intelligently limiting access as part of a Zero Trust framework.
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Should I Stay or Should I Go? Artificial Intelligence (And The Clash) has the Answer to Your Employee Access Dilemma. - Security Boulevard
Insilico enters into a research collaboration with Boehringer Ingelheim to apply novel generative artificial intelligence system for discovery of…
HONG KONG, April 14, 2020 /PRNewswire/ --Insilico Medicine is pleased to announce that it has entered into a research collaboration with Boehringer Ingelheim to utilize Insilico's generative machine learning technology and proprietary Pandomics Discovery Platform with the aim of identifying potential therapeutic targets implicated in a variety of diseases.
Insilico enters into a research collaboration with Boehringer Ingelheim
"Insilico Medicine is very impressed with the Research Beyond Borders group at Boehringer Ingelheim capabilities in the search of potential drug targets. In this collaboration, Insilico will provide additional AI capabilities to discover novel targets for a variety of diseases to benefit the patients worldwide. We are very happy to partner with such an advanced group," said Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine.
"We believe that Insilico's exclusive Pandomics platform will provide huge boost to our ability to explore and identify drug targets. We look forward to using AI to significantly improve the drug discovery process and contribute to human health," said from Dr. Weiyi Zhang, Head of External Innovation Hub, Boehringer Ingelheim GreaterChina.
In September 2019, Insilico Medicineannounced a $37 million round led by prominent biotechnology and AI investors.
About Insilico MedicineSince 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Since its inception, Insilico Medicine raised over $52 million, published over 70 peer-reviewed papers, applied for over 20 patents, and received multiple industry awards.
Websitehttp://insilico.com/
Media ContactFor further information, images or interviews, please contact:ai@insilico.com
About Boehringer Ingelheim Improving the health of humans and animals is the goal of the research-driven pharmaceutical company Boehringer Ingelheim. The focus in doing so is on diseases for which no satisfactory treatment option exists to date. The company therefore concentrates on developing innovative therapies that can extend patients' lives. In animal health, Boehringer Ingelheim stands for advanced prevention.
Family-owned since it was established in 1885, Boehringer Ingelheim is one of the pharmaceutical industry's top 20 companies. Some 50,000 employees create value through innovation daily for the three business areas human pharmaceuticals, animal health and biopharmaceuticals. In 2018, Boehringer Ingelheim achieved net sales of around 17.5 billion euros. R&D expenditure of almost 3.2 billion euros, corresponded to 18.1 per cent of net sales.
As a family-owned company, Boehringer Ingelheim plans in generations and focuses on long-term success. The company therefore aims at organic growth from its own resources with simultaneous openness to partnerships and strategic alliances in research. In everything it does, Boehringer Ingelheim naturally adopts responsibility towards mankind and the environment.
More information about Boehringer Ingelheim can be found on http://www.boehringer-ingelheim.com or in our annual report: http://annualreport.boehringer-ingelheim.com
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New Bright Pattern AI Survey Finds 78% of Companies Have or Plan to Deploy AI In Their Call Center – Associated Press
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SOUTH SAN FRANCISCO, Calif., April 14, 2020 /PRNewswire/ -- Adoption of artificial intelligence continues to increase in U.S. contact centers. According to Canam Research, 78% of contact centers in the U.S. report plans to deploy artificial intelligence in their contact center in the next 3 years, with an overwhelming number (97%) of survey respondents planning to use artificial intelligence to support agents as opposed to 7% who plan to use AI to replace some or all of their current call center staff. Top uses of artificial intelligence include bots, self-service, and AI for quality management.
These insights stem from a survey sponsored by Bright Pattern, the leading provider of AI-powered omnichannel cloud contact center software for innovative enterprises. The survey examined the current state of U.S. contact centers usage and preferences around artificial intelligence in the contact centers. Bright Pattern surveyed companies of all sizes and industries in the 2020 Contact Center AI Benchmark Trend Report.
Survey Respondents Top Goals for Implementing AI:
Everyone has been talking about AI for improving the customer experience but few companies know where to start, said Ted Hunting, Senior Vice President Marketing, Bright Pattern. We conducted this research to better understand what customers need. It resulted in the creation of our BrightStart Solution Packs for AI which help customers immediately deploy AI in their contact centers.
Call Center AI Key Findings:
Find out more about the current state of AI in the contact center by downloading the 2020 Contact Center AI Benchmark Trend Report.
Survey Methodology Bright Pattern commissioned third-party research consultancy Canam to conduct an online survey of over 300 U.S. contact center executives from a total pool of 14 industry categories.
Bright Pattern announced initial customer experience survey findings in April and will continue to release additional insights in the coming months. For more details about the survey methodology or to receive a free copy of the report, contact the Bright Pattern media relations team at marketing@brightpattern.com.
About Bright PatternBright Pattern provides the simplest and most powerful AI-powered contact center for innovative midsize and enterprise companies. With the purpose of making customer service brighter, easier, and faster than ever before, Bright Pattern offers the only true omnichannel cloud platform with embedded AI that can be deployed quickly and nimbly by business userswithout costly professional services. Bright Pattern allows companies to offer an effortless, personal, and seamless customer experience across channels like voice, text, chat, email, video, messengers, and bots. Bright Pattern also allows companies to measure and act on every interaction on every channel via embedded AI omnichannel quality management capability. The company was founded by a team of industry veterans who pioneered the leading contact center solutions and today are delivering architecture for the future with an advanced cloud-first approach. Bright Patterns cloud contact center solution is used globally in over 26 countries and 12 languages.
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New Bright Pattern AI Survey Finds 78% of Companies Have or Plan to Deploy AI In Their Call Center - Associated Press
Artificial intelligence used to measure impact of Coronavirus on American construction – News – GCR
Analysis by camera firm OxBlue has used artificial intelligence (AI) from construction site data to determine the drop in construction productivity across America due to the Coronavirus pandemic.
OxBlue is using data from commercial construction projects, which excludes single-family residential construction.
Using almost near real-time field data and comparing it to previous activity across all 50 states, OxBlue has determined that construction has declined by 5% throughout March 2020 in the US, based on the weighted average of the construction volume for each state.
The analysis found:
The two states with the most severe decline in activity were subject to Covid-19 quarantine restrictions, with Pennsylvanias issue to close non-life-sustaining businesses meaning building work was reduced by 77%. Michigan experienced a 74% drop of construction work after ordering residents to stop work on March 23rd.
12 states that are yet to issue Coronavirus restrictions saw an increase in productivity.
States with high construction construction outputs also saw large declines in activity, such as a 43% decline in New York, which ordered all non-essential construction to stop, and a 57% drop for building work in Massachusetts.
OxBlue found that construction in Americas northeast reduced the most by 34%.Images courtesy of OxBlue
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Artificial intelligence used to measure impact of Coronavirus on American construction - News - GCR
Artificial Intelligence and the Insurer – Lexology
No longer used solely by innovative technology companies, AI is now of strategic importance to more risk-averse sectors such as healthcare, retail banking, and even insurance. Built upon DAC Beachcrofts depth of experience in advising across the insurance market, this article explores a few ways in which artificial intelligence is changing the insurance industry.
How might AI change insurance?
Artificial intelligence (AI) is an increasingly pervasive aspect of modern life, thanks to its role in a wide variety of applications. The technological advancement and applicability of AI systems has exploded due to, cheaper data storage costs, increased computing resources, and an ever-growing output of and demand for consumer data. As such, we expect to see change in several critical aspects of the insurance industry.
Of course, it is important to note that insurance is a large and complex industry. Even in light of the perceived advantages discussed above, insurers may not always find it easy to integrate AI within products or backend systems. A Capgemini survey revealed that as of 2018, only 2 per cent of insurers worldwide have seen full-scale implementation of AI within their business, with a further 34% still in ideation stages. Furthermore, there are important ethical considerations which have yet to be addressed, with critics warning that AI could lead to detrimental outcomes, especially in relation to personal data privacy and hyper-personalised risk assessments. While more work needs to be done to understand the various implications of AI in insurance, it nevertheless remains an important and fascinating space to watch.