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Computational Blurring As Resolution For AI Autonomous Car Roving Eye 

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Self-driving cars are able to capture the surroundings on the path to the destination. To protect privacy, sensors could be calibrated to ensure anything in the background is blurry. (Credit: Getty Images)  

By Lance Eliot, the AI Trends Insider 

Have you ever taken a picture and realized that a person in the snapshot appears blurry due to your camera being out of focus? I’m sure you have. 

In some cases, the blur happens by accident, whereby you should have set the focus but failed to do so. It was merely an oversight. If you discovered the issue right away, hopefully, you were able to quickly take another photo and delete the remiss one. Problem solved.   

There are also situations involving the use of a blur for intentional purposes. 

Perhaps you have a few friends that want their pictures taken. Behind them are people partying and making quite a scene. You don’t want the background to overtake the attention to the foreground, namely the gaggle of your best friends. So, you set the focus to make the background blurry and ensure that the foreground is nice and sharp.  

Suppose that you indeed take such a picture and keep it around on your smartphone. A few weeks later, someone asks you if George or Samantha were in attendance at the event. You are pretty sure they were, though your memory is a bit hazy (potentially due to the numerous margaritas that you had).   

Wait for a second, they might have been in the background of that photo that you took of your dearest friends. You go ahead and pull up the picture to check it out. Unfortunately, the blur is overwhelming and there are no easy means to discern who else was captured in the snapshot. 

Why all this discussion about blurs in images?   

Blurring techniques can potentially offer a level of privacy for those that might be captured on an image or a video. This might entail blurring the face of someone. It could involve blurring their entire body and whatever kinds of motions they made. The blur is obscuring information and making it difficult to ascertain what was in the image or video. That can be a good thing if you are aiming to provide privacy to those caught on tape.   

As with most things in life, there is also a downside. In the example above, the blurred portion of the photo was unhelpful in answering the question about whether George or Samantha was at the party. There are likely lots of instances where blur makes some potentially useful information only marginally handy and possibly entirely unusable.   

Shifting gears (I promise to come back to the blurs in a moment), let’s talk about cars. 

The future of cars consists of self-driving cars. These are cars that have an AI driving system at the wheel of the vehicle. The driving actions are undertaken by the AI. No human driver is making use of the driving controls. 

An important element of self-driving cars is the use of various sensors to detect the driving scene. These sensory devices are the veritable eyes and ears of the AI driving system. For most self-driving cars, the types of sensors encompass video cameras, radar units, LIDAR units, ultrasonic devices, and the like. The sensors are mounted on the vehicle and are used to figure out where the roadway is, where other cars are, where pedestrians are standing, and so on. 

This seems relatively innocuous and there isn’t much attention being given to the plethora of sensors that self-driving cars contain. No big deal, it seems, since the sensors are logically required to sense the world that surrounds the vehicle. Without those sensory devices, the AI driving system would be blind to what is happening around the car.   

Here’s the rub. Those sensors can capture a lot more than you might at first imagine that they do.   

Imagine that you put a video camera on the top of your conventional car. You turned it on and set it to record continuously. You then drive from your home to the local grocery store. What happens during that rather routine drive? Your video camera is capturing all the activity that you perchance come across.  

For example, after backing down your driveway, you drive down the block to the corner. Turns out that your neighbors next door were in their front yard. They were tossing a baseball back and forth with their children. That activity is now captured onto your video camera recording.   

I believe you get the gist of the matter.   

With merely one video camera mounted on your conventional car, it will be collecting videos about the daily lives of anyone that you happen to drive past. Let’s up the ante. Suppose that all of your neighbors put video cameras on the rooftops of their cars too. They will also now be recording anything that they encounter while on any driving journey.   

Welcome to the emerging world of self-driving cars.   

Those heralded self-driving cars are going to be capturing imagery and other data about whatever they detect, wherever they go, all the time that they are underway. Keep in mind there is an assumption that eventually, we will predominantly have zillions of self-driving cars on our roadways, and very few conventional cars. 

Returning to your neighborhood and the idea of video cameras mounted on a few neighborhood conventional cars, ratchet this up to assume that all cars that come down your street will have a full suite of state-of-the-art sensors (because they are self-driving cars). Those advanced vehicles will be amassing a lot of information about the comings and goings on your block.   

If self-driving cars were empty and simply roaming the community to be available for any ride requests, they would be capturing daily activity.   

Whenever someone takes a ride in a self-driving car, it will capture the surroundings that are along the path to the stated destination. By riding in a self-driving car from your home to the local store, the self-driving car will record video and other data about whatever was taking place during that time period. 

Please sit down for this next shocker.   

I don’t want to get you on the edge of your seat, but imagine that this massive amount of data was collated and assembled to try and piece together the daily efforts in a city or town. In theory, you could pull together the data from all the self-driving cars and pretty much recreate a semblance of where people were, when they were there, what they did while there (assuming they were outside or otherwise visible), etc.   

I’ve referred to this as the roving eye of the coming era of self-driving cars. 

You could say that this roving eye will be a marvelous addition to our society. There are numerous positive uses. For example, you want to see the latest real estate in an area that you are considering buying a home. It is conceivable that the data from self-driving cars could be used to see exactly what the homes look like, nearly up-to-the-minute.   

This can also be used for crime-fighting. A burglar tries to break into someone’s house. The crook scoots away before being caught. Turns out that there were self-driving cars that happened to be along that street during the time period of the criminal activity. The sensory data is examined, and the identity of the thief is figured out.   

There are some notable downsides too. 

Do you want just anyone to know where you were on last Monday or Tuesday? Presumably, an inspection of data from self-driving cars might show that you were in front of your house, mowing the lawn, on Monday morning. You then left your house and walked down the street to visit a friend at another house. You stayed there for about two hours. And so on.   

Some people are worried about privacy intrusion from video cameras that are mounted on telephone poles or that are used by people as they carry their smartphones. Those are peanuts in comparison to the magnitude of video and other sensory data capturing those self-driving cars will undertake. The more we adopt and utilize self-driving cars, the greater the amount of observing of our daily lives that will occur. It’s as simple as that.   

Are we doomed to come under the crush of a Big Brother dystopian world by accepting self-driving cars as our preferred mode of transportation?   

Sadly, not many are considering this issue, and it won’t visibly arise until there are enough self-driving cars that the kind of overlapping and semi-continuous recording rises to a level high enough to be noticed. Until then, we will be laying the seeds for the future that will catch us by “surprise” about what we have done to ourselves over time.   

Shucks, you might be thinking, if this is a looming problem, perhaps something ought to be done, sooner rather than later. There must be some means to keep from digging a hole that appears to be a quite disturbing abyss.   

Aha, allow me to bring up an old friend of sorts, namely the blur. 

The earlier discussion about the blurring of images was in fact the “answer” before I had presented you with the question at hand.   

Similar to how a blurring effect was able to mask whether George or Samantha was at the wild party, the same kind of notion and capacity could be used for dealing with the data that the roving eye detects and collects. 

Here is an intriguing question to ponder: Will the advent of AI-based true self-driving cars and their roving eye be potentially made more societally palatable via the use of blurring? 

Let’s unpack the matter and see.   

For my framework about AI autonomous cars, see the link here: https://aitrends.com/ai-insider/framework-ai-self-driving-driverless-cars-big-picture/   

Why this is a moonshot effort, see my explanation here: https://aitrends.com/ai-insider/self-driving-car-mother-ai-projects-moonshot/ 

For more about the levels as a type of Richter scale, see my discussion here: https://aitrends.com/ai-insider/richter-scale-levels-self-driving-cars/   

For the argument about bifurcating the levels, see my explanation here: https://aitrends.com/ai-insider/reframing-ai-levels-for-self-driving-cars-bifurcation-of-autonomy/ 

Understanding The Levels Of Self-Driving Cars   

As a clarification, true self-driving cars are ones where the AI drives the car entirely on its own and there isn’t any human assistance during the driving task.   

These driverless vehicles are considered Level 4 and Level 5, while a car that requires a human driver to co-share the driving effort is usually considered at Level 2 or Level 3. The cars that co-share the driving task are described as being semi-autonomous, and typically contain a variety of automated add-on’s that are referred to as ADAS (Advanced Driver-Assistance Systems).   

There is not yet a true self-driving car at Level 5, which we don’t yet even know if this will be possible to achieve, and nor how long it will take to get there.   

Meanwhile, the Level 4 efforts are gradually trying to get some traction by undergoing very narrow and selective public roadway trials, though there is controversy over whether this testing should be allowed per se (we are all life-or-death guinea pigs in an experiment taking place on our highways and byways, some contend). 

Since semi-autonomous cars require a human driver, the adoption o/f those types of cars won’t be markedly different than driving conventional vehicles, so there’s not much new per se to cover about them on this topic (though, as you’ll see in a moment, the points next made are generally applicable). 

For semi-autonomous cars, it is important that the public needs to be forewarned about a disturbing aspect that’s been arising lately, namely that despite those human drivers that keep posting videos of themselves falling asleep at the wheel of a Level 2 or Level 3 car, we all need to avoid being misled into believing that the driver can take away their attention from the driving task while driving a semi-autonomous car.   

You are the responsible party for the driving actions of the vehicle, regardless of how much automation might be tossed into a Level 2 or Level 3. 

For why remote piloting or operating of self-driving cars is generally eschewed, see my explanation here: https://aitrends.com/ai-insider/remote-piloting-is-a-self-driving-car-crutch/   

To be wary of fake news about self-driving cars, see my tips here: https://aitrends.com/ai-insider/ai-fake-news-about-self-driving-cars/ 

The ethical implications of AI driving systems are significant, see my indication here: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/   

Be aware of the pitfalls of normalization of deviance when it comes to self-driving cars, here’s my call to arms: https://aitrends.com/ai-insider/normalization-of-deviance-endangers-ai-self-driving-cars/   

Self-Driving Cars And Roving Eye Blurring   

For Level 4 and Level 5 true self-driving vehicles, there won’t be a human driver involved in the driving task. All occupants will be passengers; the AI is doing the driving.   

One aspect to immediately discuss entails the fact that today’s AI is not sentient. 

In other words, the AI is altogether a collective of computer-based programming and algorithms, and most assuredly not able to reason in the same manner that humans can. I mention this aspect because many headlines boldly proclaim or imply that AI has turned the corner and become equal to human intelligence. As if that wasn’t bad enough, the outsized headlines seek to amp further the matter by contending that AI is reaching superhuman capabilities (for why the use of “superhuman” as a moniker is especially misleading and inappropriate). 

Why this emphasis about the AI not being sentient? Because I want to underscore that when discussing the role of the AI driving system, I am not ascribing human qualities to the AI.   

Please be aware that there is an ongoing and dangerous tendency these days to anthropomorphize AI. In essence, people are assigning human-like sentience to today’s AI, despite the undeniable and inarguable fact that no such AI exists as yet.   

With that clarification, you can envision the AI driving system doesn’t natively somehow “know” that the sensors are capturing a lot of information that might be considered intrusive.   

Those are facets that would need to be programmatically devised by the automaker or self-driving tech firm that makes the AI driving system. If they aren’t considering those facets, there won’t be anything somehow innately in the AI driving system that will “realize” that society doesn’t want that kind of pell-mell collecting of our daily activities.   

With that important context, let’s dig into how this might work.   

Recall that when discussing the act of taking a picture, one point made was that the background could be out of focus and thus blurry, and likewise the foreground could be out of focus and blurry.   

There’s not much debate that the foreground of any sensory detection by a self-driving car is going to be crucial for the driving of the vehicle. As such, the foreground is ostensibly going to have to be kept in focus.   

The more open-ended question is whether the background needs to be kept in focus too. In other words, suppose that the sensors were calibrated to ensure that anything in the background was blurry. This might help to overcome the otherwise wanton avid capturing of daily activities that are not particularly crucial to the driving of the vehicle.   

Of course, you can readily argue that this dividing line between the foreground and the background is altogether untenable.   

Suppose that a dog is running around in someone’s front yard. We probably would want the self-driving car to detect that a dog is up ahead, and though currently inside a yard, the dog might decide to dart into the street once the self-driving car comes along.   

If there is a purposeful blurring when the sensors are trying to detect the driving scene, it could be that vital clues about the surroundings would no longer be readily usable. This in turn could mean that the AI driving systems will not be able to drive as safely as we would hope for.   

Some would assert that it makes absolutely no sense to intentionally undercut the capabilities of the sensors. Indeed, those proponents would undoubtedly contend that we need even stronger sensors that have increasingly piercing capabilities, being able to do detection that is far above that of what humans might be able to do.   

Under that rather strident thinking, we might wish to momentarily herein set aside the notion of trying to prevent the sensors from capturing whatever they can potentially detect. Assume that the sensors are going to be allowed to detect as much as they can.   

The sensory data flows into the onboard processors of the self-driving car. At that juncture, the data is mathematically examined for purposes of driving the car. The AI driving system tries to computationally interpret the data to figure out the driving scene. Upon doing so, the AI driving system figures out the driving action to undertake and emits commands to the vehicle accordingly. 

You could suggest that the data from the sensors could now be discarded since it has been used for its primary purpose. In that way of thinking, there is no need to worry about what is contained in the data. Just dump it out, after it has been used for the driving act.  Ergo, the data cannot now presumably be used for any nefarious purposes since it isn’t sitting around anymore. The moment that the sensory data has been analyzed for driving purposes, make sure it gets deleted. That is the end of the road for the sensory data. 

Well, that presents a couple of challenges.   

First, it means that you can’t potentially use the data for the other augmentable upside possibilities that were mentioned earlier. The data won’t be around, and therefore it can’t be used to figure out the latest aspects of real estate or be used to catch those despicable criminals. Some would argue that you are possibly tossing out the baby with the bathwater (an old-time expression).   

Secondly, you cannot necessarily guarantee that the data will be deleted. Once you’ve let the data into the onboard systems, this is like letting the horse out of the barn, or the cat out of the bag. Sure, you might believe that the data is going to be deleted, and the system might be programmed accordingly. Nonetheless, the data can potentially be kept, despite the otherwise desired notion of deleting it.   

In that viewpoint, you either are going to not capture the data at all, or you are going to capture it and need to do something with it. 

This is where an intentional blurring comes to play. 

One approach consists of taking the data after it has been examined for driving purposes and then blurring the data so that those elements that are generally considered irrelevant to the driving act can no longer be readily discerned. You don’t necessarily delete those aspects, you blur them. 

A difficult question arises about when the right timing is to do the blurring.   

If you do so while the data is fresh and just brought into the onboard systems, this means that you need the computational resources on-board to do this type of blurring action while the car is underway. Some would argue that whatever computational processing you’ve got ought to go entirely towards the driving act. Do not usurp those precious processing cycles from the life-or-death matters of driving the vehicle.   

Okay, from that perspective, we might have some background process that does the blurring when the self-driving car is parked and not underway. Or basically whenever there is spare processing time available.   

Another notion is that you could do the blurring once the data has been uploaded into the cloud. You see, it turns out that self-driving cars are going to be using OTA (Over-The-Air) electronic communications to connect with the cloud of the fleet operator or automaker of the self-driving car. This would be used to readily push down crucial updates to the AI driving system. It can also be used to upload data from the self-driving car and into the cloud.   

Thus, some would say that you shouldn’t use any processing on-board the self-driving car for the blurring and instead let it happen in the cloud. The self-driving car would upload whatever data it has collected. This data would be in its rawest form. The cloud processing by the fleet operator or automaker would be programmed to then blur the data. 

Sorry to report that this chain of where the data is going and what its status consists of will open a bit of Pandora’s box.   

For example, suppose that the raw data in its entirety is being kept on board the self-driving car. Once it gets loaded up into the cloud, perhaps at that juncture it is deleted from the onboard processors (a copy now exists in the cloud). Unfortunately, this does mean that for some length of time, the data is sitting there in the vehicle, in all its glory. There is nothing blurred as yet. This leaves open the chance that the data could be somehow siphoned or copied and now be made available with everything it has to show. 

That’s why some vehemently argue that the data ought to be blurred at the soonest possible opportunity. 

Of course, there are other matters intertwined. The data is likely in an unencrypted format upon first flowing into the onboard systems. Some would urge that the data be encrypted right away. In that manner, you don’t necessarily need to worry about the blurring, since anyone that could surreptitiously get the data won’t have anything useful due to the encryption. 

This brings up that there are two camps typically at loggerheads here. One camp says that the full and unblurred data should never be allowed to leave the car. In that sense, it should not be allowed to be uploaded to the cloud. Only once it has been blurred, and possibly encrypted too, can it be uploaded. The other camp says that it is fine to upload the whole shebang, and as long as it is encrypted, you just blur it after getting into the cloud. 

For more details about ODDs, see my indication at this link here: https://www.aitrends.com/ai-insider/amalgamating-of-operational-design-domains-odds-for-ai-self-driving-cars/ 

On the topic of off-road self-driving cars, here’s my details elicitation: https://www.aitrends.com/ai-insider/off-roading-as-a-challenging-use-case-for-ai-autonomous-cars/ 

I’ve urged that there must be a Chief Safety Officer at self-driving car makers, here’s the scoop: https://www.aitrends.com/ai-insider/chief-safety-officers-needed-in-ai-the-case-of-ai-self-driving-cars/ 

Expect that lawsuits are going to gradually become a significant part of the self-driving car industry, see my explanatory details here: https://aitrends.com/selfdrivingcars/self-driving-car-lawsuits-bonanza-ahead/   

Conclusion   

There are a lot more monkey wrenches that can be thrown into this thorny matter. 

Let’s suppose that the data does get blurred. We are presuming this implies that there is no longer the qualm about being able to detect that your neighbors were playing catch with their kids in their front yard.   

Sometimes, that which can be blurred can, later on, be unblurred.  

This means that the blurring might be undone. If the images or video is allowed to be copied, you could use all sorts of unblurring techniques to try and turn the blurred aspects into something discernible. It might not be recast into its original pristine state, but at least given sufficient definition that perhaps it once again is intruding on privacy.   

The cat and mouse gambit of the blurring algorithms is an ongoing battle. Someone comes up with a newer and better blurring routine. Someone else then comes out with a new and improved unblurring approach. Round and round it goes. 

At this time, few are worrying about the roving eye of self-driving cars. 

Over time, perhaps my exhortations will only become a blur, though I am really hoping they become unblurred in time for appropriate thought and action to be taken about this mesmerizing and rather clear-cut dilemma.   

There really is no blur about it. 

Copyright 2021 Dr. Lance Eliot  

http://ai-selfdriving-cars.libsyn.com/website 

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Source: https://www.aitrends.com/ai-insider/computational-blurring-as-resolution-for-ai-autonomous-car-roving-eye/

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7 Ways Artificial Intelligence is Improving Healthcare

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Emerging technologies have the potential to completely reshape the healthcare industry and the way people manage their health. In fact, tech innovation in healthcare and the use of artificial intelligence (AI) could provide more convenient, personalized care for patients.

It could also create substantially more value for the industry as a whole—up to $410 billion per year by 2025.

This graphic by RYAH MedTech explores the ways that technology, and more specifically AI, is transforming healthcare.

How is Technology Disrupting the Patient Experience?

Tech innovation is emerging across a wide range of medical applications.

Because of this, AI has the potential to impact every step of a patient’s journey—from early detection, to rehabilitation, and even follow-up appointments.

Here’s a look at each step in the patient journey, and how AI is expected to transform it:

1. Prevention

Wearables and apps track vast amounts of personal data, so in the future, AI could use that information to make health recommendations for patients. For example, AI could track the glucose levels of patients with diabetes to provide personalized, real-time health advice.

2. Early Detection

Devices like smartwatches, biosensors, and fitness trackers can monitor things like heart rate and respiratory patterns. Because of this, health apps could notify users of any abnormalities before conditions become critical.

Wearables could also have a huge impact on fall prevention among seniors. AI-enabled accelerometer bracelets and smart belts could detect early warning signs, such as low grip strength, hydration levels, and muscle mass.

3. Doctors Visits

A variety of smart devices have the potential to provide support for healthcare workers. For instance, voice technology could help transcribe clinical data, which would mean less administrative work for healthcare workers, giving them more time to focus on patient care.

Virtual assistants are expected to take off in the next decade. In fact, the healthcare virtual assistant market is projected to reach USD $2.8 billion by 2027, at a CAGR of 27%.

4. Test Results

Traditionally, test results are analyzed manually, but AI has the potential to automate this process through pattern recognition. This would have a significant impact on infection testing.

5. Surgery / Hospital Visits

Research indicates that the use of robotics in surgery can save lives. In fact, one study found that robot assisted kidney surgeries saw a 52% increase in success rate.

Robotics can also support healthcare workers with repetitive tasks, such as restocking supplies, disinfecting patient rooms, and transporting medical equipment, which gives healthcare workers more time with their patients.

6. Rehabilitation

Personalized apps have significant care management potential. On the patient level, AI-enabled apps could be specifically tailored to individuals to track progress or adjust treatment plans based on real-time patient feedback.

On an industry level, data generated from users may have the potential to reduce costs on research and development, and improve the accuracy of clinical trials.

7. Follow-ups and Remote Monitoring

Virtual nurse apps can help patients stay accountable by consistently monitoring their own progress. This empowers patients by putting the control in their own hands.

This shift in power is already happening—for instance, a recent survey by Deloitte found that more than a third of respondents are willing to use at-home diagnostics, and more than half are comfortable telling their doctor when they disagree with them.

It’s All About the Experience

Through the use of wearables, smart devices, and personalized apps, patients are becoming increasingly more connected, and therefore less dependent on traditional healthcare.

However, as virtual care becomes more common, healthcare workers need to maintain a high quality of care. To do this, virtual training for physicians is critical, along with user-friendly platforms and intentionally designed apps to provide a seamless user experience.

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Source: https://www.visualcapitalist.com/7-ways-artificial-intelligence-is-improving-healthcare/

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The ‘Cyber Attacks’ Winter is Coming — straight for small firms in India Inc.

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Cyber intrusions and attacks have increased exponentially over the last decade approximately, exposing sensitive information pertaining to people and businesses, thus disrupting critical operations, and imposing huge liabilities on the economy. 

Cybersecurity is a responsibility that employees and leaders across functions must shoulder simply because it is the gospel truth – you cannot protect what you cannot see. As organizations have shifted to the work-from-home model due to the outbreak of the COVID-19 pandemic, it’s increasingly important to keep your company’s data secure. 

While the pandemic has led to near or complete digitalization of operations amongst financial institutions, it’s also increased the potential for cyberattacks that lead to adverse financial, reputational, and/or regulatory implications for organizations. 

According to Accenture, cybercrime is said to cost businesses $5.2 trillion worldwide within five years. “With 43% of online attacks now aimed at small businesses, a favorite target of high-tech villains, yet only 14% prepared to defend themselves, owners increasingly need to start making high-tech security a top priority,” the report continues.

A recent McAfee study shows global cybercrime costs crossed US$1 trillion dollars in 2020, up almost 50% from 2018.

India too saw an exponential rise in cybersecurity incidents amid the coronavirus pandemic. Information tracked by the Indian Computer Emergency Response Team (CERT-In) showed that cybersecurity attacks saw a four-fold jump in 2018, and recorded an 89 percent growth in 2019.

The government has set up a Cyber Crisis Management Plan for countering cyber-attacks effectively, while also operating the Cyber Swachhta Kendra (Botnet Cleaning and Malware Analysis Centre).

Banks and Financial Institutions (FIs) are some of the highest targeted market sectors. An analysis by Can we hyperlink this: https://www.fitchratings.com/videos/exploring-bank-cybersecurity-risk-13-04-2021?mkt_tok=NzMyLUNLSC03NjcAAAF82rxN_2lbDTsEp4tfBu4tUGP7i6wyb1OGpyNY0Z8lQPhdz9C7KQ-NIriTcJqNSDyb9qfQ_essxS-TdNWMgJesb-RA4yN4t7T-XqXmVfWW4dau36SW6ZE 

“>FitchRatings in collaboration with SecurityScorecard reveals that banks with higher credit ratings exhibited better cybersecurity scores than banks with lower credit ratings. 

Bharti Airtel’s chief executive officer for India, Gopal Vittal, in a letter to the telco’s 307.9 million subscribers, detailed out how Airtel is carrying out home delivery of SIM cards and cautioned subscribers from falling prey to cyber frauds. He cautioned them against the rapid rise in cyber frauds, highly likely via digital payments. “There has been a massive increase in cyber frauds. And as usual, fraudsters are always finding new ways to trick you,” he added in the letter. 

Barcelona-based Glovo, valued at over $1 billion, that delivers everything from food to household supplies to some 10 million users across 20 countries, came under attack recently when the “hacker gained access to a system on April 29 via an old administrator platform but was ejected as soon as the intrusion was detected”, according to the company.

The attack came less than a month after Glovo raised 450 million euros ($541 million) in funding. 

According to Kaspersky’s telemetry, close on the heels of coronavirus-led pandemic and subsequent lockdown in March 2020, saw a total number of meticulously planned attacks against remote desktop protocol (RDP) jumped from 93.1 million worldwide in February 2020 to 277.4 million 2020 in March — a whopping 197 percent increase. In India, the numbers went from 1.3 million in February 2020 to 3.3 million in March 2020. In July 2020, India recorded its highest number of cyberattacks at 4.5 million.

The recent data breach at the payment firm Mobikwik, affected 3.5 million users, exposing Know Your Customer (KYC) documents such as addresses, phone numbers, Aadhaar card details, PAN card numbers, and so on. The company, however, still maintains that there was no such data breach. It was only after the Reserve Bank of India’s intervention that Mobikwik got a forensic audit conducted immediately by a CERT-IN empaneled auditor and submitted the report. 

Security experts have observed a 500% rise in the number of cyber attacks and security breaches and a 3 to 4 times rise in the number of phishing attacks from March until June 2020.

These attacks, however, are not just pertaining to the BFSI sector, but also the healthcare sector, and the education sector.

Image Source: BusinessStandard.com

What motivates hackers to target SMBs? 

Hackers essentially target SMBs because it’s a source of easy money. From inadequate cyber defenses to lower budgets and/or resources, smaller businesses often lack strong security policies, cybersecurity education programs, and more, making them soft targets. 

SMBs can also be a ‘gateway’ to larger organizations. As many SMBs are usually connected electronically to the IT systems of larger partner organizations, it becomes an inroad to the bigger organizations and their data. 

How can companies shield themselves from a potential cyberattack: 

As a response to the rising number of attacks in cyberspace, the Home Ministry of India issued an advisory with suggestions on the prevention of cyber thefts, especially for the large number of people working from home. Organizations and key decision-makers in a company can also create an effective cybersecurity strategy that’s flexible for adaptation in a changing climate too. Here are a few use cases: 

  • CERT-In conducted ‘Black Swan – Cyber Security Breach Tabletop Exercise’, in order to deal with cyber crisis and incidents emerging amid the COVID-19 pandemic, resulting from lowered security controls. 
  • To counter fraudulent behavior in the finance sector, the government is also considering setting up a Computer Emergency Response Team for the Financial Sector or CERT-Fin.
  • Several tech companies have come forth to address cybersecurity threats by building secure systems and software to mitigate issues like these in the foreseeable future. For example, IBM Security has collaborated with HCL Technologies to streamline threat management for clients through a modernized security operation center (SOC) platform called HCL’s Cybersecurity Fusion Centres. 

Some of the ways through which companies can mitigate potential risks include: 

  • Informing users of hacker tactics and possible attacks
  • Establish security rules, create policies, and an incident response plan to cover the entire gamut of their operations
  • Basic security measures such as regularly updating applications and systems
  • Following a two-factor authentication method for accounts and more

While these measures are some of the ways to be on top of your game in the cybersecurity space, they will also help in sound threat detection while helping gain better insights into attacks and prioritizing security alerts so that India is better prepared for an oncoming attack and battling any unforeseen circumstance that might result in huge loss of data, resources and more. 

Coinsmart. Beste Bitcoin-Börse in Europa Source: https://www.mantralabsglobal.com/blog/the-cyber-attacks-winter-is-coming-straight-for-small-firms-in-india-inc/

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Paris-based Shift Technology becomes the latest insurtech unicorn in France after raising €183.2 million

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Shift Technology, the French startup that has created a solution that enables its insurance clients to detect fraudulent claims, is now worth $1 billion after raising its fourth round of funding. The startup, which also operates in the UK and the US, will expand its team of data scientists, particularly in France.

The AI-based insuretech startup recently announced that it had raised $220 million or around €183.2 million in a series D from Advent International, Avenir Growth, Accel, Bessemer Venture Partners, General Catalyst, Iris Capital and Bpifrance. This latest funding should enable it to structure its R&D, while its offer has expanded since its foundation in 2013. Shift Technology initially focused on fraud detection, but the startup now intends to offer a tool capable of managing the entire chain. It also aims to continue its deployment in the UK and the US, strengthened by its recent unicorn status, whereby its valuation now exceeds one billion dollars.

Originally, the startup sought to facilitate the customer compensation process offered by insurers in the event of a claim – water damage, car accident, etc. Described as the number one fear of policyholders by Jeremy Jawish, CEO and co-founder of Shift Technology and, as such, a major issue for their clients. Once this brick was laid, during its first years of existence, the startup decided to go beyond declaration fraud by making its solution a decision-making aid for insurers. They now offer automated closure of claims files and detection of underwriting fraud. These complementary products are already in production with its customers, who are, to date, around one hundred in some 25 countries. This production was made possible thanks to the previous funding round of €53 million in March 2019.

Shift Technology says it has already analysed 2 billion claims on behalf of insurers since its inception. According to CEO Jeremy Jewish, they receive the data provided by insurers, as well as a number of public data about the claimant. Their algorithms read, among other things, the claim declaration before determining whether to file an appeal or carry out a check for money laundering. The Banque Postale has adopted its solution to accelerate the management of claims for its customers. So has the Axa group, which is also a user. For the latter, the aim is to “limit the manual actions that its employees have to carry out. And to satisfy its customers, Shift Technology is counting on its team of data scientists, which it claims to be “the largest in the insurance sector” and which will be further strengthened.

With 350 employees, the company says that recruitment will be the main focus of its investment strategy following its Series D. “We’re going to recruit a lot in France and a little in the US,” says Jérémy Jawish, who also wants to “approach the health insurance sub-sector more aggressively. Shift Technology says it wants to set up “the largest French centre dedicated to artificial intelligence in insurance” with 300 experts by 2023. With an underlying aim, the startup wants to show that “champions are being created in France”. CEO Jérémy Jawish adds that the COVID-19 crisis has had “a big impact” on its activities according, but has not slowed down the pace of its market openings. A pace that should remain fairly steady.

Shift Technology aims to become an international player in its market. To do this, the french company is counting on its ‘unique’ model based on a single vertical – insurance again and again. However, competition, especially in the US, is a key driver for them to stay on top of their game. As a reminder, this Series D round brings the total amount of funds raised by the company since 2013 to $320 million (nearly €267 million).

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.eu-startups.com/2021/05/paris-based-shift-technology-becomes-the-latest-insurtech-unicorn-in-france-after-raising-e183-2-million/

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AI

Paris-based Shift Technology becomes the latest insurtech unicorn in France after raising €183.2 million

Avatar

Published

on

shift_technology

Shift Technology, the French startup that has created a solution that enables its insurance clients to detect fraudulent claims, is now worth $1 billion after raising its fourth round of funding. The startup, which also operates in the UK and the US, will expand its team of data scientists, particularly in France.

The AI-based insuretech startup recently announced that it had raised $220 million or around €183.2 million in a series D from Advent International, Avenir Growth, Accel, Bessemer Venture Partners, General Catalyst, Iris Capital and Bpifrance. This latest funding should enable it to structure its R&D, while its offer has expanded since its foundation in 2013. Shift Technology initially focused on fraud detection, but the startup now intends to offer a tool capable of managing the entire chain. It also aims to continue its deployment in the UK and the US, strengthened by its recent unicorn status, whereby its valuation now exceeds one billion dollars.

Originally, the startup sought to facilitate the customer compensation process offered by insurers in the event of a claim – water damage, car accident, etc. Described as the number one fear of policyholders by Jeremy Jawish, CEO and co-founder of Shift Technology and, as such, a major issue for their clients. Once this brick was laid, during its first years of existence, the startup decided to go beyond declaration fraud by making its solution a decision-making aid for insurers. They now offer automated closure of claims files and detection of underwriting fraud. These complementary products are already in production with its customers, who are, to date, around one hundred in some 25 countries. This production was made possible thanks to the previous funding round of €53 million in March 2019.

Shift Technology says it has already analysed 2 billion claims on behalf of insurers since its inception. According to CEO Jeremy Jewish, they receive the data provided by insurers, as well as a number of public data about the claimant. Their algorithms read, among other things, the claim declaration before determining whether to file an appeal or carry out a check for money laundering. The Banque Postale has adopted its solution to accelerate the management of claims for its customers. So has the Axa group, which is also a user. For the latter, the aim is to “limit the manual actions that its employees have to carry out. And to satisfy its customers, Shift Technology is counting on its team of data scientists, which it claims to be “the largest in the insurance sector” and which will be further strengthened.

With 350 employees, the company says that recruitment will be the main focus of its investment strategy following its Series D. “We’re going to recruit a lot in France and a little in the US,” says Jérémy Jawish, who also wants to “approach the health insurance sub-sector more aggressively. Shift Technology says it wants to set up “the largest French centre dedicated to artificial intelligence in insurance” with 300 experts by 2023. With an underlying aim, the startup wants to show that “champions are being created in France”. CEO Jérémy Jawish adds that the COVID-19 crisis has had “a big impact” on its activities according, but has not slowed down the pace of its market openings. A pace that should remain fairly steady.

Shift Technology aims to become an international player in its market. To do this, the french company is counting on its ‘unique’ model based on a single vertical – insurance again and again. However, competition, especially in the US, is a key driver for them to stay on top of their game. As a reminder, this Series D round brings the total amount of funds raised by the company since 2013 to $320 million (nearly €267 million).

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.eu-startups.com/2021/05/paris-based-shift-technology-becomes-the-latest-insurtech-unicorn-in-france-after-raising-e183-2-million/

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