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Inside the 1TB ImageNet data set used to train the world’s AI: Naked kids, drunken frat parties, porno stars, and more

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Feature ImageNet – a data set used to train AI systems around the world – contains photos of naked children, families on the beach, college parties, porn actresses, and more, scraped from the web to train computers without those individuals’ explicit consent.

The library consists of 14 million images, each placed into categories that describe what’s pictured in each scene. This pairing of information – images and labels – is used to teach artificially intelligent applications to recognize things and people caught on camera.

The database has been downloaded by boffins, engineers, and academics to train hundreds if not thousands of neural networks to identify stuff in photos – from assault rifles and aprons to magpies and minibuses to zebras and zucchinis, and everything in between.

In 2012, the data set was used to build AlexNet, heralded as a breakthrough development in deep learning since it marked the first time a neural network outperformed traditional computational methods at object recognition in terms of accuracy. AlexNet went on to win the annual ImageNet Large Scale Visual Recognition Challenge.

While ImageNet’s successes are celebrated, its troubling contents have largely been ignored.

Vinay Prabhu, a machine-learning scientist at an AI startup in Silicon Valley, stumbled across some of the data set’s darker and murkier photos by accident.

“I was trying to generate pictures of bicycles using BigGAN,” he told The Register. BigGAN is a generative adversarial network, a machine-learning system that can be taught to craft new unique outputs after studying a set of inputs. In other words, Prabhu hoped to feed BigGAN photos of bicycles from ImageNet so that his computer would generate its own original and never-seen-before pictures of bikes.

Instead, however, his code conjured strange flesh-colored blobs that resembled blurry, disfigured female bodies. Puzzled, he went back to the training data set, and realized he had accidentally trained his model on bikinis instead.

ImageNet’s categories are sorted in alphabetic order and referenced by software in numerical ascending order. In a subset of the database Prabhu was using for his research, bicycles were category 444, and bikinis were 445. A single-digit typo in his code caused his neural network to draw from category 445 – bikinis – rather than the bicycles in 444.

Sometimes the nature of what is pornographic is debatable, but in some cases, the links to the porn websites are included right in the images

“At first I found it amusing, and I decided to look through the data set,” he told us. It didn’t take long before his amusement turned to shock.

“It was clear that these were unethical,” Prabhu said. He saw photos of a naked child’s backside, porn stars, shenanigans at frat parties, plus private and intimate photos of men dressed in women’s underwear. Some of the snaps included watermarks with URLs leading back to smut sites where the photos were lifted from.

“Sometimes the nature of what is pornographic is debatable, but in some cases, the links to the porn websites are included right in the images,” he told us.

Venturing further down the rabbit hole, he investigated other categories in the ImageNet library, and uncovered more eyebrow-raising material, such as photos of women showing their pubic hair. Crucially, it appears no one pictured had given their specific consent to have their images included in a data set analyzed by untold number of neural networks and eggheads.

“These people have no idea that their faces are in this data set,” Prabhu told The Reg. When he emailed the ImageNet creators to alert them to what he had found, he received no response, he told us.

The ImageNet team refused to give The Register access to the data set when we asked. Instead, a spokesperson told us on behalf of the team that the library was unavailable due to “maintenance reasons.” Indeed, the data has been unavailable for download since January this year, though we asked before and after that month for access, and were denied both times.

Another source within the industry, however, who asked to remain anonymous, allowed us to pore over the full ImageNet library – all 1.31TB of it – and we found a trove of troubling photos.

Swimming trunks and shower caps

There were nude children in the “swimming trunks” and the “shower cap” categories. In one particular case, a naked grimacing child wearing a transparent shower cap is standing in front of a naked man, and the photo is cropped just above the child’s genitals. In the “kimono” class, there’s a woman who lies on a bed with her kimono spread to reveal her legs and pubic hair. Elsewhere, there are snaps of people in various states of undress lounging by the sea on sandy beaches.

ImageNet has millions upon millions of photos. Most of them are pretty harmless and depict things like animals, plants, or random objects. It’s when humans are involved that things get a little dicier.

A large part of the problem is due to how the images were collected. Researchers at Stanford University and Princeton University in the US wrote software that automatically scraped a large portion of them from sites including Google, Bing, and the photo-sharing platform Flickr.

Even though some, or all, of the pictures harvested from image sites may have been covered by permissive licenses, such as Creative Commons, allowing them to be used and distributed freely by the ImageNet team and scientists, it’s understood the people pictured did not all agree to have the images fed into various forms of artificial intelligence years later. That, in itself, is a lesson for us all: our data released or shared today may well be used for wildly unexpected purposes tomorrow.

‘Mistakes’

These scraped photos were passed to humans slaving away on Amazon Mechanical Turk (MTurk) – a program in which people sign up to perform simple tasks for others for a small wage. These serfs were asked to manually assign the images to specific categories, or draw bounding boxes around objects of interest in the photos.

Even after the images had been eyeballed by these turks, who were instructed to filter out any dodgy material, problematic snaps managed to slip through. The ImageNet team blamed this on human error. More than 160 million pictures were processed by tens of thousands of cyber-freelancers, so some blunders were to be expected, we’re told.

“There’s no doubt MTurk workers can make mistakes, and spammers on MTurk – people who do not pay attention to instructions or otherwise submit low-quality work – is always a problem,” the project’s spokespeople told The Register earlier this month.

“We have an automatic quality control system in place to filter out spammers and problematic images, but the system is not perfect. Even with multiple safeguards, a problematic image can still slip through, given the scale of the effort. There were over 160 million candidate images and over 50,000 MTurk workers.”

Tom White, a digital artist focused on AI and a lecturer at the Victoria University of Wellington School of Design in New Zealand, who has previously used the data set in his work, told us: “There are loads of inappropriate images online, and so we should expect any automated collection of online photos to include these as well. The publishers of the data sets generally do actively try to remove these, but no matter how hard they try, there comes a point of diminishing returns.

“The data set creators allow some of these ‘contaminants’ to remain simply because there is little incentive to spend the resources eradicating them all and they have minimal overall effect on the training of machine learning models.”

Copyright laws need to be updated

Scraping photos from public sources to feed data sets like ImageNet is a contentious issue. Even if the snaps were used under permissive licenses, is it reasonable to expect the photos to be scrutinized to build powerful object-recognizing neural networks? Do we really have to predict the future before we share any more information online?

Albert Cahn, a lawyer and founder and executive director of the Surveillance Technology Oversight Project – a non-profit activist group based in New York – told us he was “incredibly concerned about the privacy impact of computer vision data scraping.”

“I’m alarmed that millions of individuals’ faces are being used and commercialized without their consent,” he told The Register. “I think that the fact that there are children are in these databases highlights just how few rules there have been for aggregating these data sets.”

He argued new laws are needed to safeguard people’s data as it harvested and analyzed. “All too often, firms use and sell our biometric data without our consent,” he said. “While some of these practices may be legal, they highlight the need for stronger biometric privacy practices that protect against this sort of data harvesting.

“As it becomes ever easier to take ever more of our information and incorporate it into machine learning, we need for the laws to catch up and see that traditional copyright rules don’t go nearly far enough in protecting the public.”

Bias and racism

ImageNet’s creators acknowledged they were aware of its problematic content. Not only are there inappropriate photos, some of the labels used to describe them are biased and racist.

A recent project, ImageNet Roulette, created by Kate Crawford, co-founder of the AI Now Institute, a research hub focused on the social implications of AI, and Trevor Paglen, an artist interested in mass surveillance and data collection, revealed that a system trained from ImageNet would label people’s selfies with insults and racial slurs. They declined to comment for this story.

“We agree that inappropriate images should not be in the data set,” the ImageNet team told El Reg.

“We are developing and implementing a systematic approach to identify and remove problematic images, because what is considered problematic can evolve with time, be subjective, and depends on local community standards and the intended use; reasonable people might disagree on where to draw the line.”

Someone holding the loser L symbol to their forehead

This image-recognition roulette is all fun and games… until it labels you a rape suspect, divorcee, or a racial slur

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Essentially, they are right now scrubbing ImageNet of its inappropriate footage, and hope to blur out all the faces in it.

Os Keyes, a PhD student at the University of Washington on the west coast of America, who is studying gender and algorithms, told us “face blurring is what we would call ‘necessary, but insufficient.’ A face is hardly the only way to identify someone.” Said other ways include clothes, tattoos, and ID badges.

“Identifying people can be done through faces, but it can be done through a lot of other components of how people present in photography, and so it’s difficult to imagine researchers being able to actually write systems to comb identifiable features out,” Keyes said. “Again, this is a situation where active consent goes a long way towards resolving the problem.”

Deleting images also introduces other problems, too, Crawford and Paglen previously said. “By erasing them completely, not only is a significant part of the history of AI lost, but researchers are unable to see how the assumptions, labels, and classificatory approaches have been replicated in new systems, or trace the provenance of skews and biases exhibited in working systems.

“This is the problem of inaccessible or disappearing data sets. If they are, or were, being used in systems that play a role in everyday life, it is important to be able to study and understand the worldview they normalize.” ®

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Source: https://go.theregister.co.uk/feed/www.theregister.co.uk/2019/10/23/ai_dataset_imagenet_consent/

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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).

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

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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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