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Artificial Intelligence

Persistent fraud threats drive consumer biometrics for payments and mobile credentials



A biometric spoof attack and new fraud report this week both indicate the challenge of ensuring financial transactions are legitimate, showing why the latest Goode Intelligence forecast includes biometrics being used for billions of dollars in payments in the years ahead.

Biometric technologies from Idex and partners Zwipe and Tag Systems are each a step closer to consumer’s wallets to help cut fraud, Google SE partners including G+D and Thales are working on mobile digital identity credentials, in addition to the ongoing work industry stakeholders are investing in health passes, and a Mitek executive shares insights on the evolving consumer biometrics ecosystem in some of our most widely-read stories of the week.

Top biometrics news of the week

By 2026, biometrics will secure more than $5.6 billion in payments, according to the latest forecast from Goode Intelligence and the most widely-read story of the week. The latest biometric payments report from Goode comes as Idex Biometrics announced a new order of its TrustedBio sensors and a Nilson Report was released highlighting the biometric card cost reduction from the partnership between Zwipe and Tag Systems.

The secure element Google is planning to use in its forthcoming Pixel 3 phones is optimized to secure digital copies of biometric passports and support mobile driver’s licenses. The company and partners are accelerating the development of the technology through the Android Ready SE Alliance, and OEM partners including G+D, NXP, STMicroelectronics and Thales are already working with the associated StrongBox applet.

A tale of an in-the-wild biometric spoof attack of some sophistication netting over $75 million in China has been reported, after a pair of hackers were prosecuted by law enforcement. The scam involved high-resolution images of people performing different actions made with data obtained on the black market, fraudulent tax invoices and hijacked smartphones.

UbiSecure’s ‘Let’s Talk About Digital Identity’ podcast is joined by NIST Computer Scientist Mei Ngan, discussing her path to joining NIST, the expansion of face biometrics both in terms of applications and market, the work the Institute has done on facial recognition with masks and demographic differentials, with an interesting segment on the serious threat of face morphing on identity documents.

The latest Identity Fraud Study from Javelin Research finds $43 billion was lost to digital identity fraud last year, meaning there has never been a better time to take advantage of increased consumer willingness to adopt biometrics. Consumers are also not willing to tolerate failed fraud claims resolution, which is too frequent, so financial institutions are under pressure from both sides.

The pandemic has driven many businesses from industries other than financial services to approach Buguroo about fending off online fraud with its behavioral biometrics, Founder and CEO Pablo de la Riva tells Startup Info. The company’s focus on comparison against personal behavioral history, rather than cluster of ‘good’ and ‘bad’ users and experience securing financial services customers gives it the edge in an industry “gaining massive momentum,” de la Riva says.

In a highlight from Biometric Update’s growing network of media partners, we present IEC e-tech magazine Co-editor Antoinette Price’s recent interview with ISO/IEC biometrics standards editor Mike Thieme on biometric presentation attacks. Thieme talks about a broader conception of presentation attacks than is sometimes thought of, challenges with PAD systems, and what Part 4 of the ISO/IEC 30107-3 standard does.

The biometrics and technologies for delegating authorization and authentication to online accounts and various digital devices for the full range of consumer applications are available now, Mitek CTO Stephen Ritter tells Biometric Update, but the broader ecosystem to support it is yet to be established. Creating the right environment for consumer trust in smart homes and the IoT will mean building trust, and may require the efforts of business giants like big banks, but in the meantime appropriate choices in biometrics implementation can give companies an edge right now.

Two International Monetary Fund officials want to break down the Big Tech silos that are preventing big data and AI from being fully utilized. Too much data is probably being collected, and too little value shared with individuals, but major barriers related to privacy and policy stand in the way of change. The prioritization of policy around data sharing and digital identity for proving vaccination status may present the opportunity to overcome those barriers, according to an opinion piece by Yan Carriere-Swallow and Vikram Haksar.

Innovatrics’ SmartFace platform now includes pedestrian and body part detection to aid with anonymous real-time detection, with its latest update. The company has also introduced an application to provide instant feedback from mobile devices placed beside a SmartFace entry point, such as a reminder to put on a mask.

Digital health pass plans continue to be announced by governments around the world, with Japan, Estonia, and New York State the latest to adopt QR-code based credentials. Pangea has developed a ‘Green Pass’ authentication system to prevent spoofs of Israel’s COVID vaccine credential, meanwhile.

A pair of new health passes have been launched, with Global ID and Unisys each partnering with healthcare organizations. A white paper was released on the topic as part of the Digital Document Security Online Event 2021, and Aware reminds of the importance of liveness for digital identity authentication, meanwhile.

Nomidio and Post-Quantum executives talk with Biometric Update about the importance of how data is encrypted to data security, and how data can be secured in the future against quantum computers capable of breaking today’s standard encryption algorithms. That future may arrive in less than five years.

A new research partnership to bring biometric pre-registration to airport experiences by Idiap and Facedapter has been announced, in the latest attempt to reduce touchpoints and time spent waiting for flights. India’s government is moving forward with its Digi Yatra plans, while SITA offers tips for airports and a K2 Security executive weighs in on impacts of COVID-19 on TSA checkpoints.

Former IATA Director General and CEO Alexandre de Juniac believes the digital identity benefits of the IATA Travel Pass could not only play a key role in restarting the industry, but also boost the OneID project and transform passenger experience, he tells Airlines. De Juniac talks about the timing of IATA’s transition to a new CEO, and how the pandemic has brought closer collaboration between industry stakeholders.

The deadline for Nigerians to register their NINs with their SIMs has been extended by two months by court order, as numerous people faced having their mobile service cut off. The biometrics-backed national identity number is necessary for ever more parts of life in the country, with the NIN now required for writing university entrance exams.

Coppernic Co-founder and CEO Kevin Lecuivre tells Provence Business about the company’s roots in Psion Teklogix, how far France lags behind many African countries in digitizing electoral processes, and the company’s prospects for 2021 in a French-language interview. The pandemic may have set back Coppernic’s plans to reach €20 million in turnover by the end of 2022, but the company is internationalizing; and hiring.

Simprints has now reached more than 1.2 million beneficiaries, Chief Product Officer Alexandra Grigore announced in a LinkedIn post. The non-profit has provided fingerprint biometrics to support social benefits programs in 14 countries so far.

Please let us know of any interviews, editorials or other content we should share with the biometrics and digital identity communities in the comments below or through social media.

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Evolution, rewards, and artificial intelligence



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Last week, I wrote an analysis of Reward Is Enough, a paper by scientists at DeepMind. As the title suggests, the researchers hypothesize that the right reward is all you need to create the abilities associated with intelligence, such as perception, motor functions, and language.

This is in contrast with AI systems that try to replicate specific functions of natural intelligence such as classifying images, navigating physical environments, or completing sentences.

The researchers go as far as suggesting that with well-defined reward, a complex environment, and the right reinforcement learning algorithm, we will be able to reach artificial general intelligence, the kind of problem-solving and cognitive abilities found in humans and, to a lesser degree, in animals.

The article and the paper triggered a heated debate on social media, with reactions going from full support of the idea to outright rejection. Of course, both sides make valid claims. But the truth lies somewhere in the middle. Natural evolution is proof that the reward hypothesis is scientifically valid. But implementing the pure reward approach to reach human-level intelligence has some very hefty requirements.

In this post, I’ll try to disambiguate in simple terms where the line between theory and practice stands.

Natural selection

In their paper, the DeepMind scientists present the following hypothesis: “Intelligence, and its associated abilities, can be understood as subserving the maximisation of reward by an agent acting in its environment.”

Scientific evidence supports this claim.

Humans and animals owe their intelligence to a very simple law: natural selection. I’m not an expert on the topic, but I suggest reading The Blind Watchmaker by biologist Richard Dawkins, which provides a very accessible account of how evolution has led to all forms of life and intelligence on out planet.

In a nutshell, nature gives preference to lifeforms that are better fit to survive in their environments. Those that can withstand challenges posed by the environment (weather, scarcity of food, etc.) and other lifeforms (predators, viruses, etc.) will survive, reproduce, and pass on their genes to the next generation. Those that don’t get eliminated.

According to Dawkins, “In nature, the usual selecting agent is direct, stark and simple. It is the grim reaper. Of course, the reasons for survival are anything but simple — that is why natural selection can build up animals and plants of such formidable complexity. But there is something very crude and simple about death itself. And nonrandom death is all it takes to select phenotypes, and hence the genes that they contain, in nature.”

But how do different lifeforms emerge? Every newly born organism inherits the genes of its parent(s). But unlike the digital world, copying in organic life is not an exact thing. Therefore, offspring often undergo mutations, small changes to their genes that can have a huge impact across generations. These mutations can have a simple effect, such as a small change in muscle texture or skin color. But they can also become the core for developing new organs (e.g., lungs, kidneys, eyes), or shedding old ones (e.g., tail, gills).

If these mutations help improve the chances of the organism’s survival (e.g., better camouflage or faster speed), they will be preserved and passed on to future generations, where further mutations might reinforce them. For example, the first organism that developed the ability to parse light information had an enormous advantage over all the others that didn’t, even though its ability to see was not comparable to that of animals and humans today. This advantage enabled it to better survive and reproduce. As its descendants reproduced, those whose mutations improved their sight outmatched and outlived their peers. Through thousands (or millions) of generations, these changes resulted in a complex organ such as the eye.

The simple mechanisms of mutation and natural selection has been enough to give rise to all the different lifeforms that we see on Earth, from bacteria to plants, fish, birds, amphibians, and mammals.

The same self-reinforcing mechanism has also created the brain and its associated wonders. In her book Conscience: The Origin of Moral Intuition, scientist Patricia Churchland explores how natural selection led to the development of the cortex, the main part of the brain that gives mammals the ability to learn from their environment. The evolution of the cortex has enabled mammals to develop social behavior and learn to live in herds, prides, troops, and tribes. In humans, the evolution of the cortex has given rise to complex cognitive faculties, the capacity to develop rich languages, and the ability to establish social norms.

Therefore, if you consider survival as the ultimate reward, the main hypothesis that DeepMind’s scientists make is scientifically sound. However, when it comes to implementing this rule, things get very complicated.

Reinforcement learning and artificial general intelligence

Reinforcement learning artificial intelligence

In their paper, DeepMind’s scientists make the claim that the reward hypothesis can be implemented with reinforcement learning algorithms, a branch of AI in which an agent gradually develops its behavior by interacting with its environment. A reinforcement learning agent starts by making random actions. Based on how those actions align with the goals it is trying to achieve, the agent receives rewards. Across many episodes, the agent learns to develop sequences of actions that maximize its reward in its environment.

According to the DeepMind scientists, “A sufficiently powerful and general reinforcement learning agent may ultimately give rise to intelligence and its associated abilities. In other words, if an agent can continually adjust its behaviour so as to improve its cumulative reward, then any abilities that are repeatedly demanded by its environment must ultimately be produced in the agent’s behaviour.”

In an online debate in December, computer scientist Richard Sutton, one of the paper’s co-authors, said, “Reinforcement learning is the first computational theory of intelligence… In reinforcement learning, the goal is to maximize an arbitrary reward signal.”

DeepMind has a lot of experience to prove this claim. They have already developed reinforcement learning agents that can outmatch humans in Go, chess, Atari, StarCraft, and other games. They have also developed reinforcement learning models to make progress in some of the most complex problems of science.

The scientists further wrote in their paper, “According to our hypothesis, general intelligence can instead be understood as, and implemented by, maximising a singular reward in a single, complex environment [emphasis mine].”

This is where hypothesis separates from practice. The keyword here is “complex.” The environments that DeepMind (and its quasi-rival OpenAI) have so far explored with reinforcement learning are not nearly as complex as the physical world. And they still required the financial backing and vast computational resources of very wealthy tech companies. In some cases, they still had to dumb down the environments to speed up the training of their reinforcement learning models and cut down the costs. In others, they had to redesign the reward to make sure the RL agents did not get stuck the wrong local optimum.

(It is worth noting that the scientists do acknowledge in their paper that they can’t offer “theoretical guarantee on the sample efficiency of reinforcement learning agents.”)

Now, imagine what it would take to use reinforcement learning to replicate evolution and reach human-level intelligence. First you would need a simulation of the world. But at what level would you simulate the world? My guess is that anything short of quantum scale would be inaccurate. And we don’t have a fraction of the compute power needed to create quantum-scale simulations of the world.

Let’s say we did have the compute power to create such a simulation. We could start at around 4 billion years ago, when the first lifeforms emerged. You would need to have an exact representation of the state of Earth at the time. We would need to know the initial state of the environment at the time. And we still don’t have a definite theory on that.

An alternative would be to create a shortcut and start from, say, 8 million years ago, when our monkey ancestors still lived on earth. This would cut down the time of training, but we would have a much more complex initial state to start from. At that time, there were millions of different lifeforms on Earth, and they were closely interrelated. They evolved together. Taking any of them out of the equation could have a huge impact on the course of the simulation.

Therefore, you basically have two key problems: compute power and initial state. The further you go back in time, the more compute power you’ll need to run the simulation. On the other hand, the further you move forward, the more complex your initial state will be. And evolution has created all sorts of intelligent and non-intelligent lifeforms and making sure that we could reproduce the exact steps that led to human intelligence without any guidance and only through reward is a hard bet.

Robot working in kitchen

Above: Image credit: Depositphotos

Many will say that you don’t need an exact simulation of the world and you only need to approximate the problem space in which your reinforcement learning agent wants to operate in.

For example, in their paper, the scientists mention the example of a house-cleaning robot: “In order for a kitchen robot to maximise cleanliness, it must presumably have abilities of perception (to differentiate clean and dirty utensils), knowledge (to understand utensils), motor control (to manipulate utensils), memory (to recall locations of utensils), language (to predict future mess from dialogue), and social intelligence (to encourage young children to make less mess). A behaviour that maximises cleanliness must therefore yield all these abilities in service of that singular goal.”

This statement is true, but downplays the complexities of the environment. Kitchens were created by humans. For instance, the shape of drawer handles, doorknobs, floors, cupboards, walls, tables, and everything you see in a kitchen has been optimized for the sensorimotor functions of humans. Therefore, a robot that would want to work in such an environment would need to develop sensorimotor skills that are similar to those of humans. You can create shortcuts, such as avoiding the complexities of bipedal walking or hands with fingers and joints. But then, there would be incongruencies between the robot and the humans who will be using the kitchens. Many scenarios that would be easy to handle for a human (walking over an overturned chair) would become prohibitive for the robot.

Also, other skills, such as language, would require even more similar infrastructure between the robot and the humans who would share the environment. Intelligent agents must be able to develop abstract mental models of each other to cooperate or compete in a shared environment. Language omits many important details, such as sensory experience, goals, needs. We fill in the gaps with our intuitive and conscious knowledge of our interlocutor’s mental state. We might make wrong assumptions, but those are the exceptions, not the norm.

And finally, developing a notion of “cleanliness” as a reward is very complicated because it is very tightly linked to human knowledge, life, and goals. For example, removing every piece of food from the kitchen would certainly make it cleaner, but would the humans using the kitchen be happy about it?

A robot that has been optimized for “cleanliness” would have a hard time co-existing and cooperating with living beings that have been optimized for survival.

Here, you can take shortcuts again by creating hierarchical goals, equipping the robot and its reinforcement learning models with prior knowledge, and using human feedback to steer it in the right direction. This would help a lot in making it easier for the robot to understand and interact with humans and human-designed environments. But then you would be cheating on the reward-only approach. And the mere fact that your robot agent starts with predesigned limbs and image-capturing and sound-emitting devices is itself the integration of prior knowledge.

In theory, reward only is enough for any kind of intelligence. But in practice, there’s a tradeoff between environment complexity, reward design, and agent design.

In the future, we might be able to achieve a level of computing power that will make it possible to reach general intelligence through pure reward and reinforcement learning. But for the time being, what works is hybrid approaches that involve learning and complex engineering of rewards and AI agent architectures.

Ben Dickson is a software engineer and the founder of TechTalks. He writes about technology, business, and politics.

This story originally appeared on Copyright 2021


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OceanDAO Launches 7th Round of Grants, valued at $224K, for Data Science, Developer, AI Research Projects



OceanDAO, a distributed autonomous organization supporting the Ocean Protocol, reveals that the 7th round is now open for submissions. More than $200,000 is being offered for Data Science, Developer, and AI Research projects according to a release shared with Crowdfund Insider.

During its first six months, OceanDAO has “made 49 grants to community projects,” the announcement noted while adding that more than 15M OCEAN tokens used were to vote in the funding initiative, “painting a promising picture of an autonomous future for the Ocean Protocol community.”

The announcement also mentioned that OceanDAO presents opportunities for public financing that’s open to data science and AI practitioners “interested in building and creating streams to sell and curate data.”

The release also noted:

“OceanDAO’s seventh round is now open for submissions with 400,000 OCEAN (valued at $224K USD) available and up to 32,000 OCEAN per project. Proposals are due by July 6th. The community voting period begins on July 8th. Interested parties can pitch project ideas and form teams on the OceanDAO Discord. More information on the submission process can be found on OceanDAO’s website. OceanDAO is the community funding initiative of Ocean Protocol, the data exchange protocol.”

The update pointed out that OceanDAO’s funding has managed to reach almost ½ million OCEAN tokens during the first six rounds since its launch. OceanDAO, the grants DAO to assist with funding Ocean Protocol community-curated initiatives, has reportedly made 49 allocations since December of last year, with its 7th round now taking submissions.

OceanDAO intends to expand the fast-evolving Ocean ecosystem, as “a key component in the Ocean’s near-term growth and long-term sustainability,” the release noted while adding that OceanDAO remains focused on making strategic investments in certain areas that can assist with expanding the Ocean Protocol ecosystem including: “building and improving applications or integrations to Ocean, community outreach, making data available on an Ocean-powered marketplace, building and improving Ocean core software, and improvements to the OceanDAO.”

Alex Napheys, OceanDAO Community & Growth Lead, stated:

“Our main goal is to support the long-term growth of the Ocean Protocol. The OceanDAO community is evolving monthly including some of the brightest and enthusiastic builders in the new data economy sector. The DAO aims to continually grow the [number] of projects it supports by onboarding the next wave to the OceanDAO community.”

As mentioned in the release, the community behind OceanDAO includes talented data scientists, engineers, builders, educators, and more. OceanDAO holds monthly rounds, during which teams are invited to apply for grants.

OceanDAO community regularly casts its votes for initiatives that aim to provide the best chance for growth and sustainability “based on the following criteria: return on investment towards growth and alignment with Ocean’s mission.”

Town Hall meetings are “held every week and are open to the public to discuss the status of projects and the future of the DAO,” the announcement confirmed.

OceanDAO backs initiatives across “all aforementioned categories with financial resources to meet their objectives.”

OceanDAO investments reportedly include:

  •, the project “creates a two-sided market and economy for crowdsourced data to enable long and short-term benefits of AI for everyone.”
  •, helping data scientists “to make better decisions when buying data online.”
  • Opsci Bay, an open science bay “for self-sovereign data flows from Lab to Market that is GDPR-compliant.”
  • Data Whale, a user-friendly “one-stop” solution that “helps data economy participants to understand the ecosystem and make smart staking decisions.”
  • ResilientML, will bring a vast collection of data sets “curated by experts in NLP for utilization directly in machine learning methods and sentiment models running in the Ocean environment and available through the Ocean marketplace.”

As noted in the release:

“As the projects drive traction in the Ocean ecosystem, it grows network fees and improves fundamentals for OCEAN, which in turn increases funds to OceanDAO available for future investments. This “snowball effect” is a core mechanism of the Web3 Sustainability Loop developed by Ocean Protocol Founder Trent McConaghy, in which both Network Revenue and Network Rewards are directed to work that is used for growth.”

Network Rewards help “to kickstart the project and to ensure funding. Network Revenue can help to push growth further once the Web3 project achieves traction at scale,” the announcement noted.

You may access the list of initiatives supported since OceanDAO’s launch here. OceanDAO has reportedly seen more than 60 proposals since December of last year, and all project proposals are publicly available to view online.

As previously reported, Ocean Protocol’s mission is to support a new Data Economy that “reaches the world, giving power back to data owners and enabling people to capture value from data to better our world.”

According to Ocean Protocol developers, data is like “a new asset class; Ocean Protocol unlocks its value.” Data owners and consumers use the Ocean Market app “to publish, discover, and consume data assets in a secure, privacy-preserving fashion.”

Ocean datatokens “turn data into data assets” and this enables data wallets, data exchanges, and data co-ops by “leveraging crypto wallets, exchanges, and other DeFi tools.” Projects use Ocean libraries and OCEAN in their own apps “to help drive the new Data Economy.”

The OCEAN token is used “to stake on data, govern Ocean Protocol’s community funding, and buy & sell data,” the announcement explained while confirming that its supply is “disbursed over time to drive near-term growth and long-term sustainability.” OCEAN has been designed “to increase with a rise in usage volume.”

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Artificial Intelligence

AI Fraud Protection Firm Servicing Digital Goods Raises $6.8 Million Seed Round



Israel-based has raised a $6.8 million Seed round led by DisruptiveAI, Phoenix Insurance, Kamet (an AXA backed VC), Moneta Seeds and other individual investors. is a “predictive AI fraud protection company” that services digital goods such as gift cards, prepaid debit cards, software and game keys, digital wallet transfers, international money transfers, tickets, and more. The company explains that sellers of physical goods have processing times that allow them to double-check charges and can withhold a shipment if needed. Digital sellers lack this buffer, so even if fraud is detected minutes later, the assailant may be untraceable. is bringing anti-fraud technological and chargeback guarantees to the digital goods sector.

“We are thrilled that our investors have placed their trust in our leadership and confidence in,” says Alex Zeltcer, co-founder and CEO. “This investment enables us to register thousands of new merchants, who can feel confident selling higher-risk digital goods, without accepting fraud as a part of business.”

The founders of, Zeltcer and Ziv Isaiah say they experienced first-hand the unique challenges faced by retailers of digital assets. During the first week of operating their online gift card business, 40% of sales were fraudulent, resulting in chargebacks.’s 98% approval rate offers a more accurate fraud-detection strategy, allowing retailers to recapture nearly $100 billion a year in revenue lost by declining legitimate customers, according to Zeltcer.

Gadi Tirosh, Venture Partner at Disruptive AI, says they believe fraud, especially in the field of digital goods, can only be fought with top-of-the-line AI technologies.

“ has both the technology and industry understanding to win this market.”

The funding is expected to be used to further develop’s predictive AI and machine learning algorithms. solution currently monitors and manages millions of transactions every month, and has approved close to $1B in volume since going live.

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