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Art app SketchAR to allow artists to list their artworks on NFT marketplaces directly

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An update to an existing art app that allows artists to access NFT marketplaces directly, could have the potential to democratize access to the NFT world for artists not currently in crypto. SketchAR, is an existing mobile app that allows artists to turn photos into illustrations using its AI-based computer vision. It is now is launching a new feature that allows users to turn their art into NFTs directly inside the app, and then sell it. Content produced on the app can also include a public community feed and digital learning courses.

The app, which boasts it has almost a million users already, will start off selecting a single ‘Creator of the Week’ from its community for their art to be NFT’d on the OpenSea marketplace. But a new feature will shortly enable any artist using the platform to create and auction an NFT on-demand.

However, there a catch. The artwork will have to be created directly in the SketchAR app in order to prove the artist is the legitimate rights holder, says the startup. This is, however, an advantage, says the startup, since very few marketplaces monitor the derivation and authenticity of artworks uploaded proactively.

And for now, it looks like there is no real equivalent app on the market, although there are of course plenty of ways to create an artwork and then upload it to an NFT marketplace in a separate process.

Andrey Drobitko, CEO and founder told me: “It’s a unique offer since it allows even amateur designers to create an art piece and turn it into an NFT without diving deep into the ecosystem, connecting their wallet to their OpenSea account and paying significant gas fees to minting.”

He said: “Since the art piece comes from the app SketchAR, it also ensures it’s authentic and wasn’t stolen – something that happened quite a few times with NFTs.”

SketchAR said it also built its own infrastructure that allows it to use Ethereum and other 2-layer solutions like Flow, Immutable, or Binance Smart Chain, to reduce costs.

“Basically it competes with artists and designers learning a lot about blockchain, how to work with it, and working directly with marketplaces like OpenSea or Rarible” added Drobitko.

“15 years ago I realized I couldn’t make much money as an artist and only continued to make art for pleasure. It’s different now and we’re excited to support artists, help them develop creative skills, and successfully monetize their artworks,” he said.

It’s estimated there are roughly 50 million artists globally, but fewer than 10% are able to make it their primary source of income.

According to NonFungible.com more than $2 billion was spent on NFTs during the first quarter of 2021 representing an increase of about 2,100% from Q4 2020.

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Source: https://techcrunch.com/2021/06/08/art-app-sketchar-to-allow-artists-to-list-their-artworks-on-nft-marketplaces-directly/

AI

A Brief Intro to the GPT-3 Algorithm

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Generative Pre-trained Transformer 3 (GPT-3) embraces and augments the GPT-2 model architecture, including pre-normalization, modified initialization, and reversible tokenization. It exhibits strong performance on many Natural Language Processing (NLP) tasks.

GPT-3 is an auto-regressive artificial intelligence algorithm developed by OpenAI, an AI-powered research laboratory located in San Francisco, California.

It is a massive artificial neural network that takes help from deep learning to generate human-like text and is trained on huge text datasets with thousands of billions of words. It is the third-generation AI language prediction model in the GPT-n series and the successor to GPT-2.

In simple words, OpenAI GPT-3 was fed inputs the ways how billions of people write and also was taught how to pick up on writing patterns based on user entry. Once few inputs are offered, the model will generate intelligent text following the submitted pattern and structure. It is also the largest AI language algorithm that produces billions of words a day.

GPT-3 working process

This artificial intelligence algorithm is a program that can calculate the word or even the character which must appear in a text given in relation to the words around it. This is called the conditional probability of words. It is a generative neural network that allows out a numeric score or a yes or no answer. It also generates long sequences of the original text as its output.

The total number of weights the OpenAI GPT-3 dynamically holds in its memory and utilizes to process every query is 175 billion.

Examples

•noun + verb = subject + verb
• noun + verb + adjective = subject + verb + adjective
• verb + noun = subject + verb
• noun + verb + noun = subject + verb + noun
• noun + noun = subject + noun
• noun + verb + noun + noun = subject + verb + noun + noun

The stream of algorithmic content in GPT-3

Every month over 409 million people view more than 20 billion pages, and users publish around 70 million posts on WordPress, which is the dominant content management system online.

The main specialty of OpenAI GPT-3 is the capacity to respond intelligently to minimal input. It is extensively trained on billions of parameters and produces up to 50,000 characters without any supervision. This one-of-a-kind AI neural network generates texts at an amazing quality, making it quite tough for a normal human to understand whether the output was written by GPT-3 or a human.

Training of the GPT-3

The training of the GPT-3 artificial intelligence algorithm has two steps.

Step – 1: It needs to create the vocabulary, production rules, and the various categories. It can be achieved by offering inputs in the form of books. For each word, the model predicts the category to that the word belongs, and afterward, a production rule should be built.

• Step – 2: The development of the vocabulary and production rules for each category takes place. This can be achieved by offering the inputs to the model with sentences. For every sentence, the model will be predicting the category to which each word belongs, and after that, a production rule should be built.

The model consists of a few tricks that allow it a provision to boost its capability to generate texts. For example, it can guess the inception of a word by understanding the context of the word. It also predicts the next word depending on the last word of a sentence. It can also predict the length of a sentence.

Conclusion

There’s a lot of hype for the GPT-3 AI algorithm right now. One can say that in the future, it will be offering more beyond the text that includes pictures, videos, and many more. Many researchers also predicted that GPT-3 would possess the capability to translate words to pictures and pictures to words.

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How AI Is Catapulting Cannabis into the Future

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John Kaweske Hacker Noon profile picture

@johnkaweskeJohn Kaweske

John Kaweske is Founder & CEO of North Star Holdings, Inc. and Tweedleaf.

We like to think we know a thing or two about artificial intelligence. We’ve seen the ominous technological future depicted in television shows and films of robots slowly amalgamating into society. But this imagery is all wrong. AI isn’t taking over the world in the form of lifelike robots. Instead, automation has been in our lives for quite some time now, and many of us are likely not even aware of it.

Driverless cars. Voice-activated home assistants. Smartphones. These are all made possible because of artificial intelligence. It’s not only changed our lives in unimaginable ways, but it’s also allowed us to collect, analyze, and interpret data that can help us better understand the world around us. And for business owners, that’s critical not only for our organizational efficacy and profitability, but it allows us to see our customers through an entirely new lens.

It’s no surprise that many industries are already taking advantage of artificial intelligence in their practices — and now, so is the cannabis industry. The legal cannabis market is predicted to reach over $66 billion by 2025. To prepare for this growth, leaders in the cannabis sector must exploit AI’s transformative powers or risk falling behind the competition. 

So, what exactly are some of the transformative powers artificial intelligence holds in the cannabis industry? Let’s find out.

Enhanced cultivation capabilities

Have you ever enjoyed the benefits of smart home technology? If you have, you know that smart lights and smart thermostats allow you to control your home’s lighting and temperature from anywhere in the world. Growers can now enjoy these same benefits. By using artificial intelligence, we are better able to manage our crops, which can generate higher yields at lower prices. This is what we do at Tweedleaf.

We use AI to adjust the pH level and moisture levels of our soil. We also use AI to help monitor and control lighting exposure to ensure our plants receive the appropriate level of photosynthesis. We even use it for pest control. By giving growers real-time updates, AI eliminates the ‘guessing game’ of cultivation. Is the growth rate slower than usual? Are nutrient levels low? Is there a pest infestation? Artificial intelligence alerts growers to any issues, so they know exactly what to fix and how to fix it.

All of this is pretty miraculous when you think about it. AI allows growers to create the perfect environment for plants rather than leaving it up to chance. Growers shouldn’t have to hope for the best; they can make this ‘best’ their reality.

But one of the biggest advantages of artificial intelligence is that it makes it possible for growers to create and breed new, customized strains. As the legal marijuana market continues to expand, access to a variety of strains ensures that all consumers can benefit from the remarkable and healing powers of cannabis. And once growers perfect their new strains, they can then use AI to lock in the correct watering, lighting, and temperature schedules that will aid in the cultivation and production of a diverse range of plants.

Recommendation apps

For many of us, our smartphones were our earliest introductions to artificial intelligence. We’ve become so accustomed to their convenience that we can’t imagine our lives without them. This is exactly why they were created — to make our lives better.

Think about the last time you shopped online. As you were shopping, the store probably sent you some ‘recommended’ items to view. Were they scarily accurate? This wasn’t by mistake. By using AI, brands can analyze your preferences and interests and pull items from their store they also think you’d like. While this may seem a bit eerie at first, we’ve eventually come to love these recommendations. Instead of spending hours browsing a site, the brands are doing the heavy lifting for us and pulling the products we’ll have the most interest in.

The cannabis industry can take advantage of these same benefits. Certain apps like Uppy can help medical marijuana users traverse the world of legal cannabis. What experience do you hope to get from a cannabis product? Do you use it to alleviate a physical injury? Do you need it to help you sleep better at night? Are you a creative person and want to gain some inspiration? Recommendation apps can pull information about you and use it to offer insight into new strains you might like to try.

This sort of capability is so invaluable because it enriches our experience and can transform our lives. 

Better customer experience/service

While artificial intelligence has many benefits for companies, its central mission is improving the customer experience. 

If you’ve ever browsed a marijuana company’s website and interacted with a chatbot, there’s a good chance you spoke with a digital budtender that was powered by AI. And chances are you probably didn’t even realize you weren’t talking to a real person.

This is how powerful artificial intelligence has become. And now, over half of people would rather speak with a chatbot than a human because it saves time, and these chatbots can often be more knowledgeable. 

Consumers — especially new cardholders — often have a lot of questions about different products at the onset yet are too intimidated and embarrassed to walk into a dispensary and approach an employee for answers. Digital budtenders can help walk people through these questions online while also providing them with insight into the cannabis products that will best fit their needs. As customers feel more comfortable, they will begin interacting with your company more and more, which will result in a higher number of sales. 

Additionally, brands can also take advantage of tools like QR codes to have this same impact in-store. By placing a QR code on your packaging, a dispensary can equip customers with all of the information they should know about a product: potency, expected effects, user reviews, lab testing, etc. And because they don’t have to approach a person with all their questions, this could also lead to more sales.

Artificial intelligence isn’t just a revolutionary technology; it’s the future of business. Companies that implement this transformative technology do so not only to the benefit of their organizations but also to their customers. 

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Artificial Intelligence

Extra Crunch roundup: influencer marketing 101, spotting future unicorns, Apple AirTags teardown

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With the right message, even a small startup can connect with established and emerging stars on TikTok, Instagram and YouTube who will promote your products and services — as long as your marketing team understands the influencer marketplace.

Creators have a wide variety of brands and revenue channels to choose from, but marketers who understand how to court these influencers can make inroads no matter the size of their budget. Although brand partnerships are still the top source of revenue for creators, many are starting to diversify.

If you’re in charge of marketing at an early-stage startup, this post explains how to connect with an influencer who authentically resonates with your brand and covers the basics of setting up a revenue-share structure that works for everyone.


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Use discount code ECFriday to save 20% off a one- or two-year subscription


Our upcoming TC Early Stage event is devoted to marketing and fundraising, so expect to see more articles than usual about growth marketing in the near future.

We also ran a post this week with tips for making the first marketing hire, and Managing Editor Eric Eldon spoke to growth leader Susan Su to get her thoughts about building remote marketing teams.

We’re off today to celebrate the Juneteenth holiday in the United States. I hope you have a safe and relaxing weekend.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist

As the economy reopens, startups are uniquely positioned to recruit talent

Little Fish in Form of Big Fish meeting a fish.

Image Credits: ballyscanlon (opens in a new window) / Getty Images

The pandemic forced a reckoning about the way we work — and whether we want to keep working in the same way, with the same people, for the same company — and many are looking for something different on the other side.

Art Zeile, the CEO of DHI Group, notes this means it’s a great time for startups to recruit talent.

“While all startups are certainly not focused on being disruptive, they often rely on cutting-edge technology and processes to give their customers something truly new,” Zeile writes. “Many are trying to change the pattern in their particular industry. So, by definition, they generally have a really interesting mission or purpose that may be more appealing to tech professionals.”

Here are four considerations for high-growth company founders building their post-pandemic team.

Refraction AI’s Matthew Johnson-Roberson on finding the middle path to robotic delivery

Matthew Johnson-roberson

Image Credits: Bryce Durbin

“Refraction AI calls itself the Goldilocks of robotic delivery,” Rebecca Bellan writes. “The Ann Arbor-based company … was founded by two University of Michigan professors who think delivery via full-size autonomous vehicles (AV) is not nearly as close as many promise, and sidewalk delivery comes with too many hassles and not enough payoff.

“Their ‘just right’ solution? Find a middle path, or rather, a bike path.”

Rebecca sat down with the company’s CEO to discuss his motivation to make “something that is useful to the general public.”

How to identify unicorn founders when they’re still early-stage

Image Credits: RichVintage (opens in a new window)/ Getty Images

What are investors looking for?

Founders often tie themselves in knots as they try to project qualities they hope investors are seeking. In reality, few entrepreneurs have the acting skills required to convince someone that they’re patient, dedicated or hard working.

Johan Brenner, general partner at Creandum, was an early backer of Klarna, Spotify and several other European startups. Over the last two decades, he’s identified five key traits shared by people who create billion-dollar companies.

“A true unicorn founder doesn’t need to have all of those capabilities on day one,” Brenner, writes “but they should already be thinking big while executing small and demonstrating that they understand how to scale a company.”

Founders Ben Schippers and Evette Ellis are riding the EV sales wave

disrupt mobility roundup

Image Credits: TechCrunch

EV sales are driving demand for services and startups that fulfill the new needs of drivers, charging station operators and others.
Evette Ellis and Ben Schippers took to the main stage at TC Sessions: Mobility 2021 to share how their companies capitalized on the new opportunities presented by the electric transportation revolution.

Scale AI CEO Alex Wang weighs in on software bugs and what will make AV tech good enough

Image Credits: Alexandr Wang

Scale co-founder and CEO Alex Wang joined us at TechCrunch Sessions: Mobility 2021 to discuss his company’s role in the autonomous driving industry and how it’s changed in the five years since its founding.

Scale helps large and small AV players establish reliable “ground truth” through data annotation and management, and along the way, the standards for what that means have shifted as the industry matures.

Even if two algorithms in autonomous driving might be created more or less equal, their real-world performance could vary dramatically based on what they’re consuming in terms of input data. That’s where Scale’s value prop to the industry starts, and Wang explains why.

Edtech investors are flocking to SaaS guidance counselors

Image Credits: Getty Images / Vertigo3d

The prevailing post-pandemic edtech narrative, which predicted higher ed would be DOA as soon as everyone got their vaccine and took off for a gap year, might not be quite true.

Natasha Mascarenhas explores a new crop of edtech SaaS startups that function like guidance counselors, helping students with everything from study-abroad opportunities to swiping right on a captivating college (really!).

“Startups that help students navigate institutional bureaucracy so they can get more value out of their educational experience may become a growing focus for investors as consumer demand for virtual personalized learning increases,” she writes.

Dear Sophie: Is it possible to expand our startup in the US?

lone figure at entrance to maze hedge that has an American flag at the center

Image Credits: Bryce Durbin/TechCrunch

Dear Sophie,

My co-founders and I launched a software startup in Iran a few years ago, and I’m happy to say it’s now thriving. We’d like to expand our company in California.

Now that President Joe Biden has eliminated the Muslim ban, is it possible to do that? Is the pandemic still standing in the way? Do you have any suggestions?

— Talented in Tehran

Companies should utilize real-time compensation data to ensure equal pay

Two women observing data to represent collecting data to ensure pay equity.

Image Credits: Rudzhan Nagiev (opens in a new window) / Getty Images

Chris Jackson, the vice president of client development at CompTrak, writes in a guest column that having a conversation about diversity, equity and inclusion initiatives and “agreeing on the need for equality doesn’t mean it will be achieved on an organizational scale.”

He lays out a data-driven proposal that brings in everyone from directors to HR to the talent acquisition team to get companies closer to actual equity — not just talking about it.

Investors Clara Brenner, Quin Garcia and Rachel Holt on SPACs, micromobility and how COVID-19 shaped VC

tc sessions mobility speaker_investorpanel-1

Image Credits: TechCrunch

Few people are more closely tapped into the innovations in the transportation space than investors.

They’re paying close attention to what startups and tech companies are doing to develop and commercialize autonomous vehicle technology, electrification, micromobility, robotics and so much more.

For TC Sessions: Mobility 2021, we talked to three VCs about everything from the pandemic to the most overlooked opportunities within the transportation space.

Experts from Ford, Toyota and Hyundai outline why automakers are pouring money into robotics

disrupt mobility roundup

Image Credits: TechCrunch

Automakers’ interest in robotics is not a new phenomenon, of course: Robots and automation have long played a role in manufacturing and are both clearly central to their push into AVs.

But recently, many companies are going even deeper into the field, with plans to be involved in the wide spectrum of categories that robotics touch.

At TC Sessions: Mobility 2021, we spoke to a trio of experts at three major automakers about their companies’ unique approaches to robotics.

Apple AirTags UX teardown: The trade-off between privacy and user experience

Image Credits: James D. Morgan/Getty Images

Apple’s location devices — called AirTags — have been out for more than a month now. The initial impressions were good, but as we concluded back in April: “It will be interesting to see these play out once AirTags are out getting lost in the wild.”

That’s exactly what our resident UX analyst, Peter Ramsey, has been doing for the last month — intentionally losing AirTags to test their user experience at the limits.

This Extra Crunch exclusive helps bridge the gap between Apple’s mistakes and how you can make meaningful changes to your product’s UX.

How to launch a successful RPA initiative

3D illustration of robot humanoid reading book in concept of future artificial intelligence and 4th fourth industrial revolution . (3D illustration of robot humanoid reading book in concept of future artificial intelligence and 4th fourth industrial r

Image Credits: NanoStockk (opens in a new window) / Getty Images

Robotic process automation (RPA) is no longer in the early-adopter phase.

Though it requires buy-in from across the organization, contributor Kevin Buckley writes, it’s time to gather everyone around and get to work.

“Automating just basic workflow processes has resulted in such tremendous efficiency improvements and cost savings that businesses are adapting automation at scale and across the enterprise,” he writes.

Long story short: “Adapting business automation for the enterprise should be approached as a business solution that happens to require some technical support.”

Mobility startups can be equitable, accessible and profitable

tc sessions

Image Credits: TechCrunch

Mobility should be a right, but too often it’s a privilege. Can startups provide the technology and the systems necessary to help correct this injustice?

At  our TC Sessions: Mobility 2021 event, we sat down with Revel CEO and co-founder Frank Reig, Remix CEO and co-founder Tiffany Chu, and community organizer, transportation consultant and lawyer Tamika L. Butler to discuss how mobility companies should think about equity, why incorporating it from the get-go will save money in the long run, and how they can partner with cities to expand accessible and sustainable mobility.

CEO Shishir Mehrotra and investor S. Somasegar reveal what sings in Coda’s pitch doc

Image Credits: Carlin Ma / Madrona Venture Group/Brian Smale

Coda CEO Shishir Mehrotra and Madrona partner S. Somasegar joined Extra Crunch Live to go through Coda’s pitch doc (not deck. Doc) and stuck around for the ECL Pitch-off, where founders in the audience come “onstage” to pitch their products to our guests.

Extra Crunch Live takes place every Wednesday at 3 p.m. EDT/noon PDT. Anyone can hang out during the episode (which includes networking with other attendees), but access to past episodes is reserved exclusively for Extra Crunch members. Join here.

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UK’s ICO warns over ‘big data’ surveillance threat of live facial recognition in public

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The UK’s chief data protection regulator has warned over reckless and inappropriate use of live facial recognition (LFR) in public places.

Publishing an opinion today on the use of this biometric surveillance in public — to set out what is dubbed as the “rules of engagement” — the information commissioner, Elizabeth Denham, also noted that a number of investigations already undertaken by her office into planned applications of the tech have found problems in all cases.

“I am deeply concerned about the potential for live facial recognition (LFR) technology to be used inappropriately, excessively or even recklessly. When sensitive personal data is collected on a mass scale without people’s knowledge, choice or control, the impacts could be significant,” she warned in a blog post.

“Uses we’ve seen included addressing public safety concerns and creating biometric profiles to target people with personalised advertising.

“It is telling that none of the organisations involved in our completed investigations were able to fully justify the processing and, of those systems that went live, none were fully compliant with the requirements of data protection law. All of the organisations chose to stop, or not proceed with, the use of LFR.”

“Unlike CCTV, LFR and its algorithms can automatically identify who you are and infer sensitive details about you. It can be used to instantly profile you to serve up personalised adverts or match your image against known shoplifters as you do your weekly grocery shop,” Denham added.

“In future, there’s the potential to overlay CCTV cameras with LFR, and even to combine it with social media data or other ‘big data’ systems — LFR is supercharged CCTV.”

The use of biometric technologies to identify individuals remotely sparks major human rights concerns, including around privacy and the risk of discrimination.

Across Europe there are campaigns — such as Reclaim your Face — calling for a ban on biometric mass surveillance.

In another targeted action, back in May, Privacy International and others filed legal challenges at the controversial US facial recognition company, Clearview AI, seeking to stop it from operating in Europe altogether. (Some regional police forces have been tapping in — including in Sweden where the force was fined by the national DPA earlier this year for unlawful use of the tech.)

But while there’s major public opposition to biometric surveillance in Europe, the region’s lawmakers have so far — at best — been fiddling around the edges of the controversial issue.

A pan-EU regulation the European Commission presented in April, which proposes a risk-based framework for applications of artificial intelligence, included only a partial prohibition on law enforcement’s use of biometric surveillance in public places — with wide ranging exemptions that have drawn plenty of criticism.

There have also been calls for a total ban on the use of technologies like live facial recognition in public from MEPs across the political spectrum. The EU’s chief data protection supervisor has also urged lawmakers to at least temporarily ban the use of biometric surveillance in public.

The EU’s planned AI Regulation won’t apply in the UK, in any case, as the country is now outside the bloc. And it remains to be seen whether the UK government will seek to weaken the national data protection regime.

A recent report it commissioned to examine how the UK could revise its regulatory regime, post-Brexit, has — for example — suggested replacing the UK GDPR with a new “UK framework” — proposing changes to “free up data for innovation and in the public interest”, as it puts it, and advocating for revisions for AI and “growth sectors”. So whether the UK’s data protection regime will be put to the torch in a post-Brexit bonfire of ‘red tape’ is a key concern for rights watchers.

(The Taskforce on Innovation, Growth and Regulatory Reform report advocates, for example, for the complete removal of Article 22 of the GDPR — which gives people rights not to be subject to decisions based solely on automated processing — suggesting it be replaced with “a focus” on “whether automated profiling meets a legitimate or public interest test”, with guidance on that envisaged as coming from the Information Commissioner’s Office (ICO). But it should also be noted that the government is in the process of hiring Denham’s successor; and the digital minister has said he wants her replacement to take “a bold new approach” that “no longer sees data as a threat, but as the great opportunity of our time”. So, er, bye-bye fairness, accountability and transparency then?)

For now, those seeking to implement LFR in the UK must comply with provisions in the UK’s Data Protection Act 2018 and the UK General Data Protection Regulation (aka, its implementation of the EU GDPR which was transposed into national law before Brexit), per the ICO opinion, including data protection principles set out in UK GDPR Article 5, including lawfulness, fairness, transparency, purpose limitation, data minimisation, storage limitation, security and accountability.

Controllers must also enable individuals to exercise their rights, the opinion also said.

“Organisations will need to demonstrate high standards of governance and accountability from the outset, including being able to justify that the use of LFR is fair, necessary and proportionate in each specific context in which it is deployed. They need to demonstrate that less intrusive techniques won’t work,” wrote Denham. “These are important standards that require robust assessment.

“Organisations will also need to understand and assess the risks of using a potentially intrusive technology and its impact on people’s privacy and their lives. For example, how issues around accuracy and bias could lead to misidentification and the damage or detriment that comes with that.”

The timing of the publication of the ICO’s opinion on LFR is interesting in light of wider concerns about the direction of UK travel on data protection and privacy.

If, for example, the government intends to recruit a new, ‘more pliant’ information commissioner — who will happily rip up the rulebook on data protection and AI, including in areas like biometric surveillance — it will at least be rather awkward for them to do so with an opinion from the prior commissioner on the public record that details the dangers of reckless and inappropriate use of LFR.

Certainly, the next information commissioner won’t be able to say they weren’t given clear warning that biometric data is particularly sensitive — and can be used to estimate or infer other characteristics, such as their age, sex, gender or ethnicity.

Or that ‘Great British’ courts have previously concluded that “like fingerprints and DNA [a facial biometric template] is information of an ‘intrinsically private’ character”, as the ICO opinion notes, while underlining that LFR can cause this super sensitive data to be harvested without the person in question even being aware it’s happening. 

Denham’s opinion also hammers hard on the point about the need for public trust and confidence for any technology to succeed, warning that: “The public must have confidence that its use is lawful, fair, transparent and meets the other standards set out in data protection legislation.”

The ICO has previously published an Opinion into the use of LFR by police forces — which she said also sets “a high threshold for its use”. (And a few UK police forces — including the Met in London — have been among the early adopters of facial recognition technology, which has in turn led some into legal hot water on issues like bias.)

Disappointingly, though, for human rights advocates, the ICO opinion shies away from recommending a total ban on the use of biometric surveillance in public by private companies or public organizations — with the commissioner arguing that while there are risks with use of the technology there could also be instances where it has high utility (such as in the search for a missing child).

“It is not my role to endorse or ban a technology but, while this technology is developing and not widely deployed, we have an opportunity to ensure it does not expand without due regard for data protection,” she wrote, saying instead that in her view “data protection and people’s privacy must be at the heart of any decisions to deploy LFR”.

Denham added that (current) UK law “sets a high bar to justify the use of LFR and its algorithms in places where we shop, socialise or gather”.

“With any new technology, building public trust and confidence in the way people’s information is used is crucial so the benefits derived from the technology can be fully realised,” she reiterated, noting how a lack of trust in the US has led to some cities banning the use of LFR in certain contexts and led to some companies pausing services until rules are clearer.

“Without trust, the benefits the technology may offer are lost,” she also warned.

There is one red line that the UK government may be forgetting in its unseemly haste to (potentially) gut the UK’s data protection regime in the name of specious ‘innovation’. Because if it tries to, er, ‘liberate’ national data protection rules from core EU principles (of lawfulness, fairness, proportionality, transparency, accountability and so on) — it risks falling out of regulatory alignment with the EU, which would then force the European Commission to tear up a EU-UK data adequacy arrangement (on which the ink is still drying).

The UK having a data adequacy agreement from the EU is dependent on the UK having essentially equivalent protections for people’s data. Without this coveted data adequacy status UK companies will immediately face far greater legal hurdles to processing the data of EU citizens (as the US now does, in the wake of the demise of Safe Harbor and Privacy Shield). There could even be situations where EU data protection agencies order EU-UK data flows to be suspended altogether…

Obviously such a scenario would be terrible for UK business and ‘innovation’ — even before you consider the wider issue of public trust in technologies and whether the Great British public itself wants to have its privacy rights torched.

Given all this, you really have to wonder whether anyone inside the UK government has thought this ‘regulatory reform’ stuff through. For now, the ICO is at least still capable of thinking for them.

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Source: https://techcrunch.com/2021/06/18/uks-ico-warns-over-big-data-surveillance-threat-of-live-facial-recognition-in-public/

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