CHICAGO (WLS) -- Have you ever wondered what your future kids will look like?

Well, there's an app for that. It's called "Remini." The app uses artificial intelligence to generate photos of what your children could look like, but one cyber security expert is voicing concerns as Remini takes social media by storm. Daisy Reyes is a self-proclaimed TikTok influencer with nearly 500,000 followers.

"I'm always looking at what's the upcoming trend, the new trends. I've seen everybody, literally everybody doing this trend. So you already know, I had to hop on it!" Reyes said.

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Reyes said she recently downloaded the Remini app, which allows you to see what your future children could look like. She uploaded a picture of herself and her boyfriend, Rex, and boom! An image of her future baby was generated by artificial intelligence.

"When she showed it to me, I was kind of stunned. I thought it did kind of look like me," said Rex Flores.

"I felt like it was a mixture, but definitely more like him," Reyes added.

Melissa McDuffie, who has nearly 200,000 followers on TikTok, said she also tried the Remini app.

"I really like this trend, because I'm at the age where I'm married, and I'm ready to start having children. I thought it would be interesting to go ahead and see what they might look like," McDuffie said. "Artificial intelligence, the AI, can take your pictures and formulate this image, and it's so realistic. I'm an aunt to eight children, and they look very similar to my nieces. And, it's exciting. They're really cute!"

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But, as fun as the app may be, cyber security expert David Barton said users should be cautious.

"The scary thing about this, Samantha, is how accurate it has been. I've seen video clips of folks who have taken the father and the mother, put them in the app and it kicks off a picture that looks like their kid. And, that's a little bit creepy. But, on the flip side, it's kind of cool. It's kind of novel," Barton said.

Barton said users need to read the terms and conditions of these types of apps to understand how your image and likeness could be used. It's also important to know what protections are in place to ensure the artificial intelligence isn't exploited by a third party.

"Are we unintentionally giving future pictures of our kids for folks who might be using it for malicious purposes? I don't know," Barton said. "My gut says, I wouldn't do it. But, I'm a little bit older and more conservative than a lot of people. If you're going to do it, understand there are risks. At the end of the day, we AI-manage our lives by the risks we deal with day in and day out."

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The social media stars say they understand any potential risk, but believe the Remini app has brought joy to many of their followers who also use it.

"We post pictures on social media every day, let alone the new AI generator with this app. So, I feel like you take that risk and agree to be on social media," McDuffie said. "For myself, I want to be a mother so bad, I could cry. More than anything, I want to be a mother. So, to be able to see an image like that made me happy."

In a statement, the parent company of Remini, which is based out of Milan, Italy, told the I-Team:

"Remini gives users the ability to imagine their lives in many different ways, with stunning realism, and we care deeply about ensuring all our users have a safe and fun experience using our app. By its very nature, the app is constantly evolving, and we will continue to take action to apply safeguards and ensure user privacy...We take data protection and privacy very seriously and have robust protocols in place to ensure we safeguard user rights while allowing them to experience and enjoy the transformative power of generative AI."

McDuffie said the app has intensified her baby fever.

"It made me hopeful for the future, and excited," McDuffie said. "Because they were cute and beautiful. They were beautiful."

Bending Spoons, the company that owns Remini, told the I-Team that facial recognition is not used in the app, and that images are encrypted and stored with a reputable U.S.-based provider, using what they say are "state-of-the-art security standards." The company said users always retain control over their data, and that it does not sell, lease, or trade users' images to any third parties.

The company said it applies comprehensive safeguards to thwart misuse of content.

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Artificial intelligence: Is Remini safe to use? Remini, baby AI generator takes TikTok by storm as some raise security concerns - WLS-TV

The EU Artificial Intelligence Act (or AI Act) is the worlds first legislation to regulate the use of AI. It leaves room for technical soft law; but, inevitably (being the first and being broad in scope), it will set principles and standards for AI development and governance. The UK is concentrating more on soft law, working towards a decentralized principle-based approach. The US and China are working on their own AI regulations, with the US focusing more on soft law, privacy, and ethics and China on explainable AI algorithms, aiming for companies to be transparent about their purpose. The AI Act marks a crucial step in regulating AI in Europe, and a global code of conduct on AI could harmonize practices worldwide, ensuring safe and ethical AI use. This article gives an overview of the EU Act, its main aspects as well as an overview of other AI legislative initiatives in the European Union and how these are influencing other jurisdictions, such us the UK, the US and China.

The AI Act: The First AI Legislation. Other Jurisdictions Are Catching Up.

On June 14, 2023, the European Parliament achieved a significant milestone by approving the Artificial Intelligence Act (or AI Act), making it the worlds first piece of legislation to regulate the use of artificial intelligence. This approval has initiated negotiations with the Council of the European Union which will determine the final wording of the Act. The final version of the AI Act is expected to be published by the end of 2023. Following this, the Regulation is expected to be fully effective in 2026. A two-year grace period similar to the one contemplated by the GDPR is currently being considered. This grace period would enable companies to adapt gradually and prepare for the changes until the rules come into force.

As the pioneers in regulating AI, the European institutions are actively engaged in discussions that are likely to establish both de facto (essential for the expansion and growth of AI businesses, just like any other industries) and de jure (creating healthy competition among jurisdictions) standards worldwide. These discussions aim to shape the development and governance of artificial intelligence, setting an influential precedent for the global AI community.

Both the United States and China are making efforts to catch up. In October 2022, the US government unveiled its Blueprint for an AI Bill of Rights, centered around privacy standards and rigorous testing before AI systems become publicly available. In April 2022, China followed a similar path by presenting a draft of rules mandating chatbot-makers to comply with state censorship laws.

The UK government, has unveiled an AI white paper to provide guidance on utilizing artificial intelligence in the UK. The objective is to encourage responsible innovation while upholding public confidence in this transformative technology.

While the passage of the Artificial Intelligence Act by the European Parliament represents an important step forward in regulating AI in Europe (and indirectly beyond, given the extraterritorial reach), the implementation of a global code of conduct on AI is also under development by the United Nations and is intended to play a crucial role in harmonizing global business practices concerning AI systems, ensuring their safe, ethical, and transparent use.

A Risk-Based Regulation

The European regulatory approach is based on assessing the risks associated with each use of artificial intelligence.

Complete bans are contemplated for intrusive and discriminatory uses that pose unacceptable risk to citizens fundamental rights, their health, safety, or other matters of public interest. Examples of artificial intelligence applications considered to carry unacceptable risks include cognitive behavioral manipulation targeting specific categories of vulnerable people or groups, such as talking toys for children, and social scoring, which involves ranking of people based on their behavior or characteristics. The approved draft regulation significantly expands the list of prohibitions on intrusive and discriminatory uses of AI. These prohibitions now include:

In contrast, those uses that need to be regulated (as opposed to simply banned) through data governance, risk management assessment, technical documentation, and criteria for transparency, are:

High-Risk AI systems are artificial intelligence systems that may adversely affect security or fundamental rights. They are divided into two categories:

(i) biometric identification and categorization of natural persons;(ii) management and operation of critical infrastructure;(iii) education and vocational training;(iv) employment, worker management and access to self-employment;(v) access to and use of essential private and public services and benefits;(vi) law enforcement;(vii) migration management, asylum, and border control;(viii) assistance in legal interpretation and enforcement of the law.

All high-risk artificial intelligence systems will be evaluated before being put on the market and throughout their life cycle.

The Generative and Basic AI systems/models can both be considered general-purpose AI because they are capable of performing different tasks and are not limited to a single task. The distinction between the two lies in the final output.

Generative AI, like the now-popular ChatGPT, uses neural networks to generate new text, images, videos or sounds that have never been seen or heard before, much as a human can. For this reason, the European Parliament has introduced higher transparency requirements:

Basic AI models, in contrast, do not create, but learn from large amounts of data, use it to perform a wide range of tasks, and have application in a variety of domains. Providers of these models will need to assess and mitigate the possible risks associated with them (to health, safety, fundamental rights, the environment, democracy, and the rule of law) and register their models in the EU database before they are released to the market.

Next are the minimal or low risk AI applications, such as those used to date for translation, image recognition, or weather forecasting. Limited-risk artificial intelligence systems should meet minimum transparency requirements that enable users to make informed decisions. After interacting with applications, users can decide whether they wish to continue using them. Users should be informed when interacting with AI. This includes artificial intelligence systems that generate or manipulate image, audio, or video content (e.g., deepfakes).

Finally, exemptions are provided for research activities and AI components provided under open-source licenses.

The European Union and the United States Aiming to Bridge the AI Legislative Gap

The United States is expected to closely follow Europe in developing its own legislation. In recent times, there has been a shift in focus from a light touch approach to AI regulation, towards emphasizing ethics and accountability in AI systems. This change is accompanied by increased investment in research and development to ensure the safe and ethical usage of AI technology. The Algorithm Accountability Act, which aims to enhance transparency and accountability of providers, is still in the proposal stage.

During the recent US-EU ministerial meeting of the Trade and Technology Council, the participants expressed a mutual intention to bridge the potential legislative gap on AI between Europe and the United States. These objectives gain significance given the final passage of the European AI Act. To achieve this goal, a voluntary code of conduct on AI is under development, and once completed, it will be presented as a joint transatlantic proposal to G7 leaders, encouraging companies to adopt it.

The United Kingdoms Pro-Innovation Approach in Regulating AI

On March 29, 2023, the UK government released a white paper outlining its approach to regulating artificial intelligence. The proposal aims to strike a balance between fostering a pro-innovation business environment and ensuring the development of trustworthy AI that addresses risks to individuals and society.

The regulatory framework is based on five core principles:

These principles are initially intended to be non-statutory, meaning no new legislation will be introduced in the United Kingdom for now. Instead, existing sector-specific regulators like the ICO, FCA, CMA, and MHRA will be required to create their own guidelines for implementing these principles within their domains.

The principles and sector-specific guidance will be supplemented by voluntary AI assurance standards and toolkits to aid in the responsible adoption of AI.

Contrasting with the EU AI Act, the UKs approach is more flexible and perhaps more proportionate, relying on regulators in specific sectors to develop compliance approaches with central high-level objectives that can evolve as technology and risks change.

The UK government intends to adopt this framework quickly across relevant sectors and domains. UK sector specific regulators have already received feedback on implementing the principles during a public consultation that ran until June 2023, and we anticipate further updates from each of them in the coming months.

The Difficult Balance between Regulation and Innovation

The ultimate goal of these legislative efforts is to find a delicate balance between the necessity to regulate the rapid development of technology, particularly regarding its impact on citizens lives, and the imperative not to stifle innovation, or burden smaller companies with overly strict laws.

Anticipating the level of success is challenging, if not impossible. Nevertheless, the scope for soft law such as setting up an ad hoc committee at a European level shows promise. Ultratechnical matters subject to rapid evolution require clear principles that stem from the value choices made by legislators. Moreover, such matters demand technical competence to understand what is being regulated at any given moment.

Organizations using AI across multiple jurisdictions will additionally face challenges in developing a consistent and sustainable global approach to AI governance and compliance due to the diverging regulatory standards. For instance, the UK approach may be seen as a baseline level of regulatory obligation with global relevance, while the EU approach may require higher compliance standards.

As exemplified by the recent Italian shutdown of ChatGPT (see ChatGPT: A GDPR-Ready Path Forward? we have witnessed firsthand the complexities involved. The Italian data protection authority assumed a prominent role and instead of contesting the suspension of the technology in court, the business chose to cooperate. As a result, the site was reopened to Italian users within approximately one month.

In line with Italy, various other data protection authorities are actively looking into ways to influence the development and design of AI systems. For instance the Spanish AEPD has implemented audit guidance for data processing involving AI systems, more detail here, while or the French CNIL has created a department dedicated to AI with open self-evaluation resources for AI businesses, more detail here. Additionally, the UKs Information Commissioners Office (ICO) has developed an AI toolkit (available here) designed to provide practical support to organizations.

From Safety to Liability: The AI Act is Prodromic to an AI Specific Liability Regime

The EU AI Act is part of a three-pillar package proposed by the EU Commission to support AI in Europe. The other pillars include an amendment to the EU Product Liability Directive (PLD) and a new AI liability directive (AILD). While the AI Act focuses on safety and ex ante protection/ prevention re fundamental rights, the PLD and AILD address damages caused by AI systems. Non-compliance with the AI Acts requirements could also trigger, based on the AI Act risk level of the AI system at issue, different forms and degrees of alleviation of the burden of proof under both the amended PLD, for the no-fault based product liability claims, the AILD, for any other (fault based) claim. The amended PLD and the AILD are less imminent than the AI Act: they have not yet been approved by the EU Parliament and, as directives, will require implementation at the national level. Yet the fact that they are coming is of immediate importance and use, as it gives businesses even more reason to follow and possibly cooperate and partake in the standard setting process currently in full swing.

Conclusion

Businesses using AI must navigate evolving regulatory frameworks and strike a balance between compliance and innovation. They should assess the potential impact of the regulatory framework on their operations and consider whether existing governance measures address the proposed principles. Prompt action is necessary, as regulators worldwide have already started publishing extensive guidance on AI regulation.

Monitoring these developments and assessing the use of AI is key for compliance and risk management. This approach is crucial not only for regulatory compliance but also to mitigate litigation risks with contractual parties and complaints from individuals. Collaboration with regulators, transparent communication, and global harmonization are vital for successful AI governance. Proactive adaptation is essential as regulations continue to develop.

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The EU Artificial Intelligence Act: What's the Impact? - JD Supra

Video Transcript

Editor's note- This transcript has been edited for clarity.

Hello and welcome to yet another edition of the NeuroOp Guru. I'm here with my good friend Drew Carey from Johns Hopkins and the Wilmer Eye Institute. Hi Drew.

Hi Andy, happy to be here.

And today we're going to be talking about the question "Is artificial intelligence for optic disc photos ready for the clinic and the ER?" So Drew, maybe you could just give us a little background on why is this even a question?

Well, I think for a long time, we've come to realize that colleagues without ophthalmologic training, ER doctors, primary care doctors, neurologists are not so good at fundoscopy, looking at the back of the eye, and specifically the optic nerve. You know, they don't do it through dilated pupils like ophthalmologist do, they don't do it a lot. So it's not a skill set that they've kept up if they ever really refined it in medical school. But there are some conditions where it is really important to look in the back of the eye and at the optic nerve, especially if you have a patient coming in with headaches and vision changes. W

e want to know is the optic nerve swollen, you know, could this be ischemic optic neuropathy, an emergency like giant cell arteritis, or papilledema, where they could have some kind of Bergeon, intracranial CNS process going on. And we can't get an ophthalmologist in every emergency room in America, but it would be feasible to put a camera. And then the question is, well, who's going to look at the picture? It should be somebody who knows what an optic nerve is supposed to look like. Or could it be an artificial intelligence that's been trained. And so I think that was the major initiative for this project was trying to improve the diagnostic value of fundoscopy in conditions where it would be desired.

And so this AI trained on thousands of photos that they just loaded in there and taught it, what it's supposed to look for.

Yeah, so there's been, you know, a lot of work in in AI and subtypes of AI, including deep learning systems, machine learning. And it does, it takes thousands and thousands of images that have been carefully combed through, and that we labeled with what we call ground truth where we know exactly what that picture represents, to train the system.

Kind of like a resident has to see thousands of cases during their training, in order to, you know, develop good clinical intuition and understanding what's going on. So for this, this group, the BONSAI consortium, based out of Singapore, they, you know, asked for pictures from neuro-ophthalmologist all across the world to try and develop a diverse training set. With patients who they knew what the diagnosis was, they knew what that optic nerve was showing. And that's what they trained it on.

And so maybe you could just walk us through these results of the BONSAI and you can see that it was already 168 times faster, but let's see if it's better. We know it can be faster. But is it better? Just maybe you could walk us through A and B here in terms of error rate?

Yeah absolutely. So what they did is they took 800 new photos that the machine had never seen before. Which is really important. You don't want to ask the artificial intelligence to answer a question that it already knows the answer to. And they so they showed that to BONSAI, and then they showed it to 30 different clinicians, six were general ophthalmologists, six were optometrists, six neurologists, six internal medicine doctors and six emergency medicine doctors.

And they asked them to classify these optic nerve photos as normal papilledema or other. And so they said, they split the groups into two different the doctors into two different groups. They said these are folks with opthalmic expertise, ophthalmologist and optometrist and the other folks, the neurologists, internal medicine and emergency medicine. And so in A, they said the error rate for the doctors with ophthalmic expertise looking at one photo of one eye, so they didn't get the benefit of two eyes. They said the error rate was about 25% for doctors with ophthalmic expertise. And for doctors without ophthalmic expertise it was close to 45%.

And the deep learning system was about 16% compared to what we knew the actual photo was. We know that that the machine is really good. You know that's what it was trained to do. And then in B they broke it down. They said these are ophthalmologists, optometrists, neurologists, internist and the emergency medicine doctors. And the ophthalmologists and optometrists were both very similar at about 25%, which is what we saw when they were together. And then the neurologists were, you know, not quite as good running around 38%. While the internal medicine doctors who, I don't remember the last time my eye was looked at in a primary care doctor office visit, was 43%.

And then the emergency medicine doctors were about 45%. And this is, they didn't even have to look inside the eye, this is a good quality fundus photo that we were able to just give the doctor and say, you know, this is, this is what the optic nerves looks like. And again, you know, the deep learning system was running around 16% for, you know, all the pictures. So that's, that's the comparison, which I think is really good. And we know that right, it's a machine, it doesn't have to stop and think about it. And you know, go through, okay, what's this blood vessel doing? What's that blood vessel do? It just looks at it and runs it through the algorithm, and takes about 25 seconds for it to look at all 800 photos.

Versus 70 minutes for the doctors, which I think 70 minutes to look at 800 photos is still pretty good. So that's what we found out. So the BONSAI had significantly higher accuracies in 100% of the papilledema cases, 87% of the normal cases, and 93% of the other cases, compared to the clinicians. So it's really good. I don't think it's ready to replace doctors, you know, neuro-ophthalmologists, because it's not perfect. And there's a lot of other clinical information.

We all know how important that history is for neuro-ophthalmology that it can't do. But it could really help to risk stratify. You know, this is somebody that really needs to see neuro ophthalmology or get an ophthalmologist in here in person to look at the patient, or say no, this is normal. Or this is somebody who needs to proceed to neuroimaging, lumbar puncture, you know, even if we can't get an ophthalmologist in here.

So do you think it's more like a decision support right now, like helps you make a decision? Or do you think it's not even that?

I think that's where it would be, you know, if this could be clinically implemented into emergency rooms, neurologist office. You know, the patient comes in and every patient with a headache, they get their blood pressure check to make sure it's not hypertensive emergency. They should get a photo of their optic nerve to make sure it's not elevated intracranial pressure or hypertensive emergency.

So and then, you know, the doctor can look at it. And the other thing that we know about the AI, compared to a doctor is it's not just a yes, no, it also gives it probabilities. It'll say I'm 100% certain this is normal. Or it'll say I'm 100% certain this is pappilledema. Ot it might say, it's probably papilladema, but I'm 65%.

You say okay, well, let's get some more data. Let's get an ophthalmologist in here to look at both eyes and ask some important questions like, do you have headaches? Do you have wooshing sounds in your ears? If you're having transient visual obscurations when you're bending over or coughing?

Well, so maybe the answer is stay tuned to this channel. But it certainly sounds like the machine is faster. And maybe even better than the doctors. The question is, is it cheaper?

Well, like you know, a lot of emergency rooms don't have an ophthalmologist on call. And if you're asking how much is it going to cost to pay somebody to cover call, he's not going to do it for free. And, you know, we could bill for photos that might be revenue generating as opposed to revenue loss. I think the big questions is regulatory.

You know, I think in the United States, we have one, that I'm aware of, FDA approved AI system, which is for retinal screening for diabetic retinopathy. They're looking at it for implementing into neuroimaging for CT scans to help to triage, this is a CT scan, we need the neuroradiologist to look at right now or put this one at the end of the pile to finish by the end of their shift. Yeah, cost is a big question. And it still has to go through FDA approval and then you know, it's still wrong 16% of the time, who's liable when it's wrong? You know, what's, what's the safety mechanism for the patient?

But compared to the safety mechanism, without it, you know, either nobody's looking or somebody's looking who's gonna be wrong, like half the time, I'd say, you know, if I was in the emergency room, have the machine, take a picture and tell me how I'm doing.

Well, Drew, as always a pleasure to chat with you. And that concludes yet another edition of the NeuroOp Guru. We'll see you guys next time.

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VLOG: Is artificial intelligence for optic disc photos ready for the ... - Ophthalmology Times

A recent study conducted by Junior Achievement USA has indicated that student reliance on artificial intelligence (AI) is expected to rise in the upcoming school year. The survey revealed that 44% of students expressed their likelihood of using AI to complete assignments. Moreover, 48% of students reported knowing someone who has utilized AI to complete tasks on their behalf.

Despite the increasing usage of AI, the majority of teenagers still consider it to be a form of cheating. Out of the students surveyed, 60% believed that using AI in their schoolwork is dishonest, while 62% viewed it as just another tool to aid them in completing their assignments.

Educators are now tasked with ensuring that students are utilizing AI as a supplementary tool rather than relying solely on it to complete their work. Many school districts have implemented policies to prevent misuse of AI, including the use of software that can detect artificially generated student work.

Some students, like Luke Nathan from All Saints Episcopal School, have faced consequences for using AI in their academic endeavors. Nathan admitted to being caught multiple times, emphasizing that the payoff is not worth the risk. He also expressed concerns about the rapid advancements of AI and its implications in an educational context.

With AIs rapid growth and its potential to analyze and interpret complex data, there is a sense of awe and caution among students. Nathan mentioned watching AI assist in stock investments and witnessing its tremendous success, highlighting its remarkable power.

As more students express their intent to utilize AI in their academic pursuits, it is crucial for educators to strike a balance between leveraging the benefits of this technology while ensuring an honest and ethical learning environment.

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Study Predicts Increased Student Use of Artificial Intelligence in the ... - Fagen wasanni