SwissCognitive Guest Blogger: Eleanor Wright, COO at TelWAI – “Fortifying the Future: Ensuring Secure and Reliable AI”

As AI becomes further integrated into various domains, from infrastructure to defence, ensuring its robustness will become a matter of national security. An AI system managing power grids, security apparatus, or financial networks could present a single point of failure if compromised or manipulated. Historical incidents, such as the Stuxnet cyberweapon, illustrate the physical and cyber damage that can be inflicted. When considering AI’s complexity, the potential for a cascade of both physical and digital harm increases dramatically.

As such, we should ask: How do we fortify AI?

AI systems must be designed to withstand attacks. From decentralisation to layering, these systems should be constructed so that control points can seamlessly enter and exit the loop without disabling the broader system. Thus, building redundancy and backup at various control points within the AI systems. For example, suppose a sensor or a group of sensors is deemed to have failed or been corrupted. In that case, the broader system must be capable of automatically readjusting to stop utilising data and intelligence gathered from said sensors.

Another strategy for strengthening AI systems involves simulating data poisoning attacks and training AI systems to detect such threats. By teaching the systems to recognise and respond to attacks or failures, they can automatically reconfigure without the need for human intervention. If an AI can learn to identify tainted data, such as statistical anomalies or inconsistent patterns, it could flag or quarantine suspect inputs. This approach leans heavily on machine learning’s strengths: pattern recognition and adaptability. However, it’s not a failsafe; adversaries could evolve their attacks to more closely mimic legitimate data, so the training would need to be dynamic, constantly updating to match new threat profiles.

Maintaining a human in the loop to enable oversight and override is considered one of the most crucial elements in the rollout of AI in various industries. Allowing humans to oversee AI decision-making and restricting autonomy can prevent potentially harmful actions taken by these systems. Whilst critical in the early stages of AI deployment as capabilities scale and evolve, there may come a point where human oversight inhibits these systems and, in itself, causes more harm than good.

Finally, AI sovereignty may prove to be the most critical element in ensuring companies and governments fully control essential algorithms and hardware powering their operations. Without this control, these systems could be vulnerable to foreign interference, including cyberattacks, espionage, or sabotage. As the use of AI increases, the sovereignty of AI systems and their components will become increasingly important. At its core, AI sovereignty is about control, whether exercised by governments, corporations, or individuals. Through the control of data, infrastructure, and decision-making power, those who build and deploy AI systems and sensors gain control of AI.

Fortification will involve integrating resilience, adaptability, and sovereignty into AI’s DNA, ensuring it is not only intelligent but also resilient and unbreakable. It can provide technological advantages, but it may also expose systems to disruption and vulnerability exploitation. As organisations race to harness AI’s potential, the question looms: Will AI enable organisations to gain a strategic advantage, or will it undermine the very systems it was designed to strengthen?

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles.

Der Beitrag Fortifying the Future: Ensuring Secure and Reliable AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

This is your last chance to nominate! The Global AI Ambassador Program 2025 closes next week—don’t miss the opportunity to recognize AI leaders shaping the future.

In the meantime AI is advancing in research, defense, healthcare, and business—here are this week’s highlights:

AI deciphers genetic mysteries in biomedical research
US Space Force outlines AI-driven space strategies
AI-powered brain implant enables robotic arm control
Self-healing AI systems strengthen cyber defense
…and more!

Stay ahead in AI—catch you next week with more updates!

Kind regards,

The Team of SwissCognitive

Der Beitrag Last Chance for Recognition erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Dalith Steiger-Gablinger, Co-Founder SwissCognitive – “AI Takes Center Stage at Davos 2025: A SwissCognitive Perspective”

As the snow-capped peaks of Davos played host to the World Economic Forum 2025, the air was thick with excitement and a palpable sense of urgency. This year’s theme, “Collaboration for the Intelligent Age,” set the stage for intense discussions on artificial intelligence (AI) and its potential to reshape our world. As co-founders of SwissCognitive, Andy Fitze and I, Dalith Steiger, had the privilege of being flies on the wall at various public side events, soaking in the insights and debates that unfolded.

The buzz around AI was impossible to ignore, with sessions ranging from “Harnessing AI for Social Innovation” to “The Pulse of AI Innovation”. Clearly, the technology has moved beyond mere hype and into the realm of transformative force. As James Ong, one of the panellists, aptly put it, “We need to rethink the philosophy and the relationship between AI and human beings.” AI is not just a tool; it’s a paradigm shift that will redefine how we work, live, and interact with the world around us.”

“We need to rethink the philosophy and the relationship between AI and human beings.” James Ong, Founder and Director of Artificial Intelligence International Institute [AIII]

One of the most striking aspects of the discussions was the emphasis on collaboration. Gone are the days of siloed AI development. The consensus at Davos was clear: to harness the full potential of AI and ensure its benefits are widely distributed, we need unprecedented levels of cooperation between governments, businesses, and civil society.

Another discussion that deeply resonates with our vision at SwissCognitive is the AI discussion in avoiding the pitfalls of the digital divide, emphasising the need for AI to “lift all boats” rather than exacerbate existing inequalities. We strongly advocated for inclusive AI development.

The ethical implications of AI were another hot topic. The sentiment that we are not just building algorithms; we are shaping the future of humanity was echoed across multiple panels, with discussions ranging from AI’s impact on privacy to its potential to either mitigate or exacerbate climate change.

As we navigated the bustling streets of Davos, Andy and I found ourselves in impromptu discussions with fellow attendees. One of the enlightening discussions was while waiting for the Meta hot chocolate or queuing for the entrance of the Dome. One thing that was present through all our exchanges. People engaged openly, with respect and humour.

The energy was infectious, with everyone from startup founders to policymakers eager to share their perspectives on AI’s future. One conversation that stuck with us was with a young entrepreneur who’s using AI to tackle food waste in developing countries. It was a powerful reminder of AI’s potential to address some of our most pressing global challenges and SDGs.

The governance of AI emerged as a critical theme throughout the forum. With the rapid pace of AI development, there’s a growing recognition that our regulatory frameworks need to evolve just as quickly. The call for adaptive, agile governance structures was loud and clear. We shouldn’t govern 21st-century technology with 20th-century laws!

“We shouldn’t govern 21st-century technology with 20th-century laws!” during a Chatham rules debate

Perhaps the most stimulating discussions, however, centred around the potential of AI to complement human capabilities rather than replace them. AI should be seen as a co-pilot, not an autopilot. As advocates of collaboration between humans and AI, Andy and I were heartened to hear leaders from different sectors emphasise the importance of involving humans in development.

“AI should be seen as a co-pilot, not an autopilot.” during a Chatham rules debate

The Open Source Revolution: A Game-Changer in the Global AI Race

Another topic that consistently emerged in our conversations was the growing importance of open source in AI development. This trend is not just reshaping the technological landscape; it’s also challenging the traditional narrative of national AI supremacy.

The United States’ commitment to investing a staggering $500 billion in AI over the next three years is undoubtedly headline-grabbing. However, as Yann LeCun, VP & Chief AI Scientist at Meta, astutely pointed out during several discussions in Davos, the real story might be the rise of open-source models rather than any single nation’s dominance.

LeCun’s perspective is particularly illuminating: “To people who see the performance of DeepSeek and think: ‘China is surpassing the US in AI.’ You are reading this wrong. The correct reading is: ‘Open source models are surpassing proprietary ones.'”

“Open source LLM models are surpassing proprietary ones.” Yann LeCun, VP & Chief AI Scientist at Meta

This shift towards open source is democratising AI development on a global scale. LeCun explained that “DeepSeek has profited from open research and open source (e.g. PyTorch and Llama from Meta). They came up with new ideas and built them on top of other people’s work. Because their work is published and open source, everyone can profit from it. That is the power of open research and open source.”

Indeed, the open-source movement in AI is gaining momentum rapidly. Models like Llama 2, Mistral, and DeepSeek are not just matching but, in some cases, surpassing the capabilities of proprietary giants like GPT-4 and Google Gemini. This trend is reshaping the AI ecosystem, offering adaptability, cost-efficiency, and privacy compliance that many enterprises find increasingly attractive.

The implications of this shift are profound. While national investments like the U.S.’s $500 billion commitment are crucial, the collaborative nature of open-source development means that innovations can come from anywhere. This global pool of talent and ideas could potentially accelerate AI development far beyond what any single nation or company could achieve alone.

Moreover, the open source movement aligns with the growing calls for AI transparency and accountability. One tech executive at Davos noted, “We’re not just building algorithms; we’re shaping the future of humanity.” Open source development allows for greater scrutiny and collective problem-solving, potentially leading to safer and more ethical AI systems.

“We’re not just building algorithms; we’re shaping the future of humanity.” CEO during a Panel in Davos

As we reflect on the discussions at Davos, it’s clear that the future of AI is not just about who can invest the most money. It’s about fostering a global ecosystem of innovation, collaboration, and shared progress. The rise of open source in AI is not just a technological trend; it’s a paradigm shift that could redefine how we approach some of the world’s most pressing challenges.

In this new landscape, the winners will not necessarily be the nations or companies with the deepest pockets but those who can best harness the collective intelligence of the global AI community. As we move forward, it will be fascinating to see how this open-source revolution continues to shape the future of AI and, by extension, our world.

“In this new landscape, the winners will not necessarily be the nations or companies with the deepest pockets, but those who can best harness the collective intelligence of the global AI community.” Andy Fitze, Co-Founder SwissCognitive

As the forum drew to a close, we left Davos with a sense of cautious optimism. The challenges ahead are significant, but so too is the collective will to address them. The conversations made it clear that we are at a pivotal moment in the development of AI, and the decisions we make now will shape its trajectory for years to come. This future belongs to the young generations. We, the older generation, must be aware that every decision we make won’t affect us, as it will affect the younger generations! This responsibility is imperative!

As we return to our work at SwissCognitive, we’re more energised than ever to continue fostering dialogue and collaboration in AI. The insights gained at Davos will undoubtedly inform our efforts to build a future where AI truly lifts all boats, creating a rising tide of innovation and prosperity for all.

“We are the change we wanna see”, Yip Thy Diep Ta, Founder & CEO @ J3D.AI, House of Collaboration

In reflecting on our experience, Andy remarked, “The technical possibilities of AI are astounding, but it’s the human ingenuity in applying these technologies that will truly change the world.” I couldn’t agree more, adding, “AI has the power to amplify our human potential, but only if we approach its development with empathy, wisdom, and a commitment to inclusivity.“

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Copyright: forbes.com – “Emotional Intelligence is More Important Than Ever in the Age of AI”

While most of us accept that artificial intelligence isn’t going to take over the world just yet, there’s a growing recognition that businesses and their employees are going to have to adapt their skills pretty swiftly. According to the 2024 Global CEO Survey from consulting firm PwC, seven out of 10 CEOs believe that AI will significantly change the way their company creates, delivers and captures value over the next three years. On the plus side, 41% believe it will increase revenue. However, those in “AI-exposed” jobs (such as administration and customer service agents) have seen 27% lower job growth, and anticipate a 25% higher skills change rate than those who are not at risk.

In most cases, AI won’t replace entire jobs, but speed up or automate certain aspects of them, often freeing staff up to work on something more satisfying or of higher value. The emotionally intelligent, human side of work is something it is unlikely to be able to replicate, at least in the near future. AI’s power lies in being able to process vast amounts of data with speed and accuracy, but its limitations become apparent when it encounters the complexity of human behaviors. It’s also known for its fallibilities, sometimes producing false responses to prompts or biased outcomes because of the data it’s working on or the way it has been programmed.

I define emotional intelligence as self-awareness, which is a critical skill in this increasingly AI-driven world. Whatever level someone is working at, it’s important that they know how to read the room and adapt how they work with a colleague or client.[…]

Read more: www.forbes.com

Der Beitrag Emotional Intelligence is More Important Than Ever in the Age of AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data and AI at Sigli – “AI for Disabilities: Quick Overview, Challenges, and the Road Ahead”

AI has enormous power in improving accessibility and inclusivity for people with disabilities. This power lies in the potential of this technology to bridge gaps that traditional solutions could not address. As we have demonstrated in the series of articles devoted to AI for disabilities, AI-powered products can really change a lot for people with various impairments. Such solutions can allow users to live more independently and get access to things and activities that used to be unavailable to them before. Meanwhile, the integration of AI into public infrastructure, education, and employment holds the promise of creating a more equitable society. These are the reasons that can show us the importance of projects building solutions of this type.

Yes, these projects exist today. And some of them have already made significant progress in achieving their goals. Nevertheless, there are important issues that should be addressed in order to make such projects and their solutions more efficient and let them bring real value to their target audiences. One of them is related to the fact that such solutions are often built by tech experts who have practically no understanding of the actual needs of people with disabilities.

According to the survey conducted in 2023, only 7% of assistive technology users believe that their community is adequately represented in the development of AI products. At the same time, 87% of respondents who are end users of such solutions express their readiness to share their feedback with developers. These are quite important figures to bear in mind for everyone who is engaged in the creation of AI-powered products for disabilities.

In this article, we’d like to talk about the types of products that already exist today, as well as potential barriers and trends in the development of this industry.

Different types of AI solutions for disabilities

In the series of articles devoted to AI for disabilities, we have touched on types of products for people with different states, including visual, hearing, mobility impairments, and mental diseases. Now, let us group these solutions by their purpose.

Communication tools

AI can significantly enhance the communication process for people with speech and hearing impairments.

Speech-to-text and text-to-speech apps enable individuals to communicate by converting spoken words into text or vice versa.

Sign language interpreters powered by AI can translate gestures into spoken or written language. It means that real-time translation from sign to verbal languages can facilitate communication, bridging the gap between people with disabilities and the rest of society.

Moreover, it’s worth mentioning AI-powered hearing aids with noise cancellation. They can improve clarity by filtering out background sounds, enhancing the hearing experience in noisy environments.

Advanced hearing aids may also have sound amplification functionality. If somebody is speaking too quietly, such AI-powered devices can amplify the sound in real time.

Mobility and navigation

AI-driven prosthetics and exoskeletons can enable individuals with mobility impairments to regain movement. Sensors and AI algorithms can adapt to users’ physical needs in real time for more natural, efficient motion. For example, when a person is going to climb the stairs, AI will “know” it and adjust the movement of prosthetics to this activity.

Autonomous wheelchairs often use AI for navigation. They can detect obstacles and take preventive measures. This way users will be able to navigate more independently and safely.

The question of navigation is a pressing one not only with people with limited mobility but also for individuals with visual impairments. AI-powered wearable devices for these users rely on real-time environmental scanning to provide navigation assistance through audio or vibration signals.

Education and workplace accessibility

Some decades ago people with disabilities were fully isolated from society. They didn’t have the possibility to learn together with others, while the range of jobs that could be performed by them was too limited. Let’s be honest, in some regions, the situation is still the same. However, these days we can observe significant progress in this sphere in many countries, which is a very positive trend.

Among the main changes that have made education available to everyone, we should mention the introduction of distance learning and the development of adaptive platforms.

A lot of platforms for remote learning are equipped with real-time captioning and AI virtual assistants. It means that students with disabilities have equal access to online education.

Adaptive learning platforms rely on AI to customize educational experiences to the individual needs of every learner. For students with disabilities, such platforms can offer features like text-to-speech, visual aids, or additional explanations and tasks for memorizing.

In the workplace, AI tools also support inclusion by offering accessibility features. Speech recognition, task automation, and personalized work environments empower employees with disabilities to perform their job responsibilities together with all other co-workers.

Thanks to AI and advanced tools for remote work, the labor market is gradually becoming more accessible for everyone.

Home automation and daily assistance

Independent living is one of the main goals for people with disabilities. And AI can help them reach it.

Smart home technologies with voice or gesture control allow users with physical disabilities to interact with lights, appliances, or thermostats. Systems like Alexa, Google Assistant, and Siri can be integrated with smart devices to enable hands-free operation.

Another type of AI-driven solutions that can be helpful for daily tasks is personal care robots. They can assist with fetching items, preparing meals, or monitoring health metrics. As a rule, they are equipped with sensors and machine learning. This allows them to adapt to individual routines and needs and offer personalized support to their users.

Existing barriers

It would be wrong to say that the development of AI for disabilities is a fully flawless process. As well as any innovation, this technology faces some challenges and barriers that may prevent its implementation and wide adoption. These difficulties are significant but not insurmountable. And with the right multifaceted approach, they can be efficiently addressed.

Lack of universal design principles

One major challenge is the absence of universal design principles in the development of AI tools. Many solutions are built with a narrow scope. As a result, they fail to account for the diverse needs that people with disabilities may have.

For example, tools designed for users with visual impairments may not consider compatibility with existing assistive technologies like screen readers, or they may lack support for colorblind users.

One of the best ways to eliminate this barrier is to engage end users in the design process. Their opinion and real-life experiences are invaluable for such projects.

Limited training datasets for specific AI models

High-quality, comprehensive databases are the cornerstone for efficient AI models. It’s senseless to use fragmented and irrelevant data and hope that your AI system will demonstrate excellent results (“Garbage in, Garbage out” principle in action). AI models require robust datasets to function as they are supposed to.

However, datasets for specific needs, like regional sign language dialects, rare disabilities, or multi-disability use cases are either limited or nonexistent. This results in AI solutions that are less effective or even unusable for significant groups of the disability community.

Is it possible to address this challenge? Certainly! However, it will require time and resources to collect and prepare such data for model training.

High cost of AI projects and limited funding

The development and implementation of AI solutions are usually pretty costly initiatives. Without external support from governments, corporate and individual investors, many projects can’t survive.

This issue is particularly significant for those projects that target niche or less commercially viable applications. This financial barrier discourages innovation and limits the scalability of existing solutions.

Lack of awareness and resistance to adopt new tools

A great number of potential users are either unaware of the capabilities of AI or hesitant to adopt new tools. Due to the lack of relevant information, people have a lot of concerns about the complexity, privacy, or usability of assistant technologies. Some tools may stay just underrated or misunderstood.

Adequate outreach and training programs can help to solve such problems and motivate potential users to learn more about tools that can change their lives for the better.

Regulatory and ethical gaps

The AI industry is one of the youngest and least regulated in the world. The regulatory framework for ensuring accessibility in AI solutions remains underdeveloped. Some aspects of using and implementing AI stay unclear and it is too early to speak about any widely accepted standards that can guide these processes.

Due to any precise guidelines, developers may overlook critical accessibility features. Ethical concerns, such as data privacy and bias in AI models also complicate the adoption and trustworthiness of these technologies.

Such issues slow down the development processes now. But they seem to be just a matter of time.

Future prospects of AI for disabilities: In which direction is the industry heading?

Though the AI for disabilities industry has already made significant progress in its development, there is still a long way ahead. It’s impossible to make any accurate predictions about its future look. However, we can make assumptions based on its current state and needs.

Advances in AI

It is quite logical to expect that the development of AI technologies and tools will continue, which will allow us to leverage new capabilities and features of new solutions. The progress in natural language processing (NLP) and multimodal systems will improve the accessibility of various tools for people with disabilities.

Such systems will better understand human language and respond to diverse inputs like text, voice, and images.

Enhanced real-time adaptability will also enable AI to tailor its responses based on current user behavior and needs. This will ensure more fluid and responsive interactions, which will enhance user experience and autonomy in daily activities for people with disabilities.

Partnerships

Partnerships between tech companies, healthcare providers, authorities, and the disability community are essential for creating AI solutions that meet the real needs of individuals with disabilities. These collaborations will allow for the sharing of expertise and resources that help to create more effective technologies.

By working together, they will ensure that AI tools are not only innovative but also practical and accessible. We can expect that the focus will be on real-world impact and user-centric design.

New solutions

It’s highly likely that in the future the market will see a lot of new solutions that now may seem to be too unrealistic. Nevertheless, even the boldest ideas can come to life with the right technologies.

One of the most promising use cases for AI is its application in neurotechnology for seamless human-computer interaction.

A brain-computer interface (BCI) can enable direct communication between the human brain and external devices by interpreting neural signals related to unspoken speech. It can successfully decode brain activity and convert it into commands for controlling software or hardware.

Such BCIs have a huge potential to assist individuals with speech impairments and paralyzed people.

Wrapping up

As you can see, AI is not only about business efficiency or productivity. It can be also about helping people with different needs to live better lives and change their realities.

Of course, the development and implementation of AI solutions for disabilities are associated with a row of challenges that can be addressed only through close cooperation between tech companies, governments, medical institutions, and potential end users.

Nevertheless, all efforts are likely to pay off.

By overcoming existing barriers and embracing innovation, AI can pave the way for a more accessible and equitable future for all. And those entities and market players who can contribute to the common success in this sphere should definitely do this.

About the Author:

In his current position, Artem Pochechuev leads a team of talented engineers. Oversees the development and implementation of data-driven solutions for Sigli’s customers. He is passionate about using the latest technologies and techniques in data science to deliver innovative solutions that drive business value. Outside of work, Artem enjoys cooking, ice-skating, playing piano, and spending time with his family.

Der Beitrag AI for Disabilities: Quick Overview, Challenges, and the Road Ahead erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How Countries Are Using AI to Predict Crime”

A world where police departments can predict when, where and how crimes will occur seems like something from a science fiction novel. Thanks to artificial intelligence, it has become a reality. Already, countries are using this technology to forecast misconduct.

How Do AI-Powered Crime Prediction Systems Work?

Unlike regular prediction systems — which typically use hot spots to determine where and when future misconduct will be committed — AI can analyze information in real time. It may even be able to complete supplementary tasks like summarizing a 911 call, assigning a severity level to a crime in progress or using surveillance systems to tell where wanted criminals will be.

A machine learning model evolves as it processes new information. Initially, it might train to find hidden patterns in arrest records, police reports, criminal complaints or 911 calls. It may analyze the perpetrator’s demographic data or factor in the weather. The goal is to identify any common variable that humans are overlooking.

Whether the algorithm monitors surveillance camera footage or pours through arrest records, it compares historical and current data to make forecasts. For example, it may consider a person suspicious if they cover their face and wear baggy clothes on a warm night in a dark neighborhood because previous arrests match that profile.

Countries Are Developing AI Tools to Predict Crime

While these countries don’t currently have official AI prediction tools, various research groups and private police forces are developing solutions.

• United States

Violent and property crimes are huge issues in the United States. For reference, a burglary occurs every 13 seconds — almost five times per minute — causing an average of $2,200 in losses. Various state and local governments are experimenting with AI to minimize events like these.

One such machine learning model developed by data scientists from the University of Chicago uses publicly available information to produce output. It can forecast crime with approximately 90% accuracy up to one week in advance.

While the data came from eight major U.S. cities, it centered around Chicago. Unlike similar tools, this AI model didn’t depict misdemeanors and felonies as hot spots on a flat map. Instead, it considered cities’ complex layouts and social environments, including bus lines, street lights and walkways. It found hidden patterns using these previously overlooked factors.

• South Africa

Human trafficking is a massive problem in South Africa. For a time, one anti-human trafficking non-governmental organization was operating at one of the country’s busiest airports. After the group uncovered widespread corruption, their security clearance was revoked.

At this point, the group needed to lower its costs from $300 per intercept to $50 to align with funding and continue their efforts. Its members believed adopting AI would allow them to do that. With the right data, they could save more victims while keeping costs down.

Some Are Already Using AI Tools to Predict Crime

Governments have much more power, funding and data than nongovernmental organizations or research groups, so their solutions are more comprehensive.

• Japan

Japan has an AI-powered app called Crime Nabi. The tool — created by the startup Singular Perturbations Inc. — is at least 50% more effective than conventional methods. Local governments will use it for preventive patrols.

Once a police officer enters their destination in the app, it provides an efficient route that takes them through high-crime areas nearby. The system can update if they get directed elsewhere by emergency dispatch. By increasing their presence in dangerous neighborhoods, police officers actively discourage wrongdoing. Each patrol’s data is saved to improve future predictions.

Despite using massive amounts of demographic, location, weather and arrest data — which would normally be expensive and incredibly time-consuming — Crime Nabi processes faster than conventional computers at a lower cost.

• Argentina

Argentina’s Ministry of Security recently announced the Artificial Intelligence Applied to Security Unit, which will use a machine learning model to make forecasts. It will analyze historical data, scan social media, deploy facial recognition technology and process surveillance footage.

This AI-powered unit aims to catch wanted persons and identify suspicious activity. It will help streamline prevention and detection to accelerate investigation and prosecution. The Ministry of Security seeks to enable a faster and more precise police response.

• South Korea

A Korean research team from the Electronics and Telecommunications Research Institute developed an AI they call Dejaview. It analyzes closed-circuit television (CCTV) footage in real time and assesses statistics to detect signs of potential offenses.

Dejaview was designed for surveillance — algorithms can process enormous amounts of data extremely quickly, so this is a common use case. Now, its main job is to measure risk factors to forecast illegal activity.

The researchers will work with Korean police forces and local governments to tailor Dejaview for specific use cases or affected areas. It will mainly be integrated into CCTV systems to detect suspicious activity.

Is Using AI to Stop Crime Before It Occurs a Good Idea?

So-called predictive policing has its challenges. Critics like the National Association for the Advancement of Colored People argue it could increase racial biases in law enforcement, disproportionately affecting Black communities.

That said, using AI to uncover hidden patterns in arrest and police response records could reveal bias. Policy-makers could use these insights to address the root cause of systemic prejudice, ensuring fairness in the future.

Either way, there are still significant, unaddressed concerns about privacy. Various activists and human rights organizations say having a government-funded AI scan social media and monitor security cameras infringes on freedom.

What happens if this technology falls into the wrong hands? Will a corrupt leader use it to go after their political rivals or journalists who write unfavorable articles about them? Could a hacker sell petabytes of confidential crime data on the dark web?

Will More Countries Adopt These Predictive Solutions?

More countries will likely soon develop AI-powered prediction tools. The cat is out of the bag, so to speak. Whether they create apps exclusively for police officers or integrate a machine learning model into surveillance systems, this technology is here to stay and will likely continue to evolve.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How Countries Are Using AI to Predict Crime erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: ilo.org – “The Artificial Intelligence Illusion: How Invisible Workers Fuel the “Automated” Economy”

Artificial intelligence (AI) is often presented as a revolutionary force poised to automate vast swathes of the economy, displacing workers, and ushering in a “post-work” era. However, behind the sleek interfaces and impressive capabilities of many AI systems lies a hidden workforce of humans. This “human-in-the-loop” model reveals a more complex reality, one where AI is less about replacing humans and more about relying on workers with decent work deficits, such as low earnings, lack of social protection benefits and occupational safety and health to sustain the AI system. This is what we look at in our  AI-enabled business model and human-in-the-loop (deceptive AI) article, which examines how these workers power  automated systems and the implications for labour markets,  society, and for the workers themselves.

Invisible labour in the development and deployment of AI

From self-driving cars to virtual assistants, the AI industry thrives on data. This data needs to be meticulously labelled, categorised, and annotated. This requires human intelligence and labour – both of which still cannot be replaced by machines. Such tasks are often outsourced to crowdworkers on digital labour platforms or to Artificial Intelligence-Business Process Outsourcing (AI-BPO) companies. These platforms fragment complex tasks into microtasks and offer small payments for each completed task. Crowdworkers, whom are also known as invisible workers because they often work behind the scenes, are essential for training AI algorithms on several functions, such as text prediction and recognition of objects.

Similarly, virtual assistants, marketed as autonomous tools, often rely on invisible workers who may be transcribing audio, verifying the virtual assistant’s understanding, or even performing tasks like scheduling meetings that AI may struggle with. Even sophisticated large language models with impressive capabilities rely heavily on human trainers to fine-tune their responses and mitigate biases, toxicity, and disturbing content. As a result, workers are routinely exposed to graphic violence, hate speech, child exploitation and other objectionable material. Such constant exposure can take a toll on their mental health and trigger post-traumatic stress disorder, depression, and reduced ability to feel empathy.[…]

Read more: www.ilo.org

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Copyright: theconversation.com – “AI and Criminal Justice: How AI Can Support – Not Undermine – Justice”

Interpol Secretary General Jürgen Stock recently warned that artificial intelligence (AI) is facilitating crime on an “industrial scale” using deepfakes, voice simulation and phony documents.

Police around the world are also turning to AI tools such as facial recognition, automated licence plate readers, gunshot detection systems, social media analysis and even police robots. AI use by lawyers is similarly “skyrocketing” as judges adopt new guidelines for using AI.

While AI promises to transform criminal justice by increasing operational efficiency and improving public safety, it also comes with risks related to privacy, accountability, fairness and human rights.

Concerns about AI bias and discrimination are well documented. Without safeguards, AI risks undermining the very principles of truth, fairness, and accountability that our justice system depends on.

In a recent report from the University of British Columbia’s School of Law, Artificial Intelligence & Criminal Justice: A Primer, we highlighted the myriad ways AI is already impacting people in the criminal justice system. Here are a few examples that reveal the significance of this evolving phenomenon.

The promises and perils of police using AI

In 2020, an investigation by The New York Times exposed the sweeping reach of Clearview AI, an American company that had built a facial recognition database using more than three billion images scraped from the internet, including social media, without users’ consent.

Policing agencies worldwide that used the program, including several in Canada, faced public backlash. Regulators in multiple countries found the company had violated privacy laws. It was asked to cease operations in Canada.

Clearview AI continues to operate, citing success stories of helping to exonerate a wrongfully convicted person by identifying a witness at a crime scene; identifying someone who exploited a child, which led to their rescue; and even detecting potential Russian soldiers seeking to infiltrate Ukrainian checkpoints.[…]

Read more: www.theconversation.com

Der Beitrag AI and Criminal Justice: How AI Can Support – Not Undermine – Justice erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Utpal Chakraborty, Chief Digital Officer, Allied Digital Services Ltd., AI & Quantum Scientist – “AI Pioneers Claim Nobel Prizes: Transforming the Future of Science”

The year 2024 will be remembered for generations, marking a historic milestone as artificial intelligence researchers make unprecedented strides in multiple Nobel Prize categories. For the first time, AI pioneers were recognized not solely for advancements in AI itself but for groundbreaking contributions to physics and chemistry. This achievement highlights how the lines between traditional sciences and computer science (specifically AI) are blurring in ways that would have seemed unimaginable just a few decades ago.

The announcement sent ripples through the scientific community when Geoffrey Hinton and John Hopfield shared the Nobel Prize in Physics, while Demis Hassabis along with two other scientists claimed the Chemistry prize. Three brilliant minds known primarily for their AI work, now recognized for transforming our understanding of the physical world.

Geoffrey Hinton and John Hopfield received the Physics Nobel for their work on understanding phase transitions in complex systems through the lens of Neural Computation. Their groundbreaking discovery showed how the mathematics of phase transitions in materials shares fundamental principles with how Neural Networks learn and process information.

Hopfield’s contribution stemmed from his revolutionary 1982 paper (Neural networks and physical systems with emergent collective computational abilities) introducing the Hopfield network, a mathematical model that showed how collections of simple units could exhibit complex behavior similar to phase transitions in physics. The model demonstrated how memory could emerge from the collective behavior of simple components, much like how magnetic properties emerge in materials.

Hinton’s work complemented this by revealing how the principles of statistical mechanics, traditionally used to understand particle behavior in physics, could explain deep learning’s success. His breakthrough came from showing that the way neural networks optimize their weights (Backpropagation) follows the same mathematical principles that govern how physical systems find their lowest energy states.

Of course, many of us know these scientists primarily for their AI contributions:

– Hopfield’s neural networks revolutionized our understanding of associative memory and laid the groundwork for modern deep learning.

– Hinton’s work on backpropagation and deep belief networks essentially created the deep learning revolution we’re experiencing today.

But it’s their ability to bridge these seemingly disparate fields that makes their Physics Nobel Prize so significant. As Hinton once said at a conference, “The brain is a physical system. Why shouldn’t its principles help us understand other physical systems?”

On the other hand, Demis Hassabis’s Chemistry Nobel came for something equally remarkable – using AI principles to solve one of chemistry’s grand challenges, protein folding. His work at DeepMind led to AlphaFold2, but the Nobel recognized his deeper insights into how the principles of reinforcement learning could reveal fundamental rules governing molecular interactions.

The prize specifically acknowledged his team’s discovery of new chemical principles through AI analysis, principles that classical scientists had missed. By training AI systems to understand molecular behavior, they uncovered previously unknown patterns in how proteins fold and interact, revolutionizing our understanding of chemical processes at the molecular level.

Most know Hassabis as the founder of DeepMind and the mind behind AlphaGo, but his journey from AI to chemistry illustrates a broader trend in science. His background in neuroscience and computer games gave him a unique perspective on how complex systems organize themselves, whether they are neural networks, game strategies, or molecular structures.

What makes these Nobel Prizes so fascinating is how they highlight the convergence of different scientific disciplines.

The work of Hinton, Hopfield, and Hassabis shows us that these aren’t separate fields anymore, they are different lenses for viewing the same reality. Their discoveries reveal a deeper unity in science that we are only beginning to appreciate.

As I write this article, I can’t help but feel we are living through a new scientific revolution. The tools of AI aren’t just helping us do traditional science faster; they are fundamentally changing how we think about science itself.

Young researchers today don’t see themselves as just physicists, chemists, or computer scientists. They are explorers in a unified landscape where:

– Physical laws inform neural network design.

– Chemical principles inspire new computing architectures.

– AI algorithms reveal new patterns in nature.

What strikes me most about these Nobel laureates is their humanity. Despite working with machines and mathematical abstractions, they never lost sight of the human element in science.

As someone who has worked in these intersecting fields, I see these Nobel Prizes as more than just recognition of brilliant work. They are a signal that the future of science lies not in specialization, but in synthesis. The next generation of scientists won’t just cross boundaries – they’ll erase them.

These Nobel Prizes aren’t just awards; they are a glimpse of science’s future. A future where the boundaries between classical physics, quantum physics, chemistry, and computation disappear and where artificial intelligence helps us see the unity that was always there.

Der Beitrag AI Pioneers Claim Nobel Prizes: Transforming the Future of Science erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “5 Archaeological Discoveries Made by AI”

Artificial intelligence (AI) transforms many industries, and archaeology is no exception. It leverages machine learning and advanced data analysis to make it easier for researchers to discover and analyze ancient artifacts and sites.

Whether using satellite imagery to locate lost civilizations, deciphering ancient texts or predicting excavation sites, AI enhances the speed and accuracy of archaeological discoveries. As interest grows in AI’s ability to uncover hidden historical insights, it’s becoming a powerful tool for shedding new light on past mysteries.

1. Mapping Lost Civilizations

AI has proven invaluable in analyzing satellite imagery to uncover ancient cities and structures that have long been hidden from view. One remarkable example is the Nazca region in Peru. Deploying an AI system led to the discovery of 303 new figurative geoglyphs in just six months. This accomplishment would have taken years with traditional methods.

AI uses machine learning algorithms to sift through vast amounts of satellite data and quickly identify patterns and anomalies human eyes might miss. This ability to process large datasets rapidly and precisely makes AI far more efficient and accurate. This allows archaeologists to make discoveries faster and on a much larger scale.

2. Uncovering Hidden Texts

AI is a trailblazer for archeologists trying to read ancient texts that are too damaged for the human eye to decipher. One groundbreaking example is the Herculaneum scrolls, buried under volcanic ash and charred beyond recognition. Deep learning techniques allow researchers to read beneath the surface of these fragile artifacts.

Machine learning algorithms identified ink regions in the flattened papyrus, which would have otherwise remained invisible. Deep learning’s ability to sort and interpret massive numbers of images revolutionizes how these texts are classified and understood. This method reveals previously unreadable content and speeds up the analysis of ancient languages to accelerate discoveries in historical research.

3. Predicting Excavation Sites

AI is increasingly used to predict the most promising excavation sites by analyzing geographical data, historical records and patterns from past discoveries. Examining these large datasets can accurately identify likely locations for hidden artifacts and ancient structures.

Technologies like retrieval augmented generation (RAG) further enhance this process by providing access to the latest reliable information and enabling archaeologists to verify their claims in real time. This combination of AI’s data processing power and advanced technologies ensures efficiency and precision. It allows researchers to focus on areas with the highest potential and reduce time and resources spent on less promising sites.

4. Restoring and Reconstructing Artifacts

AI is crucial in reconstructing fragmented artifacts and structures by helping archaeologists visualize and restore damaged or lost pieces. It uses generative adversarial networks to rapidly manipulate portraits and landscapes and predict missing elements. One notable example is the RePAIR project, which aims to piece together ancient frescoes from thousands of fragments discovered in Pompeii.

AI systems analyze these fragments, predict how they fit together and help restore the art. This technology has also been applied to ancient pottery and sculptures, where AI predicts the shape of missing pieces, allowing archaeologists to recreate the original forms. Speeding up the reconstruction process and improving accuracy transforms restoration work, saving time and making it possible to recover more historical treasures.

5. Studying Human Evolution

AI enhances the study of ancient human migration patterns by analyzing genetic material and fossil evidence with unprecedented precision. Researchers can process complex datasets using deep learning models to trace how early humans moved and settled across different regions.

For example, deep learning models used to study the Mesopotamian floodplain environment achieved an impressive 80% detection accuracy in identifying archaeological sites. This level of precision allows scientists to understand human migration routes and settlement patterns. It also offers insights into the movements of ancient populations that would be difficult to uncover through traditional methods.

Why Staying Informed About AI Advancements Matters

Staying informed about the role of AI in archaeology opens the door to understanding new, groundbreaking discoveries that change how people view the past. AI’s potential to uncover even more hidden historical insights is immense as technology advances.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag 5 Archaeological Discoveries Made by AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.