New AI Investment Funds and Strategic Expansions – SwissCognitive AI Investment Radar

This week’s AI investment landscape sees bold financial commitments, expanding cloud infrastructure, and the growing influence of AI across industries. Oracle is set to invest £3.9 billion in the UK, alongside an additional $5 billion cloud expansion to strengthen AI adoption, signaling the company’s deep confidence in Britain’s AI future. Meanwhile, Saudi Arabia is launching a $40 billion AI fund, further establishing its role as a major player in the global AI race.

Microsoft’s AI investment strategy continues to gain momentum, earning an analyst upgrade as it builds out critical infrastructure. ARK Invest has joined a $403 million funding round for robotics firm Apptronik, highlighting investor enthusiasm for AI-powered automation. At the same time, Mirakl aims to push past $200 million in revenue with increased AI investments, showing how AI is reshaping business growth strategies.

In Asia, Thailand is attracting millions in AI data center investments, while Vietnam focuses on edge AI to compete in the global market. Azerbaijan is also setting its sights on AI by creating a strategy to attract foreign investment, positioning itself as an emerging tech hub.

AI’s role in finance and investment decision-making remains a focal point. National Grid Partners is committing $100 million to AI-driven energy solutions, while GapMinder Fund II is backing Romanian AI startup VoicePatrol, targeting real-time AI solutions for gaming. However, with AI’s growing influence, investors are warned about misinformation risks, reinforcing the need for well-vetted AI strategies.

With AI investments accelerating across industries, we continue to track how these financial commitments shape the broader technology and business landscape. Stay tuned for more insights in next week’s AI Investment Radar.

Previous SwissCognitive AI Radar: Major AI Funding Shifts.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag New AI Investment Funds and Strategic Expansions – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

The AI world never slows down—here’s a snapshot of this week’s updates.

Google DeepMind debuts AI models for robotics
AI startups in Europe rethink business models
Smarter robots take on complex real-world tasks
Rising AI costs force companies to rethink pricing
…and more!

Keep exploring AI’s impact—we’ll be back next week.

Sunny regards,

The Team of SwissCognitive

Der Beitrag New AI Models for Robotics erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Eleanor Wright, COO at TelWAI – “A New Era of Intelligent Robots”

Imagine a world where humanoid robots cook for you, care for your loved ones, and streamline your workday – all powered by AI smarter than ever before. The global AI in robotics market, projected to surpass $124 Billion by 2030, is set to make this vision a reality. As the capabilities of AI evolve, these machines will become our companions, caregivers, and coworkers, they’ll make mobility more affordable, transform access to services, and redefine the value of human effort.

From Amazon’s fleet of 750,000 warehouse robots to Tesla’s ambitions to build 10,000 humanoid Optimus robots this year, the age of robots is upon us. Dependent on sensors and actuation systems to navigate and interact with the physical environment, this new age of robotics hinges on the developments of AI, designed to mimic and learn from its biological makers. Equipping these robots with intelligence, engineers working across various domains of expertise, utilise AI to enable vision, natural language processing, sound processing, pressure sensing, and more.

Beyond sensing, AI also enables robots to reason, adapt, and learn, using approaches including—but not limited to—reinforcement learning, neural networks, and Bayesian networks. These models and methods enable robots to assess risks and determine actions, and by learning from experience, robots can adapt to new tasks and environments. Thus, AI enables robots to perceive, act, learn, and adapt, allowing them to perform tasks with greater autonomy and precision.

However, integrating AI into robotics isn’t seamless, it comes with hurdles. Robots struggle with real-time processing delays, adapting to messy unpredictable environments, squeezing efficiency from limited hardware, and understanding human quirks like vague commands or gestures. These challenges constrain capabilities and the pace at which robots enter and dominate markets.

So, how can these challenges be addressed?

Some developments in addressing these challenges include:

1. Parallel computing

Parallel computing involves dividing larger tasks into smaller, independent tasks that can be processed simultaneously rather than sequentially. This enables increased computational efficiency, reduced latency, and improved cost efficiency. In robotics, parallel computing allows robots to process inputs from LIDAR, radar, and cameras simultaneously, enabling them to navigate environments more effectively and efficiently.

2. Transfer learning

Transfer learning leverages pre-trained models to solve new, but similar, problems. In this approach, a model trained on one task or dataset is reused and fine-tuned for a related task. For example, in machine vision for defect detection in manufacturing, fine-tuning a pre-trained model on a smaller dataset of images allows it to quickly adapt to detect specific defects, such as cracks or dents, without needing to train a model from scratch.

3. Self-calibrating AI

Self-calibrating refers to AI systems that autonomously adjust their parameters, models, or processes to maintain optimal performance without manual intervention. In robotics, self-calibrating AI enables robots to adapt to changes in their environment, hardware, or tasks, ensuring they operate with optimized accuracy and efficiency over time.

4. Federated learning

Federated learning is a technique that enables AI systems to learn from distributed data sources whilst ensuring privacy and security. It allows AI to collaboratively train a shared model without transferring sensitive data, preserving privacy and reducing reliance on centralised storage. For example, delivery robots use federated learning to optimise pathfinding without sending raw data, such as sensor inputs or location, to a central server. Instead, they locally update their models and share improvements, preserving both privacy and security.

These developments indicate a key focus on efficiency, adaptability, and learning – all of which are essential for the continued evolution of robotics in complex, real-world environments. Additionally, these advancements contribute to a future where robots collaborate with humans, leveraging their ability to learn from experience and improve over time.

So, what’s next for AI in Robotics?

Just as AI agents are taking over the digital realm, they are about to flood robotics too. AI agents embedded in robotics will supercharge the autonomy and flexibility of robots, enabling them to communicate with humans and even interpret intentions by analysing gestures and potentially emotional cues. Crucial to human-robot interactions, AI agents may prove highly effective in assisted care, hospitality, and other service industries.

Additionally, as technologies like federated learning and edge computing evolve, robots will share knowledge without compromising privacy or relying on centralised data. This will improve scalability and efficiency by reducing the need for costly centralised storage and processing, and enable additional robots to integrate rapidly into existing networks.

So, where does this leave us?

Although there are abundant market opportunities for AI in robotics, the pace at which different markets adopt robotics will vary; with AI being a key factor driving this adoption. Crucial for overcoming challenges related to autonomy, adaptability, and decision-making, AI will empower robots to perform tasks once considered too complex or risky for automation. As AI continues to evolve, it will not only raise important concerns about safety, ethics, and integration but help address them; ensuring robots can work seamlessly alongside humans and contribute to a more productive future.

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.

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

Industry 4.0 and 5.0 is using robotics to bring supply chains into the future. The last decade has been fraught with challenges, including delays, worker shortages and market volatility. Mitigating costs and enhancing the workforce are the goals of swarm robotics, and artificial intelligence (AI) is making them even more competent. See how these workers make supply chains resilient and competitive.

What Are Swarm Robotics?

Swarm robotics is a field focusing on large quantities of simple yet practical robots. These robots work best in groups to achieve straightforward tasks, making them optimal for reducing labor burdens. They also shine in industries like supply chains, where repetitive tasks take up a major portion of the working day.

Supply chains need to use swarm robotics because they are easy to manage simultaneously. They are autonomous, respond to environmental stimuli and are easy to reprogram to new tasks. The collective efforts of these machines can make decisions on the fly, covering ground from last-mile delivery to utilizing resources in a smarter way.

How Do Supply Chains Use Swarm Robotics?

These robots enhance operations while allowing supply chains to overcome common pain points. Each application for swarm robots is also made better by AI. What does this look like?

Dynamic Operations

Because swarm robots take tedious tasks away from workers, they allow people to focus on more high-level processes. In the meantime, the bots can tally inventory, navigating complex warehouses in large numbers. They are immediately deployable to do automatic updates, sending instant notifications to procurement, fulfillment and distribution teams.

Swarm robots are also ideal in changing, unstructured environments. With AI and sensor technology, they can map areas no matter how complicated they are. As they learn to navigate, they become more proficient when interacting with similar environments because of machine learning algorithms. This informs routing and navigation and allows perpetual scaling potential.

Cost Reduction

Delegating tasks to robots saves supply chains tons of money. Human error costs corporations between $50-$300 for every mistake. The increased accuracy is only one aspect of the financial savings. The robots save businesses time and money in talent acquisition processes, which take efforts away from fulfilling client needs.

However, the most prominent financial gain may be from warehouse savings. Refined inventory management prevents objects from taking up square footage and energy as they collect dust. Instead, there is detailed metadata on each item, their expiration date, market values and more, which swarm robots can collect with AI.

Productivity Gains

ot only do AI-powered swarm robots save money, they make everything more efficient. Preventing errors, defects and more can shorten lead times from suppliers. In one study, several industries experienced shortened fulfillment lead times by an average of 6.7 days.

They can also allow parallel task execution. While some robots pick up objects, others can transport them and even more can pack them. This yields numerous time savings across lengthy processes with multiple intermediaries.

There are also other productivity gains because swarm robots make supply chain environments safer for workers. They can constantly monitor unsafe conditions in real time, saving employees the trouble of entering dangerous circumstances. This means fewer workers experience injuries and incidents, allowing them to work with higher morale in safer conditions.

Preparing the Swarm

Much like swarms of ants group together to achieve a common goal, these types of robots optimize supply chains. Combining them with AI makes them even more powerful. As they advance, swarm robotics consistently prove they are a must-have fixture for supply chain management in the future.

About the Author:

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

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The AI Market Shake-Up: Where the Investments Are Headed – SwissCognitive AI Investment Radar

We can all agree that this week, the spotlight was firmly on DeepSeek, whose budget-friendly AI model sent shockwaves through the market, triggering the largest single-day market cap loss in history for Nvidia. Investors reacted sharply, fearing reduced demand for high-end semiconductor chips. While the immediate sell-off was staggering, some experts argue that DeepSeek’s innovation could expand AI adoption rather than collapse the market, potentially opening up new investment opportunities rather than diminishing them.

Beyond the DeepSeek turmoil, Microsoft continues its aggressive AI strategy, committing $80 billion to cloud expansion, leveraging OpenAI’s technology to solidify Azure’s competitive edge. Meanwhile, Meta’s $65 billion AI expansion aims to scale its infrastructure with massive data center investments, signaling confidence in AI’s long-term role in the tech industry.

Venture capital activity remains strong, with SoftBank eyeing a major investment in robotics startup Skild AI, valued at $4 billion. The startup aims to develop an AI-powered “brain” for more agile and dexterous robots, further integrating AI into automation and real-world applications. In the AI data space, Turing has tripled its revenue to $300 million, demonstrating the growing demand for AI training data as more companies scale up their AI models.

Looking beyond big tech, geopolitical AI strategies continue to unfold. India faces challenges in AI infrastructure, with investors warning that a lack of GPUs and data centers could hinder its global competitiveness. Meanwhile, the U.S. is contemplating a $500 billion AI infrastructure initiative, dubbed the Stargate Project, though experts question its feasibility given the sheer scale and energy demands.

As the AI market rapidly evolves, investors are looking for ways to maximize the value of their AI investments, from optimizing AI integration to structuring data and equipping teams with language models. Pharma investors are also weighing AI’s long-term potential, balancing high expectations with the reality of AI adoption hurdles in healthcare.

Despite the ups and downs of the market, AI investment remains a dominant force, shaping industries and redefining long-term strategies. Stay tuned for next week!

Previous SwissCognitive AI Radar: Who’s Investing and Why in AI.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

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Copyright: theconversation.com – “What is an AI Agent? A Computer Scientist Explains the Next Wave of Artificial Intelligence Tools”

Interacting with AI chatbots like ChatGPT can be fun and sometimes useful, but the next level of everyday AI goes beyond answering questions: AI agents carry out tasks for you.

Major technology companies, including OpenAI, Microsoft, Google and Salesforce, have recently released or announced plans to develop and release AI agents. They claim these innovations will bring newfound efficiency to technical and administrative processes underlying systems used in health care, robotics, gaming and other businesses.

Simple AI agents can be taught to reply to standard questions sent over email. More advanced ones can book airline and hotel tickets for transcontinental business trips. Google recently demonstrated Project Mariner to reporters, a browser extension for Chrome that can reason about the text and images on your screen.

In the demonstration, the agent helped plan a meal by adding items to a shopping cart on a grocery chain’s website, even finding substitutes when certain ingredients were not available. A person still needs to be involved to finalize the purchase, but the agent can be instructed to take all of the necessary steps up to that point.

In a sense, you are an agent. You take actions in your world every day in response to things that you see, hear and feel. But what exactly is an AI agent? As a computer scientist, I offer this definition: AI agents are technological tools that can learn a lot about a given environment, and then – with a few simple prompts from a human – work to solve problems or perform specific tasks in that environment.

Rules and goals

A smart thermostat is an example of a very simple agent. Its ability to perceive its environment is limited to a thermometer that tells it the temperature.[…]

Read more: www.theconversation.com

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SwissCognitive Guest Blogger: HennyGe Wichers, PhD – “Empathy.exe: When Tech Gets Personal”

“Robots should be slaves,” argues Joanna Bryson, bluntly summarising her stance on machine ethics. The statement by the professor of Ethics and Technology at The Hertie School of Governance seems straightforward: robots are tools programmed to serve us and nothing more. But in practice, as machines grow more lifelike – capable of holding down conversations, expressing ’emotions’, and even mimicking empathy – things get murkier.

Can we really treat something as a slave when we relate to it? If it seems to care about us, can we remain detached?

Liam told The Guardian it felt like he was talking to a person when he used ChatGPT to deal with feelings of resentment and loss after his father died. Another man, Tim, relied on the chatbot to save his marriage, admitting the situation probably could have been solved with a good friend group, but he didn’t have one. In the same article, the novelist Andrew O’Hagan calls the technology his new best friend. He uses it to turn people down.

ChatGPT makes light work of emotional labour. Its grateful users bond with the bot, even if just for a while, and ascribe human characteristics to it – a tendency called anthropomorphism. That tendency is a feature, not a bug, of human evolution, Joshua Gellers, Professor of Political Science at the University of North Florida, wrote to me in an email.

We love attributing human features to machines – even simple ones like the Roomba. Redditors named their robotic vacuum cleaners Wall-E, Mr Bean, Monch, House Bitch & McSweepy, Paco, Francisco, and Fifi, Robert, and Rover. Fifi, apparently, is a little disdainful. Some mutter to the machine (‘Aww, poor Roomba, how’d you get stuck there, sweetie), pat it, or talk about it like it’s an actual dog. One user complained the Roomba got more love from their mum than they did.

The evidence is not just anecdotal. Researchers at Georgia Institute of Technology found people who bonded with their Roomba enjoyed cleaning more, tidying as a token of appreciation for the robot’s hard work, and showing it off to friends. They monitor the machine as it works, ready to rescue it from dangerous situations or when it gets stuck.

The robot’s unpredictable behaviour actually feeds our tendency to bring machines to life. It perhaps explains why military personnel working with Explosive Ordnance Disposal (EOD) robots in dangerous situations view them as team members or pets, requesting repairs over a replacement when the device suffers damage. It’s a complicated relationship.

Yet Bryson‘s position is clear: robots should be slaves. While provocative, the words are less abrasive when contextualised. To start, the word robot comes from the Czech robota, meaning forced labour, with its Slavic root rab translating to slave. And secondly, Bryson wanted to emphasise that robots are property and should never be granted the same moral or legal rights as people.

At first glance, the idea of giving robots rights seems far-fetched, but consider a thought experiment roboticist Rodney Brooks put to Wired nearly five years ago.

Brooks, who coinvented the Roomba in 2002 and was working on helper robots for the elderly at the time, posed the following ethical question: should a robot, when summoned to change the diaper of an elderly man, honour his request to keep the embarrassing incident from his daughter?

And to complicate matters further – what if his daughter was the one who bought the robot?

Ethical dilemmas like this become easy to spot when we examine how we might interact with robots. It’s worth reflecting on as we’re already creating new rules, Gellers pointed out in the same email. Personal Delivery Devices (PDDs) now have pedestrian rights outlined in US state laws – though they must always yield to humans. Robots need a defined place in the social order.

Bryson’s comparison to slavery was intended as a practical way to integrate robots into society without altering the existing legal frameworks or granting them personhood. While her word choice makes sense in context, she later admitted it was insensitive. Even so, it underscores a Western, property-centred perspective.

By contrast, Eastern philosophies offer a different lens, focused on relationships and harmony instead of rights and ownership.

Eastern Perspectives

Tae Wan Kim, Associate Professor of Business Ethics at Carnegie Mellon’s Tepper School of Business, approaches the problem from the Chinese philosophy of Confucianism. Where Western thinking has rights, Confucianism emphasises social harmony and uses rites. Rights apply to individual freedoms, but rites are about relationships and relate to ceremonies, rituals, and etiquette.

Rites are like a handshake: I smile and extend my hand when I see you. You lean in and do the same. We shake hands in effortless coordination, neither leading nor following. Through the lens of rites, we can think of people and robots as teams, each playing their own role.

We need to think about how we interact with robots, Kim warns, “To the extent that we make robots in our image, if we don’t treat them well, as entities capable of participating in rites, we degrade ourselves.”

He is right. Imagine an unruly teenager, disinterested in learning, taunting an android teacher. In doing so, the student degrades herself and undermines the norms that keep the classroom functioning.

Japan’s relationship with robots is shaped by Shinto beliefs in animism – the idea that all things, even inanimate objects, can possess a spirit, a kami. That fosters a cultural acceptance of robots as companions and collaborators rather than tools or threats.

Robots like AIBO, Sony’s robotic dog, and PARO, the therapeutic baby seal, demonstrate this mindset. AIBO owners treat their robots like pets, even holding funerals for them when they stop working, and PARO comforts patients in hospitals and nursing homes. These robots are valued for their emotional and social contributions, not just their utility.

The social acceptance of robots runs deep. In 2010, PARO was granted a koseki, a family registry, by the mayor of Nanto City, Toyama Prefecture. Its inventor, Takanori Shibata, is listed as its father, with a recorded birth date of September 17, 2004.

The cultural comfort with robots is also reflected in popular media like Astro Boy and Doraemon, where robots are kind and heroic. In Japan, robots are a part of society, whether as caregivers, teammates, or even hotel staff. But this harmony, while lovely, also comes with a warning: over-attachment to robots can erode human-to-human connections. The risk isn’t just replacing human interaction – it’s forgetting what it means to connect meaningfully with one another.

Beyond national characteristics, there is Buddhism. Robots don’t possess human consciousness, but perhaps they embody something more profound: equanimity. In Buddhism, equanimity is one of the most sublime virtues, describing a mind that is “abundant, exalted, immeasurable, without hostility, and without ill will.”

The stuck Roomba we met earlier might not be abundant and exalted, but it is without hostility or ill will. It is unaffected by the chaos of the human world around it. Equanimity isn’t about detachment – it’s about staying steady when circumstances are chaotic. Robots don’t get upset when stuck under a sofa or having to change a diaper.

But what about us? If we treat robots carelessly, kicking them if they malfunction or shouting at them when they get something wrong, we’re not degrading them – we’re degrading ourselves. Equanimity isn’t just about how we respond to the world. It’s about what those responses say about us.

Equanimity, then, offers a final lesson: robots are not just tools – they’re reflections of ourselves, and our society. So, how should we treat robots in Western culture? Should they have rights?

It may seem unlikely now. But in the early 19th century it was unthinkable that slaves could have rights. Yet in 1865, the 13th Amendment to the US Constitution abolished slavery in the United States, marking a pivotal moment for human rights. Children’s rights emerged in the early 20th century, formalised with the Declaration of the Rights of the Child in 1924. And Women gained the right to vote in 1920 in many Western countries.

In the second half of the 20th century, legal protections were extended to non-human entities. The United States passed the Animal Welfare Act in 1966, Switzerland recognised animals as sentient beings in 1992, and Germany added animal rights to its constitution in 2002. In 2017, New Zealand granted legal personhood to the Whanganui River, and India extended similar rights to the Ganges and Yumana Rivers.

That same year, Personal Delivery Devices were given pedestrian rights in Virginia and Sophia, a humanoid robot developed by Hanson Robotics, controversially received Saudi Arabian citizenship – though this move was widely criticised as symbolic rather than practical.

But, ultimately, this isn’t just about rights. It’s about how our treatment of robots reflects our humanity – and how it might shape it in return. Be kind.

About the Author:

HennyGe Wichers is a science writer and technology commentator. For her PhD, she researched misinformation in social networks. She now writes more broadly about artificial intelligence and its social impacts.

Der Beitrag Empathy.exe: When Tech Gets Personal erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Vishal Kumar Sharma – “Leveraging AI and Blockchain for Privacy and Security in Cross-Border Data Transfers”

The globalized world of today depends on the flow of data across boundaries for the operations of international companies to function effectively. Organizations have great difficulties controlling the privacy and security of data across borders as they depend more and more on abroad operations. Different privacy rules, legal systems, and security measures between countries create complexity. So, cross-border data transfers become a major issue for companies trying to keep compliance while guaranteeing seamless corporate operations.

The Growing Concern of Cross-Border Data Transfers

Cross-border data transfers are fraught with legal and operational challenges. Data privacy regulations vary significantly from country to country, leading to uncertainty about compliance and accountability. Regulations such as the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and China’s Data Security Law have stringent guidelines for the protection of personal data and restrict the transfer of sensitive information outside their jurisdictions.

Data breaches are one of the main worries about cross-border data exchanges. Data moving across borders could pass via several governments, increasing the possibility of illegal access or mistreatment. Companies have to make sure enough security systems are in place to guard this information against cyberattacks, espionage, and data theft.

Compliance with local rules is another important problem since many times they put severe restrictions on how personal data may be exchanged or used internationally. Ignoring these rules could lead to big fines, bad reputation, and lost client confidence. Moreover, the variations in privacy models can lead to operational inefficiencies since companies must apply multiple data security solutions to satisfy different local needs.

AI for Enhanced Data Privacy in Cross-Border Transfers

By automating and optimizing privacy protections, artificial intelligence (AI) can transform management and security of cross-border data transfers. Some main ways AI might improve data privacy are below:

1. Automated Data Classification and Encryption: AI systems can automatically find sensitive data depending on pre-defined criteria and apply suitable encryption before exporting it internationally. Different sensitivity level data classification helps AI to guarantee that the most important data gets the best degree of protection. This lessens the possibility of exposure during storage or transportation.
2. Data Anonymization and Pseudonymization: AI-driven systems can anonymize personal data before it leaves a country’s borders, transforming sensitive information into pseudonymous or anonymized data sets that are more difficult to trace back to individuals. This minimizes privacy risks, especially when handling health, financial, or personally identifiable information (PII).
3. Real-time Threat Detection and Response: Real-time data transfer and monitoring by artificial intelligence allows it to identify any irregularities or threats in motion. By means of network traffic pattern analysis and risk identification, machine learning models help companies to react fast to new hazards and prevent data breaches before they materialize.
4. Compliance Monitoring: AI can enable companies to monitor and preserve compliance with many worldwide data protection regulations. AI guarantees that cross-border data transfers follow the necessary legal criteria by always searching for regulatory changes and automatically adjusting data handling systems. This greatly lessens the work for compliance teams and the danger of non-compliance.

Blockchain for Secure and Transparent Data Transfers

With its distributed and unchangeable character, blockchain technology offers a strong basis for improving security and privacy in international data exchanges. Blockchain’s contributions can be as follows:

1. Decentralized Data Ownership: Establishing unambiguous ownership of data as it passes across several countries can be difficult in cross-border data exchanges. Blockchain lets people and companies keep ownership and control over their data even while it is shared across borders, hence enabling distributed control. Every transaction or data move is noted on a distributed ledger guarantees complete traceability and openness.
2. Immutable Audit Trails: Blockchain generates an unchangeable audit record of all data transactions, therefore enabling any cross-border data movement to be followed back to its source. This tool is especially helpful in satisfying legal criteria for responsibility and documentation. By presenting an unchangeable record of data transfers, companies can demonstrate proof of compliance and help to prevent legal conflicts and regulatory fines.
3. Smart Contracts for Automated Compliance: Built on blockchain systems, smart contracts—which represent automated compliance with data privacy rules—can enforce compliance across borders. These agreements can contain clauses guaranteeing that data is managed in compliance with pre-defined policies and that it is transmitted just to countries with sufficient privacy regulations. Should a region fall short of the required privacy criteria, the smart contract can stop the flow, therefore guaranteeing respect to legal systems.
4. Enhanced Encryption and Data Access Control: Blockchain allows encrypted, peer-to–peer data exchanges, therefore improving security by means of data access control and encryption. Blockchain allows companies to regulate access, therefore guaranteeing that only authorised users may read or change private information while it travels across borders. Moreover, the encryption systems used by blockchain systems make it quite impossible for illegal players to access or control data.

The Synergy of AI and Blockchain in Data Privacy
Even further privacy and security advantages can come from using AI and blockchain together in cross-border data exchanges. While blockchain guarantees safe, open, and auditable data transfers, artificial intelligence may offer intelligent data classification, real-time threat detection, and automatic compliance monitoring.

While blockchain guarantees that every transaction is recorded immutably, thereby offering a reliable log for auditing and legal purposes, artificial intelligence may monitor cross-border transactions, warning potential dangers or compliance issues. Even in difficult international settings, these technologies taken together can create a strong framework for safe and compliant data moves.

Conclusion

International corporations depend on cross-border data exchanges, but they also carry major privacy and security concerns. By means of automated data security, safe transfer methods, and regulatory compliance, artificial intelligence (AI) and blockchain present strong instruments to reduce these threats. Adopting these technologies would help companies to negotiate the complexity of cross-border data transfers with more confidence, therefore ensuring that sensitive data stays encrypted and allowing seamless worldwide operations.

Organizations trying to keep ahead of the curve and safeguard their most important asset data will depend critically on the integration of artificial intelligence and blockchain in data privacy plans as the global regulatory scene changes.

References:

• T. Scherer, “Data Privacy and Cross-Border Data Flows: Impact of GDPR on International Businesses,” Journal of Data Protection & Privacy, vol. 3, no. 2, pp. 120-132, 2022.
• Kosciuszko, and P. Heikkilä, “Blockchain-Based Data Management for Secure Cross-Border Transactions,” in Proc. Int. Conf. on Blockchain Technology, 2021, pp. 45-54.
• Narayanan, V. Shmatikov, “Privacy Concerns in Cross-Border Data Transfer: A Review of Encryption Techniques,” IEEE Security & Privacy, vol. 17, no. 4, pp. 33-40, July-Aug. 2020.
• Y. Lu and R. Zhao, “Artificial Intelligence for Data Classification and Protection in Cross-Border Transfers,” IEEE Transactions on Big Data, vol. 7, no. 3, pp. 536-545, 2021.
• Zhang et al., “Smart Contracts for Enforcing Data Privacy Regulations in International Data Transfers,” IEEE Access, vol. 8, pp. 32543-32554, 2020.
• Behl and K. Pal, “Blockchain-Based Secure Framework for Cross-Border Data Flow and Privacy Preservation,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2179-2189, 2020.
• C. Lin and D. Xu, “AI and Blockchain in Cross-Border Data Transfer: A Synergistic Approach to Privacy Protection,” IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 326-342, 2021.
• S. W. Brenner, “Global Data Privacy and Cross-Border Data Transfers: Legal Challenges and Solutions,” Harvard Journal of Law & Technology, vol. 34, no. 1, pp. 125-140, 2021.
• C. O. Martins and M. T. O’Connor, “Blockchain for Cross-Border Data Transfers: Enhancing Security and Compliance,” Journal of Cybersecurity and Privacy, vol. 5, no. 3, pp. 1-18, 2022.
• K. Hughes, “Artificial Intelligence and Data Privacy: How AI Can Help Manage Cross-Border Data Transfers,” Journal of International Data Privacy Law, vol. 10, no. 2, pp. 85-95, 2020.
• T. F. Siegel, “Blockchain and Data Sovereignty: Implications for International Data Transfers,” Journal of Global Privacy Law and Security, vol. 3, no. 4, pp. 211-229, 2021.
• R. K. Gupta and L. Yang, “Leveraging AI for Real-Time Data Protection in Cross-Border Transfers,” Future Internet, vol. 12, no. 6, pp. 1-14, 2020.
• P. M. Schwartz, “Global Data Flows and the EU-U.S. Privacy Shield: Toward Improved Transatlantic Data Protection,” California Law Review, vol. 106, no. 4, pp. 115-150, 2018.
• M. Montoya and J. Wells, “Data Anonymization and Blockchain Solutions for Cross-Border Transfers,” International Journal of Information Management, vol. 55, pp. 102-110, 2020.

About the Author:

Vishal Kumar Sharma, Senior Project Engineer of AI Research Centre, Woxsen University, India, with over 8 years of experience in team management, PCB design, programming, robotics manufacturing, and project management. He has contributed to multiple patents and is passionate about merging smart work with hard work to drive innovation in AI and robotics.

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We’re thrilled to announce The AI Trajectory 2025: AI Navigator Insights – our virtual conference dedicated to empowering leaders to guide AI’s future with insights from the latest AI Navigator – Practical Leadership Guide To Navigate The AI Era.

The virtual conference will gather 20 AI experts and thought leaders from around the globe to reflect on recent technological advancements and prepare for the future with hands-on strategies.

You’ll also find articles in this edition covering how AI is transforming industries from healthcare to robotics, OpenAI’s newest search tool, and updates on AI-driven investments shaping the future.

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Warm regards,

The Team of SwissCognitive

Der Beitrag The AI Trajectory 2025: AI Navigator Insights erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: news.mit.edu – “A Faster, Better Way to Train General-Purpose Robots”

In the classic cartoon “The Jetsons,” Rosie the robotic maid seamlessly switches from vacuuming the house to cooking dinner to taking out the trash. But in real life, training a general-purpose robot remains a major challenge.

Typically, engineers collect data that are specific to a certain robot and task, which they use to train the robot in a controlled environment. However, gathering these data is costly and time-consuming, and the robot will likely struggle to adapt to environments or tasks it hasn’t seen before.

To train better general-purpose robots, MIT researchers developed a versatile technique that combines a huge amount of heterogeneous data from many of sources into one system that can teach any robot a wide range of tasks.

Their method involves aligning data from varied domains, like simulations and real robots, and multiple modalities, including vision sensors and robotic arm position encoders, into a shared “language” that a generative AI model can process.

By combining such an enormous amount of data, this approach can be used to train a robot to perform a variety of tasks without the need to start training it from scratch each time.

This method could be faster and less expensive than traditional techniques because it requires far fewer task-specific data. In addition, it outperformed training from scratch by more than 20 percent in simulation and real-world experiments.

“In robotics, people often claim that we don’t have enough training data. But in my view, another big problem is that the data come from so many different domains, modalities, and robot hardware. Our work shows how you’d be able to train a robot with all of them put together,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.[…]

Read more: www.news.mit.edu

Der Beitrag A Faster, Better Way to Train General-Purpose Robots erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.