Who’s Betting, Where, and Why in AI – SwissCognitive AI Investment Radar

As global AI funding levels remain elevated, this week’s investment activity reveals a tightening pattern: fewer hubs, bigger bets, and sharper focus. Silicon Valley, Beijing, and Paris now account for 80% of global AI funding, while other regions navigate capital scarcity and look for niche leverage. Meanwhile, Amazon’s CEO used his annual letter to justify billions already spent, calling AI investments a necessity for long-term competitiveness.

In San Francisco, startup Virtue AI secured $30 million to tackle deployment risk, a concern that’s becoming more pronounced as adoption scales. UK-based Synthesia reported $100 million in revenue and welcomed Adobe Ventures as a new backer, underscoring the value of enterprise AI tools that are already delivering results. And in China, a newly launched $8 billion AI fund backed by government and finance ministries will channel early-stage investments into foundational research and startup formation.

CEE continues to gain investor attention as a cost-efficient and increasingly capable AI development region, while Korea saw a domestic political pledge of $70 billion toward AI initiatives. On the infrastructure front, Nvidia’s $500 billion long-term strategy—including chips and supercomputing partnerships—continues to drive share price gains, while nEye Systems closed a $58 million round to push optical chip development further into the AI stack.

Big tech players aren’t staying out of the startup scene either. Alphabet and Nvidia reportedly invested in SSI, the new venture by OpenAI co-founder Ilya Sutskever, and ex-OpenAI CTO Mira Murati’s startup is reportedly eyeing a massive $2 billion seed round. CMA CGM’s €100 million partnership with Mistral AI brings logistics into the funding spotlight, and the trend toward agentic AI for financial research continues to spread across fintech.

Previous SwissCognitive AI Radar: AI Funding Highlights.

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 Who’s Betting, Where, and Why in AI – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How AI Transforms EV Charging Networks”

People who drive gas-powered vehicles can lug a fuel can around if they ever run out while driving. For electric vehicle (EV) owners, it isn’t as easy. Many fear being stranded on the side of the road, which is why charging infrastructure is so important. However, chargers are often unreliable or outright out of order. Is artificial intelligence the solution?

Why EV Charging Networks Need an Overhaul

The current state of EV charging networks is less than ideal. Harvard Business School research revealed that charging stations are largely unreliable — and drivers are aware and dissatisfied. They can only successfully recharge using nonresidential stations an estimated 78% of the time, meaning one in five chargers in the United States don’t work. This makes them less reliable than the average gas pump.

Omar Asensio — the Harvard Business School fellow who led the study — said the main reason for this substandard reliability is that no one’s maintaining the stations. While these complex machines require extensive maintenance to keep the circuitry in peak shape, they are often neglected.

When electrical systems break down, equipment damage is not the only outcome. Potentially dangerous situations will occur unless companies perform electrical system maintenance regularly. Loose connections and fried circuits can ignite materials or shock users, causing injuries or death.

While the seemingly obvious solution is for drivers to recharge at home, people use home chargers just 10% of the time, according to one software company. Although modern batteries can reach hundreds of miles on a single charge, many people fear theirs will run out of power before they reach their destination, leaving them stranded. Besides, installation can be expensive, depending on their location and the type of at-home station they choose.

Companies Could Change EV Charging With AI

AI could help companies resolve the sector’s current charging challenges. For starters, it could autonomously manage loads, distributing power efficiently and safely among multiple stations. Reducing grid load — especially during peak hours — helps prevent EV charging equipment from damaging transmission lines, circuit breakers or transformers.

A study from the University of Michigan’s Transportation Research Institute proves this point. It states that large-scale, unmanaged EV charging could cause sudden current draw fluctuations, damaging the electrical grid. This inconsistency can lead to inefficient energy consumption, resulting in transformer strain. An outage is the likely outcome of accelerated equipment wear and energy waste.

Much of the U.S. power grid is already on its last legs. For instance, around 70% of the transmission lines are nearly three decades old, nearing their expected life span of 50 to 80 years. Minimizing strain with AI-powered smart load management can prevent outages while ensuring every battery is fully recharged.

A more comprehensive solution leverages predictive maintenance. Machine learning models can anticipate possible outcomes. They can use embedded, internet-enabled sensors to identify faults like a fried circuit or frayed wire. Maintenance teams would get real-time alerts, minimizing unplanned downtime.

AI could even improve battery health monitoring, maximizing charging efficiency. A research team from the United Kingdom’s Cambridge and Newcastle Universities discovered a machine learning method is 10 times more accurate than the current industry standard technique. It measures electrical pulses instead of tracking current and voltage during charge and discharge cycles. Improving EV battery reliability could transform the charging network’s layout.

Where would companies place new stations? With AI, they could analyze metrics like EV demand, travel frequency and location to determine where to build them. They could also optimize charging network design by plugging their budget, desired density and grid capacity into the algorithm.

Improving EV Charging Infrastructure With AI

Access to a reliable charging network is tightly intertwined with people’s opinions of EVs themselves — meaning companies can only make this mode of transportation more popular if they improve the reliability of the underlying infrastructure. AI is one of the few technologies that could help them fast-track this achievement.

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 AI Transforms EV Charging Networks erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dileep Kumar Pandiya – “AI for Transformative Enterprise Growth: Insights from a Principal Engineer”

You know, it’s amazing to think about. Imagine your sales team closing deals twice as fast. Or your supply chain just adapting on the spot when the market shifts. Honestly, it’s not something from the future—it’s happening now, all thanks to AI.

I have been working in tech for almost 18 years, and I’ve seen how these tools turn ambitious ideas into actual results. I want to show you what that looks like in real life—where AI didn’t just help businesses grow, it completely changed the game.

How AI Unlocks Growth in Enterprises

What if your business could predict customer needs before they even knew them? AI makes this possible. It’s no longer about guesswork or reacting late; it’s about proactive strategies powered by data.
Take a retail chain struggling with overstock issues. By implementing AI to forecast demand using real-time trends, they reduced inventory waste by 20% and increased availability of high-demand items by 15%. It’s a transformation that goes beyond efficiency—it’s about building smarter, more agile businesses.

AI Copilot: Redefining Sales with AI

Sales has always been about timing and relationships. But what if AI could help you focus on the right opportunities at exactly the right moment? That’s the promise of AI Copilot.
When we launched Copilot, the goal was simple: empower sales teams to act smarter and faster. By integrating AI, I built a platform that could analyze millions of data points in seconds to identify high-potential accounts. The result: Sales teams were no longer overwhelmed by data they were driven by insights.
Here’s what stood out most to me: within three months, Copilot wasn’t just saving time—it was generating millions in additional revenue. Seeing the tangible impact on businesses and hearing feedback like “I can’t imagine working without this” made every late night worth it.

Scaling Smarter with AI and Microservices

Think of a system that can process thousands of real-time events every second, with no downtime. That’s what we built with the Phoenix Project, a scalable platform that uses AI and microservices to empower B2B clients.
One client used this platform to optimize marketing campaigns dynamically. Instead of waiting weeks for data analysis, they could adjust strategies on the fly, improving lead quality by 30% and cutting acquisition costs dramatically. It’s proof that scalability isn’t just a technical goal—it’s a business imperative.

Lessons for Enterprises Ready to Embrace AI

Here’s a story I often share: A small business hesitant to invest in AI started with a single pilot project—automating customer inquiries with AI chatbots. Within six months, they expanded the system to handle order tracking, inventory checks, and even personalized product recommendations. Today, they credit AI for a 25% increase in customer retention.
My takeaway is to start small, but think big. AI’s value compounds over time, so even small steps can lead to significant transformations.

Future Trends in AI and Enterprise Growth

The future isn’t just about doing things faster—it’s about doing them smarter. Imagine systems that can explain their decisions clearly or tools that work alongside humans to tackle complex problems.
One trend I’m particularly excited about is real-time decision-making. For example, picture a global logistics company rerouting shipments during a storm, avoiding delays and cutting costs. This kind of agility is becoming the new standard, and businesses that embrace it early will set themselves apart.

Final Thoughts

AI is the foundation for building the future of business. Whether it’s transforming sales strategies, driving efficiency, or enabling agility, the opportunities are immense. My advice: Don’t wait for the perfect moment to start. Take a step, learn, and grow with AI.

About the Author:

Dileep Kumar Pandiya is a globally recognized Principal Engineer with over 18 years of groundbreaking work in AI and enterprise technology. He has pioneered transformative AI-driven platforms and scalable systems, driving innovation for Fortune 500 companies like ZoomInfo, Walmart, and IBM. His leadership has redefined sales technology and digital transformation, earning him prestigious awards and international acclaim for his contributions to business growth and industry advancement. Known for his ability to blend visionary thinking with practical solutions, Dileep continues to shape the future of enterprise technology.

Der Beitrag AI for Transformative Enterprise Growth: Insights from a Principal Engineer 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.

Der Beitrag Leveraging AI and Blockchain for Privacy and Security in Cross-Border Data Transfers erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cepr.org – “Distance From Innovation is a Barrier to the Adoption of Artificial Intelligence”

While distance may seem unimportant in the face of technological progress in transportation and communication, several studies have found that distance is a barrier to the diffusion of inventive activity and technology adoption. This column examines US firms’ adaptation and adoption of artificial intelligence in response to AI innovation. The authors find that distance from innovation hotspots reduces growth in AI research jobs as well as in jobs adapting AI to new industries, with the effect driven by AI publications rather than AI patents. Twenty percent of the overall distance effect is explained by the presence of state borders, which may impede migration and thus flows of tacit knowledge.

The extent to which geographic distance is a barrier to technological knowledge transfer is of interest to governments of countries distant from centres of knowledge creation or technology production; to entrepreneurs deciding where to locate a new firm that will need to remain abreast of technological developments; and to national or local policymakers seeking to influence the decisions of such entrepreneurs. These agents may value knowledge transfer as an input to further knowledge creation, or as a prerequisite for the adoption of new technology practices.

Distance may seem unimportant in the face of technological progress including the telephone, modern means of transportation, email, texting, the worldwide web, and video conferencing. Yet, several studies have found that distance is a barrier to the diffusion of inventive activity and to the cross-country diffusion of technology adoption, and prior work has also shown that US state borders are barriers to citations of patents. ¹ Cross-location collaboration and citing of academic papers and patents have been increased by shorter travel times, other papers have found.² […]

Read more: www.cepr.org

Der Beitrag Distance From Innovation is a Barrier to the Adoption of Artificial Intelligence erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How AI Is Transforming Drone Delivery”

E-commerce has grown exponentially since the COVID-19 pandemic drove people out of stores and into their homes. Old habits die hard as consumers continue relying on their trusty devices to make purchases.

Although business has gone well for online retailers, meeting customer expectations with timely deliveries poses considerable challenges. Fortunately, artificial intelligence (AI) advancements have paved the way for leveraging drone delivery. However, some issues must be addressed before widespread adoption.

The Rise of Drone Delivery

The convenience of e-commerce has evolved shopping, whether for food, clothing, electronics or last-minute gifts. The consumer landscape has set its sights on drones for fast, reliable delivery to fulfill these orders.

Though still in its infancy, drone delivery has the potential to reduce distribution times, improve accuracy, and lower standard shipping costs for businesses and buyers. From 2019 to 2022, customers received over 660,000 drone delivery flights, with even more tests conducted to develop and improve this method.

However, despite drones’ efficiencies, the demand outweighs current capabilities. For instance, 10 years after announcing 30-minute drone deliveries for items under 5 pounds, Amazon only fulfilled 100 shipments using the technology in the United States.

If Amazon can overcome the regulatory and logistics hurdles, its promise of 30-minute deliveries will significantly appeal to consumers. The market value for same-day delivery was $5,795.64 million in 2021, while experts predicted the demand to rise by 12.65% by 2027. Drones can play a critical role in achieving customer satisfaction.

Drone Delivery: An AI Revolution

AI has rapidly infiltrated every industry, with more companies integrating the technology into their business models. The use of AI in drone delivery fleets is a game-changer for e-commerce and has already proven its effectiveness. Here are three ways AI-powered drones have changed how orders get distributed.

1. Improved Navigation

Conventional drones use GPS to navigate, which may be unreliable in some areas. However, AI-backed drones rely on real-time sensors and computer vision to take in their surroundings.

High-resolution cameras and light detection sense various landscapes, synthesizing the information in real time via an AI algorithm. Image and object recognition allows the drone to pinpoint potential obstacles and track different objects to avoid crashing.

LiDAR sensors also use ranging lasers to map the terrain and measure distances — drone features commonly found in the surveying sector.

2. Optimized Routes

AI can process large quantities of traffic information, such as routes, congestion, rerouting, weather conditions and buildings. Drones aren’t restricted to roadways, so the algorithm can plan the appropriate path for optimized delivery of goods with reduced flight duration.

The drone delivery fleet is also more eco-friendly than traditional methods, producing nearly 47 times fewer greenhouse gases and less energy.

3. Enhanced Security

AI’s ability to detect and avoid obstacles protects the drone and its packages. The system can also circumvent unauthorized areas during flight. As a result, in-flight safety is guaranteed and orders are delivered securely.

Companies can also use AI to detect maintenance issues with drones, allowing them to ensure their fleet is up to date and prevent malfunctions.

AI Drones in the Future

Although AI-powered drones are a feat in technological advancements, the method isn’t perfect — a reason widespread adoption has been slow. For instance, AI may have a 74-day shorter breach time frame, saving companies around $3 million more than those who don’t use it. However, drone privacy is still a hotly debated topic.

The Federal Aviation Administration (FAA) requires drones to publicize their locations, meaning anyone can view the destinations and track their flight routes. A privacy breach could lead to unsolicited advertisements and the release of personal information.

Drones are also vulnerable to state and city regulations. Phoenix would be an ideal location to set up drone delivery. However, much of the city has restricted airspace, with several small airports and an Air Force base. The FAA would hesitate to allow drones to fly without a pilot for safety reasons.

Additionally, although rural areas could greatly benefit from drone delivery fleets, densely populated suburban and metropolitan regions might pose more of a problem. AI-powered drones can navigate obstacles, but trees, swimming pools, animals, cars and people may challenge the most advanced software. Inclement weather may also be an issue, as not every area has dry, sunny weather conditions year-round.

AI-Powered Drone Delivery Has Potential

AI-backed drones can improve e-commerce and ensure consumer satisfaction in package delivery. However, industries must overcome regulatory and logistics issues before the world relies on them to do the job correctly.

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 AI Is Transforming Drone Delivery erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: nature.com – “Using machine learning algorithms to enhance IoT system security”

The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand.

To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain.

The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT.

The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1.

It highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy, and also illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.[…]

Read more: www.nature.com

Der Beitrag Using Machine Learning Algorithms To Enhance IoT System Security erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Credit: This interview by Andreas Jäggi with Dalith Steiger-Gablinger has been published in German as “80 Prozent der Menschen wollen durch künstliche Intelligenz (KI) effizienter werden” – “80% Of People Want To Become More Efficient Through Artificial Intelligence (AI)”

Dalith Steiger-Gablinger is co-founder of the award-winning AI start-up SwissCognitive. With her extensive international experience in AI strategy consulting, Dalith will deliver the keynote at the Swiss Conference for HR/Internal Communication – Fit for the Future: AI in HR and Internal Communication – Perikom

What does SwissCognitive actually do?

We identify promising start-ups in the field of Artificial Intelligence (AI) around the world in order to bring them together with investors. We advise start-ups on their development and strategy and open doors to strategic partners and customers in line with our motto. “We unleash the potential of AI in business.”

How do you rate the Swiss AI start-up scene?

We are well positioned and have many great spin-offs from ETH/EPFL, the universities, and other higher education institutions. Unfortunately, venture capital is more difficult to find here than in the USA, for example. I would like to see us develop a culture that prefers to invest rather than inherit because we help the next generation the most with innovation in our country.

How do you define AI?

For me, artificial intelligence is an inappropriate term. After all, we’re not talking about artificial birds when we talk about airplanes. Artificial intelligence conjures up the idea that AI is supposed to copy the intelligence of humans. And that’s scary. What we mean by AI is intelligent systems that use data and algorithms to find solutions that we humans would never have thought of, for example. Or systems that can carry out work processes more effectively and accurately.

What can’t AI do?

Enjoying human time. And what it should not do are things that only humans are entitled to do. Falling in love or getting married, for example. What I’m trying to say is that not everything that is possible is necessary.

Where is AI already being used in business?

For example, it has been very successful in the medical and healthcare sector. In image recognition, AI is used to detect malignant cell growth at an early stage. There are also various applications in increasing productivity, such as in the supply chain and logistics, which also have a positive effect on CO2 reduction. When using AI, I recommend asking yourself whether a process should simply be improved or whether even new business models are needed.

And what about the acceptance of AI?

Studies show that over 80 percent of people want help from AI systems to work more efficiently and use it to further their careers. For me, this figure reflects a high level of acceptance for the use of AI. Employers run the risk of losing their employees to the competition if they do not keep up with this development.

Where is AI being used in HR?

A lot is already being done in the recruiting process. And when used well, AI can help us to make our unconscious biases visible when making personnel decisions and help us to have more diverse, creative, motivated and efficient employees. Much more will be possible in the future. It is worth to mention the possibility of recognizing employees who are on the verge of resigning at an early stage and finding solutions with them on how they can continue to contribute their potential to the company.

And where do you see the potential in communication?

I also see great and fascinating opportunities here. For example, I can articulate ideas well, but I am only able to translate them into an emotional, convincing visual world with the support of AI. Or people who, for various reasons – including a disability – have struggled with verbal expression are given new access with the help of AI. Also, at the level of research, analysis, summaries and translation, we are already seeing immense efficiency gains through artificial intelligence (AI).

Perikom’s question is: How do you use artificial intelligence in communications or HR in your company?

Event information:

Swiss Conference for HR/Internal Communication Fit for the Future – AI in HR and Internal Communication

May 16, 2024, 13:00, at the HWZ University of Applied Sciences in Business Administration Zurich

Where is AI succeeding in delivering on the promise of more efficient? Where are AI tools already in use and bringing relief to work in HR and internal communication?

Keynotes

• “What opportunities does AI offer for our day-to-day work?” Dalith Steiger-Gablinger, SwissCognitive
• “Deep fakes, AI and the credibility of the media”. Mathias Heller, Netzwerk Faktencheck, and Florian Notter, Product Manager AI, both SRF

Break-out sessions

• “Using AI with your own digital assistants: how it works.” Pascal Rosenberger, eggheads
• “Artificial intelligence in recruitment”. Fabio Blasi, Head of AaraNetos Cantonal Hospital
• “Application of AI for the entire value chain of internal communication.” Daniel Jörg, Farner Consulting
• “Opportunities through AI for personnel management.” Ambros Scope, Zurich Insurance

Complete program and registration: perikom website

About Perikom:

Professional association for personnel management and internal communication. Promoting cooperation between communication and HR experts. For effective internal communication, it is essential that human resources and organizational communication work together. Find out more: www.perikom.ch

Original article in german.

Der Beitrag 80% Of People Want To Become More Efficient Through Artificial Intelligence (AI) erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Sandeep Saharan, Assistant Professor, AI Research Centre, Department of AI and Analytics, School of Business, Woxsen University, Vice President, Woxsen University and Dr. Hemachandran Kannan, Professor and Director, AI Research Centre, Department of AI and Analytics, School of Business, Woxsen University – “Exploring the Cognitive Psychology of Consumer Behavior in the Age of Artificial Intelligence”

In the dynamic and always changing realm of digital marketing, maintaining a competitive edge is essential for achieving favorable outcomes. With the increasing connectivity and engagement of customers on social media platforms, companies are using data analytics to optimize their marketing tactics. The amalgamation of data analytics with social media marketing is influencing the trajectory of advertising and enabling organizations to execute more precise, customized, and impactful campaigns compared to previous practices. This article aims to examine the interdependent association between data analytics and social media marketing, elucidate the advantages it presents, and elucidate how enterprises might exploit this convergence to stimulate expansion and involvement.

The Rise of Social Media Marketing: The pervasive use of social media has become an indispensable component of our everyday existence. Social media platforms provide as a means of establishing connections with friends, engaging with preferred companies, and exploring novel goods and services. Consequently, social media platforms have undergone significant transformations, becoming as influential marketing tools that provide unique capabilities in terms of audience reach and engagement. The substantial number of people present on social media platforms such as LinkedIn, Twitter, Instagram, Facebook, and TikTok renders them very advantageous environments for companies to establish connections with their intended target demographics. Nevertheless, the primary obstacle is in differentiating oneself within the densely populated digital environment and providing material that effectively connects with people. Data analytics plays a crucial role in this context. The field of data analytics encompasses the processes of gathering, scrutinizing, and interpreting data in order to arrive at well-informed and rational decision-making. Within the realm of social media marketing, the use of data analytics empowers firms to get valuable information pertaining to user behavior, preferences, and prevailing trends. These insights possess significant value in the customization of marketing tactics that are not only efficient but also relevant and current.

The Data-Driven Approach to Social Media Marketing

The era of just depending on intuition or subjective estimations to develop marketing initiatives has become obsolete. The use of data-driven methodologies in social media marketing is significantly reshaping the business. Here’s how it works:

Audience Insights

Data analytics tools provide firms a plethora of information pertaining to their social media audience. This encompasses several factors such as demographic characteristics, geographical location, areas of interest, and even real-time indicators of user involvement. With this acquired information, marketers has the ability to generate content that effectively communicates with their intended target audience.

Content Optimization

The examination of various content formats, such as photographs, videos, infographics, and articles, enables organizations to enhance their content strategy. Data analysis allows marketers to get insights into audience preferences and identify successful strategies, allowing them to concentrate their efforts on effective approaches.

Timing and Frequency

The use of data analytics may facilitate the identification of optimal time periods for content posting, hence maximizing exposure and interaction. Additionally, it may provide valuable information about the optimal frequency of content sharing, ensuring that the audience is neither overwhelmed or alienated.

Competitor Analysis

Businesses may obtain a competitive advantage by closely monitoring the social media activity and engagement metrics of their rivals. Data analytics
technologies have the capability to identify deficiencies in the market, prospects for
distinctiveness, and domains in which rivals are succeeding.

Campaign Performance

The evaluation of marketing campaigns’ effectiveness heavily relies on the monitoring of essential performance metrics, often referred to as key performance indicators (KPIs). These KPIs include conversion rates, click-through rates, and return on investment (ROI). The use of data analytics allows the continuous monitoring and modification of campaigns in real-time, with the aim of optimizing outcomes.

The Tools of the Trade

Data Analytics for Social Media: In order to optimize the use of data analytics within the context of social media marketing, enterprises depend on a diverse array of tools and platforms.

Here are some of the key players in the field:

1. Google Analytics

This multifunctional instrument offers valuable insights on the volume of website traffic that originates from various social media networks. The use of this tool aids organizations in monitoring conversions, analyzing user behavior, and assessing the influence of social media on website efficacy.

2. Facebook Insights

Facebook Insights provides a detailed analysis of page performance, audience demographics, and engagement analytics for companies who have a presence on the Facebook platform. The use of this instrument is crucial for enhancing the effectiveness of Facebook marketing endeavors.

3. Hootsuite

Hootsuite is a comprehensive social media management software that facilitates companies in the scheduling of posts, monitoring of social media discussions, and analysis of performance data across various social networks.

4. Sprout Social

Sprout Social provides a comprehensive range of social media management solutions including analytics, publishing, and interaction functionalities. This analysis offers significant perspectives on the demographics of the audience and their patterns of involvement.

5. Buffer

Buffer streamlines the procedure of arranging and disseminating social media content. Additionally, it provides analytics tools to assess the efficacy of content across several platforms.

6. Google Analytics 360

Google Analytics 360 offers enhanced analytics and data integration functionalities to cater to the needs of bigger organizations, enabling them to get a more thorough assessment of their marketing effectiveness.

Benefits of the Convergence

Why Data Analytics Matters: The amalgamation of data analytics with social media marketing presents several advantageous outcomes for firms:

Targeted Advertising

The use of data analytics enables organizations to effectively segment their audience and implement precise advertising strategies. This practice not only
enhances the relevancy of advertisements
but also optimizes the efficiency of ad expenditure.

Content Personalization

By comprehending user preferences and behavior, organizations have the ability to provide customized content that effectively connects with specific users, hence enhancing user engagement and conversion rates.

Improved ROI

Marketing initiatives that are informed by data are more probable to provide a greater return on investment. Marketers has the ability to enhance resource allocation efficiency and improve campaigns by using real-time data.

Real-time Insights

Data analytics offers timely and valuable information into the success of campaigns. This enables marketers to promptly modify their strategies and take advantage of developing trends or possibilities.

Competitive Advantage

Companies that use data analytics in their social media marketing strategies have a competitive advantage by proactively anticipating industry trends, understanding customer preferences, and adapting to market dynamics.

Leveraging Data for Social Media Success

Best Practices

In order to properly use the potential of data analytics in the realm of social media marketing, organizations are advised to adhere to the following set of recommended practices:

Set Clear Objectives

Establishing clear and well-defined objectives for social media marketing endeavors is crucial. These objectives may include several aims, including but not limited to augmenting brand recognition, generating higher volumes of online visitors, or enhancing sales performance. It is essential that data analytics be aligned with these stated goals.

Choose the Right Metrics

The primary focus should be on the key performance indicators (KPIs) that are most important to your organization. These metrics could consist of engagement rates, conversion rates, click-through rates, and consumer acquisition costs.

Use Multiple Data Sources

Integrate data from many sources, including social media platforms, customer relationship management (CRM) system, and website analytics, to provide a holistic perspective of your target audience and organizational performance.

Invest in Training

It is essential to ensure that the marketing staff has comprehensive training in the proficient use of data analytics solutions. The acquisition of knowledge and the enhancement of skills are vital in the context of this swiftly progressing domain.

A/B Testing

Conduct many experiments using diverse content, posting schedules, and advertising formats in order to ascertain the most effective means of engaging with your target audience. A/B testing enables the refinement of plans via the use of insights derived from data analysis.

Monitor and Adapt

It is important to consistently evaluate the efficacy of one’s social media efforts and remain flexible in response to evolving trends and shifts in audience behavior.

Data Privacy Compliance: Throughout the process of collecting and using client data, it is crucial to safeguard user privacy and comply with data protection standards.

Conclusion

The Future of Social Media Marketing: The combination of data analytics and social media marketing is revolutionizing the way in which businesses establish relationships with their target consumers. In today’s world characterized by an abundance of data, the ability to analyze and respond effectively to data is a valuable skill that can have a significant impact on the success or failure of a marketing endeavor. With the continuous advancement of technology, it is anticipated that more advanced data analytics tools and methodologies would emerge, hence augmenting the capabilities of social media marketing. The future trajectory of marketing is dependent on the ability of individuals to utilize data effectively in order to provide customized, pertinent, and influential information to their designated demographic. By implementing this convergence, businesses can position themselves strategically for success in the era of digitalization, when data is the most important asset for attaining marketing excellence.

About the Authors:

Sandeep Saharan is Assistant Professor at AI Research Center, Department of AI and Analytics, School of Business, Woxsen University. He received his Bachelor of Technology degree in Computer Science and Engineering from M.D. University, and then Master of Engineering degree in Computer Science and Engineering from Thapar University. He is pursuing Ph.D. with the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology. He has published research articles in reputed international journals as well as in international conferences, such as, IEEE Transactions on Intelligent Transportation Systems, Future Generation Computer Systems, Computer Communications, Applied Mathematics and Information Sciences, and IEEE Globecom. His research interests are in the areas of evolutionary optimization, game theory, and intelligent transportation system. He is an active member of various organizations, such as, IEEE, ACM, and CSI.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

Der Beitrag The Convergence of Data Analytics and Social Media Marketing erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data Science at Sigli – “Bridging the Gap: How AI Can Facilitate Social Inclusion Of People With Disabilities”

A recent study conducted by the UK national disability charity revealed that around 60%, which is almost two-thirds, of people with disabilities are chronically lonely. Even a bigger percentage of respondents (70%) admitted that they feel that social isolation has an impact on their mental health, which can also lead to an increase in mental health conditions in combination with the disabilities that they already have.

And here, we can observe a very important dissonance. On the one hand, a lot of people with disabilities can’t feel that they are a part of society due to the restrictions that they have to face every day. So, they do not have the possibility to communicate with other individuals a lot.

On the other hand, many of them can’t live fully independently. Due to their disorders, they can’t handle even routine tasks. As a result, they need to rely on the support of their family members, nurses, or tutors. Consequently, they are obliged to interact with others to deal even with the simplest everyday processes.

Amid all these issues, questions related to social isolation and lack of independence shouldn’t be ignored by organizations that work with people with disabilities.

Given all the new opportunities that emerging technologies like AI open, they can make a huge contribution to such projects. And it is great to see the growing interest of the world’s community and international institutions in the adoption of AI to help all groups of society.

According to the European Network of National Human Rights Institutions), “AI holds significant potential to positively impact the lives of persons with disabilities by addressing various challenges and providing innovative solutions. From speech-to-text applications to vision-enhancing tools, AI can break down barriers and provide new avenues for communication and interaction.”

In this article, we offer you to take a closer look at AI-powered projects and initiatives aimed at breaking social barriers for people with disorders of various types.

AI and communication

Speech-to-text and text-to-speech tools are widely applied to help people with hearing and speech impairments communicate with others. In one of our previously written articles, we’ve already described the power of Artificial Intelligence in enhancing the way people with such disorders interact with the world around them.

Thanks to virtual assistants enriched by such features, people can communicate with others in the traditional way. And this approach presupposes a real-time dialogue with practically no delays. As a result, they can solve a lot of tasks (like making calls to book a table at a restaurant, setting an appointment with a doctor, or ordering a delivery) without waiting for help from others. You may say that such tasks can be performed via sending a message. But let’s be honest. Quite often, a phone call is the fastest way to get what you want. And in this case, they can rely on AI-powered virtual assistants like Siri.

But this functionality is not the only one that can be ensured thanks to Artificial Intelligence. AI can fully change the way society can communicate with those who use sign language. With modern solutions, you do not need to know all the signs and their meaning to communicate with those who can’t express thoughts with the help of verbal means. AI algorithms can recognize signs and transform them into natural languages and vice versa.

The AI-powered era in marketing

And that’s cool that there are projects like Signapse AI, which helps businesses adapt their marketing strategies to the needs of everyone. What does it mean? The offered solution can translate written text into a Sign Language video which businesses can place on their websites, social media accounts, and blogs.

It will be sensible to note that all the information can be delivered to people with hearing disorders via written texts. Yes, it is possible. But such projects allow companies to make their communication with everyone vivid and engaging enough. Moreover, that’s a good way to demonstrate that you respect all your existing and potential and appreciate them regardless of any disabilities that they may have.

Autism and social skills

However, while discussing barriers in communication, we should mention not only speaking or hearing disabilities. In this context, it is also crucial to talk about people with autism spectrum disorder.

For individuals with autism, it can be extremely challenging to participate in various formats of social interactions. And AI-powered tools for social skill training can bring huge value. Such tools can simulate different role-playing real-life scenarios, analyse people’s behaviour in offered situations, and provide real-time feedback. It means that, in this case, AI is applied to create a supportive, controlled, and fully safe environment where users can practice and enhance their social skills.

One of the projects that are working on delivering such solutions is SocialMind. It is a member of the Microsoft for Startups Founders Hub. This platform relies on AI and NLP to improve the social skills of children with autism. Thanks to AI, the social skills training can be personalized for each child based on his/her specific needs and learning style.

But that’s not the only possible AI use case in this domain. AI can let people with autism communicate via alternative means. How is it possible? AI technology can recognize non-verbal or limited verbal means. Then language processing algorithms, predictive text, and voice recognition tools can transform them into natural language. As a result, individuals with autism get more freedom in expressing their thoughts. They can engage in various activities and take part in conversations.

AI and consumption of content of different formats

Books, movies, and art are among those things that, in this or that form, are present in our social life and affect it.

Let us mention a very simple example. When you move to a new country and try to integrate into a new community, sooner or later you may feel that you are a stranger. It can happen even if you perfectly know the local language and have already learned the names of local supermarkets. You may start feeling that you do not know the cultural context. For example, you do not know what songs were popular when people from your generation went to school and what cartoons they liked. As a result, you simply can’t understand the majority of memes and jokes.

But, of course, it is not a problem in comparison to what people with hearing or vision disorders experience when it comes to the cultural context.

However, thanks to AI, we can introduce alternative forms of consuming and producing different content. And it’s not only about social and cultural aspects. Such tools can be also of great use for educational and professional purposes.

Live captioning and screen readers are expected to greatly change the game for people with disabilities. Real-time captioning solutions make the participation of individuals with hearing disorders in various online events possible.  At the same time, image and text readers can be used by those with visual impairments and people with dyslexia.

AI and accessibility of public spaces

It’s amazing to observe that today, public spaces are gradually becoming more accessible thanks to AI. Smart city solutions can rely on systems powered by Artificial Intelligence for sharing real-time data on accessible transportation options (like wheelchair-accessible ramps or buses that can be easily used by people with limited mobility).

Architects and designers can benefit from AI-powered tools for creating so-called disability-friendly urban planning and buildings.

And already today there are apps that use the power of AI to provide accessibility information about various public spaces.

For people with visual disorders, the market can offer smart glasses and AI-powered apps that use smartphone cameras. Special cameras can be placed on a person’s eyeglass frames. They utilize optical character recognition technology. With it, they can transform digital or printed text into real-time auditive feedback. Some other solutions of this kind can also be powered by stereo sound sensors and GPS technology. They can recognize colours, read signs, and provide spoken directions.

Moreover, there are applications like Seeing AI by Microsoft that can safely navigate people with visual impairment. AI algorithms can identify objects and people caught by the device camera and then audibly describe what is happening around them.

While Seeing AI is a universal app that can be used in many situations, similar technologies can be more industry-specific. For example, cameras powered by computer vision can be installed in gyms and can notify people about any threats caused by irregular use of equipment. Such a solution can be of great use for a very wide audience. But it will have the highest importance for people with visual impairments.

AI and independent living

Let’s be honest, the modern world is built in such a way that even a lot of healthy people can stay at home, work from home, order deliveries, communicate with others only via chats, and still feel completely okay about it. Total digitalization (in which AI also takes an important position) makes it possible for everyone. However, even when all these opportunities are available, people still feel that they are a part of society. They can easily leave their homes if they want to do it and go wherever they want. They do not depend on others.

But we can’t say just the same about people with disabilities. Many of them can’t live fully independently due to many reasons which causes a lot of discomfort to them. Nevertheless, AI is here to address such issues.

For example, smart controllers used in smart home systems can greatly increase the safety of people with vision and cognitive disabilities. They can track whether all home appliances are turned off when they are not used, monitor possible water leaks, etc. Assistants with voice control can be used to manage a lot of tasks. Meanwhile, cameras with computer vision can recognize who is standing at the door and inform a person.

Special smart controllers can also be responsible for detecting various threats and dangerous situations related to people’s health. Already today, there are projects that offer to install sensors that will detect falls and the absence of movements during a particular period. Such solutions are highly relevant for families with elderly people living alone. If a dangerous pattern is identified, the system will send notifications to nurses or other authorized individuals who can take the required measures.

For people with limited mobility, there are quite a lot of voice-controlled devices and smart robotic products. But some solutions that are available today are ground-breaking. For example, what about walking simply by thinking about it? Thanks to electronic brain implants, it can be possible. The system is still not widely available and is at an experimental stage. Nevertheless, a 40-year-old Dutch man who was paralyzed after an accident got the possibility to walk again. The electronic implants wirelessly transmit his thoughts to his legs and feet via an implant on his spine.

Neurosurgeon Prof Jocelyne Bloch at Lausanne University, who carried out the surgery and inserted the implants, explained the project’s goal in the following way:

“The important thing for us is not just to have a scientific trial, but eventually to give more access to more people with spinal cord injuries who are used to hearing from doctors that they have to get used to the fact that they will never move again.”

And that’s exactly how we at Sigli (Cortlex) view the mission of introducing tech innovations – to make impossible things possible for everyone.

But even such cutting-edge projects are not at the highest level of what we can expect to see in this field in the near future. Research and experiments are going on.

Conclusion

Have we mentioned all the existing AI-powered projects aimed at facilitating everyday tasks for people with disabilities? Definitely no. This list could be practically endless.

Without any doubt, it is extremely exciting to explore the potential of AI in this aspect. And it is even more exciting to make our contribution to this huge common initiative – ensuring that our world is comfortable for everyone. If you need any technical assistance in the realization of such projects, we would be happy to hear from you.

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 How AI Can Facilitate Social Inclusion Of People With Disabilities erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.