SwissCognitive Guest Blogger: Samir Anil Jumade – “The Relentless Tide of Technological Disruption: Are You Ready?”

The world is evolving at an unprecedented pace, driven by rapid technological advancements. Many industries that once seemed invincible have either vanished or are on the verge of collapse due to their failure to adapt. The rise of artificial intelligence (AI), automation, blockchain, and digital platforms is fundamentally reshaping how businesses operate.

In this article, we explore how past giants like Kodak and Nokia disappeared, how today’s industries are facing a similar existential crisis, and how individuals and businesses must prepare for this inevitable transformation.

The Rise and Fall of Industry Giants

Remember Kodak? In 1997, they employed 160,000 people and dominated the photography market, with their cameras capturing 85% of the world’s images. Fast forward a few years, and the rise of mobile phone cameras decimated Kodak, leading to bankruptcy and the loss of all those jobs. Kodak’s story isn’t unique. A host of once-dominant companies, like HMT, Bajaj, Dyanora, Murphy, Nokia, Rajdoot, and Ambassador, failed to adapt and were swept aside by the relentless tide of technological change. These weren’t inferior products; they simply couldn’t evolve with the times.

This isn’t just a nostalgic look back. It’s a stark warning. The world is changing faster than ever, and we’re on the cusp of another massive transformation – the Fourth Industrial Revolution. Think about how much has changed in the last decade. Now imagine the next ten years. Experts predict that 70-90% of today’s jobs will be obsolete within that time frame. Are we prepared?

Look at some of today’s giants. Uber, the world’s largest taxi company, owns no cars. Airbnb, the biggest hotel chain, owns no hotels. These companies, built on software and connectivity, are disrupting traditional industries and redefining how we live and work. This disruption is happening across all sectors.

Consider the legal profession. AI-powered legal software like IBM Watson can analyze cases and provide advice far more efficiently than human lawyers. Similarly, in healthcare, diagnostic tools can detect diseases like cancer with greater accuracy than human doctors. These advancements, while offering immense potential benefits, also threaten to displace a significant portion of the workforce.

The automotive industry is another prime example. Self-driving cars are no longer science fiction; they’re a rapidly approaching reality. Imagine a world where 90% of today’s cars are gone, replaced by autonomous electric or hybrid vehicles. Roads would be less congested, accidents drastically reduced, and the need for parking and traffic enforcement would dwindle. But what happens to the millions of people whose livelihoods depend on driving, car insurance, or related industries?

Even the way we handle money is transforming. Cash is becoming a relic of the past, replaced by “plastic money” and, increasingly, mobile wallets like Paytm. This shift towards digital transactions offers convenience and efficiency, but also raises questions about security, privacy, and the future of traditional banking.

From STD Booths to Smartphones: A Revolution in Communication

Think back to the time when STD booths lined our streets. These public call offices were once essential for long-distance communication. But the advent of mobile phones sparked a revolution that swept STD booths into obsolescence. Those who adapted transformed into mobile recharge shops, only to be disrupted again by the rise of online mobile recharging. Today, mobile phone sales are increasingly happening directly through e-commerce platforms like Amazon and Flipkart, further highlighting the rapid pace of change.

The Evolving Definition of Money

The concept of money itself is undergoing a radical transformation. We’ve moved from cash to credit cards, and now mobile wallets are gaining traction. This shift offers convenience and efficiency, but it also has broader implications. As we move towards a cashless society, we need to consider the potential impact on financial inclusion, security, and privacy.

The Message is Clear: Adapt or Be Left Behind

The message is clear: adaptation is no longer a choice; it’s a necessity. We must embrace lifelong learning and upskilling to navigate this rapidly changing landscape. We need to foster creativity, critical thinking, and problem-solving skills – qualities that are difficult for machines to replicate. The future belongs to those who can innovate, adapt, and thrive in a world increasingly shaped by technology. The question is: will you be ready?

Additional Points to Consider:

· The environmental impact of technological advancements, both positive and negative.

· The ethical considerations surrounding AI and automation.

· The role of government and education in preparing the workforce for the future.

· The potential for new industries and job roles to emerge. By staying informed and proactive, we can harness the power of technology to create a better future for all.

References:

1. D. Deming, P. Ong, and L. H. Summers, “Technological Disruption in the Labor Market,” National Bureau of Economic Research, Working Paper No. 33323, Jan. 2025.
2. K. Hötte, M. Somers, and A. Theodorakopoulos, “Technology and Jobs: A Systematic Literature Review,” arXiv preprint arXiv:2204.01296, Apr. 2022.
3. D. Acemoglu and P. Restrepo, “Assessing the Impact of Technological Change on Similar Occupations,” Proceedings of the National Academy of Sciences, vol. 119, no. 40, e2200539119, Oct. 2022.
4. D. Acemoglu and P. Restrepo, “Occupational Choice in the Face of Technological Disruption,” National Bureau of Economic Research, Working Paper No. 29407, Oct. 2021. 5.S. 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.

About the Author:

Samir Jumade is a passionate and experienced Blockchain Engineer with over three years of expertise in Ethereum and Bitcoin ecosystems. As a Senior Blockchain Engineer at Woxsen University, he has led innovative projects, including the Woxsen Stock Exchange and Chain Reviews, leveraging smart contracts, full nodes, and decentralized applications. With a strong background in Solidity, Web3.js, and backend technologies, Samir specializes in optimizing transaction processing, multisig wallets, and blockchain architecture.

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SwissCognitive Guest Blogger: Ethan Millar – “Born To Be A Bot: Then Why Does Building AI Chatbots For Enterprises Fail?”

Gaining deep insight with artificial intelligence tools is the trend for businesses to operate. Both small businesses and large enterprises are compelled to use AI technologies. AI chatbots communicate with more complex sessions. Companies that have already completed digital transformation should be moving towards a new generation of chatbots. SMEs can also take advantage of this new trend.

The new generation of AI chatbots comes with complex neural connections to have conversations. It is scalable as developers use deep learning tools. It eventually helps enterprises to bankroll AI-based intents with a high-tech approach. Unless the developer knows the pros, cons, and effects of deep learning tools on training chatbots, the very purpose of accurate deliverables gets lost in translation.

This 10-minute reading material is a virtual assistant for the developer to understand how deep learning tools maximize their potential. Moreover, it is also aimed at leadership with companies to understand why a bot-building project has met with failure. How can be brought back to work?

As both are inter-connected, this post focuses on IT developers in large enterprises and lean departments of small businesses.

Lessons to learn from the developer’s perspective

Have you just met with a failure in an AI Chatbot-based project, recently? It is not success that teaches. Failure adds a valued experience while dealing with different approaches to creating chatbots. Many companies fail initially in their efforts. It becomes the ideal base for understanding how a CRM developer can help an enterprise monetize through deep learning tools.

Three things count:

1. Deep learning does not involve or solve everything for business solutions. Some applications can do without it.
2. All enterprises cannot deal with specialized tools unless they have the requirement.
3. All developer tools are not meant for monetizing.

If an enterprise uses only deep learning tools, then only about 1/3^rd of its potential will be realized and the rest will remain untapped. The developer needs an overall understanding to tap it.

Two systems for learning

An IT team of a company) will need to research AI Chatbots and their specific requirements. It will avoid aberrations related to conversations with humans and machines. Earlier virtual assistants like Cortana, Siri, and Alexa set the bar for new bots. They still work with smartphones, appliances, and other home-based devices. They work on 2 systems – Supervised learning and unsupervised learning which require natural language processing capabilities. Since 2020, 85% of customers have been dealing with chatbots by making inquiries. The human connections have reduced.

Supervised Learning

The software is developed after getting data from real-world requests. Correlations are established between ‘tags’ and ‘user-intents’ which are marked for learning and engaging the customer. In such a case, deep learning tools achieve a high level of accuracy. Specialized tools are developed for this purpose. The only hitch here is if the data collected is insufficient or not suitable then the functionality and success are trapped.

Unsupervised learning

Again, in this case, too, a good database is required to understand the customer intent of the chatbot. When it is not supervised, it works independently. There is no need for human supervision while it functions nor does it require specific tags to prompt it to work.

The failure rate increases if the database does not provide a wide range of variables. The quality is not good enough for it to be released in the public domain. Even if it does come out, it will have limited success. The data volumes required are large for deep learning tools to be effective. And, it goes without saying that poor data does not give the required results and also affects business.

Chatbots will continue to grow

Despite the failure rate, AI chatbots will grow and many companies experiment with their capabilities. Consumers are already hooked on them and enjoy the services of such virtual assistants. They find an opportunity to add value to their routine tasks. Every public company wants to reduce customer care efforts, and this is a solution that has promise in the real world.

The only reason why it fails is due to the data required for tags and the user intent in each company is diverse. In some cases, it is limited to a certain extent. Hence, deep learning tools need careful deployment by the developer. They require a well-structured database and good examples for training the system. Getting advanced systems to work requires a good degree of inference latency, interpretability, and reproducibility to understand the data and train the program.

Developer’s skills are tested

A complex toolset may not be the answer for a training program to converse. It took years and several failed tests for Siri or Alexa to reach the stage where they are now. E-commerce giants using machine learning tools have survived as they have a constant flow of data to test and train. In the final analysis, a complete overview of components is required before they can be channeled and ready for public use or limited enterprise utility.

If developers choose hybrid systems, advanced NLP, and AI algorithms and do not rely on the 2 main systems there are bright chances of creating the right chatbot.

Now we turn our focus on the functionality and advantages of AI bots for real-time business needs.

AI chatbots are the new Jeeves

Your wish is my command!

Are you still confused about the diverse functions of AI deep learning? chatbots? Here is a simple description of the new automated ‘Jeeves’ in the corporate world. They are computer programs that communicate with the user as messengers. Some are advanced enough to handle instructions in the absence of the programmer.

It may sound like sci-fi but it is gaining traction as it is a time saver and do various tasks efficiently for different departments. For small businesses, it reduces overheads while multi-tasking.

How can it be deployed?

Most people are used to texting messages to each other as their main form of communication on social media or FB messenger even for work. This is the way even customer care is handled worldwide. Now chatbots are designed to take over.

Once you are familiar with deep learning and how it influences business processes the possibilities of its uses are unlimited. For example, they can be embedded in websites to answer 24×7 any customer queries. It is a live chat and once the user signs up on the website, the chatbot is functional.

Where is it most influential and popular? In businesses where customer services need to be handled with care. Today, pharma, real estate, and financial companies also use AI chatbots successfully.

Smart business advantage

Ai Chatbots are more common than you think. Google Assistant, Apple’s Siri and Amazon’s Alexa are all chatbots serving various functions. They are not only useful but are extremely popular. A smart chatbot increases your company’s visibility thereby boosting sales.

Earlier it was possible only for large companies to invest in AI deep learning. Now more avenues have opened up for small businesses to take advantage of this feature. Chatbots can be integrated into many areas of a company’s business. Chatbots use natural language processing in combination with machine learning to respond accurately to a customer’s requests.

They have been created to recognize an inquiry and provide an appropriate answer. With advances in the tool and features, they record previous questions and answers. They are geared to offer a personal experience to the user. As a service, it upgrades the company’s overall profile to settle disputes and provide customer satisfaction.

Ideal social media tools

AI chatbots have proven to be excellent social media marketing tools. Their efficiency is only set to increase in the coming years. AI provides personalized, real-time content targeting that produces 20 percent more sales opportunities. It can also be utilized for behavioral targeting methods for specific buyers. This is a sleek advantage for small companies that cannot hire expensive marketing managers.

Using this technology data and statistics prove to be useful to make decisions through predictive analysis. Machine learning can be applied in marketing to optimize for successful campaigns. Automaton reduces time gaps for performances and many sectors are turning towards bots to increase productivity and interactions.

Evolving innovation

With new developments, the way conversations are perceived is changing. This platform has already introduced voice bots and crypto tokens, messengers for blockchain.  Companies like Google, Apple, and Amazon are already developing new conversational platforms for better customer interaction. Perhaps this evolution will help solutions to be more forthcoming.

As 2024 is underway, the use of AI chatbots is no longer a luxury. It has become essential. With ChatGPT, Gemini, Bing, and Claude making an influential impact, it is hard to ignore them for business operations. Leaders require content generation and customization to streamline. AI bots can reason with limited inaccuracies with the user.

Closing thoughts

If you have failed once, now with experience take advantage of the new ‘Jeeves’ and its sophisticated commands. It’s time your developers take a fresh take on creating the right chatbot and reduce operational challenges.

About the Author:

Ethan Millar is a technical writer at Aegis Softtech especially for computer programming like artificial intelligence, emergency technology, Big Data, data analytics, and CRM for more than 8 years. Also, have basic knowledge of AI and technology are vast fields with numerous experts contributing to various aspects of research, development, and application.

Der Beitrag Born To Be A Bot: Then Why Does Building AI Chatbots For Enterprises Fail? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Andy Fitze, Co-Founder of SwissCognitive, World-Leading AI Network
Copyright: inside-it.ch – Andy’s AI Almanac: Technology Trilogy Engineer – die Köche der Zukunft

Let’s start from the beginning. We humans still believe that we understand non-linear processes. But in reality, we don’t. We can’t even get a bathtub halfway to a comfortable bathing temperature after the hundredth time. We are not made for things that we cannot grasp linearly. We are so overwhelmed by a simple weather forecast that we usually don’t even know what the weather is going to be like after reading the weather report. Intuition doesn’t want to enter our heads.

This leads me to 3 conclusions:

1. Things are complex, and we have simplified them so that we can understand and apply them. And that’s a good thing otherwise we’d all be overwhelmed. Anyone who goes skiing in winter, for example, knows this. There are the 3×3 rules of avalanche safety. That’s how it’s understood and it’s practical, but the reality is much more complex. So complex that we don’t understand it and can’t process it, especially not in the terrain at minus 20 degrees.
2. We deduce the future from experience, even and especially with highly volatile systems. We call this intuition, but what we mean is “competent behaviour in the face of complete cluelessness”. We tend to behave in this way when complexity is involved, perhaps in order to create a certain logic. For example, we believe that the next logical step after assisted driving is autonomous driving. In other words, we describe this change as “one step”. In reality, the technology is 1000 times more complex in this single step. When Steve Ballmer made fun of Apple in 2007, Microsoft sold millions of smartphones a year, and Apple none. Today, Apple sells that amount in one day.
3. We like to forget so badly we are practically world champions in it. Individual and social amnesia, so to speak. Before boarding the plane, we check our boarding pass to remember our seat number, but as soon as we get on the plane, we have forgotten everything.

And now we are surprised about AI, as if we had forgotten that calculators, Wikipedia, Google search, Excel, smartphones and even our kitchen stove have long surpassed us.

Wake up! With our experience, logic, and knowledge, we will hardly grasp technological developments, far less predict them.

We need to engage more intensively with technology. Much more!

What we see today with AI and its rapid development dazzles us. We are amazed, stunned and therefore blind to see what is coming.

My prognosis:

1. AI applied directly will boost productivity.
2. AI applied in technologies will define new markets.
3. AI merged with multiple technologies will change the foundations of our world.

We are already experiencing the first one today. The second one is also already advancing in the B2B sector. But the third one will be the most exciting. For example, the combination of AI, crypto, and blockchain will fundamentally change the trade of all assets in this world. And AI, biotech, and quantum will completely alter our understanding of ourselves, nature, and the healthcare industry.

For the first time in history, we will be able to answer complex questions with complex systems. I admit that I am very excited. This is beyond our current understanding.

Therefore, I suggest a new professional category: Technology Trilogy Engineer – chefs who understand how to rediscover technology recipes.

Original article: www.inside-it.ch

Der Beitrag Technology Trilogy Engineer – The Chefs of the Future erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Meike Krautscheid – “The Synergy of AI & Blockchain – What are the Use Cases?”

The world of technology has experienced a whirlwind in recent years, driven by two overwhelming forces: the hype surrounding artificial intelligence and the meteoric rise in the realm of cryptocurrencies and blockchain. While the AI hype has been accompanied by rapid technological advancements and mass adoption through platforms like ChatGPT, the blockchain world, particularly the realm of cryptocurrencies, has seen its share of ups and downs. However, crypto enthusiasts eagerly await the next upswing.

While the once-dominant cryptocurrency hype was initially overshadowed by the unstoppable wave of artificial intelligence, a question arises:

How can the worlds of AI and blockchain harmoniously converge and potentially unleash synergy?

Within the blockchain community, the belief is widespread that the true magic of the technology unfolds when combined with other groundbreaking technologies. Alongside the Internet of Things (IoT), sensors, Smart Contracts, and Ricardian Contracts, Artificial Intelligence is coming under the spotlight. But before we delve deeper into the potential synergy between AI and blockchain, it is crucial to establish a foundation by understanding what blockchain is and what makes it unique.

Blockchain technology has revolutionized the way we can store and manage data. As early as the late 1980s, scientists Scott Stornetta and Stuart Haber recognized that the increasing flood of data would pose a challenge: the need to determine the time of data creation, authenticate it, and verify it to prevent fraud, such as tampering with transactions by backdating and editing.

The scientists’ approach was to use a kind of mathematical “blender” (cryptographic hash function) to generate a unique serial number, known as a hash, which is as unique as a fingerprint, for each file. This makes even the slightest change in a file detectable. Documents previously encrypted in data blocks using hash values and timestamps and chained together are resistant to retroactive alterations; only new data can be easily added.

Stornetta and Haber have been offering this system through their central company since the 1990s, allowing users to timestamp their files with a digital timestamp that proves the authenticity and integrity of the file at a specific time. This is a crucial tool for securing the integrity of electronic documents and data.

The innovation of a central timestamp system developed by Stornetta and Haber served as the template for the decentralized system in the Bitcoin blockchain. In Bitcoin, timestamping occurs in a decentralized network without the need for a central authority. Each transaction is hashed and protected by cryptographic keys. A protocol and consensus mechanism ensure that coins cannot be double-spent, and data cannot be retroactively manipulated. The order of transactions and blocks in Bitcoin is secured through the Proof-of-Work mining process. Even if multiple actors in the Bitcoin network fail, falter, or attempt dishonest actions, the system remains robust and continues to be a trusted, decentralized, and secure method for transferring value and data.

A decentralized blockchain is crucial for data integrity because it ensures that no central authority has the ability to retroactively manipulate data. Similar to a global accounting system, the blockchain updates its records simultaneously and decentralizes the origin of data. Moreover, it enables transparent tracking of changes to the data, including the detection of manipulations.

How can these advantages of data security through blockchain now intersect with Artificial Intelligence?

High-quality datasets are essential for developing powerful AI models. AI entities require high-quality data to learn patterns and make accurate predictions or decisions. For example, when the Retrieval Augmented Generation (RAG) framework is employed to retrieve results from an internal source, a blockchain safeguard can be used to verify that the data assets returned are authentic and that the content extracted from these assets aligns with the original consensus against these assets. However, it’s important to note that this is not meant for everyday use, as it is highly costly and is suitable for specific critical cases, such as mortgage documents and financial statements. Think of it as two databases converging: the vectorized database from RAG and the blockchain decentralized database using a consensus mechanism that is widely accepted as the standard. Therefore, the synergy with blockchain could improve the reliability of training data for AI models and enable more effective use of AI in various applications.

With the rise of generative AI-generated digital content, the boundary between reality and fiction is growing increasingly ambiguous. It’s becoming difficult to determine which images and videos are genuine, technically manipulated, or entirely AI-generated. However, a potential solution arises: we can label media content, including Deep Fakes, with universal indicators and facilitate the verification of the authenticity of such content through a blockchain by storing a simple hash of the content. This technology can confirm that the content remains unaltered and genuine, whether it is stored or indexed in the blockchain, and it is verifiable by anyone.

The potential of the alliance between AI and blockchain can also be explored in areas such as the Internet of Things (IoT), financial markets, Smart Cities, supply chain management, personalized medicine, and more.

In the field of Supply Chain Management, the combination of Artificial Intelligence and Blockchain technology could enable the analysis of data while ensuring a seamless tracking of the origin and the entire product supply chain. Usually, such data is centrally stored in data lakes, and when it is, there is a risk of data manipulation or the possibility that information does not reach relevant stakeholders in the supply chain in real-time.

AI algorithms can validate data before it’s entered into the blockchain to ensure it meets predefined criteria and standards. Real-time fraud detection is also made possible as AI models continuously monitor transactions for anomalies, with the transparency of the blockchain ensuring secure recording. Furthermore, AI data analysis facilitates informed decision-making, providing valuable insights and predictive analytics. This benefits supply chain quality assurance and empowers consumers to verify the quality and authenticity of products – provided that producers grant access to this data.

In Smart Cities, AI agents (AIAs) or Convolutional Neural Networks (CNNs), in conjunction with data stored on the blockchain, could enable a more economical and resilient urban economy. This combination allows for real-time data processing, crucial for urban emergencies, traffic control, and improving citizens’ quality of life. Convolutional Neural Networks (CNNs) are relevant for analyzing visual data in Smart Cities, including traffic pattern recognition, environmental monitoring, and security applications, while AI agents can recognize patterns and make intelligent decisions, such as resource allocation.

Similarly, Blockchain and AI offer numerous advantages in the healthcare sector. Firstly, blockchain allows the decentralized storage and secure encryption of health data, protecting it from hackers and unauthorized access. Patients have control over who can access their data, and with the help of Zero-Knowledge Proofs (ZKPs), patients can share their data without revealing their identity and compromising their privacy. AI agents can then access this data, identify patterns, and make informed decisions. For example, if DNA data is available, it can be used to detect rare genetic diseases.

Furthermore, imagine an AI trading bot that operates on the blockchain without revealing its detailed workings but proves its effectiveness through Zero-Knowledge Proofs. The combination of machine learning (ML) and yield farming also takes place on-chain, with crucial parts of the process remaining confidential. Blockchain enables verification and transparency of information, with critical parameters protected by ZKPs.

It’s worth noting that all transactions occurring on the blockchain can be traced using analytics tools. For example, the blockchain intelligence company Gray Wolf Analytics provides a tool that uses artificial intelligence to understand on-chain and off-chain activities. If fraudulent transactions are detected, financial and cybercriminal activities can be prevented or traced by relevant authorities.

There will also be a revolution in the software sector, as modern NoCode super-app builder platforms will be used to create apps, APIs, and websites with the help of AI. While AI initiates software creation, the use of blockchain creates a secure and verifiable environment for bug-free versions that can be verified by any user.

In another scenario, AI could serve as a “sheriff” monitoring punctuality to meetings. If someone arrives late, the AI triggers a Smart Contract on the blockchain, resulting in a donation from the tardy person to charitable projects. However, there is a certain risk associated with the use of these technologies, especially in authoritarian states concerning the monitoring of legal violations, as individuals’ identities could potentially be listed on a social rating or blacklist on the blockchain, leading to significant restrictions.

Blockchain technology could potentially address issues that come with the use of AI. In the context of Generative Artificial Intelligence (GAI), a challenge is that it might use copyrighted content to generate new content, potentially leading to conflicts with copyright owners. By utilizing digital signatures and hash functions, data integrity is significantly improved, allowing for cryptographic verification that a data record existed at a specific point in time and remained unchanged.

In a later article, we will delve deeper into how blockchain ensures transparent tracking of the creation and modification of content, addressing the legal aspects related to GAI and potential copyright infringements.

We can expect that in the future, blockchain will help distinguish between good and bad data. While blockchains offer ideal attributes for storing critical data, which can be a valuable data source for AIs, it’s important to consider that models like the Generative pre-trained Transformer (GPT-3) were trained on approximately 45 terabytes of text files – a massive amount of data. Given that storing data on the blockchain incurs monetary costs, it’s likely that only indexes like pointers or the most essential data will be directly stored on the blockchain for use as data sources for AIs. Economic and other incentives will be crucial with blockchain usage. Beyond a minimum of revenues that must occur, there are additional challenges to overcome, including scalability, interoperability, and legal issues like GDPR.

It’s worth noting that both AI and blockchain technologies are still in their developmental stages, but we can anticipate exciting developments and innovations on the horizon, with numerous opportunities yet to be explored.

About the Author:

Meike Krautscheid is an entrepreneur and expert in blockchain-based applications. Her extensive knowledge of blockchain, NoCode, AI and related technologies has established her as a recognized thought leader. Meike is a sought-after keynote speaker at international conferences and events. Furthermore, she shares her expertise and vision through lectures and workshops at renowned universities worldwide. Through her dedication, she engages with a worldwide audience and plays an active role in spreading innovations.

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Tracking the Tides of Global Tech Investment – SwissCognitive AI Radar

In this edition, we’re zooming into the strategic financial flows that are not just creating ripples but are reshaping the global technology landscape. From the heart of Europe to Israel, from the bustling innovation hubs in India to the disciplined corridors of Swiss financial giants, this is a tale of bytes and bucks.

Our journey begins with Aleph Alpha, a trailblazer in Germany’s AI scene, which has just interlaced its fate with a massive $500 million tech investment, standing as a testament to Europe’s growing aspiration to carve out its own niche in the AI domain. PwC Switzerland follows suit, earmarking CHF 50 million to advance AI, signifying a steadfast march toward digital sagacity and a global vanguard in AI services.

Traveling across the Mediterranean, we find Israel harnessing the power of AI to bridge the gap between investors and the tech sphere, while a crypto billionaire’s altruism ushers in a groundbreaking initiative, supplying AI chips to fuel innovation amidst hardware scarcities.

From the pulse of the markets where AI is redefining investing, to the front lines where defense startups like Shield AI are securing hefty investments for AI-driven defense strategies, we see a common thread – the pursuit of progress and the unwavering belief in AI’s potential.

Through this narrative, we witness a diverse array of sectors – from fintech to defense, each spinning its own yarn of innovation, all woven together by AI’s transformative thread. As fund managers wrestle with data readiness for AI integration, and Indian startups navigate through fluctuating financial weathers, the entire world seems to be marching to the beat of the AI drum.

Let’s trace the money trails that empower AI solutions and platforms, revolutionizing industries and redefining what it means to invest in technology. Here is the SwissCognitive AI Radar: an atlas of investments where each dot connects to form the constellation of tomorrow’s AI landscape.

Previous SwissCognitive AI Radar: Global Investments Reshaping Tomorrow.

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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SwissCognitive Guest Blogger: Ivan Assenov – “The Evolution of Software: AI, NoCode, Blockchain’s Disruption of Traditional Programming”

In the modern era, traditional programming touches nearly every aspect of our lives, whether it’s through our work, the games we play, or the apps we use daily. Over the past 75 years, software has become ubiquitous, but it’s also gained a reputation for being notoriously buggy and expensive. This imperfection, combined with the high costs and time involved in creating software — especially at the enterprise level — means many software products struggle to maintain profitability despite their popularity. With consumers constantly seeking more for less, the software industry is in a cycle of relentless reinvention. The past decade has brought forth three revolutionary trends poised to redefine the landscape: NoCode/LowCode development, artificial intelligence, and blockchain. In this article, we’ll delve into how these three innovations, working in tandem, might shape the future of software.

If we break down a software cycle today on a very high level, it usually consists of having some requirements, some developers writing some code, then deployment is done and the software is available for use by the end users. There is so much more to this cycle known in the software industry as SDLC – Software Development Life Cycle but in a nutshell, it is not that complicated. And yet, tens of thousands of books had been written, certifications had been issued, and whole divisions in the corporate world had been devoted to dealing with the side effects of how software functions or malfunctions. From cybersecurity, compliance, DevOps, agile coaches, and scrum masters, a massive amount of cash is injected to keep the machine floating in order to sync all sides.

What if we are to optimize all of this chaos with a few simple steps that will fundamentally transform how software is written, maintained, and distributed?

Self-tuning software and self-releasing software

As the digital realm evolves, the demands on software grow exponentially. Picture a world where a software product, be it a sophisticated website or an engaging mobile app, has a high daily active user (DAU) count and the innate capability to evolve independently. What if this software could automatically optimize itself to enhance its DAU and /or Average Time on Page(ATP), enact AI-driven fixes, and autonomously deploy updates? The claim below is that such a vision is achievable through the combined prowess of AI, NoCode, and Blockchain.

To truly grasp this vision, let’s delineate its elements:

• We aim to amplify already operational software, not start afresh.
• The primary goal? Boost the DAU/ATP, potentially by refining the user interface or tailoring user experiences.
• All modifications are AI-driven, ensuring adaptability and responsiveness.
• These changes are not just theoretical; they are auto-deployed to the live environment in real-time.
• Users remain at the forefront. Their experience is seamless, undisturbed by the ongoing evolution.
• Finally, and most crucially, every autonomous change is TRUSTED, guaranteeing both functionality and security.

Diving deeper, we will explore how AI, NoCode platforms, and the immutable nature of Blockchain technologies can make such a futuristic vision a reality.

Below we will go over each one of them using AI, Nocode, and Blockchain technologies.

Prerequisites:

For software to truly be self-evolving, its origin is pivotal. It should be constructed using a leading-edge NoCode super app builder platform, complemented by its unique declarative language. Such a foundation heralds an age where software is not just functional but is also learning and adapting in real time.

For the system’s initial setup, human intervention will be crucial to fine-tune the preliminary system prompts. This hands-on approach will be maintained for the initial thousands of iterations to ensure optimal performance.

Enhancing DAU and ATP through UI automated Adjustments

The synergy of machine learning and generative AI holds immense potential in our mission to elevate the user experience. By weaving these AI technologies into metrics that evaluate DAU/ATP, site performance, and user interactions, we can incrementally perfect the user interface to resonate more deeply with users.

Let’s visualize a fundamental webpage design: it comprises a label, some text, and a button. Over successive releases, subtle modifications, be it in their positioning, color schemes, default settings, or shapes, are introduced. The ramifications of these changes on user engagement are then meticulously analyzed.

What sets this system apart is its foundation in NoCode. This means that the incorporation and fine-tuning of these elements can be actioned through APIs. Adding a layer of sophistication, the system has the capacity to roll out varying UI layouts across distinct geographical locales, thereby evaluating the sway of regional and cultural nuances on user behavior. With continuous feedback, the system hones in on an interface configuration that garners maximal user traction.

Each interaction a user has — ranging from the duration spent on tasks to the nuanced patterns of cursor or finger movements — is documented. When this treasure trove of data is aligned with its respective release version, it provides invaluable insights. It’s pivotal to implement the Semantic versioning approach, typified by its trio of numerals, ensuring smooth tracking and management of these iterations. For a seamless experience, it’s imperative that every integrated plugin or mini-app conforms to this versioning framework.

At the heart of the initiative lies the profound capabilities of AI. For us to realize seamless automation of these alterations, it’s vital to synchronize our Language Learning Model (LLM) with the API overseeing the creation and refinement of mini-apps within the NoCode super app ecosystem. To bolster the quality and range of the data, a strategic move would be to roll out diverse versions across multiple regions or user demographics. This would furnish us with a broad spectrum of data, enabling sharper insights into patterns of progression or setbacks.

Addressing the crucial aspect of security and privacy, there are a couple of pathways we can tread. The Retrieval-augmented generation (RAG) system offers one solution. Alternatively, a privately hosted LLM stands as another viable choice. By incorporating a dedicated embedding API interfacing with a vector database, we can assure the continuity of data updates. Currently, the accessibility of RAG is commendable, with numerous providers extending generative model services. Merging these with embedding functions and vector databases becomes a relatively straightforward task. For entities operating on leaner budgets or those with distinct requirements, more rudimentary machine-learning models are available, though they might lean more heavily on manual supervision. On the flip side, a privately hosted LLM could be an ideal fit for expansive enterprise networks that can bear the accompanying expenses.

Equipped with this newfound flexibility in deployment decisions, it’s crucial to recognize the looming challenge: maintaining trust. With every new change introduced, the overarching question remains—how will these alterations serve real-time users, especially if the entire operation is expected to function without human intervention?

There could be a few steps we need to establish:

Instead of simply recording primary events like clicks, scrolls, or page navigations, we advocate for a deeper capture of interactions. This should encompass touch actions on mobile and mouse actions on desktop, stored in a consistent key-value pair format. For instance:
{ “eventType”: “click”, “elementId”: “button123”, “timestamp”: “2023-10-06T14:30:00Z”, “location”: [x,y] }.

• Textual Interpretation: Transform the structured key-value pairs into user-friendly textual descriptions. Using our earlier example, this might read as:
  “The user clicked on button123 at coordinates x,y.”
• Text Embedding: Utilize Natural Language Processing (NLP) models such as Word2Vec, FastText, or even sophisticated variants like BERT to generate embeddings from these textual insights.
• Vector Database Storage: Archive the created embeddings in a vector database, making sure they’re aptly indexed for swift retrieval.
• AI-Driven Search Optimization: Employ AI to sift through the saved embeddings, pinpointing patterns from prior interactions. This enables the system to bolster positive user experiences or adjust in cases of identified regressions.
• Feedback Loop for Continuous Enhancement: Introduce a feedback mechanism, consistently fine-tuning and retraining the system, ensuring an ongoing refinement in user interaction capture and interpretation.
• Human Oversight: While the emphasis is on automation, the importance of human monitoring, especially in the nascent stages, cannot be overstated. It guarantees that system optimizations align with intended outcomes and maintain a human-centric approach.

Once all components are seamlessly aligned, a critical element remains for our autonomously managed and deployed model: establishing TRUST. Users need the assurance that the software autonomously deployed is not just operational, but is precise, unbiased, and safe.

Before delving into the process of establishing trust, let’s first discuss code coverage, an integral aspect that subsequently ties back to trust. Among the multitude of code coverage types (more than 15), Statement Coverage (C1) and Branch Coverage (C2) stand out as the most prominent.

Statement coverage assesses whether every code line has been addressed at least once during automated testing, while branch coverage evaluates how many control branch pathways (like ‘if’ statements) have been executed.

Today’s developers may only intermittently apply these tests, and even then, not to their fullest extent, leading to incomplete and outdated tests over time.

For genuine trust in automated releases, we must:

• Ensure that both prevalent code coverage types are extensively addressed.
• Independently verify that this coverage is trustworthy.

To accomplish this, we propose leveraging certified public blockchain transactions to log actual runs of C1 and C2 tests, introducing two levels of assurance: Level 1, where both C1 and C2 are 95% or higher, and Level 2, with both C1 and C2 at a full 100%.

It’s imperative that the chosen blockchain be public, affordable (costing mere thousands of pennies), adhere to the foundational Bitcoin protocol, and make no compromises. Currently, only a handful of blockchains meet these standards. This means in the future a new blockchain must be created to suit these activities.

Access to unit tests should be granted at the Blockchain node levels, accommodating those keen on mining blocks. Moreover, the declarative language fueling the NoCode platform components and plugins should be fully open-source. Furthermore, plugin codes derived from this declarative language should be accessible to miners upon registration.

Envision a scenario where every plugin, NoCode element, or mini-app possesses a certification indicating its trust level. This blockchain could either store the certification’s hash or its entire content sourced from the tests.

Below is an example of such a structure

Merging these components results in an autonomous system — externally observable, yet internally equipped and self-reliant to execute requisite tasks. As the digital realm undergoes swift metamorphosis, the bedrock of trust remains unshaken. While our drive for flawless automation is commendable, it should not eclipse the users’ innate need for reliability and security. Marrying meticulous testing methods with blockchain’s unwavering transparency, we transcend beyond just delivering software; we promise serenity. Poised at this technological precipice, our forward gaze is filled not just with eagerness but an unwavering pledge to honor the trust.

In this era dominated by code, the essence of trust remains inherently human.

About the Author:

Ivan Assenov‘s mastery of NoCode/LowCode has impacted hundreds of millions in the financial domain. Combining insights from high-frequency trade transactions with blockchain and generative AI systems, he stands as a software visionary.

Der Beitrag The Evolution of Software: AI, NoCode, Blockchain’s Disruption of Traditional Programming erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

“Generative AI: A New Frontier for VC Investments” Q&A with Heinrich Zetlmayer, Assaf Araki and Bo Percival

As the world continues to witness the transformative potential of Generative AI, and venture capitalists progressively invest in this emerging technology, your questions and concerns have never been more important.

In this follow-up article, we delve deeper into the thought-provoking questions gathered from our global audience during the recent “Generative AI: A New Frontier for VC Investments” conference. We’re thrilled to present you with a curated selection of responses from our distinguished panellists: Heinrich Zetlmayer, Assaf Araki, and Bo Percival.

Let’s keep the conversation going. Read on to explore these comprehensive responses from our experts, and get a front-row seat to the evolving narrative of Generative AI in the VC world.

For the conference details, agenda, speaker line-up, and handouts CLICK HERE.
For the conference recording CLICK HERE

Zeev Abrams: Although Heinrich mentioned that having proprietary models (and IPs) is important, considering the rate of change in the AI landscape (faster than any other field I’ve ever been in!), how important is that “barrier” for a startup, compared with getting market traction and a strong potential market?

[Heinrich Zetlmayer]: If you go too “thin” as a startup in the market you might get quick initial traction but the challenge will be to hold on to that beyond first 12 months. Also investors will challenge it. So you need a good strategy to expand from initial gains.

[Assaf Araki]: For the last 20 years, data scientists have been focused on open-source models; after the breakthrough in deep learning a decade ago, the reliance on open source has increased, and today, all the main DL libraries are open source. The AI community is collaborative and open source is a core value (data scientists are from Mars, and developers are from Venus, both CS but different in culture). Even if a company has a breakthrough in a proprietary model, it will only last for a short time as another innovation will overcome it. The core IP in AI should be at the product level. Bringing together an ensemble of models to create a stable product that optimizes prediction for the business KPI (vs. the highest model accuracy). Startups should focus on combining innovation across different algorithms and integrate it smoothly into their application; The company IP is to make the solution robust, doesn’t hallucinate, or become unstable in production while bringing real business value.

Q: Eleanor Wright: Will bias in venture capital lead to bias in AI?

[Heinrich Zetlmayer]: There are +10k venture capital firms. The question of bias in AI is an important one but i think goes beyond VCs.

[Bo Percival]: I think this is a great question and one that anyone in VC spaces need to consider carefully. We already see this happening as a larger proportion of AI based funding has automatically been funnelled into typical tech ecosystems (e.g. the Bay and The Valley). At the Venture Fund we advocate and act on making atypical investments for exactly this reason. It has been said that AI tech is an extension of the values of those who create it. To this point, that we need to ensure that there is diverse representation of those underrepresented values that we don’t traditionally see represented in frontier tech spaces. This may include emerging markets, diverse co-founders and investments in early-stage companies in atypical domains.

Q: Antonio Sainz: The real creators of AI/ML are a few players, for VC the way is more to find use case generators and use all the tools that exist and can be used, is this your point of view?

[Heinrich Zetlmayer]: Yes and No. I know of very deep AI/ML startups that are more small/mid-sized and can excel in their niche, and are attractive targets for VCs as well. But… AI is a general-purpose technology, so theoretically, you can use it anywhere, and therefore you need to select an area of focus as VC or investor. For us, we have seen that analyzing AI applications in industry verticals allows us to structure the complexity and yield attractive investment possibilities.

[Assaf Araki]: I agree that a small number of players are the creators of AI algorithms, but the creation of AI products is endless. AI is another way of writing SW by the algorithm, not the entire product. To build a financial service product, you must know how to write SW and have business acumen. That still needs to change with AI, and you still need business acumen. Context is highly needed to build a good product. Building models without context can take you a long way, but adding context is the last mile; without it, the product is incomplete.

[Bo Percival]: Particularly in the work of UNICEF, we believe strongly in AI work addressing real problems that face the less represented populations. We are passionate about problems and where there are tools created by the few, we want to ensure that are both accessible and inclusive of the many (or in many cases, the minority. The challenge we face is that those ‘few’ often represent a specific subset of the population who may not have or share the experiences of the many. For this reason, we believe VCs need to reflect carefully on how existing tools may include or exclude underrepresented populations and further amplify the very problems we are aiming to address.

Q: Danny Mwala: How will AI assist in the proliferation of cyber crime and crime criminals some who are state supported actors bearing in mind that there is a high deficit of Cyber security professionals around the globe?

[Heinrich Zetlmayer]: All technologies are very fast used also by criminals but we have seen in the crypto industry and in the cybersecurity industry that quickly startups are created that fight cybercrime and add more and more automation which in return alleviates a bit the lack of skilled staff. AI and ML will certainly contribute to automation of fraud detection. Law enforcement agencies are also a typical a sponsor/first client of startups in tech crime detection and prevention space.

[Bo Percival]: I am not an expert in cybercrime and there are people better positioned to answer this question. That being said, my experience tells me that not only do we have capacity gap globally, but we have particularly concerning capacity gaps in specific geographies and domains. The risk here, is that if this capacity is not filled in an intentionally inclusive and equitable way then vulnerable populations stand to be exploited at significantly higher rates than other groups.

Q: Kuzey Çalışkan: What will be the key industries in the future?

[Heinrich Zetlmayer]: I think we live in the “digital decade” which makes IT combined with ever more progress in the medical/health sector probably the most important sectors.

[Bo Percival]: Great question, I wish I had an answer to this one. To be honest, due to the rapid pace of change, I don’t think we truly know all the industries today that will be key for tomorrow. My only hope is that whatever they are, they are inclusive, equitable and accessible for people everywhere and not just restricted to the Western, Education, Industrialised, Rich and Deomcratised countries at the cost of others.

Q: 5. Arvind Punj: Can anyone comment on the change in the education system which is needed because of the LLM models impacting learning?

[Heinrich Zetlmayer]: I cannot give a complete answer here but the curriculums need to have more and more information technology in it because this is what everybody needs to be skilled at in working responsibly with it.

[Assaf Araki]: AI should be mandatory for undergrads in CS and Engineering, we see some early adopters, but this is still a grad school topic in general.

[Bo Percival]: Education is a key component of the work of UNICEF and discussions are already taking place on how we can leverage LLMs and similar technology for greater positive potential for education. I think because the broader conversation on this topic is so nascent it’s too early to say in which direction this change is taking us. What we hope at UNICEF is that we are able to harness these and future technologies to increase the access and effectiveness for education purposes, while at the same time not losing important key human factors that should not be lost. If these technologies increase critical thinking, creativity, and other similar skills, that is fantastic. However, we also envision a world in which every child has access to education and that the growth and development of children is not perceived through a lens where technology is seen as a panacea or a replacement to education methodologies that are not focuses solely on concepts of learning as a road toward computational thinking alone.

Q: Margaret Glover-Campbell: When it comes to regulation, how can we ensure multiple points of view are taken into consideration? How do we safeguard against views that are too restrictive or too liberal?

[Heinrich Zetlmayer]: Regulations are made by the lawmakers in each country or region. It is important that the various viewpoints are voiced enough in public with enough public discussion so that lawmakers notice.

[Bo Percival]: Regulation is a critical conversation in the ability to move the ethical and responsible adoption of AI around the globe. Therefore, it will be important for representation to be both diverse and equitable. It will be important for regulators to ensure that opportunities are provided for the engagement of different voices to shape the development of regulation, regardless of colour, culture, or creed. Aspiring to a single vision for all will likely lead to an outcome that benefits the majority and further reinforces existing inequities in society. What is defined as ‘too’ one way, or another often depends on where on that spectrum an observer places themselves. Ensuring that there is representation from all ends of the spectrum including those on the furthest margins may not safeguard against going ‘too’ far in one direction of the other, but at the very least it should hopefully at least ensure that no one is excluded in ways that have been all ‘too’ common in the past.

This being said, as mentioned by the Secretary General just last week, the UN should be playing a critical role in facilitating these kinds of discussions to ensure that there is a neutrality to how these conversations are being had and how we can push toward a more free, open, inclusive and secure digital future for us all to live in.

Q: Nanjun Li: Do you think the world will be more divided as generative AI deepening the division of labour?

[Heinrich Zetlmayer]: AI and automation will definetly bring changes to the labour market as it is a large productivity enhancer. On the positive it will alleviate some shortages on the labour market but much more work has to be done to understand the societal impacts.

[Bo Percival]: I think it’s hard to predict one way or the other, and as we know from previous experience, what divides us rarely comes down to a specific issue alone, let alone a specific technology. While technology does have both the capacity and the potential to amplify division, it also has the potential to unite.

Q: mi nova: What is your view of TAM for Conversational Voice AI​, as a front end to Web3 solutions?

[Heinrich Zetlmayer]: I don’t have a number for that but expect it to be the main interface in the mobile space.

Q: Boris Bend: Thinking beyond the atypical: How do you see the world changing once true AGI will be achieved and when do you personally expect that this may be achieved? (There are quite a few experts that expect that this could happen much faster than most people believe due to the current exponential progress of AI research.)

[Bo Percival]: As someone with a background in cognitive psychology, I think the idea of ‘AGI’ is something that is still somewhat contested. While there have been some notable reports of us being close, I think that there are still more questions than answers on this topic. It would be remiss of me to take a position or make a prediction on when I personally think this could happen, as I still believe there are still many questions on what ‘true’ would mean in this case. To be able to claim ‘true’ we would have to first accept an assumption based on traditional and somewhat outdated definition of ‘intelligence’. Even the tests we use these days are still contentious in terms of what they measure. We would also need to be able to say with assertion whether or not the imitation of human intelligence constitutes ‘true’ intelligence. While this is an interesting thought experiment, I think that to understand AGI we would first need to truly understand the ‘I’, and I believe that understanding is moving much slower than the current progress of technology.

Q: Any thoughts on Healthtech like Apple Vision for patient treatments? There is definitely a demand for this from the sector but until now, there are not much change. (Nurses have a heavy workload in this area (except for robot surgery and MRI scans)).

[Heinrich Zetlmayer]: Healthtech is a very large area for AI and there are many startups and AR/VR etc.. are additional important innovations. Until it comes to the patient, hurdles have to be overcome in each national health system which each has its own setup of medical care system between doctors/health providers, health insurances and regulation. That slows it.

[Bo Percival]: Health is another critically important topic area for UNICEF. In fact, this year The Venture Fund will be releasing a public call for applications related to health, both mental and physical, to which, I am sure the AI applications will be considerable.  The challenge however with health, is the difference between technology that is engaging and technology that is efficacious. As we all know, technology can be designed to engage users and keep them connected, however, what the technology sector struggles with is the time it takes to evaluate a health intervention to better understand it’s efficacy in the medical sense. I believe this creates an areas of high risk in the health sector. In a survey done in 2019, of the approx. 60,000 health related apps in the Apple and Play stores only around 3.5% had any empirical evidence to support their efficacy.

If we want to leverage AI effectively, we should be finding the problems in healthcare that are most suited to the value that AI brings and applying them to that. Unfortunately however, we start with the solution and try and find a problem. It is most concerning to me that this conversation and the breakthroughs in this area are led by tech companies and not necessarily by academic or health institutions. The risk we run here is that we are developing solutions for shareholders and that the most needed solutions may not be the most profitable ones.

Q: Eleanor Wright: How protectable is AI IP?

[Heinrich Zetlmayer]: I am not a lawyer who can answer that better from the legal side. The legal protection of IP is often overestimated, more often the topic is: do I have unique data sets, unique employees, know how to access, unique models and unique market access in combination that allow me a sustainable competitive advantage? Many base AI components will be and are available as open source or from providers.

[Assaf Araki]: See my reply to Arvind Punj above. It is irrelevant because of the pace of innovation and the enormous effort invested in global research.

[Bo Percival]: I’m not sure I’m the best one to comment on this. However, as a core part of UNICEF’s Venture Fund’s thesis, we invest intentionally in open-source products, including open data, open models and open products. For us, we see it as paramount that contributions to organisations like ours result in investments that deliver Equitable returns. It is for this reason that we would actually like less AI to be protected so it is transparent and can be leveraged to make the world better for children.

Q: Antonija Hinckel Osojnik: Do you consider environmental footprint of AI? This is now part of regulative negotiations.

[Heinrich Zetlmayer]: We expect a sharp decline in training and running costs for AI and there will be much technological development. It is therefore difficult to judge the impact, at least for us. So currently I think the best lever is at the level of data centers/computer farms and to make sure they are optimized.

[Bo Percival]: Yes, indeed, we can’t be working for future generations unless we are working for a better climate. In addition to the climate conscious practices that we have embedded into the organisation, we also invest in reviews and evaluations of our Fund and our portfolios to ensure that we are being not only climate aware but we are taking climate action.

Q: Marufa Bhuiyan: Based on the data and investment, Which country is the AI capital of the world?

[Heinrich Zetlmayer]: Silicon Valley, due to its combination of large tech firms and universities, certainly is a center but the landscape is rapidly evolving and we will have startups all over the global map. Geography will not be a good criterion for searching for great companies to invest in.

[Assaf Araki]: There are different ways to measure it. One is by publications in the main AI conferences such as NeurIPS; the second is by papers on arxiv, and the third is by location of the leading AI companies. Ultimately, it is like asking who is the c apital of CS around the world. You have many competencies centers in North America, Europe, China, and Israel.

There are different ways to measure it. One is by publications in the main AI conferences such as NeurIPS; the second is by papers on arxiv, and the third is by location of the leading AI companies. Ultimately, it is like asking who is the c apital of CS around the world. You have many competencies centers in North America, Europe, China, and Israel.

[Bo Percival]: Unfortunately, I don’t have the data to support an informed response to this. What I would say is that we should strive to ensure that no specific country or city attains a ‘dominance’ over the field of AI. If we have learnt anything from history, it is that access to these technologies is key for more equitable development. As a result, we advocate strongly to ensure that technologies and building the capacity to develop these technologies should be accessible to people not just based on where they live but based on what enables better livelihoods and development. We would hate to think that we repeat errors of times past and we want to strive for a digitally decolonised future.

About the Authors:

Heinrich Zetlmayer is the founder, CEO and managing partner of BVV. His journey with BVV started at the launch of Lykke Corporation; the global trading platform based on blockchain technology. Heinrich saw a need for the investment of blockchain activity in the market, and with Lykke and his partners he set out to create BVV. Heinrich has a unique experience as the previous Vice President of IBM, Co-CEO of ESL, and an active member of the board in Lykke and Skaylink.

Assaf Araki is an Investment Director in Israel. He joined Intel Capital in 2018. In his role, Assaf is focused on investing in data, analytics, and machine learning platforms and applications worldwide. He has been involved in several investments including Anyscale, Opaque, OtterTune, Ponder, and Verta. Before Intel Capital, Assaf was an engineering lead on the Intel AI team leading multiple machine learning projects to reduce cost, increase revenue, accelerate the process, and improve products.

Bo Percival is a ‘geek for good’ working at the intersection of technology, economics, innovation, and social justice, using his diverse qualifications in psychology, design, economics, marketing, and interpreting to promote positive development. Currently, he is serving as a Senior Advisor for UNICEF’s Office of Innovation Ventures team, applying his extensive experience in open innovation across various fields in over 25 countries worldwide.

Der Beitrag Unlocking the Generative AI Investment Frontier: Expert Q&A – Part 2 erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Tania Peitzker – “Where did metaverses & XR come from? A brief mapping of this immersive, emerging tech.”

The counterpart to this commentary for SwissCognitive in Zürich is an opinion piece for my other ambassadorial role at Ethical Intelligence in Edinburgh. The EI article is a Medium blog post featuring tables, charts and diagrams about “HOW THE EVOLUTION OF THE INTERNET IMPACTS OUR PLANET & OUR HEALTH – XR INNOVATIONS IN THE CLIMATE EMERGENCY”. Now some may consider this title somewhat negative and depressing; how can you make the internet and all its wonderful innovations responsible for the climate change crises around the world? Could it be to do with the fact that far too many members of Gen Z, not to mention the Millenials and Generations X-Y, have comparatively unsatisfactory social lives – not to mention poor mental health – due to their playing a minimum of 6 hours a day online ie they’re absorbed in the digital leisure of computer games?  Nobody can deny that the web is the source and enabler of many time-sapping activities on various communications platforms (no longer Facebook, Twitter & WhatsApp, now the favoured platforms of the Alphas and Gen Zers are Discord, Twitch and TikTok).

I explain why the www is damaging the planet and our personal health in the EI Medium post: smartphones and costly digital screens are the culprits. It’s not necessarily the internet per se which to its credit and through evolutionary “leaps forward” is, as a concerted industry effort, trying to use renewable energy sources for its massive and multitudinous data centres everywhere. As well as move into Quantum and Edge Computing which will reduce the raw power needed to process the ever-increasing trillions of bytes and data each of us help to create. No, the real problems are our devices which are often designed for “temporary use” only ie not recyclable and wrongly thrown away after being manufactured with a huge carbon footprint.

In the EI article, I ask the reader to consider how our phones consume more fossil fuel energy with every post, every text sent, and video watched on YouTube and On Demand film consumed leisurely on Netflix and Co. If you want to know why the world must inevitably shift its comms and leisure to “screenless devices” and Immersive Technologies, click on the counterpart article above for the whole rationale and for every day solutions we can all undertake today.

In this piece for SwissCognitive by contrast, I examine how screenless communications are the emerging Mission Statements for Web 3.0/Web 4.0 entrepreneurs and their Extended Reality experiences, services and hardware. Not just for climate impact reasons; the proponents and creators of XR are devotees of improved Customer Experience (UX). In the Leisure and Experience Economies respectively, the combined focus is and by necessity the convenience of the UX and its overall “enjoyability”. Repeat customers in the mid to late 21st century will only come back if you make them feel good during a great experience. It is no longer just about a quality product or service, it’s the age-old adage, “how do you make others feel”.

Consumers are moving the dial on these concerns. Ethically, for example, when metaverse users will boycott a celebrity band that uses unethically mined cryptocurrencies (I discuss this at length in my Kindle textbook, see below for the link). They won’t buy NFT merchandise if the mid-century boy bands eg K Pop don’t declare their carbon footprint in creating these bespoke Non Fungible Tokens for sale in one of the proto-metaverses. Let’s now look more closely at what “a proto-” or “The” metaverse is and why we should all care about definitions and defining features of these largely unregulated digital spaces. And at the same time answer, the all-important and soon-to-be Frequently Asked Question, who wants to be a (2d or 3D) avatar anyway?

On the Evolutionary Internet – from Virtual Reality to metaverses in XR

First of all, I ask you to take a quick look at the two detailed timelines below. They explain the evolution of Virtual and Augmented Reality hardware (HW) into increasingly interactive SaaS experiences (Software as a Service integrated into the HW). In these diagrams, I map out the progression of metaverse platforms to the current day – as their predecessor formats were and still are VR games – as well as the Play Station mediums that used Virtual Reality gaming via a screen and a console. Computer games, as most of us realise, have transitioned from portable HW (a console in the home) to online. They have become more and more three dimensional in terms of their graphics, images, movements – and avatars. The Massively Multiplayer Games of today are interactive in terms of you can meet and play with your friends in these digital space ie their avatars.

Most importantly for the owners of and investors in these hugely popular platforms, you and many other millions of players can create your own, personalised “In Game” experience by wearing a VR headset or just as a desktop or screen-based bit of kit. Personalisation comes from the digital “skins” you wear as an avatar, how you accessorise it and even what you purchase for it.

Paradoxically, virtually operated metaverses have become “physically experienced reality”: the evolutionary turning point when the avatars – you and your friends – are able to customise themselves more, communicate freely and do stuff In Game that wasn’t necessarily about the game. The two timelines above show a “convergence” or a rapid merging of the various “immersive technologies” that currently exist in 2022. Bearing in mind that most of the hardware – from VR headsets to keyboards/consoles and screens – are being improved upon at a rapid rate to make them more lightweight, easy to use and enjoyable.

Mapping the Ownership and Evolution of Emerging Metaverses

This brings us to the Venn diagram below that maps out the emerging proto-metaverses, gaming and video conferencing platforms that have “pivoted”, as well as Augmented Reality corporations pivoting into more Extended Reality or hybrid experiences. We have seen a remarkable development of the XR Emerging Tech from large scale, incredibly expensive holograms – from holographic avatars on complicated stages with heavy, industrial equipment, to Special Effects at leisure theme parks like Disneyland and hundreds of 3D interactive experiences at galleries and museums in tech consumer centres like London – to portable holographic UX. My colleagues and I in Europe and Silicon Valley have developed and fine-tuned such cognitive interfaces to make these latest XR formats, hybrid VR, Augmented Reality and holograms; making them more interactive through Conversational AI. That is the subject of a third article that I will be publishing soon on Medium.com with another one of the associations I’m involved with – stay tuned!

For now, I ask you to study the Venn diagrams below and consider how they function as “digital mud maps” to chart the landscape of these emerging proto-metaverses and the builders who own them. Some people say that these could be “murky waters” due to the machinations and paradoxical lack of transparency of the crypto world. It crypto industry, as I write in November 2022, certainly seems to have had its act of “market cleansing” as the German saying goes (a business clean-up Bereinigung, or in Switzerland, Schwizzer Duetsch would describe it as “going through the place with a broom”).

Cryptocurrencies have famously been promoted for their devotion to being transparent on the blockchain and democratising the Financial Industries through decentralisation. However, the spectacular crashes of the main currencies and the actual billion-dollar bankruptcies registered this year are evidence to the contrary.

Get Practice & Education: Our New Realities in Metaverses and Beyond

If you would like Deep Dives into these issues and characteristics, please buy my Kindle etextbook on these topics. Textbook on metaverses, interfaces, Extended Reality & Immersive Technologies (6 book series) Kindle edition (amazon.co.uk) Each ebook is on average 27 pages to make it more digestible for our communal Attention Deficit Syndrome we often share after being hooked on tweets and one liner texts! They make up the first 30 000 words of a 100 000 word book I am writing for a mainstream publisher. For any queries about this work or future commissioned books, White Papers and articles, please contact my literary agent via the portfolio website given at the end of this article.

If dedicated reading of lengthy texts is just not your thing or preferred method of consuming knowledge, then I urge you to do my recently published online course with iversity in Berlin. It is affordable and comprehensive. Best of all, it takes you less than 60 minutes to consume! So just on an hour for the lessons without doing the challenges, exercises or communicating with the other participants. In fact, two of the short 8 units with their individual reading materials are available for FREE as a sneak preview, courtesy of Springer Nature. Here are is the direct link for your convenience and enjoyment  metaverses, extended reality & cognitive interfaces (iversity.org)

Heads up: right now I am creating my first ever MOOC on all these aspects of the metaverse, XR, cognitive interfaces and Immersive Tech with my new alma mater, the University of Silicon Valley (www.usv.edu ). If you would like to know more about the upcoming Massive Open Online Courses we’re offering at USV, please drop me a line in the Computer Science Department where I’m an interdisciplinary Adjunct Professor tpeitzker@usv.edu

See you in the metaverse (here is a guest invitation to mine: it’s free, no crypto or VR headset required, and it is emerging as a hybrid museum in XR!  MRAM matriarchies regen ag museum (taniapeitzker.expert)

We can chat in my MRAM museum on Spatial.io. Just drop me a line with the time slot suggestions as to when we can meet live, face to face, as our own individualised, ever-changing 3D avatars in my customised, commercialised metaverse

Der Beitrag Where did metaverses & XR come from? A brief mapping of this immersive, emerging tech erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Utpal Chakraborty, Chief Digital Officer, Allied Digital Services Ltd., AI & Quantum Researcher – “How NFTs and Web3 is going to Threaten the Traditional Finance”

I strongly feel – “The pretty thing about the heck NFT is – those silly things that you probably do not care at all today can be your biggest asset tomorrow”. Believe it or not that’s how the crazy technology world is moving today.

Academic definition of Non-fungible Token or NFT is – NFTs are a new class of digital assets that are unique, indivisible, and immutable. NFTs represent ownership of digital assets on the blockchain ecosystem.     

In fact, they can be used to represent ownership of any unique asset, such as a certificate for an object in the digital or physical realm. NFTs are digital asset representations that are likened to digital passports because each token contains a unique, non-transferable identification to distinguish it from other tokens. Unique NFT data makes it easy to verify ownership and transfer tokens between owners while transacted. They contain ownership information for easy identification and transfer of tokens between token holders.  

The most important thing that we need to know is NFTs are unique cryptographic tokens that exist on the blockchain and cannot be replicated.  They were originally useful for trading digital assets, and now they have multiple use cases. NFTs are mostly built on the blockchain, which makes up the majority of ERC-721 tokens (a standard for representing ownership of non-fungible tokens).     

NFTs offer record-breaking immutability and unique ownership features, represent someone’s ownership of digital assets such as social media posts, digital art, paintings, signatures, and more. They have unique properties such that they cannot be traded or exchanged for other assets with the same financial value due to the non-fungibility in nature. In other words, NFTs are non-fungible tokens and assets on the blockchain with unique characteristics, information and metadata that distinguish them from millions of other assets.     This makes NFT digital assets completely different.

In addition to tokenizing intangible digital content, NFTs can also represent a tokenized version of real assets, including land and buildings. NFTs provide undeniable proof of ownership that is safer than any deed to land. As NFTs get integrated into financial infrastructure, it will be possible to implement the same concept of tokenized parcels of land and its value and location in the physical world.

In the future, digital real estate mainly refers to the ownership of digital assets, and each NFT owner will have a place in Metaverse. You can use NFTs to fully own the virtual space in the metaverse. This means that NFTs in Metaverse can represent ownership of anything, including game assets, avatars, and real estate. Metaverse assets can be traded using Metaverse coins as NFTs.     

Metaverse, along with DeFi (Decentralized Finance) and NFTs (Non-Fungible Tokens), has real-world use cases in the virtual world for the Finance Industry.  

As the gaming industry has already demonstrated that the money-making gaming system will bridge the gap; and NFTs will pave the way for exchange of contracts in the Metaverse marketplace and many more use cases linked to financial industry and others. Among all the technological breakthroughs, NFTs will disrupt the traditional metaverse paradigm of user interaction, socialization, and social commerce.

Also, all these are intimately associated with Web3, and AI is in built in Web3. So, AI is definitely going to bring that Intelligence for which it’s made for. But this time AI is not in the centre of the stage, rather it’s an enabler in a Web3 kind of an ecosystem. Having said that, AI is going to play a major role from the background to make the Metaverse and Web3 environment intelligence and user friendly.

Beyond the hype associated with multi-million dollar sales of digital art, avatar etc. ; the utility of NFT will be to create something that resembles a genuine human society based on free trade of goods, services or ideas and most importantly the radical concept like  “Social Contracts”. By providing a digital representation of physical assets, NFTs are going to represent a step forward in rethinking modern financial systems.     

Nevertheless, NFTs are already a key component of the emerging Metaverse, where feasibility relies heavily on asset tokenization. Non-fungible tokens and the technology behind NFTs play a critical role in the evolution of the Metaverse. Non-fungible token, meaning digital records of ownership stored on a blockchain will become the centrepiece of a functional economy, allowing for the authentication of assets, ownership, and even identities. Hence, I see a great future of NFTs in the emerging virtual world (Metaverse) and it’s utility pertaining to financial industry in the virtual universe. 

Der Beitrag How NFTs and Web3 is going to Threaten the Traditional Finance erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Melissa Robertson – “Blockchain and artificial intelligence: on the wave of hype”

Researchers are actively engaged in discussing the benefits of the combination of these technologies. The last statistic is that PwC predicts that AI will add $15.7 trillion to the global economy by 2030, resulting in a 14% increase in global GDP. Gartner predicts blockchain technology will add $3.1 trillion in business value by that same year.

What is blockchain?

Blockchain is a distributed, decentralized, immutable system that stores encrypted data. Artificial Intelligence is an engine or even a brain. It enables analysis and decision-making based on collected data.

Blockchain can be used in industries that need to process large amounts of data (online video), providing storage, research, and exchange of data. Cryptography ensures that all blockchain data is:

• be invariably written to the blockchain;
• verifiable concerning the privacy of the user and the information being processed;
• quickly disseminated due to the distributed nature of the blockchain.

Blockchain is a distributed registry that stores data as blocks. Each block contains information associated with the previous block in the chain. This data is protected by cryptography and cannot be altered even by the person who created it. Blockchain does not rely on a central authority to verify and validate new data but relies on a mathematical process called the consensus mechanism.

Blockchain can make artificial intelligence more consistent and understandable for humans. It allows you to track and understand why artificial intelligence made that decision during machine learning. Blockchain can record all the data and variables that artificial intelligence uses to create a machine learning decision.

What is artificial intelligence?

Artificial intelligence is the ability of computers to learn actions and tasks that previously required human intelligence. AI uses neural networks that mimic human intelligence and the human nervous system through machine learning. It allows artificial intelligence to improve over time and develop intelligent algorithms.

Artificial intelligence can analyze, classify, and predict various data sets. Machine learning is used to learn from the data provided, creating better models. Thus, the data sets provided by AI are extremely important and must be constantly updated to ensure the effectiveness of artificial intelligence models.

Today, artificial intelligence refers to three areas: machine learning, deep learning, and artificial neural networks. Each of these spheres is present in a person’s life. We use computers, phones, and software every day. It’s impossible to imagine our lives without artificial intelligence.

How Artificial Intelligence and Blockchain are Connected?

Blockchain is an innovative digital information storage system storing data in an encrypted, distributed ledger format. In work, the data is encrypted and distributed across multiple computers, which creates tamper-proof. It is a secure database that can only be read and updated by those with permission.

There are a few examples on the web today of blockchain and artificial intelligence being interconnected. Academics and scientists conducted the study. But we see the two concepts working well together. There are examples of them working together:

• Artificial Intelligence provides an opportunity to move away from the standard algorithm and solve problems more intelligently and efficiently. It can polish its skills in real-time with enough data, which means improved hashing efficiency.
• Blockchain can store data in a single format thanks to hash functions. It will standardize the data and significantly reduce its size because the blockchain will convert it into a string that exceeds a given length.
• AI can use algorithms to handle data in a cryptographically closed form, guaranteeing that it is permanently encrypted. It would make the data usable without putting human privacy at risk since no one would have access to it.
• Bitcoin is a perfect example of a power-hungry blockchain. The hashing algorithms in the Bitcoin blockchain require significant computing power to verify each character set before finding the correct one to validate the transaction.
• Introducing artificial intelligence will help move away from the brute-force analysis used by conventional computers and allow the blockchain to solve problems more intelligently, efficiently, and cost-effectively. A machine-learning-based data mining algorithm can learn decryption quickly.

Today’s computers are extremely fast, but they also require a constant supply of data and instructions, without which it is impossible to process information or perform tasks. Therefore, the blockchain used on standard computers requires significant computing power because of the encryption processes.

Secure data monetization could be the result of combining blockchain and artificial intelligence. Monetization of collected is a source of revenue for many companies. Among the big and famous ones are Facebook and Google resources.

Data is always in the public domain. Any information can be used against a person. So the data we store can be used against us. Blockchain is a secure system to protect data. It is based on a cryptographic key that protects the information. It also allows us to monetize the data as we see fit. Personal information will not go out of bounds. This makes it possible to fight biased algorithms and create different data sets in the future.

This makes it easier to understand how artificial intelligence solves a problem.

It also allows people to check whether the data is valid. Combining the benefits of AI with the immutability of blockchain could be a significant step toward transparency in algorithmic decision-making.

Artificial intelligence is used to create template projects. Blockchain technology is very different from the usual model. Blockchain creates a network with open access from anywhere in the world. Today, blockchain is the decentralized niche that drives all cryptocurrencies.

But the technology is also used across industries to enable decentralization. Some specialized projects focus on using blockchain technology to facilitate the wider dissemination of data. These projects are becoming the basis for decentralized artificial intelligence.

Conclusion

Both blockchain and artificial intelligence are technologies that are groundbreaking in their respective fields. However, when combined, these two technologies have the potential to revolutionize the industry. This connection remains to be explored and incorporated into some work processes. The collaboration of the two progressive technologies has the potential to optimize work processes.

About the Author:

Melissa Robertson is a freelance writer who believes that with well-chosen words, you can convey the right idea to a wide audience. Melissa is attentive to details, responsible, and ready to develop in the profession.

Der Beitrag Blockchain and artificial intelligence: on the wave of hype erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.