Author: Jean Chalopin

Hologram Technology and AI-Based Chatbots

Posted on by Jean Chalopin

Integrating hologram technology and AI-based chatbots is an exciting new frontier in digital communication. Hologram technology provides a new way to interact with information and data, while AI-based chatbots are changing how people communicate with businesses and organisations. Together, these technologies offer unique opportunities for organisations to engage with customers, employees and other stakeholders in more meaningful ways.

The market for hologram technology and AI-based chatbots is snowballing. According to a report from ResearchAndMarkets.com, the global holographic display market will reach US$13.5 billion by 2026, growing at a CAGR of 26.8% from 2020 to 2026. Meanwhile, the global AI-based chatbot market is expected to reach US$1.3 billion by 2024, growing at a CAGR of 24.3% from 2019 to 2024.

What Is Hologram Technology?

Hologram technology is a cutting-edge digital visual solution that allows users to project three-dimensional images into real-world environments. The technology uses light and projection systems to create an illusion of a solid object, which can be viewed from multiple angles and appears to have depth. Holograms can be used for various applications, including entertainment, advertising, and educational purposes.

One of the significant benefits of hologram technology is that it can help businesses to stand out and capture the attention of their customers. Using holograms to showcase their products, companies can offer a unique and engaging experience that can differentiate them from their competitors. For example, hologram technology can be used to create interactive product displays that allow customers to explore a product from all angles, providing a more immersive experience.

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Another benefit of hologram technology is that it can be used to improve the efficiency of communication between employees and customers. With hologram technology, employees can remotely participate in meetings and presentations, allowing them to connect with colleagues and customers from anywhere in the world. Additionally, holograms can be used to conduct virtual product demonstrations, making it easier for businesses to showcase their products and services to customers.

Furthermore, hologram technology can also be used to improve training and development opportunities for employees. With holograms, employees can receive hands-on training and experience simulations in a controlled and safe environment. This type of training can be beneficial for industries such as construction, aviation, and healthcare, where hands-on training is required to ensure the safety and well-being of employees and customers.

What Are AI-Based Chatbots?

AI-based chatbots are computer programs designed to simulate human conversations with users. They use artificial intelligence and machine learning algorithms to understand and respond to user requests in natural language. Chatbots break down the user’s input into individual words and phrases and then analyse them to determine the user’s intent. Based on the intent, the chatbot selects a response from a predetermined list of options or generates a response using deep learning algorithms.

One of the key benefits of using AI-based chatbots is that they can simultaneously handle a large volume of customer interactions, 24/7, without human intervention. This means that customers can receive fast and efficient support outside business hours. Chatbots also offer a convenient and accessible way for customers to interact with a company, as they can be integrated into websites, messaging apps, and other digital platforms.

Some of the companies that are using AI-based chatbots effectively include:

Bank of America. Bank of America’s virtual assistant, Erica, uses natural language processing and machine learning to help customers manage their finances and answer questions about their accounts.

H&M. The fashion retailer has integrated chatbots into their customer service operations, allowing customers to use messaging apps to receive fast support with their orders and returns.

Sephora. Sephora’s chatbot, named ‘Sephora Assistant’, uses AI to provide customers with personalised beauty recommendations and product information.

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Overall, AI-based chatbots offer businesses a cost-effective and efficient way to interact with customers. Their capabilities constantly improve as advancements in artificial intelligence and machine learning continue.

Hologram Technology and AI-based Chatbots: Working Together

Hologram technology and AI-based chatbots can work together to provide a more immersive customer experience. With hologram technology, a computer-generated 3D image of a person or object is projected into the real world, giving the illusion of a physical presence. By integrating AI-based chatbots into this technology, businesses can create virtual assistants that can interact with customers in real time and provide personalised support.

For example, a customer might approach a holographic display and ask questions such as ‘What are your hours of operation?’ The AI-based chatbot would recognise the customer’s voice, process the request, and respond appropriately through the holographic image. The chatbot can also use the customer’s previous interactions and preferences to personalise the interaction and provide a more tailored experience.

One company that is using this technology effectively is Lowe’s, the home improvement retailer. Lowe’s has developed a virtual assistant called ‘The Lowe’s Holoroom’, which uses holographic technology and AI-based chatbots to help customers plan and visualise their home improvement projects. 

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Google rolled out a project in 2021 that utilises holograms in chats. According to the futuristic idea, users can transform into life-size 3D holographic replicas of themselves in virtual chat booths, giving the impression that they are in the same room as you.

The Challenges

There are several challenges in combining hologram technology with AI-based chatbots, including:

Technical complexity. Hologram technology requires specialised hardware and high-performance computing resources, making it challenging to integrate with AI-based chatbots. Additionally, the development of holographic displays that can interact in real-time with AI-based chatbots is still in its early stages.

Cost. Implementing hologram technology can be expensive, which may limit its widespread adoption. This high cost can make it difficult for companies to integrate hologram technology with AI-based chatbots, as both technologies require significant investment.

Interoperability. Hologram technology and AI-based chatbots are separate technologies, each with its own standards and protocols. Integrating these technologies seamlessly and effectively can be challenging, as they may not be designed to work together.

User experience. Creating a seamless and intuitive user experience that effectively combines hologram technology and AI-based chatbots can be difficult. A key challenge is ensuring that the technology is easy to use and provides a consistent and engaging experience for customers.

Privacy and security. Integrating hologram technology and AI-based chatbots raises privacy and security concerns, as the technology can collect and store sensitive customer data. Ensuring the security and privacy of this data is a critical challenge that must be addressed.

Despite these challenges, the potential benefits of combining hologram technology with AI-based chatbots are significant. As technology advances, we will likely see continued innovation and progress in this field.

Closing Thoughts

It is difficult to say whether hologram technology is the future of AI-based chatbots, as these technologies are constantly evolving. While hologram technology has the potential to provide a more interactive customer experience, it also presents several challenges, such as the need for specialised hardware and high-performance computing resources. Additionally, the cost of implementing hologram technology is currently high, which may limit its widespread adoption.

That being said, AI-based chatbots and hologram technology are two of the most promising advancements today, and they have the potential to complement each other in many ways. As both technologies continue to advance, we will likely see more companies exploring the possibilities of integrating them to create new and innovative customer experiences.

While hologram technology may play a role in the future of AI-based chatbots, it is too soon to predict the exact trajectory of this field. The integration of these technologies will continue to evolve, and we will likely see various approaches to combining AI-based chatbots and hologram technology in the future.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

What Is Asset Tokenization?

Posted on by Jean Chalopin

In recent years, a new trend has emerged in the world of finance: asset tokenization. This technology promises to revolutionise how assets are bought, sold, and traded. Asset tokenization is converting physical assets into digital tokens on a blockchain network.

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In this article, we will delve into what asset tokenization is, the technology behind it, its benefits, and its challenges. We will explore the market size of asset tokenization and how it is projected to grow in the coming years. According to a report by MarketsandMarkets, the global asset tokenization market size is expected to reach US$5.6 billion by 2026, with a compound annual growth rate of 19.0% from 2021 to 2026.

Asset tokenization can disrupt traditional finance by democratising investment access and increasing liquidity. However, some challenges need to be addressed, such as regulatory frameworks, security concerns, and the need for interoperability between different blockchain networks.

What Is Asset Tokenization?

The concept of asset tokenization is relatively new, emerging in the early 2010s with the development of blockchain technology. The first asset to be tokenized was Bitcoin, a digital currency that uses blockchain technology to record transactions and create new units.

However, it wasn’t until later that other assets began to be tokenized. In 2017, a company called tZERO became the first to launch a regulated security token trading platform, allowing for tokenizing assets such as real estate, private equity, and debt.

Asset tokenization is a process that involves turning physical assets, such as real estate, art, or commodities, into digital tokens that can be bought and sold on a blockchain network.

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Think of it like a digital version of a stock certificate. Like owning a share of a company through a stock certificate, you can now own a fraction of an asset through a digital token. For example, instead of buying an entire house, you can own a fraction of that house through a digital token.

This is important because it allows for fractional ownership, making it easier for people to invest in assets that were previously only available to large institutional investors. It also increases liquidity, which means buying and selling these assets is easier and creates new investment opportunities for people who may not have had access to them before.

As technology evolves and becomes more accessible, asset tokenization is likely to become an increasingly important part of the financial landscape.

Asset Tokenization Technology

The technology behind asset tokenization is based on blockchain, a decentralised digital ledger that records transactions across a network of computers. Blockchain technology allows for secure, transparent, and tamper-proof transactions, making it an ideal platform for asset tokenization.

Like cryptocurrencies, digital tokens can be bought and sold on the network. Below is an overview of the process. 

To tokenize an asset, a smart contract is created on the blockchain that defines the terms of the investment, such as the ownership structure and the rights and responsibilities of the investors. This smart contract is then used to create digital tokens representing fractional ownership in the asset.

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These digital tokens are then traded on the blockchain network, just like cryptocurrencies. Investors can buy and sell these tokens on exchanges or through peer-to-peer transactions. As real-world assets back the tokens, their value is tied to the underlying asset.

For example, imagine a real estate developer who wants to raise capital for a new development project. They could tokenize the development project by creating digital tokens that represent fractional ownership. These tokens could then be sold to investors on a blockchain network, providing the developer with the capital needed to fund the project. Investors would then own a portion of the development project and receive a share of the profits when the project is completed and sold.

Another example is the tokenization of fine art. Artwork can be challenging to value and sell due to its subjective nature. By tokenizing fine art, investors can buy and sell fractional ownership in the artwork, making it a more liquid investment. Additionally, the blockchain ledger provides a transparent ownership record, reducing fraud risk.

The Benefits

Asset tokenization offers several benefits over traditional financial transactions. One significant advantage is increased liquidity. Traditional investments, such as real estate or private equity, can be difficult to buy and sell due to high transaction costs and limited market access. Asset tokenization allows for fractional ownership, which means investors can buy and sell smaller portions of an asset, making it easier to liquidate their investment.

Another benefit is increased transparency. The blockchain ledger used in asset tokenization provides a transparent record of ownership, reducing the risk of fraud or errors. The ledger is immutable, meaning once a transaction is recorded, it cannot be altered, providing a secure and tamper-proof record of ownership.

Asset tokenization also offers greater accessibility to a broader range of investors. Previously, real estate or private equity investments were only available to large institutional investors or accredited individuals. Tokenization allows for fractional ownership, meaning investors with smaller amounts of capital can participate in investments that were previously out of reach.

In addition, asset tokenization offers greater efficiency in the investment process. Traditional investments often require a complex and lengthy transaction process involving intermediaries such as brokers and lawyers. Asset tokenization eliminates the need for intermediaries, reducing costs and time.

Asset tokenization offers numerous benefits over traditional financial transactions, including increased liquidity, transparency, accessibility, and efficiency.

The Risks and Challenges

While asset tokenization offers numerous benefits, it also presents risks and challenges.

One of the main risks is the potential for regulatory uncertainty. Asset tokenization is a relatively new concept, and regulations are still developing. Different countries have different laws regarding the use of blockchain technology, and there is no universal framework for asset tokenization. This lack of regulatory clarity can create uncertainty for investors and companies and may slow the adoption of asset tokenization.

Another challenge is the risk of hacking or security breaches. Blockchain technology is secure and tamper-proof but not invulnerable to attack. If a hacker gains access to the blockchain ledger, they could alter transaction records, steal assets, or compromise investor information. Companies must take appropriate security measures to mitigate this risk.

Asset tokenization may also face challenges related to market acceptance. Despite the potential benefits, there may be resistance to investing in digital assets. Investors may be wary of the technology or not fully understand the implications of investing in a digital token. Companies and platforms offering asset tokenization must educate investors and build trust in the technology.

Finally, asset tokenization may face challenges related to scalability. As more assets are tokenized and traded on blockchain networks, the volume of transactions could potentially overwhelm the system, leading to slower processing times and increased costs. Blockchain technology is still in its early stages, and it remains to be seen whether it can handle the scale required for widespread asset tokenization.

Companies Using Asset Tokenization

There are several companies and platforms that are using asset tokenization in various ways. Here are some examples:

Harbor. Harbor is a blockchain platform that specialises in tokenizing private securities. The company’s platform allows issuers to offer securities to a wider range of investors, with fractional ownership, lower transaction costs, and increased liquidity. Harbor has tokenized assets such as real estate, private equity, and venture capital.

Securitize. Securitize is a blockchain platform that enables the tokenization of traditional securities such as stocks, bonds, and derivatives. The platform allows issuers to offer securities to a wider range of investors, with greater efficiency and transparency. The company has worked with several companies to tokenize securities, including Blockchain Capital and SPiCE VC.

tZERO. tZERO is a regulated platform for trading security tokens. The platform enables the trading of tokenized assets such as private equity, real estate, and debt securities. tZERO offers increased transparency, lower transaction costs, and increased liquidity for investors.

Vertalo. Vertalo is a blockchain platform that enables the tokenization of alternative assets such as private equity and real estate. The platform offers a suite of tools for issuers to manage their tokenized assets, including compliance tools, investor management, and cap table management.

Closing Thoughts

Asset tokenization is a relatively new concept, but it can potentially revolutionise the financial industry in the coming decade. As blockchain technology continues to evolve and regulations around tokenization become more explicit, we can expect to see significant growth in the use of asset tokens.

The primary driver of this growth will be the increased demand for alternative investments. Asset tokenization allows investors to access alternative investments, such as art, real estate, private equity, and venture capital, with lower transaction costs and greater efficiency.

In addition, we can expect to see continued innovation in the asset tokenization space. New platforms and technologies will emerge, offering greater functionality and efficiency for tokenized assets. This innovation will help to reduce transaction costs further, increase liquidity, and improve the overall user experience for investors.

However, some challenges must be overcome for asset tokenization to reach its full potential. These challenges include regulatory uncertainty, security risks, market acceptance, and scalability. Companies and regulators must work together to address these challenges and create a framework supporting asset tokenization growth.

In conclusion, the future of asset tokenization is bright, with significant growth expected in the coming decade. As blockchain technology evolves and regulations become more evident, we expect to see widely increased adoption of asset tokens as investors search for new ways to diversify their portfolios.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io.

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

What Are Neural Implants?

Posted on by Jean Chalopin

Neural implants, also known as brain implants, have been the subject of extensive research in recent years, with the potential to revolutionise healthcare. These devices are designed to interact directly with the brain, allowing for the transmission of signals that can be used to control various functions of the body. 

While the technology is still in its early stages, there is growing interest in its potential applications, including treating neurological disorders, enhancing cognitive abilities, and even creating brain-machine interfaces. 

According to Pharmi Web, the brain implants market is expected to grow at a CAGR of 12.3% between 2022 and 2032, reaching a valuation of US$18 billion by 2032. 

During the forecast period, the market for brain implants is expected to experience significant growth, primarily due to the increasing prevalence of neurological disorders worldwide and the expanding elderly population. As the number of individuals in the ageing demographic continues to rise, so does the likelihood of developing conditions such as Parkinson’s disease, resulting in a surge in demand for brain implants.

This article will explore the technology behind neural implants and the benefits and considerations associated with their use.

Understanding Neural Implants

Neural implants are electronic devices surgically implanted into the brain to provide therapeutic or prosthetic functions. They are designed to interact with the brain’s neural activity by receiving input from the brain or sending output to it. These devices typically consist of a set of electrodes attached to specific brain regions, and a control unit, which processes the signals received from the electrodes.

The electrodes in neural implants can be used to either stimulate or record neural activity. Stimulating electrodes send electrical impulses to the brain, which can be used to treat conditions such as Parkinson’s disease or epilepsy. Recording electrodes are used to detect and record neural activity, which can be used for research purposes or to control prosthetic devices.

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To function correctly, neural implants require a control unit responsible for processing and interpreting the signals received from the electrodes. The control unit typically consists of a small computer implanted under the skin and a transmitter that sends signals wirelessly to an external device. The external device can adjust the implant’s settings, monitor its performance, or analyse the data collected by the electrodes.

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Neural implants can treat neurological disorders, including Parkinson’s disease, epilepsy, and chronic pain. They can also help individuals who have suffered a spinal cord injury or amputation to control prosthetic devices, such as robotic arms or legs.

The Benefits of Neural Implants

Neural implants have the potential to provide a wide range of benefits for individuals suffering from neurological disorders. These benefits include:

Improved quality of life. Neural implants can significantly improve the quality of life for individuals suffering from neurological disorders such as Parkinson’s disease, epilepsy, or chronic pain. By controlling or alleviating the symptoms of these conditions, individuals can experience greater independence, mobility, and overall well-being.

Enhanced cognitive abilities. Neural implants also have the potential to enhance cognitive abilities, such as memory and attention. By stimulating specific regions of the brain, neural implants can help to improve cognitive function, particularly in individuals suffering from conditions such as Alzheimer’s disease or traumatic brain injury.

Prosthetic control. Neural implants can also be used to control prosthetic devices, such as robotic arms or legs. By directly interfacing with the brain, these devices can be controlled with greater precision and accuracy, providing greater functionality and independence for individuals with amputations or spinal cord injuries.

Research. Neural implants can also be used for research purposes, providing insights into the workings of the brain and the underlying mechanisms of neurological disorders. By recording neural activity, researchers can gain a better understanding of how the brain functions and develop new treatments and therapies for a wide range of neurological conditions.

While there are significant benefits associated with neural implants, many challenges and considerations must be considered.

The Challenges

There are several challenges to consider regarding the use of neural implants.

Invasive nature. Neural implants require surgery to be implanted in the brain, which carries inherent risks such as infection, bleeding, and damage to brain tissue. Additionally, the presence of a foreign object in the brain can cause inflammation and scarring, which may affect the long-term efficacy of the implant.

Technical limitations. Neural implants require advanced technical expertise to develop and maintain. Many technical challenges still need to be overcome to make these devices practical and effective. For example, developing algorithms that can accurately interpret the signals produced by the brain is a highly complex task that requires significant computational resources.

Cost. Neural implants can be costly and are often not covered by insurance. This can limit access to this technology for individuals who cannot afford the cost of the implant and associated medical care.

Ethical considerations. Using neural implants raises several ethical considerations, particularly concerning informed consent, privacy, and the potential for unintended consequences. For example, there may be concerns about using neural implants for enhancement or otherwise incorrectly. 

Long-term durability. Neural implants must be able to function effectively for extended periods, which can be challenging given the harsh environment of the brain. The long-term durability of these devices is an area of active research and development, with ongoing efforts to develop materials and designs that can withstand the stresses of the brain. 

While the challenges associated with neural implants are significant, ongoing research and development in this field are helping to overcome many of these obstacles. As these devices become more reliable, accessible, and affordable, they have the potential to significantly improve the lives of individuals suffering from a wide range of neurological conditions.

Companies Operating in the Neural Implant Space

Several companies are developing neural implants for various applications, including medical treatment, research, and prosthetics. 

Neuralink, founded by Elon Musk, is focused on developing neural implants that can help to treat a range of neurological conditions, including Parkinson’s disease, epilepsy, and paralysis. The company’s initial focus is developing a ‘brain-machine interface’ that enables individuals to control computers and other devices using their thoughts.

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Blackrock Microsystems develops various implantable devices for neuroscience research and clinical applications. The company’s products include brain implants that can be used to record and stimulate neural activity and devices for deep brain stimulation and other therapeutic applications.

Medtronic is a medical device company that produces a wide range of products, including implantable devices for treating neurological conditions such as Parkinson’s, chronic pain, and epilepsy. The company’s deep brain stimulation devices are the most widely used for treating movement disorders and other neurological conditions.

Synchron is developing an implantable brain-computer interface device that can enable individuals with paralysis to control computers and other devices using their thoughts. The company’s technology is currently being tested in clinical trials to eventually make this technology available to individuals with spinal cord injuries and other forms of paralysis.

Kernel focuses on developing neural implants for various applications, including medical treatment, research, and cognitive enhancement. The company’s initial focus is developing a ‘neuroprosthesis’ that can help treat conditions such as depression and anxiety by directly stimulating the brain.

Closing Thoughts

The next decade for neural implants will likely see significant technological advancements. One central area of development is improving the precision and accuracy of implant placement, which can enhance the efficacy and reduce the risks of these devices. Another area of focus is on developing wireless and non-invasive implant technologies that can communicate with the brain without requiring surgery.

Machine learning and artificial intelligence advancements are also expected to impact neural implants significantly. These technologies can enable the development of more sophisticated and intelligent implants that can adapt to the user’s needs and provide more effective treatment. Additionally, integrating neural implants with other technologies, such as virtual and augmented reality, could lead to exciting new possibilities for treating and enhancing human cognitive function.

The next decade for neural implants will likely see significant progress in the technology and its applications in treating a wide range of neurological and cognitive conditions. However, ethical and regulatory considerations must also be carefully considered as the field advances.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Tokenization Truly Disrupts

Posted on by Jean Chalopin

Blockchain technology has revolutionised how we think about asset ownership, management, and investment. Tokenization is one of the many innovations that have arisen from this technology. It can disrupt existing asset life cycles, accelerate product innovation, and create customised, hyper-personalised options for investors. 

This article will explore tokenization and how it differs from traditional methods. We will delve into the benefits and drawbacks of tokenization, examine real-world use cases, and analyse market statistics to gain a deeper understanding of this transformative technology.

What Is Tokenization?

Tokenization is a process that involves converting traditional assets, such as real estate, artwork, or securities, into digital tokens that can be traded on blockchain networks. These tokens are essentially digital representations of the underlying assets. 

They provide investors with a way to own and trade assets in smaller fractions rather than owning the entire asset outright. This allows for increased liquidity, lower barriers to entry, and greater transparency. Think of it like a digital representation of a physical asset, which can be bought and sold in smaller parts, with ownership tracked securely and transparently on a blockchain network.

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One real-world example of tokenization in finance is the tokenization of company shares. In traditional finance, owning shares in a company means holding a paper certificate or digital record representing a percentage of ownership. This makes it difficult to trade or sell smaller portions of the shares, as the minimum tradable amount is usually one full share.

With tokenization, a company can convert their shares into digital tokens representing smaller ownership fractions. For example, a company could tokenize its shares, with each token representing one thousandth of a share. Investors can then purchase as many tokens as they wish, allowing them to invest in the company with smaller amounts of capital.

Another example is payment card tokenization. Payment card tokenization is replacing sensitive payment card information, such as the card number, with a unique token. This token can then be used in place of the actual card information for payment transactions. Here is a simplified explanation of how payment card tokenization works:

  1. When a customer provides their payment card information during a transaction, the merchant’s payment processor securely captures the information.
  2. The payment processor then generates a unique token to represent the card information.
  3. The token is securely stored in the payment processor’s system, along with reference to the original card information.
  4. When a payment transaction is initiated using the token, the payment processor retrieves the original card information using the reference and completes the transaction on behalf of the customer.
  5. The merchant never sees or stores the customer’s payment card information, which helps to protect against data breaches and fraud.
  6. The token can only be used for transactions with the specific merchant or payment processor that generated it and is useless to anyone who may intercept it.

Overall, payment card tokenization helps to increase the security and privacy of payment transactions by reducing the amount of sensitive information that is shared and stored.

The Technology Behind Tokenization

The technology behind tokenization is based on blockchain, a distributed ledger technology that allows for secure, transparent, and tamper-proof data recording. When an asset is tokenized, it is converted into a digital representation on the blockchain network. This digital representation is called a token, essentially a unique string of code representing ownership of the underlying asset.

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Tokens can be programmed to represent various types of assets, such as real estate, artwork, stocks, or commodities. The programming of tokens can be customised to meet the specific needs of the asset being tokenized. For example, a token can be programmed to represent a certain fraction of an asset, or it can be programmed to pay out a certain percentage of returns on the asset.

Tokenization also offers greater transparency and security. Because ownership of tokens is recorded on a blockchain network, it is tamper-proof and transparent. This makes it easier to verify ownership and track the movement of assets, which can help to prevent fraud and other illegal activities.

Financial Use Cases

Tokenization has several use cases in finance that can benefit both issuers and investors. Here are some of the most common use cases for tokenization in finance.

Fractional ownership. Tokenization allows investors to buy and own a fraction of an asset that was previously not possible due to high entry barriers. For example, a piece of real estate can be tokenized and divided into multiple digital tokens, allowing investors to buy and sell a fraction of the property. This opens up new investment opportunities for retail investors and reduces the risk associated with owning a single asset. Companies like Harbor are using tokenization to offer fractional ownership in real estate assets.

Capital raising. Tokenization can be used to raise capital for new projects or businesses. By issuing digital tokens, companies can raise funds from a global pool of investors without the need for intermediaries like investment banks. This can be a more efficient and cost-effective way to raise capital. Companies like Securitize and Tokeny are providing tokenization solutions for capital raising.

Trading and liquidity. Tokenization can make it easier to trade assets that were previously illiquid or traded on traditional markets with high fees and barriers to entry. Digital tokens can be traded 24/7 on decentralised exchanges, increasing liquidity and reducing trading costs. Companies like tZERO and OpenFinance are building decentralised exchanges for tokenized securities.

Compliance and regulation. Tokenization can help issuers comply with securities regulations by automating compliance checks and providing transparency in ownership and transactions. Blockchain networks can also ensure that only authorised investors can trade certain securities. Companies like Polymath and TokenSoft are providing compliance solutions for tokenized securities.

Tokenization has several use cases in finance, including fractional ownership, capital raising, trading and liquidity, and compliance and regulation. Companies like RealT, Securitize, tZERO, and Polymath are using tokenization to disrupt traditional finance and offer new opportunities to investors.

Challenges of Tokenization

While tokenization offers several advantages over traditional finance, several challenges need to be addressed.

Regulation. Tokenization requires compliance with various regulations and laws, which can vary by jurisdiction. This can be challenging for companies that operate across multiple regions and must navigate different regulatory frameworks.

Liquidity. Tokenized assets can be illiquid, meaning they may not be easily tradable or exchangeable. This can be a significant challenge for investors who need to sell their assets quickly or for companies that need to raise capital.

Investor protection. Tokenized assets may not have the same level of investor protection as traditional securities, such as shareholder voting rights or disclosure requirements. This can increase the risk of fraud or abuse.

Interoperability. Tokenization requires interoperability between different platforms and systems, which can be challenging due to the lack of standardisation in the industry.

Adoption. Tokenization is a relatively new concept, and many investors and businesses may be hesitant to adopt it due to the lack of understanding or familiarity with the technology.

Despite the challenges of regulation, tokenization also has the potential to increase compliance and reduce fraud. Tokenized assets can be subject to ‘smart contracts’, which are self-executing agreements that can automate compliance requirements and reduce the risk of fraud or errors in the investment process. This can increase trust in the investment process and reduce the need for costly and time-consuming audits and regulatory oversight.

In terms of liquidity, while tokenized assets may be illiquid in some instances, as we’ve already noted, tokenization can also increase liquidity for assets that were previously illiquid or difficult to trade. Tokenization can enable secondary asset markets, increasing liquidity and providing an exit strategy for investors.

Ultimately, the benefits of tokenization outweigh the potential disadvantages. 

Closing Thoughts

Tokenization has the potential to be a genuine disruptor in the finance industry, particularly within asset management. Tokenization enables fractional ownership of assets, opening up investment opportunities to a broader range of investors and increasing liquidity for previously illiquid assets. Additionally, tokenization can increase transparency and efficiency in transactions, reduce fraud and increase compliance while potentially lowering costs.

However, there are challenges to overcome, such as regulatory compliance, interoperability, and investor protection. Adopting tokenization may be slow due to a lack of familiarity with the technology and concerns about the risks and benefits.

Overall, while tokenization has the potential to be a game-changer, its success will depend on overcoming these challenges and convincing investors and businesses of its value proposition. If successful, tokenization could benefit the finance industry significantly, revolutionising the investment process and opening up new opportunities for investors and companies alike.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Robots and, Touch

Posted on by Jean Chalopin

Robots are thriving with artificial intelligence (AI) integration. According to recent studies, the global robotics market is expected to reach $200 billion by 2024, with a compound annual growth rate of 17%. 

With AI advancements, robots are becoming more autonomous and capable of performing various tasks, from manufacturing and healthcare to retail and hospitality. However, despite these advancements, most robots lack a sense of touch, hindering their ability to interact with objects and environments in a nuanced, human-like way. 

To truly revolutionise the way we live and work, there is a pressing need to develop robots with a sense of touch.

The Importance of Touch for Robots

A sense of touch is critical for the robotics industry to progress because it dramatically enhances a robot’s ability to interact with its environment and perform tasks more human-likely. Without a sense of touch, robots are limited to rigid and repetitive motions, unable to adjust their movements based on objects’ texture, shape, and weight. 

By incorporating a sense of touch, robots could be programmed to handle delicate items, such as fragile electronics or perishable goods, with greater precision and care. Additionally, a sense of touch would allow robots to adapt to changing environments, making them more versatile and flexible in their applications. 

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With this newfound ability, robots could revolutionise industries ranging from manufacturing and healthcare to retail and hospitality, providing a more efficient and cost-effective solution for various tasks. Therefore, a sense of touch is a crucial step in advancing the robotics industry and bringing it closer to becoming a fully integrated part of our daily lives.

Developing Touch Sensors for Robots

Engineers use AI to develop a sense of touch for robots by incorporating sensors that can detect pressure, temperature, and texture. These sensors, known as tactile sensors, are integrated into the robot’s skin or outer surface, allowing it to sense the physical properties of objects it interacts with. 

The sensor data is then processed by AI algorithms, which use machine learning techniques to recognise patterns and make predictions based on the data received. By analysing the sensor data in real-time, the AI algorithms can allow the robot to distinguish between objects and environments, such as hard and soft surfaces or hot and cold temperatures.

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In addition, AI algorithms can continuously improve their performance over time as the robot gathers more data and experiences through its interactions with the world. In this way, engineers can use AI to create robots with a sense of touch that can make nuanced, human-like decisions, greatly expanding their abilities and applications.

The Benefits of a Sense of Touch

Developing a sense of touch brings numerous benefits to robots, including:

  • Enhanced precision and care in handling delicate and fragile items, such as fragile electronics or perishable goods.
  • Increased versatility and flexibility in adapting to changing environments and interacting with different surfaces and objects.
  • Improved safety in detecting and responding to obstacles, reducing the risk of collisions and other accidents.
  • Greater efficiency in performing tasks, as robots can make more informed decisions about how to interact with their surroundings.
  • Expansion of robots’ abilities and applications, making them more capable and valuable in industries ranging from manufacturing and healthcare to retail and hospitality.

Several industries could take advantage of robots with a sense of touch. 

Industry Use Cases

Integrating a sense of touch into robots offers numerous benefits across various industries, greatly enhancing their abilities and efficiency. From manufacturing to healthcare, retail to hospitality, a sense of touch dramatically expands the potential applications of robots, making them more capable and valuable in our daily lives.

Manufacturing

The manufacturing industry is one of the earliest adopters of robots and integrating a sense of touch is expected to bring significant improvements to the industry. With the ability to sense the physical properties of objects they interact with, robots with a sense of touch can handle delicate and fragile items, such as fragile electronics or perishable goods, with greater precision and care. 

This reduces the risk of damage and increases efficiency in the manufacturing process, leading to lower costs and higher-quality products. Companies such as Boston Dynamics, which specialises in robotics research and development, are already exploring the potential of robots with a sense of touch in the manufacturing industry.

Healthcare

In the healthcare industry, robots with a sense of touch have the potential to revolutionise the way medical procedures are performed. For example, robots with a sense of touch can assist with surgeries by providing a stable and precise platform for surgical instruments, allowing for improved accuracy and control. 

Additionally, robots with a sense of touch can also be used to assist with physical therapy, providing more accurate and effective treatments by sensing the physical properties of the patient’s body and responding in real time. Companies such as Intuitive Surgical, which develops robots for minimally invasive surgery, are already exploring the potential of robots with a sense of touch in the healthcare industry.

Retail

The retail industry is also poised to benefit from robots with a sense of touch. For example, robots with a sense of touch can handle and sort merchandise, providing a more efficient and cost-effective solution for various tasks. Additionally, robots with a sense of touch can be used in customer service, providing a more human-like experience by sensing and responding to customers’ needs and preferences. Amazon uses robots in its fulfilment centres, exploring the potential of robots with a sense of touch in the retail industry.

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Hospitality

In the hospitality industry, robots with a sense of touch can significantly enhance the customer experience by providing a more personal and human-like interaction. For example, robots with a sense of touch can be used as concierges, providing information and assistance to guests, or as restaurant servers, taking orders and serving food. 

Additionally, robots with a sense of touch can also be used in hotels for cleaning and maintenance, providing a more efficient and cost-effective solution for these tasks. Hilton is exploring the use of robots in its hotels. 

Integrating a sense of touch into robots offers numerous benefits across various industries, greatly enhancing their abilities and efficiency. With the ability to sense the physical properties of objects they interact with, robots with a sense of touch can handle delicate and fragile items, provide more accurate and effective treatments, provide a more efficient and cost-effective solution for various tasks, and provide more personal and human-like interaction. 

Risks and Challenges

Developing a robot with a sense of touch presents several challenges and risks that must be addressed to ensure its success. One of the biggest challenges is the technical difficulty of creating a system that can accurately and reliably detect and respond to physical touch. This requires sophisticated algorithms and sensors that can process information from the environment and react in real-time.

Another challenge is ensuring the safety of people and objects in the environment. Robots with a sense of touch must be able to safely interact with their environment and avoid causing harm to people or damaging objects. This requires careful consideration of the design of the robot and its controls, as well as its algorithms and sensors, to ensure that it operates responsibly. 

One example of a robot with a sense of touch gone wrong is the case of a robot at a Volkswagen factory in Germany in 2015. The robot, which was designed to handle car parts, accidentally grabbed and crushed a worker. The worker suffered severe injuries and had to be taken to the hospital, and later died. 

The incident was later determined to result from a programming error in the robot’s control system, which caused it to behave in a way that was not intended. The incident highlighted the importance of careful design and testing of robots with a sense of touch to ensure their safety and reliability.

And Addressing the Challenges

In addition to these technical challenges, several risks are associated with developing a robot with a sense of touch. One of the most significant risks is that the robot may malfunction or fail, leading to accidents or injuries. This risk can be mitigated through careful testing and development, as well as ongoing monitoring and maintenance of the robot.

Another risk is that the robot may be used in ways that are not intended or that cause harm. For example, a robot with a sense of touch could be used in manufacturing to handle dangerous or hazardous materials, leading to accidents or harm to workers. This risk can be mitigated through careful consideration of the design of the robot and its controls, as well as through education and training for those who will use the robot.

Finally, there is also a risk that the development and use of robots with a sense of touch may lead to job loss and other social and economic consequences. This risk can be mitigated through careful consideration of the impact of the technology on society, as well as through efforts to provide education and training for those who may be affected.

Closing Thoughts

The quest to give robots a sense of touch is an ongoing process, but the advancements that have been made so far are impressive. Robots with touch sensors are already being used in various industries, from manufacturing to healthcare, and are having a significant impact. As technology continues to advance, robots with a sense of touch will likely become even more widespread, offering new possibilities for the field of robotics.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Precision Medicine and AI With Blockchain

Posted on by Jean Chalopin

Precision medicine has emerged as a promising approach to providing personalised treatments for patients based on their genetic makeup, lifestyle, and environment. However, this approach requires vast amounts of data to be collected, analysed, and securely shared among healthcare providers and researchers. 

Artificial intelligence (AI) and blockchain technology offer potential solutions to these challenges by enabling data-driven and secure decision-making. According to a recent report by Market.us, the global precision medicine market is projected to reach $254 billion by 2032, growing at a compound annual growth rate (CAGR) of 21.1% from 2023 to 2032. 

This article will explore how AI and blockchain are transforming precision medicine and improving patient outcomes.

What Is Precision Medicine?

Precision medicine, or personalised medicine, is a healthcare approach that tailors medical treatments to individual patients based on their genetic information, environmental factors, lifestyle, and other personal characteristics. Unlike the traditional ‘one-size-fits-all’ approach, precision medicine aims to provide targeted and effective treatments that can improve patient outcomes and reduce healthcare costs.

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To achieve this, precision medicine requires vast amounts of data to be collected, analysed, and shared securely among healthcare providers and researchers. This is where AI and blockchain technology comes in. AI can analyse large datasets and identify patterns and correlations that human analysts may miss. At the same time, blockchain technology can provide a secure and transparent platform for sharing and accessing data.

AI can also help drug discovery by analysing large genomic, proteomic, and metabolomic data datasets to identify new drug targets and develop personalised treatments. For example, AI algorithms can analyse patients’ genomic data and predict their likelihood of responding to a particular drug or developing adverse effects.

What Is Blockchain?

Blockchain is a distributed ledger technology that enables secure, transparent, and tamper-proof record-keeping of transactions and data. It uses cryptographic techniques to create an unalterable chain of blocks that contains a record of all transactions and data entered into the system. The chain is maintained by a network of nodes, each of which has a copy of the ledger, and any changes to the ledger must be validated and approved by the network.

Blockchain technology supports precision medicine in several ways.

Firstly, blockchain provides a secure and tamper-proof platform for storing and sharing patient data. In a traditional healthcare system, patient data is stored in a centralised database vulnerable to data breaches and hacking attacks. In contrast, blockchain technology uses a decentralised system, making it difficult for hackers to breach the system and steal sensitive patient information.

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Furthermore, blockchain technology ensures the privacy and confidentiality of patient data by using cryptographic techniques to encrypt patient data. Patient data is stored in blocks linked together using cryptographic hashes, creating an unalterable and transparent ledger of patient data. Authorised parties can only access this ledger with the necessary permissions, and any changes made to the ledger are recorded and visible to all authorised parties.

Blockchain technology can also support clinical trials and drug discovery by providing a secure and transparent platform for sharing data among researchers and healthcare providers. Clinical trials often involve collecting large amounts of sensitive patient data, which researchers must share securely to ensure patient privacy and confidentiality. Blockchain technology can provide a secure and transparent platform for sharing data among researchers while ensuring the privacy and confidentiality of patient data.

Another advantage of using blockchain technology in precision medicine is the ability to create smart contracts. Smart contracts are self-executing contracts that use blockchain technology to automate the negotiation and execution of contractual terms. In precision medicine, smart contracts can be used to create agreements between patients, healthcare providers, and researchers that specify how patient data will be collected, analysed, and shared. The blockchain can automatically enforce these agreements, ensuring that all parties adhere to the agreed-upon terms.

Why Does Precision Medicine Need AI and Blockchain?

AI and blockchain technology each play a crucial role in enabling processes that enhance the effectiveness of precision medicine.

AI enables the analysis of large and complex datasets in a timely and efficient manner, identifying intricate patterns and correlations. With AI, healthcare providers and researchers can develop more accurate and personalised treatments based on a patient’s unique characteristics. However, without secure and transparent platforms for sharing data, the effectiveness of AI in precision medicine would be limited.

Understanding the Precision Medicine Sector

Several companies are leading the field in precision medicine, each with its own unique approach to this innovative field. 

One example is 23andMe, a personal genomics and biotechnology company offering consumers genetic testing and analysis services. 23andMe provides insights into an individual’s ancestry, genetic health risks, and carrier status for certain inherited conditions. The company aims to empower individuals with knowledge about their genetic makeup and help them make informed decisions about their health.

Another example of a company leading the field in precision medicine is Foundation Medicine, a molecular information company specialising in the genomic profiling of cancer patients. The company’s genomic tests help oncologists match patients with targeted therapies and clinical trials based on the genetic characteristics of their tumours. The goal is to provide more personalised and effective cancer treatments.

IBM Watson Health is a health information technology company that uses machine learning and artificial intelligence to help healthcare providers make better clinical decisions. The company’s offerings include genomics, imaging, clinical trial matching tools, and population health and patient engagement solutions.

GRAIL is a biotechnology company that is developing a blood test for the early detection of cancer. The test analyses fragments of DNA that are shed by tumours into the bloodstream, to detect cancer at an earlier stage when it is more treatable. The test is currently being evaluated in large-scale clinical trials.

Finally, Veracyte is a genomic diagnostics company that focuses on providing molecular diagnostic tests for thyroid and lung cancer. The company’s tests help healthcare providers make more informed treatment decisions, reducing unnecessary surgeries and treatments. These companies are just a few examples of the many innovative organisations leading the way in precision medicine, using cutting-edge technologies and approaches to improve patient outcomes and transform healthcare.

Considerations With Precision Medicine

When it comes to precision medicine, some technical, regulatory, clinical and ethical considerations need to be taken into account.

Technical

  • Advanced data analysis techniques like machine learning and natural language processing are needed to extract valuable insights from large and complex datasets.
  • There is an additional need for secure and interoperable data-sharing platforms to enable collaboration among healthcare providers and researchers.

Ethical

  • It’s vital to ensure the privacy and confidentiality of patient data and obtaining informed consent from patients for using their data.
  • The potential for data analysis and interpretation bias could result in inaccurate or discriminatory treatment decisions.
  • Providers must ensure equitable access to precision medicine technologies, while addressing disparities in healthcare access and outcomes. 
  • Hurdles exist within the ownership of patient data, as well as the potential for private companies’ commercialization of patient data.

Regulatory

  • Paramount remains the need for compliance with relevant laws and regulations, such as those related to data protection, patient rights, and clinical trials.
  • The industry requires regulatory oversight and approval of precision medicine technologies and treatments.

Clinical

  • There are concerns surrounding the validation and verification of precision medicine treatments, as their safety must first be verified. 
  • However, we must integrate precision medicine into clinical workflows and decision-making processes, while providing specialised training. 

Social

  • Precision medicine impacts society as a whole, including its potential to exacerbate existing health disparities or lead to the creation of new ones.
  • The potential for precision medicine to contribute to the democratisation of healthcare and the empowerment of patients, as well as its role in shaping public policy and healthcare delivery models.

Despite the many considerations that need to be taken into account, precision medicine is still considered a revolutionary field in healthcare. The ability to tailor medical treatments and interventions to individual patients based on their unique genetic, environmental, and lifestyle factors can transform healthcare in previously unimaginable ways.

Closing Thoughts

Precision medicine promises more personalised and effective treatments, earlier disease detection, and improved patient outcomes. While technical, ethical, regulatory, clinical, and social considerations must be addressed, precision medicine’s potential benefits cannot be ignored. 

As researchers and healthcare providers continue to work on developing and implementing precision medicine technologies and treatments, it is essential to carefully consider the implications of these innovations and ensure that they are used responsibly.

As healthcare becomes more personalised and patient-centred, the ability to tailor medical treatments and interventions to individual patients will become increasingly important. Moreover, precision medicine can reduce healthcare costs by avoiding unnecessary treatments and improving the efficiency of clinical trials and drug development. 

As our understanding of the genetic, environmental, and lifestyle factors that contribute to disease continues to improve, precision medicine will become an increasingly important tool in the fight against complex and chronic diseases.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

Why Life Expectancy Is Increasing

Posted on by Jean Chalopin

Our average life expectancy has increased from 45 years in the 1850s to nearly 80 years today as a result of medical science. Researchers believe that our life spans will continue to grow, but there is an eventual hard limit.

Advances in medicine that are driving this lengthening life span range across a vast spectrum, including diagnostic developments, medical devices, prescription drugs, and procedures.  These medical interventions are joined with healthier lifestyles, a more holistic approach to medicine, and more accurate and earlier diagnoses.

We will take a look at how medical science and technological advances have contributed to our lengthening lifespans.  

Healthy Lifestyles and Life Expectancy

We are increasingly more conscious of the need to maintain a healthy lifestyle. Such a lifestyle comes in the form of improved diet and nutrition, exercising regularly, maintaining our mental and emotional health, and regularly assessing our health.

The healthy lifestyle trends that started with the 1970s running craze and subsequent 80s aerobics craze have more recently grown into fitness and healthcare wearables that allow consumers to monitor their personal health–keeping track of steps, activity, sleep, heart rate, stress, and other vital signs. 

The IDC predicts that the total wearable market will grow at a rate of 13.4% in the next five years, with an expected 219.4 million units being sold in 2022.  

The wearables of today span multiple medical and health functions, from fitness trackers to smart health watches, including wearable ECG devices, and blood pressure monitors, biosensors, and more. These devices can collect physical and medical data with various levels of usefulness. They can monitor, analyse, and even predict health and mental well-being when paired with mobile and desktop applications.  

The covid pandemic accelerated a growing trend toward telehealth and remote monitoring.  This trend can be leveraged to move us in the direction of preventative healthcare for conditions such as heart disease and stroke.

There are now several wearable makers in the healthcare space, including Interplex, that has been a supplier to many manufacturers and disruptive wearable companies. They have a diabetes monitoring system that can help keep patients’ blood sugar levels more standard.

Courtesy of Interplex

Wearables and Health Diagnoses

Wearables are no longer new to the market, and their usefulness and quality have consistently improved. They collect multiple data points related to one’s health, and when applying professional analysis to the collected data, they are now able to make early detection possible, which helps with disease prevention and in proposing better treatments. Currently, medical laboratories are providing up to 70% of lab testing to physicians in order to provide accurate diagnoses and treatment plans.

Clinical lab testing results for diagnostic decision-making are an essential part of clinical medicine. The selection of laboratory tests available to doctors has grown exponentially since they first surfaced in the 1920s. Now a wide array of tests can diagnose, screen for, and research disease, while others can monitor treatments and therapies to ensure effectiveness.  It is now possible to design tests and equipment that fit the exact specifications needed for medical diagnosis, and this has moved into the area of genetic diseases.

Medical Treatment

Advances seen in medical equipment and treatment protocols have contributed to improvements in patient outcomes. A particular area of advancement is in surgical treatment.  This advancement is mainly in a movement toward more precise surgical operations as well as minimally invasive procedures.  

Equipment now being used can make cuts with lasers with high precision enabling delicate surgeries to be performed on the brain or eye, or they can even focus radio or other waveforms that ultimately produce surgical-like outcomes below the skin, without the surgeon having to make a single incision. 

With these advances, minimally invasive but advanced laparoscopic surgery (keyhole), hysteroscopic surgery, and myomectomies are just a few of the procedures that have resulted from advancements in medical technology. Other medical fields have benefitted from these advances, including neurology, interventional equipment, and cardiology.  

Beyond surgical precision and minimal invasiveness, mobile medical technologies are advancing, bringing medical technology and equipment into more hospitals, doctors’ offices, emergency rooms, and even homes, making a significant contribution to medical treatment and health outcomes.  

Telemedicine

Healthcare professionals are increasingly using mobile medical equipment and devices from medical workstations to specialised equipment for telehealth, to deliver medical care to their patients wherever they may be (both patients and medical professionals).

Through the increase in transportable and telehealth solutions, mobile medicine is expanding the reach of healthcare far beyond the traditional hospital and clinical setting.  The fields of teleradiology, telenephrology, and telepsychiatry are just a few examples of mobile medicine that have now become more commonplace and will likely continue to grow over the next decade.  With advancing technology, more of these “tele” medical fields will be available and contribute to a significant change in the medical industry. 

Courtesy of the CCHP

In the future, doctors with specialties will be able to practise much of their medicine from anywhere in the world, not needing to see their patients in person directly. This will be aided by virtual reality, augmented reality, and machines capable of testing, diagnosis, and even surgery from afar. The possibilities are endless in this space, and with 5G and soon-to-be 6G, much of this advancement we will likely see over the next two decades.  

IoT Devices

Advancements in low-cost sensor tech, dependability, increased data storage and transmission capabilities, and low power consumption has meant that new devices will be possible that can change how we view medicine. With the increasing number of IoT devices coming to market that are connecting our homes, businesses, supply chains, and vehicles, we will also see similar devices for ourselves.  

These devices will initially monitor specific health issues, allowing us to identify when a specific problem is occurring and potentially deal with it automatically. This is already happening with Implantable Cardioverter Defibrillators (ICDs).  

ICDs are being implanted into patients. In the case of a cardiac event, they are informing medical professionals of a problem and providing a shock to the patient that will restart their hearts and save their lives.  

Courtesy of the American Heart Association

These kinds of devices will expand with the IoT and become more common for many of our common ailments.  

In the future, we will likely see devices with multiple functions, such as monitoring, aiding, and preventing devices all in one, able to identify many different ailments when they first become a problem and treat them before they grow in severity.

Life Expectancy

Much of the life expectancy gains we have seen over the past 150 years have been due to improvements in infant mortality and the advent of antibiotics and immunizations. Now that 1 in 5000 Americans is 100 or over, researchers are investigating the ageing process and how to slow it.

According to biologist Andrew Steele, the author of Ageless, we have been treating medicine in an unsystematic way. We have been focused on the endpoints of ageing, problems like heart disease and cancer, but not addressing the fundamental causes for these maladies.  

But this is changing, and medicine is slowly shifting to a holistic approach where we first understand these hallmarks but then come up with treatments that intervene with them directly. This would mean a switch to preventative treatments, which can proceed earlier in life and stop people from getting age-related diseases in the first place.  

For example, treatments for cellular senescence (chronic inflammation) already exist that target many redundant cells by killing them, preventing them, or removing them from the body, along with a toxic set of molecules that accompany them, contributing to cancer and heart disease. 

These drugs have been shown to help extend the lives of mice, with fewer cancers, cataracts, and heart disease, even making them less frail as they age. Eventually, these same drugs may be given to humans.

Closing Thoughts

We have made several advances in medical science that have extended our lifespans and made us healthier. The technology we are now creating is directly impacting our health, being more connected with our doctors, and allowing us and them to receive information sooner–keeping us healthier.

In the coming decades, we will likely use genetic engineering to prevent genetic diseases from appearing at all. It is an exciting time for the medical field, and we, as patients, will most certainly be the beneficiaries.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

How ChatGPT Shapes the Future

Posted on by Jean Chalopin

In recent years, the AI industry has grown significantly, with forecasts that the worldwide market will reach $190.61 billion by 2025, expanding at a CAGR of 36.2% between 2020 and 2025. The Covid-19 pandemic has only hastened this rise, as businesses have been forced to adjust swiftly to remote working and growing digitisation. 

The pandemic has brought to light the significance of technology in industries such as healthcare and e-commerce.

Introducing ChatGPT

ChatGPT is an AI model created by OpenAI that can potentially influence the AI market’s evolution in various ways. ChatGPT may be linked to a wide range of applications and services that need natural language processing (NLP), such as customer service, chatbots, and virtual assistants. This may raise the need for NLP-based AI solutions, which would help the AI industry flourish.

ChatGPT may also be used to train other AI models, which can accelerate the development and implementation of AI-powered apps and services. This can improve the efficiency of the AI development process, contributing to the growth of the AI market.

Furthermore, ChatGPT’s capacity to create human-like writing, which can be utilised for various content creation and optimisation activities, has the potential to propel the AI market forward. ChatGPT, for example, may produce product descriptions, marketing text, and even news pieces, reducing the time and effort necessary for content generation while enhancing output quality. 

Below is a simple example of how it can write a product description for Coca-Cola within seconds. 

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The Benefits of ChatGPT

One of the primary benefits of ChatGPT is its ability to help users improve their writing and language skills. ChatGPT can help individuals become more effective communicators by providing real-time feedback and suggestions, whether they are writing emails, composing reports, or creating content for social media. 

For example, sales and marketing professionals can use ChatGPT to improve their email writing, helping them to better engage with prospects and customers. Additionally, educators can use technology to help students improve their writing and critical thinking skills without needing human grading and feedback.

Another critical benefit of ChatGPT is its ability to support knowledge management and collaboration. By using the technology to automate repetitive tasks, such as summarising reports or answering frequently asked questions, organisations can free up time and resources for more strategic initiatives. 

This can help companies become more efficient, increase productivity, and enhance customer service. For example, customer service teams can use ChatGPT to respond quickly to customer inquiries and resolve issues, reducing wait times and improving the customer experience.

The example below shows how a customer might be able to resolve a query about their home insurance without speaking to a human.

How ChatGPT Augments Roles

ChatGPT can significantly augment the functions of different departments in an organisation, including Data, IT, Marketing, Development, Finance, and Compliance.

Data 

For Data teams, it can assist in processing large amounts of data to provide insights and support decision-making. It can benefit data teams in their coding endeavours, particularly when it comes to writing code in SQL or Python. 

ChatGPT’s ability to provide suggestions for completing code snippets, identify syntax errors and suggest corrections, and generate complete code snippets based on specific requirements, can save data teams valuable time and effort. Furthermore, it can serve as a repository of coding knowledge that can be easily shared among team members. 

For example, if a data team member is working on a SQL query and encounters a roadblock, they can ask ChatGPT for advice on how to proceed. It can then provide suggestions for optimising the query or offer alternative solutions based on its vast knowledge of SQL coding best practices. By utilising its coding capabilities, data teams can improve their coding efficiency and accuracy, freeing them up to focus on more complex tasks.

IT

IT teams can use ChatGPT to automate various IT operations tasks and build a knowledge management system. It may also be incorporated with IT systems to give users rapid and accurate replies to technical assistance enquiries, decreasing the IT team’s burden.

Furthermore, ChatGPT can create a knowledge management system to store and retrieve information about IT systems and procedures, increasing the team’s productivity. IT teams may also use its natural language processing skills to examine massive quantities of log data and give insights into system performance and potential faults.

Marketing

Marketing teams can use ChatGPT to generate high-quality content and build conversational AI chatbots for customer service and sales. You can watch a video below on how ChatGPT built an entire marketing campaign in minutes. 

https://www.youtube.com/watch?v=gI1UL1cHHjk
Source

Marketing teams still need to ask the right questions, but ChatGPT saves time and efficiency. 

Finance

For Finance teams, it can be integrated into financial systems to assist with data analysis and decision-making. It may assist finance teams in making more informed decisions and improving financial planning and forecasting. 

ChatGPT may also help finance teams automate operations, including calculating financial ratios, creating reports, and tracking spending. Furthermore, ChatGPT’s natural language processing skills may be utilised to analyse financial data and discover trends, allowing finance teams to recognise opportunities and possible hazards quickly.

Compliance

Compliance teams can use ChatGPT to ensure compliance with regulations and standards by automating various compliance tasks. 

It may also aid in the categorisation and classification of enormous volumes of data, as well as the investigation of complicated legislation and laws. Furthermore, it may give real-time responses to staff inquiries, decreasing the time spent on manual research and enhancing the compliance team’s productivity. The capacity of the language model to interpret and create human-like writing makes it a powerful tool for firms wanting to strengthen their compliance procedures.

By augmenting the roles of different departments, ChatGPT can help organisations increase productivity and improve the quality of their work. Some entrepreneurs are using the technology to brainstorm business ideas. It’s like having a friend to bounce your thoughts between. 

Risks of ChatGPT

Despite these benefits, there are also some risks associated with ChatGPT that must be considered. 

One of the primary risks is the potential for the technology to promote cheating and plagiarism. For example, students may use technology to generate homework assignments, or employees may use it to create reports and presentations without doing the necessary research and analysis. 

To mitigate this risk, it is essential for organisations to communicate the acceptable use of the technology clearly and to have clear policies and procedures in place to monitor and enforce compliance.

Another risk is the potential for the technology to perpetuate bias and harmful stereotypes. As the model has been trained on a large corpus of text, it may generate offensive or inappropriate language or reinforce negative stereotypes. It is vital for organisations to use the technology responsibly and ethically and to regularly review and update the training data to ensure that it is inclusive and free from bias.

AI for People

Despite these risks, companies are already using ChatGPT in innovative and impactful ways. For example, OpenAI partnered with the non-profit organisation ‘AI for People’ to develop a tool that uses ChatGPT to support mental health and well-being. 

The tool uses natural language processing and machine learning to provide users with personalised feedback and support, helping them manage stress, anxiety, and depression. OpenAI has also worked with news organisations and journalists to develop an AI-powered writing assistant that can help writers quickly generate high-quality, accurate news articles.

Copy.ai

Another example of a company positively using ChatGPT is Accenture, a leading global professional services firm. Accenture has developed a tool called ‘Copy.ai’ that uses ChatGPT to help businesses quickly generate high-quality marketing and advertising content. 

By using the technology to automate routine tasks, such as writing product descriptions and creating social media posts, Accenture is helping its clients become more efficient and effective in their marketing efforts.

Closing Thoughts

ChatGPT is a powerful tool that has the potential to help individuals and organisations across different roles to adapt and develop new skills. While some risks are associated with the technology, companies are already using it innovatively to drive positive outcomes. The key is to use it responsibly. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment.  Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business.  Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

How Smart Cities Use Blockchain 

Posted on by Jean Chalopin

Smart cities, which use technology to improve citizens’ quality of life and optimise urban services, have rapidly grown in popularity. According to a recent report, the global smart city market is projected to reach $158 billion by 2022. 

By utilising blockchain technology, smart cities can enhance their security, transparency, and efficiency in supply chain management, voting systems, and energy consumption. Implementing blockchain in smart cities benefits the citizens and creates sustainable and resilient urban environments.

This article explains what smart cities and blockchain technology are, and how they work together. 

What Are Smart Cities?

A smart city is an urban area that leverages technology and data to improve its citizens’ quality of life, optimise resource use, and create more sustainable and efficient communities. By integrating connected devices and systems, smart cities analyse data to provide more efficient and effective services, from transportation to energy management to public safety.

One prime example of a smart city is Singapore, which has implemented numerous initiatives to enhance the living experience of its citizens. For example, the city-state has a sophisticated transportation system that uses data and technology to manage traffic flow and reduce congestion, making it easier and faster for residents to get around. 

Singapore has also implemented smart waste management systems that use sensors to optimise collection schedules, reduce the amount of waste sent to landfills, and increase recycling rates.

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Another example of a smart city is Amsterdam, which strongly focuses on sustainability and green energy. The city has implemented several initiatives to reduce its carbon footprint and increase the use of renewable energy sources. 

For example, Amsterdam has a smart grid system that integrates renewable energy sources, such as wind and solar power, into the city’s energy mix. This helps reduce the city’s dependence on fossil fuels and increase clean, sustainable energy use. 

Additionally, Amsterdam has implemented smart lighting systems that use sensors to automatically adjust the brightness of streetlights based on the presence of people, vehicles, and bikes, saving energy and reducing light pollution.

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These are just a few examples of innovative initiatives implemented in smart cities worldwide. By leveraging technology and data, smart cities are creating more livable, sustainable, and efficient urban environments for their citizens.

What Is Blockchain?

Blockchain technology is a decentralised, distributed digital ledger that securely records transactions and information. It uses cryptography to link blocks of information together in a chain, creating a permanent and unalterable record of all transactions.

At the heart of blockchain technology sits a network of computers, called nodes, that work together to validate and process transactions. Each node has a copy of the entire blockchain, and the network’s consensus must validate any changes to the blockchain. This decentralised and distributed structure makes the blockchain resistant to tampering, hacking, and fraud.

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The benefits of blockchain technology are numerous. One of the most significant benefits is increased security and transparency, as the decentralized and distributed nature of the blockchain makes it nearly impossible to alter or tamper with the information once it has been recorded. Additionally, blockchain technology can reduce the need for intermediaries, such as banks, to process transactions, lowering costs and increasing efficiency. It also enables secure and transparent tracking of assets, such as supply chains, voting systems, and intellectual property. It provides a safe and transparent platform for creating and managing digital assets like cryptocurrencies.

One example of how blockchain could work in smart cities is in the voting process. Voting systems can be susceptible to tampering, fraud, and errors. By implementing a blockchain-based voting system, each vote would be recorded as a secure transaction on the blockchain, providing a transparent and tamper-proof record of the election results. This would increase trust in the voting process and ensure that the results are accurate and fair.

Blockchain is a secure and trustworthy way of recording transactions and information in a decentralized manner, making it a valuable tool for various applications, including smart cities.

Why Smart Cities Need Blockchain Technology

Smart cities require secure, transparent, and decentralised technology to succeed. This is where blockchain technology comes in. It provides a secure, transparent platform for managing data and transactions in a decentralised manner.

One of the biggest challenges in smart cities is ensuring the integrity and security of the data that is collected and processed. Blockchain technology provides a secure and tamper-proof way of recording information, making it a valuable tool for ensuring the integrity of data in smart cities. 

Additionally, the decentralised nature of blockchain makes it resistant to hacking, tampering, and other forms of fraud, providing a secure platform for collecting and processing sensitive data.

Another reason why smart cities need blockchain technology is to improve efficiency and reduce costs. By implementing blockchain-based systems, smart cities can reduce the need for intermediaries, such as banks, to process transactions. This can increase efficiency and reduce costs, freeing up resources that can be used to enhance other services and improve the quality of life for citizens.

Finally, blockchain technology enables secure and transparent tracking of assets, such as supply chains, voting systems, and intellectual property. In smart cities, this can be used to improve transparency and accountability and enhance the management of resources, such as energy and waste.

How Smart Cities Are Created With Blockchain

Building a smart city with blockchain technology requires careful planning, research, and collaboration. Here are some steps to get started:

  1. Research and plan: Research existing smart cities and understand the challenges and opportunities they face. Identify areas where blockchain technology can be used to improve the quality of life for citizens, such as in managing waste, energy, transportation, and voting systems. Develop a plan for how blockchain technology can be used to solve specific problems and improve specific services in your city.
  2. Build partnerships: Building a smart city with blockchain technology requires collaboration and partnerships. Partner with blockchain developers, government agencies, academic institutions, and the private sector to share knowledge, resources, and expertise.
  3. Choose the right technology: There are many different blockchain technologies available, each with its strengths and weaknesses. Choose the best technology suited to your specific needs and goals, and consider security, scalability, and interoperability factors.
  4. Develop a pilot project: Start small by developing a pilot project to test your ideas and demonstrate the potential of blockchain technology. Choose a problem or service that can be improved with blockchain technology and create a proof-of-concept project to demonstrate the potential of the technology.
  5. Engage with the community: Building a smart city with blockchain technology requires community engagement and participation. Engage with citizens and stakeholders to understand their needs and concerns and involve them in the planning and implementation of blockchain-based solutions.
  6. Monitor and evaluate: Continuously monitor and evaluate your pilot project to understand its impact and identify areas for improvement. Share your results with the community and stakeholders to demonstrate blockchain technology’s benefits and encourage its wider adoption.

Building a smart city with blockchain technology requires careful planning, research, and collaboration. Following these steps, you can journey towards a more secure, efficient, and sustainable future.

How Did Singapore Become a Smart City?

Singapore has become a smart city through government leadership, innovative technology, and community engagement. The government of Singapore has taken a proactive approach to transform the city into a smart city. It has provided funding, resources, and support for developing and deploying smart city solutions. The government has also established policies and regulations that encourage innovation and collaboration between the public and private sectors, creating a supportive environment for developing smart city initiatives.

Singapore has also made significant investments in technology, including deploying smart city infrastructure, such as smart grids, sensors, and digital networks. This has enabled the city to collect and process large amounts of data, providing valuable insights into energy consumption, traffic flow, and waste management.

In addition to government leadership and investment in technology, community engagement has been crucial to the success of Singapore’s smart city initiatives. The government has worked closely with citizens and stakeholders to understand their needs and concerns and involved them in planning and implementing smart city solutions. This has helped to create a sense of ownership and involvement among citizens and encouraged their participation in developing a more sustainable, efficient, and livable city.

Risks Associated With Smart Cities

Smart cities can face many risks if they do not integrate blockchain technology. One significant risk is the security of sensitive information. In the absence of blockchain, smart cities may use centralised databases and systems to store and manage data, which can be vulnerable to cyber attacks, data breaches, and other forms of digital crime. This can compromise personal data, financial information, and critical infrastructure, causing harm to citizens and the city as a whole.

Another risk is the lack of transparency and accountability in data management. Without blockchain, there is a risk that data may be manipulated or misused without being detected, leading to potential privacy violations and ethical concerns. This can erode trust in government and civic institutions and undermine the legitimacy of smart city initiatives.

In addition, smart cities without blockchain may struggle to manage the increasing amounts of data generated by connected devices and sensors. This can lead to data silos and a lack of interoperability between different systems, hindering the ability of smart cities to make data-driven decisions and achieve their goals.

Finally, without blockchain, smart cities may not be able to ensure their data’s long-term security and preservation. This can result in the loss of valuable historical data and the inability to build on past achievements, which can hinder the progress of smart city initiatives.

Closing Thoughts

It is unlikely that every city will become a full-fledged smart city. The adoption of smart city technologies will likely vary depending on a range of factors, including the level of development of the city, the availability of resources and funding, and the priorities and needs of the citizens.

For those cities that do embrace smart city technologies, the future looks promising. With the integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, smart cities will be able to streamline their operations, provide more efficient and personalised services, and enhance the quality of life for their citizens.

The future of smart cities is bright and holds immense potential. As technology advances and the world becomes increasingly connected, more cities will likely adopt smart city technologies to improve the lives of their citizens and create more sustainable urban environments.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

How Generative Chat AI Operate

Posted on by Jean Chalopin

Generative chat AI is an exciting technology that has been making waves in recent years. It refers to computer programs designed to interact with humans using natural language processing and can generate responses that seem to be coming from a real person. 

These AI systems are capable of analysing and understanding the context of a conversation and can create responses that are not only relevant but also coherent.

According to PitchBook data, generative AI investment rose by 425% between 2020 and December 2022, totalling $2.1 billion last year. This is an especially astounding performance, given a general decline in tech investment in 2022.

This article will dive into technical details that make generative chat AI possible. We’ll explore natural language processing, deep learning, and neural networks and how they are used to train these AI systems. We’ll also touch on some challenges developers face when creating generative chat AI and how they work to overcome them.

What Is Generative Chat AI?

Generative chat AI refers to computer programs that use natural language processing (NLP) to generate human-like responses to a user’s input. These AI systems are designed to interact with humans in a way that feels natural, as if you were chatting with another person. Unlike rule-based chatbots that rely on pre-written responses, generative chat AI is capable of generating new responses on the fly based on the context of the conversation.

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At the heart of generative chat AI is a technology called deep learning, a type of machine learning that involves training neural networks on large amounts of data. By feeding these neural networks with vast amounts of text data, such as chat logs or social media posts, they can learn to generate human-like responses.

The training process involves teaching the neural network to recognise patterns in the data, such as common sentence structures, idioms, and other linguistic features. Once the network has learned these patterns, it can generate new responses that fit within the context of the conversation. The more data the neural network is trained on, the better it becomes at generating natural-sounding responses.

How Does Generative Chat AI Work?

Generative chat AI works by using a combination of natural language processing (NLP) and deep learning, specifically through the use of neural networks. Neural networks are a machine learning algorithm that can recognize patterns in data and learn to make predictions based on that data.

In the case of generative chat AI, the neural network is trained on large amounts of text data, such as chat logs or social media posts. This training process is called deep learning because the neural network has multiple layers of interconnected nodes that allow it to recognize increasingly complex patterns in the data.

During training, the neural network learns to identify linguistic patterns and relationships between words and phrases. For example, it might know that certain words tend to be used together in specific contexts or that certain terms are more likely to occur in response to particular prompts. This training process enables the neural network to generate new responses relevant to the conversation’s context.

Once the neural network has been trained, it can generate responses to user input in real time. When a user inputs a message, the generative chat AI system uses NLP techniques to analyse the text and determine the context of the conversation. Based on this context, the system then uses the trained neural network to generate a relevant and coherent response.

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The success of generative chat AI depends mainly on the training data quality and the neural network’s complexity. Developers must ensure that the training data is diverse and representative of the conversations the system will likely encounter in real-world situations. Additionally, they must design neural networks capable of handling the complexity of natural language and generating accurate and engaging responses.

Use Cases for Generative Chat AI

The future holds many potential use cases for generative chat AI, but there are already a few ways that businesses are making the most of the opportunity. 

Coding

Generative AI can understand user coding requirements in countless languages such as Python, SQL, and Excel formulas. You can ask it to write or debug your code, and the AI returns step-by-step instructions on implementing it. 

Below is a snippet of what the popular Chat GPT platform can provide using a simple question. The more specific the user is with a question, the better the output. 

Copywriting

Users can provide generative chat AI with a topic overview, context and tone. The output is a loose summary that can speed up the copywriting process, allowing humans to focus on the more creative parts. 

Currently, the results are imperfect; see our section on augmenting roles rather than replacing them below. Still, they can make marketing teams far more efficient by giving them a solid starting point. 

Before posting anything written by AI, it is vital to check the accuracy of the information. Outputs are based on the data AI reads, which can be filled with bias and fake content. 

Customer Service

Sales teams can use generative chat AI to sort through all previous customer interactions across all channels (such as web conferences, phone calls, emails, and instant messages) and then direct it to create the next answer.

Consider yourself a salesperson who must react to a client’s query. Imagine how AI could assist you in coming up with the ideal response based on understanding the account history. An article in Wall Street Journal (membership required) talks about some businesses already adopting AI for this purpose. 

Augmenting Roles, Not Replacing Them

One of the key benefits of generative AI is that it can automate routine tasks, allowing humans to focus on more complex and creative work. For example, a chatbot can handle basic customer inquiries, freeing human customer service agents to handle more complex issues requiring empathy and critical thinking.

However, it’s essential to recognize that generative AI cannot replicate human creativity, empathy, and intuition. There will always be tasks and situations requiring a human touch, such as complex problem-solving, creative work, and emotional intelligence.

Moreover, the widespread adoption of generative AI could potentially lead to job displacement and a loss of human jobs. To mitigate this risk, companies should take a responsible approach to AI adoption, ensuring they are using it to augment human capabilities rather than replace them entirely.

In practice, this means that companies should carefully consider how generative AI can be used to complement human work rather than replace it. This might involve retraining employees to work alongside AI, redesigning job roles to take advantage of AI capabilities, or providing opportunities for employees to learn new skills that will be in demand as AI becomes more prevalent.

The Challenges of Generative Chat AI

Generative chat AI faces several challenges. 

The first major challenge is obtaining high-quality training data. Generative chat AI models require large amounts of diverse and representative training data to learn how to generate appropriate responses to various user inputs. However, obtaining such data can be difficult, especially for specialised or niche domains or languages with limited digital content.

Another challenge is ensuring that the AI model does not produce biassed outputs. AI models are trained on data, which may include inherent biases in language use or representation of certain groups or perspectives. If the training data is biassed, the AI model may learn to produce outputs that reinforce or amplify those biases, potentially leading to harmful or discriminatory user interactions.

And Possible Solutions

To address these challenges, it’s important to carefully curate and evaluate the training data used to train the generative chat AI model. This may involve sourcing data from diverse and representative sources, applying quality control measures to filter out biassed or irrelevant data, and using techniques like adversarial training to ensure that the model can handle a variety of inputs and outputs.

Another approach is to evaluate the outputs of the AI model and implement techniques like debiasing or reweighting to mitigate any potential biases. This can involve human oversight and intervention and ongoing monitoring and adjustment to ensure that the model remains fair.

A further challenge is the consistency of generative AI. Users expect a natural and engaging dialogue where responses flow smoothly from one to another and build upon previous messages. However, generative chat AI models may struggle to maintain coherence and consistency, especially when dealing with complex or unpredictable user inputs. 

For example, the model may generate off-topic or irrelevant responses or contradict previous statements made in the conversation. To address this challenge, AI models may require additional training or techniques like attention mechanisms, which can help the model focus on relevant parts of the conversation and generate more coherent responses.

Closing Thoughts

The future of generative chat AI is promising as advancements in natural language processing and machine learning pushes the boundaries of what’s possible. In the coming years, we can expect to see more sophisticated and context-aware AI models capable of engaging in rich and natural conversations with users. 

These models may incorporate advanced techniques like sentiment analysis, emotion detection, and personality modelling, allowing them to tailor their responses to individual users and create more personalised experiences. However, as with any technology, some potential risks and challenges must be addressed, such as maintaining an ethical and responsible use of AI, ensuring transparency and accountability, and addressing potential biases in the data used to train these systems.

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. By using this, you agree that the information does not constitute any investment or financial instructions. Do conduct your own research and reach out to financial advisors before making any investment decisions.

The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltec.io. 

The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltec.io. 

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.

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