Category: Fintech

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.

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.

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. 

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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 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.

How Generative AI Transforms Web3

Posted on by Jean Chalopin

As the development of Web3 continues to gain momentum, artificial intelligence is set to revolutionise how we interact with decentralised networks. Among the many AI techniques available, generative AI is gaining increasing attention for its ability to create new and unique content, which has the potential to transform the Web3 landscape.

Generative AI can be used to produce everything from text and images to music and video, providing users with a wealth of new opportunities to engage with Web3 in exciting and innovative ways. 

According to a report by ResearchAndMarkets, the generative AI market is projected to reach $200.73 billion by 2032, indicating a growing demand for this technology across various industries. 

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However, as with any new technology, significant challenges must be addressed, such as ensuring ethical use and mitigating potential biases. In this article, we will explore the key concepts of this form of AI and discuss its potential benefits and challenges to the Web3 ecosystem.

What Is Generative AI?

Generative AI is a type of artificial intelligence designed to create new and original content, such as images, text, music, and even video, without human intervention. Unlike traditional AI models, which are trained to recognize patterns and make decisions based on those patterns, generative AI is focused on generating new data that does not exist in its training data set. 

This is achieved by using machine learning algorithms, such as neural networks, to analyse large data sets and identify patterns that can be used to generate new content. 

Generative AI can be used in a wide range of applications, from creative industries such as music and art to more practical fields like medicine and finance, where it can be used to generate new drug compounds or financial models. 

  •  Art: It can create original pieces of art that range from abstract to more realistic forms. For instance, The Portrait of Edmond de Belamy was created by a French art collective using generative AI, and sold at Christie’s auction house for $432,500.
  • Music: AI-generated music is becoming increasingly popular, with some AI tools allowing users to create their own unique tracks. A good example is Amper Music, an AI-powered music composition platform enabling users to create and customise their original music.
  • Writing: Generative AI can also be used to create original written content, including news articles and even novels. For instance, OpenAI’s GPT-3 model (Chat GPT) has been used to write articles that are difficult to distinguish from those written by humans.
  • Virtual Clothing: It can also be used to create unique virtual clothing for use in the metaverse or other digital platforms. For instance, The Fabricant, a digital fashion house, has created a range of virtual clothing using generative AI.
  • Video games: AI can also be used to create original video games, from procedural content generation to NPCs with their own personalities and decision-making abilities. An example is Hello Games’ ‘No Man’s Sky’, which uses procedural generation to create an entire universe of unique planets and creatures.
  • Finance: AI can be used to analyse vast amounts of financial data and generate predictions and insights to inform investment decisions. For instance, the hedge fund Numerai uses generative AI models to analyse financial data and generate trading signals.

In other words, generative AI is the perfect technology to support Web3. 

What Is Web3?

Web3 refers to the next generation of the internet, which is focused on decentralisation, security, and user control. Unlike the current Web2, which is dominated by a few large corporations that collect and control user data, Web3 is based on decentralised networks that enable users to own and control their data. 

One of the key features of Web3 is the concept of the metaverse, a virtual world where users can interact with each other in real-time using avatars and digital assets. The metaverse is expected to be a key component of Web3, providing users with a new way to interact with each other and with digital content.

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Another important feature of Web3 is smart contracts, which are self-executing contracts with the terms of the agreement between buyer and seller being directly written into lines of code. Smart contracts can be used to automate a wide range of processes, from financial transactions to supply chain management, without the need for intermediaries.

Generative AI can play a significant role in the Web3 ecosystem by creating new and unique digital assets for use in the metaverse and other decentralised applications. For example, generative AI can be used to create virtual clothing, art, and other assets that can be bought and sold within the metaverse. 

Additionally, generative AI can be used to create smart contracts that are more efficient and secure than traditional contracts, thereby reducing the need for intermediaries. However, as with any new technology, there are also potential risks and challenges associated with using generative AI in the Web3 ecosystem, including the potential for bias and the need to ensure ethical use. 

Challenges of Using Generative AI Within Web3

While generative AI has the potential to bring many benefits to the Web3 ecosystem, there are also significant challenges and risks associated with its use, particularly in the metaverse. 

One of the biggest challenges is the potential for bias in the data used to train the generative AI models. If the data used to train the models is biassed, the generated content may also be biassed, perpetuating existing inequalities and marginalising certain groups. It is, therefore, essential to ensure that the data used to train the models is diverse and representative of all groups.

Another challenge is the potential for misuse of generative AI in the metaverse. For example, generative AI could be used to create realistic deepfake videos or other forms of disinformation, which could have severe consequences for individuals and society.

Furthermore, there is also the issue of ethical considerations surrounding the use of generative AI in the Web3 ecosystem. For instance, generative AI could be used to create realistic avatars of real people without their consent, raising serious privacy concerns. 

There is also the question of who owns the rights to the generated content and how it can be used, particularly if it is sold for profit.

To mitigate these challenges and risks, it is essential to establish best practices and guidelines for the ethical use of generative AI in the metaverse and other Web3 applications. This includes ensuring that the data used to train the models is diverse and representative, establishing clear guidelines for using generated content and implementing effective mechanisms for detecting and preventing the misuse of generative AI. 

Closing Thoughts

Generative AI has the potential to play a significant role in the Web3 ecosystem, particularly in the development of the metaverse and other decentralised applications. Generative AI can be used to create new and unique digital assets, such as virtual clothing and art, which can be bought and sold within the metaverse. 

Additionally, it can be used to create smart contracts that are more efficient and secure than traditional contracts, reducing the need for intermediaries. However, there are also significant challenges and risks associated with using generative AI in the Web3 ecosystem, including the potential for bias and misuse. 

To mitigate these risks, it is important to establish best practices and guidelines for the ethical use of generative AI. Despite these challenges, the future of generative AI in the Web3 ecosystem looks promising, with the potential to create innovative content while ensuring that it is used 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.

Ethereum Is a Base Layer of Computing

Posted on by Jean Chalopin

Ethereum has maintained a strong position as the second-largest cryptocurrency by market capitalisation after Bitcoin. However, it has gained widespread recognition for its unique technology, smart contracts, and the decentralized applications (dApps).

Ethereum goes far beyond the general abilities of first-generation cryptocurrencies. Since its launch in 2015, Ethereum has grown to become much more than just a digital currency. It has positioned itself as a base layer of computing, serving as the foundation for a variety of applications that rely on its unique abilities combined with its robust and secure infrastructure.

Ethereum’s Benefit, Smart Contracts, and the EVM

At its core, Ethereum, like its big brother Bitcoin, is a decentralised, open-source blockchain platform. However, Ethereum also enables developers to build decentralised applications. 

dApps are possible with Ethereum, because it offers a programmable platform that allows developers to create smart contracts, which are self-executing contracts with the terms of the agreement between two or more parties (usually a buyer and seller) being directly written into lines of code. These smart contracts are executed on the Ethereum Virtual Machine (EVM), a decentralised runtime environment that can execute code on the blockchain.

Smart Contract Basics

Ethereum’s smart contracts are built on its blockchain technology, which is a decentralised, secure, and transparent ledger that records all transactions and interactions on the network.  Smart contracts are designed to enable, verify, and enforce the negotiation or performance of a contract without the need for intermediaries such as banks, lawyers, or notaries, nor their escrow accounts. While there are similar blockchains, Ethereum is the most popular blockchain for smart contracts.  

A smart contract’s encoded terms, stored on a blockchain, are then executed by the blockchain when certain conditions, known as ‘triggering events’, are met. Most of this code is a combination of simple ‘if-then’ statements. For example, in a simple, smart contract for a vending machine, the triggering event is the insertion of the coin: ‘if a coin is inserted’, ‘then’ the trigger releases the treat.

One benefit of blockchain-based smart contracts is that they are immutable, meaning the contract cannot be changed once deployed on the blockchain. Immutability ensures that the terms of the agreement are always executed as written, without the risk of tampering or fraud.  It also means that a smart contract must be carefully crafted before it is deployed.

The Ethereum Virtual Machine

The Ethereum Virtual Machine (EVM) is a key component of the Ethereum blockchain. It is a virtual machine responsible for executing Ethereum’s smart contracts and recording transactions on the blockchain.  

The EVM is a software environment that allows developers to write smart contracts in high-level programming languages, the most common is Solidity (Ethereum’s native smart contract programming language), and then compile them into bytecode that can be executed on the Ethereum network. The EVM is designed to be platform-independent, meaning that smart contracts can be executed on any device that is running an Ethereum node.

The EVM’s primary benefit is that it allows for the creation of dApps that can run on the Ethereum blockchain. These dApps can provide a wide range of services, such as digital identity, voting systems, supply chain management, and decentralised finance, discussed further below.  

Because the EVM is a decentralised platform, these services can be provided without the need for intermediaries or centralised authorities, helping reduce costs and increase the transparency of these functions.

The Ethereum Virtual Machine Architecture and Execution Context Courtesy of github

Another benefit of the EVM is that it provides a high level of security for smart contracts.  Smart contracts are executed on the EVM in a sandboxed environment, which means that they are isolated from the rest of the network and cannot access any external resources without explicit permission. This can help to prevent hacks and other security breaches that can occur in traditional software environments.

Ethereum’s Power

While built from simple triggering events, smart contracts can be used for a wide range of applications. This versatility has made Ethereum a popular choice among developers looking to build decentralised applications. Let’s look at a few of the ways these dApps are being utilised.

Digital Identity and Voting Systems

Ethereum is being used for digital identity and voting systems by providing a secure and transparent way to verify and authenticate users. In a digital identity system, an Ethereum smart contract can be used to store and manage user identities, including their personal information and verification documents.  

This system can help to prevent identity theft and fraud by ensuring that only authorised users can access the system. In a voting system, smart contracts can be used first to confirm a voter’s identity and then to ensure that votes are recorded and counted accurately while maintaining the anonymity of the voters.  

This two-tiered system can help increase the voting process’s transparency and integrity while reducing the risk of tampering or fraud. Overall, using smart contracts for digital identity and voting systems can increase security, transparency, and trust in these critical areas.

Supply Chain Management

Ethereum’s blockchain technology has also found its way into the world of supply chain management. By using smart contracts, businesses can create a decentralised, tamper-proof ledger that records every step of the supply chain by enabling the creation of decentralised supply chain solutions that utilise blockchain technology and smart contracts.  

These solutions, known as blockchain supply chain solutions, can provide a high level of transparency and security in the supply chain, by allowing all parties involved (businesses and consumers) to track and verify the movement of goods and information in real-time from the origin to the final destination. 

Smart contracts can be used to automate many of the processes in the supply chain, such as payment processing, quality control, and inventory management, which can reduce the risk of errors and fraud. Additionally, blockchain technology can help reduce the risk of counterfeiting and ensure the authenticity of products, which is particularly important in industries such as pharmaceuticals and luxury goods. 

Overall, Ethereum’s impact on supply chain management has the potential to increase efficiency, reduce costs, and improve the overall transparency and security of the supply chain.

Gaming

Another area where Ethereum is making significant inroads is in the field of gaming. 

Using smart contracts, game developers can create provably fair decentralised games that use blockchain technology, offering transparent and auditable gameplay. These blockchain games, are built on the Ethereum blockchain and allow players to own, trade, and sell in-game items and currency as digital assets.  

This system creates a new level of ownership and control for players, allowing for intermediary-less decentralised transactions, and creating a new form of digital economy. Blockchain games also have the potential to reduce the risk of fraud and hacking, as all transactions are recorded on the blockchain and cannot be altered.  

Additionally, blockchain games can provide players with a new level of transparency and fairness, as using smart contracts can ensure that the game rules and rewards are enforced without central authorities.  

Overall, Ethereum’s impact on gaming has opened up new possibilities for player ownership, security, and fairness, and has the potential to revolutionise the gaming industry as we know it.

Decentralised Finance

Ethereum’s impact on Decentralised Finance (DeFi) has been immense, as it has become the primary blockchain used for DeFi applications. DeFi refers to a new generation of financial services that operate on a decentralised, blockchain-based platform. 

These services include lending, borrowing, trading, and investing and are built using smart contracts that automate many of the processes involved in traditional finance. By eliminating intermediaries and providing a more transparent and secure platform, DeFi has the potential to democratise access to financial services and provide more opportunities for people to participate in the global financial system without relying on traditional financial institutions.  

Ethereum’s programmable blockchain has enabled the creation of a wide range of DeFi applications, such as decentralised exchanges (DEXs), stablecoins, lending platforms, prediction markets, and yield farming platforms. The use of Ethereum’s native token, Ether (ETH), has also become a key component of the DeFi ecosystem, as it is used as collateral for loans and as a means of exchange on many DeFi platforms.  

Overall, Ethereum’s impact on DeFi has opened up new opportunities for financial innovation and has the potential to disrupt traditional finance in a significant way.

DAOs

In recent years, Ethereum has also been used to create decentralised autonomous organisations (DAOs), which are organisations that are run by code rather than a central authority.  

DAOs are governed by a set of rules encoded in smart contracts, and decisions are made through a decentralised voting system. This creates a new form of organisational structure that is transparent, efficient, and free from centralised control.  

The options that DAOs bring are far-reaching and make the democratisation of many organisations and groups that were once impossible, possible.

Closing Thoughts

Ethereum has become a base layer of computing, serving as the foundation for a variety of applications that rely on its robust and secure infrastructure. 

Its ability to support a vast array of use cases, from DeFi and gaming to supply chain management and DAOs, has made it a popular choice among developers looking to build decentralised applications. As blockchain technology continues to mature and become more widely adopted, Ethereum is well-positioned to play a leading role in the decentralisation of various industries.

With the switch to a Proof of Stake (POS) consensus mechanism, the entire Ethereum blockchain system is becoming faster and more energy efficient, allowing more users to participate in the infrastructure. This combination makes Ethereum more competitive and enables it to retain its status as a computing base layer. 

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.

Ethereum’s Smart Contracts Explained

Posted on by Jean Chalopin

Blockchain technology is a game-changing phenomenon that has disrupted multiple industries by enabling safe, decentralised solutions for diverse transactions and operations. Implementing smart contracts is one of the most prominent uses of blockchain technology. 

A smart contract is a self-executing contract in which the conditions of the buyer-seller agreement are directly encoded into lines of code. In 2013, Ethereum, the second largest blockchain network, pioneered the notion of smart contracts. Smart contracts have now become a vital element of many businesses, providing efficient and secure solutions for various business activities.

What Are Ethereum Smart Contracts?

They are self-executing contracts with the terms of the agreement between buyer and seller being written into lines of code. These contracts run on the Ethereum blockchain, a decentralised and secure platform. The code in the smart contract is automatically executed when specific conditions are met, eliminating the need for intermediaries and increasing the efficiency and security of the transaction.

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Ethereum smart contracts are written in Solidity, a computer language comparable to JavaScript. The code defines the circumstances under which the contract will be carried out and the actions that will be executed if those requirements are satisfied. A smart contract, for example, might be used to transfer ownership of a digital asset from one party to another whenever specific criteria are met.

One of the primary advantages of smart contracts is that they can automate the process of contract execution, saving time and lowering the risk of human mistakes. As a result, they are ideal for a variety of industries, including banking, real estate, supply chain management, and others.

How Do Ethereum Smart Contracts Work?

Smart contracts automate the process of executing specific conditions when triggered by events, such as a transfer of funds. The requirements are pre-written in the code and enforced automatically once met. For instance, a smart contract can immediately release payment to a seller only after the buyer receives a product. In this way, smart contracts enforce the terms of an agreement automatically.

A smart contract process follows steps similar to the below example of buying and selling a product. 

  • The buyer and the seller agree on the terms of the sale, including price and delivery date.
  • The buyer sends the agreed-upon amount of cryptocurrency, typically Ether, to the smart contract’s address.
  • The smart contract code verifies if the conditions of the sale have been met, such as the receipt of the agreed-upon amount of cryptocurrency.
  • If the conditions are met, the smart contract executes the terms of the agreement automatically. For example, it transfers ownership of the product to the buyer.
  • The buyer now has access to the product and the seller has received payment. Both parties can trust that the smart contract has fulfilled and enforced the agreement.
  • The Ethereum blockchain records the details of the transaction, including the product ownership transfer and payment. This provides a secure and permanent record of the transaction.
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This process provides a secure and transparent way for individuals to buy and sell products using cryptocurrency. By using smart contracts, the risk of fraud and the need for intermediaries is reduced, and the process of buying and selling products is streamlined and automated.

The Technology Behind Smart Contracts

The Ethereum blockchain powers the technology underneath. This decentralised and distributed ledger securely records transactions and data. Smart contracts are self-executing computer programs that run on the Ethereum blockchain and enforce the terms of an agreement automatically. 

Developers write these contracts in a high-level programming language and compile them into low-level bytecode, which the Ethereum blockchain stores. The Ethereum Virtual Machine, a computer network that runs the Ethereum blockchain, executes the bytecode. When someone makes a transaction on the Ethereum blockchain, it triggers the smart contract to run and enforce the agreement’s terms. 

The decentralised and distributed nature of the ledger ensures the security and transparency of the agreement’s terms, as multiple computers store the transaction details, and anyone can audit them. By using smart contracts, individuals can automate various agreements and transactions, reducing the risk of fraud and the need for intermediaries.

Benefits of Ethereum Smart Contracts

Ethereum smart contracts offer numerous benefits to individuals and organisations. They reduce transaction costs and increase efficiency by eliminating the need for intermediaries. The self-executing nature of smart contracts ensures that the terms of an agreement are automatically enforced, increasing the security and transparency of transactions. 

In addition, using a decentralised and distributed ledger eliminates the risk of fraud, as all transactions are recorded on multiple computers and can be audited by anyone. 

A recent survey by Deloitte showed that 72% of executives believe that smart contracts will play a significant role in the future of business. At the same time, the market for decentralised finance (DeFi) applications built on the Ethereum blockchain has grown to over $40 billion in just a few years. These statistics show that Ethereum smart contracts are poised to play a significant role in shaping the future of various industries and revolutionising the way we do business.

Industries Benefiting From Ethereum Smart Contracts

Ethereum smart contracts have the potential to revolutionise various sectors by providing secure and efficient solutions for different business processes. 

Logistics

Smart contracts can be used in the supply chain sector to automate tracking items as they move through the supply chain. This can increase the supply chain’s efficiency and transparency, lowering the risk of fraud and ensuring that items are delivered on time.

Real Estate

Smart contracts can be used in real estate to simplify purchasing and selling property, removing the need for middlemen such as real estate agents. Smart contracts can save time, money, and minimise the risk of fraud by automating the process.

Healthcare

Ethereum smart contracts can transform the healthcare industry by automating and optimising numerous operations. For example, electronic health records (EHRs) can be securely stored and maintained on the blockchain using smart contracts, boosting patient data privacy and security while making it easier for healthcare practitioners to access and exchange information. 

Smart contracts may also help clinical studies by automating processes like delivering payments to participants when specific milestones are fulfilled and collecting and storing participant data.

Gaming

Ethereum smart contracts have the potential to change the gaming industry by allowing gamers to engage with games and participate in the gaming economy in new and inventive ways. They, for example, may be used to build decentralised, player-driven markets where users can buy, sell and exchange virtual commodities and currencies. The blockchain secures these markets, giving participants more transparency and security while participating in transactions. 

Smart contracts could automate the hosting of in-game tournaments, such as awarding prizes and collecting entrance fees from players. Smart contracts can also build decentralised gaming platforms where participants can play games and earn rewards directly from the platform.

Companies Using Ethereum Smart Contracts

Many companies have adopted Ethereum smart contracts to provide secure and efficient solutions for their business processes. Some of the companies using Ethereum smart contracts include:

  • Microsoft: Microsoft has adopted Ethereum smart contracts to provide a secure and transparent platform for managing the supply chain of its products.
  • JPMorgan Chase: JPMorgan Chase is using them to increase the efficiency and security of its cross-border payments.
  • Accenture: Accenture uses them to provide secure and transparent solutions for its clients’ supply chains.

Companies will continue to adopt blockchain technology as it evolves and offers significant business benefits. 

Getting started with Ethereum smart contracts requires a company to understand Ethereum and blockchain technology. They can begin by educating themselves on the Ethereum blockchain, smart contracts, and the Solidity programming language. 

Hiring a team of developers with experience in Ethereum and blockchain technology is also a great idea. This team will develop, test, and deploy the company’s smart contracts. 

The next step would be choosing a development environment, such as Remix, Truffle, or Ganache, to build and test their smart contracts. 

Finally, the company can deploy their smart contracts on the Ethereum blockchain and start using them to automate its business processes, increase transparency and security, and reduce costs. With the right team, resources, and determination, any company can get started with Ethereum smart contracts and leverage the power of decentralised technology.

Closing Thoughts

According to a report by Grand View Research, the global smart contract market is expected to reach $1.4 billion by 2025, growing at a CAGR of 25.2% from 2020 to 2025.

The future of the Ethereum blockchain is exciting and holds great potential for growth and development. In the next ten years, we can expect to see the following:

  • Increased Adoption: As more individuals and organisations become aware of the benefits of decentralised technology, we can expect to see a significant increase in Ethereum blockchain adoption. 
  • Expansion of Decentralised Applications: The Ethereum blockchain allows for the creation of decentralised applications (dApps) that can run on the blockchain. We can expect to see the continued growth of this ecosystem with the development of new and innovative dApps.
  • Development of New Use Cases: As the Ethereum blockchain evolves, it will likely lead to the creation of new use cases and applications. This could include decentralised finance, prediction markets, and more.
  • Scaling Solutions: Scalability has been a significant challenge for the Ethereum blockchain. However, with the development of new scaling solutions, such as sharding, we can expect the Ethereum blockchain to be able to handle more transactions and become more widely adopted.
  • More Competition: As the Ethereum blockchain grows, we can expect to see more competition from other blockchain platforms. However, the Ethereum blockchain has a large and established community, giving it a competitive advantage.

Overall, the future of the Ethereum blockchain is bright, and we expect to see continued growth and development in the coming years. The decentralised and distributed nature of the blockchain provides the potential for dramatically enhanced security, transparency, and efficiency in various industries.

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.

Blockchain Oracles in DeFi

Posted on by Jean Chalopin

We need to take a step back to understand blockchain oracles and why they are created. Oracles are problem solvers for many smart contracts launched on blockchains. 

But what is a smart contract, and what problem do oracles need to solve? We will start by answering these two questions and then explain how they solve problems for the DeFi space.  

Blockchain Oracles in Brief

Blockchain oracles are complicated computerised systems that connect data from the outside world (referred to as ‘off-chain’) with a blockchain (or ‘on-chain’).

Blockchains, Cryptos, Smart Contracts, and a Problem

The majority of blockchains have their own native cryptocurrency that is used to transfer value, enable the protocol’s operations, or to facilitate governance. Some blockchains (the most well-known being Ethereum) can also be used to build smart contracts. These are blockchain based self-executing contracts with the terms of the agreement between two or more parties, usually a buyer and seller, being directly written into lines of code. 

Smart contracts will execute predetermined actions automatically when defined conditions are met, and by being built on the blockchain, they are traceable, irreversible, and unchangeable (immutable). These smart contracts are executed ‘trustless’, not requiring a third party, and if written correctly, can be designed to carry out nearly any contract imaginable. 

If a buyer wishes to purchase a home with cryptocurrency, a simple, smart contract could be written for the transaction. It would say something like the following, ‘If the Buyer sends the required funds to the Seller, then the deed of the home at X location is transferred from the Seller to the Buyer’. 

When the conditions of the smart contract are met, it is irreversibly executed in accordance with its programming. There is no need for traditional third parties to initiate, manage, or execute such a contract.  

There is, however, a problem with this system. Blockchains need a way for smart contracts to be able to use external off-chain data so that the smart contracts can have applications in the real world. 

With the real estate example above, off-chain data may include proof of successful payment or the evidence of a deed receipt. Because blockchains are generally self-contained, the connection to the real world is a problem; this is where the problem-solving nature of oracles comes into play.  

Oracles Connects Blockchains to Off-Chain Data

Oracles provide a way for a blockchain and its smart contracts to interact with off-chain data.  Oracles are similar to another computing system, an application programming interface (API), but to the world outside the blockchain. 

There are several instances where real-world data must be communicated to a closed on-chain system. This data is critical when smart contracts rely on real-world events to execute correctly. 

Crypto oracles will query, verify, and authenticate the needed external data and then relay it to the closed blockchain system. This authenticated data will then be used to validate the smart contract.  

Inbound and Outbound Oracles

Oracles will generally establish two-way lines of communication with a blockchain; data that can be sent inward to the blockchain or outward. While outbound oracles can provide information from the blockchain to the real world, inbound oracles that bring into the blockchain off-chain data remain much more common. 

This imported information can be from nearly any source, asset prices and their fluctuations, proof of payments, weather conditions, flight information, pollution measurements, sports scores, and so on.

A common example of an inbound oracle in the form of a smart contract would be written as follows, ‘If asset A hits the defined price P, then place a buy order of U units’. 

An outbound oracle could be used when a smart contract’s conditions are fulfilled on-chain.  For example, a simple smart contract could be created that will unlock a web-enabled smart lock on a real-world storage unit. Once the correct amount of cryptocurrency is received as a payment to a defined crypto wallet, the unlock signal is sent to the real-world lock.  

Software and Hardware Oracles

The majority of crypto oracles are processing digital information, but not exclusively. Software oracles provide data from digital sources, such as servers, websites, and databases.  

Hardware oracles, on the other hand, deliver data directly from the real world. Software oracles can provide real-time information, exchange rates, flight information, pricing information, and the like. Hardware oracles can provide data from video cameras, weather monitors, barcode scanners, and similar. 

The Centralised Oracle Problem

Centralised oracles are under the control of a single entity, and these tend to be the sole providers of information to a smart contract. This system requires that the participants of a smart contract place a significant amount of trust in this single entity. 

A centralised oracle also means that there is a single point of failure, having no redundancy.  This point of failure threatens the security of a smart contract if the connection to the oracle or the oracle itself becomes compromised. The smart contract’s effectiveness and accuracy depend heavily on the data provided. Therefore, centralised oracles retain a tremendous amount of power over smart contracts.  

The reason that smart contracts were invented in the first place was to avoid counterparty risk and reliance on a third-party intermediary. Oracles allow contracts to be performed between trustless parties, but the more centralised they are, the more risk they bring with them, and they become the middleman they were intended to replace. 

This is known as the oracle problem, and it means that the preservation of fairness, security, and privacy, along with the avoidance of over-centralisation, which ultimately damages the relationship between blockchains and their smart contracts.

Decentralised Oracles

Decentralised oracles achieve a trustless and deterministic result that relies on cause and effect rather than a single relationship. We view this as a clear step in the right direction, as blockchain networks operate by distributing trust among multiple participants.

By combining many different data sources and creating an oracle system that is not controlled by a single entity, a decentralised oracle network has the potential to provide smart contracts with increased levels of security and fairness. 

Because centralised oracles can become compromised, many blockchain projects, such as Chainlink, MakerDAO, Band Protocol, and Augur, are or have already developed decentralised oracles. 

Oracles and DeFi

Oracles are used in decentralised finance (DeFi) applications for the same reason, to bring external data onto blockchains, which are then used to execute smart contracts. 

For example, consider a DeFi application with a smart contract programmed to trade cryptocurrency based on a major exchange price. In order to execute this contract, the smart contract needs access to the current price of the cryptocurrency. This data can be provided by an oracle, which fetches the current price from the exchange’s API and feeds it into the smart contract. 

The use of oracles in DeFi applications allows for the creation of very complex financial instruments and applications that are based on real-world data and events, bringing greater functionality and flexibility to the DeFi space.  

Because oracles are responsible for providing external data to smart contracts, it is crucial that the data they provide remain accurate and safe from tampering. Many DeFi applications use multiple oracles and require that they come to a consensus on the data provided.

In our currency trading example above, the exchange API is a single source. If there are multiple decentralised sources obtaining the same price info to gain a consensus, then this could be a decentralised DeFi oracle, automatically more trustworthy than a single API.

Closing Thoughts

The blockchain oracle is the problem solver, bringing external real-world off-chain data on-chain and vice versa. With oracles, smart contracts can expand their functionality, especially useful for the DeFi space, where these enhanced smart contracts create advanced financial instruments. 

When oracles are decentralised, they become even more trustworthy, making them a fantastic option for creating cheap and reliable smart contacts that no longer require a trusted third-party intermediary. As more oracles are created, bringing a more comprehensive array of data sources on-chain, smart contract use should also expand. 

Smart contracts may well turn into the solution that creates mass crypto acceptance.

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.

AI and Its Many Forms

Posted on by Jean Chalopin

Artificial intelligence (AI) is no longer just a science fiction concept but a technological reality that is becoming increasingly prevalent daily. There are several forms of AI, each with unique characteristics and applications. 

This article will explore the various forms of AI today, including machine learning, natural language processing, computer vision, expert systems, and robotics. By examining each type of AI, we can better understand how these technologies function and the potential benefits they can offer society. By understanding the different forms, we can also better appreciate their implications for the future of various industries and the overall economy.

The Different Types of AI

There are various types of AI, each with specific qualities and uses.

AI can be classified as either narrow or general based on the scope of its tasks. Narrow AI, also known as weak AI, is designed to perform specific and highly specialised tasks. 

For example, a chatbot that can answer customer service questions or an image recognition system that can identify particular objects in photographs are examples of narrow AI. Narrow AI systems are designed to complete specific tasks efficiently and accurately but are limited in their ability to generalise beyond those tasks.

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In contrast, general AI, also known as strong AI or artificial general intelligence (AGI), is designed to perform various tasks and can learn and adapt to new situations. It aims to replicate the cognitive abilities of humans, including problem-solving, decision-making, and even creativity. It seeks to create machines that can perform any intellectual task that a human can.

While we have made significant progress in developing narrow AI, we are still far from achieving general AI. One of the main challenges is creating machines that can learn and generalise from a wide range of data and experiences rather than just learning to perform specific tasks. Additionally, general AI will require the ability to reason and understand context in a way currently impossible for machines.

Below are the typical applications. Most of these are still narrow bar expert systems which are beginning to show some aspects of general AI. 

Machine Learning

Machine learning is one of the most common forms of AI and involves training algorithms on large datasets to identify patterns and make predictions. For example, Netflix uses machine learning to recommend shows and movies to viewers based on their previous viewing history. 

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This technology has also been applied to healthcare to help diagnose and treat medical conditions.

Natural Language Processing

Natural language processing (NLP) is another form of AI that allows computers to understand, interpret, and respond to human language. One real-world application of NLP is chatbots, which many companies use to provide customer service and support. For example, Bank of America uses an NLP-powered chatbot to help customers with their banking needs.

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Computer Vision

Computer Vision is a form of AI that enables machines to interpret and understand visual information from the world around them. One example of this is the use of computer vision in self-driving cars. Companies such as Tesla use computer vision to analyse data from sensors and cameras to make real-time decisions about navigating roads and avoiding obstacles.

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Expert Systems

Expert systems are AI systems that use rules and knowledge to solve problems and make decisions. These systems are often used in industries such as finance and healthcare, where making accurate decisions is critical. For example, IBM’s Watson is an expert system that has been used to diagnose medical conditions and provide treatment recommendations.

Robotics

Robotics is another form of AI involving machines performing physical tasks. One real-world application of robotics is in manufacturing, where robots are used to assemble products and perform other tasks. For example, Foxconn, an electronics manufacturer for companies like Apple, uses robots to assemble products on its production lines.

It’s important to note that we now have primarily narrow AI designed to perform specific tasks. However, the ultimate goal of AI is to develop general AI which can perform a wide range of tasks and learn and adapt to new situations. While we may not have achieved general AI yet, developing narrow AI systems is an essential step towards that goal. The interrelated and supportive nature of these different forms is what allows us to make progress towards this ultimate goal.

How People Perceive AI

Artificial intelligence is often perceived as a futuristic concept still in its early stages of development. However, the truth is that it is already a commonplace technology that is widely used in various industries. Many companies have quietly incorporated it into their operations for years, often in narrow, specialised forms that are not immediately apparent to the general public.

For example, AI algorithms are commonly used in online shopping websites to recommend products to customers based on their previous purchases and browsing history. Similarly, financial institutions use it to identify and prevent fraud, and healthcare providers use it to improve medical diagnoses and treatment recommendations. It is also increasingly used in manufacturing and logistics to optimise supply chain management and reduce costs.

Despite its prevalence, many people still associate AI with science fiction and futuristic concepts like robots and self-driving cars. However, the reality is that it is already deeply integrated into our daily lives. As AI continues to evolve and become even more sophisticated, its impact on various industries and our daily lives will become known to all.

Closing Thoughts

The development of general AI will profoundly impact many industries, including healthcare, transportation, and manufacturing. It will be able to perform a wide range of previously impossible tasks, from diagnosing complex diseases to designing and creating new products. 

However, with this increased capability comes a need for increased responsibility and regulation. As AI becomes more integrated into our daily lives, it will be essential to ensure that it is used ethically and with the best interests of society in mind. In the future, it is likely to become an even more integral part of our lives, transforming how we live, work, and interact with technology.

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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