May, 2023 - Trusting Disruption by Deltec

How Generative AI Transforms Web3

Posted on May 31, 2023May 31, 2023 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.

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.

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 May 26, 2023May 26, 2023 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.

Ethereum’s Smart Contracts Explained

Posted on May 23, 2023May 23, 2023 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.

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.

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.

Blockchain Oracles in DeFi

Posted on May 17, 2023May 17, 2023 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.

Can AI and Biotech Conquer Death?

Posted on May 15, 2023May 15, 2023 by Jean Chalopin

Death is a certainty that all living beings must face, but what if we could beat it? The advances in biotechnology and artificial intelligence, or AI, have raised the question of whether death can be conquered.

This article explores the potential of biotech and AI to achieve immortality, farfetched as it seems, and the pros and cons associated with this idea.

Advancements in Biotech

Biotechnology is a rapidly evolving field that is focused on using biological processes, systems, and organisms to create new products, technologies, and solutions. Significant advancements in biotech have led to the development of treatments and therapies that can extend human life. One of the most promising areas of biotech research is the development of stem cell therapies.

Stem cells are undifferentiated cells that can differentiate into different types of cells and tissues in the body. Stem cell therapies involve transplanting stem cells into damaged or diseased tissues to regenerate and repair them. This can be used to treat various conditions, including spinal cord injuries, heart disease, and Parkinson’s disease.

Another area of biotech research that has the potential to extend human life is gene therapy. Gene therapy involves introducing genetic material into a patient’s cells to treat or prevent disease.

This can be used to treat genetic disorders, such as cystic fibrosis, and to prevent age-related diseases, such as Alzheimer’s disease.

Advancements in AI

AI focuses on developing machines that can perform tasks that typically require human intelligence, such as perception, learning, reasoning, and decision-making. AI can transform many industries, including healthcare, by providing new tools and solutions to improve patient outcomes and extend human life.

One of the most promising applications of AI in healthcare is precision medicine. Precision medicine involves genetic and other data to tailor medical treatments to individual patients. AI can be used to analyse vast amounts of data to identify patterns and insights that can be used to develop personalised treatment plans.

Another area of research that can potentially extend human life is the development of autonomous medical systems. Autonomous medical systems are machines that can perform medical tasks without human intervention. These systems can be used to monitor patient health, administer medications, and perform surgical procedures.

AI and Biotech

AI can help progress the biotech industry in fields such as stem cell treatment and gene therapy, which we mentioned above.

While stem cell treatment has shown promise in treating a range of diseases, it is still a relatively new field, and much is not yet understood about how stem cells work and how they can be effectively used in therapy.

AI can play a vital role in advancing stem cell treatment by helping to identify the best type of stem cell for a given condition and optimising the conditions under which the stem cells are grown and differentiated into specific cell types. It can also help to predict the likelihood of success for a given stem cell therapy and identify potential side effects or complications.

One company that is working on using AI to advance stem cell treatment is Insilico Medicine. The company uses AI to develop new drugs and therapies for various diseases, including cancer, fibrosis, and ageing. The company’s platform uses deep learning algorithms to analyse large amounts of data and identify potential drug targets and therapies. The video below is an explainer of one of their products.

Similarly, gene therapy is a new field that can benefit from AI; which can play a crucial role in advancing gene therapy by helping to identify the best targets for gene therapy and optimising the delivery of genes to the target cells. It can also help predict gene therapy’s potential outcomes and identify possible side effects or complications.

One company that is working on using AI to advance gene therapy is Homology Medicines. The company is developing gene therapies for various genetic diseases, including phenylketonuria (PKU) and sickle cell disease. The company’s platform uses AI to design and optimise the delivery of gene therapies to make gene therapy more effective and accessible.

The Issues With Conquering Death

The idea of conquering death with biotech and AI has several potential benefits and risks. Some of the most significant pros and cons are outlined below.

Pros

Improved quality of life: Conquering death could significantly improve the quality of life for older adults. We could eliminate many of the problems associated with ageing, such as chronic diseases and disability.
Advancements in science and technology: Immortality could lead to significant advances in science, technology, and culture by allowing our brightest minds to continue contributing to society.
Increased productivity: If people lived indefinitely, they would have more time to contribute to society, leading to increased productivity and economic growth.

Cons

Overpopulation: One of the most significant risks associated with conquering death is the potential for overpopulation. With people living indefinitely, the world’s population would continue to grow, straining resources and exacerbating environmental issues.
Unequal distribution of access: Ethical considerations are associated with unequal access to immortality technology. If only the wealthy and powerful could access these technologies, it could exacerbate existing inequalities.
Loss of cultural traditions: Immortality could lead to the loss of cultural traditions and the stagnation of cultural evolution.

The Challenges With Conquering Death

While AI and biotech hold significant promise for advancing medicine and extending human life, many challenges must be overcome before death can indeed be conquered.

One of the primary challenges is the ethical implications of using these technologies to extend life. While many people would welcome the opportunity to live longer, healthier lives, there are concerns about the potential consequences of such an advancement.

For example, there may be questions about who would have access to these technologies and how they would be distributed. There may also be concerns about the impact on the planet and the potential strain on resources if the population continues to grow as people live longer.

Another challenge is the complexity of the human body and the many factors that can impact health and longevity. While AI and biotech can help identify potential therapies and treatments, much is still not yet understood about how the body works and how it can be effectively treated.

For example, there are many different types of cancer, each with unique characteristics and challenges. Developing effective therapies for each type of cancer will require a deep understanding of the underlying biology and a willingness to experiment with new approaches.

There are also challenges related to developing and regulating new therapies and treatments. Developing new drugs and therapies is a long and expensive process. There is always a risk that a promising treatment will fail in clinical trials or have unforeseen side effects. In addition, there are regulatory challenges related to getting new therapies approved and ensuring they are safe and effective for humans.

Finally, there are challenges related to using AI and biotech in healthcare. For example, there may be concerns about the accuracy and reliability of AI algorithms, particularly when making decisions about human health. There may also be questions about how AI and biotech will impact the roles of healthcare providers and whether machines in the future will replace them.

Market Statistics and Use Cases

The biotech and AI industries are rapidly growing and have significant potential to transform healthcare and extend human life. According to a report by Grand View Research, the global biotech market size was valued at $1,023.92 billion in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 13.9% from 2022 to 2030. The report cites the growing demand for biopharmaceuticals and increasing investment in biotech research as key market growth drivers.

Several companies are working in biotech and AI to develop new therapies and solutions that can extend human life. One such company is Unity Biotechnology, which focuses on developing therapies targeting the underlying causes of age-related diseases. The company’s lead program is a senolytic therapy that targets senescent cells, which are cells that have stopped dividing and contribute to age-related diseases.

Another company in the biotech space is Moderna, best known for developing one of the first COVID-19 vaccines. The company is also working on developing mRNA therapies that could be used to treat a range of diseases, including cancer and rare genetic disorders.

In AI, several companies are developing solutions to improve patient outcomes and extend human life. One such company is Deep Genomics, which uses AI to create new therapies for genetic diseases. The company’s platform combines genomics and machine learning to identify genetic mutations that cause disease and develop new therapies to treat them.

Closing Thoughts

The idea of conquering death with biotech and AI is a tantalising prospect but comes with significant challenges and risks. While biotech and AI have the potential to extend human life and improve the quality of life in old age, there are also substantial ethical considerations associated with immortality.

As these two industries continue to evolve, it is crucial to consider the challenges and risks and work towards developing solutions that can extend human life sustainably and ethically. The goal is to improve the quality of life for all, not just for some.

AI and Its Many Forms

Posted on May 12, 2023May 12, 2023 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.

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.

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.

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.

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.

DeFi Explained

Posted on May 10, 2023May 10, 2023 by Jean Chalopin

Decentralised finance (DeFi) is rapidly revolutionising the financial industry by offering innovative financial products and services that are decentralised, transparent, and accessible to everyone. DeFi operates on blockchain technology and allows individuals to take control of their finances without intermediaries.

According to Cointelegraph, the DeFi market has seen tremendous growth, with the total value locked in DeFi protocols surpassing $70 billion in January 2023. As DeFi continues gaining momentum, it is expected to change how the world thinks about and interacts with finance.

What Is DeFi?

Unlike traditional finance, which relies on intermediaries such as banks and financial institutions, it is built on decentralised networks that allow for direct peer-to-peer transactions and offer more transparency, security, and accessibility.

At its core, DeFi leverages blockchain technology to create a new financial infrastructure that is open and accessible to anyone with an internet connection. This infrastructure is based on smart contracts, self-executing agreements that enforce the terms of a contract without the need for intermediaries. This means that its users can access a range of financial products and services, such as lending, borrowing, trading, and insurance, without going through a traditional financial institution.

Financial firms and institutions are taking notice and are looking to incorporate its benefits into their operations. The transparency and security offered can help to reduce the risk of fraud and increase efficiency in financial transactions.

Additionally, its decentralised nature means that it has the potential to offer financial services to individuals who are currently underserved by traditional finance, such as those in developing countries or those with limited access to conventional financial services.

How do DeFi and Blockchain Work Together?

Decentralised finance and blockchain technology are two sides of the same coin, enhancing the other to create a new financial ecosystem. DeFi leverages blockchain technology to provide a decentralised and transparent infrastructure for financial transactions, while blockchain technology offers the security and immutability necessary.

Blockchain technology, the underlying technology, is a decentralised and secure ledger that records transactions across a network of computers. This decentralised nature means there is no central point of control or single point of failure, making blockchain networks highly resistant to hacking and tampering. The transparency and immutability of blockchain technology make it ideal for DeFi, as it allows for all transactions to be recorded publicly and makes it difficult for anyone to alter the records.

DeFi takes advantage of this security and transparency to offer various financial services, such as lending, borrowing, trading, and insurance, without intermediaries. For example, a sample lending platform may allow users to lend and borrow assets using smart contracts, with the platform’s underlying blockchain technology providing the security and transparency necessary for transactions. In this way, DeFi leverages blockchain technology to offer a new, decentralised financial infrastructure accessible to anyone with an internet connection.

DeFi and blockchain technology work together to create a new financial ecosystem that is decentralised, transparent, and secure. The decentralised nature of blockchain technology provides the security and transparency necessary for DeFi to function effectively. At the same time, it leverages blockchain technology to offer financial services without intermediaries. This combination has the potential to change the way the world thinks about and interacts with finance, making financial services more accessible and secure for everyone.

DeFi and Traditional Finance

Traditional finance firms need to care about DeFi because it represents a significant shift in the financial landscape. It offers a new way for people to manage their financial assets and transactions without relying on centralised intermediaries like banks. This decentralised model has proven to be secure, transparent, and accessible to people worldwide, making it an attractive alternative to traditional finance.

By ignoring DeFi, traditional finance firms risk being left behind as more people flock to decentralised alternatives. They need to stay ahead of the curve and understand the growing ecosystem to adapt and evolve their own services to meet the market’s changing demands.

Furthermore, DeFi has the potential to disrupt traditional finance and impact the bottom line of these firms. Traditional finance firms must take DeFi seriously and find ways to integrate it into their business models to remain relevant and competitive.

How Are Start-Ups Using DeFi?

Aave is a DeFi start-up that offers decentralised lending and borrowing services. The platform allows users to deposit their digital assets as collateral and then borrow other assets at a flexible interest rate without needing a central authority.

Aave uses smart contracts to automate the lending and borrowing process and ensure that each loan’s terms are transparent and fair. The platform also offers features like flash loans, which allow users to borrow funds without collateral for a short time, and liquidity pools, which enable users to earn interest on their deposited assets.

Compound is another start-up revolutionising the lending and borrowing world. The platform allows users to deposit and lend various digital assets, including cryptocurrencies, stablecoins, and non-fungible tokens.

Like Aave, Compound uses smart contracts to automate the lending and borrowing process, but it also includes a unique feature called ‘cTokens’, which allows users to earn interest on their deposited assets. cTokens are unique because they represent a user’s stake in a particular asset within the Compound platform, and their value changes in real-time based on market conditions.

Uniswap is a decentralised exchange that allows users to trade cryptocurrencies in a trustless manner. Unlike traditional centralised exchanges, Uniswap doesn’t require users to deposit their funds into a central exchange, which reduces the risk of theft and hacks. Uniswap uses a unique liquidity pool model where users can provide liquidity to the platform in exchange for a share of the trading fees.

Source

The platform’s automated market maker algorithm ensures that users can trade token pairs without needing an order book. This makes it easy for users to trade even less popular tokens that might not be listed on centralised exchanges.

DeFi start-ups are using decentralised finance to disrupt traditional finance and offer new financial services that are secure, transparent, and accessible to people all over the world. By using smart contracts and other blockchain technologies, these start-ups are creating a new financial ecosystem free from centralised intermediaries’ limitations and restrictions.

Moving to a DeFi Model

Fidelity Investments is a traditional finance firm exploring DeFi to offer new financial services to its customers. The company has launched a new division called Fidelity Digital Assets that provide custody and trading services for cryptocurrencies, making it one of the first large traditional finance firms to embrace DeFi.

Fidelity is using DeFi to offer its customers access to new investment opportunities in the cryptocurrency market and reduce the barriers to entry that have traditionally made it difficult for institutional investors to participate in the market.

Goldman Sachs is another traditional finance firm that is exploring DeFi. The company has been actively engaged in DeFi’s value proposition and creating DeFi products. Goldman Sachs is collaborating with other businesses to develop a digital assets framework, per a press release from November 2022.

JP Morgan is another traditional finance firm that is moving into DeFi. The company has been exploring blockchain technology for several years and working on its DeFi initiatives. For example, JP Morgan initiated its first DeFi trade on blockchain in 2022. Project Guardian, a trial programme run by the Monetary Authority of Singapore (MAS) to investigate potential DeFi applications in wholesale finance markets, enabled the trade.

Traditional finance firms are exploring DeFi to offer new financial services to their customers and stay ahead of the curve in an ever-changing economic landscape. By embracing DeFi, these firms can reduce barriers to entry and offer secure, transparent, and accessible financial services to their customers.

Risks and Challenges

One of the main risks associated with DeFi is security. Since it is built on decentralised networks, it is more vulnerable to hacking and other forms of cybercrime. Smart contracts, which are used to automate the process of lending, borrowing, and trading in DeFi, are particularly vulnerable to security threats. For example, if a hacker can exploit a vulnerability in a smart contract, they can steal funds from users or manipulate the platform in other ways.

Another challenge is scalability. As more people use DeFi platforms, the networks can become congested, leading to slow transactions and high gas fees. This can make it difficult for users to participate in DeFi platforms, especially during times of high demand.

Since DeFi is a relatively new technology, there is still a lot of uncertainty about how it will be regulated in the future. Some countries have already taken steps to regulate DeFi, while others have been more cautious. This uncertainty can make it difficult for DeFi platforms to operate and discourage investors from participating in the market.

Lack of liquidity is still associated with DeFi. Although DeFi platforms have snowballed in recent years, they still have relatively small liquidity pools compared to centralised exchanges. This can make it difficult for users to trade their assets and lead to price volatility.

Finally, DeFi can also be challenging for non-technical users. Since it is built on complex technology, it can be difficult for users unfamiliar with blockchain and cryptocurrency to participate in DeFi platforms. This can make it difficult for DeFi to achieve widespread adoption and discourage users from participating in the market.

Despite these risks, by integrating the right technology, such as blockchain, DeFi will still disrupt and revolutionise the industry.

Closing Thoughts

The future of DeFi is exciting and filled with endless possibilities. In the next ten years, we can expect to see it become more accessible and user-friendly, allowing more people to participate in the market. This will likely increase the number of DeFi platforms and the size of the DeFi market.

Additionally, as DeFi grows and matures, we expect to see more innovation in the space, including new financial products and services built on decentralised networks. This will likely include everything from new forms of lending and borrowing to new insurance products and investment opportunities. Overall, the future of DeFi is bright, and we expect continued growth and innovation over the next decade.

Blockchain and AI

Posted on May 8, 2023May 8, 2023 by Jean Chalopin

According to a report by Allied Market Research, the global blockchain technology market was valued at $3 billion in 2020 and is expected to grow to $39.7 billion by 2025. Similarly, the AI market is projected to grow to $190 billion by 2025, according to a report by MarketsandMarkets.

With the increasing demand for both blockchain and AI, combining these technologies can revolutionise many industries and transform the way we do business.

What Is Blockchain?

Blockchain technology is a decentralised, distributed ledger that allows for secure and transparent transactions without intermediaries. It was first introduced in 2008 by an unknown individual or group of individuals under the pseudonym Satoshi Nakamoto to facilitate Bitcoin transactions.

The technology works by recording transactions in blocks linked together to form a chain, hence the name ‘blockchain’. Each block contains a cryptographic hash of the previous block, ensuring the chain’s integrity.

The benefits of blockchain technology include increased security, transparency, and efficiency. By eliminating the need for intermediaries, such as banks, transactions can be completed faster and at a lower cost. The technology’s decentralised nature also makes it more resistant to fraud and hacking. Blockchain is used in various industries, including finance, healthcare, and supply chain management.

What Is AI?

AI, or artificial intelligence, refers to the ability of machines to perform tasks that would typically require human intelligence, such as learning, reasoning, and problem-solving. The history of AI traces back to the 1950s when researchers first began developing algorithms for machine learning. Since then, AI has evolved to include many technologies, including neural networks, natural language processing, and computer vision.

AI has rapidly transformed the finance industry by providing faster, more accurate decision-making capabilities and improving operational efficiency. Some examples of how AI is being used in finance include:

Fraud detection: AI-powered fraud detection systems use machine learning algorithms to identify unusual behaviour patterns and detect fraudulent activities.
Trading and investment: AI-powered trading algorithms use natural language processing (NLP) to analyse news articles, social media, and other data sources to identify patterns and predict market movements.
Customer service: Financial institutions use chatbots and virtual assistants to provide customer service and support.

Financial firms worldwide are increasingly turning to artificial intelligence (AI) technologies to improve their efficiency, automate their processes, and provide better customer service. Three examples of financial firms that have successfully adopted AI are Capital One, Citigroup, and Ping An.

Capital One, a US-based financial institution, has implemented natural language processing (NLP) to enhance customer service. Its virtual assistant, Eno, can understand and respond to customer inquiries in natural language, available via the company’s mobile app, website, and text messages. The system has helped Capital One reduce wait times and enhance customer satisfaction. The company has also used machine learning to detect and prevent fraudulent activity.

Citigroup, a multinational investment bank, has been utilising computer vision to analyse financial data. Its research team has developed an AI-powered platform to analyse financial statements and other data to identify patterns and trends.

The platform can also provide predictive insights, assisting investors in making well-informed decisions. The system has improved Citigroup’s research capabilities and enabled the company to provide superior investment advice to its clients.

Ping An, a Chinese insurance and financial services company, has been using machine learning to improve its risk management. Its risk management platform, OneConnect, can analyse large amounts of data to identify potential risks and provide real-time insights.

The system can also offer tailored risk assessments for different types of businesses. OneConnect has assisted Ping An in reducing its risk and enhancing its operational efficiency.

Financial firms are increasingly adopting AI technologies to remain competitive and enhance customer service. By leveraging NLP, computer vision, and machine learning, financial institutions can streamline operations, improve customer service, and make informed decisions. Firms that fail to embrace these technologies may risk falling behind their competitors.

Why AI and Blockchain Must Work Together

AI and blockchain are two of the financial services industry’s most innovative and disruptive technologies. While they are often seen as separate technologies, AI and blockchain are becoming increasingly interdependent for several reasons.

One of the most significant advantages of blockchain is its ability to provide secure, transparent, and tamper-proof transactions. However, blockchain cannot detect fraud, which is where AI comes in.

By integrating AI and blockchain, financial firms can build more secure and transparent systems that leverage AI’s fraud detection capabilities to enhance the trustworthiness of blockchain. This combination can offer improved security and transparency in transactions, which is crucial in financial services.

Another advantage of integrating AI and blockchain is the improved accuracy and efficiency of financial services. Smart contracts built on blockchain can automate financial transactions and self-execute when predefined conditions are met. By integrating AI, smart contracts can also be made more intelligent and capable of automatically adjusting to changing conditions. This integration can lead to the creation of more efficient and accurate financial systems.

Integrating AI into the blockchain can also help financial firms to detect and mitigate risks more quickly and effectively. AI can analyse vast amounts of data in real-time, making it an ideal tool for risk management. For example, AI can identify anomalies in financial transactions and flag them for review or rejection, making detecting fraud and other risks easier. This benefit can lead to better risk management, an essential component of financial services.

The integration of AI and blockchain can also help financial firms to comply with regulations more effectively. Financial rules are complex and ever evolving, making compliance a significant challenge for financial firms. By combining AI and blockchain, financial firms can improve their ability to comply with regulations and reduce the costs and risks associated with non-compliance. For example, blockchain can provide an immutable record of transactions, while AI can be used to analyse the data and ensure that it complies with regulations.

AI Creates New Business Models

Finally, integrating AI and blockchain opens up new business models and opportunities for financial firms. Decentralised finance (DeFi) applications are leveraging AI and blockchain to create new financial products and services that are more efficient, accessible, and affordable than traditional financial services. The combination of AI and blockchain technology creates new opportunities for financial firms, leading to the development of new financial products and services that were not possible before.

In practice, many examples of financial firms are already successfully leveraging AI and blockchain to enhance their services. For instance, Ripple, a blockchain-based payments solution, has integrated AI to improve its fraud detection and risk management capabilities. JPMorgan Chase is using blockchain to develop a decentralised platform for tokenising gold, and AI is being used to analyse the data generated by the platform. Visa also leverages blockchain and AI to enhance its fraud detection and prevention capabilities.

AI and blockchain can transform financial services, enhancing security, transparency, accuracy, efficiency, risk management, compliance, and new business models. By working together, AI and blockchain can create synergies that make them greater than the sum of their parts. Financial firms embracing AI and blockchain are likely better positioned to succeed in an increasingly competitive and complex financial services landscape.

Closing Thoughts

The future of AI-enabled blockchain in financial services is promising, with significant advancements expected in the next decade. Here are some potential developments:

Financial firms will continue integrating AI and blockchain to improve their operations, increase efficiency, and reduce costs.
By combining AI’s ability to analyse data with blockchain’s secure and transparent ledger, financial firms can develop systems that provide more secure and private transactions.
Decentralised finance (DeFi) applications are already leveraging AI and blockchain to create new financial products and services.
As AI and blockchain become more integrated into financial services, regulatory oversight will increase.
Integrating AI and blockchain will likely create new business models and revenue streams for financial firms.

Overall, the future of AI-enabled blockchain in financial services looks bright, with continued growth and development expected in the next decade. As financial firms increasingly adopt and integrate these technologies, we can expect to see significant advancements in efficiency and security as new business opportunities emerge.

Longevity and the Future

Posted on May 5, 2023May 5, 2023 by Jean Chalopin

With continuous advancements in medical technology, the science of longevity has seen incredible progress in the past few decades. According to the World Health Organization, the global average life expectancy increased from 64.2 years in 1990 to 72.6 years in 2019.

The same report states that, in high-income countries, life expectancy at birth can reach up to 80 years. With ongoing research and advancements, there is a high probability that the average life expectancy will continue to rise in the future. In this article, we will explore the advances in the science of longevity, including the latest discoveries, potential future developments, and ethical considerations.

The Science of Longevity

The primary goal of longevity research is to improve the quality of life by extending the number of healthy years an individual can enjoy.

Several research areas contribute to the science of longevity, including genetics, epigenetics, stem cell research, and nutrition. Recent studies show that our lifestyle habits and environment also significantly determine our life span.

Lifestyle Habits

Studies show that our lifestyle habits and environment can significantly impact our lifespan. For example, a study published in the American Journal of Clinical Nutrition found that eating a diet rich in fruits, vegetables, whole grains, nuts, and legumes reduces mortality risk from all causes, including cardiovascular disease and cancer.

Similarly, a study published in the British Medical Journal found that quitting smoking can add up to 10 years to a person’s life expectancy. The study also found that even those who quit smoking in their 60s can still add several years to their lifespan.

Other studies have looked at the impact of exercise on lifespan. A study published in the journal PLOS Medicine found that individuals who engaged in regular physical activity had a reduced risk of premature death from all causes, including cardiovascular disease and cancer.

Stress is also a factor that can impact lifespan. A study published in the journal ‘Science’ found that chronic stress can accelerate ageing at the cellular level by shortening telomeres. The study suggests that stress management techniques like mindfulness meditation and yoga may help slow ageing and extend lifespan.

These studies demonstrate that our lifestyle habits and environment can significantly impact our lifespan. Making healthy lifestyle choices, such as eating a nutritious diet, quitting smoking, engaging in regular physical activity, and managing stress, can help to extend our healthy years and improve our overall quality of life.

Genetic Research

Genetic research has made significant progress in identifying the genes contributing to ageing and age-related diseases. Studies have identified several genetic variants associated with an increased risk of Alzheimer’s, cancer, and heart disease.

Researchers are also exploring the potential of gene editing technologies, such as CRISPR, to modify genes associated with ageing and disease.

One study published in Nature Genetics found a genetic variant associated with an increased risk of Alzheimer’s disease that affects the immune system’s ability to clear beta-amyloid protein from the brain.

Beta-amyloid protein is a hallmark of Alzheimer’s disease. Another study published in the journal Nature Communications identified a genetic variant associated with an increased risk of heart disease that affects the metabolism of fats in the liver.

Epigenetics Research

Epigenetics is the study of changes in gene expression without altering the underlying DNA sequence. Recent research has shown that epigenetic changes can significantly impact ageing and age-related diseases.

For example, a study published in Aging Cell found that specific epigenetic changes in the brain are associated with cognitive decline in ageing adults. Another study published in Nature Communications found that DNA methylation changes in the blood are associated with ageing and age-related diseases, such as cancer and cardiovascular disease.

Stem Cell Research

Stem cell research focuses on developing therapies to regenerate damaged tissues and organs. Recent advancements in stem cell research have shown promising results in animal studies, including restoring damaged heart tissue and reversing age-related muscle loss.

A study published in the journal Cell Stem Cell found that injecting old mice with muscle stem cells from young mice improved muscle function and strength in the older mice. Another study published in the journal Nature found that transplanting neural stem cells into the brains of ageing mice improved cognitive function.

Nutrition Research

Nutrition research has shown that a healthy diet can significantly impact our lifespan. Studies have shown that diets high in fruits, vegetables, whole grains, and lean protein can reduce the risk of chronic diseases and improve overall health. Researchers are also exploring the potential of calorie restriction and intermittent fasting to extend lifespan.

Case Study in Okinawa

The Okinawan population in Japan is a fascinating case study in the science of longevity. Okinawa is known for having one of the highest percentages of centenarians in the world, with a significant number of individuals living beyond 100. Researchers have been studying the factors that contribute to the long lifespan of Okinawans for many years.

One of the critical factors that researchers have identified is the Okinawan diet, which is high in fruits, vegetables, and whole grains and low in calories and saturated fat. The traditional Okinawan diet consists of sweet potatoes, vegetables, tofu, seaweed, and fish. The diet is rich in antioxidants and anti-inflammatory compounds, which may help to reduce the risk of chronic diseases such as cardiovascular disease and cancer.

Regular physical activity is another factor that contributes to the longevity of Okinawans. Many Okinawans engage in physical activity, such as walking, gardening, and traditional martial arts practices. This physical activity may help to reduce the risk of age-related diseases and maintain physical function in old age.

Social connections are also a crucial factor in the longevity of Okinawans. Many Okinawans maintain strong social connections throughout their lives, which can provide emotional support and a sense of purpose. Studies have shown that social isolation is associated with increased mortality risk and poor health outcomes, emphasising the importance of social connections for overall health and longevity.

In addition to these lifestyle factors, genetic and environmental factors may also contribute to the longevity of Okinawans. Researchers have identified several genetic variations that may play a role in the long lifespan of Okinawans, including variations in genes related to insulin sensitivity and inflammation. Environmental factors, such as low pollution levels and high exposure to natural light, may also contribute to the longevity of Okinawans.

Potential Future Developments

The future of longevity research looks promising, with ongoing advancements in medical technology and genetic analysis. Here are some potential future developments in the field of longevity.

Anti-Aging Drugs

Several drugs that can delay ageing and age-related diseases are currently in development. These drugs work by targeting specific genes and proteins that are associated with ageing and age-related diseases.

Gene Editing

Gene editing technologies such as CRISPR can potentially modify genes associated with ageing and disease. Researchers are exploring the potential of these technologies to extend lifespan and reduce the risk of age-related diseases.

Regenerative Therapies

Regenerative therapies such as stem cell treatments have shown promising results in animal studies. Researchers are exploring the potential of these therapies to regenerate damaged tissues and organs in humans.

Artificial Intelligence

Artificial intelligence (AI) can potentially revolutionise the field of longevity research. AI can analyse large datasets and identify patterns to help researchers develop new therapies and treatments.

Ethical Considerations

The potential to extend lifespan raises several ethical considerations that must be addressed. One concern is the unequal distribution of life-extending therapies.

If these therapies are only available to the wealthy, it could widen the gap between the rich and the poor. Another concern is the potential for overpopulation and strain on resources if the population continues to age and live longer. Researchers and policymakers must consider these ethical implications as they develop new therapies and treatments.

Closing Thoughts

In conclusion, the science of longevity has made significant progress in recent years, thanks to advancements in medical technology and research. Genetic, epigenetics, stem cell, and nutrition research have contributed to our understanding of ageing and age-related diseases.

Future developments in anti-ageing drugs, gene editing, regenerative therapies, and artificial intelligence promise to extend a healthy lifespan. However, researchers must also consider the ethical implications of extending lifespan, including unequal distribution of therapies and strain on resources. With ongoing research and advancements, the future looks bright for the science of longevity.

Can Robots Become Sentient With AI?

Posted on May 3, 2023May 3, 2023 by Jean Chalopin

AI-powered robots’ potential to become sentient has sparked heated discussion and conjecture among scientists and technology professionals. Concerns regarding the ethical consequences of producing robots with human-like awareness are growing as AI technology improves.

The current AI in the robotics industry is worth more than $40 billion and is likely to grow in the future years. According to MarketsandMarkets, AI in the robotics market will be worth $105.8 billion by 2026, with a CAGR of 19.3% from 2021 to 2026.

This article will discuss what sentience means in robotics, along with the possible benefits and challenges.

Robots and AI

Artificial intelligence refers to the ability of machines or computer programs to perform tasks that typically require human intelligence. This includes perception, reasoning, learning, decision-making, and natural language processing. AI systems can be trained using large amounts of data and algorithms to make predictions or perform specific actions, often improving over time as they are exposed to more data.

There are several types of AI, including narrow or weak AI, which is designed for a specific task, and general or strong AI, which can perform any intellectual task that a human can. AI is used in many industries to improve efficiency, accuracy, and decision-making, including healthcare, finance, and customer service.

However, it is essential to note that AI is not a replacement for human intelligence but rather an extension that can assist and enhance human capabilities. Ethical considerations around AI, such as its impact on jobs and privacy, are essential to keep in mind as it advances and becomes more integrated into our daily lives.

What Is AI Sentience in Robotics?

The notion of AI sentience refers to the ability of a robot or artificial system to have subjective experiences such as emotions, self-awareness, and consciousness. This extends beyond a robot’s capacity to complete tasks or make decisions based on algorithms and data to construct a genuinely autonomous being with its own subjective experiences and perceptions.

In robotics, AI sentience means that a robot is designed to execute particular activities and can make decisions, feel emotions, and interact with the environment in a manner comparable to that of a human being.

One example of AI sentience in robotics is the case of the AI robot named ‘Bina48’. Bina48 was created by a company called Hanson Robotics and is designed to exhibit human-like qualities such as emotions, self-awareness, and the ability to hold conversations. Bina48 was created using information and data collected from its human ‘source’, a woman named Bina Rothblatt.

The robot uses advanced AI algorithms to process information and respond to stimuli in a way that mimics human behaviour. Bina48 has been used in various experiments to test the limits of AI sentience and has been shown to exhibit a range of emotions and respond to different situations in a way that suggests a level of consciousness. This robot is a fascinating example of the potential for AI sentience in robotics and the future of AI technology.

How Does AI Sentience Work?

AI sentience in robotics would work through the implementation of advanced AI algorithms that allow robots to process and analyse information in a way that mimics human consciousness. This would involve creating a self-aware AI system that can make decisions, hold conversations, experience emotions, and perceive its surroundings in a similar manner to a human being.

The AI system would need to have a high level of cognitive processing power and be able to analyse and respond to stimuli in real-time. Additionally, the AI system would need to be able to learn from experience and adapt its behaviour accordingly, which would require the development of advanced machine learning algorithms.

To achieve sentience, the AI system would also need access to a large amount of data that it could use to understand the world and make decisions. This data could come from sensors, cameras, or other sources and would need to be processed and analysed in real-time to enable the robot to make informed decisions.

The process for creating AI sentience would be similar to the one below.

Data Collection: The first step in creating AI sentience would be to collect vast amounts of data from various sources. This data would be used to train machine learning algorithms and help the AI system understand the world and make informed decisions.
Pre-Processing: The collected data would then undergo pre-processing to clean, format and make it ready for use in training the AI model.
Model Training: The processed data would then be used to train an advanced machine learning model that would enable the AI system to recognise patterns, make predictions and perform tasks.
Model Validation: The trained model would then be tested and validated to determine its accuracy and ability to perform the intended tasks.
Integration With Robotics: The trained and validated AI model would then be integrated into a robot or system to give it the ability to process and analyse data, make decisions and exhibit human-like qualities such as emotions and self-awareness.
Continuous Learning: The AI sentience system would need to continuously learn and adapt as it interacts with the world, which would require the implementation of advanced reinforcement learning algorithms and the ability to access and process large amounts of real-time data.

Why AI Sentience?

AI experts are striving to achieve sentience in robotics because it would represent a significant breakthrough in the field of AI and demonstrate the ability of machines to process information and make decisions in a manner similar to human consciousness. Sentience in robots would open up new possibilities for their functionality and application, including the ability to perform complex tasks, interact with the environment in a more intuitive and human-like way, and exhibit human-like qualities such as emotions and self-awareness.

Additionally, the development of sentient robots could have important implications for fields such as healthcare, manufacturing, and entertainment by providing new and innovative solutions to existing problems. The drive to achieve AI sentience in robotics is driven by the desire to push the boundaries of what is possible with AI technology and to explore the potential of machines to change our world for the better.

One example of how AI sentience is being used in healthcare is through the development of virtual nursing assistants. These AI-powered robots are designed to assist nurses in patient care and provide patients with a more personalised and compassionate experience. The virtual nursing assistants use advanced AI algorithms to process information about a patient’s condition, symptoms, and treatment history and can provide real-time recommendations and support.

Additionally, these robots can use natural language processing and advanced conversational AI to hold conversations with patients, answer their questions, and provide emotional support. By providing patients with a more personalised and human-like experience, virtual nursing assistants can help improve patient outcomes, increase patient satisfaction, and reduce the burden on healthcare providers. This is just one example of how AI sentience is being used in healthcare to transform the delivery of care and improve patient outcomes.

There are several companies working on developing AI-powered virtual nursing assistants, but no company has yet created a fully sentient AI nurse. Some companies in this field include:

Cogito: A company that develops AI-powered virtual assistants to improve customer engagement and support.
Lemonaid: A company that uses AI to provide virtual consultations and prescription services.
Woebot: A company that uses AI and machine learning to provide individuals with mental health support and counselling.

These are just a few examples of companies working on developing AI-powered virtual nursing assistants. However, it is essential to note that these systems are not fully conscious and do not possess true self-awareness or emotions. The development of AI sentience in healthcare is still in its early stages, and it may be several years before fully sentient AI systems are deployed in real-world healthcare settings.

The Risks and Challenges

The development of AI sentience in robotics is a complex and challenging field, and it comes with several risks and challenges that must be carefully considered and addressed. These risks and challenges can be broadly categorised into three areas: technical, ethical, and social.

Technical Risks and Challenges

One of the most significant technical risks and challenges of creating AI sentience in robotics is the difficulty of making a truly self-aware and conscious machine. Despite significant advances in AI technology, we are still far from fully understanding the nature of consciousness and how it arises from the interaction of neurons in the brain. To create AI sentience, we must first have a deep understanding of how consciousness works and how it can be replicated in machines.

Another technical challenge is ensuring that sentient robots are capable of making decisions that are safe and ethical. For example, if a sentient robot is programmed to prioritise its own survival over the safety of humans, it could potentially cause harm to those around it. To address this challenge, developers must carefully consider the ethical implications of their AI systems and ensure that they are programmed with the right goals and values.

Ethical Risks and Challenges

The development of AI sentience in robotics raises many important ethical questions, including guaranteeing that sentient robots treat humans with respect and dignity and safeguarding that they do not cause harm to those around them. There is also the question of ensuring that sentient robots are treated fairly and with respect and how to prevent them from being abused or exploited.

Another ethical challenge is ensuring that sentient robots have the right to privacy and freedom of thought. For example, if a sentient robot is capable of experiencing emotions and forming its own thoughts and opinions, how can we ensure that these thoughts and opinions are protected from outside interference or manipulation?

Social Risks and Challenges

Finally, the development of AI sentience in robotics raises several social risks and challenges, including ensuring that sentient robots are accepted and integrated into society and that they do not cause social or economic disruption. For example, if sentient robots become capable of performing many of the tasks that humans currently perform, it could lead to significant job loss and economic disruption.

In addition, there is the question of ensuring that sentient robots are used responsibly and ethically. For example, how can we ensure that sentient robots are not used for harmful or malicious purposes, such as in developing autonomous weapons?

Closing Thoughts

The answer to whether AI will ever become sentient is still unknown. While there have been significant advances in AI technology, experts are still divided on whether it is possible to create genuinely self-aware and conscious machines. Some believe this is a natural next step in the development of AI, while others believe that it may be technically impossible or too risky to pursue.

As for the question of whether we should let AI become sentient, opinions are also divided. Those who believe that AI should become sentient argue that it could lead to significant benefits, such as increased efficiency, improved decision-making, and the creation of new forms of intelligence. However, those who are opposed argue that the risks associated with AI sentience, such as the potential for harm to humans and the disruption of social and economic systems, are too significant to justify the development of this technology.

Ultimately, deciding whether AI should become sentient is a complex and controversial issue that requires careful consideration of the potential benefits and risks. It is crucial to have open and honest discussions about this issue and to ensure that any decisions made are based on a thorough understanding of the technology and its potential implications.