Author: Jean Chalopin

Ageing and AI

Posted on by Jean Chalopin

The field of longevity medicine is reinvigorating. Until recently, the initiative of slowing and reversing ageing was not considered ‘proper science’ or a reasonable use of public funds. 

Ageing was regarded as an unavoidable and permanent component of the human condition. It was not a disease but rather a susceptibility to disease that develops over time and cannot be reversed. The medical establishment viewed people who argued against it as entertaining mavericks at best and disreputable charlatans at worst.

The sneering has not gone away entirely, but there is now a significant investment in addressing ageing, rather than just the diseases that emerge with age, such as cancer, heart attacks, and Alzheimer’s.

Funding and approval are in place for the TAME (Targeting Aging With Metformin) project, showing that the medical profession is engaging in tackling the ageing process. 

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Billionaire cryptocurrency community members are emerging as the most prominent anti-ageing promoters, such as Richard Heart. The interest from influential figures could lead to sizable donations in the field and lead the fight against ageing. 

Artificial Intelligence Addresses Longevity

Applying modern artificial intelligence techniques to healthcare, particularly deep neural nets and reinforcement learning, is one reason for the shift in attitudes toward ageing. Neural networks are data-processing algorithms that work in layers, with each layer taking data from the previous layer as input and passing an output up to the next layer.

The outcomes do not have to be binary, but they can be weighted. Reinforcement learning algorithms adapt their approach in response to feedback from their surroundings.

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A significant contribution of AI towards anti-ageing is something known as ageing clocks. 

Age is much more than how many birthdays you’ve had. Stress, sleep, and diet all have an impact on how our organs deal with the wear and tear of daily life, which may cause you to age faster or slower than people born on the same day. That means your biological age may differ significantly from your chronological age—the number of years you’ve lived.

Your biological age is more likely to reflect your physical health and even mortality than your chronological age. However, calculating it is far from simple. Scientists have spent the last decade developing ageing clocks, which analyse markers in your body to determine your biological age.

The basic idea behind ageing clocks is that they will tell you how much your organs have degraded and, thus, how many healthy years you have left. The accuracy of hundreds of ageing clocks developed in the last decade, on the other hand, varies greatly. And scientists are still debating an important question: What does it mean to be biologically young?

When an AI is trained to predict age using specific types of biological data, it learns biology. The hope is that these AIs will eventually help us understand how ageing works.

How Does AI in Ageing Work?

AI will analyse the vast amounts of health data we collect, including time series (longitudinal) data and comparative data across social and national groups. The patterns it uncovers in the data will lead to the generation of hypotheses to test. The ageing clocks will reveal treatments’ effectiveness – or lack thereof.

AI can identify trends and determine casualties by analysing patterns in medical data, images, and other sources.

AI algorithms analysed daily photographs of mice in a project to extract potential ageing markers and develop lifespan control solutions. The same learning process for visual biomarkers can then be applied to other species, including humans.

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In addition to image analysis algorithms, AI can detect recurring patterns in human ageing to identify biomarkers. AI can identify ageing trends in different populations by sifting through mass data on various blood tests, retinal scans, muscle analyses, and more, or by comparing human data to other species.

Longevity and Psychological Age

Some academics believe that psychological age defines us much more than biological age. 

People age at different rates, and their ageing dynamics are shaped and defined by their mindset and environment. Our psychological state, in turn, influences our perception of time and health status. 

An individual’s psychological age, also known as subjective age, is based on how young or old they perceive themselves to be compared to their chronological age, depending on an individual’s self-assessment of the degree of ageing and how this perception influences their overall well-being.

Exciting research on AI-powered engines reveals novel tools that can estimate one’s psychological age and future well-being based on a psychological survey. Essentially, these AI tools can be used to determine ways to improve and maximise long-term well-being based on information inferred from available datasets.

There are two models that exist as predictors of chronological and subjective age, considering around 50 psychological features. Both clocks had modifiable features that could be altered through social and behavioural interventions. More importantly, it may be useful in shifting personal perceptions of ageing toward a mindset that promotes productive and healthy behaviours.

AI has made it possible to find the best path to emotional stability. As previously stated, this new deep learning model can predict a person’s current psychological age and guide future well-being by providing personalised recommendations for improving mental resilience. 

The model’s SOM offers a set of non-trivial, personalised paths to improved well-being that can be used as a reference for the cognitive behavioural therapy and online mental health approaches. Perhaps this tool can be used as a supplement or stand-alone approach to adjusting one’s psychological age and providing emotional and mental stability.

Closing Thoughts

AI can recommend health solutions and identify potential issues by analysing the effects of therapeutic treatments, preventive measures, different lifestyles, and other factors.

On a more fundamental level, understanding how proteins and cells respond positively or negatively to treatments allows AI to contribute to the efficient development of medicine.

Deep learning’s power may soon help us alleviate the discomforts of biological ageing.

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

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

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

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

What Is Generative AI?

Posted on by Jean Chalopin

Generative AI is a rapidly developing field of artificial intelligence that has been making waves in recent years. Using advanced algorithms, generative AI can create original and often impressive content, such as images, music, and even text, without direct human input. 

This article will delve deeper into generative AI, exploring what it is, how it works, and its potential uses.

Understanding Generative AI

Unlike other types of AI designed to complete specific tasks, such as image recognition or language translation, generative AI is programmed to learn from existing data and generate new content based on that information. 

The key to this process is the use of deep neural networks, designed to simulate how the human brain works, allowing the AI system to learn from patterns and generate new content.

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One of the most impressive aspects of generative AI is its ability to create content that is often difficult to distinguish from something a human would produce. For example, generative AI can be used to create realistic images of people who don’t exist or to generate music that sounds like it was composed by a human musician. The image below is AI-generated and not of a real person.

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This has exciting implications for various industries, from art and entertainment to marketing and advertising.

Against Other Forms of AI

Generative AI is distinct from other forms because it is designed to create something new rather than simply perform a specific task. This contrasts with different types of AI, such as supervised learning or reinforcement learning, which are focused on solving a particular problem.

For example, supervised learning algorithms are commonly used in image recognition software to identify and classify objects within a given image. In contrast, generative AI can be used to create original ideas, such as realistic portraits of people who don’t exist or entirely new landscapes that have never been seen before.

Another example of a different type of AI is natural language processing (NLP), which is used to analyse and understand human language. While NLP can generate text, it is typically focused on tasks such as language translation or sentiment analysis. In contrast, generative AI can be used to create entirely new pieces of text, such as short stories, poetry, or even news articles.

Most of the AI we see today is still based on machine learning, which involves training a model on a large dataset to identify patterns and make predictions. This is done by feeding the machine learning algorithm a set of labelled data, allowing the system to learn from the data and identify patterns that can be used to make predictions on new, unseen data. 

While machine learning has already had a significant impact on many industries, from healthcare to finance to transportation, the ability to create entirely new content has the potential to revolutionise these fields completely.

Ultimately, the critical difference between generative AI and other types of AI is the focus on creativity and originality. 

The Benefits of Generative AI

Generative AI is a rapidly developing field with numerous potential benefits.

One industry that could improve significantly from generative AI is fashion. With the ability to generate unique designs and patterns, it has the potential to transform the fashion industry. Designers can use it to create new designs, allowing them to produce unique and eye-catching pieces that stand out from the competition. By using it, designers can also save time and resources, allowing them to focus on other aspects of the creative process.

A second industry that stands to gain is gaming. With the ability to generate unique characters, landscapes, and environments, it has the potential to revolutionise the gaming industry. Game designers can use it to create original game elements that are unique and engaging for players. It enables game designers to save time and resources, allowing them to focus on other aspects of the game development process.

Finally, generative AI has the potential to shift the healthcare industry. Using it, researchers can create new drugs and treatments, allowing them to treat diseases and illnesses. It can also be used to analyse medical images and data, allowing doctors and researchers to diagnose and treat patients more accurately. With its ability to create new content and analyse large amounts of data, generative AI can potentially transform how we approach healthcare.

Successful Case Studies

Several companies are already using generative AI to great effect in their applications. Here are a few examples:

Adobe is using generative AI to develop new tools and features for its Creative Cloud suite of products. For example, Adobe’s Sensei platform uses generative AI to analyse images and suggest improvements. The company has also used it to develop new fonts and predict which colours will be popular in the coming year.

OpenAI is a research organisation focused on advancing AI safely and responsibly. The company has developed several generative AI models, including GPT-3, a language model that can generate text that is often difficult to distinguish from something a human would write. GPT-3 has many potential applications, from natural language processing to chatbots. The revolutionary Chat GPT platform is based on these models.

IBM uses generative AI to develop new solutions for various industries, including healthcare and finance. For example, the company has developed a system to analyse medical images and provide more accurate diagnoses. It has also used it to create new financial risk models.

Nvidia is a leading provider of graphics processing units (GPUs) that are used in various applications, including gaming, scientific research, and machine learning. The company is also investing heavily in generative AI and has developed several models that can generate realistic images and even entire virtual environments.

These companies are just a few examples of how generative AI is already being used to create new opportunities and drive innovation in several industries. As the technology develops, it will be interesting to see how it is integrated into even more applications and use cases.

The Risks

While generative AI has enormous potential, several risks are also associated with the technology. One of the most significant risks is its potential to be used for malicious purposes. 

For example, it can be used to create realistic-looking fake images, videos, and audio, which can be used for deception or propaganda. In the wrong hands, these tools could be used to manipulate public opinion, create fake news, or even commit fraud. 

Another risk of generative AI is its potential to perpetuate biases and inequalities. Its models are only as good as the data they are trained on, and if the data is biassed, then the model will be biassed as well. 

For example, a generative AI model trained on predominantly white and male data may be more likely to generate images and text biassed against women and people of colour. This can perpetuate existing inequalities and reinforce harmful stereotypes.

In one study published in 2018, researchers found that several leading facial recognition algorithms were significantly less accurate at identifying the faces of people with darker skin tones, particularly women. This bias was pervasive across multiple algorithms from different companies. The researchers attributed it to the fact that the training datasets used to develop the algorithms were overwhelmingly white and male.

A third risk of generative AI is its potential for cyberattack use. For example, generative AI can generate realistic-looking phishing emails, which can trick people into giving up sensitive information or clicking on links that download malware onto their devices. Additionally, generative AI can generate realistic-looking social media profiles, which can be used for impersonation or other online attacks.

Overall, while it has enormous potential for positive applications, it is vital to be aware of the risks associated with the technology. As the technology continues to develop, it will be necessary for developers and users of generative AI to take steps to mitigate these risks and ensure that the technology is being used responsibly and ethically. This will require ongoing research, development, collaboration, and coordination among stakeholders in various industries.

Closing Thoughts

Generative AI has made tremendous progress in recent years, and there is no doubt that the technology will continue to evolve and improve in the coming decade. One of the most promising areas of development for generative AI is in the realm of creative applications. For example, generative AI is already being used to generate music, art, and even entire literature. As technology advances, we can expect to see more creative works generated by AI and even collaborations between human and machine artists.

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.

The Future of AI-Based Art

Posted on by Jean Chalopin

The worldwide art market, worth billions of dollars, has historically been a traditional and restricted sector. However, as artificial intelligence advances, the art industry is shifting towards a more democratised and accessible market, introducing the phenomenon of AI-based art.

AI-powered technologies can now generate creative works of art, question the validity of existing works, and even estimate their worth. While some are concerned about AI’s ability to disrupt the industry, others view it as a way for artists to reach a larger audience and collectors to access a greater range of investment options.

According to an Art Basel and UBS estimate, the global art industry will be worth $64.1 billion in 2020. However, the market has been on a rising trend in the last decade. The worldwide art market was valued at $39.7 billion in 2011, representing a 61% increase over the previous ten years.

It is important to note that these data are estimations, and the art market is notorious for its lack of openness, making determining the exact value of the market complex.

The Rising Art Market

There are several reasons why the value of the art market has been on a rising trend over the last decade:

  1. Increasing wealth. As wealth has increased globally, more people have been able to invest in art, driving up prices.
  2. Globalisation. The art market has become more global, with a broader range of buyers and sellers participating worldwide. This has increased the demand for high-quality art and has led to a rise in prices.
  3. Investment demand. Art is seen as a safe haven asset, and many investors have been buying art to diversify their portfolios and protect against economic uncertainty.
  4. Technology. The growth of technology has made it easier for buyers and sellers to connect, increasing transparency and efficiency in the market.
  5. Awareness and education. Increased awareness and education about the art market have led to more people becoming involved and interested in collecting, further driving up demand and prices.

These factors have contributed to the overall rise in the art market’s value over the last decade. However, despite the growth in the traditional market, there is a continued focus on AI-based art. 

What Is AI-Based Art?

AI-based art is artwork that is made or aided by machines. This can take various forms, from entirely created art pieces generated by algorithms to works that utilise AI technology in their development or presentation. AI-based art may use multiple AI models, such as machine learning and computer vision, to create one-of-a-kind and creative artwork.

Machine learning is utilised to produce art by training algorithms on vast datasets of existing art. These datasets teach the algorithm patterns and styles, which they then employ to create new works. In contrast, computer vision allows editing and improving existing pictures and synthesising new images based on visual inputs.

There are several platforms and websites that offer tools for creating AI-based art:

DeepArt.io: A platform that allows users to upload an image and have it transformed into a unique piece of art using AI algorithms.

Pikazoapp.com: An app that uses AI to remix existing images and turn them into unique works of art.

Let’s Enhance: An AI tool to upscale and enhance images.

RunwayML: An open-source platform that offers a wide range of AI models for creative purposes, including art.

Artbreeder: A platform that allows users to breed unique art pieces by combining existing art and AI algorithms.

Online markets, cryptocurrency-based marketplaces, and direct sales are all methods for selling AI-based art. Artsy and Saatchi Art, for example, offer a forum for artists and collectors to buy and sell art, including AI-based art. Cryptocurrency-based marketplaces such as SuperRare and Rarible enable the purchase and sale of AI-based art using cryptocurrency. 

Artists can also sell their AI-based paintings to collectors directly through their websites or personal networks. Furthermore, galleries and shows specialising in digital and new media art may be viable platforms for selling AI-based art. 

The ideal method for selling AI-based art will be determined by the artist’s aims, target audience, and genre of work. Portrait of Edmond de Belamy, made by the Paris-based art collective Obvious, is one of the most valuable AI-generated art to date. That painting sold for $432,500 at a Christie’s auction in October 2018.

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The Benefits of AI-Based Art

Despite the continuous expansion of the traditional art business, interest in AI-based art is expanding. This can be attributable to a variety of factors.

For starters, AI-based art provides a more accessible and democratised market, enabling a broader spectrum of people to engage as producers or consumers. Because AI-generated artwork has lower production costs than conventional art, they are more affordable to a broader audience. 

Furthermore, the application of AI in art expands the definition of art and offers new avenues for creativity and self-expression. Some perceive the emergence of AI-based art as providing new investment opportunities in the form of one-of-a-kind artwork made by robots.

Finally, the use of technology and artificial intelligence in the art business simplifies and improves procedures from conception to sale. These characteristics, taken together, lead to the rising interest in AI-based art, even as the traditional art industry expands.

Industry Use Cases for AI-Based Art

Advertising and marketing, film and video game creation, and fashion and textile design are some of the major industrial use cases for AI-based art.

AI algorithms can create new pictures or improve old ones for use in advertising and marketing materials, easing the creative process and allowing for more remarkable design and visual effects versatility. In 2022, Heinz went viral after asking AI to draw its interpretation of ketchup. 

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AI algorithms can be used to build virtual sets, characters, and special effects in the film and video game industries, possibly decreasing the time and expense associated with traditional production techniques. 

AI algorithms can augment the fashion and textile design sectors by producing new patterns and designs for textiles, as well as aid in the design process by giving suggestions and coming up with new ideas. 

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AI-based art and imagery are also being applied in fields such as interior design, product design, and architecture, to name a few. The application of AI in these industries is opening new avenues for creative expression and problem-solving. It has the potential to transform how art and design are created and consumed.

The Challenges and Risks

The use of AI in creating art is a relatively new field, and there are several challenges and risks associated with AI-based art. One of the main challenges is the lack of a clear legal framework. 

Currently, there is no consensus on whether AI-generated art can be considered a work of authorship and who should own the rights to such works. This lack of clarity can lead to disputes over ownership and copyright and could stifle the growth of the AI-based art market.

A further challenge is evaluating the quality and value of AI-generated art. Unlike traditional artworks, which are typically evaluated based on the skill and talent of the artist, the value of AI-generated art is often tied to the technology used to create it and the algorithms that drive it. This can make it difficult to determine the actual value of an AI-generated artwork and can lead to inconsistencies in pricing and sales.

Another risk associated with AI-based art is the potential for the widespread use of AI to lead to a homogenisation of artistic styles and techniques, resulting in a lack of diversity and originality in the art market. There is also a risk that AI-generated art could be used for unethical purposes, such as creating deep fake images or generating false information.

There is also the issue of privacy and data protection. AI algorithms are trained on vast amounts of data, and it is crucial to ensure that this data is appropriately protected and that the use of AI algorithms does not violate the privacy of individuals. There is also a risk that AI algorithms could perpetuate biases and stereotypes present in the training data, leading to further marginalisation and discrimination. 

Despite these challenges and risks, there is the potential for AI-based art to bring new opportunities and excitement to the art market, making it more accessible and allowing for new forms of creative expression. By embracing and carefully managing the challenges and risks associated with AI-based art, it is possible to unlock its full potential and create a new era of artistic innovation.

Closing Thoughts

The impact of AI-based art on the art market and traditional industries is a subject of ongoing debate and discussion. Some believe that AI-based art has the potential to bring new opportunities and excitement to the art market, making it more accessible and allowing for new forms of creative expression. At the same time, there are concerns that the increasing use of AI in art creation could devalue traditional art forms and reduce the importance placed on the artist’s hand and personal touch.

In traditional industries such as advertising, interior design, and product design, the use of AI-based art and images can bring greater efficiency, cost savings, and provide new opportunities. However, there are also concerns that the increasing reliance on AI in these industries could lead to a loss of unique perspectives and human touch and potentially result in a homogenisation of design.

Whether AI-based art will be good or bad for the art market and traditional industries remains to be seen. The impact of AI-based art will likely vary depending on context and use case, but its meteoric rise so far is a marvel in itself. 

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

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

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

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

What Is Liquid Staking?

Posted on by Jean Chalopin

In cryptocurrency, staking has become an increasingly popular way for investors to earn passive income. However, a new concept has emerged that takes staking to the next level: liquid staking. This innovative approach can potentially revolutionise the staking landscape and bring even more people into cryptocurrency.

But what exactly is it? This article will delve into this new concept’s details and explore its benefits and risks. We’ll also look at some of the most promising liquid staking projects in the cryptocurrency sector and discuss their potential for growth and adoption. 

Whether you are a seasoned cryptocurrency investor or just starting, understanding the ins and outs of liquid staking is essential for staying ahead of the curve in this rapidly evolving field.

Understanding Liquid Staking

Liquid staking refers to a process that allows investors to stake their assets while still maintaining the ability to use them for other purposes. 

The idea has been around for a few years, but it was not until 2020 that the technology to make it a reality emerged. This was primarily due to the development of Ethereum 2.0, which introduced a new staking mechanism that made it possible to stake ETH while still holding a liquid form of the asset, called a stake token.

The technology behind liquid staking involves a complex system of smart contracts and protocols that enable investors to stake their assets and receive rewards in return. These rewards are typically paid out in the form of additional tokens, which can be traded or sold on the open market. The process of staking itself is typically done through a validator node, which is responsible for verifying network transactions and maintaining the blockchain’s integrity.

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As the popularity of liquid staking has grown, so too have the number of projects and platforms that offer this service. Some of the most notable projects include Lido, Rocket Pool, and Ankr, each offering a unique approach with distinct advantages and disadvantages.

Liquid Staking Versus Traditional Staking

Staking is a popular way for cryptocurrency investors to earn passive income by participating in network validation and transaction processing. Traditional staking methods typically involve locking up assets for a set period of time in exchange for staking rewards. However, liquid staking is a newer approach that offers investors more flexibility and control over their assets.

The main difference against traditional staking methods is that it permits investors to maintain control over their assets while still earning staking rewards. With traditional staking, assets are locked up for a set period, which can limit the ability of investors to use or trade those assets.

On the other hand, liquid staking enables investors to stake their assets and receive liquid tokens in return, which can be used for other purposes. This allows investors to continue to trade or use their assets while still earning staking rewards.

Another difference between liquid staking and traditional staking methods is the potential for higher staking rewards. Liquid staking platforms often offer higher rewards than traditional staking, making it more attractive to investors.

Upcoming Projects

There are several platforms and projects that offer liquid staking services, but three of the most well-known are Lido, Rocket Pool, and Ankr.

Lido is a decentralised staking platform that enables users to stake their ETH and receive stETH in return. StETH is a liquid form of ETH that can be traded or used in other ways while still earning staking rewards. 

Lido operates through a network of validators that secure the Ethereum network and process transactions. The platform has gained popularity due to its ease of use and high staking rewards, which have consistently been among the highest in the industry.

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Rocket Pool is another decentralised staking platform that allows users to stake their ETH and receive rETH in return. Like stETH, rETH is a liquid form of ETH that can be traded or used while still earning staking rewards. 

Rocket Pool operates through a network of node operators that provide staking services to users. The platform is designed to be highly scalable and decentralised, focusing on security and transparency.

Ankr is a platform that provides staking services for multiple cryptocurrencies, including ETH, BTC, and DOT. The platform operates through a validator network that secures the blockchain and processes transactions. 

Ankr’s liquid staking service enables users to stake their assets and receive liquid staking tokens in return, which can be used for other purposes while still earning staking rewards. Ankr’s platform is designed to be user-friendly and accessible, focusing on ease of use and security.

Overall, these three projects represent some of the most promising and innovative approaches to liquid staking in cryptocurrency. While each platform has its strengths and weaknesses, they all share the goal of making staking more accessible and user-friendly for investors.

The Pros and Cons

As with any new technology, there are both benefits and drawbacks to using liquid staking platforms.

One of the main advantages of liquid staking is that it allows investors to earn staking rewards while still maintaining the ability to use their assets for other purposes. This makes it more flexible than traditional staking, which requires assets to be locked up for a set period. Additionally, liquid staking platforms typically offer higher staking rewards than traditional staking, making it more attractive to investors.

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However, there are also risks associated with liquid staking. One of the main concerns is the potential for smart contract vulnerabilities or other security issues that could lead to loss of funds. Another concern is the potential for market volatility, which could lead to significant price fluctuations in the underlying asset.

When it comes to specific platforms, there are also pros and cons to consider. For example, Lido has been praised for its ease of use and high staking rewards, but some users have raised concerns about the centralisation of the platform and the potential for censorship. 

Rocket Pool, on the other hand, has been praised for its scalability and decentralisation, but it is still in the early stages of development. It may not be as user-friendly as some other platforms.

The pros and cons of liquid staking will vary depending on the specific platform and the investor’s needs. While risks are involved, many investors have found that the benefits of liquid staking outweigh the potential drawbacks.

Closing Thoughts

Liquid staking is a modern concept in cryptocurrency that allows investors to earn staking rewards while maintaining control over their assets. 

Despite the risks noted in this article, the popularity of liquid staking is expected to continue to grow in the coming years. As more investors become interested in earning passive income from cryptocurrency investments, the demand for liquid staking platforms will likely increase. 

Additionally, as the technology continues to evolve, we are likely to see even more innovative approaches emerge, with a focus on improving security, scalability, and user experience.

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

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

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

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

What Is Messenger RNA?

Posted on by Jean Chalopin

The flow of genetic information is an essential process in biology that involves the transfer of genetic material from DNA to RNA to proteins. At the heart of this process is a molecule called messenger RNA (mRNA). 

Without mRNA, cells would not be able to create the proteins necessary for various cellular processes, and genetic information would not be able to flow from the nucleus to the cytoplasm.

This article discusses what mRNA is and its role in molecular biology. 

Defining Messenger RNA

Messenger RNA is a ribonucleic acid (RNA) molecule that plays a central role in the flow of genetic information in cells. mRNA molecules are transcribed from DNA in the cell nucleus and carry the genetic information encoded in the DNA to the ribosomes, the cellular organelles responsible for protein synthesis.

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The mRNA molecule is a single-stranded RNA molecule complementary to the DNA sequence from which it was transcribed. The sequence of the mRNA molecule is determined by the order of nucleotides in the DNA template strand, with each three nucleotides, called a codon, corresponding to a specific amino acid. The sequence of amino acids, in turn, determines the sequence of the protein that will be synthesised.

mRNA synthesis is initiated by binding an enzyme called RNA polymerase to a specific region of the DNA called the promoter. The RNA polymerase then unwinds the double helix structure of the DNA and begins transcribing the DNA sequence into a complementary mRNA molecule. As the mRNA molecule is synthesised, it is processed to remove non-coding regions called introns and join the coding regions, called exons, to create a mature mRNA molecule.

The mature mRNA molecule is then exported from the nucleus and travels to the cytoplasm, where it binds to ribosomes. The ribosome reads the sequence of nucleotides on the mRNA molecule in groups of three, each corresponding to a specific amino acid. As the ribosome moves along the mRNA molecule, it synthesises a protein by joining its amino acids in the order specified by the mRNA sequence.

mRNA in Therapeutic Intervention

Messenger RNA has become an essential tool in therapeutic intervention due to its ability to control protein expression and serve as a template for the production of specific proteins. mRNA-based therapies offer several advantages over traditional protein-based therapies and small molecule drugs.

First, mRNA-based therapies are highly specific, encoding the exact protein of interest. This specificity allows for targeted therapies that selectively block a disease-causing protein’s activity or replace a missing or defective protein.

Second, mRNA molecules are rapidly degraded in cells, allowing for precise control over the protein expression level. This feature makes mRNA-based therapies more flexible and adaptable than traditional protein-based therapies, where the protein dosage can be challenging to regulate.

Finally, mRNA molecules can be modified chemically to improve their stability, increase their efficiency, and target them to specific cells or tissues. These modifications allow for greater control over the delivery and distribution of the mRNA molecule and its encoded protein.

mRNA Vaccines

One of the most significant advances in messenger RNA-based therapies has been the development of mRNA vaccines, such as the Pfizer-BioNTech and Moderna COVID-19 vaccines. These vaccines use mRNA molecules encoding the spike protein of the SARS-CoV-2 virus to stimulate an immune response against the virus. 

The mRNA is encapsulated in lipid nanoparticles and delivered to cells, where it is translated into the spike protein. The immune system recognizes the protein as foreign and produces antibodies against it, protecting against infection.

Source

Messenger RNA-based therapies have also shown promise in treating various diseases, including cancer, where they can be used to induce the expression of tumour-suppressing proteins or target cancer cells for destruction by the immune system. In one recent study, mRNA was used to encode chimeric antigen receptor (CAR) T cells, genetically engineered immune cells that target and destroy cancer cells. The mRNA-encoded CAR T cells were highly effective in killing cancer cells in vitro and in mouse leukaemia models.

mRNA-based therapies offer a powerful new disease treatment and prevention approach, potentially revolutionising medicine.

mRNA Use Cases

There are several use cases for mRNA-based therapies.

Cancer Immunotherapy

In a study published in Nature, researchers used mRNA to program immune cells to attack cancer cells. The mRNA encoded a chimeric antigen receptor (CAR) that recognized and targeted cancer cells. When the mRNA was delivered to immune cells in vitro, the resulting CAR T cells were highly effective in killing cancer cells. The researchers suggest that this approach could be used to develop personalised cancer immunotherapies.

Source

Genetic Disease Therapy

In a study published in the New England Journal of Medicine, researchers used mRNA to treat a patient with cystic fibrosis. The mRNA encoded a functional copy of the CFTR gene, which is mutated in patients with cystic fibrosis. The mRNA was delivered to the patient’s lungs via nebulisation. The treatment improved lung function and reduced respiratory symptoms, suggesting that mRNA-based therapies could effectively treat genetic diseases.

Vaccine Development

The development of mRNA-based vaccines has been one of the most exciting recent applications of mRNA technology. The Pfizer-BioNTech and Moderna COVID-19 vaccines, which use mRNA to encode the spike protein of the SARS-CoV-2 virus, have been highly effective in preventing COVID-19 infection. 

A study published in the New England Journal of Medicine found that the Pfizer-BioNTech vaccine was 95% effective in preventing COVID-19 infection in clinical trial participants.

These case studies demonstrate the potential of messenger RNA-based therapies in various applications, including vaccines, cancer immunotherapy, and gene therapy.

Risks With mRNA

Despite the promise of messenger RNA (mRNA) as a powerful tool for therapeutic intervention, there are several risks and challenges associated with its use.

One of the primary concerns is the potential for off-target effects, where the mRNA molecule produces unintended proteins or triggers an immune response against normal cells. This risk can be minimised through careful selection and design of the mRNA molecule and its delivery system, but it remains a significant challenge in developing mRNA-based therapies.

Delivering mRNA molecules to their target cells presents another challenge. mRNA is a large, hydrophilic molecule that rapidly degrades in the bloodstream and other extracellular fluids. Delivering mRNA molecules to cells is essential for their effectiveness in protein translation. Researchers have developed several approaches to overcome this challenge, such as utilising lipid nanoparticles to protect mRNA molecules from degradation.

Another risk associated with mRNA-based therapies is the potential for immune system activation or adverse reactions. Though these are generally mild and short-lived, mRNA vaccines have been associated with side effects such as injection site pain, fever, and fatigue. More serious adverse events have been reported in rare cases, such as anaphylaxis.

Finally, there is a risk that the rapid degradation of mRNA molecules in cells could limit the effectiveness of mRNA-based therapies. mRNA molecules have a relatively short half-life in cells, which could limit the duration of protein expression and the therapeutic effect. However, this risk can be mitigated by using modified mRNA molecules that are more stable or by administering multiple doses of the mRNA over time.

Closing Thoughts

While mRNA-based therapies offer many exciting disease treatment and prevention possibilities, they also present significant challenges and risks. Careful consideration and planning will be required to maximise the potential benefits of mRNA-based therapies while minimising the risks.

However, recent case studies have demonstrated the potential of mRNA-based therapies in various applications, including vaccines, cancer immunotherapy, and gene therapy. The development of mRNA-based therapies represents an exciting frontier in biomedicine, with the potential to revolutionise how we treat and prevent disease.

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.

Drug Discovery and AI

Posted on by Jean Chalopin

There is very little that is efficient in drug discovery and development. Approximately 90% of medications that reach commercialisation fail. Each one costs more than $1 billion and takes ten years to develop.

However, technical developments in data collection are advancing artificial intelligence in drug discovery, which might open the door to discovering treatments for disorders that have eluded medical researchers for millennia.

How Does Drug Discovery Work? 

Drug discovery procedures are often drawn-out and laborious. Academic or industrial scientists create molecules. They search for ‘targets’ (such as proteins), where the molecule can go in the body to deliver treatment.

Researchers must ensure that the molecule doesn’t mistake a healthy protein for a target. Otherwise, a drug floating about in the body may bind to and destroy a healthy cell, causing a poisonous effect. Once a target is obtained, it is removed from the body and tested against molecules in the laboratory to see what sticks.

However, when clinical trials move forward, many of these medications fail due to unanticipated toxicity in the body or the drug itself not performing as well in people as it did in the lab. This is why most investments fail. 

Source

Fortunately, artificial intelligence (AI) can improve the efficiency of drug discovery. 

AI Improves Drug Discovery

Platforms for drug discovery can more accurately forecast the effects of drugs early on by utilising data. AI links molecules with targets and models how they will behave within the body, increasing the likelihood that they will survive clinical trials and reduce patient toxicity rates.

Although the impact of AI on conventional drug discovery is still in its infancy, when AI-enabled capabilities are added to a traditional process, they can significantly speed up or otherwise improve individual steps and lower the costs of conducting expensive experiments. AI algorithms can alter the majority of discovery jobs (such as the design and testing of molecules) so that physical trials are only necessary to confirm findings.

Pharma companies are partnering with AI drug discovery platforms, with Amgen and Generate Biomedicines announcing a deal worth up to £1.9 billion in 2022.  

Source

Pharma businesses need to prepare for a future in which AI is frequently utilised in drug research, given the revolutionary potential of AI. The applications are many, and pharma businesses must decide where and how AI can best contribute. 

New players are ramping up quickly and providing considerable value. In practice, this entails taking the time necessary to comprehend the full impact that AI is having on R&D. This includes separating hype from real accomplishment and realising the distinction between standalone software solutions and end-to-end AI-enabled drug discovery.

The AI-First Approach

There are five elements to an AI-first approach in drug discovery.

Vision and Strategy

Companies must create an AI roadmap that outlines specific, high-value use cases compatible with certain discovery initiatives. Focus and prioritisation are crucial. 

Businesses should choose a limited number of use cases that are dispersed throughout the various research stages. Otherwise, AI will be viewed as a sideline and not directly related to the company’s R&D strategy or financial objectives.

Technology and Data

Prior to creating a complete tool or platform, concentrate on developing a proof-of-concept algorithm: the bare minimum analysis that verifies your capacity to draw insightful conclusions from your data in a particular scientific environment. If the insights prove worthwhile, you can spend money industrialising the tool and improve the user interface.

AI Partnerships

To be the preferred partner for big AI players, pharma companies must adopt new behaviours and ways of working. Partnerships are a powerful strategy for accelerating AI discovery and building a true value proposition. 

Companies should consider how they share data, what their culture looks like in the context of AI, and how quickly they can adapt their business model to new technology. Although those things may not seem critical to the business, they will be for potential AI partners. 

Internal Resource Management

Data scientists and engineers are a unique breed. They do not always fit into companies and cultures that are primarily focused on medicine.

But pharmaceutical businesses need more than just expertise in data science and software. Senior decision-makers will probably need to be trained on how AI-generated recommendations are made if only to stop suggestions from being revalidated using conventional methods. To interpret and adequately test the results of the algorithms, medical scientists must be knowledgeable about the analytical methodologies required but not necessarily fluent in them.

Drug Discovery Datasets

Data is pivotal for successful AI and machine learning deployments. Large-scale datasets help to build models for machine learning that can evaluate whether molecules have investable potential. 

Researchers can quantify the strength of molecules in binding to a protein. Specific drug interactions and combinations do not react favourably and must be avoided by patients. Vast data volumes help to identify the positive and negative combinations quickly. 

Embedding AI Within Drug Discovery

AI represents a new era in drug discovery. Companies will need to couple a clear vision with a healthy amount of ambition to be successful. 

Choose an area to apply AI and be specific about the improvements you want to see. Decide whether to alter the discovery program using an AI-first model or to utilise AI for optimising the present discovery process. 

Further, it can be challenging to scale AI. Teams frequently adhere to well-established procedures and feel at ease using instruments with a long history of success. Businesses need to demonstrate their commitment to AI by focusing on complete processes and thoroughly reevaluating current operating methods. 

It’s best to include the entire organisation in the AI journey. In order to overcome hesitancy, management should emphasise the transformational vision, share value proofs and lessons from inside teams, and gradually develop a wave of enthusiasm.

AI is here for good, and it is for us to harness its power. What can be better than using AI to cure previously unsolvable diseases?

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

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

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

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

The Future of NFTs

Posted on by Jean Chalopin

NFTs had their breakout year in 2021, bringing to the art world a digital revolution. They became one of the year’s fastest-growing asset classes.  

Non-fungible token technology has allowed artists to offer digital originals while cutting out art broker intermediaries, also being able to receive royalties on their work’s secondary sales.  However, art is just the simplest of use cases of the growing functionality being realised with NFTs, blossoming into a new world of web 3.0.

NFTs have an evolving utility that is expanding daily. NFTs are already building communities, enabling novel and tradable assets for gaming, and providing the foundations for ownership and identity outside the coming metaverse. This article will delve into several aspects that NFTs will be used in our lives going forward and why they are beneficial in these roles.  

NFTs and Digital Ownership

Because of their blockchain-based immutable nature, NFTs provide a complete history and proof of ownership, what the art world calls provenance, for digital assets and for any other class that is represented by a non-fungible token. 

This functionality allows for the creation of unique digital assets or items that anyone can buy or sell freely with confidence in an open marketplace.

NFTs of today have already evolved, creating further utility spanning a variety of industries:

  • Digital community keys
  • Ownership of a username and assets in the metaverse
  • Ownership of game assets, including avatars and virtual real estate

As our online world shifts from web 2 to web 3, NFTs will form the foundations of digital communities, assets, and economies.

NFTs as Entry Keys

One of the first use case evolutions of NFTs is as a form of ‘membership pass’ for a digital community. The ownership of NFTs that were part of a collection, like the Bored Ape yacht Club (BAYC) or the CryptoPunks, became the keystones that were required for membership in the communities that holders built.  

More recently, the picture NFT collections such as Oni-Ronin have expanded on this idea, giving owners exclusive access to workshops and events as well as free airdrops of additional NFTs and even private raffles for a trip to Japan.

This type of entry is moving beyond the digital space, with NFTs now being used to provide their holders access to in-person events. Because NFTs are an immutable proof of ownership maintained on their blockchain, NFTs are in a position to solve some of the most common issues with event ticketing, such as digital theft and forging.  

Digital Identities and Assets Redefined

There is no need to worry about someone taking your username in the metaverse. Some NFTs already allow for the ownership of custom “.eth” Ethereum wallet addresses (Ethereum Name Service).

Courtesy of dune.com

So far, there have been nearly 3 million names created by over 600,000 participants.

Being in NFT form, these custom addresses can be integrated into other decentralised applications or Dapps, and they simplify the previously complex wallet addresses, allowing them to be personalised and much easier to remember. Rather than a long string of numbers and letters like ‘0x0078784ef055b06FC5A76B90c26’, it would be a much simpler address, like an email address or Instagram name such as ‘johnsmith.eth’.   

Additional projects, such as NFT.com, use NFTs to provide the custom ownership of a personal profile like www.nft.com/johnsmith. The owner can share and display their NFTs on a decentralised social media network.  

Courtesy of NFT.com

Tradable and Exportable NFTs

Gaming is a nascent sector where NFTs are already proving their utility. NFTs allow players to own in-game assets. There are some crucial differences between the typical ‘owning’ of assets in games and what NFTs provide. Projects such as DeFi Kingdoms, which is on the Harmony blockchain, have their own ‘NFT heroes’. These heroes can be bought, sold, and even rented out on an open market. 

Along with providing ownership of these in-game assets, these Heroes can be productive assets. They can be sent on quests and earn in-game items (also in NFT form). The gained in-game assets can be exchanged for cryptocurrency or used to build other items to ‘power up’ the heroes.

The integration of NFTs into blockchain-based games like DeFi Kingdoms, Axie Infinity, and Crabada have created new and vibrant in-game economies where the NFTs are valued based on their attributes and statistics. The amount of time played is rewarded by these games, resulting in increased earnings and greater chances of finding rare item drops. 

The Metaverse Economy

Beyond usernames, Ethereum wallet addresses and in-game characters, NFTs are becoming the technology used as a foundation for assets in the metaverse. The Sandbox already uses NFTs to represent the ownership of digital land, virtual spaces, as well as furniture, décor, and other metaverse assets. In November of 2021, the Sandbox saw a peak monthly sale of NFT assets totaling $47.4 million changing hands. 

However, transactions have since plummeted to only about 1.1-1.2 million per month. Buyers have run the gambit of companies and celebrities, including Snoop Dogg, the South China Morning Post, and Atari, all purchasing their own real estate within The Sandbox’s metaverse. 

NFTs have only just started to revolutionise the ownership and trading of digital assets, providing the foundations of digital communities and blockchain gaming, but they are poised to move well beyond these digital borders. 

Blockchain is the Key

NFT’s utility is based on the use of blockchain tech. These decentralised digital ledgers are almost impossible to hack or alter. Beyond their use in proving the ownership of unique digital assets, NFT technology has nearly limitless applications beyond 8-bit art and in-game swords.  

It is easy to imagine a world with a deed to a home existing as an NFT. Rather than having to conduct a title search every time a property is sold, the NFT deed would be a ledger of all changes, showing who is the current owner, when they took ownership, from whom, and the price paid. Closing would be as simple as fulfilling the requirements of a smart contract.

Such a process would be much more secure–no one could forge the ownership of a home because the log of ownership would be transparent and unalterable on the blockchain.

Not Just Real Estate

The real-life applications go far beyond real estate. NFTs would be helpful in any environment where the ownership of something should be tracked and proven. Rather than keeping paperwork needed to prove that you purchased and have ownership of something, an NFT could provide a record of the ownership history of an item and could be used for either a sale or a warranty.  

NFTs could be applied to the bidding process of any job or project, ranging from simple gig work to government infrastructure projects. NFTs can also allow for built-in timekeeping and pricing mechanisms, which can make them a digital work order which can be changed in real-time as a job progresses.  

A prospective college student could mint an NFT which represents their application profile, allow colleges to bid on them, offering admission and scholarships, turning the college acceptance process on its head.  

A Secure Transaction Platform

Paper-based legacy transactions are called red tape for a reason. They are inefficient, require human intervention, and can be misplaced or lost. However, paper transactions have one advantage over more recent cloud-based documentation that is being used today; a paper document’s authenticity is often easier to prove. Cloud-based documents have the potential to be altered, hacked, or duplicated, costing companies millions in losses yearly.  

NFTs can solve both of these problems. They provide documentation and digital transactions with a new layer of security while concurrently improving transaction efficiency. Those involved with the transaction can see the life of the NFT from creation to the current version.

NFTs form a virtually unhackable, encrypted system that is easily distributed and unalterable. Identity theft could be greatly reduced or eliminated. The NFT’s underlying asset is tracked and verifiable, providing confidence and security. 

Closing Thoughts

Widespread NFT adoption could bring us many benefits. As businesses incorporate more blockchain technologies into their operations and a wider adoption happens among consumers, the sum of benefits is hard to limit.

NFTs will likely become the ‘how’–how we are identified, how we transfer personal data, and how we engage in digital commerce, particularly as the metaverse increases in popularity. Instead of overpriced art, NFTs will be seen as digital objects bringing much-needed ease to everyday business and to our daily lives.   

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.deltecbank.com.

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.deltecbank.com.

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.

Smart Cities

Posted on by Jean Chalopin

Smart cities are urban areas that combine technology and data to improve the quality of life of inhabitants and visitors, increase sustainability, and create more efficient systems to be used by all. 

The smart city concept has been around since the 1970s. However, it has only recently gained significant traction due to technological advancements and the increasing focus on sustainability and efficiency.

Understanding Smart Cities

One of the critical components of a smart city is the use of technology to gather and analyse data. This data can be used to optimise city services such as transportation, energy, and waste management. 

For example, data can be obtained and processed to optimise traffic flows, reduce energy consumption, and reduce waste. This leads to a more efficient and sustainable city, which ultimately benefits the residents who live there.

Image courtesy of Tech Target

Smart cities also aim to improve the quality of life for their citizens by making the city more accessible and livable. This goal is achieved by improving the city’s infrastructure and services, such as public transportation, healthcare, and education. 

For example, a smart city might have a network of sensors and cameras that monitor air quality and traffic patterns, allowing city officials to respond to problems more quickly by rerouting traffic to less congested areas. 

Origins of the Smart City

The concept of “smart cities” has existed for several decades, but it has evolved since its origins and become more widespread with recent technological advances. However, the intelligent city idea goes back to the 1970s, with Los Angeles’ first urban big data project named: ‘A Cluster Analysis of Los Angeles’.

It isn’t easy to pinpoint a single person or organisation as the originator of the ‘smart city’ term. Smart cities as a term first appeared in the 1990s and were defined with several definitions that included six dimensions to measure a smart city’s development:

· Smart people

· Smart economy

· Smart governance

· Smart mobility

· Smart life

· Smart environment

Another early pioneer in smart cities is Enrique Peñalosa, the former Mayor of Bogotá, Colombia, from 1998-2001 and from 2016-2019. Peñalosa introduced innovative urban development and transportation policies, including creating a bike lane network and implementing a bus rapid transit system.

In recent years, innovative city development has been driven by private sector companies, such as IBM, Siemens, and Cisco, as well as government initiatives and research organisations. For example, IBM was the first company to use the term ‘Smart City’ in their Smarter City Challenge program, which developed their centralisation of data vision of urbanisation with a security focus that crosses the world.

The Smart City Council, a global organisation focused on promoting the development of smart cities, was founded in 2012 and has become a leading voice in the field.

Overall, the idea of smart cities has been developed and shaped by several individuals, organisations, and governments over the years and continues to evolve as technology advances and urban populations continue to grow.

The Smartest Cities

One example of a flourishing smart city is Amsterdam in the Netherlands. Amsterdam has implemented several innovative smart city initiatives, including a smart grid that optimises energy consumption, a smart transportation system that reduces congestion and improves traffic flow, and a smart waste management system that reduces waste and increases recycling. These initiatives have helped Amsterdam to become a more efficient and sustainable city while also improving the quality of life for its residents.

Another example is Singapore, which has been named one of the world’s smartest cities. Singapore has implemented several smart city initiatives, including a smart transportation system that uses technology to optimise traffic flow and reduce congestion. 

Additionally, Singapore has implemented a smart energy grid that uses data to optimise energy consumption and reduce waste. These initiatives have helped Singapore to become a more sustainable and efficient city while also improving the quality of life for its residents.

Dubai’s Smart City project has adopted a strategy calling for the transformation of around 1,000 government services, focusing on the following six key sectors: 

· Transportation

· Infrastructure

· Communications

· Economic services

· Urban planning

· Electricity

Dubai implemented many initiatives within the above six sectors, which fall under the following categories:

· Simple and open access to data

· Smart transportation

· Optimising energy resources

· Smart parks and beaches

· Smartphone apps for policing

· New designated master control room

The Challenges

While the benefits of smart cities are clear, some significant challenges make smart city development and implementation difficult. One critical challenge is privacy and security. The use of technology and the creation of data in a smart city means that a large amount of personal information is being collected, which raises significant privacy concerns. 

In addition, because of this data and the control these systems have over the lives of so many, there is a risk of hacking and cyber-attacks, which could compromise the security of the city’s systems. Imagine the disruption of water, power, traffic, or other city systems by a nefarious actor for criminal or terror reasons.

Another challenge must be dealt with is the high cost of implementing smart city initiatives. The technology and infrastructure required to create a smart city can be expensive, and there is a legitimate risk that the costs of such an implementation could outweigh the benefits. 

The Future of Smart Cities

The future of smart cities is promising and exciting as technology advances and urban populations grow. With the rise of several new technologies, including the Internet of Things (IoT), 5G networks, and artificial intelligence, smart cities have the potential to become even more efficient, sustainable, and livable in the years to come.

One of the key areas where smart cities are likely to continue to evolve is transportation. Self-driving cars, intelligent traffic management systems, and connected transportation networks are just a few examples of how technology will continue revolutionising how we move around cities. This will not only make transportation more efficient, but it will also help to reduce congestion and improve air quality.

Another area where smart cities are likely to evolve is in the area of energy management. With the increasing focus on sustainability and the need to reduce carbon emissions, smart cities will likely continue investing in renewable energy sources such as solar and wind power. Additionally, smart cities will likely be at the forefront of developing more efficient energy systems, using data and technology to optimise their energy consumption and reduce waste.

The development of smart cities is also likely to impact how their inhabitants live and work significantly. With the rise in popularity of the gig economy and the increasing number of remote workers, smart cities will have to adapt to accommodate these changes. These developments may include developing coworking spaces, flexible housing options, and integrating technology and connectivity into public spaces.

While the future of smart cities is exciting, some challenges must be addressed. One of the toughest challenges is ensuring the technology and infrastructure are secure and protecting citizens’ privacy. 

In addition, with the ever-increasing amount of data being collected by smart cities, how this data is used will significantly impact the perception of its collection by the city’s residents. Therefore, it is essential that this data is stored and used responsibly and safeguards are in place to protect against hacking and cyber-attacks.

Closing Thoughts

The future of smart cities is exciting and holds great promise for the nearly 5 billion of us that live in urban areas. With advancements in technology and the increasing focus on sustainability and efficiency, smart cities have the potential to become even more livable, efficient, and sustainable in the years to come. 

However, it is essential that smart city challenges of privacy, security, implementation costs, and inclusiveness are addressed and that smart city initiatives are implemented sustainably. 

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

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

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

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

AI in Healthcare

Posted on by Jean Chalopin

When you think about technological breakthroughs from history, the full promise is never what it initially does but what it eventually enables. If you go as far back as the steam engine, it cost far more than other power sources when first commercialised. However, as soon as it enabled faster transportation and cheaper product shipping, suddenly, it did not seem so expensive. 

AI in healthcare is the modern-day steam engine. Although applications are still relatively sparse, the fourth industrial revolution of data and digital is starting to enable the new future. 

Source

The market for artificial intelligence in healthcare, estimated to be worth USD 10.4 billion in 2021, is anticipated to increase at a CAGR of 38.4% from 2022 to 2030. Key factors propelling the market’s expansion are the expanding datasets of digital patient health information, the desire for individualized treatment, and the rising demand for lowering healthcare costs.

The Current State of AI in Healthcare

Despite having the highest healthcare spending in the world, the United States now has inferior individual health outcomes than most other industrialised countries.

People of all generations need healthcare that is tailored to their requirements. Millennials want to be able to order their meals and receive medical advice from the same place—their sofa. In contrast, groups like the baby boomer generation take a totally different tack. 

They are far more likely to want a primary care physician, so we can move away from these systems’ one-size-fits-all approach to actual care delivery–toward leveraging data and AI to genuine care.

For AI to be successful in the 21st century, there are three vital components.

Responsibility

Sometimes, problems are unsuitable for AI; deciphering intent is paramount. Similarly, poor data and algorithm management might unintentionally introduce biases into analyses, with negative consequences for people.

Competence

Innovations must function, and the health ecosystem must agree on what constitutes an acceptable margin of error. The same forgiveness that is extended to a human physician who makes a single error is not extended to computer systems that prescribe cancer therapies.

Transparency

Being open about the limits of data and AI in healthcare can aid in the maintenance of confidence in the face of imperfect performance.

Early adopters of AI in healthcare have already enabled breakthroughs paving the way for a shift from scepticism to a beginning of trust, as well as a jump from efficiency to better efficacy.

Use Cases for AI in Healthcare

There are several ways in which AI is influencing the healthcare sector. 

Medical Diagnoses

Misdiagnosis is a significant problem in the healthcare industry. According to recent research, around 12 million people in the United States are misdiagnosed yearly, with cancer patients accounting for 44% of them. AI is assisting in overcoming this problem by increasing diagnostic accuracy and efficiency.

AI-enabled digital medical solutions, such as computer vision, provide accurate analysis of medical imaging, such as patient reports, CT scans, MRI reports, X-rays, mammograms, and so on, to extract data that is not apparent to human eyes.

Source

While AI can analyse most medical data quicker and more accurately than radiologists, it is still not sophisticated enough to replace radiologists.

Automation in Patient Care

Poor communication is seen as the worst aspect of the patient experience by 83% of patients. AI can assist in overcoming this obstacle.

AI can automate reminders, payment issues and appointment management. Clinicians can spend more time caring for patients than doing administrative work. AI can also do a lot of the background work of analysing data and ensuring patients are assigned to the correct doctor or department. 

AI in Surgery

Healthcare robot AI is making procedures safer and smarter. In complex surgical operations, robotic-assisted surgery allows doctors to attain more precision, safety, flexibility, and control.

It also allows for remote surgery to be conducted from anywhere in the world in locations where surgeons are not available. This is especially true during worldwide pandemics when social distance is required.

Source

The primary benefits of robotic surgery include the following:

  • Reduction in hospital stay time after a procedure
  • Reduced pain relative to human-operated surgery
  • Decreased chance of post-surgery complications

Sharing Medical Data

Another advantage of using AI in healthcare is its capacity to handle enormous volumes of patient data.

Diabetes, for example, affects more than 10% of the US population. Patients may watch their glucose levels in real-time and get data to manage their progress with doctors and support personnel using tools like the FreeStyle Libre glucose monitoring device driven by AI.

Research and Development

AI has a wide range of applications in medical research. It can help to find new drugs or repurpose existing ones. In this example, AI was used to analyse cell images and understand which were most effective for patients with specific diseases. A conventional computer is slow to spot the differences that AI can find in seconds. 

Staff Training

AI tutors can provide instant feedback to students, allowing them to learn skills safely and effectively. In the example, students could learn skills 2.6 times faster and 36% better than those who are not taught with AI.  

Virtual patients can help with remote training. During the pandemic, AI supported skill development remotely when group gatherings were impossible. 

AI-based apps are being created to aid nurses in various ways, including decision support, sensors to alert them of patient requirements, and robotic assistance in difficult or dangerous circumstances.

Overcoming Challenges with Healthcare AI

There are some best practices to follow for healthcare sector incumbents to overcome the barriers associated with AI and seize the opportunities. 

First, systems must be explainable. You don’t want to be in a position where an AI system detects cancer, and the radiologist cannot explain the decision. Prioritise building hybrid explainable AI.

AI-powered medical diagnoses are accurate but not flawless. AI systems can make mistakes that have profound implications. More testing of your AI models is a smart strategy to improve accuracy and reduce false positives. 

Due to privacy and ethical limitations in the healthcare industry, gathering training medical data might be complex. Even when automated, this procedure can be costly and time-consuming. Investing in privacy-enhancing technology can help reassure users that their data is safe when acquiring and processing sensitive medical data.

Another critical obstacle to adopting AI in healthcare is patient resistance. At first sight, robotic surgery may frighten patients, but their reservations may dissipate when they learn about the benefits. To solve this dilemma, patients must be appropriately educated.

Closing Thoughts

Clinicians need to become aware of the potential of this new technology and grasp that the world is changing. It is readily adapting AI to improve the patient experience, to eliminate errors, and to ultimately save more lives. 

In a human-centric field such as medicine, AI can never fully replace doctors–their care, empathy, touch, and years of experience. What AI can do, today, is eliminate the barriers to delivering care in a globalising, rapidly growing world that is falling behind with its healthcare. 

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

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

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

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

AI and Space Exploration

Posted on by Jean Chalopin

Artificial intelligence (AI) has improved our terrestrial living standards for decades. However, can these practical computer algorithms be applied to applications beyond our planet, and if so, how can AI assist us in our space missions and interstellar exploration?

AI can help astronauts and ground-based space operations. AI is already becoming a vital component of space travel and its exploration, helping conduct tasks humans would otherwise be unable to perform while in space, such as the analysis of cosmic occurrences, system controls, the charting of stars, black holes, and more.  

Many agencies and companies, like NASA, the European Space Agency (ESA), SpaceX, and Google, already use AI to find new celestial objects and improve astronauts’ lives in space. We will look at how AI is being used to aid in space exploration and what the future of Ai in space will bring.

Understanding AI

AI is a set of computer programs designed to match the thinking of humans. AI can be used to build ‘smart machines’ that perform various tasks that would otherwise require humans and their intelligence to run, in some cases much faster than a team of humans.

AI-Driven Rovers

NASA has already built autonomous rovers (such as the Perseverance rover) that use AI to complete their tasks and overall mission. These rovers can roam a planet’s surface, currently on Mars, and they are using AI to make decisions about the best routes to avoid obstacles and not require the earth-based mission control’s permission. Autonomous rovers are integral to some of the most important discoveries made on Mars.

The Perseverance rover, courtesy of NASA

Robots and Assistants

A larger field of AI is called natural language processing (NLP), which involves programming computers to understand speech and text. A subfield within NLP is called sentiment analysis, also called emotional AI or opinion mining. 

Sentiment analysis is the foundation of intelligence-based assistants designed to support astronauts’ future missions to our Moon, Mars, and beyond. So while science fiction fans may be worried about 2001’s Hal-like problems, there will be failsafe mechanisms in place, and these assistants will significantly benefit the crew.  

AI assistants will be used to understand and anticipate a crew’s needs, including their mental health and emotions, to take action in daily activities and emergencies. Moreover, robots will help astronauts with physical tasks such as docking or landing the spacecraft, repairs that would require a spacewalk and its elevated risk, and much more.   

Intelligent Navigation Systems

We use GPS-based navigation systems like Google and Apple maps to define and explore our planet. However, we don’t have a similar tool that we can utilise for extraterrestrial objects and travel. 

As a result, space scientists have had to get creative without GPS satellites orbiting around Mars and the Moon. In collaboration with Intel in 2018, NASA researchers developed their intelligent navigation system that allows for non-earth navigation, first on the Moon, and is intended to train it to explore other planets. The model was trained using millions of photos from several missions, allowing it to create a virtual moon map.  

Processing Satellite Data

Satellites can produce massive amounts of data. For example, the Colorado-based space tech company, Maxar Technologies, has image data of 110 petabytes and adds about 80 terabytes to this daily. 

AI algorithms process such data efficiently. Machine learning algorithms study millions of images in seconds, analysing any changes in real time. Automating this process using AI allows satellites to take images independently when their sensors detect specific signals.  

In the UK, Leeds University researchers analysed the ESA’s Gaia satellite image data, applying machine learning techniques, and found over 2000 new protostars. Protostars are infant stars in the process of forming within dust and gas clouds. 

AI also aids in remote satellite performance prediction, health monitoring, and informed decision-making.  

Mission Operations and Design

AI can aid space missions by conducting autonomous operations. An Italian start-up, AIKO, developed its MiRAGE software, a library to enable autonomous space mission operations, as a part of the ESA’s tech transfer program.

Courtesy of the European Space Agency

MiRAGE allows a spacecraft to conduct autonomous replanning while detecting internal and external events and then take the appropriate action so that the ground-based decisions do not affect the overall mission objectives.  

AI and machine learning can be utilised to evaluate operational risk analysis to determine safety-critical missions. Risk mitigation systems can also process vast amounts of data from normal operations and previous performance. After training a model to identify and classify risk, it can conduct a risk assessment and make recommendations or take action in real time.  

Mission Strategy

During the Perseverance mission, the ‘Entry, Descent, and Landing’ or EDL flight dynamics team relied on an AI for both scheduling systems and mission planning to get through the ‘7 minutes of terror’ when the craft entered the Martian atmosphere until it touched down; the lag time for radio signals made it impossible to steer the craft manually from the earth.  

Courtesy of NASA

Engineers and scientists see scheduling as an excellent task for AI to help with, as these systems need precise planning and would otherwise demand excessive human resources. Spacecraft can be programmed to determine how to execute commands autonomously according to specified functions based on past data and the current environment.

Location of Space Debris

The European Space Agency has stated that 34,000 objects larger than 4 inches threaten the existing space infrastructure. The US’ Space Surveillance Network is tracking 13,000 of these objects. Satellites deployed in the low Earth orbit can be designed to prevent becoming space debris by completely disintegrating in a controlled way. 

Researchers are actively working to prevent the possibility of satellites colliding with space debris. Collisions can be avoided by designing collision avoidance manoeuvres or building machine-learning models to transmit the processes to in-orbit spacecraft, improving decision-making.  

Likewise, pre-trained neural networks onboard a spacecraft can help guarantee the spaceflight’s safety, allowing for increased satellite design flexibility while minimising orbit collisions.

Data Collection

Like Maxar Technology’s image data, AI automation will aid in optimising the vast amount of data collected during scientific missions such as deep space probes, rovers, and Earth-observing craft. AI will then be used to evaluate and distribute this data to the end users. 

Using spacecraft-installed AI, it will be possible to create datasets and maps. In addition, AI is excellent at finding and classifying regular features, such as common weather patterns, and differentiating them from atypical patterns, such as volcanic-caused smoke.

How can we determine what data needs to be provided to the end users to process? AI can minimise or eliminate unimportant data, allowing networks to work more efficiently, transmitting cascading important data, with essential data having a priority, and keeping the data stream running to capacity.  

Discovery of Exoplanets

The Kepler Space Telescope was designed to identify and determine the frequency of Earth-sized planets that orbit sun-like stars, looking for Goldilocks zone planets. This process requires precise and automatic candidate assessment, accounting for the low signal-to-noise ratio of far-away stars. 

Google and other scientists developed the AstroNet K2 convolutional neural network (CNN) to solve this issue. The K2 can establish whether or not Kepler’s signal is an actual exoplanet or a false positive. After training, the AI model was 98% accurate, and it found two new exoplanets, named the Kepler 80g and 90i, which circle the Kepler 80- and 90-star systems.  

Closing Thoughts

AI has the potential to do many things that a human could not. They can also produce solutions to problems quickly and allow for decisions to be made autonomously that would require significant human power to complete. AIs are a way for us to continue to explore beyond our atmosphere and soon beyond our solar system.

A Solar Trip

AI will be helping NASA’s Parker Solar Probe, which will explore our Sun’s atmospheric corona. In December 2024, the probe will come within 4 million miles of the Sun’s surface. It will need to withstand temperatures up to 2500℉ and will help us learn how our Sun interacts with planets in our solar system, using its magnetometer and an imaging spectrometer. In addition, there is a goal to understand solar storms that can disrupt our current communication technologies. 

Robonauts

We will likely see AI space assistants alongside astronauts or robots conducting deep space missions to new planets. Currently, NASA is working with SSL (formerly ‘Space Systems Loral’) to test how AI can be used to reach beyond our solar system. 

Our decisions with AI allow for more risky missions and testing. These kinds of missions will enable us to make discoveries that will change human life and our future. 

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

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

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.deltecbank.com.

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

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