Category: Sustainability

Longevity and the Future

Posted on by Jean Chalopin

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

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

The Science of Longevity

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

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

Lifestyle Habits

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

Source

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

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

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

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

Genetic Research

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

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

Source

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

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

Epigenetics Research

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

Source

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

Stem Cell Research

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

Source

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

Nutrition Research

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

Case Study in Okinawa

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

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

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

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

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

Potential Future Developments

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

Anti-Aging Drugs

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

Gene Editing

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

Regenerative Therapies

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

Artificial Intelligence

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

Ethical Considerations

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

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

Closing Thoughts

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

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

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.

Can Robots Become Sentient With AI?

Posted on by Jean Chalopin

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

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

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

Robots and AI

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

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

Source

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

What Is AI Sentience in Robotics?

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

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

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

Source

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

How Does AI Sentience Work?

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

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

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

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

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

Why AI Sentience? 

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

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

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

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

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

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

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

The Risks and Challenges

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

Technical Risks and Challenges

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

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

Ethical Risks and Challenges

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

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

Social Risks and Challenges

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

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

Closing Thoughts

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

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

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

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.

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 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.

Blockchain and Supply Chain Management

Posted on by Jean Chalopin

One industry for which blockchain tech has been particularly beneficial is the management of global supply chains. With more connected devices, this will become even more prevalent. This article will introduce the basics of supply chain management, and then explain how blockchain technologies aid in its optimisation. 

Supply Chain Management

The goal of all supply chain management is to streamline a company’s supply-side operations, from the planning to its after-sales services, to reduce costs and enhance overall customer satisfaction. 

Supply chain management, or SCM, is the control of the complete production flow, beginning with raw materials and ending with the final product or service at the destination. SCM also handles material movement, information storage and movements, and finances associated with the goods and services.

While supply chain and logistics can be confused, logistics is only one part of the complete supply chain. Supply chain management traditionally involves the steps of planning, sourcing, production, delivery, and post-sale service for the central control of the supply chain. 

That said, the SCM process begins with selecting suppliers that source raw materials that will eventually be used to meet the needs of customers. Next, a decision of, if the manufacturer will deliver themselves or outsource these tasks. And once delivered, the seller must decide if and what after-sales services will be provided, such as return and repair processing, which may or may not be needed to ensure customer satisfaction.  

Courtesy of All Things Supply Chain

Modern SCM systems have software management helping to decide everything from goods creation, inventory management, warehousing, order fulfilment, product and service delivery, information tracking, and after-sales services. 

Amazon, for example, uses numerous automated and robotic technologies to store goods in the warehouse as well as pick and pack orders for shipment. They are now beginning to use drones to deliver packages weighing less than five pounds in selected test regions.  

Supply Chain Evolution

The digital supply network is beginning to combine new technologies, like artificial intelligence (AI), blockchain, and robotics, into the supply chain, adding additional information from several sources to deliver valuable data about goods and services along the supply chain.

The supply chain starts with a strictly physical and functional system but then links to a vast network of data, assets, and activities. By using AI algorithms, businesses are now extracting insights from massive datasets to manage their inventory proactively, automate warehouses, optimise critical sourcing connections, reduce delivery times, and develop customer experiences that will increase satisfaction.

Additionally, AI-controlled robots can help automate manual tasks such as picking and packing orders, delivering raw materials and manufactured goods, moving items during distribution, and scanning boxed items.  

Amazon claims that by using its robots, it can hold 40% more inventory, which allows it to fulfil its on-time Prime shipping commitments.  

Blockchain’s Impact on Supply Chain Management

Blockchain-based supply chains differ from traditional supply chains, and they can automatically update the transaction data when a change occurs. This attribute enhances traceability along all parts of the supply chain network.  

Blockchain-based supply chain networks excel with private-permissioned blockchains carrying limited actors rather than public, open blockchains that are better suited for financial applications.

There are four key actors in blockchain-based supply networks:

1.     Standard organisations. These develop blockchain rules and the technical standards, such as Fairtrade, to create environmentally friendly supply chains.

2.     Certifiers. These certify individuals for their involvement in supply chain networks.

3.     Registrars. These provide network actors with their distinct identities.

4.     Actors. These are producers, sellers, and buyers that participate on the blockchain that are certified by a registered auditor or certifier in order to maintain the system’s credibility.

Key actors in a blockchain-based supply chain courtesy of Cointelegraph

Ownership of a product and its transfer by a blockchain actor is a fascinating feature of the structure and flow of a blockchain-based supply chain. But we must ask if blockchain-based supply chain management makes the system more transparent?

As the related parties are required to fulfil the conditions of smart contracts and then validate them before transfers or exchanges are complete, ledgers are updated with all the transaction information after the participants have completed their duties and processes. This system means that there is a persistent layer of transparency in any one blockchain-based supply chain.

Further, the chain can specify the nature, quality, quantity, location, product dimensions, and ownership of the goods transparently. This results in a customer having a view of the continuous chain of custody, potentially from raw materials to final sale.

Blockchain-Based Traceability

When referring to supply chains, traceability is the capacity to pinpoint previous and current inventory locations and a record of product custody. Traceability involves tracking products while they move through a convoluted process, from raw material sourcing to merchants and customers, often passing through several geographic zones.

Traceability is a significant benefit of blockchain-driven supply chain innovation as a blockchain consists of a decentralised open-source ledger recording data. This ledger is replicable among users, and transactions happen in real-time.  

The result is a blockchain-built supply chain that is smarter and more secure because it means that products can be tracked through a robust audit trail. Concerned parties can access the origin, price, date, quantity, destination, certification, and additional data using a blockchain.

By connecting supply chain networks through a decentralised system, blockchain has the potential to enable frictionless movement between suppliers and manufacturers.

Benefits of blockchain-based traceability, courtesy of Cointelegraph

Producers and distributors can record information such as the product origin, quality, purity, and nutritional value securely using the collaborative blockchain network. Additionally, having access to the product history gives buyers further assurance that the items purchased are from reputable producers, making the supply chain more sustainable.

Finally, if any health concerns or non-compliance with safety standards issues are discovered, the needed action can be taken against the manufacturer, aided by the information stored on the blockchain’s ledger. 

Tradeability

Blockchain technology in SCM has a unique advantage over traditional supply chains, which is tradeability. Blockchain platforms can ensure tradeability by using tokenized assets.  Blockchain tokenization converts a tangible asset, digital asset, product, or even a service, into a token on the blockchain. A token is a thing that digitally represents ownership of that single product that it tracks, and the token can be exchanged in that market.  

Blockchain participants can transfer ownership of these tokens without needing to exchange the physical assets because they are tradeable. Additionally, automated smart contract payments can help identify ownership of licence software, services, and products accurately and immutably on the blockchain. 

Ownership consensus is provided via blockchain participants. There is no disagreement over transactions on the chain by design. Every entity on the chain uses the same ledger version. There are no disagreements possible, the ledger is the rule of law. 

Companies prefer the tokenisation of assets over direct payments in fiat currency because smart contracts enable peer-to-peer payments, which are generally faster and more cost-effective than traditional currency transfers. Also, token payments prevent fraudsters from using chargeback situations and stealing from companies. 

Closing Thoughts

The demand for blockchain-based supply chains is related to the need for information demanded by the supply chain’s participants, as is the case for the production of goods using ethical standards. Blockchain tech in supply chain management can address concerns that traditional supply chains cannot manage, or require the preparation of burdensome paperwork or certifications.  

Additionally, a decentralised, immutable record of organisations and transactions combined with the digitisation of physical assets makes it possible to track products all along the supply chain from source to manufacturing, and then to delivery to the final consumer.

Like all things blockchain and crypto, blockchain-based supply chains have yet to reach mainstream adoption. Because blockchain technology remains in its infancy stage, it is governed by different laws for each nation, affecting the supply networks.  

Despite these barriers, we expect blockchain-based solutions to replace conventional supply chain networks. Large companies have shareholders that demand sustainability and ethical sourcing information, as well as cost savings. The benefits of blockchains will push businesses toward their use for supply chains, and they will likely become the more common management solution. 

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.

Artificial Intelligence and Biomedicine

Posted on by Jean Chalopin

Two unlikely interweaving sciences, artificial intelligence and biomedicine, have changed our health and lives. These two sciences have now intertwined further, aiding scientists, medical professionals, and, ultimately, all of us to improve our ongoing health so we can live better lives. This article will introduce some of the ways these two sciences are working together to solve medical mysteries and problems that have plagued us for generations.

Combining With Artificial Intelligence

The field of biomedical sciences is quite broad, dealing with several disciplines of scientific and medical research, including genetics, epidemiology, virology, and biochemistry. It also incorporates scientific disciplines whose fundamental aspects are the biology of health and diseases. 

In addition, biomedical sciences also aim at relevant sciences that include but are not limited to cell biology and biochemistry, molecular and microbiology, immunology, anatomy, bioinformatics, statistics, and mathematics. Because of this wide breadth of areas that biomedical sciences touches, the research, academic, and economic significance it spans are broader than that of hospital laboratory science alone.  

Artificial intelligence, applied to biomedical science, uses software and algorithms with complex structures designed to mirror human intelligence to analyse medical data. Specifically, artificial intelligence provides the capability of computer-trained algorithms to estimate results without the need for direct human interactions. 

Some critical applications of AI to biomedical science are clinical text mining, retrieval of patient-centric information, biomedical text evaluation, assisting with diagnosis, clinical event forecasting, precision medicine, data-driven prognosis, and human computation. 

Medical Decision Making

The Massachusetts Institute of Technology has developed an AI model that can automate the critical step of medical decision-making. This process is generally a task for experts to identify essential features found in massive datasets by hand. 

The MIT project automatically identified the voicing patterns of patients with vocal cord nodules (see graphic below). These features were used to predict which patients had or did not have the nodule disorder.

Courtesy of MIT

Vocal nodules may not seem like a critical medical condition to identify. However, the field of predictive analytics has increasing promise, allowing clinicians to diagnose and treat patients. For example, AI models can be trained to find patterns in patient data. AI has been utilised in sepsis care, in the design of safer chemotherapy regimens, to predict a patient’s risk of dying in the ICU or having breast cancer, among many others.

Optoacoustic Imaging

At the University of Zurich, academics use artificial intelligence to create biomedical imaging using machine learning methods that improve optoacoustic imaging. This technique can study brain activity, visualise blood vessels, characterise skin lesions, and diagnose cancer. 

The quality of the images rendered depends on the number of sensors used by the apparatus and their distribution. This novel technique developed by Swiss scientists allows for a noteworthy reduction in the number of sensors needed without reducing the image quality. This allows for a reduction in the costs of the device and increases the imaging speed allowing for improved diagnosis. 

To accomplish this, researchers started with a self-developed top-of-the-end optoacoustic scanner with 512 sensors, which produced the highest-quality images. Next, they discarded most of the sensors, leaving between 32 and 128 sensors. 

This had a detrimental effect on the resulting image quality. Due to insufficient data, different distortions appeared on the images. However, a previously trained neural network was able to correct for these distortions and could produce images closer in quality to the measurements obtained with the 512-sensor device. The scientists stated that other data sources could be used and enhanced similarly.  

Using AI to Detect Cancerous Tumours

Scientists at the University of Central Florida’s Computer Vision Center designed and trained a computer how to detect tiny particles of lung cancer seen on CT scans. These were so small that radiologists were unable to identify them accurately. The AI system could identify 95% of the microtumors, while the radiologists could only identify 65% with their eyes.

This AI approach for tumour identification is similar to algorithms used in facial recognition software. It will scan thousands of faces, looking for a matching pattern. The University group was provided with more than 1000 CT scans supplied by the National Institutes of Health with the Mayo Clinic collaboration. 

The software designed to identify cancer tumours used machine learning to ignore benign tissues, nerves, and other masses encountered in the CT scans while analysing the lung tissue.  

AI-Driven Plastic Surgery

With an always-increasing supply of electronic data being collected in the healthcare space, scientists realise new uses for the subfield of AI. Machine learning can improve medical care and patient outcomes. The analysis made by machine learning algorithms has contributed to advancements in plastic surgery. 

Machine learning algorithms have been applied to historical data to evolve algorithms for increased knowledge acquisition. IBM’s Watson Health cognitive computing system has been working on healthcare applications related to plastic surgery. The IBM researchers designated five areas where machine learning could improve surgical efficiency and clinical outcomes:  

  • Aesthetic surgery
  • Burn surgery
  • Craniofacial surgery
  • Hand and Peripheral Surgeries
  • Microsurgery

The IBM researchers also expect a practical application of machine learning to improve surgical training. The IBM team is concentrating on measures that ensure surgeries are safe and their results have clinical relevance–while always remembering that computer-generated algorithms cannot yet replace the trained human eye.

The researchers also stated that the tools could not only aid in decision making, but they may also find patterns that could be more evident in minor data set analysis or anecdotal experience.

Dementia Diagnoses

Machine learning has identified one of the common causes of dementia and stroke in the most widely used brain scan (CT) with more accuracy than current methods. This is small vessel disease (SVD), a common cause of stroke and dementia. Experts at the University of Edinburgh and Imperial College London have developed advanced AI software to detect and measure small vessel disease severity.  

Testing showed that the software had an 85% accuracy in predicting the severity of SVD. As a result, the scientists assert that their technology can help physicians carry out the most beneficial treatment plans for patients, swiftly aiding emergency settings and predicting a patient’s likelihood of developing dementia. 

Closing Thoughts

AI has helped humans in many facets of life, and now it is becoming an aid to doctors, helping them identify ailments sooner and determine the best pathways to tackle diseases. AI performs best with larger data sets, and as the volume of data increases, the effectiveness of AI models will continue to improve.  

The current generation of machine models uses specific images and data to solve defined problems. More abstract use of big data will be possible in the future, meaning that more extensive data sets of disorganised data will be combined, and high-quality computers (potentially quantum computers) will be allowed to make new inferences from those data sets. 

For example, when multiple tests like blood pressure, pulse-ox, EKG, bloodwork, and other tests, including CT and MRI scans, are all combined, the models may see things that doctors did not piece together. This is when machine learning will take medicine to the next level, providing even more helpful information to doctors to help us live longer and healthier 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.

Utilising Quantum Entanglement

Posted on by Jean Chalopin

Quantum entanglement is a phenomenon whereby two or more quantum systems become connected so that the state of one system can affect the state of the other(s), even when separated by large distances. Classical physics does not explain this connection, or “correlation,” between the systems. 

It has been a subject of intense study and debate since it was first proposed by Albert Einstein, Boris Podolsky, and Nathan Rosen in 1935. Quantum entanglement is one of the fundamental properties behind quantum computing, and its potential impact on finance, business, and society is excellent. 

We will discuss the history of this scientific field, ongoing research, how entanglement relates to the exciting field of quantum computing, and how these technologies may solve some essential and potentially fundamental questions as we advance.

What Is Quantum Entanglement?

The concept of quantum entanglement was born in the 20th century during the atomic age. One of the first and most famous examples of quantum entanglement is the Einstein-Podolsky-Rosen (EPR) paradox. 

In this thought experiment, two particles are created at the same point in space and separated considerably. The spin state of one particle is measured, and it is found to be ‘up’. According to classical physics, the other particle’s spin state should also be ‘up’ or ‘down’ with a 50-50 chance. 

This graphic, courtesy of NASA/JPL-Caltech, is intended to explain “entangled particles.” Alice and Bob represent photon detectors, which were developed by the Jet Propulsion Laboratory (under NASA) and the National Institute of Standards and Technology.

However, in quantum mechanics, the state of the second particle is instantaneously affected by the measurement of the first particle, meaning that its spin state is also ‘up’. This concept is known as ‘spooky action at a distance’ and was considered by Albert Einstein to be a flaw in quantum mechanics.

This graphic, courtesy of NASA/JPL-Caltech, is intended to explain ‘entangled particles’. Alice and Bob represent photon detectors developed by the Jet Propulsion Laboratory (under NASA) and the National Institute of Standards and Technology.

Back in 1964

The phenomenon of quantum entanglement was first experimentally demonstrated by physicist John Bell in 1964. Bell proposed an inequality, now known as Bell’s inequality, which stated that certain measurements of entangled particles would always produce specific results if classical physics were correct. 

However, experiments have repeatedly shown that the results of these measurements violate Bell’s inequality, proving the existence of quantum entanglement.

One of the most critical implications of quantum entanglement is the concept of quantum teleportation. Quantum teleportation is the process of transferring the state of a quantum system from one location to another without physically moving the system. For example, in 1993, physicist Charles Bennett and his team successfully teleported a photon’s state over a few metres. 

Since then, scientists have been able to teleport the state of atoms, ions, and even larger objects over increasingly larger distances, both in and outside the laboratory. Quantum entanglement has also been used to create highly secure forms of encryption. 

For example, in a quantum key distribution (QKD) process, two parties can communicate securely by sharing a secret key encoded in entangled particles. Furthermore, because any attempt to measure the state of these particles will alter it, any third party attempting to intercept the communication can be detected. We’ll discuss the implications of QKD a bit more in a subsequent section.

The Potential of Quantum Entanglement

Several research areas are looking at the potential uses of quantum entanglement for practical applications. One possible application of quantum entanglement is in the field of quantum sensing. 

Quantum sensors use entanglement properties to measure physical phenomena with unprecedented accuracy. For example, quantum sensors can measure temperature, pressure, and acceleration more precisely than classical sensors. Quantum sensors can also detect faint signals, such as gravitational waves, that are otherwise difficult to detect.

Quantum entanglement is also being researched as a possible technology for quantum communication. A quantum communication network would use entangled particles to transmit information, making the communication highly secure and resistant to eavesdropping.

In medicine, quantum entanglement is being researched to develop quantum-based diagnostic tools. 

For example, in a study published in the journal Nature Communications, researchers from China proposed a new method to detect cancer cells using entangled photons; this technique is highly sensitive, non-invasive, and could be used for early cancer diagnosis.

Quantum Entanglement and Quantum Computing

Quantum entanglement and quantum computing are closely related. Quantum computing relies on the properties of quantum mechanics, including superposition and entanglement, to perform certain types of calculations much faster than their classical computer counterparts. In a quantum computer, information is stored in qubits, which exist in superposition and entanglement. Using its qubits, a quantum computer can solve specific complex mathematical problems, such as factoring large numbers, that would take a classical computer an impractical amount of time.

Quantum communication (introduced above) is also being incorporated into quantum computing. Quantum communication allows qubits to be shared between different locations, which is necessary for the distributed nature of quantum computing. This enables quantum computing to be performed on a large scale, with many qubits distributed across different locations, allowing for more powerful quantum algorithms.

Possible Uses of Quantum Entanglement

Quantum entanglement has the potential to be used in several ways, in finance, business, healthcare, and more.

  1. Quantum cryptography. Quantum key distribution (QKD) allows two parties to communicate securely by sharing a secret key encoded in entangled particles. QKD could be used to protect sensitive financial transactions such as online banking or stock trading. On the other hand, quantum computers could also break many current encryption algorithms to protect sensitive information. This code-breaking could have significant implications for online transactions, medical records, and communications security.
  2. Quantum computing. Quantum computers could be used to solve complex optimization problems in finance, such as portfolio optimisation or risk management.
  3. Quantum machine learning. Quantum machine learning (QML) is a field that combines the power of quantum computing with machine learning algorithms. QML could be used to analyze large sets of financial data, such as stock market trends, and make more accurate predictions, which could have applications in fields beyond finance, such as healthcare and transportation.
  4. Quantum internet. The idea of a quantum internet is based on using quantum entanglement to transmit information in a highly secure way. This could be used to create a new kind of internet that would be highly resistant to hacking, which could be important for financial institutions that must protect sensitive information.
  5. Quantum random number generation. Quantum entanglement can generate truly random numbers, which could be used to generate secure encryption keys for financial transactions and encode sensitive information.
  6. Drug discovery. Quantum computers could be used to simulate the behaviour of molecules. This ability could accelerate the drug discovery process and make it more efficient, produce better health outcomes for many patients, and prevent the growing problem of antibiotic resistance.
  7. Optimization problems. Quantum computers could solve specific optimization problems faster than classical computers. Such optimization could have applications in logistics, finance, and energy management.
  8. Quantum simulation. Quantum computers could simulate the behaviour of quantum systems with high accuracy. This simulation could study the properties of materials, predict the behaviour of complex systems, and understand the properties of fundamental particles such as quarks and gluons.
  9. Quantum chemistry. Quantum computers can be used to simulate the behaviour of chemical compounds and predict their properties, which could help speed up the discovery of new materials, catalysts, and drugs.

It’s important to note that these are only potential applications, but all show great promise, and most are currently in the research and development stages. It will likely take some time before they become practical technologies. However, many overlap, and if one were to be solved, a host of other applications would soon follow.  

Closing Thoughts

Quantum entanglement is a mysterious and fascinating phenomenon. It has already been used to create highly secure forms of encryption and to teleport the state of quantum systems over large distances. The study of quantum entanglement continues to be an active area of research, with scientists working to uncover its true nature, properties, and potential uses. 

Despite its many potential applications, the true nature of quantum entanglement still needs to be fully understood. Some theories propose that entanglement is a fundamental aspect of the universe, while others suggest that it results from more complex interactions between particles. 

Whatever the truth behind quantum entanglement, scientists will continue to research how it and the quantum computers built on its principles can solve some of the most challenging problems and questions we are currently asking. Suppose it does live up to the hype. In that case, quantum entanglement could prompt a new technological age and fundamentally alter our understanding of physics. 

Disclaimer: The information provided in this article is solely the author’s opinion and not investment advice – it is provided for educational purposes only. Using this, you agree that the information does not constitute 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.

The 60-40 Portfolio: What Went Wrong

Posted on by admin

For decades called the ‘balanced’ portfolio, the 60-40 portfolio allocation of 60% to stocks and 40% to bonds served as the quintessential go-to for stable growth, income, diversification, and inflation protection. It worked wonders in the decades leading to the dot-com bubble, known as one of the worst financial events for now-seasoned investors. 

Yet the 60-40 portfolio persists even after the Global Financial Crisis despite a 2022 negative return of -18%, reminiscent of the -22% crash of 1937. What’s next for 2023? 

This article delves into the pitfalls of the hallmark 60-40 portfolio, what went wrong, and what we can do moving forward into the post-pandemic era of high rates and high inflation. 

What Is a 60-40 Portfolio? 

Again, the 60-40 portfolio is an industry-standard investment strategy that allocates 60% of the portfolio to stocks and 40% to bonds. This asset allocation is based on the idea that stocks have the potential to generate higher returns over time but also carry higher risk and volatility relative to bonds. 

Stocks, in general, remain closely tied to the overall health of the economy. Periods of low rates elevated consumer sentiments, and increasing supply orders in a smoothly functioning supply chain–suggesting expected demand–tend to bode well for stocks. 

Stocks, therefore, represent the ‘growth’ we want in a portfolio and can perform well if timed well with an increasing trend or theme. For example, electric vehicles and climate change represent two persistent investment themes despite the Covid-19 pandemic.

Bonds represent the base ‘income’ necessary for a more conservative investor, such as an individual saving for retirement or for a child’s college plan. In theory, it all works well on paper so long as the two asset classes are uncorrelated. 

Why Correlation Matters

Should their correlation turn positive, which was the case for 2022, then bonds present an inordinate amount of risk for an insufficient amount of return. In other words, the risk-reward ratio initially used to create your portfolio is now out of balance. 

Bonds follow the negative trajectory of equities typically when rates are rising on the back of high inflation, supply chain disruptions, or an exogenous event, such as a global pandemic. The usual ‘flight’ to bonds that would, in theory, alleviate the damage done by a 60% allocation to stocks does not occur as institutional investors quickly foresee the incoming damage owing to duration. 

How Duration Is Harmful

Bond duration showcases any one bond’s sensitivity to changes in interest rates, often the Fed’s policy rate. In technical terms, it represents the weighted average time until the bond’s cash flows (inclusive of coupon payments and principal) are received by the investor. 

When interest rates rise, the value of a bond falls. The opposite remains true. Think of it this way: If we can receive a better deal from a more recent bond, then surely the old bond is worth less–and so it is. Bond duration demonstrates this price change. 

For example, a bond duration of 10 years means that this bond’s price will change by 10% for every 1% change in interest rates. If rates rise by 1%, then we can expect to see a 10% price fall. The effect dampens the higher the bond’s yield, but it’s an established rule of thumb we cannot ignore. 

How Active Monetary Policy Ruins ‘60-40’

January 1980 began with a Fed funds rate of 14%. You read that correctly. In order to combat inflation of also 14%, the Federal Reserve manufactured a recession on its own accord. It threw an ice bucket of water onto an overheating economy. 

This set the precedence for constant market manipulation for the following decades to come up to today. It’s the belief in a ‘soft landing’ towards recession or a ‘quick recovery’ following a disaster that keeps central banks worldwide motivated. 

And they have reason to believe–it does work to some extent. For example, many financial executives cite 1994’s soft landing by Alan Greenspan. However, there is the valid argument that it all came down to luck. 

What central bank activity certainly does is inject volatility into an asset class chosen to buffer against the very same thing–volatility–by creating expected cash flows. Again, the concept works fine in theory as long as the sources of volatility are uncorrelated between stocks and bonds. 

When they derive from the same source, such as a central bank, then asset classes themselves become correlated–leading to a type of 100% either-way portfolio. 

Return to Supply and Demand

The global pandemic introduced rampant inflation in 2022 as supply chains buckled under the weight of austere government policies, afraid of what it might be like to repeat 1918’s Spanish flu in a hyperconnected world. Dry bulk shipping prices skyrocketed as the white collar world came to add a new word to our growing dictionary, ‘hybrid working’. 

In late 2021, the lag between event and aftermath led to the transitory inflation debacle in which the economic severity of a global pandemic was woefully under-expected and under-stated. 

When inflation hit due to rising supply costs passed onto consumers, the Fed following 1994’s example, decided to raise rates as rumblings of recession began in late 2022. Consumer spending fell as duration ensured a dark path ahead for bonds. 

If the demand for the products of large companies remained robust, then the 60-40 portfolio would have fared better. However, that demand fell in 2022, such as with Apple’s annual iPhone release. 

Long-term investors then, knowing that a 60-40 portfolio depends on a healthy ‘bull market’, look to a source of healthy demand despite rising inflation and rising rates. 

Alternative Portfolios

Yale’s endowment fund serves as a great model for investing in alternative assets and bucking the 60-40 portfolio. In its year ending June 2022, it earned a return of 1%, well exceeding S&P 500’s -16% loss. In the year ending June 2021, it earned a return of 40%. 

Yale publicly documents its gradual veering away from traditional asset classes and how it favours the alternative. Leveraged buyouts, venture capital investments, and absolute return strategies form its three greatest allocations. Domestic equity, on the other hand, remains minuscule. 

And Yale is not alone. Kansas State and the University of Michigan also represent top performers in one of the worst years for retail investors. It boils down to how endowment portfolios think differently to the standard 60-40. 

They operate truly in the long term and seek to diversify through alternative strategies, such as commodities, hedge funds, and private real estate, often approaching a portfolio like a long-term legacy plan.

Wealth Planning 

As opposed to wealth management, wealth planning pays specific attention to long-term goals, incorporating a holistic view of an investor’s entire estate and how they might be able to invest through alternative methods. 

2023 marks the year of wealth planning. Inflation and correlated volatility highlight the weaknesses of the 60-40 portfolio. Endowment funds and a true long-term perspective showcase the inflation-stopping and jaw-dropping power of what it means to diversify amongst different sources of demand growth. 

Disclaimer: The author of this text, Paul Winder, has a career that spans over 30 years in the financial services sector with emphasis on creating products and services in the international tax treaty and estate planning arena. Paul is Head of Fiduciary Products & Markets at Deltec Bank & Trust and CEO of Deltec Fund Services, www.deltec.io.

The co-author of this text, Conor Scott, CFA, has been active in the wealth management industry since 2011. Mr. Scott is a Writer for 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. This information should not be interpreted as an endorsement of cryptocurrency or any specific provider, service, or offering. It is not a recommendation to trade. 

What Is a Global Citizen? 

Posted on by Jean Chalopin

The term ‘Global Citizen’ has earned increasing attention in recent years as people worldwide become more aware of our interconnectedness and our actions’ impact on one another.

At its core, being a Global Citizen means recognising that we are all part of a larger global community and that we are responsible for taking actions that promote the well-being of people and the planet, both locally and globally. This includes valuing diversity, respecting human rights, promoting peace and justice, and working to address global challenges such as poverty, inequality, climate change, and other issues that affect people across borders.

Global Citizenship is not limited to nationality, religion, or culture. It’s a mindset and a way of living that emphasises our common humanity and our shared responsibility for the world we live in. It involves actively engaging with others, learning about different perspectives and cultures, and taking action to address social, economic, and environmental issues in our own communities and beyond.

A Global Citizen has the following key traits: 

  1. Awareness: Having a global perspective and understanding of how our actions impact others in different parts of the world.
  2. Empathy: Showing empathy for those who are different from us and being willing to learn from their experiences.
  3. Action: Taking action to address issues that affect people and the planet, such as volunteering, donating to charities, and supporting policies that promote social and environmental justice.
  4. Collaboration: Working with others across different backgrounds, cultures, and sectors to find solutions to complex global challenges.

Being a Global Citizen is about recognising that our actions have consequences that extend past our immediate surroundings. It’s to take responsibility for promoting a more just, equitable, and sustainable world for all people, regardless of where they were born, where they live, and how they identify.

This article delves into these traits shared by Global Citizens worldwide, what they care about today, and examples of what they’ve done with their success. 

A Global Citizen Is Aware

They maintain constant awareness of the interconnected world–that there are expanded ripple effects to our actions. They understand that there are social, economic and environmental issues that affect people in different parts of the world and that it’s essential to be informed about these issues.

For example, a Global Citizen makes a point to learn different cultures and languages and seeks opportunities in line with their passions. They recognise not only the potential to make a difference but to benefit themselves and add to their life while doing so. 

Although renowned late Beatle singer George Harrison passed away two decades ago, he lived ahead of his time and set the definition for a Global Citizen. In 1971, he organised the first benefit concert, The Concert for Bangladesh, which strove to raise awareness and help save the lives of 10 million East Pakistani refugees suffering from disease and starvation. 

Five decades on, this concert remains one of Mr. Harrison’s hallmark achievements and added to the permanent legacy of world-changing music. 

A Global Citizen Is Empathetic

They waste no time in showing empathy for others, especially those who are different from themselves. They recognise that there are many factors that influence a person’s experiences, such as their culture, gender, sexuality, religion, economic status, and race. A Global Citizen seeks to understand these factors and remain open to learning from others.

For example, a Global Citizen might participate in cultural exchange programs, learn a new language, or volunteer to support people in marginalised communities. They are aware that their own experiences are limited and that they can benefit from understanding the life stories of others. 

Wawira Njiru earned the headline’ novice cook to international icon’ for her dedication to transforming Kenyan schoolchildren’s lives through access to food. She launched the group Food for Education which targets explicitly the cycle of poverty and showcases how it can end with a simple, good meal. She has now served over nine million meals. 

A Global Citizen Understands Action

They don’t hesitate when the time comes to stop planning, and start doing. A Global Citizen is someone who takes action to address global challenges, both locally and globally. They recognise that there are many issues facing our world, such as poverty, inequality, climate change, and other environmental issues, and they’re willing to take action to address these challenges. 

For example, a Global Citizen might volunteer for a local charity, donate to a global relief organisation, or advocate for policies that promote social and environmental justice. They recognise that their own actions can make a difference and that collective action is necessary to create positive change.

Hamdi Ulukaya, founder and CEO of Chobani, champions fully-paid parental leave, local food banks, and trustworthy food programs for local schools. Further, he founded the Tent Partnership for Refugees, which seeks to provide hiring, training, and mentorship for refugees. Most are aware of the world’s crises, but it comes down to extending a helping hand. 

A Global Citizen Collaborates

They don’t live on an island, alone, king of a kingdom-of-one. A Global Citizen is someone who works with others to find solutions to complex global challenges. They recognise that no one person, organisation, or nation can solve these challenges alone, and that collaboration across different backgrounds, cultures, and sectors is necessary.

For example, a Global Citizen might participate in community forums, join a global network of activists, or collaborate with organisations to address issues such as poverty, climate change, or social injustice. They recognise that different perspectives and experiences can enrich the solutions we develop, and that working together is necessary to create meaningful change.

Richard Curtis has worked with numerous organisations and projects, such as Comic Relief, Red Nose Day, Projet Everyone, and Make Poverty History. His collaborations have led to the fundraising of over a billion US dollars for the benefit of children and vulnerable people worldwide. 

Closing Thoughts

Awareness, empathy, action, and collaboration–in that order. Global Citizens understand they’re on Earth to contribute, to do something, and help make something great. They utilise their unique talent or profession, be that music, business management, or cooking, and make it the bedrock of an enterprise far greater than themselves. 

Ironically and because of it, they become much more; memorable individuals we read about in magazines or hear of in podcasts. And it doesn’t take $10,000–not even $1,000. Wawira started with a single Kenyan meal, and later became one of the world’s most remarkable success stories under 30 years old. 

By combining a singular passion with a greater focus, entrepreneurs transition from the realm of ‘good’ to ‘great’. It’s this reason that prompted Deltec to launch the Deltec Initiatives Foundation, empowering young Bahamians, and Deltec Cares, a global disaster relief effort. 

Disclaimer: 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, Conor Scott, CFA, has been active in the wealth management industry since 2011. Mr. Scott is a Writer for 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. This information should not be interpreted as an endorsement of cryptocurrency or any specific provider, service, or offering. It is not a recommendation to trade. 

Robotics and AI 

Posted on by Jean Chalopin

While it is obvious that artificial intelligence (AI) and robotics are different disciplines, robots can perform without AI. However, robotics reaches the next level when AI enters this mix. 

We will explain how these disciplines differ and explore spaces where AI is utilized to create envelope-pushing robotic technology. 

Robotics in Brief

Robotics is a subset of engineering and computer science where machines are created to perform tasks without human intervention after programming.

This definition is broad, covering everything from a robot that aids in silicon chip manufacturing to the humanoid robots of science fiction, and are already being designed like the Asimo robot from Honda. In global finance, we’ve had robo-advisors working with us for some years already. 

Courtesy of Honda

Robots have traditionally been used for tasks that humans are incapable of doing efficiently (moving an assembly line’s heavy parts), are repetitive, or are a combination. For example, robots can accomplish the same task thousands of times a day, whereas a human would be slower, get bored, make more mistakes, or be physically unable to complete it.

Robotics and AI

Sometimes these terms are incorrectly used interchangeably, but AI and robotics are very different. In AI, systems mimic the human mind to learn through training to solve problems and make decisions autonomously without needing specific programming (if A, then B).  

As we have stated, robots are machines programmed to conduct particular tasks. Generally, most robotics tasks do not require AI, as they are repetitive and predictable and not needing decision-making.

Robotics and AI can, however, coexist. Robotic projects that use AI are in the minority, but such systems are becoming more common and will enhance robotics as AI systems grow in sophistication.

AI-Driven Robots

Amazon is testing the newest example of a household robot called Astro. It is a self-driving Echo Show. The robot uses AI to navigate a space autonomously, acting as an observer (using microphones and a periscopic camera) when the owner is not present. 

This type of robot is not novel; robotic vacuums have been in our homes, navigating around furniture, for almost a decade. But even these devices are becoming “smarter” with improved AI. 

The company behind the robot vacuum Roomba, iRobot, announced a new model that uses AI to spot and avoid pet poop.  

Robotics and AI in Manufacturing

Robotic AI manufacturing, also known as Industry 4.0, is growing in scope and will become transformational. This fourth industrial revolution may be as simple as a robot navigating its way around a warehouse to systems like that of Vicarious, who designs turnkey robotic solutions to solve tasks too complex for programmed-only automation.  

Vicarious is not alone in this service. For example, the Site Monitoring Robot from Scaled Robotics can patrol a construction site, scanning and analyzing the data for potential quality issues. In addition, the Shadow Dexterous Hand is agile enough to pick soft fruit from trees without crushing it while learning from human examples, potentially making it a game changer in the pharmaceutical industry. 

Robotics and AI in Business

For any business needing to send things within a four-mile radius, Starship Technologies has delivery robots equipped with sensors, mapping systems, and AI. Their wheeled robot can determine the best routes to take on the fly while avoiding the dangers of its navigating world.

Courtesy of Starship Technologies

In the food service space, robots are becoming even more impressive. Flippy, the robotic chef from Miso Robotics, uses 3D and thermal vision, learning from the kitchen it’s in, and acquiring new skills over time, skills well beyond the name it earned by learning to flip burgers.  

Flippy, the robot chef from Miso Robotics

Robotics and AI in Healthcare

Front-line medical professionals are tired and overworked. Unfortunately, in healthcare, fatigue can lead to fatal consequences.

Robots don’t tire, which makes them a perfect substitute. In addition, Waldo Surgeon robots perform operations with steady “hands” and incredible accuracy.

Robots can be helpful in medicine far beyond a trained surgeon’s duties. More basic lower-skilled work performed by robots will allow medical professionals to free up time and focus on care. 

The Moxi robot from Diligent Robotics can do many tasks, from running patient samples to distributing PPE, giving doctors and nurses more of this valuable time. Cobionix developed a needleless vaccination administering robot that does not require human supervision. 

Robotics and AI in Agriculture

The use of robotics in agriculture will reduce the effect of persistent labor shortages and worker fatigue in the sector. But there is an additional advantage that robots can bring to agriculture, sustainability. 

Iron Ox uses robotics with AI to ensure that every plant gets the optimal level of water, sunshine, and nutrients so they will grow to their fullest potential. When each plant is analyzed using AI, less water and fertilizer are required producing less waste. 

The AI will learn from its recorded data improving that farm’s yields with every new harvest.

The Agrobot E Series has 24 robotic arms that it can use to harvest strawberries, and it uses its AI to determine the ripeness of the fruit while doing so.

Courtesy of Agrobot

Robotics and AI in Aerospace

NASA has been working to improve its Mars rovers’ AI while working on a robot to repair satellites.  

Other companies are also working on autonomous rovers. Ispace’s rover uses onboard tools, and maybe the device hired to lay the ‘Moon Valley’ colony’s future foundation.  

Additional companies and agencies are trying to enhance space exploration with AI-controlled robots. For example, the CIMON from Airbus is like Siri in space. It’s designed to aid astronauts in their day-to-day duties, reducing stress with speech recognition and operating as a system for problem detection.   

When to Avoid AI?

The fundamental argument against using AI in robots is that, for most tasks, AI is unnecessary. The tasks that are currently being done by robots are repetitive and predictable; adding AI to them would complicate the process, likely making it less efficient and more costly.

There is a caveat to this. To date, most robotic systems have been designed with AI limits in mind when they were implemented. They were created to do a single programmed task because they could not do anything more complex. 

However, with the advances in AI, the lines between AI and robotics are blurring. Outside of business- or healthcare-driven uses, we’ve noticed how AI facilitates the relatively new, lucrative field of algorithmic trading becoming increasingly available to retail investors. 

Closing Thoughts

AI and robotics are different but related fields. AI systems mimic the human mind, while robots help complete tasks more efficiently. Robots can include an AI element, but they can exist independently too.  

Robots designed to perform simple and repetitive tasks would not benefit from AI. However, many AI-free robotic systems were created, accounting for the limitations of AI at their time of implementation. As the technology improves, these legacy systems may benefit from an AI upgrade, and new systems will be more likely to build an AI component into their design. This change will result in the marrying of the two disciplines.  

We have seen how AI and robotics can aid in several different sectors, keeping us safer, wealthier, and healthier while making some jobs easier or performed more efficiently entirely by robots. However, we also consider a possible change in employment structure. People will be outsourced to robots, and they must be accounted for with training and other options for employment. 

With the combination of AI and robotics, significant changes are on our horizon. This combination represents the very forefront of innovation. 

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.

design and development by covio.fr