Category: Robotics

What Are Neural Implants?

Posted on by Jean Chalopin

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

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

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

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

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

Understanding Neural Implants

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

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

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

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

The Benefits of Neural Implants

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

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

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

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

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

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

The Challenges

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

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

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

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

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

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

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

Companies Operating in the Neural Implant Space

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

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

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

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

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

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

Closing Thoughts

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

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

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

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

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

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

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

Robots and, Touch

Posted on by Jean Chalopin

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

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

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

The Importance of Touch for Robots

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

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

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

Developing Touch Sensors for Robots

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

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

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

The Benefits of a Sense of Touch

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

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

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

Industry Use Cases

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

Manufacturing

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

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

Healthcare

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

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

Retail

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

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Hospitality

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

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

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

Risks and Challenges

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

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

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

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

And Addressing the Challenges

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

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

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

Closing Thoughts

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

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

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

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

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

AI and Its Many Forms

Posted on by Jean Chalopin

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

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

The Different Types of AI

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

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

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

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

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

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

Machine Learning

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

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

Natural Language Processing

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

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Computer Vision

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

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Expert Systems

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

Robotics

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

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

How People Perceive AI

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

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

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

Closing Thoughts

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

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

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

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

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

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

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.

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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. 

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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.
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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.

AI in Healthcare

Posted on by Jean Chalopin

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

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

Source

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

The Current State of AI in Healthcare

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

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

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

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

Responsibility

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

Competence

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

Transparency

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

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

Use Cases for AI in Healthcare

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

Medical Diagnoses

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

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

Source

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

Automation in Patient Care

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

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

AI in Surgery

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

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

Source

The primary benefits of robotic surgery include the following:

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

Sharing Medical Data

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

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

Research and Development

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

Staff Training

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

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

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

Overcoming Challenges with Healthcare AI

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

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

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

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

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

Closing Thoughts

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

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

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

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

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

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

AI and Space Exploration

Posted on by Jean Chalopin

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

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

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

Understanding AI

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

AI-Driven Rovers

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

The Perseverance rover, courtesy of NASA

Robots and Assistants

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

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

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

Intelligent Navigation Systems

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

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

Processing Satellite Data

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

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

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

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

Mission Operations and Design

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

Courtesy of the European Space Agency

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

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

Mission Strategy

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

Courtesy of NASA

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

Location of Space Debris

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

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

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

Data Collection

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

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

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

Discovery of Exoplanets

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

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

Closing Thoughts

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

A Solar Trip

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

Robonauts

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

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

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

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

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

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

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.

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.

The Uses of Chatbots Like ChatGPT

Posted on by Jean Chalopin

When we used to hear the word “chatbots,” pain often comes to mind. Frustration with the novel was the norm. With chatbots that were mostly able to receive a question and reply, “I am sorry, I can’t answer that. However, I will contact someone that can help you with it. You should receive a reply in 24 hours.” 

Yet, chatbots have come a long way, and the next-generation bots, like the new Chat GPT and those under development by Google, are excellent. They will become a vital part of the customer experience and take the burden of repetitive tasks, simple tasks, and questions away from agents while improving satisfaction results by quickly providing the info clients need.  

Chatbots in Brief

Chatbots had evolved since their inception when programmers wanted to surpass the Turing Test and create artificial intelligence. For example, in 1966, the ELZA program fooled users into thinking they were talking to a human.  

A chatbot is a computer program often using scripts that can interact with humans in a real-time conversation. The chatbot can respond with canned answers, handle different levels of requests (called second and third-tier issues), and can direct users to live agents for specific tasks.  

Chatbots are being used in a wide variety of tasks in several industries. Mainly in customer service applications, routing calls, and gathering information. But other business areas are starting to use them to qualify leads and focus large sales pipelines. 

The first chatbots of over 50 years ago were intended to show the possibilities of AI. In 1988 Rollo Carpenter’s Jaberwacky was designed more for entertainment but could learn new responses instead of relying on canned dialog only. As they progressed, chatbots surpassed “pattern matching” and were learning in real-time with evolutionary algorithms. Facebook’s Messenger chatbot of 2016 had new capabilities and corporate use cases.  

The general format of a Chatbot system takes inputs looking for yes-no answers or keywords to produce a response. But chatbots are evolving to do more comprehensive processes, including natural language processing, neural networks, and other machine learning skills. These chatbots result in increased functionality, enhanced user experiences, and a more human-like conversation that improves customer engagement and satisfaction.  

Benefits of Chatbots

Improved customer service. Clients want rapid and easy resolutions. HubSpot found that 90% of customers want an immediate response to customer service issues. 

This is seen with the increase in live chat, email, phone, and social media interactions. Chatbots can provide service to users 24/7, handling onboarding, support, and other services. Even robo-advisors can use chatbots as a first line of contact. 

More advanced systems can pull from FAQs and other data sources that contain unstructured data like old conversations and documents. Chat GPT uses a massive supply of information up to its 2021 cutoff point.

Improved sales. Chatbots can qualify leads and guide buyers to information and products that fit their needs, producing a personalized experience that builds conversions. For example, they can suggest promotions and discount codes to boost purchase likelihood. They can also be a checkout page aid to reduce cart abandonment. 

Money savings. The goal of chatbot deployment for service and sales support is often to reduce casts. Chatbots can service simple and repetitive tasks allowing human agents to focus on complex issues. 

For example, if a small HR team is slowed with holiday and benefits questions, a chatbot can answer 90% of these, lessening the HR team’s load. An Oracle survey found that chatbots could produce savings of more than half of a business’s upfront costs. While the upfront costs of chatbot implementation are high, the long-term cost savings in staff equipment, wages, and training will outweigh the initial spending.  

Chatbot Implementation Mistakes

While chatbots cannot do everything yet, and it will be a long time before they can do many tasks, they have a skill set that can be used. They can help humans, allowing them to work on more human-required tasks.

No human option. This is a mistake many companies make. Chatbots cannot solve all problems, and the client should have a way to escalate their interaction to a human who can solve it.  

Lacking customer research. A bot needs to know what to look for and what to address. If an implementation starts with the most common and time-consuming questions and decides if a chatbot can solve these, it will prove its value many times over. 

Neglecting tool integration. A well-built chatbot will be part of the contact center platform, aiding agents and supervisors. Able to pull info from multiple sources and escalate to a live agent with useful contextual information allowing the agent to quickly take over from where the chatbot ended.

Use Cases of Chatbots

How can businesses use chatbots? Here are a few examples of great implementations improving customer service and outcomes.  

Retail Banking

Banks or online brokers will generally field simple questions from depositors and borrowers. However, many may come at times of vulnerability. The rising cost of living means a closer focus on finances. Clients may have pending transactions, payments, fraud, or other issues; technology could allow them to monitor these in real time. 

If there is only a call center to address these issues, they will have added pressure. But these can be addressed across multiple channels. A banking chatbot with sentiment analysis can handle text-based digital channels (web chatbot, social media, SMS messaging). 

Launched on the website, mobile app, and social media, this virtual assistant can handle first and second-tier queries (credit card payments, checking account balances). The implementation of sentiment analysis can detect upset customers, quickly getting them to a natural person. 

Chatbots can also aid with the creation of balance alerts, alter other settings, and set up payment reminders, ensuring that both the present issue is solved and the likelihood of a future issue is reduced. 

Property Management

As a commercial and residential real estate business grows, more calls are coming in from customers covering a wide range of issues (rent, maintenance, renovations, and potential customers). As a result, they are taking up the contact center’s resources. For example, a chatbot could answer routine renters’ questions, guide them to self-service solutions, or submit a service ticket. 

Chatbots can also collect info that will allow the direction of their query to relevant categorical information or help from the related agent. This reduces high call volume and becomes a source to produce tickets 24/7, not just when the office is open, providing notifications to the clients when their submissions are updated. Chatbots can also be set for rent reminders via text and provide online payment options to improve on-time payments—a win-win for the user and the company’s bottom line.  

Logistics

Logistics customers want to know where their items are and in real-time. Accurate tracking info is more widely available, but with logistics, there are many variables to contend with on the global level. In addition, high volumes of location requests can overpower a company; even if they are simple requests, they stretch a company’s resources. 

A chatbot can deflect many calls from the call center to automated phone response or web services that have a text chat service, providing callers with a way to track their packages and lowering the strain on the service staff, allowing them to focus on complicated issues.  

Direct-to-Consumer Retail

Online retailers have a lot of spinning plates. Supply chain, warehousing, couriers, drop shippers, and other order fulfillment, and running an E-com site. When one piece fails, there are unhappy customers. If a manufacturer has assembly issues for a hot new product, the company may experience high call volume and service requests, resulting in many refunds and returns. 

An AI-powered chatbot like ChatGPT can be a lifesaver, guiding customers to troubleshooting and instructional media such as video tutorials or the webpage’s knowledge base. It can also take customer feedback and use this information to improve service outcomes, further optimizing flow. 

It can also be helpful in the returns process, streamlining the system, resolving returns without the need for a human team member. In addition, by deflecting most inbound calls to self-service, the call center’s volume is decreased, reducing wait times and producing cost savings. The chatbot could also generate viable leads helping consumers find the right products for their needs while upselling products and services through personalized recommendations.  

Closing Thoughts

All of the use cases for chatbots provided above are currently being employed and are solutions that use chatbots that are less sophisticated than ChatGPT. However, chatbots can provide higher levels of service that can instantaneously scale with a business while doing so at an attractive ROI. 

There are thousands of chatbot implementations possible for today’s businesses, allowing customers to get the real-time service they need with more personalization and specificity than before; this will only continue to improve and expand, allowing more to be provided to consumers.

As chatbots improve their capabilities, their use will likely broaden in scope and volume. Many things humans did in the past, or do now, will be replaced by the faculties of ever-advancing chatbots. These humans will need to be trained to do other work or higher-level service tasks so that we don’t have a glut of out-of-work service personnel. 

On the other hand, this training will result in more satisfying work for employees, which in the long run can improve their lives. Balance is needed to gain further acceptance of chatbots by employees and the populace as a whole.

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

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

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

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

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