March, 2023 - Trusting Disruption by Deltec

AI and Space Exploration

Posted on March 28, 2023March 28, 2023 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.

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.

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.

Blockchain and Supply Chain Management

Posted on March 22, 2023March 22, 2023 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.

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.

Artificial Intelligence and Biomedicine

Posted on March 17, 2023 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.

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.

Utilising Quantum Entanglement

Posted on March 14, 2023 by Jean Chalopin

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

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

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

What Is Quantum Entanglement?

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

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

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

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

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

Back in 1964

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

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

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

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

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

The Potential of Quantum Entanglement

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

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

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

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

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

Quantum Entanglement and Quantum Computing

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

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

Possible Uses of Quantum Entanglement

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

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

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

Closing Thoughts

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

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

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

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

The 60-40 Portfolio: What Went Wrong

Posted on March 11, 2023March 11, 2023 by admin

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

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

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

What Is a 60-40 Portfolio?

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

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

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

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

Why Correlation Matters

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

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

How Duration Is Harmful

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

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

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

How Active Monetary Policy Ruins ‘60-40’

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

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

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

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

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

Return to Supply and Demand

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

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

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

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

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

Alternative Portfolios

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

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

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

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

Wealth Planning

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

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

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

The co-author of this text, Conor Scott, CFA, has been active in the wealth management industry since 2011. Mr. Scott is a Writer for Deltec International Group, www.deltec.io.

The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees. This information should not be interpreted as an endorsement of cryptocurrency or any specific provider, service, or offering. It is not a recommendation to trade.

Blockchain, AI, and IoT

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

Artificial intelligence (AI), the Internet of Things (IoT), and blockchain are the most promising and rapidly evolving technologies of our time.

Combined, these three technologies solve many problems across many different industries, including supply chain management, finance, healthcare, and manufacturing. We will explore how this combination can change many aspects of our lives.

Supply Chain Management

One potential use case for combining AI, IoT, and blockchain is tracking and managing goods moving through a supply chain. By using IoT sensors to gather data on the location and condition of goods and blockchain to create a transparent and immutable record of that data, it is possible to create a real-time, end-to-end view of the supply chain.

This system can help to improve efficiency, reduce the risk of fraud, and increase transparency for all parties involved.

Another potential use case for combining these technologies is optimising logistics and transportation. By using AI to analyse data from IoT sensors and make predictions about demand, shipping routes, and other factors, logistics companies can make more informed decisions about how to move goods more efficiently. Being immutable, blockchain can also create a tamper-proof record of shipping data, which can help improve transparency and reduce the risk of fraud.

Additionally, these technologies can be combined in smart contracts, which can automate and streamline supply chain transactions by using AI to identify and execute contract terms and blockchain to ensure that the contract terms are executed transparently and securely.

Financial Services

In financial services, the first potential use case for the combination of AI, IoT, and blockchain is in the field of fraud detection and prevention.

By using IoT sensors to gather data on financial transactions, and blockchain to create an immutable and transparent record of that data, it is then possible to use AI algorithms to identify patterns and anomalies that indicate fraudulent activity. This combination helps financial institutions detect and prevent fraud more quickly and effectively, reducing costs for the company and the client.

Another potential use case for the combination of these technologies is risk management. By using AI to analyse data from IoT sensors and other sources, financial institutions can gain a more comprehensive view of the risks they are exposed to and make more informed decisions about managing those risks.

Finally, like with the supply chain, these technologies can be combined in intelligent contracts. Financial institutions can automate and simplify the contract execution process, reducing the need for manual intervention and increasing efficiency. The cost-benefit of such a solution could be significant by preventing human error, creating a trustless environment, and providing nearly minute-by-minute updates.

Healthcare

Combining AI, IoT, and blockchain technologies can also significantly impact the healthcare industry.

One potential use case for combining AI, IoT, and blockchain in healthcare is the management of electronic medical records (EMRs). Using IoT sensors to collect and transmit data to the blockchain makes it possible to create a secure and tamper-proof patient data record. AI algorithms can then be used to analyse this data and identify patterns that can help improve patient care on the individual level and speed up the discovery of new treatments for all.

Another potential use case is in the field of personalised medicine. Personalised healthcare is a new concept that could turn the medical world on its head. For example, the way cancer drugs are currently tested, a group of patients with a particular type of cancer is given a drug, and its effectiveness for the overall group is determined. A patient’s cancer cell DNA would be tested with personalised medicine, and a cocktail of drugs effective at treating cancer that fit that genetic profile could be prescribed.

Using IoT-enabled devices to collect data on a patient’s health, combined with blockchain to create a secure and transparent record of that data, AI can analyse the data and make personalised treatment recommendations. This can help doctors provide more individualised care to patients, leading to better health outcomes.

Additionally, blockchain tech can create secure and transparent medical supply chains, allowing for the tracking and traceability of medical products and devices from manufacturer to patient. While all supply chains are essential, ensuring that patients receive safe and effective treatments that have been shipped adhering to required standards and reducing the risk of counterfeit drugs and medical devices will save lives.

Manufacturing

Combining AI, IoT, and blockchain technologies can significantly impact the manufacturing industry. By leveraging these technologies, manufacturers can create more efficient and cost-effective operations and improve the overall quality of their products. In addition, these technologies can provide significant benefits by improving the manufacturing process’s efficiency, transparency, and security.

One potential use case for combining these technologies in manufacturing is in the field of predictive maintenance. By using IoT sensors to collect data on the performance of manufacturing equipment, AI algorithms can then analyse massive amounts of data and predict when equipment is likely to fail.

This system can help manufacturers schedule maintenance timely and cost-effectively, reducing downtime and increasing overall efficiency. Such information is already being applied to advanced systems such as aeroplanes, blurring the lines between manufacturing and services.

Additionally, blockchain tech can create secure and transparent traceability systems for products, from raw materials sourcing, production, and logistics to product traceability and warranty management. This can help to ensure that products are safe and of high quality and can help to protect a company’s reputation and brand.

With the increasing significance of environmental, social, and governance (ESG) issues, manufacturers and the consumers of their goods care more about the sustainable practices of companies. A clear and transparent trail that can be followed on an immutable blockchain will give confidence to those who value ESG issues.

Ongoing Concerns

As organisations look to implement AI, IoT, and blockchain technologies, it is crucial that they also consider the potential risks and challenges associated with these technologies. One of the essential considerations is data privacy and security.

Collecting and storing large amounts of data through IoT sensors and blockchain technology can present significant privacy and security risks. Personal information, including health and financial data and other compassionate information, can be vulnerable to breaches, hacking, and cyber-attacks. Organisations must take the necessary steps to protect this data, such as implementing robust security protocols, encrypting data, and regularly monitoring potential threats.

A study by PwC highlights that the growing use of IoT in healthcare has raised privacy concerns among patients and healthcare providers and regulatory challenges for organisations that handle patient data. Furthermore, another study by Deloitte states that blockchain technology can be used to implement robust security protocols and data encryption, as well as data sharing and access controls, which can help to mitigate these risks. The correct balance of these technologies will be needed.

Another vital consideration is regulatory compliance. The use of these technologies is subject to a range of laws and regulations, including data protection and privacy laws, financial regulations, and healthcare laws. Organisations must comply with all relevant regulations and have the processes and procedures to meet regulatory requirements.

A report by the World Economic Forum highlights that regulations and standards are needed to ensure the safe and responsible use of these technologies while also enabling innovation and growth.

To address these concerns, organisations should work with data privacy and security experts and legal and regulatory compliance experts to develop a comprehensive strategy for technology implementation. This strategy should include a thorough analysis of the potential risks and benefits of the technologies and a plan for mitigating those risks. Additionally, organisations should be prepared to invest in the necessary infrastructure and resources to ensure the security and privacy of their data.

Closing Thoughts

Combining AI, IoT, and blockchain tech significantly benefits various industries. For example, in financial services, they can be used to improve fraud detection and prevention, risk management, and brilliant contract execution. In healthcare, they can be combined to manage electronic medical records, improve personalised medicine, and secure medical supply chains. Finally, in manufacturing, they can be used for predictive maintenance, supply chain management, and product traceability.

Each use case demonstrates how combining these technologies can improve transparency, security, and efficiency in different industries. By leveraging the power of AI, IoT, and blockchain, organisations can gain a more comprehensive view of their operations and make more informed decisions, leading to better outcomes for their customers and an improved bottom line.

These systems are now being considered even more significantly, with proposed smart cities taking advantage of them for optimised infrastructure. Furthermore, it is easy to imagine using the data created and analysed by these technologies to be further combined for other uses, some of which may still be unseen.

It is important to note that while these technologies have the potential to bring significant benefits, there are also challenges to be addressed. For example, ensuring data privacy and security and addressing regulatory concerns are significant challenges that need to be addressed. Nevertheless, with the right approach and partners, organisations can successfully implement these technologies and reap the benefits they can offer.

Combining these three new technologies represents a significant opportunity for organisations across various industries. As their use in transparency, security, and efficiency expands beyond business sectors, they will begin to help society and the earth.

The Financial Planning Process

Posted on March 3, 2023March 3, 2023 by admin

Financial planning or wealth planning is the process of creating an achievable roadmap for your life’s goals. The financial planning process involves several intensive steps, such as:

Defining your financial goals
Assessing your current financial situation
Developing a realisable plan of action
Implementing that plan
Monitoring and adjusting

Whether saving for retirement, paying off debt, or planning for a major purchase or life event, tailored financial planning means the difference between success and failure. The financial planning process itself must be well-structured, thorough, and comprehensive.

After painting a complete picture of your finances, the capable advisor defines the primary realisable goals relevant to you and how to achieve them. Of course, the ultimate goal remains long-term financial stability and success through the well-ordered mind.

This article explores the five essential steps that are the foundation for a well-ordered financial planning process. In the greater scope of wealth management, financial planning continues to be a complex speciality requiring both experience and compassion.

Source: finexplained

Defining Your Financial Goals

As the critical first step of the financial planning process, it helps you clarify what you want, what’s achievable, and what you want to achieve in the long term. Further, it provides a path forward. There are five considerations to keep in mind.

Short- vs long-term. We must identify whether your goals are short-term (less than one year), medium-term (one to five years), or long-term (more than five years). This aids goal prioritisation and the relevance level of available strategies.

Measurable. After time durations are determined, we examine the specific numbers to which we must hold ourselves accountable. This lets us see where we could have met specific objectives.

Realistic. Ambitious goals come with high risk, while realisable goals enable us to moderate that risk, especially over longer durations. Considering the current income stream, we can identify any weak points and define strategies for remedying them.

Prioritised. An essential question inside any financial planning process: what can we do without, and what is imperative? Pre-paying school fees for possible tax benefits is a high-priority item, while an additional car is not.

Value-aligned. Your values and priorities dictate how you spend your disposable wealth. Otherwise, why hire a financial planner? Your passions and beliefs should enter many of your financial and life goals.

Assessing Your Current Financial Situation

The second step of the financial planning process, it provides an essential baseline for evaluating your forward progress and the necessary plan of action.

Income. We must evaluate the varied sources of income and their levels of consistency. For example, salary payments versus sporadic rental income. Then we factor in taxes, deductions, and all matters relevant to your legal jurisdictions.

Assets. We then need to review the total value of your assets, including savings accounts, retirement accounts, investment portfolios, private funds, real estate, and other sources. Not only does accurately understanding your net worth open up new doors, but it guides the timeline for realising more significant goals.

Liabilities. In short, we must ensure that all unnecessary liabilities are handled with the utmost care and urgency. While more time may be needed for property or loan balances, removing minor matters immediately improves your financial momentum and well-being.

Cash flow. In the final but essential portion, we need to determine the current cash flow picture and how it can be adjusted to meet your financial goals. This is one area where experience and financial acumen becomes critical.

Developing a Realisable Plan of Action

The third step of the financial planning process, developing a realisable action plan, entails producing a concrete strategy for achieving your financial goals.

Set targets. After setting your financial goals through to bequests and the next generations of your family, set your smaller, achievable targets. The overall goal is to know how one achievement feeds into the next.

Identify obstacles. We’re all familiar with the timeless maxim: life happens. So what are the expected and possibly unexpected obstacles you might face in your journey? Your advisor must account for these and structure finances accordingly.

Choose the right strategies. Yes, easier said than done, but this is the substance of any worthwhile financial plan. What are the vital commitments? What are the appropriate structures? How many generations are in the family? Dozens of questions comprise this point.

Monitor your progress. Some ideas feel good in the mind or work until the market or the Fed takes a turn for the worse. Your advisor must always be reachable in the event changes are needed.

Implementing Your Financial Plan

The final step of the initial financial planning process, implementing your plan, must be done carefully and guided by experience. Just as timing investments significantly impacts returns, time also impacts long-term financial plans.

Automate your savings. As an essential “Rich Dad Poor Dad” technique, define your monthly portfolio contribution before spending your regular income. This not only brings mental well-being and confidence, but ensures that your financial plan keeps to your desired goals.

Stay disciplined. By defining your significant purchases for the next five years with a financial advisor, you can avoid unnecessary expenditures or liabilities while limiting debt exposure. In addition, a worthy financial planner gently reminds you of your long-term ambitions whenever appropriate.

Remain ready to re-evaluate. This can be negative or positive. If the real estate or cryptocurrency markets take an upswing, then the immediate cash boon should be included if favourable. If events turn unfavourable, then it’s best to prioritise and move forward.

Closing Thoughts

Define, assess, develop, implement, and then monitor. These five steps comprise a great financial planning process. We say: don’t settle for anything less. This is the baseboard, the bare minimum you should expect.

Financial planning differs from private banking or traditional wealth management because it focuses more on the individual and the long term. It is far more idiosyncratic, considering hopes, fears, desires, and flaws. As personal dreams make the best north star, compassion and an experienced ear make the best financial plan.

The co-author of this text, Conor Scott, CFA, has been active in the wealth management industry since 2011. Mr. Scott is a Writer for Deltec International Group, www.deltec.io.