Blockchain and AI

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

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

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

What Is Blockchain?

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

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

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

What Is AI?

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

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

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

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

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

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

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

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

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

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

Why AI and Blockchain Must Work Together

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

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

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

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

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

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

AI Creates New Business Models

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

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

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

Closing Thoughts

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

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

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

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.

Longevity and the Future

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

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

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

The Science of Longevity

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

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

Lifestyle Habits

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

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

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

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

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

Genetic Research

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

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

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

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

Epigenetics Research

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

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

Stem Cell Research

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

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

Nutrition Research

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

Case Study in Okinawa

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

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

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

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

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

Potential Future Developments

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

Anti-Aging Drugs

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

Gene Editing

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

Regenerative Therapies

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

Artificial Intelligence

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

Ethical Considerations

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

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

Closing Thoughts

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

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

Can Robots Become Sentient With AI?

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

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

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

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

Robots and AI

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

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

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

What Is AI Sentience in Robotics?

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

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

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

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

How Does AI Sentience Work?

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

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

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

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

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

Why AI Sentience?

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

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

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

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

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

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

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

The Risks and Challenges

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

Technical Risks and Challenges

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

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

Ethical Risks and Challenges

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

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

Social Risks and Challenges

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

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

Closing Thoughts

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

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

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

Ageing and AI

Posted on April 28, 2023April 28, 2023 by Jean Chalopin

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

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

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

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

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

Artificial Intelligence Addresses Longevity

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

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

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

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

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

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

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

How Does AI in Ageing Work?

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

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

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

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

Longevity and Psychological Age

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

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

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

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

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

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

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

Closing Thoughts

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

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

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

What Is Generative AI?

Posted on April 26, 2023April 27, 2023 by Jean Chalopin

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

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

Understanding Generative AI

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

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

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

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

Against Other Forms of AI

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

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

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

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

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

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

The Benefits of Generative AI

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

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

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

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

Successful Case Studies

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

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

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

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

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

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

The Risks

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

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

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

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

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

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

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

Closing Thoughts

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

The Future of AI-Based Art

Posted on April 24, 2023April 24, 2023 by Jean Chalopin

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

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

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

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

The Rising Art Market

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

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

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

What Is AI-Based Art?

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

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

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

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

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

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

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

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

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

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

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

The Benefits of AI-Based Art

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

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

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

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

Industry Use Cases for AI-Based Art

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

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

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

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

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

The Challenges and Risks

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

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

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

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

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

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

Closing Thoughts

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

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

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

What Is Liquid Staking?

Posted on April 20, 2023April 20, 2023 by Jean Chalopin

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

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

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

Understanding Liquid Staking

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

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

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

Source

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

Liquid Staking Versus Traditional Staking

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

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

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

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

Upcoming Projects

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

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

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

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

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

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

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

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

The Pros and Cons

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

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

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

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

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

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

Closing Thoughts

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

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

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

What Is Messenger RNA?

Posted on April 18, 2023April 18, 2023 by Jean Chalopin

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

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

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

Defining Messenger RNA

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

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

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

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

mRNA in Therapeutic Intervention

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

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

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

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

mRNA Vaccines

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

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

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

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

mRNA Use Cases

There are several use cases for mRNA-based therapies.

Cancer Immunotherapy

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

Genetic Disease Therapy

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

Vaccine Development

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

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

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

Risks With mRNA

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

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

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

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

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

Closing Thoughts

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

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

Drug Discovery and AI

Posted on April 13, 2023April 13, 2023 by Jean Chalopin

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

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

How Does Drug Discovery Work?

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

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

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

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

AI Improves Drug Discovery

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

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

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

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

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

The AI-First Approach

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

Vision and Strategy

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

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

Technology and Data

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

AI Partnerships

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

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

Internal Resource Management

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

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

Drug Discovery Datasets

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

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

Embedding AI Within Drug Discovery

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

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

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

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

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

The Future of NFTs

Posted on April 11, 2023April 11, 2023 by Jean Chalopin

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

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

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

NFTs and Digital Ownership

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

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

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

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

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

NFTs as Entry Keys

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

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

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

Digital Identities and Assets Redefined

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

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

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

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

Tradable and Exportable NFTs

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

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

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

The Metaverse Economy

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

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

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

Blockchain is the Key

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

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

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

Not Just Real Estate

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

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

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

A Secure Transaction Platform

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

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

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

Closing Thoughts

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

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