Artificial Intelligence

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

Ethical Considerations

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

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

Closing Thoughts

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

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

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

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

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

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

Can Robots Become Sentient With AI?

Posted on 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.

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?

AI in Healthcare

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

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

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

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

The Current State of AI in Healthcare

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

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

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

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

Responsibility

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

Competence

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

Transparency

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

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

Use Cases for AI in Healthcare

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

Medical Diagnoses

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

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

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

Automation in Patient Care

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

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

AI in Surgery

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

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

The primary benefits of robotic surgery include the following:

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

Sharing Medical Data

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

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

Research and Development

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

Staff Training

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

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

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

Overcoming Challenges with Healthcare AI

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

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

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

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

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

Closing Thoughts

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

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

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.

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.

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.