Category: Natural Language Understanding

Hologram Technology and AI-Based Chatbots

Posted on by Jean Chalopin

Integrating hologram technology and AI-based chatbots is an exciting new frontier in digital communication. Hologram technology provides a new way to interact with information and data, while AI-based chatbots are changing how people communicate with businesses and organisations. Together, these technologies offer unique opportunities for organisations to engage with customers, employees and other stakeholders in more meaningful ways.

The market for hologram technology and AI-based chatbots is snowballing. According to a report from ResearchAndMarkets.com, the global holographic display market will reach US$13.5 billion by 2026, growing at a CAGR of 26.8% from 2020 to 2026. Meanwhile, the global AI-based chatbot market is expected to reach US$1.3 billion by 2024, growing at a CAGR of 24.3% from 2019 to 2024.

What Is Hologram Technology?

Hologram technology is a cutting-edge digital visual solution that allows users to project three-dimensional images into real-world environments. The technology uses light and projection systems to create an illusion of a solid object, which can be viewed from multiple angles and appears to have depth. Holograms can be used for various applications, including entertainment, advertising, and educational purposes.

One of the significant benefits of hologram technology is that it can help businesses to stand out and capture the attention of their customers. Using holograms to showcase their products, companies can offer a unique and engaging experience that can differentiate them from their competitors. For example, hologram technology can be used to create interactive product displays that allow customers to explore a product from all angles, providing a more immersive experience.

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Another benefit of hologram technology is that it can be used to improve the efficiency of communication between employees and customers. With hologram technology, employees can remotely participate in meetings and presentations, allowing them to connect with colleagues and customers from anywhere in the world. Additionally, holograms can be used to conduct virtual product demonstrations, making it easier for businesses to showcase their products and services to customers.

Furthermore, hologram technology can also be used to improve training and development opportunities for employees. With holograms, employees can receive hands-on training and experience simulations in a controlled and safe environment. This type of training can be beneficial for industries such as construction, aviation, and healthcare, where hands-on training is required to ensure the safety and well-being of employees and customers.

What Are AI-Based Chatbots?

AI-based chatbots are computer programs designed to simulate human conversations with users. They use artificial intelligence and machine learning algorithms to understand and respond to user requests in natural language. Chatbots break down the user’s input into individual words and phrases and then analyse them to determine the user’s intent. Based on the intent, the chatbot selects a response from a predetermined list of options or generates a response using deep learning algorithms.

One of the key benefits of using AI-based chatbots is that they can simultaneously handle a large volume of customer interactions, 24/7, without human intervention. This means that customers can receive fast and efficient support outside business hours. Chatbots also offer a convenient and accessible way for customers to interact with a company, as they can be integrated into websites, messaging apps, and other digital platforms.

Some of the companies that are using AI-based chatbots effectively include:

Bank of America. Bank of America’s virtual assistant, Erica, uses natural language processing and machine learning to help customers manage their finances and answer questions about their accounts.

H&M. The fashion retailer has integrated chatbots into their customer service operations, allowing customers to use messaging apps to receive fast support with their orders and returns.

Sephora. Sephora’s chatbot, named ‘Sephora Assistant’, uses AI to provide customers with personalised beauty recommendations and product information.

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Overall, AI-based chatbots offer businesses a cost-effective and efficient way to interact with customers. Their capabilities constantly improve as advancements in artificial intelligence and machine learning continue.

Hologram Technology and AI-based Chatbots: Working Together

Hologram technology and AI-based chatbots can work together to provide a more immersive customer experience. With hologram technology, a computer-generated 3D image of a person or object is projected into the real world, giving the illusion of a physical presence. By integrating AI-based chatbots into this technology, businesses can create virtual assistants that can interact with customers in real time and provide personalised support.

For example, a customer might approach a holographic display and ask questions such as ‘What are your hours of operation?’ The AI-based chatbot would recognise the customer’s voice, process the request, and respond appropriately through the holographic image. The chatbot can also use the customer’s previous interactions and preferences to personalise the interaction and provide a more tailored experience.

One company that is using this technology effectively is Lowe’s, the home improvement retailer. Lowe’s has developed a virtual assistant called ‘The Lowe’s Holoroom’, which uses holographic technology and AI-based chatbots to help customers plan and visualise their home improvement projects. 

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Google rolled out a project in 2021 that utilises holograms in chats. According to the futuristic idea, users can transform into life-size 3D holographic replicas of themselves in virtual chat booths, giving the impression that they are in the same room as you.

The Challenges

There are several challenges in combining hologram technology with AI-based chatbots, including:

Technical complexity. Hologram technology requires specialised hardware and high-performance computing resources, making it challenging to integrate with AI-based chatbots. Additionally, the development of holographic displays that can interact in real-time with AI-based chatbots is still in its early stages.

Cost. Implementing hologram technology can be expensive, which may limit its widespread adoption. This high cost can make it difficult for companies to integrate hologram technology with AI-based chatbots, as both technologies require significant investment.

Interoperability. Hologram technology and AI-based chatbots are separate technologies, each with its own standards and protocols. Integrating these technologies seamlessly and effectively can be challenging, as they may not be designed to work together.

User experience. Creating a seamless and intuitive user experience that effectively combines hologram technology and AI-based chatbots can be difficult. A key challenge is ensuring that the technology is easy to use and provides a consistent and engaging experience for customers.

Privacy and security. Integrating hologram technology and AI-based chatbots raises privacy and security concerns, as the technology can collect and store sensitive customer data. Ensuring the security and privacy of this data is a critical challenge that must be addressed.

Despite these challenges, the potential benefits of combining hologram technology with AI-based chatbots are significant. As technology advances, we will likely see continued innovation and progress in this field.

Closing Thoughts

It is difficult to say whether hologram technology is the future of AI-based chatbots, as these technologies are constantly evolving. While hologram technology has the potential to provide a more interactive customer experience, it also presents several challenges, such as the need for specialised hardware and high-performance computing resources. Additionally, the cost of implementing hologram technology is currently high, which may limit its widespread adoption.

That being said, AI-based chatbots and hologram technology are two of the most promising advancements today, and they have the potential to complement each other in many ways. As both technologies continue to advance, we will likely see more companies exploring the possibilities of integrating them to create new and innovative customer experiences.

While hologram technology may play a role in the future of AI-based chatbots, it is too soon to predict the exact trajectory of this field. The integration of these technologies will continue to evolve, and we will likely see various approaches to combining AI-based chatbots and hologram technology in the future.

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

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

What Are Neural Implants?

Posted on by Jean Chalopin

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

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

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

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

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

Understanding Neural Implants

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

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

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

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

The Benefits of Neural Implants

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

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

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

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

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

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

The Challenges

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

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

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

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

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

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

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

Companies Operating in the Neural Implant Space

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

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

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

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

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

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

Closing Thoughts

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

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

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

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

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

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

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

How ChatGPT Shapes the Future

Posted on by Jean Chalopin

In recent years, the AI industry has grown significantly, with forecasts that the worldwide market will reach $190.61 billion by 2025, expanding at a CAGR of 36.2% between 2020 and 2025. The Covid-19 pandemic has only hastened this rise, as businesses have been forced to adjust swiftly to remote working and growing digitisation. 

The pandemic has brought to light the significance of technology in industries such as healthcare and e-commerce.

Introducing ChatGPT

ChatGPT is an AI model created by OpenAI that can potentially influence the AI market’s evolution in various ways. ChatGPT may be linked to a wide range of applications and services that need natural language processing (NLP), such as customer service, chatbots, and virtual assistants. This may raise the need for NLP-based AI solutions, which would help the AI industry flourish.

ChatGPT may also be used to train other AI models, which can accelerate the development and implementation of AI-powered apps and services. This can improve the efficiency of the AI development process, contributing to the growth of the AI market.

Furthermore, ChatGPT’s capacity to create human-like writing, which can be utilised for various content creation and optimisation activities, has the potential to propel the AI market forward. ChatGPT, for example, may produce product descriptions, marketing text, and even news pieces, reducing the time and effort necessary for content generation while enhancing output quality. 

Below is a simple example of how it can write a product description for Coca-Cola within seconds. 

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The Benefits of ChatGPT

One of the primary benefits of ChatGPT is its ability to help users improve their writing and language skills. ChatGPT can help individuals become more effective communicators by providing real-time feedback and suggestions, whether they are writing emails, composing reports, or creating content for social media. 

For example, sales and marketing professionals can use ChatGPT to improve their email writing, helping them to better engage with prospects and customers. Additionally, educators can use technology to help students improve their writing and critical thinking skills without needing human grading and feedback.

Another critical benefit of ChatGPT is its ability to support knowledge management and collaboration. By using the technology to automate repetitive tasks, such as summarising reports or answering frequently asked questions, organisations can free up time and resources for more strategic initiatives. 

This can help companies become more efficient, increase productivity, and enhance customer service. For example, customer service teams can use ChatGPT to respond quickly to customer inquiries and resolve issues, reducing wait times and improving the customer experience.

The example below shows how a customer might be able to resolve a query about their home insurance without speaking to a human.

How ChatGPT Augments Roles

ChatGPT can significantly augment the functions of different departments in an organisation, including Data, IT, Marketing, Development, Finance, and Compliance.

Data 

For Data teams, it can assist in processing large amounts of data to provide insights and support decision-making. It can benefit data teams in their coding endeavours, particularly when it comes to writing code in SQL or Python. 

ChatGPT’s ability to provide suggestions for completing code snippets, identify syntax errors and suggest corrections, and generate complete code snippets based on specific requirements, can save data teams valuable time and effort. Furthermore, it can serve as a repository of coding knowledge that can be easily shared among team members. 

For example, if a data team member is working on a SQL query and encounters a roadblock, they can ask ChatGPT for advice on how to proceed. It can then provide suggestions for optimising the query or offer alternative solutions based on its vast knowledge of SQL coding best practices. By utilising its coding capabilities, data teams can improve their coding efficiency and accuracy, freeing them up to focus on more complex tasks.

IT

IT teams can use ChatGPT to automate various IT operations tasks and build a knowledge management system. It may also be incorporated with IT systems to give users rapid and accurate replies to technical assistance enquiries, decreasing the IT team’s burden.

Furthermore, ChatGPT can create a knowledge management system to store and retrieve information about IT systems and procedures, increasing the team’s productivity. IT teams may also use its natural language processing skills to examine massive quantities of log data and give insights into system performance and potential faults.

Marketing

Marketing teams can use ChatGPT to generate high-quality content and build conversational AI chatbots for customer service and sales. You can watch a video below on how ChatGPT built an entire marketing campaign in minutes. 

https://www.youtube.com/watch?v=gI1UL1cHHjk
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Marketing teams still need to ask the right questions, but ChatGPT saves time and efficiency. 

Finance

For Finance teams, it can be integrated into financial systems to assist with data analysis and decision-making. It may assist finance teams in making more informed decisions and improving financial planning and forecasting. 

ChatGPT may also help finance teams automate operations, including calculating financial ratios, creating reports, and tracking spending. Furthermore, ChatGPT’s natural language processing skills may be utilised to analyse financial data and discover trends, allowing finance teams to recognise opportunities and possible hazards quickly.

Compliance

Compliance teams can use ChatGPT to ensure compliance with regulations and standards by automating various compliance tasks. 

It may also aid in the categorisation and classification of enormous volumes of data, as well as the investigation of complicated legislation and laws. Furthermore, it may give real-time responses to staff inquiries, decreasing the time spent on manual research and enhancing the compliance team’s productivity. The capacity of the language model to interpret and create human-like writing makes it a powerful tool for firms wanting to strengthen their compliance procedures.

By augmenting the roles of different departments, ChatGPT can help organisations increase productivity and improve the quality of their work. Some entrepreneurs are using the technology to brainstorm business ideas. It’s like having a friend to bounce your thoughts between. 

Risks of ChatGPT

Despite these benefits, there are also some risks associated with ChatGPT that must be considered. 

One of the primary risks is the potential for the technology to promote cheating and plagiarism. For example, students may use technology to generate homework assignments, or employees may use it to create reports and presentations without doing the necessary research and analysis. 

To mitigate this risk, it is essential for organisations to communicate the acceptable use of the technology clearly and to have clear policies and procedures in place to monitor and enforce compliance.

Another risk is the potential for the technology to perpetuate bias and harmful stereotypes. As the model has been trained on a large corpus of text, it may generate offensive or inappropriate language or reinforce negative stereotypes. It is vital for organisations to use the technology responsibly and ethically and to regularly review and update the training data to ensure that it is inclusive and free from bias.

AI for People

Despite these risks, companies are already using ChatGPT in innovative and impactful ways. For example, OpenAI partnered with the non-profit organisation ‘AI for People’ to develop a tool that uses ChatGPT to support mental health and well-being. 

The tool uses natural language processing and machine learning to provide users with personalised feedback and support, helping them manage stress, anxiety, and depression. OpenAI has also worked with news organisations and journalists to develop an AI-powered writing assistant that can help writers quickly generate high-quality, accurate news articles.

Copy.ai

Another example of a company positively using ChatGPT is Accenture, a leading global professional services firm. Accenture has developed a tool called ‘Copy.ai’ that uses ChatGPT to help businesses quickly generate high-quality marketing and advertising content. 

By using the technology to automate routine tasks, such as writing product descriptions and creating social media posts, Accenture is helping its clients become more efficient and effective in their marketing efforts.

Closing Thoughts

ChatGPT is a powerful tool that has the potential to help individuals and organisations across different roles to adapt and develop new skills. While some risks are associated with the technology, companies are already using it innovatively to drive positive outcomes. The key is to use it responsibly. 

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.

How Generative Chat AI Operate

Posted on by Jean Chalopin

Generative chat AI is an exciting technology that has been making waves in recent years. It refers to computer programs designed to interact with humans using natural language processing and can generate responses that seem to be coming from a real person. 

These AI systems are capable of analysing and understanding the context of a conversation and can create responses that are not only relevant but also coherent.

According to PitchBook data, generative AI investment rose by 425% between 2020 and December 2022, totalling $2.1 billion last year. This is an especially astounding performance, given a general decline in tech investment in 2022.

This article will dive into technical details that make generative chat AI possible. We’ll explore natural language processing, deep learning, and neural networks and how they are used to train these AI systems. We’ll also touch on some challenges developers face when creating generative chat AI and how they work to overcome them.

What Is Generative Chat AI?

Generative chat AI refers to computer programs that use natural language processing (NLP) to generate human-like responses to a user’s input. These AI systems are designed to interact with humans in a way that feels natural, as if you were chatting with another person. Unlike rule-based chatbots that rely on pre-written responses, generative chat AI is capable of generating new responses on the fly based on the context of the conversation.

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At the heart of generative chat AI is a technology called deep learning, a type of machine learning that involves training neural networks on large amounts of data. By feeding these neural networks with vast amounts of text data, such as chat logs or social media posts, they can learn to generate human-like responses.

The training process involves teaching the neural network to recognise patterns in the data, such as common sentence structures, idioms, and other linguistic features. Once the network has learned these patterns, it can generate new responses that fit within the context of the conversation. The more data the neural network is trained on, the better it becomes at generating natural-sounding responses.

How Does Generative Chat AI Work?

Generative chat AI works by using a combination of natural language processing (NLP) and deep learning, specifically through the use of neural networks. Neural networks are a machine learning algorithm that can recognize patterns in data and learn to make predictions based on that data.

In the case of generative chat AI, the neural network is trained on large amounts of text data, such as chat logs or social media posts. This training process is called deep learning because the neural network has multiple layers of interconnected nodes that allow it to recognize increasingly complex patterns in the data.

During training, the neural network learns to identify linguistic patterns and relationships between words and phrases. For example, it might know that certain words tend to be used together in specific contexts or that certain terms are more likely to occur in response to particular prompts. This training process enables the neural network to generate new responses relevant to the conversation’s context.

Once the neural network has been trained, it can generate responses to user input in real time. When a user inputs a message, the generative chat AI system uses NLP techniques to analyse the text and determine the context of the conversation. Based on this context, the system then uses the trained neural network to generate a relevant and coherent response.

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The success of generative chat AI depends mainly on the training data quality and the neural network’s complexity. Developers must ensure that the training data is diverse and representative of the conversations the system will likely encounter in real-world situations. Additionally, they must design neural networks capable of handling the complexity of natural language and generating accurate and engaging responses.

Use Cases for Generative Chat AI

The future holds many potential use cases for generative chat AI, but there are already a few ways that businesses are making the most of the opportunity. 

Coding

Generative AI can understand user coding requirements in countless languages such as Python, SQL, and Excel formulas. You can ask it to write or debug your code, and the AI returns step-by-step instructions on implementing it. 

Below is a snippet of what the popular Chat GPT platform can provide using a simple question. The more specific the user is with a question, the better the output. 

Copywriting

Users can provide generative chat AI with a topic overview, context and tone. The output is a loose summary that can speed up the copywriting process, allowing humans to focus on the more creative parts. 

Currently, the results are imperfect; see our section on augmenting roles rather than replacing them below. Still, they can make marketing teams far more efficient by giving them a solid starting point. 

Before posting anything written by AI, it is vital to check the accuracy of the information. Outputs are based on the data AI reads, which can be filled with bias and fake content. 

Customer Service

Sales teams can use generative chat AI to sort through all previous customer interactions across all channels (such as web conferences, phone calls, emails, and instant messages) and then direct it to create the next answer.

Consider yourself a salesperson who must react to a client’s query. Imagine how AI could assist you in coming up with the ideal response based on understanding the account history. An article in Wall Street Journal (membership required) talks about some businesses already adopting AI for this purpose. 

Augmenting Roles, Not Replacing Them

One of the key benefits of generative AI is that it can automate routine tasks, allowing humans to focus on more complex and creative work. For example, a chatbot can handle basic customer inquiries, freeing human customer service agents to handle more complex issues requiring empathy and critical thinking.

However, it’s essential to recognize that generative AI cannot replicate human creativity, empathy, and intuition. There will always be tasks and situations requiring a human touch, such as complex problem-solving, creative work, and emotional intelligence.

Moreover, the widespread adoption of generative AI could potentially lead to job displacement and a loss of human jobs. To mitigate this risk, companies should take a responsible approach to AI adoption, ensuring they are using it to augment human capabilities rather than replace them entirely.

In practice, this means that companies should carefully consider how generative AI can be used to complement human work rather than replace it. This might involve retraining employees to work alongside AI, redesigning job roles to take advantage of AI capabilities, or providing opportunities for employees to learn new skills that will be in demand as AI becomes more prevalent.

The Challenges of Generative Chat AI

Generative chat AI faces several challenges. 

The first major challenge is obtaining high-quality training data. Generative chat AI models require large amounts of diverse and representative training data to learn how to generate appropriate responses to various user inputs. However, obtaining such data can be difficult, especially for specialised or niche domains or languages with limited digital content.

Another challenge is ensuring that the AI model does not produce biassed outputs. AI models are trained on data, which may include inherent biases in language use or representation of certain groups or perspectives. If the training data is biassed, the AI model may learn to produce outputs that reinforce or amplify those biases, potentially leading to harmful or discriminatory user interactions.

And Possible Solutions

To address these challenges, it’s important to carefully curate and evaluate the training data used to train the generative chat AI model. This may involve sourcing data from diverse and representative sources, applying quality control measures to filter out biassed or irrelevant data, and using techniques like adversarial training to ensure that the model can handle a variety of inputs and outputs.

Another approach is to evaluate the outputs of the AI model and implement techniques like debiasing or reweighting to mitigate any potential biases. This can involve human oversight and intervention and ongoing monitoring and adjustment to ensure that the model remains fair.

A further challenge is the consistency of generative AI. Users expect a natural and engaging dialogue where responses flow smoothly from one to another and build upon previous messages. However, generative chat AI models may struggle to maintain coherence and consistency, especially when dealing with complex or unpredictable user inputs. 

For example, the model may generate off-topic or irrelevant responses or contradict previous statements made in the conversation. To address this challenge, AI models may require additional training or techniques like attention mechanisms, which can help the model focus on relevant parts of the conversation and generate more coherent responses.

Closing Thoughts

The future of generative chat AI is promising as advancements in natural language processing and machine learning pushes the boundaries of what’s possible. In the coming years, we can expect to see more sophisticated and context-aware AI models capable of engaging in rich and natural conversations with users. 

These models may incorporate advanced techniques like sentiment analysis, emotion detection, and personality modelling, allowing them to tailor their responses to individual users and create more personalised experiences. However, as with any technology, some potential risks and challenges must be addressed, such as maintaining an ethical and responsible use of AI, ensuring transparency and accountability, and addressing potential biases in the data used to train these systems.

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

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

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

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

AI and Its Many Forms

Posted on by Jean Chalopin

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

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

The Different Types of AI

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

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

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

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

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

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

Machine Learning

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

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

Natural Language Processing

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

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Computer Vision

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

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Expert Systems

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

Robotics

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

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

How People Perceive AI

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

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

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

Closing Thoughts

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

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

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

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

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

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

Blockchain and AI

Posted on 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.

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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.

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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.

Can Robots Become Sentient With AI?

Posted on by Jean Chalopin

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

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

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

Robots and AI

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

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

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

What Is AI Sentience in Robotics?

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

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

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

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

How Does AI Sentience Work?

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

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

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

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

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

Why AI Sentience? 

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

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

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

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

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

  • Cogito: A company that develops AI-powered virtual assistants to improve customer engagement and support.
  • Lemonaid: A company that uses AI to provide virtual consultations and prescription services.
  • Woebot: A company that uses AI and machine learning to provide individuals with mental health support and counselling.
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These are just a few examples of companies working on developing AI-powered virtual nursing assistants. However, it is essential to note that these systems are not fully conscious and do not possess true self-awareness or emotions. The development of AI sentience in healthcare is still in its early stages, and it may be several years before fully sentient AI systems are deployed in real-world healthcare settings.

The Risks and Challenges

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

Technical Risks and Challenges

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

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

Ethical Risks and Challenges

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

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

Social Risks and Challenges

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

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

Closing Thoughts

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

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

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

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

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

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

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

What Is Generative AI?

Posted on 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.

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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.

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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.

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.

Brain-Computer Interfaces

Posted on by Jean Chalopin

Brain-computer interfaces are devices that allow people to control machines with their thoughts. This technology has been the stuff of science fiction and even children’s games for years. 

Mindflex game by Mattell

On the more advanced level, brain-computer technology remains highly experimental but has vast possibilities. First to mind (no pun intended), would be to aid those with paralysis in creating electrical impulses that would let them regain control of their limbs. Second, the military would like to see its service members operating drones or missiles hands-free on the battlefield.  

There are also concerns raised when a direct connection is made between a machine and the brain. For example, such a connection could give users an unfair advantage, enhancing their physical or cognitive abilities. It also means hackers could steal data related to the user’s brain signals.  

With this article, we explore several opportunities and issues that are related to brain-computer interfaces.  

Why Do Brain-Computer Interfaces Matter?

Brain-computer interfaces allow their users to control machines with their thoughts. Such interfaces can aid people with disabilities, and they can enhance the interactions we have with computers. The current iterations of brain-computer interfaces are primarily experimental, but commercial applications are just beginning to appear. Questions about ethics, security, and equity remain to be addressed. 

What Are Brain-Computer Interfaces? 

A brain-computer interface enables the user to control an external device by way of their brain signals.  A current use of a BCI that has been under development is one that would allow patients with paralysis to spell words on a computer screen. 

Additional use cases include: a spinal cord injury patient regaining control of their upper body limbs, a BCI-controlled wheelchair, or a noninvasive BCI that would control robotic limbs and provide haptic feedback with touch sensations. All of this would allow patients to regain autonomy and independence.

Courtesy of Atom Touch

Beyond the use of BCIs for the disabled, the possibilities for BCIs that augment typical human capabilities are abundant. 

Neurable has taken a different route and has created headphones that can make you more focused, not requiring a user’s touch to control, but can work with a wink or nod and will be combined with VR for a better experience.

Courtesy of Neurable

How do BCIs Work?

Training

Generally, a new BCI user will go through an iterative training process. The user learns how to produce signals that the BCI will recognize, and then the BCI will take those signals and translate them for use by way of a machine learning algorithm. Machine learning is useful for correctly interpreting the user’s signals, as it can also be trained to provide better results for that user over time. 

Connection

BCIs will generally connect to the brain in two ways: through wearable or implanted devices. 

Implanted BCIs are often surgically attached directly to brain tissue, but Synchron has developed a catheter-delivered implant that taps into blood vessels in the chest to capture brain signals. The implants are more suitable for those with severe neuromuscular disorders and physical injuries where the cost-benefit is more favorable. 

A person with paralysis could regain precise control of a limb by using an implanted BCI device attached to specific neurons; any increase in function would be beneficial, but the more accurate, the better.  Implanted BCIs can measure signals directly from the brain, reducing interference from other body tissues. However, most implants will pose other risks, primarily surgical-related like infection and rejection. Some implanted devices can reduce these risks by placing the electrodes on the brain’s surface using a method called electrocorticography or ECoG.  

Courtesy of the Journal of Neurosurgery

Wearable BCIs, on the other hand, generally require a cap containing conductors which measure brain activity detectible on the scalp. The current generation of wearable BCIs is more limited, such as only for augmented and virtual reality, gaming, or controlling an industrial robot. 

Most wearable BCIs are using electroencephalography (EEG) with electrodes contacting the scalp to measure the brain’s electrical activity. A more recent and emerging wearable method incorporates functional near-infrared spectroscopy (fNIRS), where near-infrared light is shined through the skull to measure blood flow which, when interpreted, can indicate information like the user’s intentions. 

OBELAB’s NIRSIT fNIRS system

To enhance their usefulness, researchers are developing BCIs that utilize portable methods for data collection, including wireless EEGs. These advancements allow users to move freely. 

The History of BCIs

Most BCIs are still considered experimental. Researchers began testing wearable BCI tech in the early 1970s, and the first human-implanted BCI was Dobelle’s first prototype, implanted into “Jerry,” a man blinded in adulthood, in 1978. A BCI with 68 electrodes was implanted into Jerry’s visual cortex. The device succeeded in producing phosphenes, the sensation of “seeing” light.  

In the 21st century, BCI research increased significantly, with the publication of thousands of research papers. Among that was Tetraplegic Matt Nagle, who became the first person to control an artificial hand using a BCI in 2005. Nagle was part of Cyberkinetics Neurotechnology’s first nine-month human trial of their BrainGate chipimplant.  

Even with the advances, it is estimated that fewer than 40 people worldwide have implanted BCIs, and all of them are considered experimental. The market is still limited, and projections are that the total market will only reach $5.5 million by 2030. Two significant obstacles to BCI development are that each user generates their own brain signals and those signals are difficult to measure.  

The majority of BCI research has historically focused on biomedical applications, helping those with disabilities from injury, neurological disorder, or stroke. The first BCI device to receive Food and Drug Administration authorization was granted in April 2021. The device (IpsiHand) uses a wireless EEG headset to help stroke patients regain arm and hand control.  

Courtesy of Neurolutions IpsiHand

Concerns With BCI

Legal and security implications of BCIs are the most common concerns held by BCI researchers. Because of the prevalence of cyberattacks already, there is an understandable concern of hacking or malware that could be used to intercept or alter brain signal data stored on a device like a smartphone.

The US Department of Commerce (DoC) is reviewing the security implications of exporting BCI technology. The concern is that foreign adversaries could gain an intelligence or military advantage. The DoC’s decision will affect how BCI technology is used and shared abroad.

Social and Ethical Concerns

Those in the field have also considered BCI’s social and ethical implications. The costs for wearable BCIs can range from hundreds even up to thousands of dollars, and this price would likely mean unequal access. 

Implanted BCIs cost much more. The training process for some types of BCIs is significant and could be a burden on users. It has been suggested that if the translations of BCI signals for speech are inaccurate, then great harm could result. 

The Opportunities of BCIs

The main opportunities that BCIs will initially provide are to help those paralyzed by injury or disorders to regain control of their bodies and communicate. This is already seen in the current research, but in the long term, this is only a steppingstone. 

The augmentation of human capability, be it on the battlefield, in aerospace, or in day-to-day life, is the longer-term goal. BCI robots could also aid humans with hazardous tasks or hazardous environments, such as radioactive materials, underground mining, or explosives removal.  

Finally, the field of brain research can be enhanced with a greater number of BCIs in use. Understanding the brain will be easier with more data, and researchers have even used a BCI to detect the emotions of people in minimally conscious or vegetative states.  

Closing Thoughts

BCIs will provide many who need them a new sense of autonomy and freedom they lack, but several questions remain as the technology progresses. Who will have access, and who will pay for these devices? Is there a need to regulate these devices as they begin to augment human capability, and who will do so? What applications would be considered unethical or controversial?  What steps are needed to mitigate information, privacy, security, and military threats?  

These questions have yet to be definitively answered—and they should be answered before the technology matures. The next step of BCIs will be information transfer in the opposite direction, like with Dobelle’s original light sensing “seeing” BCI of the 1970s, or computers telling humans what they see, think, and feel. This step will bring a whole new set of questions to answer.

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

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

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

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

Spotting Deepfakes

Posted on by Jean Chalopin

A deepfake is a piece of image, audio, or video content using artificial intelligence to create a digital representation by replacing the likeness of one person with another. This advanced technology is becoming more common and convincing, leading to misleading news and counterfeit videos. 

We will delve deeper into deepfakes, discuss how deepfakes are created, why there are concerns about their growing prevalence, and how best to detect them so as not to be fooled into believing their content.  

Rise of the Machines

Advances in computers have allowed them to become increasingly better at simulating reality. What was once done taking days in the darkroom can be done in seconds using photoshop. For example, five pictures of the Cottingley Fairies tricked the world in 1917.  

Modern cinema now relies on computer-generated characters, scenery, and sets, replacing the far-flung locations and time-consuming prop-making that were once an industry staple.  

Source: The Things

The quality has become so good that many cannot distinguish between CGI and reality.

Deepfakes are the latest iteration in computer imagery, created using specific artificial technology techniques that were once very advanced but are beginning to enter the consumer space and will soon be accessible to all.  

What Are Deepfakes?

The term deepfake was coined from the underlying technology behind them, deep learning, a specific field of Artificial Intelligence (AI) or machine learning. Deep learning algorithms have the ability to teach themselves how to solve problems better, and this ability improves the more extensive the training data set provided to them. Their application to deepfakes makes them capable of swapping faces in video and other digital media, allowing for realistic looking but 100% fake media to be produced.  

Source: The Army War College

While many methods can be applied to create deepfakes, the most common is through the use of deep neural networks (DNNs). These DNNs use autoencoders that incorporate a face-swapping technique. The process starts with a target video that is used as the basis of the deepfake (on the left above) and from there, a collection of video clips of the person (Tom Cruise) that you wish to overlay into each frame of the target video.

The target video and the clips used to produce the deepfake can be completely unrelated. The target could be a sports scene or a Hollywood feature, and the person’s videos to insert could be a collection of random YouTube clips.

The deep learning autoencoder is an artificial intelligence program tasked with selecting YouTube clips to understand how the person looks from several angles, accounting for different facial patterns and environmental conditions. It will then map that person into each target video frame to make it look original. 

An additional machine learning technique called Generative Adversarial Networks or GANs is added to the mix, which detects any flaws and improves the deepfake through multiple iterations. GANs are themselves another method used to create deepfakes. They rely on large amounts of data to learn how to create new examples that mimic the real target. With sufficient data, they can produce incredibly accurate fakes.  

Deepfake Apps

Deepfake apps have also hit the consumer market, such as Zao, FaceApp, DeepFace Lab, Face Swap, and the notorious and removed DeepNude–a particularly dangerous app that generated fake nude images of women.

Several other versions of deepfake software that have varying levels of results can be found on the software development open-source community GitHub. Some of these apps can be used purely for entertainment purposes. However, others are much more likely to be maliciously exploited.

How Are Deepfakes Being Used?

While the ability to swap faces quickly and automatically with an app and create a credible video has some interesting benign applications, in Instagram posts and movie production, deepfakes are obviously dangerous. Sadly, one of the first real-world deepfake applications was in the creation of synthetic pornography. 

Revenge Porn

2017 saw a Reddit user named “deepfakes” create a forum for porn featuring face-swapped actors.  Since then, the genre of “revenge porn” has repeatedly made the news. These deepfake use cases have severely damaged the reputations of celebrities, prominent figures, and even regular people.  According to a 2019 Deeptrace report, pornography constituted 96% of deepfake videos found online, and this has only dropped to 95% in 2022.  

Political Manipulation

Deepfakes have already been employed in political manipulation. Starting in 2018, for example, a Belgian political party released a video of, at the time, President Donald Trump giving a speech that called on Belgium to withdraw from the Paris climate agreement. The former president Trump never gave that speech. It was a deepfake. 

The Trump video was far from the first deepfake created to mislead, and many tech-savvy political experts are bracing for the future wave of fake news featuring convincingly realistic deepfakes. We have been fortunate not to have so many of them during the 2022 midterms, but 2024 may be a different story. They have, however, been used this year to change the course of the war in Ukraine.  

Non-Video Deepfakes

Just as deepfake videos have taken off, their audio counterparts have also become a growing field with many applications. Realistic deepfake audio can be created with similar deep learning algorithms using samples of a few hours of the target voice. 

Once the model voice has been created, that person can say anything, such as the audio deepfake of Joe Rogan. This method has already been used to perpetrate fraud, and will likely be used again for other nefarious actions.

There are beneficial uses for this technology. It could be used as a form of voice replacement in medical applications, as well as in specific entertainment situations. If an actor was to die before the completion of the movie or before a sequel is started, their voice could be fabricated to complete lines that were not yet spoken. Game programmers can make characters who can say anything in real-time with the real voice rather than using a limited script recorded by the voice actor.  

Detecting Deepfakes

With deepfakes becoming ever more common, our society must collectively adapt to the spotting of deepfake videos in the same way that we have become attuned to detecting various kinds of fake news online. 

As is the case with all types of cyber security, there is a cat-and-mouse game where a new deepfake technology must emerge before a relevant countermeasure is created. This process is a vicious cycle, like with computer viruses, which is an ongoing challenge to avoiding the harm that can be done.

Deepfake Indicators

There are a few tell-tale giveaways that help in spotting a deepfake.

The earlier generation of deepfakes were not very good at animating faces, and the resulting video felt unnatural and obvious. However, after the University of Albany released its blinking abnormality research, newer deepfakes have incorporated natural blinking into their software–eliminating this problem.

Second, look for unnatural lighting. The deep fake’s algorithm will often retain the illumination of the provided clips that were used to create the fake video’s model. This results in a lighting mismatch. 

Unless the audio is also created with a deep fake audio component, it also might not match the speech pattern of the person that is the target. The video and the audio may look out of sync unless both have been painstakingly manipulated.  

Fighting Deepfakes Using Technology

Even though the quality of deepfakes continues to improve and appear more realistic with technical innovation, we are not defenseless to them. 

Sensity, a company that helps verify IDs for KYC applications, has a deepfake detection platform that resembles an antivirus alert system.  

The user is alerted when they are viewing content that has signs of AI-generated media. Sensity’s system uses the same deep learning software to detect as is used to create the deepfake videos.  

Operation Minerva uses a more straightforward approach to identifying and combating deepfakes.  They employ a method of digital fingerprinting and content identification to locate videos made without the target’s consent. It can identify examples of deepfakes, including revenge porn, and if identified, it will send a takedown notice to sites that Operation Minerva polices. 

There was also a Deepfake Detection Challenge by Kaggle, sponsored by AWS, Facebook, Microsoft, and the Partnership on AI’s Media Integrity Steering Committee. This challenge was an open, collaborative initiative to build new ways of detecting deepfakes. The prizes ranged up to a half million dollars.  

Closing Thoughts

The advent of deepfakes has made the unreal seem real. The quality of deepfakes is improving and combating them will be more problematic as the technology evolves. 

We must remain diligent in finding these synthetic clips that can seem so real. They have their place if used for beneficial reasons, such as in entertainment and gaming, or med-tech to help people regain speech. However, the damage they can do on personal, financial, and even social levels has the potential to be catastrophic. Responsible innovation is vital to lasting success.

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

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

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

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