PredictWise

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Beyond the Hype: The Enduring Value of Predictive AI in a GenAI World

Artificial intelligence could revolutionize conventional eye-tracking technology. Developers would benefit in several ways from incorporating it into accessibility testing. Could this predictive alternative replace today’s standard techniques? What Is AI-Powered Predictive Eye Tracking? AI-powered predictive eye-tracking is an advancement in conventional eye-tracking technologies. It uses a machine learning model to predict where people will look. Its purpose is to use their fixations and scan paths — where their gaze settles and how it jumps from point to point — to inform development processes and improve accessibility testing. There are several ways AI-powered predictive eye tracking can work. The first involves a massive training dataset, where engineers feed tens of thousands of data points to an algorithm. Typical references include attention heatmaps — a visual representation of fixation depicted using cool and warm colors — and past research on how users respond to factors like layout, orientation, color, size, and shape. Another approach involves simulations. Since neural networks are designed to behave like a human brain, they can accurately imitate fixation and scan paths. Alternatively, AI engineers can use a machine learning model to develop synthetic attention heatmaps. Generally, as long as the training data is relevant and preprocessed, their results will be realistic. Professionals can also embed an advanced algorithm into a camera to capture individuals’ eye movements as they scan an image, video, website, or app. These computer vision systems interpret visual data in real-time to determine fixation and scan paths. It can use near-infrared projection or track how the pupil moves in relation to the sclera — the eye’s white outer area. The point of AI-powered predictive eye tracking in accessibility testing is to make platforms as accessible as possible. While many developers account for those with common, permanent visual impairments, they often overlook others. Approximately 15% of the global population — over one billion people — experience situational disabilities. For example, they may have cataracts, night blindness, or broken limbs. With AI, everyone can be accommodated. How Is It Different from Conventional Eye Tracking? Conventional eye-tracking technology differs from AI-powered predictive eye-tracking in that it uses the pupil-center corneal reflection technique. A specialized camera and projection system sends near-infrared light into the user’s eyes. It uses the resulting reflection on the cornea — the transparent, outermost part of the eye that covers the iris and pupil — to establish their relative position. This way, it can measure where an individual’s eyes move. AI-powered systems don’t need to project near-infrared patterns because the algorithms can interpret visual information when embedded into computer vision systems. They can train on existing research to uncover hidden trends. Conversely, conventional eye-tracking technology uses basic mathematical algorithms to calculate fixations and scan paths. Conventional tools are generally inaccurate because someone must manually synthesize eye movement data points into aggregated information blocks. Some information is lost, miscategorized, or misinterpreted in the process, muddling intent. Using these findings for forecasts does not always return precise results. In accessibility testing, the little details matter. For people with disabilities, whether a platform is intuitive is often the difference between being able to use it and having to find an alternative. Since AI has predictive capabilities, it is the clear choice for advanced eye-tracking. Use Cases for AI-Powered Predictive Eye Tracking Several use cases for AI-powered predictive eye tracking in accessibility testing exist. Cursor Control Some people with disabilities lack full use of their limbs or are incapable of fine motor control. With AI-powered predictive eye tracking, they can fully participate in accessibility testing by moving the cursor with their eyes. To click, they can blink twice in rapid succession, fix their gaze on one spot for a predetermined amount of time, or press a hand-held switch. Gaze Prediction Gaze prediction uses past eye movement information to forecast where people will look and what will interest them. These algorithms can analyze behavioral information within seconds — before the user realizes they’re about to make a decision. These real-time adjustments help make the user experience (UX) more intuitive. Personalized Learning Because of the high potential for personalized learning that AI-powered tools provide, experts expect the education sector to play a large role in AI’s growth, with the market projected to be worth $1,345 billion by 2030. Specifically, AI-powered predictive eye tracking can transform accessibility in educational settings, allowing students of all abilities to engage more fully in the learning experience. This technology will let students navigate digital learning resources using only their gaze, fostering an inclusive environment for learners who might otherwise struggle to keep up with their peers. Intuitive UI During accessibility testing, developers can use AI-generated heatmaps to forecast what users will focus on during sessions. Since eye tracking reveals unconscious design preferences, they can use this information to eliminate user interface bloat, ensuring the UX is as streamlined and intuitive as possible. Video Captioning Basic video captioning models translate visual input into language, transcribing videos for people who are deaf, hard-of-hearing or cognitively impaired. This technology is often unavailable or inaccurate. Predictive AI outperforms those transcriptions, whether embedded into an eye-tracking camera or a mounted fisheye lens that monitors on-screen images. Advantages of Integrating Predictive Eye Tracking Into Accessibility Testing The primary benefit of integrating this AI technology into accessibility testing is the reduced hardware costs. Since these systems don’t need to project near-infrared patterns or use specialized cameras, building them is more affordable. Moreover, since algorithms can interact with multiple users simultaneously, developers only need one model for all analyses. Another one of the major advantages of AI is precision. Unlike conventional eye-tracking tools that need information aggregated into strict, predefined blocks, it can account for numerous variables. People with disabilities often scan differently. For example, those with autism have fewer gaze fixations, and those with attention-deficit/hyperactivity disorder have relatively unpredictable eye movements. An advanced algorithm eliminates guesswork by detecting subtle patterns, enabling it to build accurate, personalized profiles for various user segments. This way, developers can tweak their UI and UX with unparalleled precision. With this level of granularity, accessibility testing becomes far more insightful and actionable. Challenges of Integrating Predictive Eye Tracking Into Accessibility Testing Like any emerging technology, AI predictive eye tracking faces some obstacles. For one, developers must consider the legality of using this information to train or inform their model. If they operate in or serve users in the European Union, they must adhere to the General Data Protection Regulation. This may add to development complexity, delaying time to completion. Privacy is crucial for compliance and reputation preservation. One potential solution is to store fixations and scan paths — using the x and y-axis to standardize data points — locally instead of on cloud servers. Anonymizing the data this way protects the users during accessibility testing and future use. Training is another issue. The model may manifest unexplainable biases or skew results if a training dataset is irrelevant or inaccurate. For example, if the references mainly consist of white individuals, it may offensively direct people with epicanthic folds to open their eyes. Companies must factor in all eye, skin, and lighting types for accessibility testing to be precise. Large resource utilization is also a potential issue. The more complex these algorithms are, the more memory and processing power users need. Fortunately, researchers have already begun exploring solutions. One research group developed a saliency prediction model that runs over four times faster and is 93.27% smaller than state-of-the-art alternatives. The Future of AI Eye Tracking in Accessibility Testing Since AI is still a relatively new technology, developers and engineers will incrementally improve it as they explore new use cases and address past pain points. While it may not replace standard near-infrared eye-tracking technology just yet, it likely will in the near future. Artificial intelligence could revolutionize conventional eye-tracking technology. Developers would benefit in several ways from incorporating it into accessibility testing. Could this predictive alternative replace today’s standard techniques? What Is AI-Powered Predictive Eye Tracking? AI-powered predictive eye-tracking is an advancement in conventional eye-tracking technologies. It uses a machine learning model to predict where people will look. Its purpose is to use their fixations and scan paths — where their gaze settles and how it jumps from point to point — to inform development processes and improve accessibility testing. There are several ways AI-powered predictive eye tracking can work. The first involves a massive training dataset, where engineers feed tens of thousands of data points to an algorithm. Typical references include attention heatmaps — a visual representation of fixation depicted using cool and warm colors — and past research on how users respond to factors like layout, orientation, color, size, and shape. Another approach involves simulations. Since neural networks are designed to behave like a human brain, they can accurately imitate fixation and scan paths. Alternatively, AI engineers can use a machine learning model to develop synthetic attention heatmaps. Generally, as long as the training data is relevant and preprocessed, their results will be realistic. Professionals can also embed an advanced algorithm into a camera to capture individuals’ eye movements as they scan an image, video, website, or app. These computer vision systems interpret visual data in real-time to determine fixation and scan paths. It can use near-infrared projection or track how the pupil moves in relation to the sclera — the eye’s white outer area. The point of AI-powered predictive eye tracking in accessibility testing is to make platforms as accessible as possible. While many developers account for those with common, permanent visual impairments, they often overlook others. Approximately 15% of the global population — over one billion people — experience situational disabilities. For example, they may have cataracts, night blindness, or broken limbs. With AI, everyone can be accommodated. 15% of the global population 15% of the global population How Is It Different from Conventional Eye Tracking? Conventional eye-tracking technology differs from AI-powered predictive eye-tracking in that it uses the pupil-center corneal reflection technique. A specialized camera and projection system sends near-infrared light into the user’s eyes. It uses the resulting reflection on the cornea — the transparent, outermost part of the eye that covers the iris and pupil — to establish their relative position. This way, it can measure where an individual’s eyes move. AI-powered systems don’t need to project near-infrared patterns because the algorithms can interpret visual information when embedded into computer vision systems. They can train on existing research to uncover hidden trends. Conversely, conventional eye-tracking technology uses basic mathematical algorithms to calculate fixations and scan paths. Conventional tools are generally inaccurate because someone must manually synthesize eye movement data points into aggregated information blocks. Some information is lost, miscategorized, or misinterpreted in the process, muddling intent. Using these findings for forecasts does not always return precise results. In accessibility testing, the little details matter. For people with disabilities, whether a platform is intuitive is often the difference between being able to use it and having to find an alternative. Since AI has predictive capabilities, it is the clear choice for advanced eye-tracking. Use Cases for AI-Powered Predictive Eye Tracking Several use cases for AI-powered predictive eye tracking in accessibility testing exist. Cursor Control Some people with disabilities lack full use of their limbs or are incapable of fine motor control. With AI-powered predictive eye tracking, they can fully participate in accessibility testing by moving the cursor with their eyes. To click, they can blink twice in rapid succession, fix their gaze on one spot for a predetermined amount of time, or press a hand-held switch. Gaze Prediction Gaze prediction uses past eye movement information to forecast where people will look and what will interest them. These algorithms can analyze behavioral information within seconds — before the user realizes they’re about to make a decision. These real-time adjustments help make the user experience (UX) more intuitive. Personalized Learning Because of the high potential for personalized learning that AI-powered tools provide, experts expect the education sector to play a large role in AI’s growth, with the market projected to be worth $1,345 billion by 2030. Specifically, AI-powered predictive eye tracking can transform accessibility in educational settings, allowing students of all abilities to engage more fully in the learning experience. expect the education sector to play expect the education sector to play This technology will let students navigate digital learning resources using only their gaze, fostering an inclusive environment for learners who might otherwise struggle to keep up with their peers. Intuitive UI During accessibility testing, developers can use AI-generated heatmaps to forecast what users will focus on during sessions. Since eye tracking reveals unconscious design preferences , they can use this information to eliminate user interface bloat, ensuring the UX is as streamlined and intuitive as possible. reveals unconscious design preferences reveals unconscious design preferences Video Captioning Basic video captioning models translate visual input into language, transcribing videos for people who are deaf, hard-of-hearing or cognitively impaired. This technology is often unavailable or inaccurate. Predictive AI outperforms those transcriptions , whether embedded into an eye-tracking camera or a mounted fisheye lens that monitors on-screen images. AI outperforms those transcriptions AI outperforms those transcriptions Advantages of Integrating Predictive Eye Tracking Into Accessibility Testing The primary benefit of integrating this AI technology into accessibility testing is the reduced hardware costs. Since these systems don’t need to project near-infrared patterns or use specialized cameras, building them is more affordable. Moreover, since algorithms can interact with multiple users simultaneously, developers only need one model for all analyses. Another one of the major advantages of AI is precision. Unlike conventional eye-tracking tools that need information aggregated into strict, predefined blocks, it can account for numerous variables. People with disabilities often scan differently. For example, those with autism have fewer gaze fixations , and those with attention-deficit/hyperactivity disorder have relatively unpredictable eye movements. have fewer gaze fixations have fewer gaze fixations An advanced algorithm eliminates guesswork by detecting subtle patterns, enabling it to build accurate, personalized profiles for various user segments. This way, developers can tweak their UI and UX with unparalleled precision. With this level of granularity, accessibility testing becomes far more insightful and actionable. Challenges of Integrating Predictive Eye Tracking Into Accessibility Testing Like any emerging technology, AI predictive eye tracking faces some obstacles. For one, developers must consider the legality of using this information to train or inform their model. If they operate in or serve users in the European Union, they must adhere to the General Data Protection Regulation. This may add to development complexity, delaying time to completion. Privacy is crucial for compliance and reputation preservation. One potential solution is to store fixations and scan paths — using the x and y-axis to standardize data points — locally instead of on cloud servers. Anonymizing the data this way protects the users during accessibility testing and future use. Training is another issue. The model may manifest unexplainable biases or skew results if a training dataset is irrelevant or inaccurate. For example, if the references mainly consist of white individuals, it may offensively direct people with epicanthic folds to open their eyes. Companies must factor in all eye, skin, and lighting types for accessibility testing to be precise. model may manifest unexplainable biases model may manifest unexplainable biases Large resource utilization is also a potential issue. The more complex these algorithms are, the more memory and processing power users need. Fortunately, researchers have already begun exploring solutions. One research group developed a saliency prediction model that runs over four times faster and is 93.27% smaller than state-of-the-art alternatives. runs over four times faster runs over four times faster The Future of AI Eye Tracking in Accessibility Testing Since AI is still a relatively new technology, developers and engineers will incrementally improve it as they explore new use cases and address past pain points. While it may not replace standard near-infrared eye-tracking technology just yet, it likely will in the near future.

AI-Powered Predictive Eye Tracking Highlights the Future of Accessibility Testing

RAG Predictive Coding for AI Alignment Against Prompt Injections and Jailbreaks

Diabetes management has long been a critical area of healthcare, demanding constant innovation to improve patient outcomes. In recent years, advancements in Artificial Intelligence (AI) and Machine Learning (ML) have opened new avenues for enhancing the precision and effectiveness of diabetes care. This article, part of a series on the future of diabetes management, explores how different AI is revolutionising the MedTech (Medical Technology) field, focusing on the development of predictive models and artificial pancreas systems. Project Motivation The motivation behind this project stems from the need to address the growing prevalence of diabetes and the challenges associated with its management. Traditional methods of monitoring and controlling blood glucose levels are often inadequate, leading to complications and reduced quality of life for patients. By leveraging AI, we aim to develop more accurate predictive models that can anticipate glucose fluctuations and automate insulin delivery, significantly improving patient care. Project Goals The primary goals of this project are to: Apply machine learning methods to derive predictive models of meal behaviour from publicly available datasets.
Evaluate the learned models in terms of prediction accuracy and their applicability in an artificial pancreas context. Relevance in the Current Landscape Since the inception of this project, there have been significant updates in the fields of AI, Large Language Models (LLMs), and MedTech. These advancements have further emphasised the relevance of our work. The integration of AI in healthcare is now more robust than ever, with new algorithms and models providing unprecedented accuracy in predictive analytics. Moreover, the ongoing development of LLMs has enhanced our ability to process and analyse vast amounts of data, offering deeper insights into patient behaviour and treatment outcomes. Future Impact As technology continues to evolve, its applications in diabetes management are expected to expand. Future work could involve integrating more sophisticated models, such as Long Short-Term Memory (LSTM) networks, to improve the prediction of blood glucose levels over time. Additionally, collaboration with healthcare providers and technology companies could lead to the development of more advanced and user-friendly artificial pancreas systems. Series Overview This article is the first in a series of my work. Each article will delve into different aspects of how AI and ML are transforming diabetes care. The upcoming articles will cover topics such as: Understanding Diabetes and Its Management: A comprehensive overview of diabetes, including types, symptoms, and current management strategies.
Artificial Pancreas and Insulin Delivery Systems: An in-depth look at open-loop and closed-loop insulin delivery systems and recent advancements in artificial pancreas technology.
Machine Learning in Diabetes Management: Detailed exploration of various machine learning methods used in predicting and managing blood glucose levels.
Carbohydrates and Their Role in Diabetes: Analysis of different types of carbohydrates and their impact on blood glucose levels, along with the concept of nutrient timing.
Data Exploration and Analysis: Methods for data collection and analysis, key findings from dietary recall questionnaires, and improvements in data analysis techniques.
Predictive Models for Meal Behaviour: Building and evaluating predictive models using supervised and unsupervised learning techniques.
Time Series Analysis in Diabetes Management: The importance of time series data in understanding meal patterns and applications of advanced time series forecasting methods like LSTM.
Professional and Ethical Considerations in AI and MedTech: Ethical issues related to AI in healthcare, professional responsibilities, and recent developments in AI ethics and regulatory frameworks.
The World of Large Language Models (LLMs) and Their Potential Use in Diabetes Management: Explore the transformative potential of Large Language Models (LLMs) in diabetes management.
Future Directions and Emerging Trends: Potential future directions for research, emerging trends, and promising research projects and collaborations.
Self-Assessment, Lessons Learned, and How to Use the Project and Its Findings: Reflection on project execution, successes, challenges, and personal growth. Practical applications of research findings, instructions for using developed software and models, and new tools and resources for further exploration. In the next article, Understanding Diabetes and Its Management, I will provide a comprehensive overview of diabetes, including the different types, symptoms, and current strategies used to manage this chronic condition. Stay tuned to learn more about the foundation upon which AI and machine learning innovations are being built. Diabetes management has long been a critical area of healthcare, demanding constant innovation to improve patient outcomes. In recent years, advancements in Artificial Intelligence (AI) and Machine Learning (ML) have opened new avenues for enhancing the precision and effectiveness of diabetes care. This article, part of a series on the future of diabetes management, explores how different AI is revolutionising the MedTech (Medical Technology) field, focusing on the development of predictive models and artificial pancreas systems. Project Motivation The motivation behind this project stems from the need to address the growing prevalence of diabetes and the challenges associated with its management. Traditional methods of monitoring and controlling blood glucose levels are often inadequate, leading to complications and reduced quality of life for patients. By leveraging AI, we aim to develop more accurate predictive models that can anticipate glucose fluctuations and automate insulin delivery, significantly improving patient care. Project Goals The primary goals of this project are to: The primary goals of this project are to: Apply machine learning methods to derive predictive models of meal behaviour from publicly available datasets. Evaluate the learned models in terms of prediction accuracy and their applicability in an artificial pancreas context. Apply machine learning methods to derive predictive models of meal behaviour from publicly available datasets. Apply machine learning methods to derive predictive models of meal behaviour predictive models of meal behaviour from publicly available datasets. Evaluate the learned models in terms of prediction accuracy and their applicability in an artificial pancreas context. Evaluate the learned models in terms of prediction accuracy and their applicability in an artificial pancreas artificial pancreas context. Relevance in the Current Landscape Since the inception of this project, there have been significant updates in the fields of AI, Large Language Models (LLMs), and MedTech. These advancements have further emphasised the relevance of our work. The integration of AI in healthcare is now more robust than ever, with new algorithms and models providing unprecedented accuracy in predictive analytics. Moreover, the ongoing development of LLMs has enhanced our ability to process and analyse vast amounts of data, offering deeper insights into patient behaviour and treatment outcomes. Future Impact As technology continues to evolve, its applications in diabetes management are expected to expand. Future work could involve integrating more sophisticated models, such as Long Short-Term Memory (LSTM) networks, to improve the prediction of blood glucose levels over time. Additionally, collaboration with healthcare providers and technology companies could lead to the development of more advanced and user-friendly artificial pancreas systems. Long Short-Term Memory (LSTM) Series Overview This article is the first in a series of my work. Each article will delve into different aspects of how AI and ML are transforming diabetes care. The upcoming articles will cover topics such as: Understanding Diabetes and Its Management: A comprehensive overview of diabetes, including types, symptoms, and current management strategies. Artificial Pancreas and Insulin Delivery Systems: An in-depth look at open-loop and closed-loop insulin delivery systems and recent advancements in artificial pancreas technology. Machine Learning in Diabetes Management: Detailed exploration of various machine learning methods used in predicting and managing blood glucose levels. Carbohydrates and Their Role in Diabetes: Analysis of different types of carbohydrates and their impact on blood glucose levels, along with the concept of nutrient timing. Data Exploration and Analysis: Methods for data collection and analysis, key findings from dietary recall questionnaires, and improvements in data analysis techniques. Predictive Models for Meal Behaviour: Building and evaluating predictive models using supervised and unsupervised learning techniques. Time Series Analysis in Diabetes Management: The importance of time series data in understanding meal patterns and applications of advanced time series forecasting methods like LSTM. Professional and Ethical Considerations in AI and MedTech: Ethical issues related to AI in healthcare, professional responsibilities, and recent developments in AI ethics and regulatory frameworks. The World of Large Language Models (LLMs) and Their Potential Use in Diabetes Management: Explore the transformative potential of Large Language Models (LLMs) in diabetes management. Future Directions and Emerging Trends: Potential future directions for research, emerging trends, and promising research projects and collaborations. Self-Assessment, Lessons Learned, and How to Use the Project and Its Findings: Reflection on project execution, successes, challenges, and personal growth. Practical applications of research findings, instructions for using developed software and models, and new tools and resources for further exploration. Understanding Diabetes and Its Management : A comprehensive overview of diabetes, including types, symptoms, and current management strategies. Understanding Diabetes and Its Management Understanding Diabetes and Its Management Artificial Pancreas and Insulin Delivery Systems : An in-depth look at open-loop and closed-loop insulin delivery systems and recent advancements in artificial pancreas technology. Artificial Pancreas and Insulin Delivery Systems Machine Learning in Diabetes Management : Detailed exploration of various machine learning methods used in predicting and managing blood glucose levels. Machine Learning in Diabetes Management Machine Learning in Diabetes Management Carbohydrates and Their Role in Diabetes : Analysis of different types of carbohydrates and their impact on blood glucose levels, along with the concept of nutrient timing. Carbohydrates and Their Role in Diabetes Carbohydrates and Their Role in Diabetes Data Exploration and Analysis : Methods for data collection and analysis, key findings from dietary recall questionnaires, and improvements in data analysis techniques. Data Exploration and Analysis Data Exploration and Analysis Predictive Models for Meal Behaviour : Building and evaluating predictive models using supervised and unsupervised learning techniques. Predictive Models for Meal Behaviour Predictive Models for Meal Behaviour Time Series Analysis in Diabetes Management : The importance of time series data in understanding meal patterns and applications of advanced time series forecasting methods like LSTM. Time Series Analysis in Diabetes Management Time Series Analysis in Diabetes Management : Professional and Ethical Considerations in AI and MedTech : Ethical issues related to AI in healthcare, professional responsibilities, and recent developments in AI ethics and regulatory frameworks. Professional and Ethical Considerations in AI and MedTech Professional and Ethical Considerations in AI and MedTech The World of Large Language Models (LLMs) and Their Potential Use in Diabetes Management : Explore the transformative potential of Large Language Models (LLMs) in diabetes management. The World of Large Language Models (LLMs) and Their Potential Use in Diabetes Management The World of Large Language Models (LLMs) and Their Potential Use in Diabetes Management Future Directions and Emerging Trends : Potential future directions for research, emerging trends, and promising research projects and collaborations. Future Directions and Emerging Trends Future Directions and Emerging Trends Self-Assessment, Lessons Learned, and How to Use the Project and Its Findings : Reflection on project execution, successes, challenges, and personal growth. Practical applications of research findings, instructions for using developed software and models, and new tools and resources for further exploration. Self-Assessment, Lessons Learned, and How to Use the Project and Its Findings Self-Assessment, Lessons Learned, and How to Use the Project and Its Findings In the next article, U nderstanding Diabetes and Its Management, I will provide a comprehensive overview of diabetes, including the different types, symptoms, and current strategies used to manage this chronic condition. Stay tuned to learn more about the foundation upon which AI and machine learning innovations are being built. U nderstanding Diabetes and Its Management,

Artificial Pancreas and Predictive Models : Is This A New Era For Diabetes Care?

Predictive modeling has become an invaluable tool in diabetes management, enabling the prediction of blood glucose levels based on dietary intake and other factors. This article explores how machine learning models are developed to predict meal behaviour and blood glucose levels, and how these predictions can be used to optimise diabetes management. Developing Predictive Models 1. Data Collection The first step in developing predictive models is collecting high-quality data. For meal behaviour prediction, data typically includes: Dietary Intake: Detailed records of the types and quantities of food consumed.
Blood Glucose Levels: Continuous glucose monitoring (CGM) data provides real-time measurements.
Insulin Dosage: Information on the amount and timing of insulin administration.
Physical Activity: Logs of physical activity, as exercise affects glucose metabolism. 2. Data Preprocessing Once data is collected, it needs to be cleaned and preprocessed. This involves: Handling Missing Values: Imputing or removing missing data points.
Normalisation: Scaling features to a standard range to improve model performance.
Feature Engineering: Creating new features that can improve model accuracy, such as time of day, meal composition, and insulin timing. 3. Model Selection Several machine learning algorithms can be used to build predictive models. Some of the most effective ones for meal behaviour include: Linear Regression Linear regression models the relationship between blood glucose levels and various input features (carbohydrate intake, insulin dosage, etc.). The equation for linear regression is: ŷ = w₀ + w₁ ⋅ x₁ + w₂ ⋅ x₂ + … + wₚ ⋅ xₚ where: ŷ​ is the predicted blood glucose level.
x₁,x₂,…,xₚ ​​ are the input features.
w₀,w₁,…,wₚ ​​ are the model coefficients. Random Forest Random Forest is an ensemble learning method that uses multiple decision trees to improve prediction accuracy. It works well with complex datasets and can handle nonlinear relationships. The prediction from a Random Forest model is the average of predictions from individual trees: where N is the number of trees, and ŷᵢ is the prediction from the i−th tree. LSTM (Long Short-Term Memory) Networks LSTM networks are a type of recurrent neural network (RNN) that excel at handling time series data. They are particularly useful for capturing long-term dependencies in sequential data, such as blood glucose levels over time. An LSTM cell can be represented by the following equations: fₜ = σ(W_f ⋅ [hₜ₋₁, xₜ] + b_f)iₜ = σ(Wᵢ ⋅ [hₜ₋₁, xₜ] + bᵢ)C̃t = tanh( W_C ⋅ [h{t−1}, xₜ] + b_C)Cₜ = fₜ * Cₜ₋₁ + iₜ * C̃toₜ = σ(Wₒ ⋅ [h{t−1}, xₜ] + bₒ)hₜ = oₜ * tanh(Cₜ) where: • fₜ is the forget gate, • iₜ is the input gate, • C̃ₜ is the candidate cell state, • Cₜ is the cell state, • oₜ is the output gate, • hₜ is the hidden state, • σ is the sigmoid function, • tanh is the hyperbolic tangent function, • W and b are the weights and biases. 4. Model Training and Evaluation Models are trained using historical data and evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) to assess their accuracy. Case Study: Predicting Post-Meal Blood Glucose Levels In our study, we developed predictive models using dietary intake, insulin dosage, and CGM data to forecast post-meal blood glucose levels. Data Collection and Preprocessing Collected dietary recall and CGM data from participants.
Normalised the data and created features such as carbohydrate intake per meal, time of day, and insulin timing. Model Training Trained linear regression, Random Forest, and LSTM models using the preprocessed data.
Evaluated model performance using MAE and RMSE metrics. Results The LSTM model outperformed the others, demonstrating its effectiveness in capturing the temporal dependencies of blood glucose levels.
The models provided accurate predictions of post-meal blood glucose levels, allowing for personalised insulin recommendations. Applications of Predictive Models Predictive models for meal behaviour have several applications in diabetes management: 1. Personalised Meal Planning Models can recommend optimal meal compositions and timings to maintain stable blood glucose levels. 2. Insulin Dosage Recommendations Accurate predictions allow for precise insulin dosage adjustments, reducing the risk of hyperglycaemia and hypoglycaemia. 3. Real-Time Alerts Integration with CGM devices can provide real-time alerts and recommendations based on predicted glucose trends. 4. Long-Term Glucose Control Continuous use of predictive models can help in maintaining long-term glucose control, improving overall health outcomes for diabetic patients. Predictive models for meal behaviour are revolutionising diabetes management by providing personalised and accurate predictions of blood glucose levels. By leveraging advanced machine learning techniques, these models can significantly improve the quality of life for individuals with diabetes. As technology continues to evolve, the integration of these models with wearable devices and health platforms will further enhance their effectiveness and accessibility. In the next article, Time Series Analysis in Diabetes Management, we will explore how time series analysis methods are used to predict blood glucose levels and identify trends, patterns, and anomalies in diabetes management. Predictive modeling has become an invaluable tool in diabetes management, enabling the prediction of blood glucose levels based on dietary intake and other factors. This article explores how machine learning models are developed to predict meal behaviour and blood glucose levels, and how these predictions can be used to optimise diabetes management. Developing Predictive Models 1. Data Collection The first step in developing predictive models is collecting high-quality data. For meal behaviour prediction, data typically includes: 1. Data Collection Dietary Intake: Detailed records of the types and quantities of food consumed. Blood Glucose Levels: Continuous glucose monitoring (CGM) data provides real-time measurements. Insulin Dosage: Information on the amount and timing of insulin administration. Physical Activity: Logs of physical activity, as exercise affects glucose metabolism. Dietary Intake: Detailed records of the types and quantities of food consumed. Dietary Intake: Blood Glucose Levels: Continuous glucose monitoring (CGM) data provides real-time measurements. Blood Glucose Levels: Insulin Dosage: Information on the amount and timing of insulin administration. Insulin Dosage: Physical Activity: Logs of physical activity, as exercise affects glucose metabolism. Physical Activity: 2. Data Preprocessing Once data is collected, it needs to be cleaned and preprocessed. This involves: 2. Data Preprocessing Handling Missing Values: Imputing or removing missing data points. Normalisation: Scaling features to a standard range to improve model performance. Feature Engineering: Creating new features that can improve model accuracy, such as time of day, meal composition, and insulin timing. Handling Missing Values: Imputing or removing missing data points. Handling Missing Values: Normalisation: Scaling features to a standard range to improve model performance. Normalisation: Feature Engineering: Creating new features that can improve model accuracy, such as time of day, meal composition, and insulin timing. Feature Engineering: 3. Model Selection Several machine learning algorithms can be used to build predictive models. Some of the most effective ones for meal behaviour include: 3. Model Selection Linear Regression Linear regression models the relationship between blood glucose levels and various input features (carbohydrate intake, insulin dosage, etc.). Linear Regression The equation for linear regression is: ŷ = w₀ + w₁ ⋅ x₁ + w₂ ⋅ x₂ + … + wₚ ⋅ xₚ where: ŷ​ is the predicted blood glucose level. x₁,x₂,…,xₚ ​​ are the input features. w₀,w₁,…,wₚ ​​ are the model coefficients. ŷ​ is the predicted blood glucose level. x₁,x₂,…,xₚ ​​ are the input features. w₀,w₁,…,wₚ ​​ are the model coefficients. Random Forest Random Forest is an ensemble learning method that uses multiple decision trees to improve prediction accuracy. It works well with complex datasets and can handle nonlinear relationships. The prediction from a Random Forest model is the average of predictions from individual trees: Random Forest where N is the number of trees, and ŷᵢ is the prediction from the i−th tree. LSTM (Long Short-Term Memory) Networks LSTM networks are a type of recurrent neural network (RNN) that excel at handling time series data. They are particularly useful for capturing long-term dependencies in sequential data, such as blood glucose levels over time. An LSTM cell can be represented by the following equations: LSTM (Long Short-Term Memory) Networks fₜ = σ(W_f ⋅ [hₜ₋₁, xₜ] + b_f)iₜ = σ(Wᵢ ⋅ [hₜ₋₁, xₜ] + bᵢ)C̃t = tanh( W_C ⋅ [h{t−1}, xₜ] + b_C)Cₜ = fₜ * Cₜ₋₁ + iₜ * C̃toₜ = σ(Wₒ ⋅ [h{t−1}, xₜ] + bₒ)hₜ = oₜ * tanh(Cₜ) where: • fₜ is the forget gate, • iₜ is the input gate, • C̃ₜ is the candidate cell state, • Cₜ is the cell state, • oₜ is the output gate, • hₜ is the hidden state, • σ is the sigmoid function, • tanh is the hyperbolic tangent function, • W and b are the weights and biases. 4. Model Training and Evaluation Models are trained using historical data and evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) to assess their accuracy. 4. Model Training and Evaluation Case Study: Predicting Post-Meal Blood Glucose Levels In our study, we developed predictive models using dietary intake, insulin dosage, and CGM data to forecast post-meal blood glucose levels. Data Collection and Preprocessing Data Collection and Preprocessing Collected dietary recall and CGM data from participants. Normalised the data and created features such as carbohydrate intake per meal, time of day, and insulin timing. Collected dietary recall and CGM data from participants. Normalised the data and created features such as carbohydrate intake per meal, time of day, and insulin timing. Model Training Model Training Trained linear regression, Random Forest, and LSTM models using the preprocessed data. Evaluated model performance using MAE and RMSE metrics. Trained linear regression, Random Forest, and LSTM models using the preprocessed data. Evaluated model performance using MAE and RMSE metrics. Results Results The LSTM model outperformed the others, demonstrating its effectiveness in capturing the temporal dependencies of blood glucose levels. The models provided accurate predictions of post-meal blood glucose levels, allowing for personalised insulin recommendations. The LSTM model outperformed the others, demonstrating its effectiveness in capturing the temporal dependencies of blood glucose levels. The models provided accurate predictions of post-meal blood glucose levels, allowing for personalised insulin recommendations. Applications of Predictive Models Predictive models for meal behaviour have several applications in diabetes management: 1. Personalised Meal Planning Models can recommend optimal meal compositions and timings to maintain stable blood glucose levels. 1. Personalised Meal Planning 2. Insulin Dosage Recommendations Accurate predictions allow for precise insulin dosage adjustments, reducing the risk of hyperglycaemia and hypoglycaemia. 2. Insulin Dosage Recommendations 3. Real-Time Alerts Integration with CGM devices can provide real-time alerts and recommendations based on predicted glucose trends. 3. Real-Time Alerts 4. Long-Term Glucose Control Continuous use of predictive models can help in maintaining long-term glucose control, improving overall health outcomes for diabetic patients. 4. Long-Term Glucose Control Predictive models for meal behaviour are revolutionising diabetes management by providing personalised and accurate predictions of blood glucose levels. By leveraging advanced machine learning techniques, these models can significantly improve the quality of life for individuals with diabetes. As technology continues to evolve, the integration of these models with wearable devices and health platforms will further enhance their effectiveness and accessibility. In the next article, Time Series Analysis in Diabetes Management , we will explore how time series analysis methods are used to predict blood glucose levels and identify trends, patterns, and anomalies in diabetes management. Time Series Analysis in Diabetes Management

Can Predictive Models for Meal Behaviour Assist in Diabetes Management?

Authors:
(1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com);
(2) Andrey Chertok, (a.v.chertok@gmail.com);
(3) Konstantin Zorin, (berzqwer@gmail.com);
(4) Cyril Shtabtsovsky, (cyril@aloniq.com). Table of Links Abstract and 1. Introduction 2 Related works 3 Dataset Overview, Preprocessing, and Features 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.3 Features 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4.2 Backtest settings 4.3 Results 4.4 Capital Growth 5 Other approaches 5.1 Investors ranking model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 6 Conclusion 7 Further Research, References and Appendix 6 Conclusion Traditionally, venture capital investment decisions have largely been guided by the investors’ intuition, experience, and market understanding. While these elements remain significant, there’s a growing recognition that these traditional approaches can be greatly enhanced by integrating data-driven insights into the investment decision-making process. Our paper comprehensively examines a predictive model for startups based on an extensive dataset from CrunchBase. A meticulous review and analysis of the available data were conducted, followed by the preparing of a dataset for model training. Special attention was given to the selection of features which include information about founders, investors, and funding rounds. The article also underlines a thoughtfully designed backtest algorithm, enabling a fair evaluation of the model’s behavior (and the simulation of a VC fund based on it) from a historical perspective. Rigorous efforts were made to avoid data leakage, ensuring training at any given point only utilized data that would have been known at that time. Several configurations were explored regarding the funding rounds at which the fund could invest in a company and the timing of exits. The primary evaluative metrics were derived from a backtest table (Table 2), which chronicles instances of company entries, exits, and the corresponding success statuses. Utilizing additional data on company valuations, we calculated the Capital Growth, illustrating the fund’s impressive economic impact over time. To sum up, this work primarily focused on the variety of input features, the integrity of the backtest, and the realistic simulation of the portfolio from a historical perspective. Additionally, we proffer a series of propositions aimed at enhancing the existing model, primarily revolving around the access to supplementary data repositories. Within the highly competitive and dynamic investment environment, the assimilation of data-driven decision-making practices transitions from being an option to becoming a necessity. As such, venture capitalists that effectively harness the potential of AI and machine learning will likely secure a significant competitive advantage, positioning themselves for success in the new era of venture capitalism. This paper is available on arxiv under CC 4.0 license. Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Authors: Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related works 2 Related works 3 Dataset Overview, Preprocessing, and Features 3 Dataset Overview, Preprocessing, and Features 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.3 Features 3.3 Features 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4.2 Backtest settings 4.2 Backtest settings 4.3 Results 4.3 Results 4.4 Capital Growth 4.4 Capital Growth 5 Other approaches 5.1 Investors ranking model 5.1 Investors ranking model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 6 Conclusion 6 Conclusion 7 Further Research, References and Appendix 7 Further Research, References and Appendix 6 Conclusion Traditionally, venture capital investment decisions have largely been guided by the investors’ intuition, experience, and market understanding. While these elements remain significant, there’s a growing recognition that these traditional approaches can be greatly enhanced by integrating data-driven insights into the investment decision-making process. Our paper comprehensively examines a predictive model for startups based on an extensive dataset from CrunchBase. A meticulous review and analysis of the available data were conducted, followed by the preparing of a dataset for model training. Special attention was given to the selection of features which include information about founders, investors, and funding rounds. The article also underlines a thoughtfully designed backtest algorithm, enabling a fair evaluation of the model’s behavior (and the simulation of a VC fund based on it) from a historical perspective. Rigorous efforts were made to avoid data leakage, ensuring training at any given point only utilized data that would have been known at that time. Several configurations were explored regarding the funding rounds at which the fund could invest in a company and the timing of exits. The primary evaluative metrics were derived from a backtest table (Table 2), which chronicles instances of company entries, exits, and the corresponding success statuses. Utilizing additional data on company valuations, we calculated the Capital Growth, illustrating the fund’s impressive economic impact over time. To sum up, this work primarily focused on the variety of input features, the integrity of the backtest, and the realistic simulation of the portfolio from a historical perspective. Additionally, we proffer a series of propositions aimed at enhancing the existing model, primarily revolving around the access to supplementary data repositories. Within the highly competitive and dynamic investment environment, the assimilation of data-driven decision-making practices transitions from being an option to becoming a necessity. As such, venture capitalists that effectively harness the potential of AI and machine learning will likely secure a significant competitive advantage, positioning themselves for success in the new era of venture capitalism. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

A Comprehensive Analysis of Startup Predictive Models

Authors:
(1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com);
(2) Andrey Chertok, (a.v.chertok@gmail.com);
(3) Konstantin Zorin, (berzqwer@gmail.com);
(4) Cyril Shtabtsovsky, (cyril@aloniq.com). Table of Links Abstract and 1. Introduction 2 Related works 3 Dataset Overview, Preprocessing, and Features 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.3 Features 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4.2 Backtest settings 4.3 Results 4.4 Capital Growth 5 Other approaches 5.1 Investors ranking model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 6 Conclusion 7 Further Research, References and Appendix 3.1 Successful Companies Dataset In this research, a company is deemed successful if it achieves one of three outcomes: Initial Public Offering (IPO), Acquisition (ACQ), or Unicorn status (UNIC), the latter being defined as a valuation exceeding $1 billion. To assemble a list of successful companies, we initially filtered for IPOs with valuations above $500M or funds raised over $100M, yielding 363 companies. For acquisitions, we applied filters to eliminate companies with a purchase price below the maximum amount of funds raised or under $100M, resulting in 833 companies. To select unicorns, we searched for companies with a valuation above $1 billion, utilizing both Crunchbase data and an additional table of verified unicorns, which led to a total of 1074 unicorns. The final dataset contains a timeline of all crucial investment rounds leading to the success event (i.e., achieving unicorn status, IPO, or ACQ), with the index of this event specified in the success_round column. This approach ensures that the dataset accurately represents the history and progress of each successful company, facilitating effective analysis. 3.2 Unsuccessful Companies Dataset To supply the model with examples of ’unsuccessful’ companies, we collected a separate dataset. We excluded companies already present in the successful companies dataset by removing those that had IPO, ACQ, or UNIC flags. We also eliminated a considerable number of actual unicorns from the CrunchBase website [16] to avoid overlap. We excluded companies that have not attracted any rounds since 2016. Additionally, we excluded companies that are subsidiaries or parent companies of other entities. Furthermore, we used the jobs dataset to exclude companies that have hired employees since 2017. Additionally, we applied extra filters to exclude companies with valuation above $100 million, as they reside in the "gray area" of companies that may not be clearly categorized as successful or unsuccessful. By applying these filters, we constructed a dataset comprising 32,760 companies, denoted by the label ’0’ for unsuccessful, and 1,989 companies, denoted by the label ’1’ for successful. This paper is available on arxiv under CC 4.0 license. Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Authors: Authors: (1) Mark Potanin, a Corresponding (authorpotanin.m.st@gmail.com); (2) Andrey Chertok, (a.v.chertok@gmail.com); (3) Konstantin Zorin, (berzqwer@gmail.com); (4) Cyril Shtabtsovsky, (cyril@aloniq.com). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related works 2 Related works 3 Dataset Overview, Preprocessing, and Features 3 Dataset Overview, Preprocessing, and Features 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.1 Successful Companies Dataset and 3.2 Unsuccessful Companies Dataset 3.3 Features 3.3 Features 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4 Model Training, Evaluation, and Portfolio Simulation and 4.1 Backtest 4.2 Backtest settings 4.2 Backtest settings 4.3 Results 4.3 Results 4.4 Capital Growth 4.4 Capital Growth 5 Other approaches 5.1 Investors ranking model 5.1 Investors ranking model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 5.2 Founders ranking model and 5.3 Unicorn recommendation model 6 Conclusion 6 Conclusion 7 Further Research, References and Appendix 7 Further Research, References and Appendix 3.1 Successful Companies Dataset In this research, a company is deemed successful if it achieves one of three outcomes: Initial Public Offering (IPO), Acquisition (ACQ), or Unicorn status (UNIC), the latter being defined as a valuation exceeding $1 billion. To assemble a list of successful companies, we initially filtered for IPOs with valuations above $500M or funds raised over $100M, yielding 363 companies. For acquisitions, we applied filters to eliminate companies with a purchase price below the maximum amount of funds raised or under $100M, resulting in 833 companies. To select unicorns, we searched for companies with a valuation above $1 billion, utilizing both Crunchbase data and an additional table of verified unicorns, which led to a total of 1074 unicorns. The final dataset contains a timeline of all crucial investment rounds leading to the success event (i.e., achieving unicorn status, IPO, or ACQ), with the index of this event specified in the success_round column. This approach ensures that the dataset accurately represents the history and progress of each successful company, facilitating effective analysis. 3.2 Unsuccessful Companies Dataset To supply the model with examples of ’unsuccessful’ companies, we collected a separate dataset. We excluded companies already present in the successful companies dataset by removing those that had IPO, ACQ, or UNIC flags. We also eliminated a considerable number of actual unicorns from the CrunchBase website [16] to avoid overlap. We excluded companies that have not attracted any rounds since 2016. Additionally, we excluded companies that are subsidiaries or parent companies of other entities. Furthermore, we used the jobs dataset to exclude companies that have hired employees since 2017. Additionally, we applied extra filters to exclude companies with valuation above $100 million, as they reside in the "gray area" of companies that may not be clearly categorized as successful or unsuccessful. By applying these filters, we constructed a dataset comprising 32,760 companies, denoted by the label ’0’ for unsuccessful, and 1,989 companies, denoted by the label ’1’ for successful. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Unicorns vs Failures: Constructing Comprehensive Datasets for Predictive Modeling

AI: Sleep Computational Neuroscience, Dreams, Loneliness, and Predictive Coding

Image source: https://www.flickr.com/photos/nida-nih/51015715423/in/photostream/ The observation that the brain makes [something described as] predictions is a gateway to exploring how placebos work. It also includes what follows after, to have therapeutic effects—in the conditions they do. Placebos involve the expectation to feel better and the experience of it. Placebos are used in studies. They, however, have presented a different challenge in testing the efficacies of psychedelics and antidepressants. There is a feature in The Atlantic, Psychedelics Are Challenging the Scientific Gold Standard, stating that "In clinical trials, participants (and the researchers studying them) generally aren’t supposed to know whether they’re getting the actual drug or a placebo, to avoid allowing people’s expectations about a treatment to shape their response to it. Blinding, as this practice is called, is a key component of a randomized controlled clinical trial, or RCT—medicine’s gold standard for demonstrating that a drug actually works. But virtually no one can take a psychedelic drug and not know it. Such failure, if it exists, might extend beyond psychedelics to several kinds of psychoactive drugs. For instance, a 2022 analysis found that many antidepressant trials fail to effectively blind participants, in part because of side effects." This contributed, in part, to a recent report by Reuters, US FDA panel votes against first MDMA-based PTSD treatment, stating that "Advisers to the U.S. Food and Drug Administration on Tuesday voted against a therapy based on the mind-altering drug MDMA for patients with post-traumatic stress disorder, marking a setback to the nascent field of psychedelic treatments. The panel voted 10-to-1 against the first MDMA-based PTSD treatment, saying the benefits did not outweigh its risks, while nine members said the available data did not show its effectiveness in PTSD patients." There might be a few conceptual approaches around this, starting with some questions. How does the human mind do what is described as prediction or anticipation? How do relays occur in the mind—afterwards—to result in relief? How can this be explained to participants and researchers in a way that neutralizes the expected side effects of psychoactive drugs against dampening the placebo effect or group identification? Brain science has established that electrical signals leap from node to node in myelinated axons, going faster in what is called saltatory conduction. This is different from continuous conduction in unmyelinated axons, where they travel directly along the length of the axon without leaping. It is postulated that in a set of electrical signals [in clusters of neurons], some electrical signals split or go ahead of others to interact with chemical signals before others follow. This means that whatever initial perception an individual has, either of sight, smell, touch, taste, or sound, could be a result of this split. If the input matches with the split, then relays continue—with the incoming signals going in the same direction. However, if the input is different, the incoming electrical signals go in the right direction. This explains what is described as predictive coding, processing, and prediction error. Simply, when two letters in a word are first seen, there could be a perception of what the word is, which may be confirmed if the rest of the letters are seen. In this case, the first and second sets of the electrical signals would go in the same direction. If the rest of the word is seen and it is different from what was perceived, then the incoming electrical signals would go in another direction. This is a mechanistic way to explain how the brain appears to predict using electrical signals. It is proposed that electrical signals are always splitting, making it feel like predictions are common in the mind. Splits are postulated to be a division of distributions—a feature in the human mind. Distributions are defined as the way some sets of signals get the outputs of other sets of signals. Distribution happens in attention [a feature] and in awareness [another feature, but less than attention]. For example, a word may result in the memory of an event, the sight of something may result in an emotion of fear, or the sound of something may result in an appetitive feeling. Also, experiences with side effects—or not, can also be described as distributions since electrical and chemical signals are involved in modulations of internal senses. Some other features of mind, including intentionality, may be responsible for distributions as well. When intent does not [a non-intent feature], arrays do. However, destinations of distributions are not always constant—they may vary, sometimes widely. There is a new paper in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Dynamic Functional Hyperconnectivity After Psilocybin Intake Is Primarily Associated With Oceanic Boundlessness, where the authors "investigated the relationship between neurobiological and experiential patterns after psilocybin by focusing on the link between dynamic cerebral connectivity and retrospective questionnaire assessment." How does the human mind work for distributions that correlate with hyperconnectivity? How do psychedelics have an effect? What is the relationship between predictions in placebos and how psychedelics work? How can hallucinations in psychedelics be explained in comparison to those in large language models? Can AI shape future clinical trials for psychedelics? Exploring the postulate of the human mind in the action potentials neurotransmitters theory of consciousness​, ​with examples and detailed animations, could work in explaining to participants and researchers that while there are expectations of side effects, features of the human mind ultimately decide experiences. So, it may not proportionately apply that the absence of side effects indicates that the participant is under placebo, open-label, or otherwise. Image source: https://www.flickr.com/photos/nida-nih/51015715423/in/photostream/ Image source: https://www.flickr.com/photos/nida-nih/51015715423/in/photostream/ https://www.flickr.com/photos/nida-nih/51015715423/in/photostream/ The observation that the brain makes [something described as] predictions is a gateway to exploring how placebos work. It also includes what follows after, to have therapeutic effects—in the conditions they do. Placebos involve the expectation to feel better and the experience of it. Placebos are used in studies. They, however, have presented a different challenge in testing the efficacies of psychedelics and antidepressants. There is a feature in The Atlantic , Psychedelics Are Challenging the Scientific Gold Standard , stating that "In clinical trials, participants (and the researchers studying them) generally aren’t supposed to know whether they’re getting the actual drug or a placebo, to avoid allowing people’s expectations about a treatment to shape their response to it. Blinding, as this practice is called, is a key component of a randomized controlled clinical trial, or RCT—medicine’s gold standard for demonstrating that a drug actually works. But virtually no one can take a psychedelic drug and not know it. Such failure, if it exists, might extend beyond psychedelics to several kinds of psychoactive drugs. For instance, a 2022 analysis found that many antidepressant trials fail to effectively blind participants, in part because of side effects." The Atlantic Psychedelics Are Challenging the Scientific Gold Standard This contributed, in part, to a recent report by Reuters , US FDA panel votes against first MDMA-based PTSD treatment , stating that "Advisers to the U.S. Food and Drug Administration on Tuesday voted against a therapy based on the mind-altering drug MDMA for patients with post-traumatic stress disorder, marking a setback to the nascent field of psychedelic treatments. The panel voted 10-to-1 against the first MDMA-based PTSD treatment, saying the benefits did not outweigh its risks, while nine members said the available data did not show its effectiveness in PTSD patients." Reuters US FDA panel votes against first MDMA-based PTSD treatment There might be a few conceptual approaches around this, starting with some questions. How does the human mind do what is described as prediction or anticipation? How do relays occur in the mind—afterwards—to result in relief? How can this be explained to participants and researchers in a way that neutralizes the expected side effects of psychoactive drugs against dampening the placebo effect or group identification? Brain science has established that electrical signals leap from node to node in myelinated axons, going faster in what is called saltatory conduction. This is different from continuous conduction in unmyelinated axons, where they travel directly along the length of the axon without leaping. It is postulated that in a set of electrical signals [in clusters of neurons], some electrical signals split or go ahead of others to interact with chemical signals before others follow. This means that whatever initial perception an individual has, either of sight, smell, touch, taste, or sound, could be a result of this split. If the input matches with the split, then relays continue—with the incoming signals going in the same direction. However, if the input is different, the incoming electrical signals go in the right direction. This explains what is described as predictive coding, processing, and prediction error. Simply, when two letters in a word are first seen, there could be a perception of what the word is, which may be confirmed if the rest of the letters are seen. In this case, the first and second sets of the electrical signals would go in the same direction. If the rest of the word is seen and it is different from what was perceived, then the incoming electrical signals would go in another direction. This is a mechanistic way to explain how the brain appears to predict using electrical signals. It is proposed that electrical signals are always splitting, making it feel like predictions are common in the mind. Splits are postulated to be a division of distributions—a feature in the human mind. Distributions are defined as the way some sets of signals get the outputs of other sets of signals. Distribution happens in attention [a feature] and in awareness [another feature, but less than attention]. For example, a word may result in the memory of an event, the sight of something may result in an emotion of fear, or the sound of something may result in an appetitive feeling. Also, experiences with side effects—or not, can also be described as distributions since electrical and chemical signals are involved in modulations of internal senses. Some other features of mind, including intentionality, may be responsible for distributions as well. When intent does not [a non-intent feature], arrays do. However, destinations of distributions are not always constant—they may vary, sometimes widely. There is a new paper in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging , Dynamic Functional Hyperconnectivity After Psilocybin Intake Is Primarily Associated With Oceanic Boundlessness , where the authors "investigated the relationship between neurobiological and experiential patterns after psilocybin by focusing on the link between dynamic cerebral connectivity and retrospective questionnaire assessment." Biological Psychiatry: Cognitive Neuroscience and Neuroimaging Dynamic Functional Hyperconnectivity After Psilocybin Intake Is Primarily Associated With Oceanic Boundlessness How does the human mind work for distributions that correlate with hyperconnectivity? How do psychedelics have an effect? What is the relationship between predictions in placebos and how psychedelics work? How can hallucinations in psychedelics be explained in comparison to those in large language models? Can AI shape future clinical trials for psychedelics? Exploring the postulate of the human mind in the action potentials neurotransmitters theory of consciousness ​, ​with examples and detailed animations, could work in explaining to participants and researchers that while there are expectations of side effects, features of the human mind ultimately decide experiences. So, it may not proportionately apply that the absence of side effects indicates that the participant is under placebo, open-label, or otherwise. the action potentials neurotransmitters theory of consciousness

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