Computers Can Predict When You're Going to Die… Here's How

Be Smart

9 May 202413:33

Summary

TLDRThe video script explores the concept of predictive analytics, a branch of mathematics that uses historical data to forecast future outcomes. It delves into the history of predictive analytics, starting with the 1600s when Lloyd's of London used data to predict the risk of sea voyages, leading to the birth of the insurance industry. The script then transitions to the present, highlighting how predictive analytics is pervasive in various sectors, from shopping to politics. The narrative takes a personal turn as the host, Joe, discusses death anxiety and the possibility of predicting life expectancy using algorithms. He introduces an AI model that predicts mortality with high accuracy by analyzing multi-factor datasets. The script concludes with a reflection on the power of human choice and the potential of predictive analytics to enhance life, encouraging viewers to stay curious and make the most of their time.

Takeaways

📉 Joe's initial reaction to comments on his age leads to a humorous reflection on death and an introduction to life-extending efforts.
📚 A 2022 survey reveals that a significant portion of Americans and Gen Z individuals think about death frequently, highlighting the prevalence of death anxiety.
💰 The global health and wellness market is worth $1.8 trillion, indicating the extent to which society is invested in extending life.
📈 Predictive analytics, a branch of mathematics, uses historical data to predict future outcomes and is widely used across various sectors.
⛵ The origins of predictive analytics can be traced back to the 1600s with the advent of maritime insurance by the Lloyd's Company of London.
🧮 Predictive analytics involves creating a 'statistical Franken-human' by aggregating data from numerous individuals to predict outcomes for a collective rather than an individual.
📱 Our daily activities generate a wealth of data that can be used to predict our behaviors and outcomes, such as where we are likely to be at any given time.
🔢 The law of large numbers is a mathematical theory that underpins predictive analytics, stating that larger data samples are more likely to reflect actual averages.
🎰 The complexity of predicting future events is simplified by considering multiple factors and their influence on outcomes, much like analyzing the contents of a bag of marbles.
🤖 Machine learning is employed to analyze increasingly complex factors and determine which ones are significant in predicting future events.
🧳 An AI mortality model trained on Danish data was able to predict survival with a high degree of accuracy, underscoring the potential of predictive analytics in life expectancy.
🚀 Despite the accuracy of predictive models, unpredictable 'black swan' events can still occur, emphasizing the limits of these models and the importance of human agency in shaping our futures.

Q & A

What is the main theme of the video?
-The main theme of the video is the concept of death anxiety and the use of predictive analytics to estimate life expectancy and risks associated with various factors.
What does the term 'thanatophobia' refer to?
-Thanatophobia is the fear of death, a perfectly natural human feeling that many people experience.
How does the global health and wellness market size relate to the video's content?
-The size of the global health and wellness market, estimated at $1.8 trillion, illustrates the extent to which people are willing to invest in products and services to extend life and improve health, reflecting the human desire to 'cheat death.'
What historical example is given to show the use of predictive analytics?
-The historical example given is the Lloyd's Company of London, which used past data to predict the risk of sea voyages and offered insurance based on those predictions, effectively birthing the insurance industry.
How does the law of large numbers play a role in predictive analytics?
-The law of large numbers is a mathematical theory stating that the larger the data sample, the more likely it is that the average of that sample will reflect what actually happens. This principle is used in predictive analytics to create more accurate predictions by analyzing larger groups of data points.
What is the significance of machine learning in improving predictive analytics?
-Machine learning allows for the analysis of more complex factors and can determine which factors are actually important in predicting outcomes. It can process all possible factors, free from human bias, and select and weight factors more objectively.
What is the accuracy of the AI predictive mortality model mentioned in the video?
-The AI predictive mortality model mentioned in the video was able to guess correctly 8 out of 10 times when tested with a set of people where half survived and half died.
How does the actuary in the video estimate Joe's life expectancy?
-The actuary uses a longevity illustrator and factors such as Joe's age, gender, and habits (like not smoking) to estimate his life expectancy to be around 86 years, with a 37% probability of living to age 90 and an 8% chance of living to age 100.
What is the role of black swan events in the context of predictive analytics?
-Black swan events are outlier events that are moments of totally unpredictable chaos. They represent the unpredictable elements that can occur despite the most accurate predictive analytics, emphasizing the inherent uncertainty in forecasting the future.
What is the conclusion the presenter draws about the power of individual choices?
-The presenter concludes that despite the accuracy of mathematical tools and predictive analytics in forecasting our lives and actions, individuals still have the power to make choices that can change those predictions and lead to different outcomes.
What is the purpose of the Patreon mention at the end of the video?
-The Patreon mention is a call to action for viewers to support the show financially. By signing up for Patreon, viewers can gain early access to videos and support the content creation process, effectively taking some control back from predictive analytics algorithms.

Outlines

00:00

😀 The Impact of Online Comments and Predictive Analytics

The video begins with Joe, the host, expressing his apprehension about reading online comments despite their potential negativity. He stumbles upon a mix of supportive and aging-focused comments, leading to a humorous and anxious reaction. This segues into a discussion about the fear of death and life-extending measures, such as a smoothie recipe found on TikTok. Joe highlights the ubiquity of death contemplation, citing a survey that shows how often people think about death. The video then delves into the concept of death anxiety and introduces the global health and wellness market. Predictive analytics is presented as a tool that uses historical data to forecast future events, with applications ranging from shopping to politics. The narrative traces the origins of predictive analytics to the 1600s, describing how it was used to assess maritime risks and gave birth to the insurance industry. Today, predictive analytics is more sophisticated, harnessing the power of computers to make highly accurate predictions.

05:02

📊 Understanding Predictive Analytics and Its Accuracy

The video continues by emphasizing the predictability of human behavior, despite our perception of free will. It explains how data trails, such as sleep patterns and daily routines, are collected unconsciously by various apps and services. This data is then aggregated to form a statistical representation of a group, known as the law of large numbers. The video uses a marble-drawing analogy to illustrate how predictive analytics works, emphasizing the importance of considering multiple factors to enhance prediction accuracy. It also touches on the role of machine learning in identifying and weighing complex factors. The segment concludes with a discussion about the kind of data needed to construct predictive algorithms, highlighting the difference between traditional human analyst selection and machine learning's unbiased, comprehensive approach. An example of an AI mortality model is given, noting its impressive accuracy in predicting life expectancy based on a large dataset from Denmark.

10:05

🎯 Actuarial Predictions and the Power of Individual Choices

The final paragraph features Joe consulting an actuary named Dale to predict his life expectancy based on his personal information. Dale uses a longevity calculator to estimate Joe's life expectancy, providing a probability of reaching age 90 and even 100. Joe is pleasantly surprised by the optimistic prediction and reflects on the importance of planning for a long life. The video acknowledges the existence of unpredictable 'black swan' events that can defy predictions. However, it reassures viewers that despite the accuracy of predictive analytics, individuals still hold the power to make choices that can alter predicted outcomes. The video concludes with a reflection on the limited time we have and the role of science and analytics in helping us make the most of it. It ends on a promotional note, encouraging viewers to support the show on Patreon for exclusive content and to have a more personal connection with the host.

Mindmap

Keywords

💡Predictive analytics

Predictive analytics is a branch of mathematics that uses historical data to make predictions about future outcomes. It is central to the video's theme as it discusses how this method is used to predict life expectancy and death, which is a significant part of the content. The script mentions that predictive analytics is prevalent in various sectors, including shopping, sports, social media algorithms, fraud detection, and politics.

💡Thanatophobia

Thanatophobia is the fear of death, a perfectly natural human feeling that the video discusses in the context of how often people think about death. It is relevant to the video's theme as it sets the stage for the discussion on death and the attempts to predict or cheat it. The script cites a survey indicating that many Americans and Gen Z individuals think about death regularly.

💡Life-extending smoothie

A life-extending smoothie is a humorous reference in the video to a concoction that Joe, the character, is trying to use to cheat death. It symbolizes the human attempt to prolong life and is an example of the video's exploration of efforts to counteract mortality. The script humorously describes the taste as 'youth and burning plastic', indicating the lengths people might go to in the quest for longevity.

💡Global health and wellness market

The global health and wellness market, estimated at $1.8 trillion, is mentioned in the video to highlight the vast industry that has grown around the desire for a longer, healthier life. It ties into the video's theme by illustrating the economic implications of the human quest to avoid death and the market forces that drive the development of products and services aimed at longevity.

💡Law of large numbers

The law of large numbers is a mathematical theory that states the larger the data sample, the more likely it is that the average of that sample will reflect what actually happens. It is integral to the video's discussion on predictive analytics, as it explains how analyzing large groups of data points can lead to accurate predictions about life expectancy. The script uses this concept to discuss how computers create a 'statistical Franken-human' to represent a group for making predictions.

💡Machine learning

Machine learning is a type of artificial intelligence that allows systems to learn and improve from experience without being explicitly programmed. In the context of the video, it is used to analyze complex factors and determine which ones are important for predicting mortality. It is a key component in the advancement of predictive analytics for life expectancy, as it allows for the consideration of a vast array of factors beyond human bias.

💡Actuary

An actuary is a professional who deals with the measurement and quantification of risk, primarily in the insurance industry. In the video, an actuary named Dale is consulted to provide an estimate of Joe's life expectancy based on certain personal data. The actuary's role is pivotal as it demonstrates the practical application of predictive analytics in assessing individual risk and life expectancy.

💡Black swan events

Black swan events are unpredictable, rare, and have severe impact. The video references these to acknowledge that despite the accuracy of predictive analytics, there will always be outlier events that cannot be foreseen. This concept is crucial as it tempers the narrative around the infallibility of predictive models and reminds viewers of the inherent unpredictability in life.

💡Free will

Free will is the power of action without the constraint of necessity or fate. The video touches on the idea that despite the predictive capabilities of analytics, individuals still possess free will to make choices that can alter predicted outcomes. This concept is central to the video's message, emphasizing the human element and agency in the face of deterministic models.

💡Data points

In the context of the video, data points refer to individual pieces of information that are collected and analyzed to make predictions. The script discusses how each person's life can be distilled into data points, which are then used by predictive analytics to forecast outcomes like death. This concept is fundamental to understanding how predictive models aggregate and interpret information to make statistical inferences.

💡Insurance industry

The insurance industry is highlighted in the video as the birthplace of predictive analytics, with the example of Lloyd's of London using past data to predict the risk of sea voyages and offering insurance accordingly. The industry's historical use of predictive methods is tied to the video's theme by showcasing the origins and evolution of the techniques now being applied to predict life expectancy and death.

Highlights

Joe confronts the reality of aging and death through comments on social media.

Life-extending smoothie recipe found on TikTok is attempted by Joe, symbolizing the human desire to cheat death.

Statistics from a 2022 survey reveal that half of all Americans think about death monthly, and one out of three Gen Z'ers daily.

Death anxiety, or thanatophobia, is identified as a natural human feeling due to the value of life.

The global health and wellness market is estimated at $1.8 trillion, reflecting the massive scale of the industry aimed at extending life.

Predictive analytics, a branch of mathematics, uses historical data to predict future outcomes and is applied in various fields.

The origins of predictive analytics are traced back to the 1600s with the advent of maritime insurance by Lloyd's Company of London.

Lloyd's of London has grown to become one of the largest insurers in the world, utilizing advanced predictive analytics.

Human behavior and life patterns are shown to have a high degree of predictability, contrary to common beliefs.

Data trails left by individuals through daily activities contribute to the vast amount of information used in predictive analytics.

The law of large numbers is introduced as a mathematical theory that increases the accuracy of predictions with larger data samples.

Predictive analytics considers multiple factors to calculate likelihoods and probabilities of future events, such as death.

Machine learning is employed to analyze complex factors and determine their importance in predicting outcomes.

An AI predictive mortality model is discussed, which was trained on Danish health and demographic statistics and showed high accuracy.

The potential for human bias in selecting factors for predictive models is highlighted, and how machine learning can help mitigate this.

An actuary provides a personalized life expectancy estimate for Joe based on his age, habits, and other factors.

The importance of planning for the future, even when armed with predictive analytics, is emphasized.

The unpredictability of 'black swan' events is acknowledged, showing the limits of predictive analytics.

The power of individual choices to alter predictive outcomes and the potential for change is discussed.

The video concludes with a reminder of the limited time humans have and the role of science in helping make the most of it.