Finding the Cause from the Effect in the Age of Big Data

hello world inverse

“Just as it would be difficult to predict where the very next drop of water is going to fall, (…). But once the water has been spraying for a while and many drops have fallen, it’s relatively easy to observe from the pattern of the drops where the lawn sprinkler is likely to be situated.”

[Hello World: Being Human in the Age of Algorithm, Hannah Fry]

In science, an inverse problem is one of the research fields to extracts the hidden law (or the mathematical formula) from observation (data). That is, the inverse problem is to find the “cause” from the “effect”. It is a similar concept of profiling a serial killer in criminology. Through all the data of victims, we anticipate the character of the serial killer. We agree that more victims make an accurate prediction of the serial killer BUT we don’t want more victims. So, the important part of the inverse problem is to find the appropriate formulation from a small data set. However, as you see the quote, it is really hard to estimate something accurately with small data. This issue has been a bottleneck of the development of an inverse problem.

In the age of Big Data, on the other hand, we collect massive data set from individuals, autonomous systems, efficient measurements, or online websites, leading to accurate prediction of the cause. So many people thought that it is easy to solve the inverse problem using massive data; that is somewhat true and many research achievements about data-driven modeling that finds the underlying laws or governing equations (or a black box model) to describe the cause and effect directly from data. However, the inverse problem is now struggling with another issue – finding “right” causality. In big data, improbable things happen all the time. This may lead to the wrong causality of input/output data. For example, there is a possibility that the correlation between two variables stems from just coincidence but the algorithm cannot distinguish this coincidence and the real causality. Hence, the human check the data-driven causality based on rigorous way. That is why the fundamental mathematics/statistics are becoming important in the age of Big Data.

As Algorithms Becoming Intelligent, We May Become Unintelligent

“There’s a hidden danger in building an automated system that can safely handle virtually every issue its designers can anticipate. (…) So they’ll have very little experience to draw on to meet the challenge of an unanticipated emergency.”

[Hello World: Being Human in the Age of Algorithm, Hannah Fry]

Using Google Maps, I was driving to Quebec in Canada from my home (in the U.S.) for late summer vacation with my family. Just after passing the border, I realized that my phone did not work and of course Google Maps lost their power, too. I made a desperate attempt to drive with only road signs as my dad did. Our world is fast becoming intelligent via recent developments in smart devices, algorithms, automated systems, and AI. We don’t need to remember our friends’ phone numbers and physical addresses anymore. Moreover, we don’t need to memorize the exact spelling of the longer word; Google search can show the correct results from the misspelling. Can we say that we (not the world) are becoming intelligent?

Large autonomous systems will be widespread inevitably. For example, autonomous cars will be popular in the near future. So, the next generation may not know (or experience) how to correct a slide on an icy road. This lack of experience may lead to a nasty accident when the autonomous system is not working. Technologies do more, we do less (e.g. thinking or experience). However, there are two sides to every story. Since the invention of the calculator (or the computer), we have developed new research fields such as numerical analysis, scientific computing, or computational biology, resulting in the enormous expansion of knowledge. I hope that the advent of the large autonomous system provides not only the answer to problems we are facing now but also the vision for the better future.

Can Artificial Intelligence Be the New Judge in the Future?

hello world justice

“The algorithm will always give exactly the same answer when presented with the same set of circumstances. (…). There is another key advantage: the algorithm also makes much better predictions.”

[Hello World: Being Human in the Age of Algorithm, Hannah Fry]

When we consider the dark side of algorithms and AI models, we always think about justice; The AI model is usually optimized for efficiency and profitability, not for justice (also see my previous post: Justice: What’s the Right Thing to Do in Data Science?). What is justice, by the way? Justice is the quality of being just or fair. Then what is “just” or “fair”? Defining the word “justice” or “just” is still an arguable issue in our society. In a slightly different context, we may say about “fair” instead. Fairness, in a narrow sense, requires consistency; The same input should produce the same output. For example, if you and I write down the same answer on the test, we should get the same score. That is the starting point to discuss fairness.

The (AI) algorithms which encapsulate detailed mathematical formulas have such fairness inherently, leading to a consistent consequence (the same input, same output). This is a big advantage of the algorithm for finding someone’s guilty consistently. Furthermore, the prediction is much accurate than human’s prediction. However, the consistency may also occur consistent error until the algorithm is adjusted. Humans, on the other hand, have their own models in their minds to judge someone’s guilty. However, this is not based on mathematics (or rigorous reasoning), leading to inconsistent outcomes for the same circumstances. Also, it is really hard to correct their bias and prejudice while the algorithm is easy to adjust their parameters for correction. Then who is more righteous in terms of fairness? 

Digging Data in the New Wild West

hello world data power

“We do well to remember that there’s no such thing as a free lunch. (…). Data and algorithms don’t just have the power to predict our shopping habits. They also have the power to rob someone of their freedom”

[Hello World: Being Human in the Age of Algorithm, Hannah Fry]

There are many FREE apps for tracking your routine such as walking, jogging, eating, book reading, shopping, or studying. Thanks to these productive apps, we can check our daily routine and change our routine for better performance. By the way, how do these free apps make money? There is no free lunch in the world. They make their profit from the data you recorded. In the age of Big Data, data is the new gold and many companies are digging such gold in our daily routines now. We might say we live in the new Wild West.

Someone might think that Data is just Data. That is true but the AI model can spot important (hidden) patterns from massive data effectively. They can dig gold in the mine by efficient tools. Moreover, they make precise categories for people’s behaviors, leading to an accurate prediction (classification) for new customers. Hence, AI models are becoming more sophisticated as increasing the number of data they collect. Amazon and other online retailers provide irresistible deals and coupons every day. Netflix and other streaming services recommend the best movies we will like so we cannot help clicking the next movie. In these days, we cannot blame a shopaholic. because (internet) shopping addiction is not caused by a lack of self-control but caused by a sophisticated AI model. That is why I purchase more books on Amazon today (Don’t blame me!).

Who Is our Future AI and What Is our Role?

hello world algorithm

“This tendency of ours to view things in black and white – seeing algorithms as either omnipotent masters or a useless pile of junk – present quite a problem in our high-tech age.”

[Hello World: Being Human in the Age of Algorithm, Hannah Fry]

In the famous marvel movie, “Avengers: Age of Ultron”, Two different AIs appeared. The first one is JARVIS (Just A Rather Very Intelligent System) that helps the Iron man – Good AI. Another one is Ultron, the AI supervillain, to destroy the world like “Skynet” in Terminator – Bad AI. Who (or what) will be in our future world? It is hard to answer this question. A lot of books written by AI experts are divided into two forecasts; AI utopia and AI dystopia. However, all the books speak with one voice that the future of AI depends on our actions. Hence, we don’t need to forecast our future in black and white. The (real) future of AI, I believe, will be in between and will be adjustable by us.

Artificial Intelligence is a system consisting of mathematical algorithms to take action that maximizes the probability of success for the given task. It is just (complicated but) a set of algorithms, not a supernatural power. That is, there is still room for understanding it and making it good. First, we should reaffirm fundamental mathematics inside of the AI algorithm as many as we can and eliminate hidden mathematical errors (or computer bugs). Second, we feed them to unbiased and correct data so that AI makes an impartial model to decide their actions. Third, we need to set clear and socially approved objectives for AI models. The first two actions are relatively practicable but the last part requires a social consensus to make a good AI model. For example, the United Nations platform, AI for Good, has tried to offer a route for sustainable development goals. So, please think about the future of AI and about your roles for making a good AIs.

[Wrap up] Book Review: Humble PI: A Comedy of Maths Errors

In our life, mathematics is very important for logical thinking based on evidence-based knowledge through rigorous mathematical analysis. Especially, when we predict something new, the power of mathematics overwhelms our instinct or heuristics. However, when using mathematics improperly, catastrophic results are waiting for us. In this book, the author, Matt Parker, said such an important role of mathematics and showed examples of disasters stemming from mathematical errors through exhilarating stories he has experienced. 

Then, what is the role of a human in mathematics? We try to use mathematics when deciding something important. And then, we should check all the types of mathematical errors to avoid the disaster. I would like to introduce his last paragraph. “Our modern world depends on mathematics and, when things go wrong, it should serve as a sobering reminder that we need to keep an eye on the hot cheese but also remind us of all the maths which works faultlessly around us.”

The following links are some quotations from the book with my thoughts.

(1) What Number Is a Really Big Number?

(2) Please Give Math More Time to Pick up the Pieces

(3) I Don’t Count on You When You Count Numbers

(4) More Approximations, More Problems in Your Life

(5) Probably, We Are Not Independent

(6) Searching for Average Man

(7) Sometimes, Simple Mathematics is Better than Our Experiences

Sometimes, Simple Mathematics is Better than Our Experiences

humble pi simple math

“If a new system is implemented, humans can be very resourceful when finding new ways to make mistakes. It can be very dangerous when humans get complacent and think they know better than the maths.

[Humble PI: A Comedy of Maths Errors, Matt Parker]

If someone has accumulated knowledge based on a lot of experiences in a particular subject, we call her/him an expert. When a paradigm is shifted or a new knowledge system comes, we often follow existing experts’ opinions without a doubt. I don’t underestimate the important role of existing experts to show a vivid and clear vision for the new age, stemming from appropriate heuristics. However, sometimes their knowledge and experiences are obsolete. For example, the experts about the Ptolemaic system lost their power in the age of the Copernican system. When a new system of knowledge is coming, what can we do? Should we stick to old ways of doing things?

Our model based on previous experiences is suitable for predicting similar tasks but vulnerable to predict rare events. The model (or knowledge) based on mathematics, however, is still robust to an extreme case or a rapid change. Moreover, our knowledge from experiences seems to be fragmentary and unconnected for understanding a big complex system while mathematics can describe it more effectively and clearly. Hence, when you do something totally new, please think one more time before act on instinct or previous experience. Human keeps on making the same mistakes over and over again. Also, previous experience may not predict something new effectively. Instead, find evidence (or facts), do mathematical thinking (or making mathematical model) with them, and take the simplest way to understand without logic fallacy. I cannot say such mathematical thinking is the best (or the only) way to be right. Rather, mathematical thinking is the way not to be wrong.