A ONE Institute
Jul 24, 2024
Today, we have set aside time to explore what kind of mindset our students should have in the era of Data.
Big Data, a lot of seemingly meaningless data, depending on how well we select and use it, we are living in an era where we can achieve enormous added value. In fact, Data is also called the crude oil of the digital age.
Companies like Tesla, which aim for autonomous driving, are collecting all the data from the process of consumers driving their cars. For a specific example, if you look at Uber, it analyzes various means of transportation in the area where the service is provided and receives different prices depending on the area.
For example, it provides services cheaply in places where public transportation is well developed, and expensively in the opposite places. I’m not sure if you remember the big issue that happened last time.
Target kept providing coupons related to pregnancy and childbirth to a minor female student and marketing, and the student’s father saw it and went to the store and got angry, saying that they were inducing the student’s pregnancy, but the student was really pregnant. We live in an era where data is used in various ways, to the point where AI knows before parents that their child is pregnant.
Google is also running a company very well using data. In the case of Google, advertising accounts for almost 80% of total revenue, so it is a company that needs to focus heavily on advertising. Google is using Data to do the following:
• Targeted advertising
• Location Tracking
• Improving Usability
• Tweaking Algorithms
• Trendspotting and analysis
• Targeted Advertising
In this case, it is to insert specific advertisements targeting consumers. When sending out advertisements, there is a function that can insert specific advertisements as needed by specifying regions, genders, ages, etc.
• Location Tracking
Google Maps also uses location tracking to target advertisements, such as exposing advertisements that can be exposed to people when installing billboards in the area based on regional gender, age population information. A lot of data is used in relation to advertising. Data is important.
In addition, data is being collected to develop generative AI, and it is an era where all data can be used, even idle chatter between people.
If AI is trained on SNS where many people use, such as Reddit, Twitter, Quora, Facebook, etc., AI that can converse similarly to humans will be born. Those who know that Tesla’s Elon Musk acquired Twitter also know that it is to obtain a source to educate AI.
In the sea of data and information, what strategy should be set up to pick out meaningful nuggets? What abilities make these things possible? Depending on the time, it is important to set a “hypothesis” to get the necessary information and create added value.
When you set up a Hypothesis and organize the data that can verify it, and verify it without distortion, added value is created.
Hypothesis can be divided into two major categories.
• Non-Directional Hypothesis
Setting a hypothesis without a specific direction. When the correlation between interest rates and house prices is given as an example, the result that is open is a Non-Directional Hypothesis.
• Directional Hypothesis
Clearly setting and verifying the relationship. Similarly, when the correlation between interest rates and house prices is given as an example, setting the relationship as a result that house prices will fall when interest rates rise is a Directional Hypothesis.
Before the Hypothesis, there is a prerequisite condition to set up a more clear hypothesis. That is Knowledge. When setting a hypothesis about the correlation between interest rates and house prices mentioned earlier, it is helpful to have a lot of background knowledge about what interest rates are, what role they play, what ripple effects are, etc. I would like to emphasize once again that you need to read a lot of books and study to support Knowledge.
I will explain Hypothesis and Knowledge using our academy as an example. We believe that the traditional system of academies (the system of going to school, teaching on the blackboard, and parting) will end in as short as 5 years and as long as 10 years. We definitely think that AI or computers will replace important parts of the academy system as AI develops. AI will study a specific subject, and when the study is over, it will create a curriculum for that subject, and AI will also create test questions for each level. The process of grading/analyzing the results of the students who solved the test questions and applying it to the class curriculum will improve AI’s teaching skills and accumulate know-how. The problem is that companies that create such AI, which will be much larger than small academies, can teach AI a specific subject and make a curriculum for that subject, but those companies do not have enough data from students.
In fact, in the academy, the students’ problem-solving behavior and correct/incorrect answers will be gathered in the form of the image above on the PDF file, but unfortunately, AI cannot learn such data. We think it is the role of the academy to collect data that can be passed on to such AI when large companies develop AI.
With such a Hypothesis, we at A-One Institute are in the process of changing all exams to Digital, and we are collecting student data through digitally converted exam questions. There will be various appearances when students solve problems, such as erasing choices, guessing answers, and skipping, and we are collecting such data for each problem.
We are also collecting data on solving problems clearly because the data on solving problems is also important. We are collecting data by analyzing how much time a student spends when solving a problem, how many choices they erased, etc.
If analog materials are stored digitally, when AI is developed and data is needed later, we are confident that this part of ours, A-One, will be used as data that generates added value. In our case, we are setting up such a hypothesis and preparing specifically. Therefore, I hope you do not forget to allocate time for Knowledge for those things while thinking about how to collect data in a way that is closely related to your life, and how to set up a hypothesis!
Today, I told you about the strategy to survive in the Data era that you were curious about. I hope it was helpful and I will see you in the next post.