In 2016, the AlphaGo vs. Lee Sedol match shocked the world by demonstrating the potential of artificial intelligence. AlphaGo’s ability to learn and its versatility showed the promise of AI, but the public needs to think about the need for AI and its potential impact. AI can be the key to solving complex problems, but it requires public understanding and cooperation.
An AI system that captured the world’s attention
March 2016 was a hot month for Go and artificial intelligence because of AlphaGo, an artificial intelligence system developed by Google DeepMind. AlphaGo won four out of five games against Lee Sedol 9, a result that many professional Go players predicted would be a landslide victory. AlphaGo’s shocking results were due to its ability to learn. AlphaGo is a system that literally grows as it accumulates quality data from game after game through machine learning, and AlphaGo also has a huge advantage over previous artificial intelligence: its versatility. AlphaGo’s generalizability means that by strengthening the number of layers of its neural network, it has greatly increased its utility beyond Go.
This achievement demonstrated the potential of AI to mimic and extend human thinking and creativity beyond the realm of technological advancement. While AI in the past has been limited to systems that specialize in specific tasks, AlphaGo’s ability to perform in domains that require complex decision-making marks the beginning of general-purpose AI. These technological advances are not just the result of programming, but a new form of intelligence with the ability to learn beyond human intuition.
“solve intelligence, use it to make the world a better place!” With these words at the top of AlphaGo’s homepage, Deep Minds throws down the gauntlet to make the world a better place with AlphaGo’s learning capabilities and versatility. And it succeeded in making a big impression on audiences around the world. However, it hasn’t yet convinced the public that it’s necessary. Some people fear that the explosion of A.I. will one day lead to a world where A.I. dominates humans. This vague fear of A.I. could hinder the development of A.I., which could be a great power for humanity, as Demis Hassabis, the founder of Deep Mind, worries. Therefore, the public needs to understand the necessity of advancing AI.
Deep minds want to make the world a better place
First, let’s consider what kind of world Deep Minds is aiming for: a better world. A world where income inequality is eliminated? A world where we don’t have to worry about disease? A world where there is peace and no war? Certainly, from the perspective of the general public, AI may seem like an unlikely means to achieve these lofty goals. But there is a connection. In this article, we’ll take a look at statistics to see why we need AI.
AI and statistics
The world is full of seemingly unsolvable problems. Solving them involves collecting and analyzing the right information to find answers. Statistics plays an important role in this process. This is because statistics is responsible for collecting data, organizing it, and deciding how to analyze it to find the best answer to the problem. Statistics has made significant advances from the past to the present, and has made great strides in all areas of problems involving uncertainty. Statistics has a wide range of applications, including biostatistics in medicine and pharmacy, sampling theory in policy formulation, and financial statistics, which is central to finance and management.
Statistics is inherently interested in populations rather than samples per se. In other words, it tries to learn about the whole from a part of it, i.e., the information collected from a sample. This is called statistical inference. In other words, it’s the same process as the problem-solving method mentioned above: collecting and analyzing the right information to find the answer. However, this statistical inference process is very similar to the behavioral decision-making process of A.I., which decides how to react based on accumulated data. It also supports two of AlphaGo’s strengths: its ability to learn and its generalizability. In the end, developing an AI is akin to creating a group of excellent statisticians. It is a group of statisticians who have the ability to learn and accumulate knowledge, or data, through various situations, and the versatility to work in various fields. Therefore, the reason why we need to develop AI is directly related to the reason why we need statistics.
The relationship between AI and statistics is interesting in itself, but it also expands the range of problems that AI can solve. For example, global issues such as climate change, predicting complex financial markets, and developing diagnostics and treatments in the medical field are just a few of the many areas where AI can intervene. As AI continues to advance, it has the potential to provide a new paradigm for solving these challenges by borrowing from the power of statistics.
AI may be the key to solving the world’s problems!
The truth is that A.I. still has a long way to go. Even the act of playing Go requires a high level of technical skill, as does the sheer number of cases that need to be analyzed, but it’s important to keep improving the system in order to help solve the world’s more complex problems. These advances are also influenced by public interest, and if the public does not recognize the true need, we may miss the key to solving the world’s challenges. In this article, I’ve used the method of analogy to compare artificial intelligence to statistics to find the need for it. It was a very simple process, but not a light one, and I hope this article will give at least one reader time to think about the necessity of developing AI.
Furthermore, the development of AI is not just a technological advancement, but an important change that will open up new possibilities for society as a whole. It will be the responsibility and obligation of all of us to understand how technological advances can change our lives, and to be proactive in dealing with those changes. Artificial intelligence is more than just an improvement in mechanical computational power; it has the potential to open up new models of collaboration between humans and machines to solve problems together. Preparing for the future that these changes will bring is no longer an option, but a necessity.
