As you think about artificial intelligence, you may ask questions like: Why is AI is so important? Why are so many large tech companies focusing their efforts on developing and applying AI tools?
From a development standpoint, one obvious reason for the growth of AI is that computer processing power has become exponentially better, which in turn allows computers to process more complex algorithms. These are the same kinds of advanced algorithms that power AI.
Is Data the New Oil?
Data is the other important element that has propelled the development of AI. In the most basic terms, without data, it would be nearly impossible to create AI products and applications.
One well-known saying often heard in the tech community is that “data is the new oil.” Today, the world’s most valuable companies are often the ones that have access to the largest quantities of data. It is not only the volume of the data that is important in business, but the quality as well.
Data is also similar to oil in that both have been misused by large corporations, resulting in enormous harm to the public. In both cases, the companies that had the earliest access to these commodities were rapid to take advantage of their benefits for their short-term financial gain without considering the possible long-term consequences of using them irresponsibly.
The excessive and improper use of oil, especially before the establishment of regulations, has led to disastrous consequences for the environment and the planet. Today, a similar phenomenon is happening with data. The giant tech companies like Google, Facebook, and Amazon collect, sell, and use enormous amounts of user data for their purposes without any regard for consumer privacy.
They have been doing this without being required to follow any ethical guidelines or data privacy protection laws, which has been creating many new kinds of problems for individuals and society in general. In the future, we need solutions that will protect the security and privacy of user data, ensuring that it can be used and shared ethically.
Personally, though, I would argue that data is even better than oil. In the years when oil was one of the most valuable commodities in the world, only a few companies were able to reap the benefits from it. Today, however, when almost anyone can learn the basics of AI and machine learning and use these skills to create valuable tools, and it is so easy to access free data sources online, everyone can benefit from the value of data.
Access to Data
In the modern world, we have an abundance of data that can be used. In contrast, 30 years ago, there was not nearly as much data about healthcare, traffic, finance, and other important industries and topics, so it was impossible to create AI-based solutions for basic problems in these areas.
Using the same logic, it is safe to assume that the technologies we have now will be even more powerful ten years from now as access to more data becomes available.
One example of this concept can be found in observing the development of self-driving cars and interconnected smart cities. The underlying component that makes these things possible is the volume of data that can be collected and analyzed to improve the performance of AI systems.
Data analysis usually relies on two kinds of information: structured data and unstructured data. To really comprehend AI systems, it is important to recognize the key differences between these two types of data.
Traditionally, structured data has been used more often than unstructured. Structured data includes simple data inputs like numerical values, dates, currencies, or addresses. Unstructured data includes data types that are more complicated to analyze, such as text, images, and video. However, the development of AI tools has made it possible to analyze more kinds of unstructured data, and the resulting analyses can then be used to make recommendations and predictions.
Powerful analytics will allow us to apply AI tools throughout society in the future.
Merrill Lynch has estimated that between 80 and 90 percent of all of the business data in the world is unstructured, meaning that the analysis of this particular type of data is extremely valuable. Results from analysis of this unstructured data could lead to a number of advantages in our modern society, including better healthcare options, safer traffic patterns, and increased access to education, among others.
Use of Data in Business and Society
Big data is also helping large companies to improve their internal and external operations. Kai-Fu Lee, a venture capitalist and CEO of Sinovation Ventures, illustrates the reasons why data is critical to large tech companies in a description of five steps that companies use to improve their AI solutions:
Obtaining More Data:
The Google search engine encompasses a huge amount of data. Likewise, Facebook would not be such a powerful social network without having access to data on people’s social trends. The key idea here is that tech companies can create services that are so powerful and useful that people are willing to allow their data to be used by the service.
Better AI-trained Product:
With Google and Facebook, your experience as a user is custom-tailored to be relevant and useful to you. This is possible because of AI-based tools that craft a personalized experience.
Greater Number of Users:
When users have a good experience with a product or service, they tend to recommend it to their friends.
A larger number of users almost always means access to more revenue.
Access to High-quality Data Scientists and Machine-Learning Experts:
As companies grow in revenue, they are better able to attract some of the world’s top experts in AI.
Eventually, the more data scientists and machine learning experts that come to work for a company, the more significant their research in AI can be, which then allows the company to become not only more valuable but also better prepared for the future.
These five steps are illustrated below in Figure 1.7. Although American tech companies were specifically used in the example, these steps are also valuable to other international companies that rely on AI, such as Alibaba, Baidu, and Tencent.
Because data is such a vital piece in the development of AI, many experts have demanded that big tech companies should release at least some of the data that they possess so that a greater number of useful applications and products can also leverage this information.
While this notion brings about significant questions to be answered, it is an unmistakable fact that in the future, it will be important to have data sets like these available for the continued development of AI products and services.
This chapter offers just a short introduction to the importance of data for AI, but hopefully, as you read, you’ll begin to think about the potential AI-based applications that you could design or develop in the future. As with many other topics in this book, if this concept has caught your attention, I recommend that you dive deeper into learning more about it in detail.
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