The Value Of Data And The New Business Model
Data collection itself, especially when it comes to large amounts of data, is a complex and expensive task that large companies can only use. Have they become more affordable for small and medium customers? Big data labeling and simplicity in artificial intelligence are a competitive advantage for companies, so you can not expect it to be easier to obtain.
This is one of the main obstacles to developing artificial intelligence technology in the field of small and medium-sized enterprises. There are significant differences across industries. The situation in some has improved, and others have not changed at all. Changes in laws and regulations are necessary. Without this, in certain fields, such as medicine, the data will not be more accessible.
In the less sensitive area of data, a lot is available. Still, another factor comes into play: the company that manages to collect this data correctly sees its value and competitive advantage and is not eager to share it. So the main driver of progress is institutions. Their data sets are generally not used for business purposes, only for research, and the largest IT company publishes valuable and necessary data sets, but it is not enough.
Every year there is more information, and the data is also enriched. As a result, more and more companies are entering the market, collecting diverse information about users on the Internet, and then selling the data or allowing other companies to use it for free.
Every year there is more information, and the data is also enriched, But the priority has always been and continues to be big business. On the other hand, experts recall that many tagged data sets, frames, and previously trained neural networks have appeared in the public domain that can be used as prototypes for certain tasks.
There are more and more opportunities to see how data works. However, these are test models that can be used to get acquainted with technology but may not be suitable for solving real-life business problems. Today, artificial intelligence technology has matured and developed to such an extent that we cannot help but wonder: Is this a new programming method or a new model for processing data? The future of the field of artificial intelligence depends on the correct understanding of the questions posed.
It is surely not a new way of programming but a new way of solving certain types of problems that have traditionally been solved through programming. This is just a new and quite effective way to process data. Programming is still a broader term, and it is a subset of it. For some tasks, before writing a complex system for a long time, filled with a large number of conditions and rules, we now train a neural network, which produces approximately the same result.
This does not save us from implementing the program itself that receives such data and does something with its processing results, not to mention the business logic on the product side, interfaces, etc. Do not forget that artificial intelligence technology is based, first and foremost, on a classic code that someone previously wrote. Almost all vendors are now trying to include machine learning modules in their solutions to be used for rapid model development and productivity.
In addition, there is a set of tools for data scientists themselves, who can automate the process of creating models, verifying them, testing them, and putting them into production. This is the address of the MLOps, and it may turn out to be one of the most promising addresses. Many manufacturers are looking in this direction. This year there have been big changes in this area.