Machine learning is one of the most promising technologies for the coming decades, a branch of artificial intelligence that studies how to make machines learn, and that could completely change the world as we know it today.
If we manage to develop machines capable of learning by themselves, they will probably do so much faster than humans. They will also be able to make more efficient use of the information acquired, getting closer and closer to executive intelligence.
Machine learning promises to bring more intelligence to all the software of the machines and devices that surround us, from a smartphone to a coffee machine or a home device, such as Amazon’s Echo or Google Home.
With that ability to learn, machines will gradually acquire new skills and abilities, achieving previously unthinkable things. This transformation will also turn around the world of business, where only those companies that learn to use artificial intelligence as an opportunity instead of as a threat will be prepared to survive in the new business environment of the future.
At present, all large companies are investing in machine learning and artificial intelligence, as well as hiring specialized personnel in these areas. There are also numerous startups and other smaller companies that, with minimal resources, investigate and try to adapt to any disruptive innovation in this discipline that offers them a comparative advantage and a chance of survival in an increasingly hostile competitive environment.
Big companies like Google, Apple, Amazon, and Facebook have already incorporated artificial intelligence into many of their products. Google’s AlphaGo artificial intelligence system won against the Go world champion, a game that until now machines have failed to dominate due to the high levels of intuition it requires. And new applications are continually emerging for Watson, IBM’s already famous artificial intelligence system.
Machine learning catalysts
Three things are needed to work in this field: artificial intelligence technology, computing power, and data.
Machine learning algorithms have been there for years, but the recent development of the other two factors has triggered the advancement of machine learning:
• The availability of a large amount of information or large data repositories (big data) originated with the arrival of social networks, mobile devices, the Internet of things, and smart cities.
• The cheapening of high-power computer equipment that allows analyzing the growing volumes of data.
The differential factor: the data
Most algorithms, libraries, and machine learning tools are in the public domain. Companies like Google have recently opened their machine learning API to third parties so that any developer can make use of Google technology in their applications at an affordable price.
And the computing power necessary to implement them is also available to any company. However, the data is not.
For this reason, and as a result of the advances that, with certainty, will be produced in artificial intelligence and machine learning, numerous experts agree that data will be the key to success so that companies can survive the inevitable business transformation that will happen in the coming years. The companies that own the data will be the ones that end up succeeding.
However, not all participants are on equal terms and not precisely due to economic issues.
Currently, the bulk of the data is owned by a few large companies (technological, financial, and consumer), so initially, SMEs and startups are at a disadvantage. However, they will have their chance if they resort to the right strategy, taking advantage of the strengths and weaknesses of large companies.Also read: Machine Learning Basics for Beginners
Big business strategy
Currently, there are large companies, such as Amazon, Google, or Facebook, who have already realized the importance of artificial intelligence in business transformation and have been working on data collection for years. These companies have everything they need to succeed in the future business environment: financing, technology, and data, so competing with them will be very complicated, both for other giants and for small startups.
At the same time, there are other traditional giants with large databases, but they often do not have the necessary technology to take advantage of that data:
• Consumer multinationals (large supermarkets, clothing chains, etc.) have all our purchase tickets, so they know what we consume, on what dates, by cities, by sex, etc. Until now they have always used this information for advertising purposes, but they begin to be aware of its value because other sectors show interest in buying those databases.
• Technological multinationals also have many data on behaviors and trends, although some of them fail to develop the products necessary to monetize such information.
• The big telephone and internet operators, the energy multinationals, the universities, all of them have large data repositories.
• Even the banks which have infinite historical databases did not seem to have a clear strategy on what to do with them until the recent appearance of the fintech.
To survive in a future scenario in which machine learning and other new technologies are already an extended reality, these companies can basically:
• Develop their own technology: an often complex option, given that they lack not only the necessary experience in these areas but also their gigantic structures. They are generally hierarchical and not very flexible and do not adapt easily to changes, which is fundamental in an environment of vertiginous transformation.Also read: How Machine Learning Impact to Supply Chain Management?
• Acquire startups: it is a fairly popular option, but it requires an essential initial investment and an exhaustive analysis of the new existing technologies and of the different startups to choose the most appropriate one for the needs of the business giant in question.
• Collaborate with startups: it also requires a thorough analysis to choose the companies with which you are interested in collaborating with. The initial investment is less, and so is the risk when working with another company because you can stop the collaboration at any time and establish a new one. Also, the process is less traumatic for both companies.