Machine learning is a technique by which programs learn iteratively from data instead of being static. Machine learning systems are used to create a model based on continuous input that can be used to make predictions or make decisions.
Machine learning systems learn from data and can adjust themselves to produce better results. The more data you have, the faster you will learn and the more accurate your results will be. It is continuous improvement, applied to knowledge.
Machine learning and data management: from difficulty to opportunity
Managing organization data is a growing challenge for companies. However, the solution to this challenge is not to focus on business processes and systems; it has to do with innovation.
Resorting to machine learning is the way to transform difficulty into opportunity and turn inconveniences into benefits, such as:
An increasing volume of data: if the management of complex, heterogeneous, fast data and in a big data environment escapes human capabilities, the same does not happen with machine learning, which takes advantage of all those zeta bytes of information and exploits the advantages of the billions of IoT sensors that are connected today to learn and contribute to creating a smarter system.
Several business users that do not stop growing: and that, although it poses a security challenge for companies, which must carefully take care of endpoint management, it is extraordinarily effective so that machine learning does not cease its continuous learning.
New habits: migrations, transformations, data integration, or advanced analytical processes are not exceptional circumstances in any organization, but patterns that are increasingly repeated, as business users opt for experimentation and organizations empower them to do so, providing them with the right tools. Machine learning takes advantage of all these inputs to continue learning and contributing to the system’s new perspectives, a more complete vision, and a deeper knowledge of each data.
This is an extraordinary opportunity to move towards a leadership model based on data that make the organization succeed, driving it towards success in each of its disruptive initiatives and allowing it to find answers to all those questions that it could never have allowed itself to respond due to limitations. Budget.
Machine learning: Benefits your business could experience
Are data your priority? Is your organization ready to unleash the potential of each bit of information? It must be clear that the results of any digital initiative can only be as good as the quality of the data based on those executed.
In addition to implementing quality software to ensure adequate standards, the decision to opt for machine learning for data management implies many benefits for all business users, such as:
- Increased speed of data delivery for critical business initiatives.
- Increase productivity and effectiveness of processes.
- Improvement of the suitability of the recommendations, when machine learning is combined with the visibility of metadata throughout the company.
- Latency reduction, thanks to the automation of many data management tasks.
Besides, among the advantages of machine learning is the fact that machine learning can be used to improve tasks that would be impossible to perform by humans or would be unproductively long or expensive, such as predictive operations or the discovery and identification of large-scale patterns.
Advantages of Applying Machine Learning In the Company
If you want to be one of those professionals who work with Machine Learning in the company you have to know the advantages of this technology:
Better customer service
Machine Learning allows you to analyze customer preferences to offer customized products automatically. In this way, their perception of the company improves and loyalty is enhanced.
Automatic learning of the management systems applied in the organization helps to avoid mistakes made. The longer it has been integrated into the system, the stronger it will be.
Concerning the previous point, machine learning tools prevent errors. The AI discards by itself the riskiest actions and those that can put in risk the development of our product or service.
This technology allows machines to be up to date concerning cyber-attacks. Given that most malware uses similar code, machine learning can easily prevent attacks from recurring.
Artificial Intelligence can easily detect which transactions are legitimate and which are not if we assign a pattern to these monetary movements.
The automation of routines or mechanical tasks that do not add value is a recurring element in the lists of benefits related to Artificial Intelligence. Thanks to Machine Learning, the machine will know what processes to deal with and, over time, will refine them and even expand the number of tasks to be performed.
Americans are the ones who have been applying Machine Learning for the longest time. According to MIT, 50% of organizations in the United States use this technology to facilitate data analysis and obtain more and better insights. 46% are interested in the competitive advantages of Machine Learning, 45% say they want to boost the speed of data collection thanks to this tool and 44% want to boost R&D to offer innovative products and services.
Types of Machine Learning
We find three types of Machine Learning depending on the level of supervision they require:
As the name says, these are Artificial Intelligence that needs some human control. In these cases, the Data Scientist establishes what type of data must be related to certain specific elements so that the machine can do the rest of the work. The professional must be in charge of introducing the inputs and outputs so that technology can find patterns in the information.
In these cases, the data is not previously labeled and the AI has much more autonomy. It is the machine that must find the relationship and the structure of the information. A higher density of information is obtained, but the sample is much wider, so it will be the Data Scientist who will be responsible for filtering it later.
This system is very different from the previous two. It works with a “rewards” system. When the machine is successful with its operations it is given a positive stimulus and if it fails, it is given a negative one. Thus, by trial and error, the machine generates patterns and learns for itself the best way to proceed according to the needs of the organization.
Examples of Machine Learning in Inventory Management
An example is the calculation of the costs of lack of stock. Suppose a customer goes to the supermarket to buy an award-winning bottle of fizzy drink from the local store, but it is sold out. What will the client do? Are you buying a reservation for another year? Or a wine bottle from another winery? Or just another champagne bottle. Or do you leave the supermarket and therefore do not buy any products? Or does it no longer return for wine, but other purchases? Or does it not come back at all? What if another supermarket is 300 meters or 15 km away and the customer does not have a car? You see it coming; “The answer depends on the situation,” but what situation?
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Welcome to the world of machine learning
Time is advancing and with it, a lot of energy has been put into the search for more efficient solutions to overcome that complexity. Especially because a brute force approach, in which we evaluate all possible outcomes and then choose the best one, was not at all beneficial from a practical point of view. However, the growing interest in artificial intelligence and machine learning was the technique that received the most attention. “The solutions in inventory management have not changed. The means have.”
You may have heard or read the words ‘DBC system’, ‘machine learning’, ‘IBM’ and the year ‘1997’ in the same sentence. The system Deep Blue Chess (DBC) IBM is the protagonist of the story that took place in 1997. Through machine learning, they managed to defeat Kasparov, the world chess champion.
The machine-learned the combinations that it should make in certain situations to obtain the best result, taking into account the possible movements that follow from it and the patterns in the game of Garry Kasparov. However, there is some mystery surrounding this story. For example, IBM did not want to deliver the machine log files. This raised the suspicion of Gary Kasparov, which made him think that IBM had cheated. Another interesting detail is that Gary Kasparov demanded a rematch and IBM rejected and dismantled the machine almost immediately.
In 2016, there was a real show of the power of machine learning. Once again, in a strategic game, this time the game ‘Go’, the machine faced the absolute world top. Here too, they succeeded in the design. Now, ‘Go’ is different from chess in the sense of the number of possible combinations it has. Another funny detail: ‘Go’ has 10 to 174 possible plate configurations. To give you an idea of this size: there are 1 million trillion trillion trillion trillion possible configurations more than with chess.
After seeing the advantages of this tool, we can say that the investment involved in having Machine Learning in the company can be extremely profitable.