Machine learning (ML) algorithms allow computers to define and apply rules that were not explicitly described by the developer.

There are quite a few articles dedicated to machine learning algorithms. This is an attempt at a “helicopter view” description of how these algorithms are applied in different business areas. This list is not an exhaustive list of courses.

The first point is that ML algorithms can help people find patterns or dependencies that are not visible to a human.

Numerical forecasting seems to be the best known area here. For a long time, computers were actively used to predict the behavior of financial markets. Most of the models were developed before the 1980s, when financial markets had access to sufficient computing power. These technologies later spread to other industries. Since computing power is cheap now, it can be used even by small businesses for all kinds of forecasting, such as traffic (people, cars, users), sales forecasts, and more.

Anomaly detection algorithms help people to scan through a lot of data and identify which cases need to be checked as anomalies. In finance they can identify fraudulent transactions. In infrastructure monitoring, they allow problems to be identified before they affect the business. It is used in manufacturing quality control.

The main idea here is that you should not describe every type of anomaly. It gives a large list of different known cases (a learning set) to the system and the system uses it to identify anomalies.

Object clustering algorithms allow a large amount of data to be clustered using a wide range of meaningful criteria. A man cannot operate efficiently with more than a few hundred objects with many parameters. The machine can do more efficient clustering, for example, for customer/prospect qualification, product list segmentation, customer service case classification, etc.

Recommendations/preferences/behavior prediction algorithms give us the opportunity to be more efficient in interacting with customers or users by offering them exactly what they need, even if they haven’t thought of it before. Recommender systems work really poorly on most services now, but this sector will improve rapidly very soon.

The second point is that machine learning algorithms can replace people. The system analyzes people’s actions, creates rules based on this information (that is, learns from people), and applies these rules by acting instead of people.

First of all, it is about all types of standard decision making. There are many activities that require standard actions in standard situations. People make some “standard decisions” and escalate cases that are not standard. There’s no reason machines can’t do that: document processing, cold calling, accounting, front-line customer support, etc.

And again, the main feature here is that ML does not require an explicit rule definition. It “learns” from the cases, which are already solved by people in their work, and makes the learning process cheaper. Such systems will save business owners a lot of money, but many people will lose their jobs.

Another fruitful area is all kinds of web scraping/data harvesting. Google knows a lot. But when you need to get aggregated structured information from the web, you still need to entice a human to do it (and there’s a good chance the result won’t be really great). The aggregation, structuring and cross-validation of information, based on your preferences and requirements, will be automated thanks to ML. The qualitative analysis of the information will continue to be carried out by people.

Ultimately, all of these approaches can be used in almost any industry. We should take this into account when predicting the future of some markets and of our society in general.