Machine learning (ML) algorithms allow computers to define and apply rules that the developer did not explicitly describe.
There are quite a few articles dedicated to machine learning algorithms. This is an attempt to give 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 most well-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 forecasts like traffic (people, cars, users), sales forecasts, and more.
Anomaly detection algorithms help people scan a lot of data and identify which cases need to be verified 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 shouldn’t describe every type of anomaly. You feed a large list of different known cases (a learning set) to the system and the system uses this to identify anomalies.
Object clustering algorithms allow large amounts 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 grouping, for example, for customer/prospect qualification, product list segmentation, customer service case classification, etc.
The algorithms of recommendations/preferences/behavior prediction give us the opportunity to be more efficient when interacting with clients or users, offering them exactly what they need, even if they have not thought about it before. Recommender systems are really bad for most services right now, but this sector is going to improve rapidly very soon.
The second point is that machine learning algorithms can replace people. The system analyzes the actions of people, creates rules based on this information (that is, learns from people), and applies these rules by acting on people’s behalf.
First of all, it deals with all types of standard decision making. There are many activities that require standard actions in standard situations. People make some “standard decisions” and escalate non-standard cases. 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. You “learn” from cases, which are already solved by people at work, and it 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 data harvesting/web scraping. Google knows a lot. But when you need to get aggregated structured information from the web, you still need to attract a human to do it (and there’s a good chance the result won’t be really good). 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.