Machine learning algorithms allow computers to define and apply rules that are not explicitly described by the developer.
There are quite a few articles devoted to machine learning algorithms. Here is an attempt to make a “helicopter view” description of how these algorithms are used in different business areas. Of course, this list is not an exhaustive list.
The first point is that ML algorithms can help people by helping them find patterns or dependencies that are not visible by 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 models were developed before the 1980s, when the financial markets gained access to sufficient computing power. Later, these technologies spread to other industries. Since computing power is cheap now, it can be used by even small businesses for all kinds of forecasts, such as traffic (people, cars, users), sales forecasts and more.
Anomaly detection algorithms help people scan lots of data and identify which cases to check for deviations. In financing, they can identify fraudulent transactions. In infrastructure monitoring, they make it possible to identify problems before they affect business. It is used for manufacturing quality control.
The main idea here is that you do not have to describe every type of anomaly. You provide a large list of various known instances (a learning set) to the system and the system uses it to detect deviations.
Object clustering algorithms make it possible to group large amounts of data using a wide range of meaningful criteria. A man cannot function effectively with more than a few hundred objects with many parameters. The machine can make clusters more efficient, for example for customer / customer qualifications, product list segmentation, customer support case classification, etc.
Recommendations / preferences / behavior prediction algorithms allow us to be more effective in interacting with customers or users by offering them exactly what they need, even if they haven’t thought about it before. Recommendation systems work really poorly in most services now, but this sector is improving very soon.
The second point is that machine learning algorithms can replace humans. The system analyzes people’s actions, builds rules based on this information (i.e., learns from people), and applies those rules that act instead of people.
First and foremost, this is about all types of standard decisions. There are many activities that require standard actions in standard situations. People make some “standard decisions” and escalate non-standard cases. There are no reasons why machines can’t do it: document processing, cold calling, bookkeeping, first-line customer support, etc.
And again, the main feature here is that ML does not require the definition of explicit rules. It “learns” from matters already resolved by people during their work, and it makes the learning process cheaper. Such systems save a lot of money for business owners, but many people lose their jobs.
Another fertile area is all kinds of data harvesting / scraping of the web. Google knows a lot. But when you need to retrieve some aggregated structured information from the web, you still need to attract a human to do it (and there is a high chance that the result will not be very good). Information, aggregation, structuring and cross-validation, based on your preferences and requirements, are automated thanks to ML. Qualitative analysis of information will still be done by humans.
Finally, all of these approaches can be used in almost any industry. We should take that into consideration when predicting the future of some markets and our communities in general.