Machine learning is a technology that has grown in popularity over the past few years. A considerable number of companies from all over the world are quickly mastering this problem and taking into account various sensations. As a result, machine learning engineers are in high demand. While there are a few apparent pros, it does have a few cons.
The concept of artificial intelligence has been around for more than half a century, and many of us have lived through previous periods of unrest, accompanied by boring streaks of frustration – “artificial intelligence winters” – let’s break down the main shortcomings of ML.
- Getting data.
Data collection is one of the most painful moments in data science and machine learning. It also happens that when companies collect data from surveys, they may collect volumes of fake and incorrect data. These reasons make data collection insufficient.
- Prone to error.
The quality of analysis and the results of AI implementation widely depend on the quality of raw data. So, data clearance is one vital step influencing the whole process. Paying additional attention to the unification and dismissal of incorrect figures while starting the ML modeling is a must-have step. Find out about the data preprocessing and cleansing in detail on this page.
One of the solutions in case, after the clearance, you do not have enough labeled data is the use of a modern approach through self-supervised learning.
- Choice of the algorithm.
Various algorithms from machine learning assortment or solutions with the introduction of neural networks can be the best option for your particular business case. It is a tricky task to evaluate which one will work better. So, quite often, it’s necessary to try several models to recognize the best option. We can estimate this as a disadvantage, demanding more time and other resources, but gaining more precise predictions or better fraud detection is definitely worth those additional efforts from the AI practitioners.
- The right team
Data scientists’ collective work is similar to a football team’s action – different performers gradually prepare a machine learning model for sale: collect data, conduct testing, set up infrastructure, write code, track metrics, and apply many operations.
No footballer can stand well on goal, play defense, and attack – these are different skills that require special training. It is the same with data sets – each performer has a specialization that is suitable only for the heavy parts of the project. Therefore, if the company does not recruit a sufficient number of qualified specialists with different disciplines in the project, then the machine learning model will not go on sale soon.
The spread of AI can also raise difficult ethical questions. Some of these may be related to the use and potential misuse of the technology in areas ranging from public surveillance and advanced military applications to social media.
The algorithms and data used to train them may introduce new biases or perpetuate existing types. Other critical issues include using personal information, privacy, cybersecurity, and “deep forgery.” This can be used to manipulate election results or commit large-scale global fraud.
Despite these challenges, AI should be of great benefit to all of us. Mainly if developers, politicians, and companies act wisely and quickly to take full advantage of these opportunities.