Exploring the Limitations of Machine Learning

Daglar Cizmeci Hacker Noon profile picture

Founder of Hence, Repeat, RCCL, Mesmerise and Marsfields.

The use of artificial intelligence is becoming more and more apparent in our everyday lives, and machine learning plays a pivotal part within that. There are many advantages to artificial intelligence as productivity can be streamlined, and tasks made easier. From virtual assistants to quick data management, the use of artificial intelligence simulates human intelligence using machines. 

Artificial intelligence itself is the use of smart machines that are capable of performing tasks in a more effective and productive manner than humans could. Although artificial intelligence mimics human intelligence, it hasn’t entered the realm of mirroring the consciousness that is expressed by humans yet.

This is still very much in development, and smart technology such as speech assistance and automated driving show that the actionary side of human intelligence is there, but the more sentient side is yet to be shown. It is possible that one-day artificial intelligence will be able to replicate human consciousness. 

What is Machine Learning?   

Machine learning is a subset of artificial intelligence that uses algorithms to accomplish tasks and result in the desired output. At its very basic core machine learning is a model that uses data sets within machines to learn and categorize them. It can automate large amounts of data and presents decisions and predictions based on the patterns within the data set. It does not necessarily need to be constantly programmed by a human and often uses an algorithm that can detect patterns within a computer and database.  

An example of everyday machine learning is the use of a search engine, such as Google, which can extract a result from an extortionate amount of data. Take football players, for instance, you can search for premier league football players, and machine learning algorithms will generate a set of structured data from a larger set of data. This, in turn, is a more efficient way of data pulling than could be done by a human within the speed that machine learning and artificial intelligence do it.

Other examples of machine learning include speech recognition, automated stock trading, and customer service.

What are the main limitations of machine learning?

It seems the development and advancement of both artificial intelligence and machine learning work to our advantage, but there are limitations when it comes to machine learning. 

As machine learning works with excessive amounts of data and focuses on actioning commands without the need for human input, the testing process for the initial algorithm can be very timely. Each narrow application has to be specifically trained and tested to ensure that computers give the desired output.

For example, with automated driving, vigorous testing in a simulated environment will take a lot of time to ensure safe real-world applications. Paired with the time it will take to ensure that each algorithm is tested thoroughly comes the training of the humans in data. The training itself can take time and requires a large amount of batch training to ensure that the algorithms within machine learning are operating as they should. 

Indeed, the nature of the training required for data and algorithm programming when it comes to machine learning, is extensive and this means that the systems themselves are not the easiest to navigate.

Machine Learning systems are known to be opaque and difficult to debug, which in application causes many problems and contributes to the time that it takes for an algorithm to work in the desired way. The algorithm itself can only handle a very narrow amount of natural language. It has to be  § and learned in ways that can learn and pattern commands that are being asked of it. For example,

the issue with accents when virtual assistants and voice assistants were first available to the general public is that the algorithm wasn’t programmed to understand all types of dialect and with testing and time, it has now been catered to meet this need. The resources that are required for machine learning to operate in a way that is satisfactory come back around to the time and the trained manpower. There are indeed limitations of machine learning when it comes to the programming itself and the algorithms which are put in place for the commands to be satisfactory. 

Although machine learning has limitations, such as the time and training required for it to function, it still serves us. The hurdles that have been overcome in artificial intelligence over the years mean that there is still room for development and advancement within machine learning.

The huge demand for artificial intelligence and the streamlined productivity that machine learning can offer us will ensure that there will be even more diversified usage of machine learning and perhaps even an algorithm that can mimic human consciousness.