4 Current Limitations in Machine Learning

Machine learning is an intelligence that helps machines to learn without being programmed. It involves embedding programs to computers which help them to react to fresh data. In performing machine learning in real-time, there are many constraints that limit the ability to outperform the best in showing its effective use.

The algorithms of machine learning perform well with the similar type of data the machine is trained with, but they fail to perform accurately when facing a new set of information. Limitations arise when the machine has to face data different from the trained set of data.

Different measures are being introduced with the advancement of research and development. With improvements in the algorithms, machine learning is becoming much better in recognizing differences between letters and pictures. But the improvement is still not up to par. Here's a list of four current limitations.

1. Application shortcomings

The application of machine learning is limited. It cannot guarantee to serve different types of immediate cases. Most of the time it fails in the first attempt because, before working out, it should get a clue about which function to use with the data set if different from the trained data set. So machine learning still needs to work out kinks when encountering an immediate set of unknown input data.

2. Trouble with discontinuous loss functions

The machine learning algorithms find it hard to tune the irregular, non-separable loss functions. If a function is facing any problem, it cannot be optimized using machine learning techniques. The faulty functions are useful in certain cases, such as in sparse representations. Without any loss in the sparsity, those functions need to be smoothed.

3. Failure to assume and make use of a particular function

Whenever a new data set is given to act on, the machine learning algorithms assume which function to make use of. There are multiple numbers of functions embedded in the machine, and the machine has to decide which function to use. It has to find out which model the data suits by comparing with the models assigned to it during the training phase.

Frequently, machine learning fails to find out which function it should use for a particular set of data. Due to a wrong assumption, the entire operation faces failure. In finding the perfect function for the particular input data set, the machine may use simple models as well as complex models. The simple ones can detect functions that may not suit the training set of data, and the complex ones somehow fit with the training set of data. In determining the model, various problems may arise, such as:

  • Overfitting

    When the model uses the complex suggestions and concentrates on the unrelated parameters in the training data set, it limits the capacity to specify when a new data is given.

  • Underfitting

    Here, the simple suggestions are only used as a result of which practically real functions get neglected. It only allows the machine to determine how neatly the model will be compatible with the new set of input data and also the speed of the machine learning algorithm.

4. Need for bulk data set

In order to train the machine with the machine learning algorithms, a large quantity of training data is used. It is awfully heavy to work with such data sets. Luckily, we have a lot of data, but working with such a big bulk of data is not a simple task.

In closing

Machine learning is a tool which will obviously carry both advantages and disadvantages. We should be smart enough to use these algorithms of machine learning in the right way. And with the ongoing advancement of technology, its limitations will be resolved soon enough.

Article written by Vaishnavi Agrawal
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