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Practical Problems Machine Learning and Artificial Intelligence are Still Facing

The way Machine Learning and Artificial Intelligence have streamlined our civilisation has been a matter of fascination. No wonder, AI and ML have changed the way industries operate. Although it has cast a deep impact on our lifestyles, Artificial Intelligence still has a long way to go.

In fact, several practical concerns related to Machine Learning and Artificial Intelligence are yet to be solved. ML fails to resolve certain tasks, and the talent deficit has always been in the background. Besides, data was never free from elements affecting it.

In this article, you will come across some of the practical issues related to Artificial Intelligence.

Reasoning ability

One of the constraints of ML algorithms is the lack of reasoning ability, beyond the application they are intended for. Reasoning power forms a powerful human trait. The AI algorithms are developed for specific purposes.

Coming to applicability, the available algorithms need to be narrowed down. Therefore, they are unable to reason out why a given method is being used, or the way their own outcomes get introspected.

Take the instance of an algorithm used in image recognition. In a particular scenario, it can differentiate oranges from apples. However, it is unable to determine whether a particular fruit is in good condition. Humans can explain this process mathematically. However, ML is unable to do so.

Scalability

Although AI applications are being used significantly, one needs to consider the scalability factor. The growth rate of data is very fast, and it comes in several forms. In the process, it becomes difficult to scale a project based on machine language.

Algorithms do not have much to do in this case. It is necessary to update these algorithms on a regular basis, as new changes are necessary to deal with data. Therefore, a regular human interference is required for scaling the project. ML and AI are not potential enough to resolve this problem.

Besides, when an increasing volume of data is shared on a platform backed by ML, it needs to be examined using intuition and knowledge. Machine Learning lags behind human intelligence, when it comes to these values.

Contextual limitation

Natural language processing (NLP) is one of the crucial mechanisms used in Machine Learning. In NLP, speech and text information are the key means to interpret languages. AI can learn words, letters, syntaxes and sentences.

However, they are unable to process the context of language. The reason is, it is not possible for algorithms to understand in what context this language is being used. In some cases, human intervention becomes inevitable. Therefore, many processes have not yet been fully automated.

Even if AI has natural language processing abilities, it remains restricted to certain areas. AI fails to grow a comprehensive idea about the situation. The mnemonic interpretations limit the scope of AI, and it does not recognise what is happening around.

Data subjected to regulatory restriction

Massive volumes of data are needed while working with ML, particular in stages like cross-validation and training. However, both general and private information comes as a package with this data. This results in various complications, and the data is subjected to regulatory restriction.

Data has been privatised by most of the tech firms. The ML applications find this data useful. However, the process involves risks, as the data could be used for wrong purposes. The concern is greater, when it is used in health insurance, medical research or other sensitive fields.

At times, data is anonymised. However, it is never free from vulnerability. For this reason, data becomes subjected to regulatory restrictions, which may prevent ML to be used to its full potential.

Internal mechanism of deep learning

This particular sub-category or branch of Machine Learning holds the credit behind the AI growth today. Previously, it was merely a theory.

However, it is one of the most significant aspects of ML now. DL (deep learning) helps in powering image recognition, voice recognition and other applications through neural networks, which have been artificially developed.

However, the internal operation of deep learning remains to be solved. Researchers still get baffled about how the advanced DL algorithms work. In deep learning, the neural networks consist of millions of neurons, increasing the abstraction at all the levels, which one cannot fully comprehend. It is for this reason that researchers have dubbed deep learning as a ‘black box’. The internal mechanism still remains to be discovered. This might open up new avenues for its application.

The training process is difficult

Enterprise software, that is traditionally used, is straightforward. The business goals are specific, and you have individual functionalities. Accordingly, the right technology can be used to develop these tools. Developing a working version of this kind of software is a matter of few months.

However, several layers are involved in Machine Learning. The engineers need to generate a program. It takes time for this program to learn the actions they are programmed for. Now, if a few more layers need to be added, it complicates the overall process.

Evidently, it takes more time to develop the ML applications. Particularly, the researchers need to train the respective algorithms. Often, uncertainty looms over the amount of time required to develop the ML programs.

Different sizes of data sets need to be evaluated. It indicates that data scientists and Machine Learning engineers are unable to ensure that the same training model will be replicated in the application.

Conclusion

Although Artificial Intelligence and Machine Learning are not free from flaws, a time is likely to arrive when technology becomes intelligent enough to tackle these issues. Presently, research on intelligent systems is still going on.

Most of the forward-thinking firms are partnering AI-based app developers to bolster their applications and websites. A patient and careful planning can prevent the risks associated with the process and generate high rewards.

Perhaps, more groundwork needs to be done on ML, particularly in understanding deep learning, rather than enhancing the aspect at this moment.The challenges need to be addressed carefully, so that they no longer remain a concern for the business organisations using them.

Aarsh, Co- Founder & COO, Gravitas AI

www.gravitas.ai

Aarsh

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