Greatest Challenges To Developing Quality AI Apps?

25 Nov

Artificial Intelligence has bought massive changes in the world since its beginning but even now it is facing some great challenges in the development and innovation of its applications.

Here we will see some of those challenges that the world of artificial intelligence is facing. Also, enterprise mobility solutions are the ones who benefit the most from artificial intelligence. Hence the people who need these mobility solutions are the ones impacted the most by these challenges. Let’s see all these challenges one by one in detail.

Challenges to the Development of AI Applications

1. Inefficient computing

The artificial intelligence requires a very advanced and efficient type of machinery and processing. Cloud computing seems to be one solution for this but if we take into consideration the current software and devices then even those do not suffice. This is one of the first challenges that Artificial intelligence solutions face. The techniques of AI such as machine learning and deep learning are the things that require top-notch speed of calculation. For these, the calculation needs to be done at a rapid rate of micro or even nanoseconds. In some cases, the speed of calculation might need to be shorter than nanoseconds.

2. Lack of Support

This challenge hampers the progress of AI software development. This happens because not many people are acquainted with what is artificial intelligence and moreover they do not understand how to use a machine that is capable of thinking and learning itself. The rejection that it faces from people is what holds it back from making progress and achieving new heights of development. Now as it is not demanded by the people hence there is no demand for it in the market and as a result, the corporations or organizations also do not invest in the AI then. This is how it faces a lack of support.

3. Unable to gain trust

Just like the name suggests it is a type of intelligence but one that is inhuman. This arises doubt in people that how a machine is able to make decisions. And it is not simple like a bank procedure where you can simply show the math algorithms and the customer understands it or at least you are able to gain the trust of the client. The procedure is much more complex when it comes to artificial intelligence. It is difficult to explain it to the general public. And hence people do not trust this easily, left alone accepting it.

4. Single-purpose specialization

Artificial intelligence so far has been able to serve specifically only limited uses. How it performs is by reading and retaining the inputs given and the output produced with it. Though it does this with only the best results that come up. But it is limited to getting better and better at only one task.
The artificial intelligence that can perform any type of task just like human beings has not been developed efficiently yet. And this is required for enterprise mobility management. Though it may soon be developed, for now, it is not there in the market.

5. Need for better Explanation

Companies and developers who are creating and developing artificial intelligence software and applications and products are not able to make the general public understand their goals and achievement. They have not made it clear to the public what all they have achieved with artificial intelligence so far.

This is what arises doubt in the minds of people. Explainable artificial intelligence should be curated and spread across to achieve preset goals. Developers should be able to explain the decision making power of artificial intelligence and moreover that it is fine and just. Only then the people will accept artificial intelligence whole-heartedly.

6. Prone to Breaches

The machine learning systems and artificial intelligence rely highly on the data that they get. And to perform better this data is often personal and sensitive in nature. This is what makes them prone to theft and breaches. And also, such types of breaches have become quite common in today’s time.

Following which rules and regulations have been made as well to create and develop such types of artificial intelligence that does not pose any threat to the person’s data and its confidentiality, security, etc. This is made for machine learning systems and artificial intelligence applications because they store a massive amount of data that is sensitive in nature.

Read the blog:- List of enterprise mobility management glossary

7. Biasion of Algorithms

AI applications usually work according to the training that they got on the earlier data. The problem arises when bad data comes into play and the AI application starts to work according to it. Therefore they need to be trained on the unbiased data and produce easily explainable algorithms.

8. Scarcity of Data

Even though the companies and organizations have an immense amount of data, the data that is useful for artificial intelligence is still not enough. Also, the most efficient artificial intelligence is one that is provided the supervised training and this type of training is learned upon by labeled data which is also scarce in nature.

So there is a need to develop and create such a machine learning system and artificial intelligence applications that can do more on less data. And also maybe with time world will be able to generate enough data sets for Artificial Intelligence and machine learning systems to work on, which is quite rare in today’s time.

Conclusion

So we saw what Artificial intelligence is and what are some of the greatest challenges that one might come across at the time of AI software development. But there is no doubt in the fact that AI has already started to take over the world yet much more growth and development is required of Artificial intelligence Solutions.

Also, AI has not been accepted by everyone so far. There are many companies and sectors left that need to adapt to artificial intelligence and its applications. But that time is not far as the industry is already trying to eradicate the challenges faced by artificial intelligence.

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