What are the Biggest Challenges with AI?

With the evolution of technology, we must ask the question ‘What are the Biggest Challenges with AI?’

The reality of human-level artificial intelligence is still a dream in the tech world. Even with all the advancements in the state-of-the-art in AI, its ability to understand the world around us is always at the level of a one-year-old child.

But stills, it’s very impressive how well our current AI technology scales. Now our software industry has entered a race to apply Artificial Intelligence in every vertical possible. While AI technology adoption is accelerating at a rapid pace, our ability to understand the potential impact of AI isn’t keeping up.

Everyone agrees to some extent that it’s only the technology that can change the world. From countering terrorism to space exploration & even creating art, AI’s potential is becoming increasingly apparent.

But still, it faces many challenges – challenges that will have to be overcome before its true potential can be achieved.

So, what are some of the big challenges we are facing with AI both today & tomorrow & what opportunities could it offer? And meeting the challenges as a task should be our utmost priority right now.

Automation and Jobs

The automotive industry is well-known for its automation enhancements & maturity. Self-driving cars are quickly becoming a reality, and no one knows how much it will change in the future & no one knows how both taxis road haulage work in the future.

But When you think it with an open mind, you will come to know that there’s going to be an awful lot of steps that have to happen after this technology is perfected. Also, safety legislation would have to change, highway & road infrastructure will have to adapt accordingly.

In-short there’s going to be a lot of moving parts brought together before drivers are phased out. 

Integrating AI

Seamlessly transiting to AI is much more complicated than adding some simple plugins for a website or just creating a Visual Basic for Applications (VBA) enhanced Excel Workbook. One must make sure that the current programs are compatible with AI requirements, and also do make sure that AI is implemented into these programs without stopping current output.

The Artificial Intelligence interface needs to be set up in such a way that data storage infrastructure, & data input are considered, & that the output is not negatively affected. Also, when this whole process is completed, then do make sure that all personals are trained on the new system.

Collecting and Utilizing Relevant Data

For an organization to successfully implement AI programs & strategies, they must maintain a constant source of relevant data to have a base set of data to make sure that AI can be useful to their selected industry. Organizations can collect data on various applications with a multitude of formats such as audio, images, videos & text.

The wide range of platforms to collect this adds to the challenges of AI and ML. To get successful, all the data must be integrated in a manner that AI can understand it, and can transform into useful results.

Man Power

As AI is a very emerging technology, few possess the required skill-sets or training needed for artificial intelligence development.

This is a significant problem in the industry, so many companies will need to allocate some additional budget towards AI development training or hire AI development specialists.

Implementation Strategies

AI is rapidly transforming every industry, but one of the major challenges of AI is the lack of a clear implementation strategy. To be successful in the tech industry, a clear strategic approach is very important, and it needs to be established while implementing AI.

This includes identifying areas that need more improvement, setting objectives with well-defined benefits, & ensuring a continuous process improvement feedback loop.

To tackle these sorts of issues, managers will need to have a solid understanding of current AI technologies, their limitations & possibilities, and keep themselves up to date about the current challenges with AI. By doing this, organizations will be able to identify areas that can be improved by AI.

One-Track Minds

A challenge that is worth considering is that the vast majority of Artificial Intelligence implementations are used in today’s modern world are highly specialized. Specialized AI is often referred to as ‘applied AI,’ and is created out one specific task & learn to become better and better at it.

Specialized AI simulates what could happen given every combination of input values, & measuring the results until the most perfect and effective output is achieved.

Generalized AI – such as that powering robot-like Star Trek’s Data and capable of turning their hand to perform any task just as human beings- will still be a time fiction dream for some time yet.

The major problem here is that we humans are natural organisms. We are capable of taking into consideration learning & data from tasks other than the one we currently work on. The ability to draw on sources other than those immediately apparent, to face any problem, is known by clichés such as “Blue sky” or “Out-of-the-box” thinking & is an element of human problem solving & ingenuity.

So this means Artificial Intelligence has to be taught to ensure that their solutions don’t cause any other sort of problem.

Conclusion

The key challenges mentioned above, which Artificial Intelligence will have to overcome shortly, are not insurmountable. But some solutions will have to be implemented before Artificial Intelligence will undoubtedly live up to its true potential.

In the case of most of them – generally, those problems which will be solved by the advance of technology – that work is well and truly underway.

Other problems will surely require human minds to come together & establish workable principles & codes of conduct, which can take a while.

The problems mentioned above are not impossible to solve. However, it does require the rapid evolution of technology and human corporation as well.

While we are moving at a rapid pace in technological advancements, we still have a long way to develop methodology, principles, & frameworks to ensure that powerful technology like AI is not misused.


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1 Response

  1. Ash says:

    I really enjoyed reading this blog

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