A few years past, it had been arduous to seek out anyone to own a significant discussion concerning computing (AI) outside educational establishments. Today, nearly everyone talks about AI. Like any new major technology trend, the new wave of {creating |or constructing} AI and intelligent systems a reality is creating curiosity and enthusiasm. People are jumping on its bandwagon adding not only great ideas but also in many cases a lot of false promises and sometimes misleading opinions.

Built by giant thinkers and academic researchers, AI adoption by industries and further development in academia around the globe is progressing at a faster rate than anyone had expected. Accelerated by the strong belief that our biological limitations are increasingly becoming a major obstacle towards creating smart systems and machines that work with us to better use our biological cognitive capabilities to achieve higher goals. This is driving an overwhelming wave of demands and investments across industries to apply AI technologies to solve real-world problems and create smarter machines and new businesses.

AI overcame many obstacles over the last decades mainly on the academic side. However, it is facing now one of its major challenges so far, which is the adoption in real-world industry scenarios and the myths and misunderstanding surrounding it. Unfortunately, with confusing and conflicting messages about what AI can and can’t do, it is challenging for industry leaders to distinguish between facts and fiction in the rapidly crowded and noisy ecosystem of enthusiasts, platform vendors, and service providers. However, once the dust settles down and things get clear, the truth of AI will endure, eventually losers and winners will be declared.

The challenge is how industry leaders would have a realistic opinion about what AI can and can’t do for their business and continuously update it so that they can lead their organizations to apply AI in the right way in solving real-world problems and transform their businesses. Also, academics and AI practitioners have the responsibility to get out of their bubble and engage with industry experts to be able to further develop the academic foundations of AI in a way that would make its real-world adoption faster, more rewarding and responsible.

The current state of AI adoption in industries

Over the previous few years, business leaders from nearly every trade are attempting to know the new switching technology referred to as computer science (AI) and the way their businesses will have the benefit of it. Sadly, as yet most of the implementations of AI-powered solutions haven’t gone on the far side Proof of ideas within the type of scattered Machine Learning (ML) algorithms with a restricted scope. Whereas this level and approach of AI adoption is wasting several opportunities and resources for corporations, it's helped to convert business and IT leaders that computer science will drive transformative and relevant innovation.

Many PoC comes nowadays square measure mistreatment primarily easy applied mathematics strategies to feature some easy prediction or classification capabilities to their analytics solutions and decision it AI solutions. This is often still outlined as associate lyrics or presumably advanced analytics that still wants intensive human intervention in understanding the end result and create a choice or take an action.

As the business processes and operational conditions ceaselessly amendment, the fresh generated information and also the continual changes in several business factors square measure reducing the amount of preciseness and also the worth such algorithms can give rendering them over time to be useless or perhaps cause dangerous selections.

Such an associate approach and its outcome square measure simply another a part of the frustrating reality that's confusing business leaders and preventative the proper adoption of subtle AI technologies within the acceptable thanks to gain valuable results.

The current approach of attempting to squeeze in few Machine Learning (ML) algorithms in some business areas for fast gains is the alone risk and may cause a reverse to AI adoption across industries triggering another “AI winter” this point on the trading facet not on the tutorial facet. Applying even mature AI technologies in such the simplest way may add some values however might add new dangerous “artificial stupidity” to the organization with harmful consequences.

Over future years, corporations will afford to continue acceptive a standing of confusion and hesitation around what AI can and cannot do, however, it will be integrated with alternative technologies to make intelligent solutions or machines and wherever to use it befittingly.

AI technologies are not yet ready for industrial adoption. Is it a Myth?

The current AI benefited from decades of great high-quality tutorial analysis. However, it’s clear that one of the major weaknesses of current AI systems is the lack of real-life experience, which is needed to make it reliably useful for all of us. When AI systems fail to give the right answer at the beginning of using it, this doesn’t usually mean that the underlying AI algorithms or mathematical models are not mature enough.

Like humans, AI algorithms need more real-world experience that might include more data created through algorithms’ own trials and errors in the real world.

Therefore, it would be unfair and technically wrong to judge AI solutions in the early stages while they still have no or little experience. This is one of the most common mistakes done today and usually led to frustration and misunderstanding around the maturity of AI underlying models. We’ve to give AI-powered solutions time to learn and be carefully evaluated before deploying them in the enterprise.

For instance, machine learning capabilities which gained enough real-world experience such as computer vision (CV) and Natural Language Processing (NLP) are the most mature and widely adopted parts of AI today. They’re the cognitive engines behind many industrial and consumer applications and products with the most positive impact on business and our personal life so far.

This is a key difference between traditional analytics and AI solutions. In analytics, software vendors build software solutions without having the actual data. On the contrary, in AI solutions, we use the problem description, actual data, domain knowledge and a set of specific goals to be able to create, train and verify ML algorithms. No Data, No Algorithms! in AI, there is no turnkey solution. This is a key mind-set shift which must happen immediately to avoid this misunderstanding.

Such shift in mind-set combined with new principles of designing distributed intelligent systems such as multi-agent distributed and interconnected cognitive systems would play a major role in deciding whether the organization’s efforts to leverage AI capabilities would succeed or just add more frustration, wasted opportunities and new risks.

Additionally, one of the key capabilities AI systems must have per design is that it should have the ability to continuously learn as well as dynamically leverage effective learning approaches over time. Selecting the right initial architecture as well as continuous learning approaches such as supervised, unsupervised, reinforcement learning or a mix of them is very important in a successful AI adoption. Lifelong Continues Learning (LLCL) is one of the main and most promising AI research areas today. However, it continues to be a challenge for the present machine learning and neural network models since the continual acquisition of recent data from non-stationary information sources typically lead to catastrophic forgetting of previously learned knowledge or abrupt decrease in the precision.

While there is a lot to be done to enable AI systems to continuously learn and evolve with their environments, most of the current AI platforms from start-ups and established vendors provide powerful tools to make this happen.

What makes or breaks AI adoption in business is not the AI academic methods and algorithms or the technology platforms built around them but the way we adopt, architect and integrate them in business solutions and industrial products.

Conclusion

Companies must carefully create a comprehensive and dynamic AI strategy and immediately start adequate execution initiatives to get ready for the new era of many intelligent things powered by AI. This strategy towards intelligent enterprise will help in creating the new Man + Machine workforce of the future and reimagine their overall business. This is urgently required before new intelligent products, solutions or services from far smaller new disruptors will become a real threat to not only their businesses but also to their very existence.

This will require the business and IT leadership to have a realistic and accurate view about what AI can and can’t do now and in the near future. Also, having someone with robust academic and practical experience in AI leading such initiatives would help organizations cut through the hype and avoid costly misunderstandings and misleading myths.

Intelligence can’t be centralized, it should be distributed and not limited to a few functional areas. A hybrid and balanced approach of embedded, edge and centralized intelligence should be considered upfront to guarantee a well-orchestrated growth of the collective intelligence of the organization across all teams, functional areas, products, and services.

Most importantly, the adoption of AI and other related technologies towards the intelligent enterprise will bring the more productive and augmented Human and intelligent Machines closer creating a powerful workforce of the future. Companies should understand that humans and machines will continue to be the two pillars of the new workforce and wisely plan to leverage their combined strengths and understand their limitations of biological and artificial nature.

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