A few years back it’s really hard to have any serious discussion regarding Reality of Artificial Intelligence outside of the academic institutions. But the scenario is totally different nowadays, almost everyone is talking about AI. Even more, everyone is sharing great ideas with enthusiasm and curiosity. But there are a lot of fake promises and misleading opinions are also raising.
AI adoption rate and enhanced development in academics had lead to the faster development of AI than ever expected. Accelerated by the deep conviction that our biological limits are increasingly becoming a major obstacle to the creation of intelligent systems and machines that collaborate with us to better utilize our biological cognitive capabilities to achieve more ambitious goals. To solve the real-world problems and create smarter machines the investment of AI technologies had increased with overwhelming demand.
Many obstacles of AI is cleared over the last few years in academics. Now the major challenges faced by AI is its adoption into real-world industries. The barriers to the development of AI is misunderstanding and myths. It is a great challenge for industry leaders to distinguish between the myths and facts of AI.
The reason behind this is noisy and crowded enthusiastic, service providers and platform vendors. The truth of the AI will be endured once the dust of its gets settled and the winners and losers will be declared eventually. The major challenge is how the industry leaders will have a realistic opinion regarding what the AI can do and can’t do.
This clarification about the AI will give the right ways to solve the real-world problems and transforming the businesses. This leads to the continuous operation of the facts.AI practitioners have a responsibility to get out of their bubble and call on industry experts to further develop the academic foundations of AI in order to make adoption in the real world faster, more rewarding and more effective. responsible.
The mess of AI adoption in industries:
All the business leaders from a few years are trying to understand how the AI will be beneficial for their business. Most implementations of AI-based solutions have not gone beyond proof of concepts (POC) in the form of machine learning (ML) dispersed algorithms with limited scope. Many opportunities and resources of the companies are getting wasted with this level of approach to AI adoption.
Simple statistical methods for adding the classification capabilities or some simple predictions in many PoC projects for the analytical solutions can be simply called as AI solutions. Human intervention is still needed for understanding while making a decision for the outcome by the analytics. The operational conditions and business process are changing continuously, the continual change in the business factors and newly generated data are reducing perception level can lead to dangerous decisions.
The current approach of trying to incorporate some Machine Learning algorithms into certain areas of activity to gain quick wins is itself a risk and could lead to a decline in industry adoption of AI. which would trigger another “AI winter” this time on the industry side. the academic side. The application of even mature AI technologies in this way can add some values, but also a new “artificial stupidity” dangerous to the organization, with catastrophic consequences.
AI systems can’t be biased:
As we use human-generated data based on rules we have created to train machine learning algorithms, this data will directly reflect our thinking and approach. These data will determine the behavior of each algorithm. This creates another misunderstanding that the problem of AI bias is irrelevant in such cases, leaving many people believing, wrongly, that the algorithms are not biased. In such cases, many companies do not know that ML algorithms can represent a high risk and even a legal burden for organizations.
The ethics, accountability, and governance of AI systems are one of the most important roles of leadership in the AI era. They must invest proactively to inform, guide and raise awareness throughout the organization. We should develop new methods and tools to expose biases using appropriate human and machine reasoning based on relevant business and technical knowledge.
Overhyped promises from Data:
Recent years have shown that, in many cases, companies lack sufficient historical data in the quality and quantity required for current anti-money laundering approaches. we need to invest considerable effort in different areas such as data engineering, data analysis, feature engineering, feature selection, predictive modeling, model selection, and verification before having algorithms initial.
Predictive analytics solutions use simple statistical models to predict something based on available historical data. This assumes that the future will follow the past in a simple and straightforward way. An assumption that in many cases proved wrong.