Who would have imagined that artificial intelligence would become a reality and more so a common talk. Despite all the impossibilities that surrounded the technology –today, AI is practically making major changes across all industries.
We have seen how AI is supercharging surveillance without human involvement, how governments are embracing AI-models to combat corruption in their systems and so on. Basically, the list of what artificial intelligence has achieved since the inception of the technology is endless.
Regarding how companies are viewing artificial intelligence, a study by the Forrester AI Readiness reveals that 40% of businesses are willing to embrace automation, and another 43% promised to employ intelligent recommendation solutions provided by AI-enhanced analytic systems.
Basically, all industries have so far felt the impact of this tech which ideally varies from online AI-platforms, robots, AI models and all other agents that operate autonomously. However, there are hurdles that need to be overcome to have the technology grow into the future without having to redesign the systems from scratch.
The Cost of Industrializing AI
The reason as to why start-ups are taking longer to fully embrace AI is because the technology is very expensive. This is mostly because there is practically no size fits for all businesses that aspire to implement AI models.
In other words, each company has to carry its own cross when it comes to designing, training and fine-tuning their AI systems. All companies have problems native to them and the solutions only come with custom-made models.
Unfortunately, creating customized AI systems can be a complicated endeavor, as it requires experts with PhDs. The process needs contribution from engineers, brain philosophers and specialists AI-trainers with deep knowledge of how machine learning works. This draws to the conclusion that only well-funded companies can delve into building custom AI models.
Also worth mentioning is that machine learning models which employ deep learning require very intense training and a lot of time. Now, what is alarming is that these models have limited reusability -even after being successful.
Well, some of them might be reprogrammable to handle different tasks, but the obvious fact is that they become obsolete when the surrounding environment changes or no longer favors their functionality. As you may know, models are created using large datasets that need to be gathered, curated and labeled. That would mean that the system could expire after the data that is fed to it becomes stale.
In summary, this means that there is something missing -even with all the success that AI is currently experiencing. As in, how can this promising technology be redesigned so that it maneuvers past future hurdles that are certain to show up?
That is Exactly Where Evolutionary Algorithms Come In
As a first-hand solution, AI systems created on robust machine learning models need to have adaptation capabilities. The undisputed fact is that the world is ever in a state of change.
New ideas, as well as things, flow in and out of systems very fast –that’s why there is a great need to invest more in evolutionary algorithms. This will enable AI systems to catch up with different contexts without the need to redesign them –thereby saving a lot of money and time that would have gone into retraining the systems.
A good example of where evolutionary algorithm would of benefits is where a business uses AI to enhance or drive online user experience.
Evolutionary Algorithms in Action
When it comes to online retailing, companies that employ AI model solutions to recommend products to customers depending on the historical data of the user, like what he or she searched for or bought the last time they visited the shop. However, it would be more promising if the intelligent model employed was able to judge the present actions of the customer so as to offer accurate recommendations.
In fact, an evolutionary algorithm can go beyond changing with the environment to actually predicting the changes and preparing to adopt before time. A good example of that is again making models that can know and predict what the customer is likely to buy next based on the current characteristics of the user.
Well, it’s not a secret that building an evolutionary algorithm might take more time. However, that would depend on the route that scientists plan to use to reach there. As in, if they can come up with AI systems that build and train (fellow) new AI models, that would probably be the shortest route to the realization of complex evolutionary algorithms that could power AI-systems with the ability to adapt to changes as they unfold.