While the technology experts across the globe these days are boasting about what AI can do and how it can bring positive changes to our day to day lives, there’s another group of people that is scared about AI taking over the world. Undoubtedly, the whole concept of know-it-all and uncontrollable AI networks is terrifying, but there are tech developers who are working on leveraging the technology to simplify a plethora of complex tasks.
AI is one of the most talked about technologies in today’s day and age, and owing to the fact that it entails numerous aspects that if put to right use, can make innumerable things easier for us. There are technology experts working on AI to make it smarter and continuously outweigh itself in its capability of offering tangible benefits.
How Smart is AI?
AI (Artificial Intelligence) is quite smart and there are a lot of tasks that can be performed by programming AI-based software solutions. Used in conjunction with Machine Learning, there are a lot of things that AI-based applications and solutions are already doing. One of the best examples of AI usage in our day-to-day life is SIRI from Apple and Amazon Alexa. Both utilize machine learning-based architecture and use AI to assist their users with certain repetitive tasks. However, their range of functionality is quite limited. Apart from these, there are several other web-based applications that are utilizing AI to deliver better functionality and there are several companies across the globe that are looking to hire artificial intelligence developers to develop AI-based applications.
If we consider the current incarnation of AI, it is not one of the smartest things. AI can be leveraged to do a few things and enhance certain applications, but, there’s a long way to go for AI to work completely on its own with efficient decision-making capabilities. However, if you have web-based applications, indeed their functionality can be greatly enhanced by introducing the AI components.
Neural Networks and AI Smart Enough to Figure Out When These Cannot Be Trusted?
AI (Artificial Intelligence) systems, also referred to as “Deep Machine Learning Neural Networks” is being increasingly used in multiple sectors but their contribution to health and transportation is quite significant as compared to other niches. With colossal information and deep knowledge, the AI is enabling the users in these sectors with smart decision-making. By presenting the right facts and data to the users from different perspectives, and by researching the data availability on a specific subject, AI-based solutions make things easier for their users.
These AI-based solutions compare huge data available on any required aspect and run comparison algorithms to deliver information that can be leveraged for making informed decisions. Also, with machine learning, the AI-based apps analyze the user’s behavior and present the options that it thinks would be useful to the users. By being intuitive and proactive, AI-based mobile and web applications are garnering immense appreciation from users.
This is one of the reasons that there is a high demand to hire AI developers to create applications that can perform some tasks in an automated fashion whilst the technology experts are working on enhancing the scope of AI. Technology experts from various research institutions these days are working on enhancing what AI can do and at the same time, they are critically examining as well as evaluating the AI-based solutions to understand their limitations.
One of the facts that have been established is that AI technology is greatly helping professionals across various industrial niches make smart and fast decisions.
Now, the question arises that the way AI networks and solutions compare information and decide on parameters on which the comparisons would be run is 100% correct.
Well, AI-based solutions in current times are as good as how their creators or developers want them to work. Another vital factor that contributes to the trustworthiness of AI networks is the data that is being used by these systems to aid decision-making. As long as the data is credible, useful, correct and doesn’t change the information when viewed from different perspectives, AI-based systems can be trusted. There are many tech companies across the globe that offer the development of AI-based solutions and one can easily hire artificial intelligence developers, but the effectiveness of any such system is not just concluded by its technical architecture. The quality of data that is being fed into the system matters a lot.
How AI Systems Are Being Tested?
There are experts who are testing AI-based systems in various scenarios and conditions to find out how much computing a system needs to do before arriving at a decision.
The experts are deploying several ways for uncertainty estimation in neural networks. But these methods seem to be relatively slower and computationally expensive as they involve comprehensive testing. However, technology experts indicate that “Deep Evidential Regression” can be used to accelerate the process and derive useful outcomes.
One of the most vital conclusions that have been derived from all the testing is that we need to make neural networks not only capable of delivering efficient decision-making but also, they need to be equipped with the capability to understand when these cannot be trusted. This can be applied broadly and can be used to assess the AI-based solutions that totally rely on the learned models.
By carefully and accurately assessing the uncertainty in the AI-based learned models, we can evaluate how much error can be expected from the system and what data/tools can be deployed in order to reduce the uncertainty. The gist of the entire uncertainty-related experimentation and assessment is to ensure that AI-based solutions not only deliver optimum performance under various scenarios but also can identify when they cannot be trusted, and thus, one stops depending on them in those situations/scenarios.
There are companies hailing from diverse niches who are looking forward to hiring AI developer and these companies should take this into consideration to clearly understand what and what not their AI systems are capable of.