A lot has been said and a lot is yet speculated about Artificial Intelligence and efficiency paradox. Whenever the topic comes under debate, the best argument against it turns out to be the problem with efficiency and the question about improvements. However, such questions come from people who usually don’t dive deep into the problem itself.
Apparently, Artificial Intelligence has had problems with coming to terms with the demands of efficiency and results. People like to discuss whether AI is the answer to complex questions or does it, itself, need answers about its operative mechanics. The speculations are not all wrong, but they are not right either.
In spite of the difficulties that AI is facing in the current era, it has produced fruitful results. The evidence of that lies in the growing interest of every tech-firm in the niche. Where the world is transforming, AI has been the cornerstone to every step it is taking in that direction. From Biology, which is a natural science, to engineering, the world is looking up to AI for a spark of brilliance soon.
The question then arises: What has hampered the progress of AI so far? It is not so simple to sum up in little explanations. In spite of all that, there are a few highlights that would substantially help the world grasp the root issue. The layman calls it efficiency issues, but the AI geeks love to call it the “Efficiency Paradox”.
The Efficiency Paradox
For the beginners, a paradox is a problem that contradicts with its cause. The causal agent is the hampering agent too. That is what has been happening to the Artificial Intelligence too. The problem of Artificial Intelligence and the efficiency paradox is not a simple thing. It is a nexus that needs unfolding and a definite solution if the world has to adapt to AI.
Artificial Intelligence relies heavily on the data set provided, and that data set has been the limiting factor to its efficiency. If not for this cause, the results from every AI engine would have been way better. Within the processes led b AI, there is a huge dependency on the provided data. This in turn causes AI systems to rely on humans a lot.
Data is entirely a human issue. It isn’t a non-issue, instead it is somewhat of pivotal importance to AI’s future. Upon a deep dive research on the causes of reduction in the efficiency of AI engines, it was deduced that the cause is the flawed data. The effect of flawed data is humongous when observed in an AI engine, just like the effect of stale fruit on the quality of juice.
Data and Efficiency
Coming to the focal point, how data affects the efficiency of an ‘AI’ system? The explanation to this is pretty simple. Artificial Intelligence can make decisions on certain points but when it comes to judging data, it relies on what is fed to it. It believes in the provided data and goes on to give the results based on that.
That is where the problem arises. Flawed data, flawed results. The blame? AI usually takes the fall for this. In the theory of efficiency paradox, the focus lies on how the data and results are related. There is always a directly proportional relation between the two. AI has improved, the data hasn’t and so haven’t the results!
It is proposed that when AI relies on data provided by humans, it does not take into account any possibility of errors. It goes on to produce results based on that data. The cause, data, brings the efficiency of the system to a very low spot. Hence, the efficiency paradox.
Answers to Artificial Intelligence and efficiency paradox
Although the statement isn’t much of a question in appearance, it is a question mark for data scientists out there. Whatever be the issue, it needs a solution for AI to flourish and nurture. The data needs to be better qualitatively to increase the efficiency of AI systems.
The best possible answer to this problem would be to hand over data collection to AI as well. This could bring in criticism but it has become a necessity. Evidently, human errors have long caused efficiency issues and with a little tweak and lot of thought to the AI models, we can revolutionize the world of data processing.
A crowdfunding type model for data collection that works on AI engines is a proposition. It surely is not that simple, but it is a step towards an approach that would put to rest the ever-lasting issue of Artificial Intelligence and efficiency paradox to rest.