The Time is Ripe to Undo the Knot between AI and Human Brain

While we are running towards advanced ai, something beyond human brains should be our destiny

We, humans, are confined to the thought that human brain is the most intellectual mechanism we have ever come across. This has put pressure on researchers and scientists to make artificial intelligence and robots that function to the level of the human intelligence. But how about keeping away the thought of the human brain and thinking beyond that? Yes, it is possible. Decades-long run towards finding an artificial intelligence system that imitates the human brain could be broken once humans think out of the box. In that case, the human brain shouldn't be our destiny, but something beyond and far more advanced.

For a very long time, artificial intelligence was seen as the science of making smarter and intelligent human-like machines. The human brain has advanced over time in responding to survival instincts, harnessing intellectual curiosity, and managing demands of nature. We have the power to think about what will work and what will not. For example, humans took birds as inspiration to create flights. But in order to make something fly, we didn't come up with the exact mechanism of a birds wings. The bird's wings are flexible while aircraft wings are inflexible and stiff. We had an intellectual instinct that designing an aircraft with flexible wings won't work. This is how the human brain finds ways to exceed our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical models in accelerating scientific pursuits.

Artificial intelligence has come a long way from being a component of science fiction to reality. Today, we have self-driving cars, smart virtual assistants, chatbots and surgical robots powered by AI. Ever since artificial intelligence entered the daily life of humans, it sparked a row over bias and discrimination. Since the fact is said 'AI algorithms are fed with real-world data,' researchers work as troubleshooters when AI overwhelms the discrimination accounts. The well-known cases of AI bias are Amazon recruiting algorithm, which famously discriminated against women, and the US government's COMPAS algorithm, which disproportionately punishes Black people. Even though manual fixes can bridge the gap between this discriminatory mechanism, it might get far worse if not dealt properly. At the core of all these issues is the human developed bias that influences machines in the modern era. The only way out of ending up with advanced technologies that serve the priority of people is to make an artificial intelligence mechanism that is beyond human brain. Since we are at the forefront of managing a global health crisis, let us look at some real-world examples of how technology is tackling the Covid-19 pandemic beyond human intelligence.

Supervised machine learning detects people vulnerable to Covid-19 

The initial phase of the pandemic challenge started with emerging clusters. The case numbers went beyond people's finger counts and the officials could no more maintain or track the new cases. With more and more mutant variants from the UK, Brazil, South Africa, etc. coming into the picture, things got more hectic for healthcare workers and government officials who were taking care of the Covid-19 situation. The only way to find whether people have Covid-19 or not is by seeking the help of technology and setting up a big data system that sorts the numbers.

Supervised learning techniques are machine learning techniques or algorithms that bind previous and current dataset with the help of labeled data to predict future events. The techniques are able to provide results in input data with an adequate training process and compare results with actual results and expectations to identify errors and modify the model according to the results. With the help of supervised machine learning, an algorithm can take in all the data and analyze particulars like person's occupation, gross income, family status, race or ZIP code, influence whether they've caught the disease or not. The algorithm can even analyze how vulnerable certain people are to contract the virus with their data.

Unsupervised machine learning finds Covid-19 patients

Lack of data has always posed a threat. It is even more severe in the case of Covid-19. To make matters worse, the data could even be wrong due to human errors. The Covid-19 data are often incomplete making it tough for analysis. People of lower socioeconomic status tend to have less access to diagnostic resources, which means that they might have had Covid already, but never tested positive.

The main objective of this work was to use clustering, an unsupervised learning technique to identify and evaluate the ideal number of clusters of countries regarding Covid-19. This way, one obtains several different groups data and compares it to see whether a set of people might have had Covid-19 in the past.