What we get wrong about artificial intelligence and machine learning

As a result, it remained largely misunderstood. Therefore, to learn artificial intelligence, one needs to eliminate misconceptions regarding it.

So, read on to learn more about it.

What have we got wrong about Artificial Intelligence?

When we talk about AI, we club it with terms like Neural Networks, Machine Learning, and Deep Learning. But it is some statistical algorithm that PhDs can only understand, and that is where we are all wrong about AI.

But that is not the real issue. The main challenge in building AI solutions is building a human-centred design, and the efficiency of these systems does not matter initially. But unfortunately, that is what most of the artificial intelligence and machine learning courses are teaching the students, which needs rectifying.

After evolving for centuries, humans interpret and think about their surroundings in their unique way, but an AI system is also incapable of doing that. It can learn to act like humans but not think the way a human can. Despite having petabytes of data, it is impossible to properly train an AI system like a human.

Therefore, the focus must shift from technology to interactive designs of AI systems. The true future of AI lies in design, not in more powerful algorithms and CPUs. They need to understand the human context and can only think and become more human. Gradually, technical prowess will have lesser importance in building great AI systems. Instead, they might know how to develop deep empathy with the users of these systems.

Current AI can't learn on its own but

Most marketable AI systems mimic human psychological information and process information in three different stages:

1. Reception: This portion of the system has receptors to receive signals and send them to a processing agent through electromagnetic signals.

2. Interpretation: Understanding what data has been recorded and how it should be processed according to user specifications.

3. Learning: It is when the system understands the entire process and learns or creates datasets for a certain task. An AI system needs to learn how a real human would complete a specific task.

Reception is the course in which receptors like eyes and ears receive signals from the environment and forward them to a processing agent, i.e. the brain in formats that can be interpreted easily by the processing system (i.e. electromagnetic signals).

It is followed by the interpretation process, where objects are identified. The references for it are searched in a library, and identified the results and sent all the matched data sets submitted by users.

Due to these capabilities, systems like Alexa and Siri have been developed. That is how the library of references can be expanded for the system to achieve its full potential. However, this is where the real challenge of the modern AI system lies because systems like Alexa and Siri are supervised manually by engineers in the backend, so these systems are not running purely on their intelligence; rather, they are AI systems that receive assistance from humans to function.

To sum up, the confusion and misconception regarding AI are quite normal and frankly not unexpected as the technology is still in its early days. Once it starts to penetrate more daily human lives, the awareness will increase, and such misconceptions will slowly fade away.

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