Background information
Humans invented technologies and improved them over time. No matter how efficient they are, there can be some limitations to them as well. For example, as I said in Transcription Technology, the technologies are still in their development stages that is why they have so many limitations, and they cannot provide such efficient results. Machine Translation technologies are not so efficient as everybody thinks. I’ve seen so many cases about their inefficient results. That is why we are going to talk about the limitations of machine learning technologies. Read on to find some interesting facts!
Artificial intelligence is replacing humans in small jobs. Like we have seen some robots in restaurants, in ATMs, and some other services
Background information
Like I have said before, humans invented machines and technologies. This is the reason why machines can never replace humans. I have seen the impacts of artificial intelligence on human works and I was terrified of it. So many hard workers have lost their jobs to a robot, natural language processing has replaced human translators who worked hard to earn their living. These are just a few cases that I mentioned. So, read on to find the limitations of Machine learning.
Limitations of Machine learning:
Science and researchers are trying to develop something capable of performing human tasks with better efficiency and performance. There is no doubt that so many of such technologies have entered different industries where they are quite helpful. But the question is, how many industries that these technologies are going to conquer? Despite their serious disadvantages, why are these technologies still working in different industries?
Transcription technology and multiple speakers:
There is no doubt that transcription technology is ruling the IT industry right now. But have you ever tried to record a conversation that includes more than 2 speakers? Or, have you ever tried to record an aggressive debate with transcription technology? Because these are some important conditions to consider if we talk about the efficiency of transcription technology. Even a human writer cannot write down any conversation that includes more than one or two speakers.
If we talk about efficient transcription technology, I would not give good ratings. Because the technology is not so efficient in recording interviews, heated up conversations, or any college or university lecture. The current technology can just record one human voice at a time, even the person has to speak a fluent language without any “hmm, ehm” etc.
Think of your normal debates. How do they work and what are the environmental conditions around you? Two people are discussing something, the third person interrupts them, and you start speaking out of nowhere. How can an in-development-stage technology possibly record such conversation and provide you with efficient and error-free results?
Background noises:
Just like a human struggle in understanding a conversation when there are loud or irritating background noises, transcription technology or machine learning technology struggles too. The technology might struggle more because it does not have a hearing ability of a human. This is the biggest drawback of machine learning.
Suppose, you are in a crowded place and trying to find a route to your home. You unlock your iPhone and say, “Hey Siri, give me a route to my home” and don’t understand it. What would you think? It will surely drive you nuts. This is why we cannot drive the best results when we are in a crowded place. So, what would be the difference between humans and technology? Neither humans can understand something when in a crowded place, nor the technology. So, why do we rate them as superior to humans? This is something to think about.
Natural language processing struggles:
NLP struggles as well. This technology may seem a bit straightforward, but possesses a lot of limitations. Different countries have different accents of the same languages. For example, you will see the difference between an American accent and a British accent. Siri, Google Assistant, or Alexa have received complaints about not understanding the British or other English accents.
On the other hand, when we talk about a human’s ability to understand or translate something in the worst condition, they will surely provide you with a better result. I don’t understand the point of comparing humans and machines in good conditions, both of them can work. if we compare natural language processing with human’s ability to understand, we will get better results from humans.
Not so creative:
Humans invented machines, so humans are the creative ones. Ever heard about a robot who discovered or invented anything? No, you won’t, because a robot’s mind is just limited to do things that it is designed to do. A normal human being takes about 10 years to reach its master’s level graduation, on the other hand, a robot can learn anything in 3 days. Do you think this is the efficiency of that robot? No, it is the lack of creativity.
Can artificial intelligence replace human services?
If we talk about some facts, Artificial intelligence is replacing humans in small jobs. Like we have seen some robots in restaurants, in ATMs, and some other services. Talking about efficiency, they might be performing better than humans, but the machines are not reliable. We heard about automatic driving cars, right? you can search for the number of accidents caused because the customers were so dependent on automatic car driving.
The same is the case with different business owners and manufacturers. When Elon Musk claimed to manufacture more than 20,000 electric cars in one month. He did so because he was dependent on robots, but human employees. When the time reached to deliver the vehicles, he only managed to deliver 2500 cars. So, he fired all of his robots and hired new human employees, and got his expected results.
So, the conclusion of this question is, humans are natural resources of nature, they are more creative, reliable, and efficient than robots. Despite some benefits of Artificial intelligence in different fields, this technology cannot completely replace humans.
Frequently asked questions:
What is machine learning?
Machine learning is derived from artificial intelligence. It is a process that can give authentic results without even taking any input. The process uses historical data about the field and identifies the incoming outcomes.
Mentions the types of machine learning:
According to the algorithm they use, machine learning technology has three types:
- Unsupervised machine learning
- Supervised machine learning
- Semi-supervised machine learning