Limitations of Artificial Intelligence: AI Can Predict Outcomes

 We’ve already begun to see more frequently the inventions of Artificial intelligence. Most of these that are becoming popular are in the consumer product space. But also growing is AI in the healthcare industry. Once research came to find out that AI Can Predict Outcomes they continued to research all the positive and great ways that this can be applied to improve the giving and receiving of care.

Despite the growing popularity of AI both in the consumer product and healthcare industries, there are still some setbacks and rough patches that need to be better refined. Let’s discuss some of these setbacks

Lack of Consciousness

No matter how smart and sophisticated a machine is, it is still a machine and this means that it lacks consciousness or self-awareness. This means that there are some tasks it will never be able to do is well is a human being would. Even the smartest programs cannot learn the way humans do. For example, it takes a human being seeing an object at one time or maybe two or three times for the human to be able to recognize it anywhere and at any time.

It’s completely different when it comes to making a computer program to learn. If you want to make it, let’s say, recognize the same object a human can in a second, you will need to feed the program with large datasets. Image recognition by a computer program can be done but only if you have enough data at your disposal and you don’t expect a program to understand the relevant context. The latter is the real issue, the one we can’t really think of a way to tackle.

So long is machines lack consciousness, they will not be able to grasp underlying contexts. You can train them to recognize visual images, text, patterns, and even sarcasm (in more advanced cases) but they still won’t be able to get the full picture. Human behaviors like winking and what it means or implies in different contexts and situations would be hard for a robot or computer program to understand. Hard or maybe even impossible. In what they lack, they make up for with their other abilities that superseded the abilities of a human brain. Computing speed isn’t the strongest side of the human brain, and neither is the ability to simultaneously deal with so many information at once.

Artificial Neural Networks
and Predicting Outcomes

Deep learning is a process that is used to make computers and machines smarter. It is an advanced form of artificial intelligence and scientists who work on deep learning work with Artificial Neural Networks. A lot of data needs to be processed before an accurate model is created to be used by Artificial Neural Networks for future predictions, the architecture of Artificial Neural Networks is becoming more and more complex.

The more complex and Artificial Neural Network becomes, the more difficult it becomes to understand how to produce the predictive models and predict outcomes. Look at it this way, if you had a very small neural network, you might be able to understand it. But once it becomes very large, and it has thousands of units per layer and maybe hundreds of layers, then it becomes quite un-understandable and eventually unpredictable.

The scientist being eternally curious embrace this unpredictability is a natural occurrence and equate it to human-like intelligence and how it evolved. When an Artificial Neural Networks evolves scientists have admitted to the fact that there are some things that may have happened almost all by themselves, determined by so many factors that it would have been impossible to understand their genesis even if had the privilege of first-hand accounts.

Just like a human’s explanation for their action is probably incomplete is there [probably are some subconscious reasons as well, same can be said for Artificial Neural Networks and artificial intelligence. It might just be part of the nature of intelligence that only part of it is exposed to rational explanation. Some of it is just instinctual, or subconscious, or inscrutable. SO does this mean that the machines have a subconscious base and at some level can think for themselves?

Computational Limitations
of AI (Big Data)

With machine learning, you can access a large amount of data and compute this data too. But it is beginning to seem like the ability to compute bid data does not match up with the amount of data being generated. It is just hard to keep up - even with artificial intelligence, machine learning, deep learning, and  neural networks.

Big data is growing at an exponential rate and supercomputers are already feeling the consequences and the fact that they are not getting any better during the past three years, should be an alarming call for scientists and engineers to focus on developing alternative architectures in order to pave the way for the more powerful technology of the future.

  • Could it be that big data would overpower our ability to process it and take over?
  • How would it look if that happened?

I guess that should be food for thought and we should all think about it is we try to come up with more powerful and more advanced computers or even come up with other solutions that a unique. There are ideas no one has thought about yet, it’s just a matter of time.

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