Artificial Intelligence

Top 3 Risks Involved in Artificial Intelligence

Top 3 risks involved in Artificial Intelligence include the risk of Algorithmic Failure, the risk of Data Malfunction and the risk of Abstraction in Information Technology. All these factors are interrelated and each one of them requires proper management. Top 3 risk issues are to be sorted out before an Artificial Intelligence system is designed and developed. Each one of them can be sorted out individually.

Algorithmic Failure:

It is considered an unacceptable risk scenario. Researchers and developers need to be very careful while designing and implementing any algorithm. There must be a focus on reliability and reproducibility. The designers should consider all the possible outcomes of their algorithm. In addition to this they should be careful about the implications of their algorithm on natural intelligence.

If the task of an AI includes managing and controlling real-time data then the programmers and Researchers should be cautious enough. They should not allow Algorithms to make assumptions or use known false facts. For instance, if an artificial intelligence software is supposed to predict the future stock prices then it may fail by over-estimating the demand and under-estimating the supply. In fact it may cause the stock prices to fall by a large amount resulting in heavy losses for the investors.

Similarly if an artificial intelligent software is supposed to perform stock trading algorithm, it may choose to purchase a security that has low volatility but high liquidity. This may sound like a good choice until the next day when the company’s financial results come out. Those who were invested in that stock may lose heavily.

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Data Malfunction:

A lot of computer systems and software are designed to collect data and analyze them. But the results of their analysis are never perfect. At times they might even give incorrect or incomplete information. So a system that claims to be able to analyze all types of data but can’t might be wrong most of the time.

Human error:

No system can do everything perfectly. There might be some problems that might arise. And human being fallible like any other machine. One possible example of this problem is in the healthcare industry. Many medical systems run on paper-based records, which have lots of errors in them.


Complex systems often take longer to train. They also require more resources. These might result in slower return on investment and increased operating costs. And it makes no sense whatsoever. Why invest your money in complicated? Instead, use simple systems and let the computers do the hard work.

The list of potential risks involved with artificial intelligence is long. It would take many books to cover all the possible issues. But at least you will know what to avoid. Now do you want to be an AI researcher?

Well, there’s another way. If you’re passionate about solving human problems and you are willing to work hard at it. This probably describes most of us who love to solve complex problems. But if you are afraid of failures… then I guess you won’t be able to continue. So this is the reason why you should get a job in a lab working on AI or Machine Learning instead of doing it in your project development business.

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What’s the big risk? The risk of replacing human intelligence with a machine. And that’s a big deal. It might prevent us from fully exploring what it means to be human.

What do you think are the other risks? Well, it sounds like the most obvious risk. And that’s the one I care the least about. What if a system cannot function well enough to keep track of what it is trying to accomplish?

That risk is called “archy” or worse: Artificial Intelligence Collapse. We know it will happen because it’s happened before. The problem is once it happens; it will be so catastrophic that humans won’t be able to survive without artificial intelligence. So what do you think? Are you willing to take that chance?

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