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How High-Tech Industry is Leveraging AI for Exponential Business Growth
Artificial Intelligence and machine learning is paving their way in almost all industries. Its next target is the high-tech industry. The emergence of Artificial Intelligence in the engineering and mechanical world has raised many questions. What is the scope of Artificial Intelligence in the high-tech industry? Is it a good idea to invest in AI? Will AI replace the engineers? Is it easy for Artificial intelligence to overtake all the high-tech fields?
There is no doubt that Artificial intelligence is developing rapidly. It is capable of versatile applications and has marked remarkable changes in many industries. We have the example of Google, Amazon, and Facebook algorithms in front of us. But with the current developments in AI, it cannot overtake the high-tech mechanical and engineering industry anytime soon. It may modify the traditional tools of the industry, but it is useless without the human workforce.
In this article, we have evaluated the scope of Artificial Intelligence in the high-tech industry. We have also discussed the obstacles to adopting AI in the industry.
AI is now part of almost every industry, including high-tech. It has made significant progress in recent years and seems to have a good scope in the tech field.
The most significant development of Artificial Intelligence is in the research field. Today, AI tools and software are much more efficient in holding the data and evaluating it. They help the researchers in making the most of the existing research. Researchers can now focus more on finding new solutions than investing time in extracting information from previous work with the help of AI.
Artificial intelligence can process and evaluate data much more quickly and efficiently than the human brain. It can hold much more data than traditional computing devices and process it in just a few seconds. Now whether you want to correlate the existing database with centuries-old data. Or need to drive results based on an extensive database, AI software and tools are there to assist you. They can serve as the efficient assistants of data analysts and may take over their roles one day.
AI tools and software work with much larger databases and are expected to make accurate judgments in most cases. For instance, a human brain may get confused in identifying a metal or chemical. But AI tools can detect it accurately and efficiently.
Similarly, fingerprint detection and face feature detection can be done swiftly and with lesser doubts using AI tools. Due to the accuracy of AI tools, it is assumed that Artificial Intelligence will replace many engineers and specialists in the future.
Though the scope of AI seems to be quite promising in the high-tech industry, there are some obstacles to adopting AI.
All AI-based projects require a lot of time and investment. Industries and organizations need special hardware and software tools for executing the AI model. Furthermore, training the model is itself a very time-consuming and costly procedure.
Since AI experiments’ success rate is not promising, many investors are reluctant to invest their resources in such projects. Thus, the limitation of investment in high-risk AI projects is one of the major obstacles in adopting it in the high-tech industry.
Building AI-based hardware and training AI models is a very time taking and tedious process. It yields results at a slower pace.
Keeping in mind the faster pace of the high-tech industry, most AI machines and models become outdated even before their actual execution. This time-lapse between the idea and its execution is a hindrance in the way of developing AI.
AI tools and software are dependent on the data feed to them. They can only process and evaluate the data that is there in the system. Anything beyond the scope of existing information is beyond the capacity of AI tools as well. Moreover, it cannot detect errors in the data fed to it.
Therefore, any human error in feeding data can result in the failure of the whole AI model. Therefore, this data dependence is another major obstacle in its adoption in the high-tech industry.
High tech industry demands quick and efficient decision-making. Unfortunately, though AI tools can make fast and efficient judgments in many situations, they lack creativity.
No AI tools, to date, can take abstract decisions based on the scenarios like a human mind can do. No doubt AI tools are versatile, yet they are far behind the creative capacity of the human brain.
Artificial Intelligence seems to have a good scope in the high-tech industry, especially in the telecommunication and computing fields. But it still has a long way to go before being adopted in the biotechnical and engineering fields.
AI is a high-risk investment. And reluctance in adopting AI is a significant barrier in the way of its development and progress.
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