It seems engineering terms are becoming buzzwords within the industry.

For example, words like The Fourth Industrial Revolution, the Internet of Things, Artificial Intelligence, the Blockchain — and more — are all misused in quickly rattled off articles and political speeches.

In fact, it seems that the people peddling these terms are not actually aware of their meaning. It is easy to refer to the Fourth Industrial Revolution without giving credence to the technologies that are bringing the revolution about.

The risk is that engineers entering the industry might be unsure of which type of engineering to pursue when buzzwords are being thrown from all angles. When it comes to terms like the Internet of Things and Artificial Intelligence...it’s a delicate balance.

 

IoT vs. AI: Which way forward?

The Internet of Things refers to connecting machines together and harvesting the data that they generate, whereas Artificial Intelligence is about engineering an intelligence comparable to human intelligence.

The overarching difference is who interprets and essentially makes sense of the data. Thus, AI can be plugged in at the technological level that can make sense of the data generated by Internet of Things technologies. As a result, in some instances, some systems can be interconnected in an Internet of Things network and have an artificially intelligent software making sense of the data it is receiving.

However, a new report by EE Times has taken a look at the revenue expectations for the Worldwide Deep Learning Chipset industry. The report seems to indicate that ‘five-sixths of the growth in semiconductors' will be in AI technologies alone by the year 2025.

The Internet of Things, merely a technological shift where an interconnected network of things (or computers) talk to each other, may be on the back foot of the semiconductor industry because AI setups are usurping that sector.

Analyzing data with deep-learning algorithms seems more of a technical task than connecting things together and needs other AI technology altogether. CPUs, GPUs, and the like are unable to process the data networks generate as effectively as algorithms can, and so those technologies fitted with the correct semiconductors have reached the market and are ready to do the job.

So what kind of semiconductors can prospective engineers expect to see in the industry? Application-specific integrated circuits, application-specific standard products, systems on a chip, and accelerators for artificial intelligence. Each chip being algorithm specific in its own right.

Those algorithm-specific and application-specific semiconductors are tailor-made for, for example, autonomous vehicles, Wall Street, farming technology, weather forecasting, etcetera.

The Engineering Institute of Technology is delivering a Professional Certificate of Competency in Machine Learning and Artificial Intelligence. The course will begin on 13 May 2019. The course is presented by industry lecturers with more than 15 years of experience in the development and management of artificial intelligence. The course teaches students incredible problem-solving skills in the machine learning world and trains them in building algorithms to automate processes regardless of the industry.

Enquire about the course today and start clearing up the basics, and start your journey on focusing what the engineering industries that exist require right now.

 

Works Cited

Shuler, Kurt. “IoT Was Interesting, But Follow the Money to AI Chips.” EETimes, EE Times, 20 Feb. 2019, www.eetimes.com/author.asp?section_id=36&doc_id=1334342.

Sync, CRM. “The Future of IOT Is AI.” TechUK - Representing the Tech Industry in the UK, TechUK, www.techuk.org/insights/opinions/item/13827-the-future-of-iot-is-ai.

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