3 Key Differences Between AI And Robotics

Robots may replace about 800 million jobs globally in the future, making about 30% of all occupations irrelevant. Also, only 7% of businesses don’t use AI currently but are looking into it. Stats like these scramble people’s heads and make them believe that robots and AI are one and the same, which has never been the case. Instead, businesses and governments use robotics-based applications that can be described as a convergence of AI and robots. Unlike what is shown in most dystopian sci-fi movies or books, not all robots are intelligent.

Copyright: forbes.com – “3 Key Differences Between AI And Robotics”

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 Artificially intelligent robots, a combined application of AI and standard automation robots, are just one of the several types of robots. Such robots use AI algorithms and models to execute more than just a repetitive series of movements and increase their autonomy—but more on that later. AI robots are highly sought-after resources today with several applications—either on their own or in combination with other technologies.

There are several differentiating factors between AI and robotics, but the three enlisted here enable people to clearly understand them.

1) AI and Robotics: Conceptual Differences

The basic definition of AI revolves around enabling machines to make complex decisions autonomously. The hardware and software tools based on AI can solve complex real-world problems by analyzing vast quantities of data and finding patterns not visible to humans in it. Machine learning and reinforcement learning fine-tune the analytical capabilities of such applications over time. Therefore, AI-based applications possess a limitless capability of becoming better at the tasks they perform.

For example, consider an app like TikTok. Like most social media applications, TikTok also uses a “social graph” to provide recommendations to users based on the pages they follow and the videos they like. TikTok’s machine learning algorithms go one step further than other social media apps by also using an “interest graph”—using video viewing duration numbers to provide suggestions to users. These suggestions will include creators and videos that bear similarities to the ones watched by users for the longest duration. For instance, if a user continues watching a cat video for, say, more than 20 seconds, TikTok’s algorithms will direct more videos on cats, other feline creatures and other animals towards their personalized video feed to eventually get them addicted to the app. As TikTok videos generally last less than a minute, they can collect massive amounts of data and accomplish peak personalization faster than other social media applications.

This is how AI works—using various kinds of data as reference to improve its working over a period of time. As stated earlier, the bigger the dataset, the better an AI-based tool will perform in terms of operational speed and accuracy.

Source: SwissCognitive