AI

Artificial intelligence (AI) has evolved from hype to reality over the past few years. Algorithmic advances in machine learning and deep learning, significant increases in computing power and storage, and huge amounts of data generated by digital transformation efforts make AI a game-changer across all industries. AI has the potential to radically improve business processes with, for instance, real-time quality prediction in manufacturing, and to enable new business models, such as connected car services and self-optimizing machines. Traditional industries, such as manufacturing, machine building, and automotive, are facing a fundamental change from the production of physical goods to the delivery of AI-enhanced processes and services as part of Industry 4.0.

Similar to the relationship between a car and petrol, data and artificial intelligence (AI) complement each other. Data manipulates AI, and Artificial Intelligence helps us to understand the data available to us. Data and AI are two of the biggest topics in technology in recent years, as both work together to shape our lives daily. The sheer amount of data available right now is weaving and it doubles every two years. However, we currently only use about 2 percent of the data available to us. Much like when oil was first discovered, it is taking time for humans to figure out what to do with the new data available to us and how to make it useful.

Artificial Intelligence and Data

Professor Amandeep S. Sidhu in his blog on AI “AI without Quality Data is useless” mentioned that even though AI technologies have existed for several decades, it’s the explosion of data that has allowed it to advance at incredible speeds. It’s the billions of searches done every day on Google that provide a sizable real-time data set for Google to learn from our typos and search preferences. Siri and Cortana would have only a rudimentary understanding of our requests without the billions of hours of the spoken word now digitally available that helped them learn our language”. 

Also, he mentioned, “each year, the amount of data we produce doubles and it is predicted that within the next decade there will be 150 billion networked sensors (more than 20 times the people on Earth). This data is instrumental in helping Artificial Intelligence devices learn how humans think and feel, and accelerates their learning curve, and also allows for the automation of data analysis. The more information there is to process, the more data the system is given, the more it learns and ultimately the more accurate it becomes”.

“In the past, AI’s growth was stunted due to limited data sets, representative samples of data rather than real-time, real-life data, and the inability to analyze massive amounts of data in seconds. Today, there’s real-time, always-available access to the data and tools that enable rapid analysis. This has propelled AI and machine learning and allowed the transition to a data-first approach”, he added.

From data comes the emerging technology Big Data. Just like data, Big Data and Artificial Intelligence complement each other. Artificial Intelligence becomes better, the more data it is given. It's helping organizations understand their customers a lot better, even in ways that were impossible in the past. On the other hand, Big Data is simply useless without software to analyze it. Humans can't do it efficiently.

How AI Uses Data?

  • Identifying abnormality- AI can analyze data to detect unusual occurrences in the data. For example, having a network of sensors that have a predefined appropriate range. Anything outside of that range is an anomaly.
  • Possibility of future outcome - Using known condition that has a certain probability of influencing the future outcome, AI can determine the likelihood of that outcome
  • AI can acknowledge patterns - Artificial Intelligence can see patterns that humans don’t
  • Data Bars and Graphs - AI can look for patterns in bars and graphs that might stay undetected by human supervision.