AI is rapidly becoming ubiquitous. Despite being in early stages, it is found in most of the things that we use daily. From personal assistants on smartphones like Siri to facial recognition software on desktops, AI is becoming commonplace. In near future, machines will learn how to make sense of real world and we will be able to see AI out there in the fields working alongside humans.
However currently, AI is still nascent. It is facing limitations and challenges in many forms. These challenges stem from the fact that AI tries to mimic human intelligence but unlike human brain, it uses computers to perform its functions. Computer processors are severely limited as compared to the human brain in terms of managing complexity. But they excel at performing same tasks repeatedly without making any errors. Hence first of all, we need specialized processors that mimic human brain in order to make AI reach its potential.
To unleash full powers of AI, another thing we require is data. AI algorithms search through huge amounts of data to find optimal solution — typically performing thousands of iterations looking through the data. Fortunately, Big Data holds the potential to provide AI with means to be successful. According to the estimates of International Data Corporation (IDC), there will exist 44 zettabytes of data globally distributed across the world. This is huge amount of information that will cause uptick in revenues from $130.1 billion in 2016 to over $203 billion in 2020 for suppliers of big data and business analytics. Hence, we can say that AI will have enough information in future to work at its full potential.
Moreover since the data will not be controlled by single organization and will be spread across the globe, distributed algorithms will come in handy for AI purposes. These algorithms run on interconnected processors and communicate with each other to achieve desired goal. They allow parallel processing, saving a lot of time that will be otherwise spent in collecting the data at one single point and then processing it.
Another big requirement of AI is fast, parallel hardware. Typically, AI requires ability to experiment with data, produce multiple results, and then look for the best one. In search of the best result (optimal solution), it needs to evaluate all results on the basis of some scores. Once it finds the best result, it then requires the ability to provide this result to the user via web service of some sort. This process needs huge processing power that current hardware is unable to provide.
This is where cloud fits in image. Clouds provide PaaS services that AI algorithms can utilize to perform their functions. Running AI algorithms in parallel on cloud platform will save time and cost of businesses. At the same time, they will be able to use cloud services to allow their customers to interact with AI applications via web.
AI is also making friends with IoT which will become one of the primary sources of data. This data will then be used by AI to extract useful patterns and provide beneficial insights.
It is safe to say that AI will rely on big data, IoT, and cloud to reach its full potential.
About the Author
Mazhar Naqvi is a CS grad student with research interests in computer networks and security. He can be reached at firstname.lastname@example.org and you can follow him on linkedin athttps://www.linkedin.com/in/mazharnaqvi
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