The core components of Artificial Intelligence

The massive surge in AI and the buzz surrounding it sometimes makes it difficult to get a handle on the technology and piece around AI. Here we make a small attempt to explain the core components, services and piece in AI.


Graphic Processing Units or GPUs - Nvidia is the largest maker of GPUs had its niche in the gaming market. It had such a lasting impression that no gamer would want to be caught without it. This was until the discovery of Bitcoin and the heavy interest in blockchain. GPUs were no longer restricted to gaming. People relied on them to mine bitcoins.With the current buzz regarding AI and the rush towards adapting it in their processes, people have realized the benefit of using GPUs for AI and deep learning. GPUs essentially are high end graphic cards that go on to regular servers. The cards as well as the extensive software stack make it easy to process large volumes of data in complex AI models. GPUs are expensive but they cut processing time from months to days. Now, you would never see a deep learning developer without a GPU. Nvidia from its end, has built an extensive software stack to support the use of these GPUs. Libraries such as Cuda are a great resource.


While the hardware is in place, the framework or libraries are the what the machine learning applications are built on. Matlab was widely used to experiment and the R programming was also heavily used.When Python libraries became available, developers switched almost immediately. Scikit-Learn is the most popular python framework most developers use to do traditional machine learning.The big push towards deep learning has lead to some great competing frameworks. Most recently, Tensorflow from Google has gained popularity as well as Pytorch. Some others in the same field are Kara’s, Caffe and Theao. Each frameworks have their own set of pros and cons.Every framework has its pro and cons. The adoption is really driven by the community available for people get support since all of these are free open sourced and not commercially sold frameworks. Tensorflow is pushing the limits with awesome features to handle text, images etc and has been widely used within Google. Our teams here are currently in love with Pytorch.


All the big cloud providers such as Amazon Web Services (AWS), Google Web Platform or Microsoft Azure have a portfolio of AI or machine learning APIs offered as cloud services. They offer text classification, sentiment analysis, image classification etc. These services can be plugged into solve simple problems within a developers application. For instance, for a broad level search, you can use the sentiment analysis API to see if your customer feedback is negative or positive.

AI Applications

These are the application used by customers which leverage AI and solve specific problems. Siri and Alexa are the ideal examples. Another great example would be the recommendation engines that we see on Amazon, Walmart, etc. The most common applications can be seen in almost every car leveraging technology from MobileEye. It has features such as the lane assist, parking sensor, and pedestrian or obstacle detection systems.Our teams at IceCream Labs have spent the better part of the last 18 months building applications focused on catalog management and merchandising for retailers and brands.
We leverage GPUs, Tensorflow, Pytorch, Keras and Caffe. We don’t use the standard cloud APIs as they are not sufficient for the problem we cover. Our application, CatalogIQ can intelligently score the quality of the product content and automatically classify products, generate keywords, improve content for SEO and search.
By leveraging secure private clouds, we can do this on from 1 product to 100 million products seamlessly. That is, really the power of AI.