Cognitive Cloud Computing Advantages And Disadvantages

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According to a recent analysis from LinkedIn, 2019’s employers are looking for a combination of both hard and soft skills. Cloud computing and artificial intelligence are topping the list of desired attributes. So is there a type of technology that combines both of these desired attributes? The answer is: Yes! These are the so-called cognitive technologies. So in this, we are going to look at the basics of Cognitive Cloud Computing Advantages And Disadvantages.


Offered usually by the AI-first companies within their cloud platforms and by pioneering startups, these cognitive technologies mimic human abilities such as vision, text analysis, and speech. More importantly, these applications make use of top-performing algorithms trained on loads of data containing images, videos, and text. Basically, companies like Google, Amazon, and Microsoft have been acquiring this data from society’s behavior for years, and now, they are selling this intelligence back to society as machine learning as a service (MLaaS).



They became part of the secret tool kit of the best analytics translators and those decision-makers responsible for matching a business problem with a feasible technology. Let me give you an example: SkinVision is an app that detects skin cancer melanoma, which is based on computer vision supported by Amazon Web Services. It has trained a proprietary algorithm to label melanoma pictures that are now being used by more than 1 million active users. Users can take pictures with their smartphones and scan their bodies frequently, under a subscription model. Considering that 1 in 5 citizens develop some kind of skin cancer, the service is now frequently offered for free by insurers to their clients. So instead of developing an app to book a visit to a dermatologist, it actually brings the specialist view directly to you in the form of computer vision.


A typical example of artificial narrow intelligence. But you still need to know how to evaluate these models according to the cost of their mistakes. It is still the job of a human to judge these mistakes in an analyst-in-the-loop approach. Just like when you buy a car, you need to know how to compare different models and make diagnostics of engine failures. But you don’t need to know how to build the entire car yourself. Another very interesting example comes from DataSine’s Pomegranate image scoring. Their machine learning models have been trained on images and text examples to understand how different content appeals to humans. It offers suggestions on words and phrases to replace, as well as colors, themes, and images, depending on the personality of your users. We’ve seen some really nice examples from participants in our Growth & AI traineeship.


Marwan from the Mobile Company recently tested Pomegranate by comparing the suggestions of the image scoring model with real click-through rates of different ad campaigns. Using this kind of image recognition, a skilled marketer will not need to run too many experiments to find images and copywriting that actually generate a higher conversion rate. Another application of computer vision was tested by Merle from Son of a Tailor. She used Google’s Auto ML Vision to build a prototype classification model for new t-shirts based on their attributes, for example, crew neck or v-neck. After you set up the environment on GCP, there are no coding skills required, but the person running the experiment can observe the performance metrics like precision and recall. Or even observe how the model would perform on new t-shirts once deployed in an application. Microsoft Azure, similarly, has a set of cognitive services that can be used to detect specific content in text and images.



Another one of our trainees, Niels, from Eyecons, tested the Content Moderator and review API to detect profanity and negative sentiment in content. This content could be posted, for example, by sports fans in forums and comments to articles. Then, a human-in-the-loop could react promptly with the best communication strategy. Hope you enjoyed these examples of cognitive technologies and are keen to know more about them, as well as the current top professional skills: cloud computing and artificial intelligence.

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