Top three soon to be trends in Artificial Intelligence (AI)

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Artificial intelligence is frequently discussed nowadays yet it’s too early to show real gains. Here are a few ways that AI is expected to make an impact sooner rather than later:

1. Training AI to Know What it Does Not Know

The next decade will determine whether humans or machines which are better in making a medical diagnosis. But nowadays more health care companies are turning to AI due to its more accurate results. The main problem here is in the case when the trained data set has no pre-defined knowledge for a new given test case. In that case, Curai has made the system flexible and has allowed the human can intervention to avoid the misclassification of unknown diseases. This approach is known as physician-in-the-loop.

2. Reducing call center burden

United Health received around 36 million calls in 2017 with 7.6 million calls transferred to a representative. It’s really hectic and burdensome to handle as many calls. So AI comes into the play. The AI platform solution uses deep learning and claim queue, Automatic Speech Recognition (ASR) to translate audio to text, and Natural Language Processing (NLP) for unsupervised clustering, to generate new call variables and automate transfer calls.

3. AI could be a solution to restore privacy

Privacy has been in the headlines lately as regulators and social media users begin to doubt the protection of their personal information that they share online or with their so-called trusted apps, social media platforms. So many companies like Cambridge Analytica are creating AI recommendation engines that are so powerful that only a little information is needed about the person making the choice and their preference is enough to determine what to recommend next.

Netflix is reported to have a great recommendation engine to suggest relatable content to its viewers. Pinterest also uses a complex recommendation engine to surface the best image for an individual out of the billions of images on Pinterest’s platform. The recommendation system uses users’ history, cookies, bestsellers, and other parameters to narrows down choices from billions to hundreds. 

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