Perform an analysis gauging whether synthesizing data offers an improvement over relying on a limited amount of “organic” data.

No matter how you deal with it, data science will play a massive role in 2018. If you’re thinking to be a data scientist, this is the best time. This article comprehensively explains the latest trends that data scientists should consider in 2018. Data scientists have become the center of the technology-oriented world that helps those who have excellent expertise and strong technical backgrounds.

Editor’s note: This post is the result of a collaboration with PredictX, a decision automation platform. Author Joni Lindes is a content writer at PredictX.

Today’s machines can identify objects in photographs, predict loan repayments or defaults, write short summaries of long articles, or recommend movies you may like. Up until now, machines have achieved mastery through laser-like focus; most machine learning algorithms today train models to master one task, and one task only. We are excited to introduce multi-task learning in our upcoming webinar, report, and prototype. Multi-task learning is an approach to machine learning that goes beyond single-task approaches and supercharges classification by allowing algorithms to master more than one task at once and in parallel.

Import AI: #100: Turning 2D people into 3D puppets with DensePose, researchers trawl for bias in language AI systems, and Baidu engineers a self-building AI system

This post is co-authored by Anusua Trivedi, Data Scientist, Microsoft; Patrick Buehler, Data Scientist, Microsoft; Dr. Sunil Gupta, Founder, Intelligent Retinal Imaging System (IRIS); and Jocelyn Desbiens, Researcher, IRIS.  

Well, to put it in simple words, machine learning is an extensively algorithm driven study which makes computer/device/software capable of learning on the basis of their own previous experience and improve the performance of a task. It also gives machines/software ability to analyze, predict and sort huge amounts of data. You Use a smart home assistant, ever wondered how you can say the same thing in different ways and it would still understand you? No just this, it becomes better at understanding you and recognizing your voice.

When I left the academic world, by the end of 2015, I had almost finished a review on the state of the art in usage of machine learning methods on neuroscience: how predictive models can be used for certain diseases (Alzheimer’s, Parkinson’s, etc) when the input data comes from structural (not functional) magnetic resonance imaging.

Demo of the chatbot