Machine Learning – A new Paradigm

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With organizations all around the world realizing the need to analyze data faster and cheaper, machine learning has come to the forefront. Majority of companies across industries are utilizing this sub-branch of AI to help understand the hidden trends and patterns in their data. This especially helps them in designing an effective strategy for their b2b sales or marketing activities. However, since the soul of machine learning is algorithms based on data, any inherent bias in data would hamper the results. It is therefore critical to evaluate the data and models at every stage, to get rid of any bias. To help this process, machine learning has evolved recently to include automated machine learning or auto-ML, which is the process of automating the time consuming, iterative tasks of machine learning model development. These new approaches have the potential to not only remove bias from the process, but also make it faster and cheaper.

Products Corner: 
The release for June emphasize our vision of making insights available to users in the simplest form, and also highlight Leni’s ability to solve for more and more complex queries with new integrations.
 
Major release for June:
  • Enhanced Machine Reasoning Capability – With this, Leni can help users identify any significant parameters driving the change in the focus areas. Leni derives its machine reasoning capabilities through an intervention of causal algorithms.
  • Analytics as an API – Leni is built on the principle of micro-services. For this release, analytics interventions, including forecasting algorithms are now available as APIs. This enables analytics capabilities to be scalable and configurable with different use case and and allows for quicker implementations.  
  • New Question Handling Capability – With this, Leni can not only solve for values of summarized KPI's, but can solve for “How many” type questions pertaining to categorical variables as well.  .
  • NLG Narratives Release Update – Leni narratives will now include references for filters specifically for single dimension charts. Narratives will also be available for all graphical representations with multiple measures.
Latest from Lymbyc: 

Pradeepta Mishra, Director AI, Lymbyc, will be the keynote speaker at the Step-in Summit 2019, where he will analyze some frameworks / procedures on how to use AI in automation testing and automation of robotic procedures.

        

 

             

Featured Articles: 

What is Machine Learning bias and how to overcome it

No matter how great your Machine Learning algorithm is, the quality of insights eventually depends on the quality of data that is being fed into the algorithm. Therefore, it is important to evaluate data and check for any inconsistencies and test for sample biases and edge cases. Read More

Why AI-powered Competitive Intelligence is a necessity

AI allows companies to identify trends and insights by analyzing reams of data, including real-time data. As a result, AI can drastically improve decision-making, and help organizations become more competitive. Read More

Automated Machine Learning - A new Paradigm

Existing Machine Learning algorithms require a certain amount of pre-processing of the dataset to make it suitable for machine learning and this requires considerable expertise and knowledge of ML. AutoML helps automate this process. Read More

LAURELS FOR LYMBYC

Lymbyc, being the world's first virtual analyst has proven its mettle in the industry . Be it the "Most innovative Data science Product" by Aegis or "the top 10 emerging Analytics startups in India to watch out for in 2018" by Analytics India Magazine, Lymbyc is making heads turns and making headlines

About

Lymbyc, a leading edge innovator in AI and Machine Learning takes inspiration from the “Limbic” brain — the part that stores, dissects, rationalizes and generates memories and actions. That’s exactly what we do — giving business users the power of a virtual analyst that is intuitive, actionable and context driven to answer all their business queries

 

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