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Generative Pre-Trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series created by OpenAI.
We can help you leverage the power of GPT-3 for your products.
Problem: Improve agent productivity suggesting right answers to customer questions.
Solution: Collected 650 tickets and applied NLP techniques like stemming, lemmatization to extract the core of the tickets. The core sentences were classified using Alpes algorithm to form the base model. This model learns incrementally every day.
Result: 90% accuracy in showing relevant results.
Problem: Build a background removal tool from photos.
Solution: Using image processing techniques we identify the main object in focus. The object pixel co ordinates are identified and the remaining pixels are converted to white..
Result: Background was removed properly in 90% of cases..
Problem :
Automate generation of presentation based on user inputs provide prediction of what presentation should contain and how content has to be arranged
Solution: Multiple prediction module such as Chart Prediction, Icon prediction, Diagram prediction, free space prediction on images, title and sub-title prediction were and added in a work flow to achieve the required automation
Result: Solution was successfully delivered and its live now for users.
Problem: To build classification model to identify between fraudulent/Non-fraudulent transactions.
Solution:Using Attributes and transactions data for remitter and beneficiary, applying feature engineering and domain expertise to lower false positives, Tuning of feature space and achieving the best accuracy rate from the various detection models
Result:Reduced fraud related costs, Relationship analysis of fraudulent networks and collusions, Improved data credibility, uncovered hidden correlations.
Alpes has the fastest learning algorithm with O(NlogN) time complexity. This will allow you to iterate training with your data fast to find the right feature set.
Algorithm can perform incremental learning. When fed with new training data you don't need to run the whole training process again. This will allow your products to imbibe new data on the fly while making decisions.
Clear understanding of why the system is learning or not learning. AI is not a blackbox anymore. This will allow you to fine tune your data and get the right data to make your product learn better.
Model size storage and compute savings of more than 20 times
Leading the research in Alpes, Dr. Kumar carries 37 years of research experience in the application of computers in the areas of industrial image processing, AI and pattern recognition, electromagnetics, fluid mechanics, and structural mechanics. He has used many numerical techniques such as Finite Element Methods (FEM), Finite Differences, Boundary Integral Equation (BIE), and minimization methods to solve various problems.
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