- Hybrid Content-Based & Collaborative Recommendation System
- Action (Clicks, Likes, Dislikes, etc.) Prediction System
- Context-Dependent Recommendation System
- Cross-Domain Recommendation System
- User Engagement & Content Diversity Optimization System
- Context-Aware Notification System
- Next Place Prediction System
- Relevant Email and Contacts Classification System
I visited the company headquarters in Bilbao and I was impressed with their Artificial Intelligence technology. Among the Digital Assistant companies that I have seen, I am most excited by the Sherpa.ai product and team, that’s why I decided to join them.
Strategic Advisor in AI at Sherpa.ai
Co-founder and former CTO of Siri
Former Head of Siri Advanced Development Group at Apple
- Advanced Statistical and Deep Learning Techniques
- Text Summarization
- Duplicated and Related News Identification
- Action Required Email Classification
- Federated Learning Framework
- Differential Privacy Framework
- Fake News Detection
- Life-Long Learning
- Among others...
Federated Learning is a novel Machine Learning paradigm aimed at learning models from decentralized private data, such as data located on users’ smartphones, in hospitals, or banks. It can be combined with Differential Privacy, a statistical technique to provide data aggregations while avoiding the leakage of individual data records, to ensure a higher degree of privacy.
The Sherpa.ai Federated Learning and Differential Privacy Framework is the framework for Machine Learning that allows collaborative learning to take place, without sharing private data. It is the most advanced and complete technology in the market for developing data privacy artificial intelligence. The framework offers multiple types of Federated Learning, i.e., horizontal, vertical and transfer Federated Learning, supporting a variety on heterogeneous data privacy-based use cases.
Sherpa.ai is leading the way in the research and development of machine learning techniques for intelligent predictive assistants, and we are paving the way for novel applications that respect users’ privacy, based on cutting edge research on Federated Learning.
Francisco Herrera, Ph.D.
Senior Associate Researcher in DL & ML at Sherpa.ai
Highly Cited Researcher (Thomson Reuters) in the areas of Engineering and Computer Sciences
Spanish National Award in Computer Science
The AI engine constantly works to analyze all the information and to always have the most relevant information available, without the need to perform searches. Our algorithms are capable of detecting underlying patterns in the data and give coherence to the information coming from different domains, a priori unrelated. The following are some examples of the capabilities of the AI engine:
- Relevant personalized recommendations from the start: The AI engine is able to characterize the preferences of a new user by integrating the knowledge of the preference patterns of the existing user population, with minimal information provided by the user, through the use of Bayesian Networks. Our models are able to offer stable user profiles in cold start situations.
- The advanced Natural Language Processing models allow us to characterize and evolve the preferences of the users with various levels of abstraction, dynamically integrating highly detailed linguistic information with general interest topics.
- Our Opinion Mining & Sentiment Analysis algorithms permit the extraction of subjective information about the content and the ability to offer personalized recommendations based on the location of the user and their habitual response to the emotional charge of the content.
- The processing of geo-location signals and the interaction of the user with the platform through advanced Unsupervised Classification and Structured Semantic Analysis techniques allows us to understand the user’s various contexts and adapt the recommendations to said contexts. Plus, a combination of Bayesian models and computational Machine Learning models allows us to predict changes in the user’s context, such as if the user is going to go to a different location soon.
- The AI engine has multiple recommendation systems adapted to the various needs of the user and the information domains. Similarly, it has general purpose recommendation systems based on hybrid models (collaborative and content-based) and on Machine Learning (Action Prediction Model).
The algorithms developed by Sherpa.ai are capable of predicting the future of our users, before they're even aware of it themselves.
JOSE A. LOZANO, Ph.D.
Algorithms & Models Senior Associate Researcher at Sherpa.ai
Ph.D. in Computer Science
Degree in Mathematics & M.Sc.
Associate editor of top journals, such as IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Evolutionary Computation.
Several best paper awards from international conferences, such as the World Conference on Computational Intelligence and the IEEE Congress on Evolutionary Computation.
Our models incorporate and apply the most recent advances in Machine Learning to Natural Language Processing and the Conversational Manager.
Deep Learning Models (Recurrent Neural Networks, Attentional Mechanism, Encoder-Decoders, etc.) and Reinforcement Learning allow us to extract the relevant information from text documents and offer our users content that is more relevant for them:
- Linguistic Analysis
- Word Embeddings
- Opinion Mining & Sentiment Analysis
- Stance Detection
- Fake News Detection
- Identification of Duplicate and Related Documents
- Automatic Summaries
Sherpa.ai incorporates five levels of linguistic analysis to eliminate all possibility of misinterpretation - morphological, syntactical, semantic, pragmatic, and functional. Its sophisticated natural language technology mimics human understanding to dismiss impossible or unlikely matches.
Packed with over 300,000 concepts and 5,000 syntactic and semantic rules, Sherpa.ai's thoroughly tested core system provides the basis for a reliable and comprehensive approach to human-computer interaction.
At the Sherpa.ai research lab, we are working on the next generation of assistants using the latest Machine Learning paradigms, such as Reinforcement Learning and Life-Long Learning.
ENEKO AGIRRE, Ph.D.
Senior Associate Researcher in NLP at Sherpa.ai
Ph.D. in Computer Science
Google Research Awards in 2016 and 2018
Sherpa.ai’s extensive resources of conceptual and linguistic information and its detailed, five-level approach to linguistic analysis makes it a highly accurate and flexible tool for building innovative natural language-based solutions.
DEBORAH DAHL, Ph.D.
Speech and Natural Language Processing Expert
Co-Principal Investigator on the Defense Advanced Research Projects Agency (DARPA) of the U.S. Department of Defense-funded project which integrated Unisys natural language understanding technology with speech recognition.