"A machine can be said to be intelligent if its answers are indistinguishable from those of a human being" Alan Turing

Artificial Intelligence, Profiling & Predictive Algorithms

Based on robust Machine Learning techniques, Sherpa’s Artificial Intelligence Algorithms predict and anticipate users’ needs before they are even expressed.

Areas of Expertise

Prediction and Recommendation Systems:

  • 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

Advanced Processing and Text Analysis:

  • Advanced Statistical and Deep Learning Techniques
  • Text Summarization
  • Duplicated and Related News Identification
  • Action Required Email Classification

Research Activities:

  • Fake News Detection
  • Federated Learning
  • Life-Long Learning
  • Among others...

Predictive Algorithms

Through advanced predictive Machine Learning algorithms based on sophisticated probabilistic and computational models, Sherpa is capable of learning the preferences and habits of the users in order to proactively provide them with personalized information that is relevant in each context. The knowledge extracted from different domains allows for the automatic identification and exploitation of latent relationships between the distinct elements present and the users, integrating multiple modalities of information.

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 (content and collaborative-based) and on Machine Learning (Action Prediction Model).
Sherpa Predictive Algorithms and intelligent technology formula Sherpa Predictive Algorithms and intelligent technology formula Sherpa Predictive Algorithms and intelligent technology formula

Sherpa 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 Sherpa

Francisco Herrera, Ph.D.

Senior Associate Researcher in DL & ML of Sherpa.ai.

Ph.D. Mathematics.

Highly Cited Researchers (Thomson Reuters) in the areas of Engineering and Computer Sciences.

Spanish National Award on Computer Science.

More than 331 Journal papers published which account for 70,435 citations in Google Scholar.

The algorithms developed by Sherpa are capable of predicting the future of our users, before they're even aware of it themselves.

Jose Antonio Lozano Sherpa

JOSE A. LOZANO, Ph.D.

Algorithms & Models Senior Associate Researcher of Sherpa.ai

Ph.D. in Computer Science.

Degree in Mathematics & M.Sc.

100 ISI journal papers published which account for than 11,079 citations in Google Scholar.

Associate editor of top journals, such as IEEE Transactions on Neural Networks and Learning Systems and IEEE Trans. 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.

Artificial Intelligence, Profiling & Predictive Algorithms

User profiling

Its personalized predictive capabilities set it apart from market competitors. Sherpa analyzes over 100,000 parameters per user per day in order to formulate and continuously update individual profiles:

  • Demographics (Business traveler, electronics buyer, golfer, etc.)
  • Personal Interests (Fashion, technology, etc.)
  • Commuting Habits
  • Social Media Habits
  • Personal Relationships (Family, friends, coworkers, etc.)
  • Favorite Brands
  • Restaurant Preferences
  • Sports Preferences
  • Concepts and Latent Topics
  • Sentiment Characteristics
  • And many more...
Sherpa interface Top brands user profiling Sherpa

NLP and Conversational Manager

The most advanced Machine Learning techniques

Our models incorporate and apply the most recent advances in Machine Learning to Natural Language Processing and the Dialogue Manager.

Deep Learning Models (Recurrent Neural Networks, Attentional Mechanism, Encoder-Decoders, etc.) and Reinforcement Learning allow us to extract the relevant information from the text documents and offer our users content that is more relevant for them:

  • Linguistic Analysis
  • Word Embeddings
  • Opinion Mining & Sentiment Analysis
  • Stance Detection
  • Hyperpartisanism
  • Fake News Detection
  • Identification of Duplicate and Related Documents
  • Automatic Summaries

Unparalleled Linguistic Intelligence

Sherpa 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.

Layers Linguistic intelligence Sherpa
  • MORPHOLOGICAL
  • SYNTACTICAL
  • SEMANTIC
  • PRAGMATIC
  • FUNCTIONAL

Built-in Natural Language Capabilities

Packed with over 300,000 concepts and 5,000 syntactic and semantic rules, Sherpa's thoroughly tested core system provides the basis for a reliable and comprehensive approach to human-computer interaction.

At 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 Sherpa

ENEKO AGIRRE, Ph.D.

Senior Associate Researcher in NLP of Sherpa.ai

Ph.D. Computer Science.

Google Research Awards in 2016 and 2018.

Over 150 international peer-reviewed articles in Natural Language Processing which account for 9,572 citations in Google Scholar.

Sherpa’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 Sherpa

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.