These patterns were used to compare with optimal patterns to understand deviations in user's flow allowing in identifying features that were used, unused and undiscovered. These patterns were mapped to user-roles to understand their affinity towards a specific feature. Based on these discoveries, in-product notifications were delivered to drive feature discovery and usage
These metrics also helped the business to predict subscription continuity of a specific user and use these insights to predict subscription revenues.
- Travel Restrictions: Status of Air-Bridges and share of routes, which are currently operational and comparison with pre-crisis levels
- Aviation Status: Track the current status of the aviation sector (groundings of aircraft, numbers of passengers travelling, etc)
- Avaition Impact: Analyse the impact of current levels of aviation sector activity on the global economy, and the current climate impact of the aviation sector
As the data-sets came from a mature CDP, customer interaction data and transaction data was structured. Over this data-set, we built an intent detection algorithm, which would calculate the intent rank based on user's frequency, recency, time spent and their depth of interaction.
This customer-product rank was made available to the collaborative-filtering algorithm along with past transaction data.
This allowed the recommendation engine to fine tune its recommendations based on past purchases and feedback from the customer, plus customers current intent rank along with cohort's purchase behavior
Based on semantics, data is organized by objects and sequenced by time, which gets converted to symbolic sequences, acting as Episodic Memories. Robots can use these memories to make real-time decisions based on past and current data.
This gives us the opportunity to generate knowledge representations dynamically, which will allow machines to abstract, reason and plan its next move.
Our approach of the adaptive model was to extract the text corpus for unique entities and group them based on POS (Parts of Speech) to create the first level of compressed characters or symbols.
Using semantic labels and dependency parsing logic, the compressed sequence is grouped with associated symbols to create a merged entity with a new symbolic representation of the complete sentence.
We have experience designing, implementing and automating learning and decision processes. Here is a list of capabilities where we can be handy.
- Heuristic/Implicit Models
- Explicit Models
- Knowledge Graphs
- Behavioral Analysis
- Probabilistic Attribution
- Collaborative Filtering
- Time-series Prediction
- Named Entity Extraction
- Semantic Role Labeling
- Inference Engine
- Markov Decision Processes
- Finite State Automation
- Rule based Triggers
- Data Dashboards
- Data Simulation
- N-dimensional Reports
Digging deep into Symbolic AI techniques
RM's proprietary techniques allow in providing decision automation solutions with great control and transparency. Our strengths lie in designing
- Declarative Memories and Inference Engine
- Object Oriented Data Structure for Behavioral Study
- Integrating NLP Techniques (Named Entities) to Declarative Memories
Started in 2016, Responsible Machines(RM) started out with a vision to enable robots or machines to learn, speak, make decisions and exhibit general intelligence.
RM's Symbolic AI techniques which solves the symbol-grounding problem allows in exhibiting machine reasoning and abstraction, which acts as a foundation to generalized learning concepts, a more promising approach to human like intelligence
percent confident in building zero-redundancy data models which help in reducing processing required for AI workloads, thereby reducing carbon footprint.
Proprietary Algorithms in the area of converting data to Symbolic Sequences, Unique Entity Extraction and Machine Reasoning
Years of Experience combined, in designing, implementing and delivering Analytical and AI products and projects
percent savings guaranteed on any AI implementation by way of optimized processing, reduced storage and a smaller data engineering team
With over 20 years of senior leadership and deep consulting experience at major consulting firms, Manoj leads the vision at Responsible Machines. Having worked with HP, Mphasis, EPAM in the past, he has a reputation for implementing strategic competitive transformation programs.
Veer has more than 15 years of experience in building analytical and AI products. His previous venture "Plumb5" solved unstructured customer data challenges and was used by large enterprises like Walmart, Unilever, Aegon, Commonwealth Bank in solving their single customer view problem.
Margaret handles operations at Responsible Machines. She brings a rich experience of working with deep science projects. She devised the GEO-DMF System for Outer Space research and worked as an Assistant Director for the former Institute for Applied Archaeological Sciences at Barry University.
Frank Boehm handles market development and strategic partnerships for Responsible Machines. He is a Research Associate at the University of Guelph and brings his rich expertise of developing advanced nanomedical diagnostic and therapeutic technologies.
ResponsibleMachines(RM) is a deep-tech AI company and was started to make AI more transparent and perform general intelligence tasks using machine reasoning and abstraction.
511, Ave of Americas, #4047
New York, NY 10011
(Manoj) +1 (917) 740-2269