Enablers for AI applications
Artificial intelligence enablers from Swisscom help you to develop AI applications easily and quickly. Thanks to standardised interfaces, our AI enablers can easily be integrated into new or existing systems. All enablers are specifically trained for their respective context.
AI Research demos
Go through the different pages to find work done by our data scientists and EPFL students working together with us.
You’ll find amazing dialogue systems and well as text, speech and image processing techniques that come alive. If you want to read more about each demo, inside the pages you’ll find links to the articles or theses that accompany them.
Where your digital transformation begins
Our lab is a collaborative space that brings together technologies, competences, methods and EPFL's research capabilities to fuel our customers' digital transformation. We help our customers to transform an idea into a solution in weeks with scalability and flexibility by design.
Our Teams
We focus primarily on natural language understanding, in both textual and spoken forms...
Contouring the IoT domain, basing itself on the connectivity and on the services provided by Swisscom.
Recent Publications
February 2019
Evaluating the Search Phase of Neural Architecture Search
Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. Evaluating NAS algorithms is currently solely done by comparing their results on the downstream task.
January 2018
GitGraph - Architecture Search Space Creation through Frequent Computational Subgraph Mining
The dramatic success of deep neural networks across multiple application areas often relies on experts painstakingly designing a network architecture specific to each task. To simplify this process and make it more accessible, an emerging research effort seeks to automate the design.
February 2018
Submodularity-Inspired Data Selection for Goal-Oriented Chatbot Training Based on Sentence Embeddings
Spoken language understanding (SLU) systems, such as goal-oriented chatbots or personal assistants, rely on an initial natural language understanding (NLU) module to determine the intent and to extract the relevant information from the user queries they take as input.