Interactive Machine Learning

With the convergence of Artificial intelligence (AI) and Machine Learning (ML), Interactive Machine Learning (IML) is where the Human-Computer Interaction (HCI) community meets the ML community. We also combine contributions from related fields such as data science, cognitive science, computer graphics, design or the arts, and natural language processing, data mining/data analytics, knowledge representation and reasoning. Our focus is to improve the interaction between humans and machines, by leveraging both state-of-the-at HCI approaches, as well as solutions that involve state-of-the-art ML techniques.

Interactive Machine Learning (IML) is the design and implementation of algorithms and intelligent user interface frameworks that facilitate machine learning with the help of human interaction.

We can “assist” AI systems in becoming self-sustaining, “lifelong” learners. This includes (1) to develop insights into the importance of the social and cultural contexts of machine learning; (2) ML systems that are viewed as goal-directed agents who actively seek information (active learning); (3) the need to pay attention to the incomplete understandings, the false beliefs, and the naive renditions of concepts that machine learners and their models bring with them to a given subject (and in future the humans presented super-human AI reasoning).

Topics

HCI and AI for ML:
– Metalevel verification and validation
– Metacognition and introspection
– Reinforcement learning
– Active learning
– Subgroups

Mixed initiative interaction:
The algorithm and the domain expert engage in a two-way dialogue to facilitate more accurate learning from less data compared to the classical approach of passively observing labeled data.
– Crowdsourcing
– Incremental learning
– Integral / one-shot learning
– Online learning

IML tools for enhancing human cognitive capabilities:
– Empower humans to make important decisions in a more informed way
– Help understand and foresee long-term implications of ML

Transparency in machine learning, causation:
– Mitigate bias in machine learning
– Evaluate machine learning models
– Render ML models more interpretable

Human interpretability in machine learning:
– Interpretation of black-box models (including deep neural networks)
– Causality of predictive models
– Visual analytics

Explainable artificial intelligence applications:
– Medical decision making
– Reliance on learned models in deployed applications
– Legal requirements in Germany and the European Union’s GDPR.

Projects

Ongoing Research Projects

Hybrid Human-Machine Translation Services

Crowdsourcing is recently used to automate complex tasks when computational systems alone fail. In this project, we investigate how humans can effectively contribute to automate natural language translation. The envisioned goal is a hybrid machine translation Read more…

Facetted Search

As medical records may cover a very long history of diseases (up to 30 years) and include a vast number of diagnoses, symptoms, results, medications, and laboratory values, we could highly benefit from advanced search Read more…

Handwriting Spellchecker

In comparison to typing, handwriting stimulates different parts of the human brain and brings into play a very different cognitive process. Many of today’s tasks benefit from digitalization and digitization of handwriting input and with Read more…

Multi-Sketch Recognition

Many of todays processes benefits from digitization and digitalization of written content when it comes to data aquisition for machine learning tasks. Through the use of state of the art handwriting and gesture recognition we Read more…

Kognit

In Kognit (2014–2015), we enter the mixed reality realm for helping dementia patients. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Memory loss is an Read more…

Interactive Machine Learning Group

German Research Center for Artificial Intelligence (DFKI)

Address

German Research Center for Artificial Intelligence
DFKI GmbH 
Stuhlsatzenhausweg 3
Saarland Informatics Campus, Geb. D 3_2
D-66123 Saarbrücken

Contact Us