Interactive Machine Learning

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

With the convergence of Artificial intelligence and Machine Learning, IML is where the  Human-Computer Interaction (HCI) and IUI 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, and knowledge representation and reasoning. Our focus is to improve the interaction between humans and machines to update ML models, by leveraging both state-of-the-art HCI and IUI approaches, as well as solutions that involve state-of-the-art ML techniques.

We can “assist” AI systems in becoming self-sustaining, “lifelong” learners. This includes  to (1) develop insights into the importance of the social and cultural contexts of machine learning; (2) create ML systems that actively seek information; (3) realise the need to pay attention to the incomplete context understandings and naive generalisations that ML systems, in particular end-to-end systems, bring with them to a given subject. We focus on multimodal multisensor interfaces for ML.

Topics

Mixed initiative interaction:
The ML system 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
  • Multimodal Multisensor Interfaces
  • Online learning
  • Speech-based ML
  • Textual entailment for ML
  • Argumentative active learning (from textual explanation feedback)

IML tools for enhancing human cognitive capabilities:

  • Help understand and foresee long-term implications of ML
  • Empower humans to make important decisions in a more informed way

Transparency and human interpretability in ML:

  • Mitigate bias in ML
  • Evaluate machine learning models
  • Render ML models more interpretable
  • Interpretation of black-box models (including deep neural networks)
  • Enhance trust in ML
  • Causality of predictive models
  • Visual analytics

Explainable artificial intelligence applications:

  • Medical decision making
  • Reliance on learned models in deployed applications
  • Legal requirements

General AI for ML:

  • Metalevel verification and validation
  • Metacognition and introspection
  • Super-human AI Reasoning
  • Reinforcement learning
  • Active Learning
  • Subgroups

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

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