The 40th International Conference on Machine Learning (ICML) took place last week in Honolulu, Hawaiʻi. As well as four invited talks, the programme boasted oral and poster presentations, affinity events, tutorials and workshops. Find out what the participants got up to over the course of the conference. Can't wait for our first invited speaker talks by the inimitable @MarzyehGhassemi and @shakir_za on Tuesday! pic.twitter.com/tNDi7RNIUt Amazing group from #LatinXinAI hiking the Makapu'u Point Lighthouse Trail to kick off our social events at @icmlconf #ICML2023 @_LXAI pic.twitter.com/cO6dKAz6x8

I will discuss a line of work in which we compile common machine learning systems into symbolic representations that have the same input-output behavior to facilitate formal reasoning about these systems. We have targeted Bayesian network classifiers, random forests and some types of neural networks, compiling each into tractable Boolean circuits, including Ordered Binary Decision Diagrams (OBDDs). Once the machine learning system is compiled into a tractable Boolean circuit, reasoning can commence using classical AI and computer science techniques. This includes generating explanations for decisions, quantifying robustness and verifying properties such as monotonicity. I will particularly discuss a new theory for unveiling the reasons behind the decisions made by classifiers, which can detect classifier bias sometimes from the reasons behind unbiased decisions.

Date: Tuesday 15th October Time: 18:30-21:00 Location: RAI Exhibition Center *** Registration is free but you must do so in advance through Meetup. The event will be in English and is open to all.

The system recognizes facial that the tools we build have an impact on people's expressions in others using the outward camera and delivers psychological wellbeing and mental health.

The invited talks presented at the Fourteenth International Conference on Principles of Knowledge Representation and Reasoning included Ontology-Based Monitoring of Dynamic Systems by Franz Baader, Knowledge Representation Meets Computer Vision: From Pixels to Symbolic Activity Descriptions by Tony Cohn, Datalog+/–: Questions and Answers by Georg Gottlob, and Situation Calculus: The Last 15 Years by Sheila McIlraith.

Abstracts of the invited talks presented at the AAAI Fall Symposium on Discovery Informatics: AI Takes a Science-Centered View on Big Data. Talks include  A Data Lifecycle Approach to Discovery Informatics,  Generating Biomedical Hypotheses Using Semantic Web Technologies,  Socially Intelligent Science, Representing and Reasoning with Experimental and Quasi-Experimental Designs, Bioinformatics Computation of Metabolic Models from Sequenced Genomes, Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science,  Predictive Modeling of Patient State and Therapy Optimization, Case Studies in Data-Driven Systems: Building Carbon Maps to Finding Neutrinos,  Computational Analysis of Complex Human Disorders, and Look at This Gem: Automated Data Prioritization for Scientific Discovery of Exoplanets, Mineral Deposits, and More.

We analyze several of the key technical and practical challenges involved in representing activity context across a large variety of knowledge, components, and applications. We present two novel broad methods that enable semantic knowledge capture and interchange, and suggest how they can be used for activity context-awareness. The first is knowledge representation and reasoning (KRR) in Rulelog, an expressively extended form of declarative logic programs that features defeasible higher-order logic formulas yet is computationally tractable, and is a draft dialect of W3C RIF. Rulelog's expressiveness enables representation of exceptions and change, and thus processes, agreements, and policies, e.g., for confidentiality. The second broad method is Textual Logic, an approach to mapping between natural language (text) and logic, where the mapping itself is logic-based. Textual Logic leverages Rulelog's expressiveness to enable relatively rapid text-based authoring of rich knowledge, reducing the knowledge acquisition bottleneck. Together, Rulelog and Textual Logic help address the potential for ontological and KRR Babel that lurks when representing activity context using previous semantic technologies.

Most approaches to semantics in computational linguistics represent meaning in terms of words or abstract symbols. Grounded-language research bases the meaning of natural language on perception and/or action in the (real or virtual) world. Machine learning has become the most effective approach to constructing natural-language systems; however, current methods require a great deal of laboriously annotated training data. Ideally, a computer would be able to acquire language like a child, by being exposed to language in the context of a relevant but ambiguous environment, thereby grounding its learning in perception and action. We will review recent research in grounded language learning and discuss future directions.

His informatics group built the reusable software platform for Stembook Despite the fact that we now have access to almost all peer reviewed (www.stembook.org), William Cohen exchanged and is orthogonal to any specific biomedical domain The growing size of the scientific literature has led to a number of ontology. We believe this approach will be extremely useful in attempts to automatically extract entities and relationships from drug discovery to break down information silos, increase information scientific papers, and then to populate databases with this extracted awareness and sharing, and integrate terminologies and information. In my group we have been exploring techniques data with documents and text, both public and private. We will for using this sort of extracted information for specific tasks, discuss applications we are currently developing in collaboration including "bootstrapping" to improve the coverage of an extraction with a major pharma.

Bill Swartout Introduced by Alan Kay at XEROX PARC in the 1970's, the desktop metaphor, which was later adopted in the Macintosh and Windows operating systems, has become the primary way we think about interacting with computers. Over the last decade, we have been developing sophisticated virtual humans at the USC Institute for Creative Technologies.