ICTopen Intelligent Machines. Science meets business, March 22 2016

Machine learning and artificial intelligence become more and more important in business and society. On Tuesday March 22, NWO and SNN organise a one day symposium entitled Intelligent Machines, where we present an overview of recent developments in this fast evolving field. The meeting aims to establish a dialogue and to build connections between academic research, industry and public institutions in the Netherlands.

The program of the day will consist of a number of international invited speakers, and plenty time for networking during poster and exhibition sessions. The aim is to present a very comprehensive overview Dutch academic and industrial research.

Confirmed speakers

Based on our experience with earlier similar events (see www.ml2015.nl), we expect a large part of Dutch academic researchers to present their research at the symposium. In addition, the meeting tends to be well attended by industry. Companies are invited to present themselves with a stand. All academic researchers as well as researchers from industry and business are invited to present their latest application oriented research with a poster.

ICTopen Natural Artificial Intelligence, March 23 2016

The track Intelligent Systems is continued on 23 March with a scientific symposium that is concerned with the integration of two central topics: the scientific understanding of intelligence and the ability to replicate intelligence in engineered systems. Central questions of the symposium include how intelligence is grounded in computation, how these computations are implemented in neural systems, how intelligence can be described via unifying mathematical theories, and how we can build intelligent machines based on these principles. The program for the second day will present the initial results from the NWO program Natural and Artificial Intelligence, which funded 6 research projects in 2014. (see h http://www.nwo.nl/en/research-and-results/programmes/natural+artificial+ntelligence/projects):

11:30 - 11:50
Tim de Bruin, Robert Babuska TUDelft
Deep Learning for Robust Robot Control
Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. The Deep Learning for Robust Robot Control project aims to further develop these methods and apply them to robotic control applications. The first step in this direction has been to apply the Deep Deterministic Policy Gradient method for a robot control problem both in simulation and in a real setup. The importance of the size and composition of the experience replay database is investigated and some requirements on the distribution over the state-action space of the experiences in the database are identified. Of particular interest is the importance of negative experiences that are not close to an optimal policy. It is shown how training with samples that are insufficiently spread over the state-action space can cause the method to fail, and,how maintaining the distribution over the state-action space of the samples in the experience database can greatly benefit learning.

11:50 - 12:10
Taco Cohen, Max Welling AMLAB, Informatics Institute, UvA
Symmetries and Equivariance in Deep Learning
Deep convolutional neural networks (CNNs, convnets) have proven to be very powerful models of sensory data such as images, video, and audio. They exploit translational symmetry in the data through convolutional operations. In this talk I will discuss how to extend convolutions to a larger group of symmetries, such as rotations, reflections etc. These "group-convolutions" result in representations that are "equivariant" to the larger group of symmetries and allow for a much higher degree of weight sharing resulting in improved statistical strength.

12:10 - 12:30
Flash Presentations

14:00 - 14:20
Moinuddin M. Haque, Paul Vogt, Afra Alishahi and Emiel Krahmer Tilburg University
Exploring patterns in children's interactions using neural network methods
Children while interacting, not only learn, but also adapt to their environment and communication partners. These interactions along with the linguistic information are richly augmented with social cues (such as eye gaze and gestures), which help to facilitate better interactions and learning. Based on the principles of cross situational learning (Quine 1960), computational models have learned to predict a word based on semantic features, using associative networks. The language game model of Steels (2003) is an example of an agent-based model in which agents interact with each other, exchange utterances and can learn from each other. Such models have been used to study language evolution. However, such models tend to implement interactions between agents using toy languages and thus do not reflect naturalistic interaction patterns. The CASA MILA (Vogt and Mastin 2013) corpus, which consists of longitudinal recordings of children interacting in naturalistic environments has been developed to overcome this problem (Vogt & Mastin 2013). The current paper presents a study to generate novel interactions based on observations from the corpus.

14:20 - 14:40
Sander Bohte CWI Amsterdam
Networks of Spiking Neurons
With deep neural networks advancing AI in leaps and bounds, the actual computational paradigms of real neurons are being reexamined to possibly create novel types of artificial neural networks. The medium of neural communication in particular, by spikes, may help to advance critical elements of deep learning, such as (energy) efficient neural communication and asynchronous computation. Potential applications range from cell-phones capable of running large deep learning programs to efficient and graceful robot control. At the same time, networks of spiking neurons offer the most direct link to future brain computer interfaces. In this talk I outline some of the current trends and successes, as well as the current challenges faced in these endeavors and the relation to advances in neuroscience.

14:40 - 15:00
Kimberley McGuire, Guido de Croon, Karl Tuyls, Bert Kappen
Exploration with a Swarm of Pocket Drones
A swarm of bees are quite magnificent if you look at them from a robotics point of view. They can detect & avoid obstacles, search for flowers, remember where they are and communicate that information to their neighbors. As a swarm they can explore a field of flowers within a short amount of time. To implement these capabilities into a swarm of pocket drones can be of benefit for many (indoor) surveillance applications. However, the current state of the art methods uses excessive computational power, sensing, memory and communication resources to enable low level control, high level navigation and swarm exploration. A micro aerial vehicle’s size is highly correlated with what it can transport and a pocket drone (which fits in the palm of your hand) cannot carry these resources. To use the state-of-the-art methods, it needs to be connected to an external computer to do the computations. The big challenge is have all the swarm exploration capabilities on-board of a pocket drone, dealing with strict hardware limitations. This project's goal is be to provide an efficient, nature-inspired solution that covers: (1) low-level navigation such as obstacle avoidance or flying through narrow corridors, (2) high level navigation allowing to reach places of interest, and (3) coordination with the other drones to explore the environment swiftly. In this presentation, I will focus most on the challenges involved in the first part, as I already obtained preliminary results allowing a pocket drone to stabilize itself with only on-board sensors. The first results are on detecting velocity and obstacles using computer vision with a low-weight stereo camera. Multiple variables can be detected with just one sensor, therefore makes other, single-purpose, sensors obsolete. The next step is for the pocket drone to efficiently manage its memory during exploration. Finally, multiple pocket drones will need to share minimal information within a certain range of each other, to make exploration decisions based on the actions of others. At the end of this project, a swarm of pocket drones can autonomously explore an indoor, GPS-deprived, building, as natural as a swarm of bees can.

15:00 - 15:20
Dominik Thalmeier SNN
learning computations in spiking neural networks
Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them.

16:05 - 16:30
Award Ceremony & Closure

In addition, researchers are invited to present their work (see ICTopen call for posters. Some posters may be upgraded to oral presentation).

Program Committee

The program of the Intelligent Systems track of ICTopen is organized by Bert Kappen (Radboud Universiteit), Paul Vogt (U van Tilburg), Tom Heskes (Radboud Universiteit), Robert Babuska (TU Delft), Guido de Croon (TU Delft), Sander Bohte (CWI), Pieter Roelfsema (NIN). The Intelligent systems track is part of the NWO annual ICTopen conference (22 and 23 March) which consists of 5 parallel tracks. The Intelligent systems track is coorganized by SNN, a non-profit organization that aims to promote research and applications on machine learning and artificial intelligence in the Netherlands.

Practical information

  • Date and time: March 22, 2016 from 10.00 am until 6.00 pm.
  • Venue: Theater de Flint, Amersfoort
  • Registration (including lunch and reception): 100 Euro before February 16, 2016; 125 Euro later.
  • Exhibition space including admission (including lunch and reception) for two persons: 1500 Euro (excl VAT). Exhibition space is 2.25 m2 stand space (1.5 x 1.5m). Includes poster wall, standup table and power outlet. Subject to availability, please inquire before February 27, 2016 at snn@science.ru.nl
  • Deadline poster abstract submission: February 15, 2016. 1 poster per registration maximum.


Registration and poster submission through www.ictopen.nl