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@@ -35,16 +35,11 @@ HAICU Central will be implemented at HMGU, Munich. Other HAICU Local units will
 * When: Tuesday, 27 August 2019, 1:00pm
 * Where: Jülich Supercomputing Centre, Rotunda, building 16.4, room 301
  
-The potential of machine learning and deep learning to advance artificial intelligence is driving a quest to build dedicated systems that accelerate such workloads at a large scale and in an autonomous fashion. A natural approach is to take inspiration from the brain by building neuromorphic hardware that emulates the biological processes of the brain using digital or mixed-signal technologies.
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-In this talk, I will present interdisciplinary approaches anchored in machine learning theory and computational neurosciences that enable the applicability of neuromorphic technologies to real-world, human-centric tasks. In particular I’ll discuss the following related challenges and their possible solutions:
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-(1) The models and tools of deep learning apply to neuromorphic hardware, but physical implementations of neural networks call for novel, continual and local learning algorithms;
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-(2) Neuromorphic technologies have potential advantages over conventional computers on tasks where real-time adaptability, autonomy or energy efficiency are necessary, but applications and benchmarks benefiting from these qualities are not yet identified;
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-(3) Challenges in memory technologies, compounded by a tradition of bottom-up approaches in the field and the lack of large-scale simulation environments block the road to major breakthroughs.
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+The potential of machine learning and deep learning to advance artificial intelligence is driving a quest to build dedicated systems that accelerate such workloads at a large scale and in an autonomous fashion. A natural approach is to take inspiration from the brain by building neuromorphic hardware that emulates the biological processes of the brain using digital or mixed-signal technologies.<br> 
+In this talk, I will present interdisciplinary approaches anchored in machine learning theory and computational neurosciences that enable the applicability of neuromorphic technologies to real-world, human-centric tasks. In particular I’ll discuss the following related challenges and their possible solutions:<br> 
+(1) The models and tools of deep learning apply to neuromorphic hardware, but physical implementations of neural networks call for novel, continual and local learning algorithms;<br> 
+(2) Neuromorphic technologies have potential advantages over conventional computers on tasks where real-time adaptability, autonomy or energy efficiency are necessary, but applications and benchmarks benefiting from these qualities are not yet identified;<br> 
+(3) Challenges in memory technologies, compounded by a tradition of bottom-up approaches in the field and the lack of large-scale simulation environments block the road to major breakthroughs.<br> 
 The recent algorithmic results I will present solve some of these challenges and pave the way toward the co-design of brain-inspired computing systems and algorithms with a mathematical viewpoint. These solutions enable the roadmap towards building a software framework for neuromorphic hardware with a Tensorflow-like workflow and leveraging the scalable, distributed, low-latency and energy- efficient nature of neuromorphic hardware.
 
 ### Talk by [Frank Rudzicz](http://www.cs.toronto.edu/~frank/): "Explainable AI — Interpretability, brains, policies and politics"
@@ -107,4 +102,4 @@ news and links to ML&DL related calls
 
 
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-last change: 12.7.2019 sw
+last change: 15.8.2019 sw