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Building bigger brains: Utilizing neuromorphic engineering as the path towards brain-scale intelligence and Be agile on data and query the world!

AI and Machine Learning

The best artificial neural network (ANN) models today are capable of extraordinary tasks.  For example, in just the last year natural language processing has progressed massively with the onset of transformer-based models such as BERT from Google and GPT-2 from Open AI.  The problem with these models is their cost - one model can take as much energy for training as multiple cars consume in their lifetime.  At Rain, we are fundamentally reimagining the processor that underlies the mathematics of all ANNs to enable a step change in both scale and efficiency.  We utilize innovations in materials, architectures, and algorithms to build the most brain-like processors ever.

Speaker: Gordon Wilson, Rain Neuromorphics

Cognitive Architecture in Models for Natural and Artificial Intelligence

The age of artificial intelligence and data science requires scalable access to data. Studies show that even today, data science teams spend up to 80% of their time in the discovery and integration process. Traditional integration approaches like ETL, where often a single engineer writes a separate script for each data source does not scale with thousands of data sources. Fusionbase is a novel approach to access data in heterogeneous data landscapes. The system implements a performant query federation layer using semantically enriched dataspaces. This technology allows virtualizing data efficiently and gives ad-hoc analytical access to data scientists on a large variety of data sources.

Speaker: Patrick Holl, Furionbase.io

Advance registration requested

Tuesday, 10/08/19


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Santa Clara Valley IEEE Computer Society

Cadence, Bldg 10
2655 Seely Ave
San Jose, CA 95134

Website: Click to Visit