On 29-30 June in Berlin, we held the RE•WORK Machine Intelligence Summit, bringing together industry leaders, influential technologists, academic researchers, data scientists and founders for two days of discussions sharing best practices to advance machine learning and AI impact and opportunities in business and society.Topics included technologies such as speech recognition, computer vision, predictive intelligence and pattern recognition, and explored applications in transport, manufacturing, robotics, healthcare, human-machine communication, retail and more through presentations, panels, open mic Q&As and networking sessions.Attending companies included Symantec, Accenture, Volkswagen, Siemens, Google, ThoughtWorks, Huawei, Intel, Samsung, SAP, Twenty Billion Neurons, Wells Fargo and more.


Increasing Quality, Value and Access to Medical Imaging
Kilian Koepsell, Co-Founder & CTO at Bay Labs

How can we bring the life-saving benefits of medical imaging to more people? At Bay Labs they are pursuing this mission by combining deep learning and ultrasound. Ultrasound, combined with deep learning, has the potential to transform medicine at the point-of-care. In this talk, Kilian presents work that shows this potential future may now be within reach, and concludes with one of Bay Labs’ efforts to bring medical imaging, powered by our deep learning technology, to those most in need.

Natural Language Interfaces Using Case-Based Reasoning
Tina Klüwer, CTO at parlamind

Customer support teams are swamped with work while customers expect fast responses and team leaders expect the team to report insights quickly. parlamind’s Artificial Intelligence enables effective listening and faster reactions to customers on all written channels. Our technology analyses customer care communication, understands customer’s intentions, monitors and visualises contacting reasons, and sends out responses autonomously. The technology combines knowledge about language and human communication with unsupervised and supervised machine learning algorithms. In its core six similarity metrics based, e.g., on syntax and word embeddings, are used for case-based reasoning and clustering. The AI continuously learns automatically from unseen data as well as from interactions with humans and directly integrates the feedback into processing.

Infinite Compute Power for GPU Accelerated Deep Learning
Axel Koehler, Principal Solution Architect at NVIDIA

NVIDIA has been a pioneer in accelerating deep learning and has been developing deep learning software, libraries and tools for a number of years. Today's deep learning solutions rely almost exclusively on NVIDIA GPU-accelerated computing to train and speed up challenging applications such as image, handwriting, and voice identification.

This presentation provides an overview about the latest hardware and software developments for deep learning at NVIDIA, focusing in particular on NVIDIA® DGX-1™, the world’s first purpose-built system for deep learning. The software stack includes major deep learning frameworks, the NVIDIA Deep Learning SDK, the DIGITS™ GPU training system, drivers, and CUDA® for rapidly designing deep neural networks (DNN). This powerful system also provides access to cloud management services for container creation and deployment, system updates, and an application repository. View more videos from the Machine Intelligence Summit here.

The next Machine Intelligence Summit takes place in New York on 2-3 November. For more information and to register, visit the event website here.

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