Welcome to AI Monday in Berlin
Since September 2018 #aimonday also takes place in Berlin. This is only natural as a Start-up capital and with 54% of all German AI companies the fourth largest global AI hub. Since then AI Monday happens every 5-6 weeks at changing locations.
We are looking forward to welcoming you to one of the next AI Monday Berlin – as guest or speaker.
Four crisp presentations incl. Q&A. Some speaker also demo their AI solutions. Followed by snacks, drinks and networking.
AI curious people, change leaders, businesses with passion for data and disruption.
Share AI-knowledge, exchange and encourage each other on our change journeys.
Always Monday after work.
Sep 2nd, 2019
doors open at 6:30pm, talks start at 7pm. Networking starts at around 8:30pm.
No Sales Pitches. No Math lectures or deep tech dives. No shallow consulting or marketing talks.
Co-Founder & CIO AIPARK
How to use computer vision on the edge to build live curb sidemaps”
Exact information about the curb side is essential for all new mobility providers like ride hailing companies or delivery fleets. AIPARK’s mobile vision platform, a system of distributed edge computing devices deployed in fleet vehicles, is capable of collecting a variety of datapoints around this and many other use cases in real time.
Robotics Engineer – InSystems Automation GmbH
Decentralized collaborative fleet of Automated Guided Vehicles (AGV)
In a rapidly changing industry, creating innovative solution is no longer an option but should be considered as an opportunity to cope with challenges such as the increasing demand on quick/fast production reconfiguration in complex setups and the high demands for “Batch Size 1” production. Intralogistics industry is already revolutionized by the application of transport robots in the production lines and warehousing. However, InSystems Automation GmbH as a specialized automatic solution developer aimed to move beyond current level and started the development of “Collaborative Systems”, which provides collaboration between production units and Automated Guided Vehicles (AGV). To realize such an adaptive setup, the transition from centralized to decentralized control unit is a great challenge. This architecture will not only remove the possible single point of failure but also increases the flexibility, independency and intelligence of the fleet of transport robots. This talk describes how InSystems is designing collaborative system by adding an IoT dimension to their AGVs to transform brownfield environment into industry 4.0.
Machine Learning Researcher – Alphamoon
How to build an acoustic tracking system with embedded AI
In the last few years its suddenly became possible to take noisy signals like images, audio or accelerometers and extract meaning from them, by using neural networks. Because we can run these networks on microcontrollers, and sensors themselves using little power, it becomes possible to interpret much more of the sensor data we’re currently ignoring. In this talk, Kira will talk about a particular case study at Alphamoon that used deep learning on a microcontroller to track moving UAVs based on acoustic sensor data.
CEO – OMQ GmbH
AI based customer service in shared mobility
Mobility is changing rapidly, and the range of shared mobility services is continuously increasing. In the same way that customers expect to be able to borrow a vehicle at every corner, they expect customer service to be just as accessible. While apps are user-friendly and straightforward, getting a quick answer is often difficult. As a customer, you have to search through countless FAQs or wait very long for an e-mail response. The customers are on the go with their smartphones and need a solution immediately. OMQ has developed an artificial intelligence that automatically answers recurring questions in real-time, whether on the website, via email or chat. The challenge is to understand customers’ intention and to give a suitable answer despite the different ways of expression. In this talk, we will use the case study from Call a Bike to show how customer requests in shared mobility can be significantly reduced with AI.