Keeping up with the speed of technical innovation is a huge challenge facing everyone working in the engineering & data field.
But with an influx of new hype cycles, how can we cut through the noise and accurately assess if the latest hype is a passing fad or the next big thing?
And, just as crucial: how can we gauge its long-term value and relevance of topics such as machine learning & data to determine the impact they will have on our work & future lives?
...comes with deep background in engineering & technology.
Meet the brightest leaders from the world of data and machine learning
Since age 15, the main goal of Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. He is often called the father of modern AI. His lab's deep learning neural networks such as LSTM have revolutionized machine learning, are now on 3 billion smartphones, and used billions of times per day, for Facebook's automatic translation (2017), Google's speech recognition (since 2015), Apple's Siri & QuickType, Amazon's Alexa, etc. He also pioneered unsupervised adversarial networks, artificial curiosity and meta-learning machines that learn to learn. He is recipient of numerous awards, and chief scientist of the company NNAISENSE, which aims at building the first practical general purpose AI. He is also advising various governments on AI strategies.
Gene Kogan is an artist and a programmer who is interested in generative systems, computer science, and software for creativity and self-expression. He is a collaborator within numerous open-source software projects, and gives workshops and lectures on topics at the intersection of code and art. Gene initiated ml4a, a free book about machine learning for artists, activists, and citizen scientists, and regularly publishes video lectures, writings, and tutorials to facilitate a greater public understanding of the subject.
Thomas Wiecki is the lead data science researcher at Quantopian, where he uses probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. Among other open source projects, he is involved in the development of PyMC—a probabilistic programming framework written in Python. A recognized international speaker, Thomas has given talks at various conferences and meetups across the US, Europe, and Asia. He holds a PhD from Brown University.
Johannes graduated in Computer Science in both Shanghai and Berlin and is an expert in software development, eCommerce and Machine Learning. In 2007, he joined Rocket Internet as a founder in residence and software engineer to contribute to online services such as Zalando, eDarling and Toptarif. After that, Johannes co-founded Visual Meta, an online company that runs shopping platforms such as LadenZeile.de and ShopAlike in 13 countries and which was sold to Axel Springer in 2011. Johannes joined home24 as Chief Technology Officer in April 2018.
Jodok loves Machine Data, BigData, Sensor Data, high traffic environments, Industrial IoT, Data Visualization,
SQL, horizontal scaling, shared nothing architecture, Kubernetes, Docker, Python, Elasticsearch, NoSQL, Linux,
Python, SCRUM, agile leadership, agile software
development, self-organizing teams.
Jodok has a widespread expertise on open-source and Big Data. He began his work with famous data-sets and recording as an early-adopter from cloud-technology. Before Crate.io he was a developer from an open-source-database which was a perfect fit for data intensive applications. Furthermore, he was CTO from StudiVZ, where he had the responsibility for the whole product, software-development and the system-operation.
Intermediate Jodok was a docent at the “Fachhochschule Dornbirn” and a leader from the Plone Foundation and Zope Foundation – two foundations for open-source-projects.
eCommerce maps the full variety of activities into the digital space. Consumers often reduce eCommerce to the web shop as it is the main sales channel and face to the customer, but it actually requires far more activities in the fields of (online) marketing, supply chain management, warehousing and distribution to run a successful eCommerce business. Within these fields, machine learning helps solve many problems that are quite specific to the company and the vertical they operate in such that the progressing commoditization of machine learning solutions falls short. This talk provides a technical overview over the many challenges that ecommerce companies face and solve by machine learning.
Our deep learning artificial neural networks have won numerous contests in pattern recognition and machine learning. They are now used billions of times per day by the world’s most valuable public companies. I will discuss latest state-of-the-art results in numerous applications, and outline how AIs will transform every aspect of our civilisation, and eventually colonise the universe, and make it intelligent.
While the hype around data science continues unabated, companies are starting to find that it is harder than anticipated to capture the promised gains. While the initial problems around data engineering and data quality have received adequate focus and brought forth various viable commercial solutions, another disconnect happens between the data science mindset and business needs. Specifically, while data scientists often care deeply about which particular machine learning method is used and maximizing predictiveness, it is often not immediately clear what value all this provides to stakeholders, leading to miscommunication and mismatched expectations. In this talk I will argue that the accuracy of a machine learning algorithm should not be the final result but be carried through into the decision making process to highlight to instead the business value added. I will demonstrate this with a case study from quantitative finance related to capital allocation, using the probabilistic programming package PyMC3 to build a statistical model and estimate parameters as well quantify the uncertainty of those estimates. The resulting model can then be used for scenario generation. By optimizing our decision making process over all possible scenarios we can demonstrate direct improvements in terms of business relevant measures, rather than more technical measures of model effectiveness.
This talk explores the use of machine learning for art and creativity. Recent advances in deep learning have made it possible to generate photorealistic images, sounds, and texts from neural networks trained on top of real-world data, inspiring a surge of creative works exploiting the rich audiovisual content captured inside of these generative models. We'll review the field's state-of-the-art, present a selection of art projects and interactive installations from the past year, as well as speculate on future directions as the science and art rapidly converge. Finally, a selection of educational resources will be presented for curious people who'd like to experiment with the technology themselves.
Manufacturing today is characterized by the need to increase efficiency and reduce costs in an increasingly competitive landscape. There are many challenges that need to be overcome to achieve this: shortage in local talent, personnel cost increases, loss of experience caused by turnover, ever growing requirements in flexibility and adaptability. The implication of this is that it is increasingly difficult to drive continuous improvement and achieve higher efficiencies at lower costs. Organizations need to embrace technology and use digitalization to harness data, create information to support the workforce to take action so that they can achieve better results by enabling data driven decision making. Crate developed a technology solution that enables data driven efficiency improvement through the use of input from the production process, combining it with the knowledge and experience of best practice approaches, and applying this in a scalable way to a large number of manufacturing sites. Crate IoT Data Platform has shown meaningful improvement across core KPIs including total productivity and an increase in the stability of the production process. Through our experience of rolling out this solution to multiple plants we have developed a repeatable template to enable factories to get up and running within months.
A format some of you might know from our "mothership" OMR:
We curate 3 companies that have a 10 minute pitch on stage.
A special day deserves a special ending: For the conference day we open up the doors for a joint dinner
& unconference evening with tons of speakers, food and good drinks.
Enjoy the rest of the day and ... network!
The lightning talk evening costs you 10€ only and is free for academia (please get in touch).
Get your ticket now, we've got limited capacity. Be part of the game.
You or your company does innovative things with data?
You are on an advanced level and are not afraid of the other speakers?
Then apply now.
Deadline for application is the 15th of march.
A total time of 10-15 minutes (Lightning) is planned for each talk.
The selection of the talks is made by our very busy curators. Therefore we ask for patience.
Eintragung im Handelsregister.
Registergericht: Amtsgericht Hamburg
§27 a Umsatzsteuergesetz: DE269764470
Magic hands for magic brands