As part of this partnership, called SBC2go, the partners will provide financial resources, mentors and marketing support to startups selected to participate in the programme.

Dr. Frank Spennemann from Daimler lab and Mercedes-Benz says partnering with Startupbootcamp Berlin will “accelerate our access to innovation and will plug us into an impressive community of alumni, mentors and investors. At the same time we support start-ups in developing business ideas and increase their market value. Due to our global presence we can open doors to new markets.”

So, what is Mercedes-Benz, Bosh and HDI looking for?

E.g. Daimler has a number of initiatives already such as the Car2Go car-sharing service which has over 7,000 vehicles in 18 cities on the road in Europe and North America

They need basic, advanced and realtime data anlytics. How basic traffic data analytics can look like, can already be seen at uber.com

Here are San Francisco’s location networks, showing the probability that a ride starts in one neighborhood and ends in another.

Having this kind of analytics in realtime, car sharing service car2go could offer e.g. dynamic pricing. This would mean an competitve advantge to DriveNow (BMW), flinkster (Deutsche Bahn) and ZebraMobil.

It is great to see that Mercedes-Benz, Bosch and HDI are supporting the Berlin start-up ecosystem. The output of this partnership will be definitely interesting.

Links:

http://www.startupbootcamp.org/blog/the-big-league-startupbootcamp-berlin-partners-up-with-mercedes-benz-hdi-and-bosch-for-sbc2go.html

http://techcrunch.com/2013/04/16/mercedes-benz-bosch-and-hdi-create-new-accelerator-with-startupbootcamp-berlin/

http://blog.uber.com/2012/01/09/uberdata-san-franciscomics/

How can it be that during the DARPA Grand Challenge an autonomous car drove 7 miles in 7 hours and 6 years later Google autonomous Vehicle drove 100 000 miles?

Peter Norvig, Chief Scientist, of Google has an explanation: “We don’t have better algorithms. We just have more data.”
The data was the Google street-view vehicle. The data came from humans who drove with the Google street-view cars the roads, equipped with detailed sensors which measured, photographed and collected all the data. The data was stored in the cloud and made available to the Google autonomous Vehicle. This is an example for rethinking human-machine symbiosis.

All this data makes the Google autonomous Vehicle project just possible. It is a fairly hard AI problem to pic a traffic light out of a video stream. It is a trivial AI problem to figure out if it is red or green if you already know it is there.

Read more: e-corner Stanford University’s Entrepreneurship Corner
Stanford Technology Ventures Program (March 6, 2013)

What Ben Horowitz is looking at investments:

1. The size of the opportunity – you need to get 30% to 40% of the market to meaningfull and to make money in technology.
2. The quality of the team – is the team good enough to build a great product that is ten-times better and take the market.
3. A bad market always beat a good team.

What Ben Horowitz likes to have in his investments:

• Megalomaniacs
• Outliers
• Shifts in technology or markets
• Market sectors that are dead – big market, a winner, bad product; this is a opportunity
• Entrepreneurs that tilt towards big markets and not a niche
• Hard core technical team
• Products then teams rather than vice versa

What Ben Horowitz believes to be the biggest challenges for entrepreneurs:

• Being both entrepreneur and inventor
• Finding product/market fit
• Managing their own psychology

Learn how business works directly from groundbreaking entrepreneurs and business leaders. This episode features Ben Horowitz, co-founder of Andreessen Horowitz, a $300 million venture fund aimed at investing in new entrepreneurs, products, and companies in the technology industry. Presented by UC Berkeleys College of Engineering’s Center for Entrepreneurship. Series: Distinguished Innovator Lectures [4/2010] [Business] [Show ID: 17365]

The main approach to solve Big Data challenges is to take out the complexity of the data sets.

Complexity itself is anti-methodology. It is against “one size fits all.”
- Tom Petzinger, Interaction of Complexity and Management

This means it makes more sense to use multiple simple models instead of one complicated model. Having a toolkit of methods and frameworks, which each fail in their own way, is a smarter approach than relying on one method or framework to deal with all situations.

Read more on:

Jurgen Appelo’s blog noop.nl

Nicolas Spiegelberg – Multi-tenant HBase Solutions at Facebook from newthinking on Vimeo.

Facebook first started looking for a distributed OLTP database solution in 2010. We ultimately chose HBase as the best solution for a variety of our workloads. Since then, we have rolled out multiple large production systems using HBase. For example, our current Messages infrastructure runs on HBase and handles over 180 billion person-to-person messages per month. This talk will discuss multiple Facebook projects that are running on HBase now, our selection criteria in choosing HBase as a good fit, and the functionality we added to open source to optimize a growing variety of use cases.

More info: berlinbuzzwords.de/sessions/multi-tenant-hbase-solutions-facebook

As head of JESS3′s strategy and operations for the last five years, COO & co-founder Leslie Bradshaw shares her insights and observations around how data, content and workforce are impacting and leveraging one another.

Leslie posits: Whether you are an agency, brand, educator or public sector organization, these trends will all play a part of how you organize, think and produce.

Originally presented for RefreshDC’s November meetup on 11/16/11.