Some previous partnerships include (in alphabetically ordered):

• Audi Autonomous Car in 2020 with NVIDIA
• BMW partners close with Delphi Automotive and Intel, which now also owns Mobileye
• Bosch partners with NVIDIA and Daimler as well as several car manufactures
• Ford bought SAIPS Machine Learning, co-invested in Velodyne (with Baidu) for it’s 2021 robot cabs agenda
• Here partners with Mobileye and NVIDIA
• Hyundai Motor presented the Ioniq Concept
• Japan created an autonomous car consortium for the 2020 Olympics in Tokyo
• Samsung Electronics for Automotive bought tech with Harman
• Volvo struggled with Uber (link)
• Volkswagen online Mobileye and Delphi in 2018
• …
• ZF and NVIDIA collaborate on embedded autonomous driving

Currently manufactures and OEMs see their need of software, hardware and data. Therefore building partnerships conventional one-to-one or one-to-few.  (Additionally past process optimisation often came from automotive – now also the IT offers more and more team process optimisation, such as with SCRUM and Agile.)

Baidu

But besides the Google car, which was in the news for years, Baidu might be even closer to Googles regular market approach:
Open software and services and creating a ecosystem. 

„Baidu, which is China’s Google, is leading the effort with big data and deep learning and recently began public trials of its own self-driving cars, despite severing its autonomous driving partnership with BMW, noting the two companies differed on how to proceed.“
https://www.forbes.com/sites/frankahrens/2016/11/28/asia-speeding-ahead-in-self-driving-vehicle-technology/#d819ea9404c5

Source: https://www.forbes.com/sites/frankahrens/2016/11/28/asia-speeding-ahead-in-self-driving-vehicle-technology/#d819ea9404c5
Baidu however, goes a step or two further, trying to build an open source like autonomous car reference implementation – like Google ownes Android.
http://www.cnbc.com/2017/04/19/baidu-driverless-cars-open-source-technology.html

Their advantage is, like Google, they don’t have a car business to loose, yet to gain. This can be a whole market disruption, as many new players on the market can arise utilising their technology.

Hardware: Eventually besides the software algorithms maybe hardware will be manufatured within the mainland Chinese ecosystem as well.
For example the top #1 fastest supercomputer in the world, Sunway TaihuLight in Wuxi runs already on only local processors with with 93 petaflops.

Read about Baidu: http://usa.baidu.com/adu/

Future

„Ford’s research has found that 84 percent of Indians and 78 percent of Chinese could see themselves owning an autonomous car, compared with only 40 percent of Americans and 30 percent of Britons, Connelly said.“

Source: http://www.cnbc.com/2016/12/06/asia-may-be-the-real-global-market-for-autonomous-cars.html

Hence, form my point of view:  Tech soft- and hardware suppliers and car manufactures / OEMs have to collaborate more, invest heavily in Deep Learning and see openness as an advantage.

Additionally, have a look at a Ptolemus Partnership Map:

Further resources:

http://www.businessinsider.de/how-uber-embarrassed-volvo-in-california-2016-12

https://www.theverge.com/2016/8/16/12499622/ford-baidu-velodyne-investment

http://www.gruenderszene.de/allgemein/tesla-ford-autonomes-fahren-analyse (german)

http://www.gruenderszene.de/automotive-mobility/delphi-mobileye-system-selbstfahrende-autos (german)

http://ngin-mobility.com/artikel/2018-mobileye-vw-daten-autonomes-fahren/ (german)

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via IBM Watson Analytics vs. Microsoft Azure Machine Learning (Part 1).

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Deep-learning startup MetaMind launches with $8M from Benioff & Khosla Ventures | VentureBeat | Deals | by Jordan Novet.

MetaMind with Richard Socher, left, and Sven Strohband, right, is the new kid on the block of Deep learning.

Sounds promising!

I would like to see some kind of comparison of MetaMind and their competitors using the same data sets. My idea: Maybe every company could give some dataset for everybody to run – so it’s easy to see which data / media formats suits most for each machine learning / deep learning solution.

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