Please inform me if you are coming as well or want to have a talk on the day.

At a later stage I will share some of our presentation here too.

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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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After the keynote panel talk and the break I will give a speech about these topics:

Give me a call or e-mail  in case you are in town!

Go to the AI Expo: https://www.ai-expo.net/europe/track/ai-enterprise-development/ 

The post Meet @ AI-Expo Europe 2017, my presentation on June 1st appeared first on ai-claudio Artificial Intelligence.

Of course there are a tons of different Frameworks but I keras

I came to I love keras as the meta language is able to work on top of TensorFlow, theano and also Microsoft CTNK is in development of supporting it.

Even for some, and I believe only a few, use cases the pure frameworks are more powerful – in most cases development time, optimisation, collaboration and education with it is amazingly efficient.

Read about keras: https://keras.io

Additionally, also Lasagne is such a good meta language approach. As I didn’t implement it yet in any projects, I can’t compare it much.

I will give a deeper insight in my following speeches and webinar.

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The focus was mostly within Predictive Maintenance & Quality – here some slides in German:

www.slideshare.net/MHPInsights/tools-techniken-bei-predictive-analytics

www.youtube.com/watch?v=Leun_qElDeY

Nowadays of course technologies changed and we merely focus on Machine Learning and especiallay Deep Learning.

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In Germany we already call a car them ‘Auto’ which is a short for ‘Automobil’, deriving from the greek αὐτός and latin ‘mobilis’ – “self moving”. Well the name has it already in it – and this year there were so many news about the selfdriving cars.

Daimlers concept car F015 has some nice elements for people inside and outside the car.

Will the be no traffic jams if all cars drive in a steady way, with a secure but short distance to each other without any surprising behaviour? No traffic lights needed when cars are connected? Order a car with a desk during daytime or a comfy couch on the evening. No need of an all purpose car – enjoy a nice sporty one if you feel like it – or an super energy efficient if you prefer.

As soon as most traffic is done by autonomous cars – or as I like to call them Auto Autos safety will increase incredible – and small cars will be as nearly as safe as huge ones.

Check out some of the latest headlines:

• Racing Self-Driving Cars Will Make Roads Safer for Everyone
  http://www.wired.com/2015/12/roborace-autonomous-vehicle-racing/
• Mercedes-Benz F 015 Luxury in Motion
  https://www.mercedes-benz.com/en/mercedes-benz/innovation/research-vehicle-f-015-luxury-in-motion/
  
• Volvo to test autonomous cars with ordinary drivers on public roads by 2017
  http://www.theguardian.com/technology/2015/feb/24/volvo-test-autonomous-cars-ordinary-drivers-public-roads-by-2017

Some numbers say, the most advanced autonomous car already drives better then 99% of the people. This is a huge increase in saftey, plus more comfort.

My opinion: A computer scientist should not decide about life and death in a car accident – in a split-second.
But nowadays that is the case, and I am a programmer and possibly need to react in an accident in less then a second. Better have a thousand people think, talk and collaborate on programming the car to find the best moral and practical solution.

Autonomous Cars and Artificial Intelligence

Autos will be a huge field for predictive analytics, as image recognition and pattern recognition as part of machine learning is one of the ost important areas for the future autonomous cars.

[This article will be extended possibly during christmas time]

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Hadoop is an open-source Apache Project derived 2006 from Yahoo! and based on papers from Google in 2003, is such a widely used system. Even though it is basically ‘mostly just’ a filesystem it’s three biggest advantages are

1. flexibility
2. scaleability
3. in-expensiveness

At the core of Hadoop is the filesystem HDFS (Hadoop Distributed File System) which stores it’s data in blocks across all DataNode machines. The data is replicated usually on a (or more) machine in the same rack, as well as on an other rack. Clients connecting to Hadoop to read or write will first question the NameNode which will tell them at which DataNodes they can attempt to access.

The current Hadoop 2.x version rely on YARN (Yet another Resource Negotiater) for that and with data and server replication there is no single-point-of-failure anymore. On top of that Hadoop uses MapReduce as key/value database. Therefore Hadoop is great for lots of data retrievals and querying.

It’s drawbacks are: the data in HDFS is not editable, only append able, it takes a lot of configuration work and without any additions and  it’s not for real-time queries.

As for machine learning the more data the merrier – so many experts, e.g. from Spring, believe that for data with an amount of 10 Terrabyte  and more Hadoop is a great solution.

SAS for Hadoop / HortonWorks

There are countless derivations and distributors, for BI cases, besides IBM there is also HortonWorks Data Plattform utilising SAS in-Memory Statistics for Hadoop and SAS Visual Statistics for machine learning.

STATISTICA by StatSoft (DELL)

StatSoft’s Statistica, since 2014 owned by DELL is one of the most well known statistic Suits, offering Statistica HP – High Performance for massive parallel processing in-memory. This software works with a wide number of systems, including Hadoop as well as SAP Hana, Windows HPC Server / Polybase / Parallel Data Warehouse, Oracle Exadata, Teradata Aster, Pig, Hive, Sqoop as well as IBM’s Netezza.
It’s been widley used worldwide abroad all branches for data analytics ans well as Predictive Analytics.

More about Statsoft

IBM’s Predictive Analytics

Besides offering IT consulting in the field of Predictive Data Analytics IBM is also offering a wide variety of own tools.

Since 2009 this includes SPSS Statistics (Statistical Package for the Social Sciences) the standard software for statistical analysis in social and other sciences.

More about IBM

And IBM’s Hadoop and Watson Analytics will be discussed in this blog as well.

SAP HANA

The german SAP HANA system is as well one of the leading software for business data and analytics. Using Hadoop as filesystem HANA is able to do predictive data analystics with the in-memory HANA system.

More about SAP HANA and Hadoop

Read more about Hadoop, IBM’s Hadoop as well as SAS for BI the following articles.

Online Classes for Big Data (Predictive) Data Analytics and similar topics

Introduction to Big Data with Apache Spark : Learn how to apply data science techniques using parallel programming in Apache Spark to explore big (and small) data. by UC Berkley @ edX

Data, Analytics and Learning : An introduction to the logic and methods of analysis of data to improve teaching and learning. by U Texas Arlington @ edX

Big Data and Social Physics : Understanding big data, how to use it to improve companies, cities, and government, and best-practice for privacy. by MIT @edX

Knowledge Management and Big Data in Business : Learn why and how knowledge management and Big Data are vital to the new business era. by HongKong Polytechnic U

Foundations of Data Analysis : This is a hands on course with a data lab to teach fundamental statistical topics such as descriptive statistics, inferential testing, and modeling. by Texas U Austin

The Analytics Edge : Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life. by MIT @ edX

And many more at edX

Or check out the Data Analyst Nanodegree @ Udacity

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All-New Google Photos is using improved face and pattern recognition. Not only the face recognition was improved, which was already available in Google Picasa and other apps. But additionally and new to consumer photo album software – it also will use pattern recognition to add automatically searchable text tags.

Additional automatically cut and edited videos known as “auto awesome” will be improved as Google Photos “Assistant” – using A.I. internally to figure out which video sequences and photos to use.

Copyright Techcrunch.com

Google Now has more then 1 billion information pieces able to show users to assist them at their current action. Simple via (A.I. powered) speech-recognition connected information are shown.

They have mentioned that since 2013, when speech recognition had a failure of about 23%, it’s down today to 8%.  So we can expect this number to go down even further.

Copyright Techcrunch.com

With the “Context” Element in Google Now, also the speech recognition will look for textual context in a current App to easier identify words, such as international names of food. Here sometimes even the correct pronunciation is a mystery to user.

Copyright Techcrunch.com

Mr. Sundar explained that Google’s machine-learning and image recognition improved much more through Deep Learning and Deep Neuronal Networks.

Copyright Techcrunch.com

Driverless Cars

Showing the cars point of view: The cars drive on A.I.

All together it’s clear that A.I. is not only at the core of Google, it’s search algorithm, but also in many products and features – some shown above. Looking forward to see more about that within the following days of Google I/O and afterwards.

(Read all about it in the live-blog at Techcrunch, also copyright owner of some pictures in this article.)

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