試想一下...

…當AI+超過人類的智慧 

我們就能解決很多問題。它不僅能夠幫助我們的日常任務,還能減少疾病,促進我們的知識的科學計算可能的解決方案,以世界的問題…

 

假若當你有一個數位分身時

假若當你有一個數位分身時
你的數位分身藉由分析你的信件,聊天紀錄,社交圖譜,以及你的演說紀錄能夠對你達到一定程度的了解,他將能夠為你處理一些不是那麼重要的選擇,為你節省大量的時間.改善工作效率,處進家庭和諧
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歡迎AI-克勞迪奧
Claudio Weck 德國和他的團隊正在做研究和開發的人工智能(AI)和機器學習(ML)。我們專注於來自神經科學方面的分析BigData對AGI以及自然語言處理(NLP),以提高和非常人性為中心的人機交互DeepLearning。我們喜歡解決和世界知識數據庫之間的連接(符號)深學習!
ai-claudio.com是從事不懈地研究,創作的文章,博客,IT諮詢和項目管理。請隨時瀏覽我們的大多是英語(和德國)的職位和我們聯繫下面的表格。

 


我們的中文 的 A.I. Resources:


Let’s meet @ MIE Summit June 20th

As a Feature Speaker I will be at the Machine Intelligence Exchange in Berlin on June 20th 2017. I will talk about Deep Learning for Internet-of-Things such as Smart Cars.

 

Screenshot MIE 2017

 

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.

Deep Learning for Autonomous Cars – Might ‘Open’ be a disruption from Asia?

While in Europe and US the classic car manufactures created often one-to-one partnerships with OEMs and tech companies in the past, in Asia now also new business models are rising:

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)

I ♥ keras for Deep Learning

As responsible for the topic and team of Data Scientists in our business area, we moved to using Deep Learning more and more instead of the common classic statistical algorithms.

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.

Machine Learning and Artificial Intelligence in the Age of Autonomous Driving

In the last time a lot of great AI core software came out.TensorFlow
TensorFlow by Google integrated the code of DeepMind and is strong in imae recognition as well as natural language processing.
Besides the OpenAI group is working on a open and bigplayer independent AI platform for the masses. Of cource also Microsoft is strong with Machine Learning on Azure and Amazons DSSTNE is still living a shaddow life.

Within Automotive there are news everyday about new accomplishmens. Like from Audi, Mercedes, Telsa and all the others. Also IT gigants like Google and open-secretly Apple as well as often critizised Uber.

Even there are many restrictions often commented – mostly within the areas of ethics and laws – it’s a clear trend towards autonomous driving with more and more driver assistance systems.

Already for me, as digital native, the software features running within a car trumps the design & interrieur etc.

Working within the Automotive area on machine learning and artificial intelligence means having great changes ahead.

So more will follow 🙂

Auto Auto – αὐτός mobilis

If you see a button with the description ‘auto’ on a machine – you know you don’t have to do much – the machine will work it out itself – and that will soon be the same for cars.

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.

read more…

Big Data, Hadoop & Machine Learning

Talking about machine learning today doesn’t go without talking about distributed fault-tolerant data storage and query system.

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.

read more…

A.I. huge at Google I/O 2015

Artificial Intelligence is a huge topic at the ongoing Google I/O 2015.
Life from the keynote:
 most products discussed have new features heavily dependant on A.I.!

 

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.

google-io-20150197

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

Copyright Techcrunch.com

read more…

A Vision Of A Driverless Future | TechCrunch

Len Epp is writing in  A Vision Of A Driverless Future | TechCrunch about conclusions of the possibility of autonomous cars. Not only the drivers habits will change but the whole industry: shop on wheels, local services, no ownership of cars needed anymore, huge variation in sizes, …

So the AI powered self-driving cars will bring a huge change in our everyday life and for many businesses – great potential for many startups too.

Bill Gates is concerned and excited about super intelligence

Bill Gates agrees that we should be worried about artificial super intelligence! 

Just a little after the ‘Future of Life Institute’ published an open letter on Research Priorities for Robust and Beneficial Artificial Intelligence, which states many risks and examples including many research ideas to avoid those risks. You can read our article Demonic A.I.! What are we afraid of? Solutions to the risks! about that.

Bill Gates: I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well. A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don’t understand why some people are not concerned

Source: thisisbillgates @ Reddit

But he also is very exited and enthusiastic about A.I.: 

read more…

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