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.

The post I ♥ keras for Deep Learning appeared first on ai-claudio Artificial Intelligence.

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

The post Machine Learning and Artificial Intelligence in the Age of Autonomous Driving appeared first on ai-claudio Artificial Intelligence.

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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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.: 

Well, he should be happy, as the development in the ice age of A.I. was not as fast as predicted. So he did not miss the development of real A.I. and will be able to play a bigger role in this field now:

Bill Gates: There will be more progress in the next 30 years than ever. Even in the next 10 problems like vision and speech understanding and translation will be very good. Mechanical robot tasks like picking fruit or moving a hospital patient will be solved. Once computers/robots get to a level of capability where seeing and moving is easy for them then they will be used very extensively.

One project I am working on with Microsoft is the Personal Agent which will remember everything and help you go back and find things and help you pick what things to pay attention to. The idea that you have to find applications and pick them and they each are trying to tell you what is new is just not the efficient model – the agent will help solve this. It will work across all your devices.

Amazing. We are as exited about A.I. as he is.! Luckily he also thinks, that becoming a software developer is still safe for now, and not currently doomed through automation.

 
Please have a look at our latest articles on A.I.

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About the dangers and far-reaching consequences was written very much in various media lately. As most readers have probably already read most of potential problems – here are solutions instead, from the brightest minds of A.I. and me.

I wrote this article for the german tech magazine Mobile Geeks. I mostly used (A.I.) automated translations and fixed only the major errors. Thats why it may sound weird, but I hope you still can get some points.

About the Future of Life Institute

“Future of Life Institute” sounds quite strange and unreal at first. It reminds me more of ‘The Hitchhiker’s Guide to the Galaxy “. But the Institute, in short FLI, is serious and has drummed up earlier this year, many of the brightest minds in and around A.I. research. They have discussed all potential disasters by A.I.  and approaches to avoid those. They have published it as an open letter with a attachment. (Here the whole letter, which you can also sign.)

Future of Life Conference Jan. 2015 of risks of A.I.
Back row, from left to right: Tom Mitchell, Seán Ó hÉigeartaigh, Huw Price, Shamil Chandaria, Jaan Tallinn, Stuart Russell, Bill Hibbard, Blaise Agüera y Arcas, Anders Sandberg, Daniel Dewey, Stuart Armstrong, Luke Muehlhauser, Tom Dietterich, Michael Osborne, James Manyika, Ajay Agrawal, Richard Mallah, Nancy Chang, Matthew Putman
Other standing, left to right: Marilyn Thompson, Rich Sutton, Alex Wissner-Gross, Sam Teller, Toby Ord, Joscha Bach, Katja Grace, Adrian Weller, Heather Roff-Perkins, Dileep George, Shane Legg, Demis Hassabis, Wendell Wallach, Charina Choi, Ilya Sutskever, Kent Walker, Cecilia Tilli, Nick Bostrom, Erik Brynjolfsson, Steve Crossan, Mustafa Suleyman, Scott Phoenix, Neil Jacobstein, Murray Shanahan, Robin Hanson, Francesca Rossi, Nate Soares, Elon Musk, Andrew McAfee, Bart Selman, Michele Reilly, Aaron VanDevender, Max Tegmark, Margaret Boden, Joshua Greene, Paul Christiano, Eliezer Yudkowsky, David Parkes, Laurent Orseau, JB Straubel, James Moor, Sean Legassick, Mason Hartman, Howie Lempel, David Vladeck, Jacob Steinhardt, Michael Vassar, Ryan Calo, Susan Young, Owain Evans, Riva-Melissa Tez, János Kramár, Geoff Anders, Vernor Vinge, Anthony Aguirre
Seated: Sam Harris, Tomaso Poggio, Marin Soljačić, Viktoriya Krakovna, Meia Chita-Tegmark
and Photographer Anthony Aguirre edited into on his left

Elon Musk, the Institute has also been boosted by a donation of US $ 10 million.

The aim of the FLI is that the research and the use of KI is used positively:

The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is valuable to investigate how to reap its benefits while avoiding potential pitfalls. (FutureOfLife.org)

There is much research needed to address all threats and to create a useful and robust A.I.. “Robust” stands for, it is guaranteed, that intelligent machines, do what you want them to do. In this article are first the imminent dangers, which are warned by the FLI, then secondly those which are further in the future:

Dangers of reasonably intelligent machines aka the weak AI

Weak A.I. begins with things like handwriting recognition and translation programs. Stock market trading computers, robot controllers and autonomous cars are also included.
These usually resolve only those tasks, which they were trained for. However, a large amount of hazards arise by them. More about the solutions to avoid these problems:

1. Laws and Ethics

 Autonomous Vehicles decide in dangerous situations over life and death

Here are ethical discussions and new laws needed. Considerations in which situations a driver and in which pedestrians have to be more protected, must be taken.

Autonome weapons to kill people without responsibility

Whether autonomous weapons systems, as here, are legal with UN human rights is doubtful. Nevertheless, against these dangers, the best solution is a global and UN-wide ban. The use is difficult to control, but a development ban will at least least largely restrict the danger.

Unemployment through automation

As stated recently in my articles, innovation, education and flexibility is important then ever for a healthy future. A social development is also necessary.

2. Verification

Coding mistakes: Who can guarantees, that a robot won’t crush someones hand?

Formal and mathematical verification of A.I. program code has to be done, hopefully like it already exists within banks and aircraft control, to help to avoiding programming errors. This is complex but useful. A secure minimal open source (robots) OS is available with SEL4 already.

Modular development also helps – then each part can be tested individually. In addition, it provides a verifiable meta-level that is in control of everything.

Learning  means making mistakes: Children sometimes get themselves into dangerous situations, that should hardly happen with (strong) robots

Even before delivery, robots and A.I. should be able to deal with as many situations as possible.

However, every real situation is different. But if robots, under safe laboratory conditions, were trained with nearly all physically possible situations in a test room with e.g. mother, children and all the usual household items – this would already help enormously.

I also think that when A.I. will be able to extract context and connections from text, images and videos, training on Wikipedia, books (documentary-)movies and other media will be extremely helpful. Although machines might not be able to feel: “moral values” and feelings, can be recognised by statistical behaviour of people. (You would then hold up a mirror to society. Oh oh)

Should there be an international stop-word for robots in your household?

3. Validation

Misunderstandings and unexpected results, such as stock market crash

Thus, even in the event of error-free A.I. and robots there will be many problems. As a task and the work environment must be specified for A.I. and robots – this has to be as precisely as possible. And we cannot always do it right. Also, our understanding of “good behavior” is often common sense and needs to be established in machine understandable form.

4. IT Security

A.I. networks might get supplied with manipulated data

To help A.I. learn better and faster, they will be connected. There are already robot databases such as RoboEarth. In order to use non-manipulated data, a trust system for A.I. systems must be established.

And general IT security and privacy of people in A.I. should play a big role at the design time.

5. Human control

Unintentional Cyber Wars

Since the first militaries sometimes automates DoS attacks shooting back, probably soon between 192 to 205 other countries – additionally other non-state powers will do the same. But Error, negligence, misunderstandings, mistakes and technical IT security gaps should be (like many others things) not trigger wars.

The solution is (would be) to allow digital weapons and controlling systems of physical weapons, at the technical level only by human control.

The dangers of intelligence explosion

The best A.I. Scientists, SciFi authors, astrologers and physicists are very divided on whether A.I. can reach a human level or even or higher level of intelligence in the foreseeable future .

If so, then the precautions above, for weak AI, are not sufficient. Therefore, they all agree that we should start looking for solutions for today.

As described in a previous article, I think due to illustrations of Shane Legg, that around 2030 A.I.  might reach human-like level.

Stephen Hawking – Transcendence is more than Scifi

Stephen Hawking – Theoretical physicist, cosmologist and one of the smartest people of our time – has used Artificial Intelligence for his research – and sees it as possibly the biggest threat to our civilisation. In all seriousness, he stated, that the movie Transcendence, which is about singularity, should be taken as a warning.

Hawking describes, that the creation of truly intelligent machines would be the greatest achievement in the history of mankind. It may, however, also be our last, if we do not learn how to deal with the risks they.

Solutions on the risks of strong A.I.:

 experience of unexpected generalizations

To ensure that A.I. and robots won’t imprison us “for our own safety” in rooms with cotton wool, systems should provide us with all the important decisions and plans they came up with. A.I. should also represent the sub-objectives and tasks in a human understandable, concise but complete way including probable and possible consequences.

Self optimising systems are not formally verifiable

A formal code verification is impossible on systems that change their own code and improve at any time. A modular design helps, possibly the control units could be minimal, more static and thus controllable.

Accidents by misperceptions of the environment by the AI

A.I. systems must recognise not only the environment, but also learn to assess how other people and systems will possibly behave. For this purpose, systems must conclude logically under uncertainty and learn to decide.  Also, they need to pay attention to negative consequences of each decision. The relationship between calculations and assumptions must be further developed to do so.

Intelligent machines beyond effective human control

To perform tasks as  good as possible, intelligent machine would naturally try to prevent to be switched off or halted likewise. Therefore is would be a naturally goal to avoid human control. Therefore receiving the wish of interception by the machines administrators should be a very high level goal.

Again, a modular structure in which the central control unit can not autonomously expanded is helpful. Additionally, the display of machines goals and sub-goals, in an intelligible form, is a must.
Adopting new knowledge from neuroscience into the AI development is needed as well.

Laws and morality are too complex for AI – and insufficient

Laws are on the one hand too extensive and complex for A.I. (or people:-)) to be understood at the whole. Moreover, they are not sufficient. Therefore, courts decide by taking conventions in account of , morals, and especially the respective situation.

Certainly, it is necessary to create laws and rules of conduct that are specifically tailored for machines. Possibly even according to the principle: anything not explicitly allowed is forbidden?

Broken or harmful A.I. without bounds

Untested, unfinished or potentially harmful A.I. should be processed, off the internet,  in containers. However, this requires a high level of IT security and well-developed systems. A.I. must be trained with real world data without the possibility spreading via steganography or airgap-hacking.

Infinite resource devouring A.I. – a natural target

In order to carry out tasks as well as possible, an intelligent system will use all resources made available. This also resources are used, which are available only by exploiting security vulnerabilities and specification.
This would allow foreign computer systems and energy sources are captured. And just limited temporal resources could be consumed excessively.

K.I. tricks us – according to Goodhart’s rule

A.I. mostly works on the reward/reinforcement systems. Each system tries the easiest and fastest way to get a reward. Finally, these systems do not try to do the task conscientiously, but rather only trying to meet the evaluation criterion.
The evaluation criterion must therefore be precisely linked to the fulfilment of the task without any possible undesirable effect.

A.I. behaves different when you look away – Heisenberg uncertainty principle

If auditors are present, students, adults, restaurants and companies behave differently than usual. Unfortunately, the same is true for intelligent machines.
But those should always stick to the rules, smart machines should therefore rather be ‘ideal’ and less ‘human’ and perhaps also receive as little information about when and what there are tested for.

We can not know, what super intelligent machines will do

This problem remains: It is impossible for us to foresee the behaviour of people or machines that are smarter than we are. We can guess, but it is not for sure.
Whether one should limit the resources in systems before crossing the IQ of 200?

Conclusion

Finally! We have been warned mostly by sci-fi authors and from time to time by journalists or individuals about the dangers of A.I.. Often many true thoughts were mixed in imaginative stories.

Now at the conference of the Future of Life Institute, a large number of leading scientists of different fields, top researchers in the industry and some others came together. They have discussed together about the potential dangers and solution ideas. With the publication of the Open Letter and the research objectives, this finally has a scientific nature, which can be cited – and contains the consensus of the A.I. elite.

Good: There are concrete solutions for many examples. Bad: Those are only concepts and the general solution is: we need to do research on it.

Although the scientists want to track these charitable goals, I guess currently the most research money is spent for the productivity increase of A.I. systems.

I agree with the points and the letter and will try to contribute a part in that research!

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The post Research Priorities for Robust and Beneficial Artificial Intelligence: an Open Letter appeared first on ai-claudio Artificial Intelligence.

The lecturers will be Pieter Abbeel and Dan Klein, which both have masses of very positive student reviews and previous experience.

Artificial Intelligence | edX.

Stay also tuned for further information as well as lecture notes and possible discussions about this course on ai-claudio.com. If you can’t wait, have a look at UDACITY Machine Learning – 1 Supervised.

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If you cannot attend, you still will be able get some of their papers and further resources on their linked event page.

Please feel free to send me further events or comments, they are as always appreciated!

January:

02-05 January 2015, The Future of AI: Opportunities and Challenges in San Juan, Puerto Rico

21-23 January 2015, International Symposium on Artificial Life and Robotics (AROB 2015), in Beppu, Japan

25-30 January 2015, AAAI Conference on Artificial Intelligence (AAAI-15) in Austin, Texas, USA.

February:

05-07 February 2015, Australasian Conference on Artificial Life and Computational Intelligence (ACALCI 2015), in Newcastle, Australia

March:

25-28 March 2015, SMART Cognitive Science International Conference in Amsterdam, Netherlands

April:

07-11 April 2015, International Neuroscience Winters Conference (17.) in Soelden, Österreich

08 – 10 April 2015, Evostar 2015 in Copenhagen, Denmark

19-22 April 2015, Spring Brain Conference, Bridging Neural Mechanisms and Cognition by FENS in Copenhagen, Denmark

22-24 April 2015, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015) (23.) in Bruges, Belgium

May

18-22 May 2015, International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) techniques in Constraint Programming (CP-AI-OR 2015) in Barcelona, Spain

19-20 May 2015 ICT Spring Europe 2015 with a AI&ROBOT AREA special in Luxembourg City, Luxembourg

26-30 May 2015, IEEE Robotics & Automation Societys Conference, (ICRA 2015) in Seattle, USA

25-28 May 2015, IEEE Congress on Evolutionary Computation (IEEE CEC 2015) in Sendai, Japan

June

01-04 June 2015, International Conference on Brain Engineering and Neuro-computing (IC-BrainEngiNe 2015) in Mykonos, Greece

14-18 June 2015 the International Conference on Artificial Intelligence and Soft Computing (ICAISC 2015) in Zakopane, Poland

17-20 June 2015 the Conference on Artificial Intelligence in Medicine (AIME 2015) (15.) in Pavia, Italy

22-25 July, Artificial General Intelligence (AGI 2015) in Berlin, Germany

25-27. June 2015, Electronics, Computers and Artificial Intelligence (ECAI 2015) in Bucharest, Romania

July

6-11 July 2015 International Conference on Machine Learning (ICML 2015) by IMLS in Lille, France

22/23-25 July, the Annual Meeting of the Cognitive Science Society (COGSCI 2015) in Pasadena, USA

25-31 July 2015, International Joint Conferences on Artificial Intelligence (IJCAI-15) in Buenos Aires, Argentina

September

28 September – 03 October 2015, IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2015) in Hamburg, Germany

2016

24-26 February 2016, International Conference on Agents and Artificial Intelligence (ICAART 2016 in conjunction with ICORES 2016 and ICPRAM 2016), Rome, Italy

Resources:

The most valuable list of Upcoming Conferences & Workshops (as well as previous ones) I could find is provided by the Biointelligence Laboratory of the School of Computer Sci. & Eng. at the Seoul National University: http://bi.snu.ac.kr/Info/conferences.html

A broad Conference Listing Page, partly in German language only:  http://www.conference-service.com/konferenzkalender/neuronale-netze.html

The Human Brain Project of the EU Events: https://www.humanbrainproject.eu/upcoming-events-viewer

The Wiki Call for Papers: http://www.wikicfp.com/cfp/

K.I. (A.I.) of the Germany in http://konferenz.kuenstliche-intelligenz.de/

The Germany A.I. Magazine: http://link.springer.com/journal/13218 

Please feel free to tell me, if you like other conferences or resources to be added!

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