The Moon For All. An Apollo Story.

Why to write about space now?

I’ve been long fascinated with space exploration, especially with Apollo Lunar program. So far I’ve read a dozen of books and watched more than dozens of documentaries, interviews and movies on the subject. I have to tell you my enthusiasm is not even close to be exhausted. As you may know this year will mark 50th anniversary of Apollo 11 first Moon landing that took place on July 20th, 1969, 20:17 UTC. It’s not  strange that last year we saw the First Man movie about Neil Armstrong hitting the theaters across the globe. This year on March 8th long awaited Apollo 11 documentary featuring never before seen hi quality video along with re-mastered videos was released in IMAX format in theaters. It seems like more is to come when we get closer to July 20th of 2019.

What books do I recommend?

I recommend Flight: My Life in Mission Control by Christopher Kraft. In my mind it is one of the most interesting books written about American space program by the founding father of mission control.

 

 

 

 

 

 

I also find Sunburst and Luminary: An Apollo Memoir book by Don Eyles very interesting from an engineer-programmer point of view. He describes in detail how the Lunar Module (LM) software was designed and behaved in practice.

 

 

 

 

 

 

It is hard to not recommend The Last Man on the Moon book by Eugene Cernan who was the last man so far on the Moon. I find this book very inspirational thanks to Cernan’s insightful thoughts and his deep personality. He was able to describe his emotions while being on the Moon with vivid colors. It feels like you join him there. 

What documentaries and movies are worth watching?

Actually, there are quite a lot documentaries that are available for free on YouTube. But there are a number of very good ones that worth the money to pay for them. I’ll mention only a number of them. Others may be found in my earlier Deep Space, Do You Copy? post.

I recommend you to watch Apollo 11 documentary that was released recently (at time of writing) in IMAX if possible. It successfully conveys the enormity of the event that unfolds before your eyes. It is possible at times to forget that you are watching the movie. I would also mention the sound track for the film by Matt Morton that helps this film to stand out.

It turns out that Apollo 11 documentary stands upon shoulders of another, today long forgotten For All Mankind documentary from 1989 by Al Reinert. It is also accompanied by very impressive sound tracks of Brian Eno and provides interesting interviews with Apollo astronauts along with nice video of them having fun on the way to the Moon and on the surface. 

Conclusion

The manned space exploration is again an exciting topic since Israeli Beresheet space craft is on its way to the Moon and a Chinese vehicle is exploring the Moon as I write these lines. US SpaceX and Canadian Space Agency partnering with NASA and others to built Gateway a Lunar Space Station for a long stay on Moon orbit and on the Moon surface on our way to Mars.  So, book your next flight with Virgin Galactic, take a seat, fasten your belts, check oxygen level and prepare for a liftoff to space.

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Good mathematics books to read and work through

Math is entertaining when you try to play with it

If you have an interest in mathematics be it pure or applied there are plenty of books were written on the subject varying by the depth of the presented material and the need to know a certain level of math to be able to not only read the book but also gain some practical insights by working through the examples and tasks. Personally, I like more books that freely use math in the description of the examples and give tasks for a reader to accomplish. It seems like this is the only way to really understand what author tried to convey. It’s like reading a book on programming and trying right away the code samples, changing them.

Following is the description of a number of popular math books that I find very insightful, useful and entertaining, since reading them not only gives an appreciation of the beauty of math, but also makes one feel better when he or she is able to find a solution to tasks in the book.

A Mathematician’s Lament

lament-1

I want to start with the article that Paul Lockhart wrote, that later was expanded to a book with the same name which is A Mathematician’s Lament. The first part of the book is essentially the article itself. So if you read only the article you read half a book already. If you have an Amazon account you may buy a Kindle version of the book and it may take you a couple of hours to finish it. Then you may return it for refund and that’s it. You got the entire book for free.

The article and consequently the first part of the book presents readers with a very strange way that math is taught in schools using very clever analogy to how music might have been taught if it would be taught like math in most schools today. 

The second part of the book tries to show some solutions to the problems of how math is taught that were described in the first part of the book.

I recommend this book to all who disliked math and thought that it was boring and  disgusting. Maybe, you’ll change you thoughts on the subject.

From popular to more hands-on math books

primes

Recently, I’ve read the The Music of the Primes book by a mathematician Marcus du Sautoy on Riemann hypothesis. Previously I read a  book about primes and as part of my studing at college learned a thing or two about them, but I never appreciated how interesting it may be to follow the path along with mathematicians trying to prove Riemann hypothesis. This hypothesis is one of the seven problems that Clay Mathematics Institute thinks worth 1,000,000 USD for one who’s able to prove it. Though less money is given to one who will disprove it. 

 

 

The Riemann hypothesis states that all interesting solutions of the equation

ζ(s) = 0, where ζ(s) is a Riemann Zeta function, ζ(s) = 1 + 1/2s + 1/3s + 1/4s + …

 lie on a certain vertical straight line which is  Re(s) = 1/2,  Re(s) stands as for a real part of the argument s.

What I liked about The Music of the primes that Marcus wasn’t afraid to show a little bit of mathematics that was related to the saga of trying to prove the Riemann Hypothesis. He also was able to create an adventurous story that connected math and physics and such a mundane thing as RSA cryptosystems that was used in Internet secure web communications. In addition, Marcus du Sautoy mentions a large number of prominent mathematicians who deserve a separate book to be written about them.

 

Math is a pleasure to play with when it is presented in an interesting manner

The last book that I want to mention in this post is the book by Vladimir Arnold who was one of the distinguished Russian mathematician and the one who solved the Hilbert thirteenth’s problem in the age of twenty. Only a number of numerous books written by Vladimir Arnold were translated into English from Russian, but even the ones that were are still very exciting to read and include lots of tasks to be resolved by a reader. I should say that Arnold’s popular math books are actually a kind of math courses. If you’ll check one of his books you’ll understand what I mean by this. What I find appealing in the Arnold’s books is his ability to explain complex topics in a simple way that is entertaining and makes you long for more. By the way if you can read in Russian you may find all of his books and many others for free at the Moscow Center For Continuous Mathematical Education web site library.

If you like physics and applied math, then you gonna find Mathematical Understanding of Nature: Essays on Amazing Physical Phenomena and Their Understanding by Mathematicians book by Vladimir Arnold very informative and entertaining at once. In it you’ll find a number of task and solutions to them drawn from various fields of physics along with a simple to grasp explanations that makes complex things seem beautiful.

There are additional Vladimir’s books that may be found in English so if you’ll find this book useful to you then there are others you can enjoy too.

It’s only the beginning

This post is only the first one in a series of post that will accompany me while I myself read and work through the popular math books and try to report on interesting gems I find in them.

 

Thoughts on physics and artificial intelligence on 2019 New Year’s eve

Make the New Year happy, because you can

It seems to me the New Year will be interesting and exciting as it always seems this way on new year’s eve. What makes me think so though is a number of books I read recently. One of the book is a collection of interviews with prominent people in the field that is known as Artificial Intelligence. The other book is about the particle physics being stuck with high hopes in String theory and why it may be a root cause of not seeing no new physics discovered so far in the Large Hadron Collider (LHC) except for Higgs boson.  

The power of the right books

In his book Architects of Intelligence: The truth about AI from the people building it Martin Ford has done something interesting by combining a numbers of interviews, more than a dozen, with people who are focused on Artificial Intelligence progress in various levels. In it you may find Geoff Hinton the founding father of Deep Learning and his colleagues Yoshua Bengio and Yann LeCun who need no special advertising (hint, search in Google). There are also a row of interviews with people like Jeff Dean and Ray Kurzweil from Google Brain that are interesting to read too.

The main point of the book is that those people were asked more or less the same questions, including how they came into field of Artificial Intelligence, what they think about Deep Learning and whether it will alone lead to Artificial General Intelligence. Will the recent advances in machine learning jeopardize jobs and what to do about that. What is interesting to see is that each person interviewed naturally had a different answer to these questions, so it helps to get a balanced view on what is the state of the art of Deep and Machine Learning in 2018.

Things that require new explanations

In her book Lost in Math: How Beauty Leads Physics Astray Sabine Hossenfelder a particle physicist discusses an interesting matter of various biases that affect theoretical physicists that set out to devise a theory that intended to explain laws of physics. For example, String Theory is discussed extensively in the book since this theory though it’s very elegant, beautiful and full of  naturalness completely failed due to the absence of any predictions that the theory envisioned. Indeed, no new particles except for Higgs boson, were found in the Large Hadron Collider and it feels like there is a time to abandon String Theory which isn’t working and check other theories that won’t be plagued with ad hoc assumptions of naturalness and apparent, and very likely deceiving, beauty of the nature. If you are interested why there was found nothing new in the particle physics in recent decades, you may find Sabine’s explanations insightful. And maybe just, maybe you’ll discover that you too like me have biases that affect our perception of the nature. 

 

So make the upcoming year as you wish it to be

Remember that as intelligent creatures we are chanced to possess a capability to set goals and achieve them when we plan and act on plans with an enthusiasm and a perseverance (and Google search).

Happy New Year!

 

 

Better Deep Learning or How To Fine Tune Deep Learning Networks

Effective Deep Learning is possible

Nowadays, when Deep Learning libraries such as Keras makes composing Deep Learning networks as easy task as it can be one important aspect still remains quite difficult. This aspect that you could have guessed is the tuning of various number, which isn’t small at all, of hyper-parameters. For instance, network capacity which is number of neurons and number of layers, learning rate and momentum, number of training epochs and batch size and the list goes on. But now it may become a less of a hassle since a new Better Deep Learning book by Jason Brownlee focuses exactly on the issue of tuning hyper-parameters as best as possible given a task in hand.

 

Why is it worth reading this book?

When I myself worked through this book from the beginning to the end, I liked that this book as other books written by Jason Brownlee followed the familiar path of self-contained chapters that provided just enough theory and detailed practical  working examples, that might be extended and build upon by practitioners. The code samples themselves are concise and can be run on an average PC without a need in GPU, but nevertheless they convey very well what author intended to show.

While playing with code samples in each chapter I found myself thinking that I was back at college again doing a lab for electrical engineering. I felt this way since each chapter provides a great number of experiments with related graphs that help understand the hyper-parameter behavior in different configurations.

How this book may help me?

Better Deep Learning may help you if you have initial experience with Deep Learning networks and you want to fine tune network performance in a more controlled way than simple trial and error. Since the book uses restricted and simple data-sets generated with Python libraries it is easy to run each experiment and get fast understanding how each hyper-parameter effects network behavior.

In addition to working code examples, the book provides a number of focused references to papers, books and other materials that are related to the content of each chapter.

Last but not least, each chapter concludes with a number of extensions that make a practitioner think harder and try to play with the chapter’s content in a much more deeper level.

Conclusion

All in all, the book provides comprehensive treatment of all hyper-parameters you may find in various types of Deep Learning networks, such as CNN, RNN, LSTM and it makes it clear that fine tuning of Deep Learning is possible even for a beginner with proper guidance which the book provides.

Stay fine tuned!

Driver drowsiness detection with Machine or/and Deep Learning.

It actually even more useful than Driver Assistant

In the previous post I mentioned that it is nice to have a mobile phone application which is capable of detecting various erroneously driven cars in front of the moving vehicle. Another more interesting application in my opinion, which is even more impact-full will be a mobile application that uses ‘selfy’ camera to track driver’s alertness state during driving and indicating by voice or sound effects that driver needs to take action. 

Why this application is useful?

The drivers among us and not only surely know that there are times when driving is not coming easy, especially when a driver is tired, exhausted by a little amount of sleep or certain amount of stress. And driving in alerted state of conciseness (against the law, by the way). This in turn is a cause of many road accidents that may be prevented should the driver be informed in timely manner that he or she requires to stop and rest. The above mentioned mobile application may assist in exactly this situation. It even may send a notification remotely to all who may concern that there is a need to call a driver, text him or do something else to grab the attention.

Is there anything like this in the wild?

As part of MIT group that is researching autonomous driving and headed by Lex Fridman the group used this approach to track drivers that drive Tesla cars and their interaction with the car. For more details, you may check out the links below with nice video and explanations.

This implementation combines best of state of the art in machine and deep learning.

face_detection_car.png

 

 

This implementation is from 2010 and apparently it is a plain old OpenCV with no Deep Learning.

face2

Requirements

  • Hardware
    • Decent average mobile phone 
  • Software
    • Operating system
      • Andorid or IPhone
    • Object detection and classification
      • OpenCV based approach using built-in DL models
  • Type of behavior classified
    • Driver not paying attention to the road
      • By holding a phone
      • Being distracted by another passenger
      • By looking at road accidents, whatever
    • Driver drowsiness detection
  • Number of frames per second
    • Depends on the hardware. Let’s say 24. 
  • Take action notifications
    • Voice announcement to the driver
    • Sound effects
    • Sending text, images notification to friends and family who may call and intervene
    • Automatically use Google Maps to navigate to the nearest Coffee station, such as Starbucks, Dunkin’ (no more donuts) and Tim Horton’s (select which applicable to you)

Then what are we waiting for?

This application can be built quite ‘easily and fast’ if you have an Android developer account, had an experience developing an Android apps. You worked a little bit with GitHub and had a certain amount of experience and fascination with machine learning, or OpenCV with DL based models. Grab you computer of choice and hurry to develop this marvelous piece of technology that will make you a new kind of person.

A possible plan of action

  • Get an Android phone, preferably from latest models for performance reasons.
  • Get a computer that can run OpenCV 4 and Android Studio.
  • Install OpenCV and all needed dependencies.
  • Run the example from Adrian Rosebrock’s blog post.
  • Install Android Studio.
  • Create a Android developer account (if you don’t have one, about $25 USD)
  • Use the Android app from this blog post as a blueprint and adapt the Pyhton code from Adrian’s implementation into Java.
  • Publish the app at Google Play Store.
  • Share the app.

 

References

Driver Assistant. Detect strange drivers with Deep Learning.

Driver assistant app. Can it be done?

I was too optimistic about making this work on Android since it takes more than a couple of seconds to process even single frame. So folks doing what I hoped in this post with OpenCV  is currently not achievable with a mobile phone.

This video which had 30 fps and was 11 seconds long took about 22 minutes to process.

larry.png

I wonder why is that there is close to none Android or IPhone applications that can in real-time detect erroneous drivers driving on the road before or sideways to you. The technology is there and algorithms, namely Deep Learning is there too. It is possible to run OpenCV based deep learning models in real-time on mobile phones and get good enough performance to detect suddenly stopping car ahead of you. Since mobile phone field of view isn’t that large, I think it will be hard if not impossible to detect erroneous driving on the sides of the car. A good example of OpenCV based object detection and classification using Deep Learning may be Mask R-CNN with OpenCV post by Adrian Rosebrock from PyImageSearch.

Requirements

  • Hardware
    • Decent average mobile phone 
  • Software
    • Operating system
      • Andorid or IPhone
    • Object detection and classification
      • OpenCV based approach using built-in DL models
  • Type of objects classified
    • Car
    • Truck
    • Bus
    • Pedestrian (optional)
  • Number of frames per second
    • Depends on the hardware. Let’s say 24. 
  • Field of View 
    • About 60 degrees
  • Type of erroneous driving detected
    • Sudden stopping
    • Zigzag driving
    • Cutting off from the side (hard to do with single forward facing phone camera)
    • etc.

Then what are we waiting for?

This application can be built quite ‘easily and fast’ if you have an Android developer account, had an experience developing an Android apps. You worked a little bit with GitHub and had a certain amount of experience and fascination with machine learning, namely OpenCV DL based models. To be able to detect some dangerous maneuvering others do there is a need to use a little bit of math to be able to detect them, as well as calculate speed, direction and distance to other cars. The effort is worth investing time into. Even a little helper can have a big impact, unless drivers start staring into the mobile phone screen looking how it’s doing while driving.

A possible plan of action

 

 

Language Acquisition. Multidisciplinary approach. Part three.

 

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Multidisciplinary approach

This post about the research of natural language acquisition will be as short as previous parts. This time I want to describe how the current research that is too linguistically focused may benefit from being open up to other disciplines, such as Machine Learning, computer Science, Neurosciences and Physics.

Currently, the language acquisition research is predominantly done by linguists. In my opinion, it is the reason why the progress in this field is so slow. It is very clear that researches that trained only in linguistic alone cannot leverage advances in other fields that are related to natural language processing, such as Neural Machine Translation which is a part of Machine Learning, Neuroimaging which is a part of Neuroscience, Neuromorphic Chips which are part of Electronics, and Dynamical Systems which are part of Physics. The mere luck of mathematical modeling is a very constraining factor, and it propelled all fields mentioned above. That is why groups that consists of generalists that have good grasp of math, machine learning, neuroscience and engineering will be most efficient in advancing the research and practical implementation of language acquisition. 

Clearly defined goal

As Jeff Hawkins from Numenta that is focused on developing general neocortex algorithm based on neurological evidence mentioned we have enough data to work on general theory of neocortex functioning. There is no lack of data, in opposite the data is in abundance. What lacks is the clear goal of what we want to achieve and clear plan how to move into right direction. It seems to me the best approach should be something along the lines of Lunar Program back in 60th and 70th of 20th century. Though there is no need to invest billions of dollars to make a progress, but dedicated people with right background and well defined goals.

References