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
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!
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.
I hope that this post will be the first one in a series of posts I want to write on the topic of second language acquisition abbreviated in linguistics as SLA. What is meant by SLA is a language that a person learns as a second language (L2) after he had acquired the first one which is a native language (L1). The research into the subject shows that the first and second language acquisitions are interconnected and may effect each other. So it makes sense to discuss first language acquisition too.
Why am I interested in this topic?
Since childhood I was interested in how we learn languages. As my life progressed from childhood to where I am now I happened to acquire two languages with a very high level of proficiency and learned a number of others to some extent. As a native Russian speaker growing in Ukraine I learned Ukrainian as a second language at school, but my knowledge of the language is quite superficial, though I can understand it when I hear it well. Then I learned and talked Hebrew for about 19 years. Even though I also studied English back in Ukraine I never knew it well before I started to learn it by mostly reading magazines back in 1999. So I would say that real experience with English language I started to gather for about 19 years too. Though, one important point to make is that I only started to use it for speaking communication purposes for about 2 years now. In addition, back in Tel Aviv University I studied a Japanese language for a year. But my diminishing knowledge of it is rudimentary.
To summarize the above I would rate my knowledge of the languages as below, when by knowledge I understand speaking, reading and writing.
I hope that this background description explains a little bit why I might be interested in understanding how we learn a new language be it second, third or N-language.
It is very strange that we know so little about how we learn first or second languages taking into consideration the advances in Neuroscience since early 2000 and Artificial Neural Networks starting from 2012 (also known now as Deep Learning). First, I heard about the subject of SLA back in 2004 when I studied Generative Linguistics in Tel Aviv University. When looking into the state of the art of the research back then I heard only about Noam Chomsky and Stephen Krashen’s research into this subject. Now almost 15 years since the state of the art of the research seems like frozen in the same place. But my intuition indicates, that by incorporating approaches from Supervised Machine Learning which includes Recurrent Neural networks such as LSTM and Convolutional Neural Networks with Attention Mechanism, along with a very promising research done at Numenta company and other approaches it is possible to make a significant progress in the field of second and first language acquisition.
The more detailed description of what I propose will be explained in further parts.
The Dawn Of Quantum Computing
There are great news for those of you who’ve heard about quantum computers and quantum computations. Finally, thanks to IBM Quantum Experience site it is possible to run experiments on a real 5 qubits Quantum Processor connecting to it via IBM Cloud interface! Isn’t this sound great? It is an advancement in computing that is equal in its importance to the transition from mechanical to digital computers and it can be hardly overestimated. I recommend to read this IBM’s article that explains the endeavor before proceeding any further.
What Is It All About?
IBM Quantum Experience is aimed to bring hands-on skills in programming quantum processor. This is not a simple task taking into consideration that Quantum Computations far complex to grasp in comparison to classical computations and requires understanding of quantum physics and linear algebra. But it can be done with effort and dedication. In addition, there is a need in motivation and curiosity.
If you have no prior knowledge of quantum computations you’d better learn a little bit at this link at IBM Quantum Computing site. In addition to this, to jump deep into experimenting with quantum algorithm there is a need to revive or acquire skills in Linear Algebra (this one is at Khan Academy) and quantum physics fundamentals concepts. Preferably, it is better to have a BSc degree in physics, engineering, mathematics etc.
How To Apply?
The application is straightforward. There is a need to register at the site. Then confirm the registration. Log in and compose your first quantum algorithm. One who has no previous knowledge in quantum computation is advised to go through very detailed guide at the site.
What Can Be Implemented On This Processor?
Well, this is exactly the question that I am asking myself right now too. I’ve found that it is possible to implement Deutsch–Jozsa algorithm and to see how it works for real. Following articles may help to understand how to do it.
What Else May Help?
There are additional resources that may come in handy tackling this topic.
And so it begins…
It’s a kind of magic
If you haven’t heard yet about Magnetic Levitation then check out the video of my final project for a BSc. in Electronics back in 2011 that did just that.
There are a number of types of magnetic levitation and you may find a lot of peculiar videos about it on YouTube. In addition, don’t miss this MagLev World Record video.
How does it work?
Specifically in my own project the maglev was implemented using digital controller that controlled the magnitude of current flowing in the electromagnet as a function of distance of a floating object from electromagnet steel core. A Hall effect sensor, actually, two of them were placed at opposite sides of electromagnet core and provided the readings of the magnetic field surrounding the electromagnet.
With a help from my friends
I’d like to thank once again Mr. Arie Shnaiderman an outstanding control engineer at ReWalk company who helped me make this instrument a real thing.
And so it begins …
This post is a very special one due to the fact that on 2/11/2016 it was announced that long ago predicted from General Relativity Theory gravitational waves were detected by
Laser Interferometer Gravitational-Wave Observatory (LIGO) in USA.
This is the most exciting discovery of the 21st century so far and its consequences are hard to predict. What is for sure that now we have a mean to probe what happens in the places such as where black holes collide.
I congratulate all of humankind with this achievement that is a great example of science teams collaboration around the world that is a way to go.
Resources you won’t want miss
- An interview with the Kip Thorne the genius behind the machines that made it a reality.
- Video from Washington’s National Science Foundation press-conference were this announcement was made.