All posts in: Neural networks

Neuromorphic computing simulates analog processes of the human brain. Digital (more properly binary) computing can *simulate* but not *duplicate* analog processes. Various scientists are working to integrate actual analog computing in binary systems. See link below.

Apart from the inherent dichotomy between analog and digital, we tend to have a limited view of the “brain.” We can’t divorce its biological host. The brain developed and always remains an integrated, inseparable part of the physical body. It is not a “thing” but a process.

Perhaps most importantly, only humans have consciousness. A brain without consciousness is, well, brain dead. We do not yet know the “architecture” of consciousness. We do know we all have intellect, which helps us to survive and to reason. We also have an identity created by our upbringing, cultural environment, etc., and we have a “database”, the knowledge we accumulate during our life and which is strictly personal and shaped by culture. The brain is more that a binary calculator.

Designing a computing system that makes logical decisions is relatively easy. An example is IBMs Big Blue beating the world chess champion. But only biological entities can have a brain that can develop its own consciousness – and subconsciousness.

The article below addresses some of these issues. It is followed by a list of resources.

A few years ago, ML algorithms looked strange and difficult for an average software engineer. ML is growing really fast. Nowadays it is easy to improve production solution by some Artificial Intelligence. You don’t need to have twenty people in your Data Scientist department if you want to extend you service with smart analytics or Artificial Intelligence.
I will show you how to apply smart search in your service.
Currently, our service is a place, where each user can share their articles, documents, videos, calendar events, tasks and etc. So we have a huge database with users’ content. Now it is a problem for a user to search a certain document or event. All items have tags and full text search. But what about video and audio files?

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The Flow Inspector is tool which can help you review your TensorFlow Graph in your Swift written program.

The video below shows the default layout of the Flow Inspector debugger and main interaction process.


The Four Parts of Debugging and the Debugging Tools
There are four parts to the debugging workflow:

  1. File Navigator – where you can select bin and source files.
  2. Source section to review your code and select certain function to review.
  3. Graph section to review your graph inside Flow Inspector.
  4. Console output section to review output and errors in your program.

Flow Inspector Alpha version is available on GitHub.

If you work with Swift for TensorFlow project, sooner or later you will face the debug problem. The root of the issue is that lldb can’t get access to TensorFlow graph in you swift program.

Official documentation describes the compilation process:

Once the tensor operations are desugared, a transformation we call “partitioning” extracts the graph operations from the program and builds a new SIL function to represent the tensor code. In addition to removing the tensor operations from the host code, new calls are injected that call into our new runtime library to start up TensorFlow, rendezvous to collect any results, and send/receive values between the host and the tensor program as it runs. The bulk of the Graph Program Extraction transformation itself lives in TFPartition.cpp.

Once the tensor function is formed, it has some transformations applied to it, and is eventually emitted to a TensorFlow graph using the code in TFLowerGraph.cpp. After the TensorFlow graph is formed, we serialize it to a protobuf and encode the bits directly into the executable, making it easy to load at program runtime.

Actually the final graph is serialized into protobuf bytes and copied directly into the executable file.

I made a small debug tool, – Flow Inspector which can handle that problem.

You can find package template and readme on my GitHub page.

The application is available here.