(testing signal)

Tag: computation

Biologically-inspired Neural Networks for Self-Driving Cars

Watch more in the videoDeep Neural Networks And Other ApproachesResearchers are always looking for new ways to build intelligent models. We all know that really deep supervised models work great when we have sufficient data to train them, but one of the hardest things to do is to generalize well and do it efficiently. We can always go deeper, but it has a high computation cost. So as you may already be thinking, there must be another way to make machines intelligent, needing less data or at…… Read more...

The Coming Age for Tech x Bio: The ‘Industrial Bio Complex’

We’ve now seen multiple trends come to fruition at the intersection of bio and technology over the past decade: A Moore’s Law for bio, thanks to computation; machine learning and AI transforming many areas of bio pharma and healthcare; the ability to not just “read”, but “write”, to bio, including CRISPR (even in just a decade). We’re also seeing the rapid unbundling of care delivery as well now, driven by “the great unlock” — which includes the unbundling of the hospital…… Read more...

A Hidden Gem of CI: Evolutionary Computation

Photo by Jr Korpa on Unsplash

Fourier Transforms (and More) Using Light

Linear transforms — like a Fourier transform — are a key math tool in engineering and science. A team from UCLA recently published a paper describing how they used deep learning techniques to design an all-optical solution for arbitrary linear transforms. The technique doesn’t use any conventional processing elements and, instead, relies on diffractive surfaces. They also describe a “data free” design approach that does not rely on deep learning.

There is obvious appeal to using light to compute transforms. The computation occurs at the speed of light and in a highly parallel fashion. The final system will have multiple diffractive surfaces to compute the final result.… Read more...

Wavelet Transforms in Python with Google JAX

A simple data compression example

Wavelet transforms are one of the key tools for signal analysis. They are extensively used in science and engineering. Some of the specific applications include data compression, gait analysis, signal/image de-noising, digital communications, etc. This article focuses on a simple lossy data compression application by using the DWT (Discrete Wavelet Transform) support provided in the CR-Sparse library.

For a good introduction to wavelet transforms, please see:

Wavelets in Python

There are several packages in Python which have support for wavelet transforms. Let me list a few:

  • PyWavelets is one of the most comprehensive implementations for wavelet support in python for both discrete and continuous wavelets.
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Top 10 Fastest Growing Technologies in Last 5 Years – IndianWeb2.com

1. Computer systems based on Biological Models –

With a CAGR growth of 67.28% in number of patents filed from 2016 to 2020, Computer systems based on biological models (Patent Class – G06N 3 & Its Subclass) is the top fastest growing technology.

As per IFI, this technology refers to computing systems where the computation is based on biological models (brains, intelligence, consciousness, genetic reproduction) or is using physical material of biological origin (biomolecules, DNA, biological neurons, etc.) to perform the computation. The computation can be digital, analogue or chemical in nature.

According to IFI, many growing sub technologies are included within class G06N of Patents (Computer systems based on specific computational models) which is the top area in terms of new technology developments right now.
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DOE Announces $26 Million to Advance Chemical and Materials Sciences with Data Science

Newswise — WASHINGTON, D.C. – The U.S. Department of Energy (DOE) announced $26 million in funding to harness cutting-edge research tools for new scientific discoveries fundamental to clean energy solutions. The 10 projects announced today will help scientists to unleash the power of data science —including artificial intelligence and machine learning (AI/ML)—on experiments, theory, and computation-based methods to tackle the basic science challenges that will enable clean energy technologies, improve energy efficiency, and advance our understanding of chemical and materials systems.

“Data science, and especially AI/ML, provides unique opportunities to leapfrog to novel capabilities for understanding fundamental properties and processes in physical and chemical systems,” said Dr.

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Introducing Packed BERT for 2x Training Speed-up in Natural Language Processing

By Dr. Mario Michael Krell, Principal Machine Learning Lead at Graphcore & Matej Kosec, AI Applications Specialist at Graphcore

Image by author.

By using a new packing algorithm, we have sped up Natural Language Processing by more than 2 times while training BERT-Large. Our new packing technique removes padding, enabling significantly more efficient computation.

We suspect this could also be applied to genomics and protein folding models and other models with skewed length distributions to make a much broader impact in different industries and applications.

We introduced Graphcore’s highly efficient Non-Negative Least Squares Histogram-Packing algorithm (or NNLSHP) as well as our BERT algorithm applied to packed sequences in a new paper [1].

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7. How TensorFlow Works?

Tensor Flow permits the subsequent:

  • Tensor Flow helps you to deploy computation to as a minimum one or extra CPUs or GPUs in a computing tool, server, or mobile device in a completely easy manner. This way the matters may be completed very speedy.
  • Tensor Flow lets you specific your computation as a statistics glide graph.
  • Tensor Flow helps you to visualize the graph using the in-constructed tensor board. You can test and debug the graph very without difficulty.
  • Tensor Flow offers the amazing regular overall performance with an capability to iterate brief, teach models faster and run more experiments. Python Course Online
  • Tensor Flow runs on almost the entirety: GPUs and CPUs—together with cellular and embedded systems—or even tensor processing gadgets (TPUs), which may be specialized hardware to do the tensor math on.
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How to do “Limitless” Math in Python

Sounds like a catchy title? Well, what we really meant by that term is arbitrary-precision computation i.e. breaking away from the restriction of 32-bit or 64-bit arithmetic that we are normally familiar with.

Here is a quick example.

This is what you will get as the value for the square-root of 2 if you just import from the standard math module of Python.

You can use Numpy to choose if you want the result to be 32-bit or 64-bit floating-point number.

But what if you wanted the result up to 25 decimal places…

1.414213562373095048801689

Or, 50 decimal places?

1.4142135623730950488016887242096980785696718753769

How are we getting these results?

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About Deep learning as subset of machine learning and AI

Deep learning has wide application in artificial intelligence and computer vision backed programs. Across the world, machine learning has added more value to a range of tasks using key methodologies of artificial intelligence such as natural language processing, artificial neural networks and mathematical logics. Off lately, deep learning has become central to machine learning algorithms which are required to do highly complex computation and handle gigantic data.

With a multi-layer neural architecture, deep learning has been solving multiple scenarios and presenting solutions that work. There are several deep learning methods which are actively applied in machine learning and AI.

Types of Deep learning methods for AI programs

1.

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How to Attack a Blockchain

Some interesting post brings to me some ideas to the table. Lets discuss how to theorecally attack a BC and commit fraud on it (never done since the start in 2008).

  • Shutting down the Internet.
  • Or shutting down all the nodes running the BC.

The 51% Attack

Its a very large flaw in public BC like BTC. If a single entity had the majority of the hash rate then it could manipulate the public ledger. This would undermine confidence and create panic among participants, prevent transactions to be recorded, reverse transactions, prevent other miners to find blocks, etc. Its very costly attack because of the computation cost.… Read more...

Criptografía ECDSA y Computación Cuántica

Los ordenadores clásicos no pueden romper ECDSA mediante ataques de fuerza bruta. No hay energía suficiente en el Sol por ejemplo para adivinar de forma correcta una sóla de las claves.

Pero un computador cuántico (de existir) puede usar un Algoritmo Shor para reconstruir una clave privada desde una clave pública. El problema: la computación cuántica es ciencia ficción.

En torno a la mitad de las direcciones BTC se hacen públicas, ya que cuando alguien envía dinero en BTC o ETH, la clave pública de la dirección pagadora se revela, y almacena para siempre en la BC.

Por tanto en algún momento del futuro todas esas direcciones corren el riesgo de ser hackeadas mediante computación cuántica.… Read more...

¿Por qué no Vivimos en una Simulación de Ordenador?

Un debate que aparece con recurrencia en determinados ámbitos. Los indicios de que vivimos (somos) una simulación realizada con algún tipo de ordenador. Una teoría que gana peso cada año que pasa sin poder refutar la Paradoja de Fermi.

El problema es que aunque el Universo fuera computable (Lloyd aporta 10^120 operaciones cuánticas), esto no significa que lo sea de una forma útil para nosotros. Wolpert prueba que 2 computadoras no se pueden simular la una a la otra, lo que implica que un computador no se puede simular a sí mismo.

Un computador no puede tener la suficiente memoria como para saber su propio estado porque necesita al menos un bit más para observar su simulación, y debe incluir ese bit en su estado.… Read more...