How an artificial brain could help us outsmart hackers

How an artificial brain could help us to outsmart hackers

  • The big conceptual difference between deep learning and traditional machine learning is that deep learning is the first, and currently the only learning method that is capable of training directly on the raw data (e.g., the pixels in our face recognition example), without any need for feature extraction.
  • When applying traditional machine learning, it is necessary to first convert the computer files from raw bytes to a list of features (e.g., important API calls, etc), and only then is this list of features fed into the machine learning module.
  • Additionally, unlike traditional machine learning, which reaches a performance ceiling as the number of files it is trained on increases, deep learning can effectively improve as the datasets grow, to the extent of hundreds of millions of malicious and legitimate files.
  • The results of benchmarks that compare the performance of deep learning vs traditional machine learning in cybersecurity show that deep learning results in a considerably higher detection rate and a lower false positive rate.
  • As malware developers use more advanced methods to create new malware, the gap between the detection rates of deep learning vs traditional machine learning will grow wider; and in coming years it will be critical to rely on deep learning in order to have a realistic chance of foiling the most sophisticated attacks.

During the past few years, deep learning has revolutionized nearly every field it has been applied to, resulting in the greatest leap in performance in the history of computer science.
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Messing around with OpenAI Gym – craftworkz

Ever heard of @OpenAI? We did some research...  #ai #openai #ArtificialIntelligence

  • OpenAI Gym is a cool platform for anybody involved with reinforcement learning algorithms.
  • To be clear, OpenAI Gym doesn’t power any algorithms itself, leaving it up to more specialised packages like TensorFlow or Theano.
  • The platform will allow you to test your algorithms in a variety of different environments without having to go through the hassle of making the right inputs available to your algorithm.
  • Data scientist at Craftworkz designing chatbots and developing robotics applications
  • That’s right, you can test the performance of your reinforcement learning algorithms on a variety of different atari games and what’s more, you can automatically upload the performance of your algorithms and compare them to other people’s approaches.

So while I was looking around for interesting Python-based AI projects I came across OpenAI Gym, backed by mister Elon Musk himself. This application aims to provide the ultimate sandbox environment…
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GitHub

Introduction to Deep Learning for Image Recognition - SciPy US 2016 w/ slides |

  • The notebook accompanies the Introduction to Deep Learning for Image Recognition workshop to explain the core concepts of deep learning with emphasis on classifying images as the application.
  • The slides used for the workshop are available
  • Python data stack is used for the workshop.
  • Unsupervised learning using Autoencoders
  • Depending on time, the following topics might be covered

Read the full article, click here.


@YhatHQ: “Introduction to Deep Learning for Image Recognition – SciPy US 2016 w/ slides |”


scipyUS2016_dl-image – Introduction to Deep Learning for Image Recognition – SciPy US 2016


GitHub