Machine Learning and Misinformation

Machine Learning and Misinformation.

  • Communication is naively defined as content and the mode of transmission — symbols manifested as images, language transmitted through speech and writing, digital files sent through the internet.
  • Most machine learning in today’s products is related to understanding — your phone can translate your voice into text and you can search photos for certain objects or people because of machine understanding.
  • Generative modeling is a machine learning technique that creates new data that mimics the data that the machine was trained on.
  • In an earlier example, image search was used as an example of computers aiding in communication by helping you find an image that approximates what your mind’s eye sees.
  • Instead of returning an image that already exists, the generative system creates an entirely new image based on the text.

Communication is an essential pillar of society. Humanity’s progression over the past millennium was largely driven by the development and evolution of commu…
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6 areas of AI and Machine Learning to watch closely

#ICYMI 6 areas of #AI and #MachineLearning to watch closely

  • Those who are able to train faster and deploy AI models that are computationally and energy efficient are at a significant advantage.
  • Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”.
  • If we want AI systems to solve tasks where training data is particularly challenging, costly, sensitive, or time-consuming to procure, it’s important to develop models that can learn optimal solutions from less examples (i.e. one or zero-shot learning).
  • Deep learning models are notable for requiring enormous amounts of training data to reach state-of-the-art performance.
  • Without large scale training data, deep learning models won’t converge on their optimal settings and won’t perform well on complex tasks such as speech recognition or machine translation.


Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.

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The major advancements in Deep Learning in 2016

The major advancements in Deep Learning in 2016

  • In a nutshell, InfoGAN is able to generate representations that contain information about the dataset in an unsupervised way.
  • This model achieves state of the art results in all but POS tagging (where it came out in second place).
  • It goes a step further, training a single model that is able to translate between multiple pairs of languages.
  • Called Generative Adversarial Networks , it has enabled models to tackle unsupervised learning.
  • The model is trained separately for each pair of languages like Chinese-English.

Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.
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