Why A.I and machine learning is the key to unlocking creativity and productivity

Why A.I and #machinelearning is the key to unlocking creativity and productivity

  • A few weeks ago, the Royal Society of Machine Learning released a report considering the state of Machine Learning in the UK.
  • As with many reports and studies around the ‘rise of the machine learning’ era the Royal Society of Machine Learning’s report shouts about the potential that A.I solutions can provide to every corner of our Society, it also highlights legitimate concerns from the same societies that A.I technology is meant…
  • The report references a survey conducted by the Society, which shows that there is an overriding concern, also aired in the national press, that jobs will be stolen by the machine, big businesses will increase profits and the unemployment lines will grow.
  • The survey also showed that there is a real concern that skill levels will be eroded and that there should be a focus on up-skilling in areas of data science and machine learning, to provide a career path to those who will be displaced by the application of these solutions….
  • Teams that are impacted using machine learning and A.I can now focus on growing their business and personal success.

A few weeks ago, the Royal Society of Machine Learning released a report considering the state of Machine Learning in the UK. It contained a lot of valuable information on the history, current implementations and future uses of A.I – I wanted to share my highlights of that report and investigate what it means for businesses.
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Unsupervised learning of 3D structure from images

Show me a 2D image, and I'll build you a 3D model... deep learning goes 3D:

  • The observation is simply
  • If only there was some way to look at a 2D scene (e.g., an image from a camera) and build a 3D model of the objects in it and their relationships
  • All experiments used LTSMs with 300 hidden neurons and 10 latent variables per generation step.
  • The 2D projection is a projection operator from the model’s latent 3D representation to the training data’s domain (either a volume or an image in the experiments): .
  • We demonstrate the ability of our model to learn and exploit 3D scene representations in five challenging tasks.

Unsupervised learning of 3D structure from images Unsupervised learning of 3D structure from images Rezende et al. (Google DeepMind) NIPS,2016 Earlier this week we looked at how deep nets can learn intuitive physics given an input of objects and the relations between them. If only there was some way to look at a 2D scene…
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