Google – Brain game pt. II.

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Google remains out front in AI but Baidu most interesting. 

  • The first results from Google’s AutoML project are beginning to surface and are implying once again (see here) that machines may end up being better coders than humans.
  • AutoML was announced at Google i/o in May 2017 and failed to attract much attention mainly because I suspect that most commentators did not grasp the significance of the concept.
  • AutoML is neural network that is capable selecting the best from a large group neural networks that are all being trained for a specific task
  • This is potentially a hugely important development as it marks a step forward in the quest to enable the machines to build their own AI models (challenge no. 3 (see below)).
  • Building models today is still a massively time and processor intensive task which is mostly done manually and is very expensive.
  • If machines can build and train their own models, a whole new range of possibilities is opened-up in terms of speed of development as well as the scope tasks that AI can be asked to perform.
  • RFM has highlighted automated model building as one of the major challenges (see here) of AI and if Google is starting to make progress here, it represents a further distancing of Google from its competitors when it comes to AI.
  • In the subsequent months since launch, AutoML has been used to build and manage a computer vision algorithm called NASNet.
  • AutoML has implemented reinforcement learning on NASNet to improve its ability to recognise objects in video streams in real time.
  • When this was tested against industry standards to compare it against other systems, NASNet outperformed every other system available and was marginally better than the best of the rest.
  • I think that this is significant because it is another example of when humans are absent from the training process, the algorithm demonstrates better performance compared to those trained by humans.
  • The previous example is AlphaGo Zero (see here).
  • I see this as a step forward in addressing RFM’s three big challenges of AI (see here) but there remains a very long way to go.
  • These problems are:
    • First: the ability to train AIs using much less data than today,
    • Second: the creation of an AI that can take what it has learned from one task and apply it to another and
    • Third: the creation of AI that can build its own models rather than relying on humans to do it.
  • When I look at the progress that has been made over the last year in AI, I think that Google has continued to distance itself from its competition.
  • Facebook had made some improvements around computer vision, but its overall AI remains so weak that it is being forced to hire 10,000 more humans because its machines are not up to the task (see here).
  • Consequently, I continue to see Google out front followed by Baidu and Yandex with Microsoft, Apple and Amazon making up the middle ground.
  • Facebook remains at the back of the pack and its financial performance next year is going to be hit by its inability to harness machine power.
  • For those looking to invest in AI excellence, Baidu is the place to look as its search business and valuation has been hard hit by Chinese regulation but is now starting to recover.
  • Baidu represents one of the cheapest ways to invest in AI available.

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