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2 1 AI and Speech Recognition
– Bots that look around for information;
– Bots that look around for information to complete a specific task; and
– Bots with social abilities and tasks (which he names social bots or chatbots).
For the first two, the reward structure is indeed pretty easy to be defined, while the
third one is more complex, which makes it more difficult to be approached nowadays.
When this third class will be fully implemented, though, we would find ourselves
living in a world where machines communicate among themselves and with humans
in the same way. In this world, the bot-to-bot business model will be something
ordinary and it is going to be populated by two types of bots: master bots and
follower bots.
I believe that research in speech recognition adds up, as well as the technology
stacks in this specific space. This would result in some players creating “universal”
bots (master bots) which everyone else will use as gateways for their (peripheral)
interfaces and applications. The good thing of this centralized (and almost monop-
olistic) scenario is, however, that in spite of the two-level degree of complexity, we
won’t have the black box issue affecting the deep learning movement today because
bots (either master or follower) will communicate between themselves in plain
English rather than in any programming language.
1.2 The Challenges Toward Master Bots
Traditionally, we can think of deep learning models for speech recognition as either
retrieval-based models or generative models. The first class of models uses heuristics
to draw answers from predefined responses given some inputs and context, while the
latter generates new responses from scratch each time.
The state of the art of speech recognition today has raised a lot since 2012, with
deep-q networks (DQNs), deep belief networks (DBN), long short-term memory
RNN, Gated Recurrent Unit (GRU), sequence-to-sequence learning (Sutskever et al.
2014), and tensor product representations (for a great overview on speech recognition,
look at Deng and Li 2013).
So, if DFL breakthroughs were able to improve our understanding of the machine
cognition, what is preventing us from realizing the perfect social bots? Well, there
are at least a couple of things I can think of.
First of all, machine translation is still in its infancy. Google has recently created a
“Neural Machine Translation”, a relevant leap ahead in the field, with the new version
even enabling zero-short translation (in languages which they were not trained for).
Second, speech recognition is still mainly a supervised process. We might need
to put further effort into unsupervised learning, and eventually even better integrate
the symbolic and neural representations.
Furthermore, there are many nuances of human speech recognition which we
are not able to fully embed into a machine yet. MetaMind is doing a great work in
the space and it recently introduced Joint Many-Tasks (JMT) and the Dynamic