There is one big problem that machine learning usually faces: The acquisition of data. This has been one of the bigger hindrances to train speech recognizers for quite some time. A nice read in this context is a blog from Arthur Chan from seven years ago, where he explains his thought on true open source dictation: http://arthur-chan.blogspot.de/2009/04/do-we-have-true-open-source-dictation.html
This problem increased, when deep learning entered the scene of speech recognition. More and more data is needed to create convincing systems. The story continues with spoken dialog management. Apple seems to want to make a step forward in this direction with the acquisition of VocalIQ: http://www.patentlyapple.com/patently-apple/2015/10/apple-has-acquired-vocal-iq-a-company-with-amazing-focus-on-a-digital-assistant-for-the-autonomous-car-beyond.html
All news tried to see this in the light of Apple's efforts towards integration into the automotive market. CarPlay http://www.apple.com/ios/carplay/ to display apps on the dashboard and what some people call iCar http://www.pcadvisor.co.uk/new-product/apple/apple-car-rumours-what-on-earth-is-icar-3626110/ were recently in the news.
I am not sure if there really is such a relation. It might be useful for Siri as well. Adaptive dialogs have been a research topic for some years, now. Maybe, it is time for this technology to address a broader market.
So far, Apple seemed to be reluctant with regard to learned dialog behavior. In the end, these processes cannot guarantee a promised behavior. This is also one of the main reasons, why this technology is not adopted as fast as in other fields where (deep) learning entered the scene. Pieraccini and Huerta describe this problem in Where do we go from here? Research and commercial spoken dialog systems as the VUI-completeness principle. They describe it as "the behavior of an application needs to be completely specified with respect to every possible situation that may arise during the interaction. No unpredictable user input should ever lead to unforeseeable behavior. Only two outcomes are acceptable, the user task is completed, or a fallback strategy is activated..." This quality measure has been established throughout years and is not available with statistical learning of the dialog strategy. In essence, this fear can be described as follows: Let's assume the user is asking "Hey, what is the weather like in Germany?". In (the very unlikely case) that it is in the data, the system may have learned that a good answer to this could be "Applepie".
Consequently, the data to train the system has to be selected and filtered. Sometimes, such a lack is discovered while the system is running. Usually, this is the worst case scenario. Recently, this happened to Apple's Siri. A question to Siri where to hide a dead body became evidence in a murder trial. Siri actually came up with some answers.
Screenshot of Siri 's answer to hide a body
Now, it has been corrected and Siri simply answers "I used to be able to answer this question.".
Similarly, Microsoft was in the news with its artificial agent Tay. Tay was meant to learn while people were interacting with it. It took less than 24 hours from the statement "Humans are super cool" to "“Hitler was right.”. Data was coming more or less unfiltered from hackers aiming to shape this attitude of Tay.
Again, the base problem is in the ethics of the data: selection and filtering. But what are the correct settings for that? Who is in charge of determining the playground? Usually, this is the engineer developing the system (and thus his ethical background).
This "other problem of machine learning" seems to be not in the focus of those developing machine learning systems. Usually, they are busy with coming up with some data at all to initially train their system at all.
However, this problem is not really new. Think of Isaac Asimov who invented the laws of robotics. He already had the idea of guidance criteria to machine behavior. Maybe, we are in the need to develop something in this light while we move on this road.
And this is also true for spoken dialog systems that actively learn their behavior from usage as adaptive dialogs. It will be awkward to see learning systems out there that change their behavior to something that was never intended by the developer. I am waiting for those headlines.
Similarly, Microsoft was in the news with its artificial agent Tay. Tay was meant to learn while people were interacting with it. It took less than 24 hours from the statement "Humans are super cool" to "“Hitler was right.”. Data was coming more or less unfiltered from hackers aiming to shape this attitude of Tay.
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| Evolvement of Tay on Twitter, from https://twitter.com/geraldmellor/status/712880710328139776/photo/1 |
Again, the base problem is in the ethics of the data: selection and filtering. But what are the correct settings for that? Who is in charge of determining the playground? Usually, this is the engineer developing the system (and thus his ethical background).
This "other problem of machine learning" seems to be not in the focus of those developing machine learning systems. Usually, they are busy with coming up with some data at all to initially train their system at all.
However, this problem is not really new. Think of Isaac Asimov who invented the laws of robotics. He already had the idea of guidance criteria to machine behavior. Maybe, we are in the need to develop something in this light while we move on this road.
And this is also true for spoken dialog systems that actively learn their behavior from usage as adaptive dialogs. It will be awkward to see learning systems out there that change their behavior to something that was never intended by the developer. I am waiting for those headlines.
