On the surface, artificial intelligence voice assistants like Siri, Alexa, Cortana and Google Assistant seem smart, connected and somewhat human.
They handle queries well and use natural speech to share information, news and keep you updated. But every so often, their true nature is exposed and they fail to come up with a proper response. In these moments, you are reminded more than ever that they’re just computer-based systems.
What’s the key to eliminating said quirks and truly launching modern AI systems and assistants into the annals of human achievement? How can developers and software engineers craft an AI machine that is truly lifelike?
Researchers from Carnegie Mellon University say they have the answer and were able to, in fact, create a conversational agent like Alexa that’s very tough to stump.
The CMU agent is called Evorus. It uses a rather unique method to authenticate speech and build new working profiles. Humans are simultaneously using and training the system’s AI, teaching it more about natural speech and ultimately helping it become less dependent on humans.
It “recruits” human workers on demand, thanks to the Amazon Mechanical Turk platform, in an effort to answer questions posed by users. Crowd workers then vote on the best answer provided.
As part of its training, Evorus monitors and records the questions, answers and communication between users. It will then later suggest the answers given — remember, the ones that are best voted by peers — for new questions that are posted.
Essentially, this means the AI is using real human responses and communication to answer future questions, albeit with its own form of input.
On a simpler level, Evorus is using real human interactions, speech and Feedback to become smarter and better. Applied in other constructs for similar AI systems, developers can really push the limits of their AI platforms, giving them not just more human and life-like speech patterns, but also more accurate responses.
What Can Human-Based Feedback and Training Do for AI?
Evorus is far from the only artificial intelligence using human feedback and interactions to learn and grow. Google’s DeepMind is another team working on human training for AI.
In fact, Google has been using latent semantic indexing for years. It’s a system for dramatically improving the results offered by their search engine. It’s a form of “smart” analytics — remarkably similar to how Evorus learns new answers — designed to look at more relevant, more accurate information. Over time, Google’s engine learns what fits best in what circumstance, which is how today’s search is so accurate and on-point.
There’s something to be said about incorporating these features and functions in the development stage of an AI or automated toolset.
Evorus, for example, is still technically in its infancy.
