Alexa, are you ready to have a body?
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Mayfield Robotics, the manufacturer of the Kuri home robot,shut down in August.
Boston Dynamics is stillstruggling to find real-world problems to solvewith its robots.
These are just a few of a string of failed robot projects, with probably more to come.
To be clear, Alexa is backed by one of the largest and richest tech companies in the world.
Amazon sits on a wealth of data, money and experience in creating tech products.
But will Amazons virtually limitless resources be enough to overcome the challenges of creating an Alexa-backed robot?
That isexactly what contemporary AI lacks.
Robots and self-driving cars usecomputer visionto analyze their surroundings and navigate the world.
At the moment, the most popular AI technique used in computer vision isdeep learning.
Deep learning algorithms ingest a huge number of examples to develop their behavior.
This is why its so hard to designrobots and self-driving cars that can navigate open environments.
These hardware additions reduce error rates (and raise the costs).
Alexa will be facing an even bigger problem if it wants to handle objects as well as navigate environments.
Robots have historically been bad at handling objects, unless in a very controlled environment.
The next question will be, what should this robot do?
These are the kind of things you could expect from an inanimate object sitting on your table.
We will expect our AI assistant to manifest human-like behavior and intelligence.
We will expect them to have many of the cognitive skills that we take for granted.
To be clear,AI assistants are already strugglingto perform tasks that require multiple steps.
Some of those problems are due to the limits of a voice-only interface.
Theyre also not very good at going back and forth between multiple steps.
Thats why tasks like playing music and setting timers remain the more popular use cases for smart speakers.
But the bigger problem of digital assistants arethe limits of contemporary AI in understanding and processing human language.
Advances in deep learning and neural networks have created breakthroughs in automated speech recognition and natural language processing.
AI is now better than ever in transforming speech to text and mapping text to commands.
But AI is still struggling to understand the context and meaning of words.
At the heart of the most complicated language processing AI algorithms is still statistics.
Your smart speaker will be able to respond to different variations of What is the weather tomorrow?
Hows the weather on Monday?
and Will it rain next week?
But that is only because it has seen thousands of similar sentences and the corresponding function they must perform.
It has no understanding of the concepts of weather, rain and weekday.
your smart speaker will not be able to respond.
But for a human, it would be a no-brainer.
But the language understanding problem will not go away.
Meanwhile, we have a tendency toanthropomorphize anything that scantly behaves or looks like humans.
Whats the optimal use for AI assistants?
What does this mean for digital assistants like Alexa, Siri and Cortana?
Here are two scenarios that work best with current AI technology.
It would sound like a single AI-powered gadget that can perform thousands of tasks.
The owner of the robot would have no way of knowing what the gear can and cant do.
Theres a lot of ground for confusion and errors.
The narrow AI approach is to have multiple Alexa-powered devices that can perform specific tasks.
This is something that Amazon has already tested successfully.
The idea would be to see more AI-powered gadgets in homes, offices and cars.
From a functional standpoint, this approach would work within the boundaries of current AI technology.
But it isnt a perfect solution.
One of the areas where AI assistants can perform augmented intelligence is AR headsets.
For instance, users can query for information while using the headset.
AR headsets also enable better cooperation between humans and AI.
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