Posts Tagged ‘AI’
February 11, 2019
Voice Assistants are growing in both popularity and capability. They are arriving in our home, cars, mobile devices and seem to now be a standard part of American culture, entering our tv shows, movies, music, and Super Bowl ads. However, this popularity is accompanied by a persistent concern over our privacy and the safety of our personal data when these devices are always listening and always watching.
There is a significant distrust of big companies like Facebook, Google, Apple, and Amazon. Facebook and Google have admitted to misusing our private data, and Apple and Amazon have admitted that system failures have led to a loss of private data.
So naturally, there would be an advantage of not sending our voices or videos into the cloud and doing the processing on-device. Then no data loss is at risk. Cloud-based queries could still occur, but through anonymized text only.
COMPUTING AT THE EDGE VERSUS THE CLOUD
Some have argued that we have carried microphones and cameras around with us for years without any issues, but I see this thinking as flawed. Just recently, Apple admitted to a facetime bug on mobile phones enabling “eavesdropping” on others.
Also, if my phone is listening for a wake word it’s a very different technology model than an IoT device that’s “always on.” Phones are usually designed to listen in arms-length situations of 2 or 3 feet. An IoT speaker is designed to listen to 20 feet! If we assume constant noise across a room that could make an assistant “false fire” and start listening, then we can think of 2 listening circles, one with a radius of 3 feet and one with a radius of 20 feet, to compare the listening area of the phone with a far-field IoT device such as a smart speaker. The phone has a listening area of π r2 or 9 π, the IoT device has a listening area of 400 π. So, all else equal the IoT device is about 44 times more likely to false fire and start listening when it wasn’t intended to.
As cloud-based far-field assistants enter the home there is a definite risk of our private data getting intercepted. It’s not just machine errors but human errors too, like the Amazon employee that accidentally sent out the wrong data to a person that requested it.
There are also other means in which we can lose our cloud-connected private data like the “dolphin attack” that can allow outsiders to listen in.
ON-DEVICE VOICE ASSISTANTS WILL BECOME MORE COMMON
February 10, 2017
The wonders of deep learning are well utilized in the area of artificial intelligence, aka AI. Massive amounts of training data can be processed on very powerful platforms to create wonderful generalized models, which can be extremely accurate. But this in and of itself is not yet optimal, and there’s a movement afoot to move the intelligence and part of the learning onto the embedded platforms.
Certainly, the cloud offers the most power and data storage, allowing the most immense and powerful of systems. However, when it comes to agility, responsiveness, privacy, and personalization, the cloud looks less attractive. This is where edge computing and shallow learning through adaptation can become extremely effective. “Little” data can have a big impact on a particular individual. Think how accurately and how little data is required for a child to learn to recognize its mother.
A good example of specialized learning is when it comes to accents or speech impediments. Generalized acoustic models often don’t handle this well, resulting in customized models for different markets and accents. However, this customization is difficult to manage, can add to the cost of goods, and may negatively impact the user experience. Yet, this still results in a model generalized for a specific class of people or accents. An alternative approach could begin with a general model built with cloud resources, with the ability to adapt on the device to the distinct voices of the people that use it.
The challenge with embedded deep learning occurs in its limited resources and the need to deal with on-device data collection, which by its nature, will be less plentiful, unlabeled, yet more targeted. New approaches are being implemented such as teacher/student models where smaller models can be built from a wider body of data, essentially turning big powerful models into small powerful models that imitate the bigger ones while getting similar performance.
Generative data without supervision can also be deployed for on-the-fly learning and adaptation. Along with improvements in software and technology, the chip industry is going through somewhat of a deep learning revolution, adding more parallel processing and specialized vector math functions. For example, GPU vendor nVidia taking has some exciting products that take advantage of deep learning. Some smaller private embedded deep learning IP companies like Nervana, Movidius, and Apical are getting snapped up in highly valued acquisitions from larger companies like Intel and ARM.
Embedded deep learning and embedded AI is here.