Compared to other AI services in the market, our technology excels in four different ways, each providing unique benefit for our users.
- Cloud-independent, better privacy
- Transparent and human readable, not a "black box"
- Highly data-efficient: better performance with less resources
- Flexible, able to be retrained without starting over
- On-device learning and personalization, tailored to the individual
The first advantage was mentioned earlier: our AI system is independent and works in a stand-alone manner. Instead of putting the AI in a data center, it can live entirely on the device. This not only makes the AI system portable, but it also means the AI responds faster and is more reliable than other systems. To reframe this from the user's perspective, devices that use our AI will not slow down or break if they have a poor internet connection. Being stand-alone also means improved privacy. When the AI makes a decision, it doesn't need to send the user’s data to the cloud or any third-party services. Everything happens locally, and your data never touches outside systems.
We also designed our AI systems to be transparent and human readable, two qualities that are vital when it comes to AI oversight and safety. We’ve heard a lot about Deep Learning systems and how they are "black boxes." When a Deep Learning system makes a decision, it’s difficult to determine how or why it came to that decision. In our AI, every decision can be dissected and understood by non-technical users. If our AI produces an output that appears biased in some way, we can identify exactly where that bias originated. From there, we can retune the AI and reduce the bias without needing to start over or retrain the system.
Our AI systems also process data more efficiently than other AI systems. This means businesses will see more results and better insights from their data, leading to faster and bigger ROI. It also means businesses can use AI without needing to pay the enormous ante of Big Data or third-party computing. This efficiency extends to stand-alone devices I mentioned earlier such as Internet of Things (IoT) devices. If required, our AI can learn from streaming data as it arrives, without needing to store the data at all and without specialized hardware.
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Devices that use our AI will not slow down or break if they have a poor internet connection.
Finally, our AI system can be modified to perform new tasks without retraining, making it much more flexible than other AI systems on the market. Most of the AI systems we see in the marketplace are trained using "supervised learning" to perform a single task or decision. For example, a supervised learning system might learn how to classify an image as a cat or a dog after being given 10,000 pictures of cats or dogs. Once trained, supervised learning systems are limited to the exact task they were trained to perform, even if some new decision or task is very similar or in the same domain. Our AI system uses "unsupervised" methods, learning continuously from all the data it receives. As a result, our AI system is much more flexible and is not limited to any single task or decision. Instead, it can change from one decision to another without retraining, using the information it has already learned.
One final advantage is worth mentioning. When our AI is running on a personal device like a smartphone or autonomous vehicle, it can personalize itself to the individual who is using that device. This is different than other forms of AI and machine learning in which the system learns an aggregate model of all users. One area where personalized AI has a lot of potential is in video games. Instead of the video game responding the same way to each player, our AI can tailor itself to the individual. This personalization would create new challenges for the player and provide opportunities to experience the game in a novel and exciting way.
In the end, these advantages provide tremendous cost savings by reducing the workload of technical personnel as well as the cost of Big Data and cloud-based AI services. They also accelerate ROI by enabling developers to get their AI products to market faster than if they were using traditional methods.