When thinking about AI, some of the big examples that come to mind are of course the smart phone. But on a larger scale, one of the visions of AI progress has always been the Smart Home. Even before the explosion of AI devices to the current market, the idea of a smart house was one that very much intrigued the general public. Watchers of Disney Channel in the late 90s will remember the iconic (and slightly traumatizing) Disney Channel movie, Smart House. The story of an AI program that runs a family’s home and becomes sentient enough to think she is human and take over their lives may have its flaws when it comes to accuracy, but it was an introduction to many young minds to the concept of machine learning.
While the smart homes of today are much different than their Disney counterpart, one thing they do share is the process of machine learning. One of the main challenges in developing smart home technology is that the AI needs to continuously adapt to the user’s behavior and evolve to meet their needs.
As users go through their day-today lives, the AI is constantly processing data to become a better version of itself. But developing an AI program that can effectively collect and learn from data requires a strong foundation of, you guessed it, more data. Unlike a smart phone or a smart watch, a smart home is not just a singular device, but an interwoven system of AI programs working to make life more efficient for the user. Like any AI program, a subpar product can end up causing the user more frustration than good and become a nuisance more than a help. This outcome is one all AI developers seek to avoid and is a problem that needs to be headed off at the very start of the AI development process- with the data used to train it. Starting with a strong foundation of high-quality data gives the developer a much higher chance of success and leads to a more efficient and usable product.
In terms of smart home voice interaction, the variety of accents and colloquial expression of users makes the voice control system fails to deliver a satisfactory smart home experience. For example, generally, a smart air conditioning system can successfully deal with command like “open the air conditioner”. However, when colloquial expression like “It’s a hot day!” is used, it would be quite difficult for the built-in AI model to understand the meaning of the natural sayings and make a corresponding response. In addition, the inverted word order, hesitant and irresolute speech phenomenon in natural human communication also pose additional challenges to the smart home system.
The problem of faulty or low-quality data is one that MagicHub seeks to help fix. Access to high quality data, while vital, is also often an issue for machine learning engineers and AI developers. The process of data annotation is time consuming and when not done right, can have disastrous consequences. With a heavy focus on conversational language data, MagicHub has a wide range of data sets that meet the needs of most AI projects, especially those of conversational AI. For smart homes specifically, the wide variety of data sets focused of conversational speech in multiple languages is especially helpful. Home is a place where people feel comfortable. They are usually less formal, and less rigid in their speech. An effective Smart Home AI system must be able to recognize language that diverges from standardized language structure. As an open-source community, MagicHub not only provides machine learning engineers with a trove of high-quality data, it is a place where AI developers can problem-solve with the help of their peers and push AI development forward with the use of communal effort. As the development of AI becomes more data-centric, MagicHub provides a much-needed centralized location for high-quality data.
Smart Home development requires multiple variations of data types as the systems not only need to understand users but be able to identify different objects in the home as well. MagicHub provides a solution to the issue of needing multiple types of data by providing these data sets in one location. For more information about the data sets available or to contribute to an open-source community, please visit www.magichub.com.
MagicHub community is a data-centric AI community that provides resources for AI developers to promote innovation and progress within the field. Aside from the actual open-source data, Magic Hub also provides a space for data producers and users to ask questions, give advice, and collectively problem solve. This open-source community also gives us insight into the specific trends in challenges that AI developers may be facing and allows us to create data sets as solutions to those challenges.
As AI has created new levels of accessibility within video conferencing, MagicHub seeks to do the same in the world of machine learning and data annotation. Our goal is not just to produce high caliber data sets to meet the needs of the current level of AI, but to be part of the progress into the future of machine learning. While we have many excellent data sets for open source, here at MagicHub we understand the importance of open-source data to the future of AI and machine learning.