Under the background of AI big data, how can smart home make use of scene data?

If the process of human exploration of artificial intelligence is compared to the process of conquering the vast ocean, then computing power, algorithms and data represent ships, power and navigation charts respectively. Computing power determines the type of ship, such as whether it is a cruise ship, a ship, or an aircraft carrier; the algorithm determines the sailing speed of the ship, because it determines whether the ship uses steam, diesel, or nuclear power; the data is a nautical chart, the richer and more accurate it is The power of data can shorten the distance between us and our destination.

In recent years, thanks to the rapid development of new technologies such as the Internet of Things, artificial intelligence, and 5G, smart homes have successively gone through the stages of "automation," single product intelligence, and "Internet of Things + home scenarios" in just a few years. "Three stages, entering the "intelligent" stage of the current "artificial intelligence + home scene", opening up the learning and exploration of human thinking and consciousness in smart homes.

In the movie "Iron Man", Tony Stark's artificial intelligence butler, Jasvi, almost satisfies all the audience's fantasies about "smart home". However, when "it" really comes into reality, although it also facilitates our life to a certain extent, it is still far from the ideal "perfection".

According to the survey of relevant practitioners, smart home technicians have not formed a good interactive relationship with the market. Although the products they develop are technologically advanced, they are poor in practicality, complicated in operation, and out of touch with market demand. The problem remains widespread. According to public data, among the products currently on the market, consumers’ lack of enthusiasm for consumption due to poor human-computer interaction experience accounts for 12.7%, and the reason for these phenomena is the lack of rich and accurate scene data. support.

As mentioned at the beginning of this article, our exploration of artificial intelligence is a process of conquering the ocean. From the start of artificial intelligence in the middle of the last century to today's exploration of the deep integration of artificial intelligence and application scenarios, under the premise that "ships" and "power" have been greatly guaranteed, once there is a training data deviation or lack of data in the "navigation chart" Support, then we will only run counter to our destination, and the distance will be farther.

1. AI data is to smart homes what nautical charts are to navigation.

Iron Man's friend, Jasvi, may be regarded as the highest goal of applying artificial intelligence to smart homes. Not only can it perform classic and funny scenes with Stark in daily life, but it can also interact with Stark in daily life. Can carry out "human-machine collaboration" and work together in tacit understanding.

All of these reflect the essence of artificial intelligence technology in the smart home field, an AI interactive revolution.

This revolution is dominated by interactive technologies such as voice interaction and visual interaction, and is supported by AI capabilities such as natural language processing and machine vision processing. At the same time, this also means that artificial intelligence’s demand for scene data almost fully covers voice, images, text, video and other fields.

In the AI system, computing power, algorithms and data are the "troika" that drive artificial intelligence, and they respectively play the role of infrastructure capabilities, guidance methods and algorithm basis. Only by collecting and labeling precise data (voice, images, text, videos, etc.) and using it for iterative training of the algorithm can a complete set of artificial intelligence data solutions be output. Jia Yuhang defines the relationship between the three as "mutual restriction and mutual promotion."

From this point of view, AI data applied in smart home scenarios is naturally the "source of living water" that feeds back solutions. If AI technology is to achieve the highest goal in smart home applications, AI data cannot be ignored and must contain richness. and accuracy, which also involves the issues of data collection and data labeling of artificial intelligence.

In fact, artificial intelligence players have a relatively consistent attitude towards AI data, including foreign giants such as Google, Microsoft, and Amazon, as well as domestic giants such as Baidu and Alibaba, all of which have made achievements in AI data. In addition, Cloud Measurement Data, as a leader in domestic data collection and annotation, has been called the "Top Five in Artificial Intelligence" by the media and SenseTime, Megvii, Tuya, and Horizon. This is also because the industry is well aware of the importance of high-quality data for AI applications, so it "combines" companies that have made achievements in different fields of algorithms, computing power, and data. In other words, this is also the industry's reliance on the development of artificial intelligence. way of expression.

From the perspective of speech recognition, for example, a cat elf recently announced that it has launched Sichuan dialect. Users can use Sichuan dialect to talk to it when using daily life and entertainment functions such as alarm clock, weather, guess what, etc.

But in fact, in addition to dialect, factors such as gender, age, speaker's speaking speed, speaking background, noise, emotion, language type, etc. are all key to forming a rich and real interaction sample. This requires handing over some specific data to artificial intelligence to forcibly summarize a specific rule. This rule has certain applicability, allowing real users to achieve a real application experience during use.

Or from the perspective of semantic recognition, when we need to turn on the air conditioner, by speaking a command, the machine may be able to understand and turn on the air conditioner. But when it comes to deep logical meanings, can machines “understand” them? Especially as the corresponding functions become more and more abundant, more and more subcategories will be split, such as temperature adjustment after the air conditioner is turned on, wind speed adjustment and other different attributes.

Therefore, if smart homes want to be truly implemented, they must rely on rich, high-quality AI data to feed back AI algorithms to help smart home industry companies improve user experience on the road to exploring the potential of AI, while achieving their own goals. cost reduction and efficiency improvement. Just like a sailing ship must require a nautical chart with complete and accurate data to reach its destination.

2. AI big data helps the industry navigate to the other side of success

At present, AI big data relies on the two major strengths of scenario laboratories and data annotation bases to provide high-quality scenario-based AI data services for smart driving, smart cities, smart finance and other fields in addition to the smart home field, and fully supports text , voice, image, video and other types of data processing.

Facing the smart home industry, the core capabilities of AI data collection include wake-up word collection, control word collection, designated corpus collection, face collection, emotion type collection, Chinese and English, domestic dialects, Southeast Asian, European, African minor languages and other collection types , support scene data collection of smart speakers, smart TVs, smart sweeping robots, etc.; the core capabilities of data labeling include character voice transcription, behavioral intentions, voiceprint recognition, domain recognition, sentence generalization, semantic segmentation and other labeling types, support smart speakers, Scene data labeling of smart TVs, smart sweeping robots, etc.

Jia Yuhang believes that the development trend of AI data in the field of smart home is developing in three directions: multimodal, emotional, and subdivided. For this, AI big data has also established its own service system to continuously meet the needs of enterprises in the field of smart home. Demand for AI data services.

The first is specialization, customization and sceneization. With the development of AI, AI data has experienced a relatively chaotic development period, and has derived different business forms. For example, in the "prehistoric stage" of data capture through crawler technology, or in the early days of artificial intelligence technology, general data sets are also "unique". Now, AI data provides services for enterprises that are about to land in the current AI industry by providing scenario-based data collection and data labeling services.

The scene laboratory and the data labeling base are the two "magic weapons" of AI data: the first is the data scene laboratory, in order to deal with all the scenes that may appear in the smart home scene, such as environmental conditions such as light, noise, background, and for Different factors such as race, language, age, gender, etc., AI data can build and simulate "real scenes" for different needs, and then provide data collection for customers in the smart home field to restore the scene; secondly, the data labeling base, through the Artificial intelligence trainers conduct professional training, and cooperate with their own data platform to carry out continuous iterations to ensure the accurate output of the entire data, and can provide professional customization for customers in security, home, driving, finance, Internet, retail, education and other fields services.

Followed by high efficiency, high precision, high quality. There is such a saying in the field of artificial intelligence: garbage in, garbage out. In other words, the quality of the data can be directly reflected in the final result through the "hardening" of the algorithm. This is actually a test of an AI data service provider's ability to control data accuracy, data process control, and data quality screening. Only high-quality AI data can accelerate the application of artificial intelligence to the greatest extent, help enterprises reduce costs and increase efficiency, and at the same time realize that good money in the market drives out bad money.

The artificial intelligence data service team of AI data has formed a complete operating system including task assignment, demand analysis, demand confirmation, data cleaning, bid confirmation, progress control, quality assurance and other processes. For example, cloud AI data sets the underlying rules for voice, text, picture, video and other categories, and has multiple review mechanisms, coupled with professional labeling logic, thinking and thinking ability, and knowledge in related fields, so as to efficiently output into one A set of high-quality and accurate smart home data solutions.

Finally, the most important data privacy security. AI data has a deep understanding of this, and enterprise data security is one of the important elements for an enterprise to form a core competitive barrier.

3. Smart home bursts out beautiful sparks

Asia's smart home market will grow to $26 billion by 2022 and $115 billion by 2030, accounting for more than 30 percent of the global market, according to a forecast by consulting firm AT Kearney. China will be key to Asia's growth. As far as the current stage of smart home is concerned, the intelligent stage of "artificial intelligence + home scene" will show continuous and long-term development potential. With the help of AI data, the smart home industry is bound to usher in strong growth and spark brilliant sparks.

I hope that the existing fields can go deeper and deeper, and at the same time expand to more fields to serve more artificial intelligence companies. I also hope that the products and projects of more artificial intelligence companies can be implemented faster, so that people can use it in their daily lives. Use; really let users feel understanding and warmth. If this vision is achieved, we who provide data annotation and data collection services will be satisfied.