AIoT stands for AI+IoT, which refers to the integration of artificial intelligence technology and the Internet of Things in practical applications. Currently, more and more people have combined AI and IoT together.

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What is AIoT?
AIoT stands for AI+IoT, which refers to the integration of artificial intelligence technology and the
Internet of Things in practical applications. Currently, more and more people have combined AI
and IoT together.
The field of AI and IoT integration has been hot in recent years. Whether it is the capital market
or mass entrepreneurship, all have shown great enthusiasm for it.
Market opportunities for human-computer interaction in the AIoT field
Since 2017, AIoT has become a hot word in the Internet of Things industry. At present, more and
more people have combined AI and IoT to see that AIoT, as the best channel for the intelligent
upgrade of major traditional industries, has become an inevitable trend in the development of
the Internet of Things.
In the market based on IoT technology, there are more and more scenarios for connecting with
people (such as smart home, autonomous driving, smart medical treatment, smart office). As
long as it is in contact with people, it is bound to involve the needs of human-computer
interaction. Human-computer interaction refers to the information interaction process between a
person and a computer using a certain dialogue language to complete a certain task in a certain
interactive manner. The scope of human-computer interaction is very wide, ranging from light
switches to dashboards on airplanes or control rooms in power plants. With the explosion of
smart terminal equipment, users have also put forward new requirements for the interaction
between humans and machines, which has gradually stimulated the AIoT human-computer
interaction market.

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AIoT development path
Taking the smart home market as an example, data shows that China's smart home market will
reach 180 billion yuan in 2018, and the smart home market will reach 357.6 billion yuan in 2020.
Analysts predict that the global smart home market will reach more than 500 billion yuan in 2021.
In the rapidly erupting AIoT market, the demands and prospects for human-computer interaction
are undoubtedly expected.
The digitalization process of human life has been going on for about thirty years. In these years,
we have experienced the evolution from the analog era to the PC Internet era and then the
mobile Internet era. At present, we are in the process of evolving into the Internet of Things era.
In terms of interaction, we can see that machines are more and more accommodating to people:
from the keyboard and mouse in the PC era to the touch screen, NFC and various MEMS sensors
in the mobile era, to the booming voice in the Internet of Things era With interactive methods
such as images/images, the threshold for use is becoming lower and lower, which has led to more
and more users being involved. At the same time, we need to pay attention to another profound
change, that is, due to the evolution of interaction methods, a large number of new dimensions
of data are constantly being created and digitized, such as work materials and entertainment
programs in the PC era, and users in the smartphone era. Habits, location, credit and currency,
and all kinds of possible new data in the Internet of Things era.
In the era of the Internet of Things, interactive methods are developing in the direction of
ontology interaction. The so-called ontology interaction refers to the basic ways of interaction
between people, such as voice, vision, movement, touch, and even taste, starting from the
person's ontology. For example, controlling household appliances by voice, or air conditioner
using infrared to determine whether it should cool down, and combining voice and infrared to
control temperature (when no one in the room is detected, even if cooling is mentioned in the TV
program, the air conditioner does not do reaction).
New data is the nourishment of AI, and a large number of new dimensions of data are creating
infinite possibilities for AIoT. From the perspective of the development path of AIoT, industry
professionals currently generally believe that it will experience three stages of stand-alone
intelligence, interconnected intelligence, and active intelligence.
Stand-alone intelligence refers to that the smart device waits for the user to initiate an
interaction request, and there is no mutual connection between the device and the device in this
process. In this situation, the stand-alone system needs to accurately perceive, recognize, and
understand various instructions of the user, such as voice and gestures, and make correct
decisions, executions, and feedback. The AIoT industry is at this stage. Take the home appliance
industry as an example. In the past, home appliances were a feature phone era. Just like the
previous mobile phone’s button type, it helps you lower the temperature and help you realize the
refrigeration of food; now the home appliances realize stand-alone intelligence, that is, voice or
mobile phones. The remote control of the APP can adjust the temperature and turn on the fan.
Smart items that cannot be interconnected are just islands of data and services, far from meeting
people's needs. To achieve the continuous upgrading and optimization of intelligent scene

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experience, the first thing that needs to be broken is the island effect of single product
intelligence. The interconnected smart scene essentially refers to a matrix of interconnected
products. Therefore, a brain (cloud or central control), multiple terminals (perceptrons) model
becomes inevitable. For example, when the user tells the air conditioner in the bedroom to close
the curtains in the living room, and the air conditioner and the smart speaker central control in
the living room are connected, they can discuss and make decisions with each other, and then
take the action of closing the curtains in the living room by the speaker; or When the user speaks
the sleep mode to the air conditioner in the bedroom at night, not only the air conditioner is
automatically adjusted to a suitable temperature for sleep, but also the TV, speakers, curtains,
and lights in the living room are automatically turned off. This is a typical scenario where the
cloud brain cooperates with the interconnected intelligence of multiple sensors.
Active intelligence refers to that the intelligent system is on standby at any time according to
various information such as user behavior preferences, user portraits, environment, etc. It has
self-learning, self-adaptation, and self-improvement capabilities, and can actively provide
services suitable for users without waiting for users to make demands. , Just like a personal
secretary. Imagine such a scene. In the early morning, as the light changes, the curtains are
automatically opened slowly, the soundbox comes with soothing wake-up music, and the fresh
air system and air conditioning start to work. When you start to wash, the personal assistant in
front of the wash station will automatically broadcast today's weather and dressing suggestions
for you. After washing, breakfast and coffee are ready. When you walk out of the house, the
electrical appliances in the house are automatically cut off and turned on again when you wait
for you to go home.
The realization of AIoT places demands on edge computing capabilities
Edge computing refers to an open platform that integrates core capabilities of the network,
computing, storage, and applications on the edge of the network close to the source of things or
data, and provides edge intelligent services nearby to meet the needs of industry digitalization in
agile connection, real-time business, data optimization, application intelligence, Key
requirements for security and privacy protection. There is a very vivid analogy in the industry.
Edge computing is like the nerve endings of the human body, which can process simple stimuli by
itself and feedback characteristic information to the cloud brain. With the implementation of
AIoT, in the intelligent connection of all things scenario, devices will be interconnected, forming a
new ecology of data interaction and sharing. In this process, the terminal not only needs to have
more efficient computing power, in most scenarios, it must also have local autonomous decision
and response capabilities. Take a smart speaker as an example. It not only needs the ability to
support local wake-up, but also the ability to reduce noise at a distance. Due to real-time and
data availability considerations, this calculation must occur on the device side rather than the
cloud.
As the most important landing scenario for AIoT human-computer interaction, the smart home
industry is attracting more and more companies to enter. Among them, there are not only
technology giants such as Apple, Google, Amazon, etc., but also traditional homes appliance

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manufacturers such as Haier and Samsung. Of course, there are also Internet upstarts such as
Xiaomi and JD.com. Based on the concept of interconnected intelligence, in the future AIoT era,
every device needs to have a certain perceptions (such as preprocessing), inference, and
decision-making functions. Therefore, each device-side needs to have certain independent
computing capabilities that do not rely on the cloud, that is, the edge computing mentioned
above.
In the smart home scenario, interacting with terminal devices through natural voice has now
become the mainstream of the industry. Due to the particularity of the home scene, home
terminal equipment needs to accurately distinguish and extract correct user commands (instead
of invalid keywords that family members accidentally say when talking), as well as information
such as sound source and voiceprint. Therefore, the smart home field Voice interaction also puts
forward higher requirements for edge computing, specifically in the following aspects:
Talk about noise reduction and wake up
The sound field in the home environment is complex, such as TV sound, multi-person dialogue,
children playing, spatial reverberation (noise from kitchen cooking, washing machine, and other
equipment). These sounds that easily interfere with the normal interaction between the user and
the device are likely to be at the same Time exists, which requires processing and suppressing
various interferences to make the voices from real users more prominent. In this process, the
device needs more information to make auxiliary judgments. A necessary function of voice
interaction in the home scene is to use the microphone array for multi-channel simultaneous
sound recording. Through the analysis of the acoustic space scene, the spatial positioning of the
sound is more accurate and the voice quality is greatly improved. Another important function is
to help distinguish the real user through voiceprint information so that his voice can be more
clearly distinguished from the interference of multiple people. All of these need to be
implemented on the device side and require greater computing power.
Local recognition
The local recognition of human-computer interaction in the home field cannot be separated from
edge computing, which specifically reflects two aspects:
High-frequency words. According to actual statistics, users have a limited number of
frequently-used keyword instructions in specific scenarios. For example, for car products, users
may use the previous/next song most often, and the most commonly used commands for
air-conditioning products may be on/off, etc. The words frequently used by these users are called
high-frequency words. For the processing of high-frequency words, it can be processed locally
without relying on the delay of the cloud, thereby bringing users the best experience.
Networking rate. In the process of smart home products, especially home appliances, networking
rate is a problem. How to let users perceive the power of voice AI without connecting to the
Internet and cultivate users is also an important role of edge computing in the current.
Balance of local/cloud efficiency
In the natural language interaction process of the home furnishing field, when all the calculations
are placed in the cloud, the acoustic calculation part will put greater pressure on cloud

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computing. On the one hand, it will cause a substantial increase in the cost of the cloud platform;
on the other hand, it will cause calculation delays. Damage the user experience. Natural voice
interaction is divided into two parts: acoustics and natural language understanding (NLP). From
another dimension, it can be regarded as business-independent (speech-to-text/acoustic
computing) and business-related (NLP) parts. Business-related parts undoubtedly need to be
solved in the cloud. For example, users need to ask about the weather and listen to music. Then
the device's understanding of the user's sentences and the acquisition of weather information
must be completed through the Internet. However, for the user's voice-to-text conversion, such
as issuing an instruction "turn on the air conditioner, increase the temperature, etc.", some or
even most of the calculations may be done locally. In this case, the data uploaded from the local
to the cloud will no longer be the compressed voice itself, but a more streamlined intermediate
result or even the text itself. The data is streamlined, cloud computing is simpler, and the
response is better. For speed.
Multi-modal demand
The so-called multimodal interaction refers to the interaction after the combination of multiple
ontology interaction means, for example, the integration of multiple senses, such as text, voice,
vision, action, environment, etc. Human is a typical example of multimodal interaction. In the
process of human-to-human communication, expressions, gestures, hugs, touch, and even smells
all play an irreplaceable role in the process of information exchange. Obviously, the
human-computer interaction of smart homes is bound to be more than just a voice mode but
requires multi-modal interaction in parallel. For example, if a smart speaker sees that a person is
not at home, it does not need to respond to the wake-up word that was mistakenly released on
the TV, and can even put itself to sleep; if a robot feels the owner is watching him, it may Will
proactively greet the host and ask if you need help. Multi-modal processing undoubtedly requires
the introduction of common analysis and calculation of multiple types of sensor data. These data
include not only one-dimensional voice data but also two-dimensional data such as camera
images and thermal images. The processing of these data does not require the ability of local AI,
which puts forward a strong demand for edge computing.
AI chip demand brought by AIoT
AI algorithm puts forward higher requirements for the parallel computing capability and memory
bandwidth of the device-side chip. Although the traditional GPU-based chip can implement
inference algorithms on the terminal, its disadvantages of high power consumption and low-cost
performance cannot be ignored. In the context of AIoT, IoT devices are endowed with AI
capabilities. On the one hand, they can complete AI computing (edge computing) while ensuring
low power consumption and low cost; on the other hand, IoT devices are different from mobile
phones, with ever-changing forms and fragmented demand. With serious changes, the demand
for AI computing power is also different, and it is difficult to provide a universal chip architecture
across device forms. Therefore, only starting from the IoT scenario and designing a customized
chip architecture can it greatly improve performance while reducing power consumption and
cost, while meeting the needs of AI computing power and cross-device forms.

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The development of the Internet of Things is no longer a separate individual. It is increasingly
combined with big data and artificial intelligence. We used to say that all things are connected,
but now we are talking about the intelligent connection of all things. It is bound to be inseparable
from intelligent interaction, which also gives many companies a new round of shuffling
opportunities.
Ali proposed the troika of intelligent connection of all things: IoT, AI, and cloud computing. The
role that Alibaba wants to play in the new IoT era is to build the Internet of Things infrastructure,
provide an open and convenient IoT connection platform for the industry, provide powerful AI
capabilities, and realize the collaborative computing of cloud, edge, and end. Goal down. This
also indicates that with the rapid development of AI, big data, cloud computing, and other
technologies, the IoT Phoenix Nirvana has become the intelligent connection of all things.
In simple terms, AIoT is all kinds of hardware devices in the Internet of Everything. With the
upcoming commercial use of 5G networks, more and more hardware devices will start to be
connected to the Internet in the future and can be controlled by voice. These are all products of
AI+IoT.