![Edge AI
Hook
Doesn’t self-driving car really run over human?
Key details
Self-driving car can detect human and stop immediately using ”Edge
AI”.
The machine which equips “Edge AI” can decide immediately.
“Edge AI” can think immediately because it is mounted on devices
such as IoT devices and sensors.
“Edge AI” processes inferences in the device based on data which is
collected at the device and decide immediately.
In contrast to Edge AI, there are Cloud AI.
The machine which equips “Cloud AI” cannot decide immediately.
It takes time for “Cloud AI” to recognize image or information.
Hence, If self-driving car uses “Cloud AI” cannot detect human and
stop immediately.
More details
The advantage and disadvantage of “Edge AI” are shown below. First, advantage of
“Edge AI” are low communication costs and security strength. “Edge AI” only sends the
data which is necessary for learning to the cloud after processing on the device.
Because the volume of data is smaller than that of cloud AI, which sends all data in the
device, ”Edge AI” can reduce communication costs. Then, “Edge AI”, which perform
inference and learning in different locations(device and cloud), can process critical data
which has the risk of information leakage while keeping it inside the device. “Edge AI”
has the advantage, which enhance the security of the network environment by
processing without going through the internet. Then, disadvantage of “Edge AI” are low
processing capacity and high hurdles for introduction and operation. Considering the
size of the device and its power consumption, there is a limit to the resources that can
be loaded in the device. Because the CPU and GPU used in ”Edge AI” have lower
specifications than those used in the cloud, it is difficult to process large data. In
addition, because inference and learning are performed in different locations, “Edge
AI” cannot execute complex and advanced processing. Then, ”Edge AI” has high
operational hurdles because making system design and maintenance operations are
easy to get more complex.
Give you some example of “Edge AI” except self-driving car. First, it is monitoring
manufacturing facility. The sensor which load ”Edge AI” enable equipment in the
factory to monitor in real time. It can detect minor changes in operating conditions and
prevent equipment from breaking and stopping. Second, it is behavior of customer
analysis. The camera which loads “Edge AI” and is installed in stores and facilities
analyze data of customer behavior. By analyzing the path which a person has walked, it
can serve a changing layout of product and reviewing of product shelves. Finally, it is
safety management of construction site. The gateway which loads ”Edge AI” provides
operation information of construction equipment and special vehicle s for
construction. By monitoring construction works in real time, it serve a safety
management of laborer.
[1] https://conexio-iot.jp/blog/40#6178bc8b23748342cb2e1a84-1635302878375](https://image.slidesharecdn.com/artificialintelligence-230611161702-c4741751/85/Artificial_intelligence-pptx-6-320.jpg)
Artificial_intelligence.pptx
- 1. AI: Neural network We have brain, so we can think But AI don’t have brain. Do you know how AI can think? The neural network is a mathematical model of our neurons in the brain. And it is a fundamental concept on which AI is based! The neural network have changed during the three AI booms. <The first AI boom「Perceptron」> The perceptron is a neural network that mimics human vision and brain function. <The second AI boom 「Multilayer perceptron」> The multilayer perceptron has three nodes. Other than that, not much different from the perceptron. <The therd AI boom 「Deep learning」> The deep learning is very famous right now. It allows neural networks to capture features themselves. So, It can learn more active. <Perceptron> The perceptron, a neural network invented in 1957, attracted attention for its ability to mimic human vision and brain function, leading to the first AI boom. However, the boom came to an end when its weaknesses were pointed out, such as its inability to learn problems that are not linearly separable. <Multilayer perceptron> The second boom occurred in 1986 with the development of the "error back propagation" method. The Error Back Propagation Method uses a "multi-layer perceptron", a neural network that is an advanced version of the perceptron, allowing for more complex learning. However, at that time, before the advent of the Internet, the boom came to an end again because there was not much data available for machine learning, and the learning accuracy of multilayer neural networks did not improve easily. <Deep learning> A technology called "auto encoder," developed in 2006, made it possible for neural networks to capture features themselves. The learning method using autoencoders and multilayer neural networks is called "deep learning," and has been the breakthrough of the third AI boom.
- 2. AI: CNN Hook What do you think this picture is of? Can a computer recognize it as you do? Key details ・What is CNN? CNN is "Convolutional Neural Network,”. It is a neural network with several deep layers and is a network that generates value mainly in the field of image recognition. CNNs are mainly valuable in the image recognition task called "general object recognition. In this field, it is utilized as an algorithm with excellent performance. More detail 1,Convolution: By restricting the connections between neurons well and using a technique called weight sharing, a process like image convolution is expressed within the framework of a neural network. 2,Pooling: Reduces the size of the feature map by extracting the features that satisfy the conditions from the specified region. 3,Affine: Instead of treating the input as a two-dimensional array, we will treat everything equally as a single list. All values get their own vote in terms of whether the current image is ○ or x, but we are not completely democratic in this process. In understanding how CNN works, there are three main layers: Convolution, Pooling, and Affine.
- 3. Artificial Intelligence: Reinforcement learning Hook A learning method that automatically derives the optimal solution Key details Reinforcement learning repeats trial and error and learns the appropriate control method. Application Examples of Reinforcement learning are Advanced Control, Autonomous Driving, Robotics, Scheduling and Calibration. Reinforcement learning being used in a real system for example, Shogi AI, Igo AI and Cleaning robot. There are 5 steps. ・First step is about an environment. This step defines the environment. Second step is about a reward. This step is necessary to repeat some times for reward. Third step is making agent. This step is necessary to choose some methods for example, choosing neural networks or lookup tables, choosing an appropriate learning algorithm. Fourth step is agent training and validation. This step prove the performance of the learned policy. Final step is policy deployment. All of them are Reinforcement learning steps. More detail ・Advanced Control: Controlling nonlinear systems is a difficult problem, often addressed by linearizing the system at different operating points. Reinforcement learning can be applied directly to nonlinear systems. ・Autonomous Driving: Given the success of deep neural networks in imaging applications, driving decision-making based on camera input is an area where reinforcement learning is well suited. ・Robotics: Reinforcement learning is useful for applications like robotic grasping, such as teaching robotic arms how to use different objects in pick-and- place applications. There are many other applications of robotics, such as human- robot and robot-robot collaboration. ・Scheduling: Scheduling problems can be found in many situations, such as controlling traffic lights or coordinating factory floor resources for a given purpose. Reinforcement learning can be used as an other to evolutionary methods for solving these combinatorial optimization problems. ・Calibration: Applications that involve manual calibration of parameters, such as electronic control unit (ECU) calibration, are good candidates for reinforcement learning. ・Neural networks: It is a mathematical model called an artificial neuron that expresses nerve cells in the human brain and their connections. ・Lookup tables: Refers to data structures such as arrays and associative arrays created to improve efficiency by changing complex calculation processing with simple array reference processing.
- 4. Artificial intelligence: sentence generation Hook AI may win the Nobel Prize in Literature in the near future !? Key details GPT-3 is the most notable current technology in sentence generation AI. GPT-3 (Generative Pretrained Transformer) is one of the pre- trained natural language processing. Natural language processing is a technology that uses AI to analyze large amounts of text data. GPT-3 learns a massive corpus of text with more than 175 billion parameters. Thanks to that, GPT-3 can automatically generates AI-written text that has the potential to be practically indistinguishable from human-written sentences, paragraphs, articles, short stories, dialogue, lyrics, and more. In the U.S. and other countries, there are a number of services that utilize GPT-3. More details ● What is GPT-3? GPT-3, a new language model from the whizzes over at OpenAI, generates AI-written text that has the potential to be practically indistinguishable from human-written sentences, paragraphs, articles, short stories, dialogue, lyrics, and more. OpenAI trained GPT-3 on a massive corpus of text with more than 175 billion (yes, with a "b") parameters, making it the largest language model—ever. Lost? In non- technical terms, GPT-3 was shown how millions of people write and taught how to pick up on writing patterns based on user entry. You just feed it some input, and the model will generate intelligent text following the submitted pattern and structure. ● How does GPT-3 work? GPT-3 is a language model, which is a statistical program that predicts the probable sequence of words. Trained on a massive dataset (from sources like Common Crawl, Wikipedia, and more), GPT-3 has seen millions of conversations and can calculate which word (or even character) should come next in relation to the words around it. When you type in an initial set of words, like "go to the store to buy...", GPT-3 will start predicting what would naturally come next based on its training. Probably something like: Eggs,Milk,Bread,Fruit,Vegetables,Snacks,Drinks,Etc. ● Downsides to GPT-3 GPT-3 may seem like the perfect AI-communications solution, but it's not without its imperfections. There are a few downsides to this powerful machine learning technology: 1, Lack of true intelligence: GPT-3 is a deep learning model that uses machine learning algorithms, but it's still not "intelligence." This AI is only using existing text to predict future results—it's not necessarily coming up with anything truly original as it lacks true understanding and meaning (unlike something like Artificial General Intelligence (AGI)). 2, Privacy risk: It's unclear whether GPT-3 retains any portion of the training data, making it a potential privacy issue. 3, Bias: GPT-3 can be fooled into creating incorrect, racist, sexist, and biased content that's devoid of common sense and real-world sensibility. The model’s output is dependent on its input: garbage in, garbage out.
- 5. Artificial Intelligence: Meta AI Hook There is a small director, who is not humans, in games! Key details Meta AI is the type of Artificial Intelligence. It is used in games to adjust the difficulty of games. In detail, it gets some information about the context of games, the level of games, and so on. After that, it adjust difficulty based on these information. So, they work like a game director. There are two types; Classical Meta AI and Modern Meta AI. Crassical Meta AI has been existing since the 1980. It judge the skill of player, and then adjust difficulty passively. Modern Meta AI has been existing since the 2007. It adjust difficulty more actively by using more particular way. For example, by the spawn position, the number, and the kind of enemy, or more sophisticated way. More details There are two example for each Meta AI. First, the use of Classical Meta AI in Xevious. Xevious is a 2-D shooting game. It was released in 1982. In this game, the game director use a humorous variable and easy Meta AI. The name of the variable is “DIFFICULTY”. It is used for judging the skill of player. If a player take more mistakes, the value of “DIFFICULTY” will be lower. And Meta AI adjust the strongness of enemy based in the value of “DIFFICULTY”. Thanks for these components, both good player and not good player can enjoy this game. Second, the use of Modern Meta AI in Left 4 Dead. Left 4 Dead is a 3- D First Person Shooting (FPS) game. It was released in 2008. The goal is to leave the city that there are a lot of zombies. The players cooperate to achieve this goal. In this game, the very complex way is used. At first, the AI estimates the “emotional strongness” of each survivor as a value. For example, when they are injured by zombie, the value become higher. The AI adjust the amount of zombie based in the value of “emotional strongness”. If the value does not reach the peak, the amounts of zombies and strongness of zombies will be higher. If the value reach the peak, the AI will give the relax time to the players. Thanks for these complex methods, this game will not be simplified, and the player more likely to continue this game.
- 6. Edge AI Hook Doesn’t self-driving car really run over human? Key details Self-driving car can detect human and stop immediately using ”Edge AI”. The machine which equips “Edge AI” can decide immediately. “Edge AI” can think immediately because it is mounted on devices such as IoT devices and sensors. “Edge AI” processes inferences in the device based on data which is collected at the device and decide immediately. In contrast to Edge AI, there are Cloud AI. The machine which equips “Cloud AI” cannot decide immediately. It takes time for “Cloud AI” to recognize image or information. Hence, If self-driving car uses “Cloud AI” cannot detect human and stop immediately. More details The advantage and disadvantage of “Edge AI” are shown below. First, advantage of “Edge AI” are low communication costs and security strength. “Edge AI” only sends the data which is necessary for learning to the cloud after processing on the device. Because the volume of data is smaller than that of cloud AI, which sends all data in the device, ”Edge AI” can reduce communication costs. Then, “Edge AI”, which perform inference and learning in different locations(device and cloud), can process critical data which has the risk of information leakage while keeping it inside the device. “Edge AI” has the advantage, which enhance the security of the network environment by processing without going through the internet. Then, disadvantage of “Edge AI” are low processing capacity and high hurdles for introduction and operation. Considering the size of the device and its power consumption, there is a limit to the resources that can be loaded in the device. Because the CPU and GPU used in ”Edge AI” have lower specifications than those used in the cloud, it is difficult to process large data. In addition, because inference and learning are performed in different locations, “Edge AI” cannot execute complex and advanced processing. Then, ”Edge AI” has high operational hurdles because making system design and maintenance operations are easy to get more complex. Give you some example of “Edge AI” except self-driving car. First, it is monitoring manufacturing facility. The sensor which load ”Edge AI” enable equipment in the factory to monitor in real time. It can detect minor changes in operating conditions and prevent equipment from breaking and stopping. Second, it is behavior of customer analysis. The camera which loads “Edge AI” and is installed in stores and facilities analyze data of customer behavior. By analyzing the path which a person has walked, it can serve a changing layout of product and reviewing of product shelves. Finally, it is safety management of construction site. The gateway which loads ”Edge AI” provides operation information of construction equipment and special vehicle s for construction. By monitoring construction works in real time, it serve a safety management of laborer. [1] https://conexio-iot.jp/blog/40#6178bc8b23748342cb2e1a84-1635302878375