KAYABA TECHNICAL REVIEW No. 66 APR. 2023

Foreword

Artificial Intelligence Will Change Industries
TAMURA Satoshi*

Deep learning is a kind of artificial intelligence (AI)that has rapidly become popular in recent years. Bylearning enormous amounts of data sets using a multilayered neural network that imitates the human brain, thetechnology can execute natural language processing, image recognition and other various tasks with high accuracy. In the history of AI, deep learning has particularly
gained the spotlight with its high performance in achieving different tasks including image recognition and natural language processing. This technology is also expected
to be further improved and applied to a variety of fields.
Now, what do you feel about the above text after having read through it? The above paragraph is actually that
automatically created by AI after I told it to "explain
what deep learning is." You probably see the text as if it
had naturally been written by a human being, grammatically correct, and properly describing the subject. Next,
look at the image in Fig. 1. This is also a piece of graphicthat I asked AI to create just with the keywords "automobile", "future", "night", and "Milky Way". Like this, images with a quality that can be compared favorably withthose drawn by human beings can be swiftly completed
by AI in just several seconds.
Deep learning has been used not only in research anddevelopment (R&D) on natural language processing andimage recognition, but also in R&D on speech processing. Familiar examples include voice commands tosmartphones and voice interfaces of smart speakers. Furthermore, some have tried to apply deep learning to signal processing or data analysis. These applications havestarted to be introduced in chemical, medical, and mechanical industries.
Now let me expand on the meaning of the words "Artificial Intelligence", "deep learning", and "MachineLearning" as shown in Fig. 2. First, artificial intelligence(AI) means to artificially implement intelligence at a level equivalent to or even higher than human intelligence.
AI covers various fields ranging from fundamental research to application engineering, including the development of humanoids and computer based Igo and Shogi.
“Machine learning” is one AI approach. For implementing human learning mechanisms and functions on a computer system, machine learning pursues appropriate

Artificial
intelligence

Machine learning
Deep learning

Fig. 1

Image drawn by AI

Fig. 2*

Associate Professor, Gifu University―1―

Artificial intelligence, machine learning, and deeplearning

Artificial Intelligence Will Change Industries

mathematical models and their algorithms as well as theirapplication. Machine learning is also related to the above
mentioned natural language processing, image processing, and speech processing as well as statistics. “DeepLearning” is a type of machine learning that uses a “deepneural network (DNN)” as a mathematical model.
It is in just the last decade that deep learning has rapidly advanced. This was achieved with the proliferationof the Internet and the higher performance of computers,
which allowed collection of large amounts of data andhigh-speed processing of the collected data, althoughdeep learning models themselves had been proposedsince olden times. With a focus on industrial applicationsof deep learning, the following introduces several examples.
Efforts have arisen to implement “Anomaly Detection”
to automate and optimize inspection of industrial products or status monitoring of manufacturing machinery.1)
With this technology, computers can judge products OK
or not OK just by scanning their images, thereby enabling systemization of inspection tasks by humans thatare quite individual skills. This may eventually alleviate
the lack of labor caused by the low birthrate and aging
society. Manufacturing machinery can be installed withmicrophones and vibration sensors for continuous monitoring of operating status, which allows the detection ofsigns of failures or the performance of machinery maintenance at optimal timings. Plant owners can expect
higher availability of production equipment and lower
maintenance cost.
Detecting human errors or potentially dangerous tasks
in the assembly process will improve product quality andhelp create a safer work environment. In addition, collecting and analyzing information about employees’ tasks
and the traffic line of moving machines and then conducting a PDCA (Plan, Do, Check, Action) cycle to develop
and execute an improvement plan will probably improvein making decisions. Then, to inform people of the behavior of AI, an effort called “Explainable AI (XAI)” hasgained attention from the viewpoint of application to industry and medicine.3) In a deep learning model for image analysis for instance, the parts of the image focusedby the computer in making decisions can be disclosed.
Another example is a system to analyze information obtained from many different sensors that is designed to beable to calculate the importance of each piece of information. Furthermore, an attempt is widely underway thatwill effectively use the findings obtained for developing
new industrial products or projecting the effectiveness ofdrugs.
Promoting these effective uses of AI described above
in industry should enhance the possibility of technical innovation and new business development. To do so, “Digital Transformation (DX)” is a must to digitize all information at the site, connect the digital information with AItechnology, and establish a new information utilizationmeans in the whole business flow. During the COVID-19
pandemic, we were physically separated from our workplace and became acutely aware of the necessity of DX
for online meetings and teleworking as well as for information exchange with sites or among employees. Beyond
DX lies AI. Toward the age of living under or even after
the pandemic as well as to be able to respond to theworld's dramatically changing circumstances, I believethat we need to use AI as much as possible.
References
1) ASAHI, et al.: "Proposal of failure prediction method of factoryequipment by vibration data with Recurrent Autoencoder",Transactions of the JSME, Vol.86, No.891, 2020.
2) TAMURA, et al.: "Multi-modal service operation estimation using DNN-based acoustic bag-of-features", EUSIPCO (international conference), 2015.
3) MIYOSHI, et al.: "Machine learning using clinical data at base-operations and their efficiency. These efforts are called"Service Engineering". R&D related to such service engineering is underway by leveraging AI technology. 2)
Concrete examples of R&D include an analysis of images captured by monitoring cameras to notify any misoperation or dangerous tasks and an analysis of information
obtained from employees' wearable sensors to identify
any issues.
Deep learning has the problem that its mathematic
models are likely to be a black box and people cannot
exactly know the information or logic used by computers―2―line predicts the efficacy of vedolizumab at week 22 in patientswith ulcerative colitis", Scientific Reports 11, 2021.