Publications
Our published Work!
2019
Nordling, Torbjörn E. M.
Automation, Digitalisation, Industry 4.0, and Artificial Intelligence Trends and Case Studies Proceedings Article
In: Mubarok, Fahmi (Ed.): The 4th International Conference on Mechanical Engineering (ICOME 2019) in Yogyakarta (Indonesia), Dept. of Mechanical Engineering, Institut Teknologi Sepuluh Nopember, Yogyakarta, Indonesia, 2019.
Abstract | Links | BibTeX | Tags: artificial intelligence, Automation, deep learning, Digitalisation, Industry 4.0
@inproceedings{Nordling2019ICOME,
title = {Automation, Digitalisation, Industry 4.0, and Artificial Intelligence Trends and Case Studies},
author = {Torbjörn E. M. Nordling},
editor = {Fahmi Mubarok},
url = {https://elib.its.ac.id/conf/icome/?page_id=147},
year = {2019},
date = {2019-08-01},
booktitle = {The 4th International Conference on Mechanical Engineering (ICOME 2019) in Yogyakarta (Indonesia)},
publisher = {Dept. of Mechanical Engineering, Institut Teknologi Sepuluh Nopember},
address = {Yogyakarta, Indonesia},
abstract = {Keynote speech: Manufacturing in the ASEAN region is predicted to have a compound annual growth rate (CAGR) of 6.6% currently. At the same time, major industrial powers are running programs to update their manufacturing industry, e.g. the U.S. “Advanced Manufacturing Partnership”, German “Industrie 4.0”, Japanese “Industry Revitalization Plan”, South Korean “Manufacturing Innovation 3.0”, and Chinese “China Manufacturing 2025”. The U.S. and Korean programs are bottom-up industry lead, while the German and Japanese programs are top-down lead by the government and academic institutions. In particular, the German Industrie 4.0 program, which “refers to the intelligent networking of machines and processes for industry with the help of information and communication technology” has received a lot of attention and the Taiwanese “Productivity 4.0” program is largely based on it. It aims towards flexible production with better coordination of production steps and machine load; convertible factories with modular production lines quickly assembled for tasks; individualized production in small quantities at affordable prices; customer-oriented solutions where the customers design products and smart products send data to the manufacturer; optimised logistics with ideal delivery routes and machines reporting when they need new material; use of data to create new business models and services; to create a resource-efficient circular economy. Currently the Industrie 4.0 market is predicted to grow with a CAGR of 37%, so a large business opportunity exists. The two key drivers of the current update of the manufacturing industry are digitalisation and automation. It is worth noting that this is part of the third industrial revolution, which according to Jeremy Rifkin is characterised by a convergence of how we communicate, power, and move information and goods through digitalisation. Digitalisation is the enabler of exponential growth. Based on the 6D's exponential framework of Peter Diamandis, we can expect the technology to become deceptive and then disruptive, which leads to dematerialisation, demonetisation, and finally democratisation. The development will be challenging for the society due to increased capital demands and reduced employment opportunities until the dematerialisation phase when scarcity is replaced with abundance leading to demonetisation that enable everyone to enjoy the products and thus democratise access. Roughly half of all time spent on work activities, equalling 15 trillion USD in wages, can be automated away using current technology according to McKinsey Global Institute. However, the adoption is predicted to take roughly 15 to 50 years, so all engineers needed to implement the automation will have more work than they can do and new work tasks and professions are created continuously. Therefore, a work shortage is unlikely to occur within the next 15 years, but a mismatch between demanded skills and available skills is already present. Rapid implementation of the third industrial revolution and upgrading of manufacturing to Industrie 4.0 standard is currently the best and only hope I see when it comes to combatting climate change, biodiversity loss, land conversion, and nitrogen & phosphorus loading, and at the same time improving the social conditions. We need to get back within the ecological ceiling of our planet or our civilisation is doomed. Artificial Intelligence (AI), in particular Deep learning, has since the ImageNet LSVRC-2012 contest established itself as one of the core technologies driving the third industrial revolution with many commercial applications. The success of Artificial Narrow Intelligence (ANI) is due to: big labelled data, GPU accelerated distributed computing, open source software, and good algorithms. The transformative power of ANI is best illustrated by how super human solution of the object category classification problem was enabled within 4 years from the best solution being practically worthless. ANI augments the cognitive ability. Therefore, ANI increases the productivity of workers skilled in machine learning exponentially. I believe that prediction will mainly be done by AIs while mechanistic understanding is provided by humans. The data dependency and advantage combined with exponential learning enable first movers to go from zero to global leadership in winner takes all fashion. The technology is likely to increase wealth inequality. To succeed in the AI era it is essential to digitise fast and specialise in a vertical to obtain global leadership within it unless the sales process or regulation provide a barrier of entry.},
keywords = {artificial intelligence, Automation, deep learning, Digitalisation, Industry 4.0},
pubstate = {published},
tppubtype = {inproceedings}
}
Keynote speech: Manufacturing in the ASEAN region is predicted to have a compound annual growth rate (CAGR) of 6.6% currently. At the same time, major industrial powers are running programs to update their manufacturing industry, e.g. the U.S. “Advanced Manufacturing Partnership”, German “Industrie 4.0”, Japanese “Industry Revitalization Plan”, South Korean “Manufacturing Innovation 3.0”, and Chinese “China Manufacturing 2025”. The U.S. and Korean programs are bottom-up industry lead, while the German and Japanese programs are top-down lead by the government and academic institutions. In particular, the German Industrie 4.0 program, which “refers to the intelligent networking of machines and processes for industry with the help of information and communication technology” has received a lot of attention and the Taiwanese “Productivity 4.0” program is largely based on it. It aims towards flexible production with better coordination of production steps and machine load; convertible factories with modular production lines quickly assembled for tasks; individualized production in small quantities at affordable prices; customer-oriented solutions where the customers design products and smart products send data to the manufacturer; optimised logistics with ideal delivery routes and machines reporting when they need new material; use of data to create new business models and services; to create a resource-efficient circular economy. Currently the Industrie 4.0 market is predicted to grow with a CAGR of 37%, so a large business opportunity exists. The two key drivers of the current update of the manufacturing industry are digitalisation and automation. It is worth noting that this is part of the third industrial revolution, which according to Jeremy Rifkin is characterised by a convergence of how we communicate, power, and move information and goods through digitalisation. Digitalisation is the enabler of exponential growth. Based on the 6D's exponential framework of Peter Diamandis, we can expect the technology to become deceptive and then disruptive, which leads to dematerialisation, demonetisation, and finally democratisation. The development will be challenging for the society due to increased capital demands and reduced employment opportunities until the dematerialisation phase when scarcity is replaced with abundance leading to demonetisation that enable everyone to enjoy the products and thus democratise access. Roughly half of all time spent on work activities, equalling 15 trillion USD in wages, can be automated away using current technology according to McKinsey Global Institute. However, the adoption is predicted to take roughly 15 to 50 years, so all engineers needed to implement the automation will have more work than they can do and new work tasks and professions are created continuously. Therefore, a work shortage is unlikely to occur within the next 15 years, but a mismatch between demanded skills and available skills is already present. Rapid implementation of the third industrial revolution and upgrading of manufacturing to Industrie 4.0 standard is currently the best and only hope I see when it comes to combatting climate change, biodiversity loss, land conversion, and nitrogen & phosphorus loading, and at the same time improving the social conditions. We need to get back within the ecological ceiling of our planet or our civilisation is doomed. Artificial Intelligence (AI), in particular Deep learning, has since the ImageNet LSVRC-2012 contest established itself as one of the core technologies driving the third industrial revolution with many commercial applications. The success of Artificial Narrow Intelligence (ANI) is due to: big labelled data, GPU accelerated distributed computing, open source software, and good algorithms. The transformative power of ANI is best illustrated by how super human solution of the object category classification problem was enabled within 4 years from the best solution being practically worthless. ANI augments the cognitive ability. Therefore, ANI increases the productivity of workers skilled in machine learning exponentially. I believe that prediction will mainly be done by AIs while mechanistic understanding is provided by humans. The data dependency and advantage combined with exponential learning enable first movers to go from zero to global leadership in winner takes all fashion. The technology is likely to increase wealth inequality. To succeed in the AI era it is essential to digitise fast and specialise in a vertical to obtain global leadership within it unless the sales process or regulation provide a barrier of entry.
2018
Liou, Yi-Fan; Tsai, Po-Jung; Huang, Zi-Yu; Chiou, Po-Chin; Chu, Hsiao-Wei; Ciou, Li-Ping; Nordling, Torbjörn E. M.
FFANEprot: Predicting Protein Functions using a Weight-sharing Multitask Neural Network Optimized by a Firefly Algorithm with Natural Enemy Strategy Proceedings Article
In: 17th International Conference on Bioinformatics (INCoB-2018), Asia Pacific Bioinformatics Network (APBioNet), New Delhi, India, 2018.
Abstract | Links | BibTeX | Tags: Convolutional neural network, deep learning, Evolutionary algorithm, Firefly algorithm, Inhibitory neurons, Natural enemy strategy
@inproceedings{Liou2018,
title = {FFANEprot: Predicting Protein Functions using a Weight-sharing Multitask Neural Network Optimized by a Firefly Algorithm with Natural Enemy Strategy},
author = {Yi-Fan Liou and Po-Jung Tsai and Zi-Yu Huang and Po-Chin Chiou and Hsiao-Wei Chu and Li-Ping Ciou and Torbjörn E. M. Nordling},
url = {http://www.incob2018.org/},
year = {2018},
date = {2018-09-01},
booktitle = {17th International Conference on Bioinformatics (INCoB-2018)},
publisher = {Asia Pacific Bioinformatics Network (APBioNet)},
address = {New Delhi, India},
abstract = {Background: The prediction of multiple functions of several proteins at once from the protein sequence alone is essential, but difficult. To solve this problem, we composed a dataset of 81,267 proteins and 1,169 Gene Ontology (GO) terms of the molecular function (MF) from the Swiss-Prot database, and used weight-sharing and multi-task learning to create FFANEprot. Results: The architecture of FFANEprot was optimised by a Firefly algorithm with a natural enemy strategy (FFANE), i.e. periodic reversals. The training and test Matthews correlation coefficients (accuracies) are 0.52 (98.84%) and 0.49 (98.67%), respectively. When analysing the trained networks, we found many completely inhibitory neurons, which typically have a small kernel size and occupy approximately 30% of the CNNs. Conclusion: FFANEprot can predict GO MF terms with high accuracy from sequence alone. Our FFANEnet source code is available at http://ffanenet.nordlinglab.org.},
keywords = {Convolutional neural network, deep learning, Evolutionary algorithm, Firefly algorithm, Inhibitory neurons, Natural enemy strategy},
pubstate = {published},
tppubtype = {inproceedings}
}
Background: The prediction of multiple functions of several proteins at once from the protein sequence alone is essential, but difficult. To solve this problem, we composed a dataset of 81,267 proteins and 1,169 Gene Ontology (GO) terms of the molecular function (MF) from the Swiss-Prot database, and used weight-sharing and multi-task learning to create FFANEprot. Results: The architecture of FFANEprot was optimised by a Firefly algorithm with a natural enemy strategy (FFANE), i.e. periodic reversals. The training and test Matthews correlation coefficients (accuracies) are 0.52 (98.84%) and 0.49 (98.67%), respectively. When analysing the trained networks, we found many completely inhibitory neurons, which typically have a small kernel size and occupy approximately 30% of the CNNs. Conclusion: FFANEprot can predict GO MF terms with high accuracy from sequence alone. Our FFANEnet source code is available at http://ffanenet.nordlinglab.org.
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