Edited, memorised or added to reading list

on 19-May-2020 (Tue)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

#palestra-ETL
The survey revealed that transportation and logistics, automotive, and technology companies are at the forefront of AI adoption, while process industries (such as chemicals) lag behind. Companies in the US, China, and India have taken an impressive lead in adoption over their counterparts in such countries as Japan, France, and Germany. The differences in speed of AI adoption across countries reflect different expectations regarding AI’s benefits.
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

AI in the Factory of the Future: The Ghost in the Machine
d to identify major challenges. The survey also delved into the relevance and adoption levels of specific AI use cases in operations and the benefits that participants expect to gain from them. <span>The survey revealed that transportation and logistics, automotive, and technology companies are at the forefront of AI adoption, while process industries (such as chemicals) lag behind. Companies in the US, China, and India have taken an impressive lead in adoption over their counterparts in such countries as Japan, France, and Germany. The differences in speed of AI adoption across countries reflect different expectations regarding AI’s benefits. While companies in emerging nations such as China tend to be enthusiastic about these benefits, those in many industrialized nations, such as Germany, have a more conservative view. Bec




AI augments, rather than replaces, existing levers that producers apply to continuously improve productivity. It is among the main technological building blocks of Industry 4.0. (See Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, BCG Focus, April 2015.) Moreover, producers can use AI to enhance traditional efficiency levers, such as automation and lean management. (See When Lean Meets Industry 4.0: The Next Level of Operational Excellence, BCG Focus, December 2017.) For example, by identifying root causes of quality issues and thus helping to eliminate defects, AI supports lean-management efforts to reduce waste.
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

AI in the Factory of the Future: The Ghost in the Machine
mpanies cited operations as the company area likely to be most affected by AI. (See Reshaping Business with Artificial Intelligence, a report by BCG and MIT Sloan Management Review, Fall 2017.) <span>AI augments, rather than replaces, existing levers that producers apply to continuously improve productivity. It is among the main technological building blocks of Industry 4.0. (See Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries, BCG Focus, April 2015.) Moreover, producers can use AI to enhance traditional efficiency levers, such as automation and lean management. (See When Lean Meets Industry 4.0: The Next Level of Operational Excellence, BCG Focus, December 2017.) For example, by identifying root causes of quality issues and thus helping to eliminate defects, AI supports lean-management efforts to reduce waste. Indeed, 40% of our study participants expect AI to become a very important driver of productivity improvement in 2030, versus 29% who consider it to be very important for productivity i




#palestra-ETL
Supply Chain Management. Demand forecasting is a key topic for applying AI within supply chain management. By better anticipating changes in demand, companies can efficiently adjust production programs and improve factory utilization. AI supports forecasting of customer demand by analyzing and learning from data related to product launches, media information, and weather conditions. Some companies use machine-learning algorithms to identify demand patterns by consolidating data from warehousing and enterprise resource planning (ERP) systems with customer insights.
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

AI in the Factory of the Future: The Ghost in the Machine
sing generative design to develop aircraft parts with completely new designs, such as bionic structures that provide the same functionality as conventional designs but weigh significantly less. <span>Supply Chain Management. Demand forecasting is a key topic for applying AI within supply chain management. By better anticipating changes in demand, companies can efficiently adjust production programs and improve factory utilization. AI supports forecasting of customer demand by analyzing and learning from data related to product launches, media information, and weather conditions. Some companies use machine-learning algorithms to identify demand patterns by consolidating data from warehousing and enterprise resource planning (ERP) systems with customer insights. Inside the Factory. Inside the factory walls, AI will bring various benefits to production and to such support functions as maintenance, quality, and logistics: Production. Our study co




#palestra-ETL
Logistics. Our study focused on in-plant logistics and warehousing, rather than on logistics along the external supply chain. AI will enable autonomous movement and efficient supply of material within the plant, which is essential to managing the growing complexity that comes with making multiple product variants and customer-tailored products. Self-driving vehicles that transport items within the plant and warehouse will use AI to sense obstacles and adjust the vehicles’ course to achieve the optimal route. Producers of health care equipment have begun using self-driving vehicles in their repair centers. Without relying on guidance from magnetic strips or conveyors, the vehicles can stop if they encounter obstacles and then autonomously determine the best route. Machine learning algorithms will use logistics data—such as data on outflow and inflow of material, inventory levels, and turn rates of parts—to enable warehouses to self-optimize their operations. For example, an algorithm could recommend moving low-demand parts to more remote locations and moving high-demand parts to nearby areas for faster access.
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

AI in the Factory of the Future: The Ghost in the Machine
hines and the production environment. For example, AI can compare drilling-machine settings with material properties and behavior to predict the risk that drilling will exceed tolerance levels. <span>Logistics. Our study focused on in-plant logistics and warehousing, rather than on logistics along the external supply chain. AI will enable autonomous movement and efficient supply of material within the plant, which is essential to managing the growing complexity that comes with making multiple product variants and customer-tailored products. Self-driving vehicles that transport items within the plant and warehouse will use AI to sense obstacles and adjust the vehicles’ course to achieve the optimal route. Producers of health care equipment have begun using self-driving vehicles in their repair centers. Without relying on guidance from magnetic strips or conveyors, the vehicles can stop if they encounter obstacles and then autonomously determine the best route. Machine learning algorithms will use logistics data—such as data on outflow and inflow of material, inventory levels, and turn rates of parts—to enable warehouses to self-optimize their operations. For example, an algorithm could recommend moving low-demand parts to more remote locations and moving high-demand parts to nearby areas for faster access. Some AI use cases apply to more than one area of operations. For example, virtual agents that are capable of language generation and processing (similar to Apple’s Siri and Amazon’s Ale




#palestra-ETL

About the Study

In February and March 2018, BCG conducted an online survey of companies in order to assess their progress in adopting AI in industrial operations, which we defined as producers’ core transformation processes, including production and related functions such as maintenance, product quality, and logistics. The survey also covered engineering and supply chain management.

The survey’s participants consisted of executives and production and technology managers from 1,096 global companies representing a broad array of producing industries: automotive, consumer goods, energy, engineered products, health care, process industries, transportation and logistics, and technology. The participants were based in Austria, Canada, China, France, Germany, India, Japan, Mexico, Poland, Singapore, the UK, and the US.

The survey sought to evaluate survey participants’ views of the relevance of AI in operations today and in 2030, to assess the current state of AI adoption, to understand companies’ future plans, and to identify major challenges. The survey also delved into the relevance and adoption levels of specific AI use cases in operations and the benefits that participants expect to gain from them.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

AI in the Factory of the Future: The Ghost in the Machine
ting IT infrastructure. You may also be interested in Artificial Intelligence and Business Browse Collection You may also be interested in Artificial Intelligence and Business Browse Collection <span>About the Study In February and March 2018, BCG conducted an online survey of companies in order to assess their progress in adopting AI in industrial operations, which we defined as producers’ core transformation processes, including production and related functions such as maintenance, product quality, and logistics. The survey also covered engineering and supply chain management. The survey’s participants consisted of executives and production and technology managers from 1,096 global companies representing a broad array of producing industries: automotive, consumer goods, energy, engineered products, health care, process industries, transportation and logistics, and technology. The participants were based in Austria, Canada, China, France, Germany, India, Japan, Mexico, Poland, Singapore, the UK, and the US. The survey sought to evaluate survey participants’ views of the relevance of AI in operations today and in 2030, to assess the current state of AI adoption, to understand companies’ future plans, and to identify major challenges. The survey also delved into the relevance and adoption levels of specific AI use cases in operations and the benefits that participants expect to gain from them. The survey revealed that transportation and logistics, automotive, and technology companies are at the forefront of AI adoption, while process industries (such as chemicals) lag behind.




#palestra-ETL
Handling and Control. Manipulating physical objects—for example, enabling robots to pick unsorted parts from a storage bin without requiring specific trainingNavigation and Movement. Maneuvering through physical environments—for example, enabling an AGV to move and optimize its routes autonomously within a factory
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

AI in the Factory of the Future: The Ghost in the Machine
ing an automated guided vehicle (AGV) to identify its best next movement Speech Generation. Communicating with humans via written text or acoustic speech—for example, reading instructions aloud <span>Handling and Control. Manipulating physical objects—for example, enabling robots to pick unsorted parts from a storage bin without requiring specific training Navigation and Movement. Maneuvering through physical environments—for example, enabling an AGV to move and optimize its routes autonomously within a factory Many industry leaders expect AI to transform processes along the value chain from end to end, including engineering, procurement, supply chain management, industrial operations (product




#palestra-ETL
Among the eight industries that our study focused on, transportation and logistics (21%), and automotive (20%) have the highest share of companies that are early adopters, while engineered products (15%) and process industries (13%) lag behind. (See Exhibit 4.) These differences reflect the industries’ different starting points and affinities for digitization. It is not surprising that automotive and technology companies are among the most advanced.
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

AI in the Factory of the Future: The Ghost in the Machine
estments in AI. In contrast, some industrialized nations, such as Japan, remain focused on conventional levers (for example, lean manufacturing) that promoted their competitiveness in the past. <span>Among the eight industries that our study focused on, transportation and logistics (21%), and automotive (20%) have the highest share of companies that are early adopters, while engineered products (15%) and process industries (13%) lag behind. (See Exhibit 4.) These differences reflect the industries’ different starting points and affinities for digitization. It is not surprising that automotive and technology companies are among the most advanced. Other industries have yet to learn many of the digital tactics that have become integral parts of those industries’ value chain over the years. The number of people that a company emplo




However, a low pressure (vs. almost no pressure) system set to a level where standard commercial pumps could easily overcome an air leak and the transport pods could handle variable air density would be inherently robust. Unfortunately, this means that there is a non-trivial amount of air in the tube and leads us straight into another problem.
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

pdf

cannot see any pdfs




#palestra-ETL
Acredita-se que a IA seja uma tecnologia transformadora e que por meio dela seja possível gerar soluções ou sistemas disruptivos com potencial para: revolucionar como nós vivemos, interagimos, trabalhamos, aprendemos, evoluímos e nos comunicamos; propiciar benefícios socioeconômicos para a sociedade; melhorar qualidade de vida; alavancar a prosperidade econômica e resolver grandes problemas que não tem soluções hoje. IA está presente em diversas aplicações atuais (reconhecimento facial, varejo, robôs, análise de crédito, saúde, financeira, jurídica, indústria, entre outras) e estará presente em muitas outras aplicações em um futuro breve.
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

FAPESP :: Chamadas de Propostas - Chamada de Propostas FAPESP – MCTIC - CGI.BR para Centros de Pesquisas Aplicadas em Inteligência Artificial
CPA não superará R$ 1 milhão por ano para a FAPESP mais R$ 1 milhão para a empresa parceira. Posteriormente, será feita uma nova chamada de propostas para a seleção de até mais quatro centros. <span>Acredita-se que a IA seja uma tecnologia transformadora e que por meio dela seja possível gerar soluções ou sistemas disruptivos com potencial para: revolucionar como nós vivemos, interagimos, trabalhamos, aprendemos, evoluímos e nos comunicamos; propiciar benefícios socioeconômicos para a sociedade; melhorar qualidade de vida; alavancar a prosperidade econômica e resolver grandes problemas que não tem soluções hoje. IA está presente em diversas aplicações atuais (reconhecimento facial, varejo, robôs, análise de crédito, saúde, financeira, jurídica, indústria, entre outras) e estará presente em muitas outras aplicações em um futuro breve. Esta percepção também está presente em outros países como USA, China, Índia, Japão, União Europeia, uma vez que estes países elaboraram estratégias específicas para IA e entendem que es




#palestra-ETL

O termo IA não é novo. Entretanto, o grande aumento do poder computacional e o acesso a dados propiciou grandes avanços práticos na aprendizagem de máquina/ Machine Learning (ML). Isso abriu oportunidades para alavancar o desenvolvimento de ferramentas de IA. Dessa forma, a Internet tornou-se fundamental para o progresso da ciência e da tecnologia com base em técnicas de IA.

Contudo, como grande parte destes dados estão na Internet, e somando-se ao crescimento de sensores conectados que compõem a Internet das Coisas (IoT), questões como a segurança dos dados e privacidade passaram a ter um grande impacto e relevância no cotidiano das empresas, do governo e da sociedade como um todo.

statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

FAPESP :: Chamadas de Propostas - Chamada de Propostas FAPESP – MCTIC - CGI.BR para Centros de Pesquisas Aplicadas em Inteligência Artificial
peito às garantias legais quanto a direitos fundamentais tais como privacidade, proteção de dados pessoais, segurança digital, ética e impacto nos empregos devam ser abrangidas pelas pesquisas. <span>O termo IA não é novo. Entretanto, o grande aumento do poder computacional e o acesso a dados propiciou grandes avanços práticos na aprendizagem de máquina/ Machine Learning (ML). Isso abriu oportunidades para alavancar o desenvolvimento de ferramentas de IA. Dessa forma, a Internet tornou-se fundamental para o progresso da ciência e da tecnologia com base em técnicas de IA. Contudo, como grande parte destes dados estão na Internet, e somando-se ao crescimento de sensores conectados que compõem a Internet das Coisas (IoT), questões como a segurança dos dados e privacidade passaram a ter um grande impacto e relevância no cotidiano das empresas, do governo e da sociedade como um todo. No Brasil, a Política Nacional de Segurança da Informação (PNSI), regulamentada pelo Decreto nº 9.637 de 2018, previu, para sua implementação, a elaboração da Estratégia Nacional de Seg




#iniciação-científica
Estas novas regras exigem que os projetos concorrentes às cotas de bolsa nestes programas institucionais tenham necessariamente aderência a uma das Áreas de Tecnologias Prioritárias do Ministério da Ciência, Tecnologia, Inovações e Comunicações (MCTIC), conforme definidas pelas portarias de no 1122 e 1329 de 2020, quais sejam: Tecnologias Estratégicas, Tecnologias Habilitadoras, Tecnologias de Produção, Tecnologias para o Desenvolvimento Sustentável e Tecnologias para a Qualidade de Vida.
statusnot read reprioritisations
last reprioritisation on reading queue position [%]
started reading on finished reading on

Manifesto da PRPGP – PIBIC/CNPq – PRPGP
s novas regras para concessão das cotas de bolsas nos Programas Institucionais de Bolsas de Iniciação Científica (PIBIC) do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). <span>Estas novas regras exigem que os projetos concorrentes às cotas de bolsa nestes programas institucionais tenham necessariamente aderência a uma das Áreas de Tecnologias Prioritárias do Ministério da Ciência, Tecnologia, Inovações e Comunicações (MCTIC), conforme definidas pelas portarias de no 1122 e 1329 de 2020, quais sejam: Tecnologias Estratégicas, Tecnologias Habilitadoras, Tecnologias de Produção, Tecnologias para o Desenvolvimento Sustentável e Tecnologias para a Qualidade de Vida. É evidente o enorme prejuízo que a observância a estas novas regras acarretarão às inúmeras linhas de pesquisa que não se enquadram dentre aquelas definidas como “Tecnologias Prioritári