Special Cases

28
2021.11
【2021 Application Example】 AI Analysis Cloud Service Platform for Remote Sensing Big Data Enables the Smooth Application of Satellite Remote Sensing Images

Although satellite remote sensing images can make all surface objects visible, it still requires a lot of time and manpower to be truly applied to the industry In order to effectively solve the problems that customers face in digesting huge amounts of image data and eliminate technical obstacles for cross-domain users to process satellite remote sensing images, ThinkTron has developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" as a new beginning for cross-domain AI applications for spatial information In recent years, in response to the impact of industrial globalization, Taiwan's agriculture has continued to transition towards technology-based and higher quality, improving the yield and quality of crops by solving problems, such as microclimate impacts and pest and disease control The demand of agriculture on images has expanded endlessly to accurately grasp the growing environment of crops In the early years when UAVs unmanned aerial vehicles were not yet popular, manual field surveys were the most basic but most labor-intensive work With the emergence of UAV drones, aerial photography operations might not be difficult, but the range that can be photographed is limited Furthermore, surveying expertise is required to accurately capture spatial information At this time, the use of satellite remote sensing data may break away from the past imagination of using image data Taiwan Space Agency TASA ODC data warehouse services In the past ten years, with the breakthrough of modern satellite remote sensing application technology, Digital Earth has become a new trend in global data collection Countries have developed data cube image storage technology, and the development of smart agriculture has become one of the largest image users Determining planting distribution is the first step in understanding crop yields Free satellite remote sensing images, powerful data warehousing support, and the team's robust image recognition technology are important supports for accelerating agricultural transformation Using satellite remote sensing image data can accelerate the development of smart agriculture However, in the past, it was difficult to extract large-area crop distribution through satellite remote sensing images, not to mention the cost If you wanted to use free information, you had to visit all websites of international space agencies, look through the wide variety of satellite specifications, and carefully evaluate the sensor specifications, image resolution, and revisit cycle After finding suitable images, you had to look at them one by one to filter the ones you need Next is downloading dozens of images that are often several hundreds of Megabytes MB each, which might exceed the capacity of your computer Also, after overcoming image access and preparing data, you must then start to confirm the downloaded image products and which bands you want, because the image you see is not just an image file jpg or png, but rather complex multi-spectral information, attribute fields and coordinate information It takes a lot of effort just to confirm the correct information Facing GIS software packages with complex functions is the start of another trouble The complex image pre-processing process and the inflexible machine learning package greatly reduce the efficiency of analyzing data After finally getting the results of crop identification, you might find that the best time for using map information may have already passed The above-mentioned complex and time-consuming satellite image processing problems are precisely the pain points of the market ThinkTron expanded from traditional machine learning to modern deep learning applications, and developed an "AI Analysis Cloud Service Platform for Remote Sensing Big Data" under the GeoAI framework, breaking through the constraints of details in the spatial information for customers Differences between the process before and after introducing the AI analysis cloud service platform ThinkTron said that Taiwan's ODC Open Data Cube system has been completed and began providing services after years of efforts from the Taiwan Space Agency TASA, formally becoming aligned with international trends The powerful warehousing technology allows users to easily capture and use image data of a specific time and spatial range according to their needs The warehouse stores multiple satellite image resources from international space agencies, including the ESA's Sentinel-1 one image every 6 days, Sentinel-2 one image every 6 days, USGS's Landsat-7 one image every 16 days, Landsat-8 one image every 16 days, and the domestic Formosat-2 one image every day and Formosat-5 one image every 2 days ThinkTron develops satellite image recognition tools based on Python Breaking free from the limitations of GIS Geographic Information System software packages, ThinkTron integrated GDAL Geospatial Data Abstraction Library based on Python, and considered computing efficiency and parallel processing when developing all tools required for satellite image processing and image recognition modeling, including coordinate system and data format conversion, grid and vector data interaction, and data intra-difference and normalization All of the tools are designed with AI applications in mind, and some commonly used tools are packaged into an open source package under the name TronGisPy to benefit the technical community ThinkTron utilized the team's understanding of satellite remote sensing images and the collected tagged data crop distribution maps to preset the image recognition modeling process, the required training data specifications, and dataset definitions This is imported into the machine learning LightGBM or deep learning CNN framework that was completed in advance, and the entire training process to be performed in the Web GIS interface, providing users with partial flexibility to freely filter images, confirm spatial and temporal ranges, select models, and adjust hyperparameters In addition to the operation of training models, it also provides historical models to output identification results, and finally displays the identification results of crop distribution on the Web GIS map In fact, agriculture is not the only industry that needs satellite remote sensing applications AI applications of spatial information have also appeared in various fields as companies in different industries aim to enhance their global competitiveness For example, surveying and mapping companies that have a large amount of map data can use the AI analysis cloud service platform to store map data while also accelerating the efficiency of digital mapping Under the severe global climate change and the risk of strong earthquakes, there is a wide variety industrial insurance, agricultural insurance, financial insurance, or disaster insurance are all inseparable from spatial information The use of remote sensing image recognition to understand insurance targets has long been an international trend AI Analysis Cloud Service Architecture for Remote Sensing Big Data

2021-11-28
【2021 Application Example】 AI Complements the Disruption of Traditional Industry Experience: Production Forecast Analysis in Plastic Recycling Process

As the number of veteran craftsmen in traditional industries diminishes In Taiwan, SMEs have always played a central role in Taiwan's industry, accompanying Taiwan through various 'economic miracles' periods But as time progresses, the old masters gradually became elderly craftsmen Coupled with the phenomenon of fewer children and changes in the overall industrial structure, fewer and fewer of the new generation are willing to enter traditional industries Now, it can be observed that the main combination on most SMEs' operational fields is formed by 'elder craftsmen' together with 'foreign workers' These experienced craftsmen, who act as living dictionaries of field experience, suffer from a lack of successors to continue the tradition, leading to a growing difficulty in sustaining on-site experience transfer in traditional industries The limits of traditional hands-on process optimization are in sight Located in Tainan Baoan Industrial District, 'Tangxian Company' was established in 1972, initially manufacturing high-quality weaving equipment It possesses the capability to manufacture machinery, and in recent years it has actively developed environmentally friendly plastic recycling equipment in response to international green energy, recycling, and environmental protection demands Ultimately, they have successfully developed low energy consumption, low waste, high purity, and high output recycling granulators with a sleek and efficient machine design supplemented by advanced intelligent control technology Tangxian Company's self-developed plastic recycling granulator equipment However, in the production process of plastic recycling, when faced with hundreds of material types and dozens of process temperatures, speed settings, what is faced is thousands of possible parameter combinations Previously, the adjustment of various production process conditions was reliant on the on-site staff the experience of the craftsmen Thus, during the transition of production of different incoming materials such as PET, PP, PE, a significant amount of raw materials would be wasted during the trial phase The professional information gap in traditional industries Tangxian Company recognizes the importance of data In the past, although process parameters were recorded, due to a lack of data capabilities at the time, it was primarily in paper form, manually written down by the operating staff, accumulating a large amount of paper data However, this also meant a lack of scientifically accurate and detailed information available for real-time reference and adjustment Process parameters logbook, records the state of about a dozen machines and production figures hourly In quality control as well, due to a lack of control over the quality of output and monitoring and feedback mechanisms for unit time production, it becomes difficult to predict the profit conditions of each batch Production management can only estimate and average cost and productivity changes over the process from the outcomes, without being able to objectively and timely restore the production conditions to reasonableness or make clearer adjustments when facing quality abnormalities Site reality left image shows recycled scraps right image shows pellet production Taiwanese manufacturers possess strong machinery manufacturing capabilities, and many modern machines now have data capabilities, recording real-time status and information via IoT But is the infrastructure of the factory's on-site and information systems ready yet When the Old Master Meets AI With government referral, Tangxian Company partnered with a Taiwanese data science company, working together to integrate AI services and optimize internal processes using AI They started with a medium-sized plastic recycling production line within the factory as a trial field After establishing a successful benchmark, this model was expanded to larger plastic recycling machinery within the factory to continue verification and application Initially, both parties converted the past handwritten paper data into digital format using OCR supplemented by manual correction Tangxian Company also worked with the supplier of the human-machine interface of the machinery to integrate the control panel and parameter data into the factory's database, allowing real-time monitoring of machine status and eliminating the complexities and potential errors of manual transcription Panel of plastic recycling granulation machine, showing current process temperatures, speeds, and power usage Meanwhile, the Taiwanese data science company further modeled dozens of parameter data through AI, conducting scenario analysis to simulate various production possibilities under environmental parameters and material inputs, identifying key characteristic parameters and providing parameter adjustment recommendations to decrease costs during the trial phase Applying data analysis to traditional industry machinery processes After the old master receives the raw materials, they only need to enter the relevant material characteristic parameters, and the system automatically generates recommended process parameters After small adjustments by the old master, they proceed with the trial production of the material, effectively reducing the waste of materials, water, electricity, and manpower caused by incorrect attempts Moreover, Tangxian Company has proactively deployed the concept of 'production pedigree' in the plastic recycling process, allowing the batch's raw materials and process parameters to be accessed by scanning a QRCode Production and sales pedigree of plastic recycling pelletizing Taiwan's SMEs have strong machinery capabilities, just waiting for the 'east wind' of data From industrial revolutions 20 to 30, even 40, many Taiwanese SMEs face challenges in transitions not just in upgrading machinery, but after investing in modern equipment and generating data, they do not know how to utilize it effectively It is impractical for these manufacturers to develop a specialized data analysis department on their own meanwhile, Taiwan also has many innovative teams with strong software capabilities in AI and data analysis, possessing the technology but lacking the field and data Therefore, if the traditional industries of Taiwan could be fully integrated with the innovative teams in AI and data analysis, it would not only address the current challenges of manpower and experience transfer faced by traditional industries but also advance Taiwan's development and application of AI significantly「Translated content is generated by ChatGPT and is for reference only Translation date:2024-05-19」

2021-01-21
【2021 Application Example】 Fongyu Uses AI Knowledge-based Fish Farming to Effectively Increase Aquatic Production by 10%

Fisheries is an important industry in an island economy However, the fish farming industry has faced severe challenges in recent years, including climate change, labor shortage, and rising costs In particular, nearly 110,000 workers in agriculture will retire due to old age over the next 10 years For this reason, the need for aquaculture to move towards smart farming is becoming increasingly urgent Founded in 2014, Fongyu Corp Ltd has developed a unique eco-friendly farming model based on its own fish farming It uses AI knowledge-based fish farming to effectively increase aquatic product production by 10, and reduced labor cost by 15 The word "Fongyu" has a profound meaning "Fong" represents good mountains and "Yu" represents good water, and is the hope that companies will allow Taiwan to always have good mountains and good waterIt is also a homophone for "having a full figure," expressing the hope that products will give consumers a full and healthy body and mind The founder of the company, Liu Chien-Shen, has been through the difficult entrepreneurial journey of becoming an apprentice in fish farming, raising funds, renting fish farms, establishing a fish farming company, building a brand, and expanding sales Labor shortage and aging workers are hidden worries in the fish farming industry Currently, fish farms in Taiwan are still mainly traditional fish farms, and farming techniques are still passed down through word-of-mouth In addition, the labor shortage and average age of workers exceeding 60 years old has made it impossible to effectively stably improve productivity and yield This farming method makes it difficult to prevent and control diseases, and greatly increases the possibility of excessive use of drugs, environmental pollution, and water quality and ecological damage, creating a vicious cycle that lowers the quality of fish farming In addition, 651 of workers in Taiwan's fish farming industry are inadequately skilled With limited support from IoT sensors, traditional fish farmers still mainly rely on their own experience and knowledge for water quality management, feeding, and disease detection Fish farming management relies heavily on the ability of individual fishermen Once experienced workers retire, the industry will not only face the issue of succession, but also the difficult of stably supplying a certain amount of harvest that meets quality standards This may cause a dilemma for the entire industry from fish farming to sales In order to improve the pain point of inability to pass on experience in fish farming, and at the same time create a "digital" foundation for fish farming, the top priority must be to collect farming behavior data and develop AI services as an important starting point Fishery digital twin technology helps fishermen transition to smart farming With the assistance of the Institute for Information Technology III, Fongyu implemented the "fishery digital twin" technology to dynamically adjust the farming schedule In other words, the fish farming schedule is adjusted according to the species, habits, and variables of the fish The use of AI in fish farming not only effectively increase aquatic production by 10, but also reduced labor cost by 15 In terms of specific methods, we first digitalized the fish ponds, feed, and decision-making behavior for each species, such as sea bass and Taiwan tilapia, and recorded the seasonal temperature changes from releasing seedlings to harvesting, all of which were digitalized, gradually recording the experience and methods of experienced workers into a rich database Based on the recorded data, we analyzed the compound variables to find the best farming behavior and generate a dynamic farming schedule The records for each pool provide data on workers' experience However, fish farming behavior generally relies on rules of thumb Even experienced fish farmers cannot ensure that they will find the best solution Therefore, new methods are proposed to solve this issue That is, "to determine the best fish farming behavior by predicting the interaction with water quality and past data on feeding, and evaluating fish farming behavior based on water quality and fish farming," and provide fishermen with the most intuitive recommendations through daily schedules To continue optimizing the dynamic fish farming calendar on a rolling basis, iterations of the model will be developed through the three-step cycle 1 Input the current fish farming calendar into the model 2 The model predicts the future environment 3 Shortcomings of the fish farming calendar are corrected based on the future environment to obtain a new version of the fish farming calendar In the process, the experience of aquaculture experts is used to establish the causal relationship between fish farming behavior and the environment The establishment of a dynamic fish farming process and technology-based fish farming recommendation services provide a traceable and detailed fish farming process It is one of the few technologies that can digitalize fish farming Fishermen can quickly and easily record their daily behaviors to build knowledge without taking up too much time, but in the long run it can reduce labor cost by 15 and increase output and revenue by an average of 10 Smart fish farming has achieved outstanding results, reducing labor cost by 15 and increasing output by 10 At the same time, the fish farming calendar can also be extended to different aquatic species, such as white shrimp, milkfish, clams, and Taiwan tilapia, to produce fish farming schedules for ponds with different specifications, and the harvested aquatic species can be traced according to different specifications, establishing vertically integrated services for safe food products Fongyu's main products are divided into two categories One is aquaculture modules, including fry, feed, materials and probiotics, production planning and processes, and monitoring, which can be sold separately or exported as modules The high-quality aquatic products produced by Fongyu have repeatedly won awards Figure Fongyursquos official website The other category is high-quality aquatic products, including seabass fillets, seabass balls, oil-free seabass balls, seabass dumplings, and seabass soup The products have won various awards, including the top ten souvenirs in Pingtung in 2017, "Barramundi Fillet" won the 2017 Eatender of the Council of Agriculture COA, "Oil-Free Barramundi Fillet" won the 2018 Eatender Gold Food Award of the COA, and "Dumplings of Barramundi" and "Barramundi Broth" won the 2019 Eatender of the COA The consecutive awards represent that the "quality" of Fongyursquos aquatic products can be seen and eaten with peace of mind In addition, Fongyu has exclusive fingerlings that meet international needs, such as Pure seawater cultured tilapia fingerlings and seawater Taiwan tilapia fingerlings from selective breeding FY-01 are items that aquaculture companies in many countries are looking forward to The company also has aquaculture modules, disease monitoring tools, and feeding materials designed in accordance with the environment, in order to provide customers with more stable income

2021-09-28
【2021 Application Example】 HRT Technology Improves Production Efficiency by 20% Through AOI Detection of Defects in VCSEL Packaging

In 2017, the launch of the iPhone X made 3D sensor technology used in Face ID highly popular, which drove the development of VCSEL, a core component in the 3D sensor module In the detection of defects in incoming packaged VCSEL, the use of AI inference models can solve the industry's issue with low yield and improve reliability to 95 VCSEL technology currently can be used in many applications and various end consumer markets, including robots, mobile devices, surveillance, drones, and ARVR VCSELs are a good solution in applications that require high-speed modulation capabilities, such as cameras and biometrics VCSEL technology has a wide range ofnbsp applications, including in drones Pictured Zoyi Technology's Agricultural Drone VCSEL technology has a wide range of applications, AI technology assists in defect detection HRT Technology stated that the packaged VCSEL market is also facing strong price competition from competitors, and needs to further reduce costs and enhance product competitiveness One of the key problems is the replacement of glass lens with epoxy resin lens The production of traditional glass lenses has high yield, but the cost is higher than that of epoxy resin lenses Due to the cutting process of epoxy resin, the side wall of cutting lines can easily have rough edges, causing it to be oversized The release of stress caused by heat during the mounting process will directly cause the optical lens to break HRT Technology pointed out that the incoming inspection of VCSEL epoxy resin lenses is very important Under the constraints of packaging space, the space for fitting the package and optical lens is limited Moreover, the optical lenses will be confined to a metal frame If the dimensional tolerances are properly controlled, stress release due to heat during mounting can easily cause the optical lens to break, resulting in a yield loss of up to 10 in the VCSEL package reliability verification, resulting in an increase in production costs In order to solve the problems above, HRT Technology hopes to use AI to monitor the size and appearance defects of epoxy resin components in the VCSEL epoxy resin lens incoming stage, verifying whether their dimensions meet specifications, whether the cutting edges are smooth, and whether there are any defects in their appearance Since traditional incoming material inspection requires a rough visual inspection by humans to distinguish the quality The problem of image collection needs to be solved first to successfully collect image data Therefore, HRT Technology first developed an Automated Optical Inspection AOI device, which includes X, Y, Z three-axis motion, high-resolution cameras, and related control software to automatically record images After collecting the image data, opencv aligns the test image and a normal image to determine differences between the two images, and then pixel mapping is used to compare the pixel area to complete initial screening Manual labeling is carried out according to the image classification above, including samples that are normal, have defects in appearance, or have different shape characteristics, and then algorithm training and verification is carried out Residual neural network ResNet or other related algorithms are used for deep learning to identify the quality of lenses Implementation of AOI inspection improves production efficiency by 20 and above Comparing the differences before and after the implementation of AI image inspection, the incoming VCSEL lens inspection before implementation only involved manual inspection of the appearance The lens is packaged on the VCSEL package that has completed die bonding After passing the general light up test, the final reliability test high temperature reflow is performed Failed samples go into the rework process However, after the implementation of AOI inspection, it can screen defective lenses sooner and reduce the cost of subsequent materials input, it can also reduce the need for rework due to failure, improving yield to 95 and above in the reliability verification This is expected to help companies reduce production costs by 10 and increase production efficiency by 20 and above The difference before and after implementing AI image detection HRT Technology pointed out that this technology is an AI application developed based on tiny images It uses deep learning algorithms to identify defects in the images The trained network automatically classifies image data to predetermined categories Defect categories can be determined through reference images, so cumbersome programming is not required In the industrial machine vision environment, deep learning is mainly used for classification tasks in applications, such as inspection of industrial products or identification of parts In the future, with the development of IoT wearable devices and the trend of energy saving, the size of optoelectronic components will continue to shrink This technology can be applied to the detection of defects in the appearance of other tiny optoelectronic components in the future

2021-12-05

Records of

新加坡國家AI戰略內容概要
2023 Singapore AI Development Policy Research: Singapore focuses on the needs of specific vertical fields when developing AI solutions

nbsp Source The National AI Strategy of Singapore, summarized by the AI HUB Project, November 2023 Singapore has been committed to developing a smart nation since 2005, formulating strategic blueprints such as "Intelligent Nation 2015 iN2015" and "The Smart Nation Initiative 2015" It has successively launched a number of AI policies and plans, which aim to fully utilize technological innovations to develop Singapore into leader in AI In 2019, Singapore formulated the 11-year National AI Strategy and proposed a 2030 vision, hoping to become a leader in AI solutions in important fields The strategy also pays special attention to promoting AI applications in key industries, such as transportation, manufacturing, and finance Singapore is also committed to cultivating the AI ecosystem and is concerned about the social risks that AI may bring At this point, Singaporersquos AI policy framework is practically set Singapore's "National AI Strategy" mainly consists of two parts, namely the "National AI Project" and "creating an AI ecosystem" By focusing on the deployment of AI models in key industries and incorporating the spirit of human-centered AI governance, Singapore has achieved an AI development path that takes into account both supervision and innovation The National AI Project refers to selecting key industries that have a high impact on Singapore's society and economy, and giving priority to the development of AI solutions The current five major projects are "Intelligent Freight Planning," "Seamless and Efficient Municipal Services," "Chronic Disease Prediction and Management," "Personalized Education through Adaptive Learning and Assessment," and "Border Clearance Operations" The projects above also show that Singapore will prioritize the development of AI applications in key industries, such as transportation and logistics, healthcare, education, and national defense Creating an AI ecosystem refers to how to facilitate Singapore's AI innovation and implementation methods At present, it has set five main goals, namely Facilitate industry-government-academia-research partnerships to realize AI commercialization, meet the demand on talent through AI education and cultivating AI talents, create a complete data structure to achieve cross-industry high-quality databases, and create a trustworthy AI environment to reduce concerns when implementing AI technology, and actively engage in international cooperation to get a say in international AI In summary, Singaporersquos AI development strategy not only focuses on industrial development and technology, but also attaches great importance to cultivating competitive AI talents and ecosystems This strategy of combining technology development and talent cultivation is also worth referencing in Taiwanrsquos subsequent development of AI nbsp nbsp

英國AI國家戰略大綱
2023 UK AI Development Policy Research: The UK established an AI regulatory framework to encourage innovation and developed an evaluation ecosystem

Source The UKrsquos National AI Strategy, summarized by the AI HUB Project, June 2023Outline of the UK National AI Strategy The UK's AI policy can be traced back to the "UK AI Sector Development Report" released in October 2017 In the Industrial Strategy White Paper released in November 2017, AI was further regarded as one of the four major challenges for future development In 2019, the UK established the AI Council to provide expert advice to senior leaders of the government and AI ecosystem It proposed an AI Roadmap in January 2021, providing 16 recommendations and strategies to the government Its core message is for the government to develop an independent national AI strategy The UK responded in the second half of 2021 and released the "National AI Strategy" based on the development path of its digital policy in the past The contents reveal the overall development vision, goals, key actions, and short, medium and long-term action plans of UK for AI in the next 10 years The UK's National AI Strategy further emphasizes the UK's vision to maintain its leading position in AI and become a global AI innovation center in the next ten years Its five main goals are 1 Ensure that all regionssectors can share the benefits of adopting AI, 2 maintain the UKrsquos leadership in AI RampD, 3 drive the UKrsquos GDP growth with the growth of the AI industry, 4 promote and maintain the UKrsquos AI literacy, and 5 establish its own AI capabilities to protect national security To achieve the above goals, the UK's National AI Strategy provides three specific action strategies The first is the long-term need to invest in and plan for the AI ecosystem, in order to maintain the UK's leadership as an AI superpower The second is to support industrial AI and digital transformation and ensure that AI is inclusive The third is to ensure that the UK has a say in AI governance, encourage innovation and investment, and protect public interests and moral values In terms of the ultimate strategic goals of actions, the UKrsquos main goal for the National AI Strategy is to improve the level of AI use by enterprises nationwide, attract international investments in AI companies in the UK, and cultivate the next generation of local technical talents, so as to promote the prosperity of the entire UK, ensure that everyone can benefit from AI, and apply AI to solve global challenges, such as climate change Another main feature of the UKrsquos AI industry is that it mainly adopts guiding principles for AI regulation As mentioned above, the UK hopes to establish a regulatory framework conducive to innovation to achieve its vision of becoming a global Trustworthy AI innovation center nbsp nbsp

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2023 Israel AI Development Policy Research: Israel established AI as national infrastructure and is actively creating an ecosystem

Source Official information from the Israeli government, summarized by the AI HUB Project, March 2023Outline of Israelrsquos AI Policy Israel is an outstanding country in AI development and high-tech industries According to the 2021 Government AI Readiness Index Report released by the Oxford Insights, Israel ranks 20th among 160 countries In addition, Israel also ranked 5th in Tortoisersquos Global AI Index In addition, Israel is also known as the "Startup Nation" Despite having a population of only over 8 million, Israel ranks at the top in terms of number of start-up companies Israel held five parliamentary elections between 2018 and 2022, and many government plans failed to be implemented smoothly As a result, the National AI Strategy was not formally implemented until 2022, even though its draft was proposed in 2019 The strategy uses the National AI Plan as its core, learns from the governmentrsquos past successful experience in investing in the field of cybersecurity, and strives to make Israel one of the worldrsquos five leading countries in the next five years Israelrsquos AI industry development approach can be divided into three main aspects Develop the infrastructure necessary for AI, create the optimal environment for AI infrastructure, and create a cross-domain sustainable ecosystem It establishes AI as Israelrsquos critical infrastructure in the future and creates a complete AI ecosystem Its specific actions include building computing facilities, human capital, data and research in related fields to establish the infrastructure required for the development of Israeli AI systems Israel establishes the best development environment for industrial growth through the development of a network security and ethical supervision environment Ultimately, it further creates a complete and sustainable ecosystem by connecting different sectors, such as implementing national development plans in health, transportation, security, and agriculture In terms of industrial development, Israel has extended the spirit of "Cyber Security is National Security" to the development of the AI industry, in order to strengthen AI-related infrastructure as the core strategy of the "National AI Strategy," and established AI as a critical infrastructure and national priority in the future However, due to long-term geopolitical risks, unstable political situations, and the successful experience of creating a startup ecosystem in the past, the spirit of innovation is deeply rooted in Israeli culture, and has further affected Israelrsquos views on the development of the AI industry

2023年9月美國《國家AI研究發展策略》版本內容比較圖片1
2023 US AI Development Policy Research: The United States carries out AI strategic planning from both a domestic and international perspective

The United States released its latest national AI policy in May 2023, ie, the National AI Research and Development Strategic Plan 2023 Update This is the second version updated after 2016 This series of strategies is also the core theme of the overall US AI policy In the past, many detailed industry policies and practices have been derived under this framework, such as the "National AI Security Final Report," "US Innovation and Competition Act," and the "CHIPS and Science Act"The "National AI Research and Development Strategic Plan" mainly elaborates on current RampD challenges in the field of AI, and uses this as the basis for planning subsequent resource investment by the US government, thereby ensuring that the US continues to maintain its leading position in the development and application of Trustworthy AI systems The ldquoNational AI Research and Development Strategic Plan 2023 Updaterdquo mainly continues the strategic planning in the first version in 2016 and the 2019 update In addition to continuing to supplement the eight strategic plans proposed in the past, a ninth strategic plan was added to the 2023 update, hoping to further incorporate international cooperation into important strategic planning for the subsequent development of AI technology and industry Main approaches of the US AI industry policy can be further analyzed from ldquoStrategy 5 Develop Shared Public Datasets and Environments for AI Training and Testing,rdquo ldquoStrategy 8 Expand Public-Private Partnerships to Accelerate Advances in AI,rdquo and ldquoStrategy 9 Establish a Principled and Coordinated Approach to International Collaboration in AI Researchrdquo of the ldquoNational AI Research and Development Strategic Plan 2023 Updaterdquo After observing, summarizing, and analyzing the contents, it can be summarized into two main approaches domestic and internationalIn terms of its domestic approach, the US focuses on expanding public-private partnerships because it believes that improving the AI model training and deployment environment is an important cornerstone This is the foundation for effectively linking government, academic research, and industry resources and needs Therefore, the US has taken a series of measures to democratize data to achieve the goal of improving AI infrastructure For example, the National Artificial Intelligence Research Resources NAIRR Task Force put forward a roadmap for national network infrastructure, and the US Congress also passed the ldquoOPEN Government Data Actrdquo In addition, lowering the access threshold for these government shared datasets is also a major focus For example, the STRIDES project Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability of the National Institutes of Health NIH is committed to integrating public cloud platforms Only one login account and password are needed to access different platforms to search for data, which is beneficial for researchers to collaborate on projects and transfer dataIn terms of its foreign approach, the United States actively engages in various bilateral cooperation methods This is mainly to ensure that the United States can continue to maintain its position as the central hub of the AI RampD ecosystem Therefore, it needs to continue to actively participate in international partnerships to share infrastructure and data resources Its specific approach is roughly divided into four aspects bilateral cooperation to develop Trustworthy AI, formulation of AI system standards and frameworks, exchange and attraction of professional talents, and development of AI to combat global threatsIn summary, the core spirit of US AI policy has always been to ensure that the US maintains its global leadership in the development and application of Trustworthy AI systems In terms of industrial development approaches, different strategic plans are proposed for domestic and international developments In addition to continuing to improve the AI shared infrastructure for domestic RampD, the US mainly expands public-private partnerships between industry, government, academia, and research institutes Internationally, the US is actively expanding bilateral cooperation to form a technology democratization alliance, further extending the concept of resource sharing to alliance partners and creating a mutually beneficial vision Source The National AI Research and Development Strategic Plan of the US, summarized by the AI HUB Project, September 2023 Comparison of different versions of the US National AI Research and Development Strategic Plan

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Announcement of the First Batch of the AI Technical Service Capability Registration and Application Mechanism in 2024, Applications Now Being Accepted!!

The Ministry of Digital Affairs MODA Administration for Digital industries ADI guides the direction of research and development RampD of technology and application services by domestic digital service providers, and provides certifications of verified performance, so as to enhance the resilience R, integration I, security S, empowerment E, and competitiveness of the industry, and drive the growth of digital economy-related industries The AI technical service capability classification and registration mechanism was specially planned and established to take stock of domestic AI technical service capabilities through a credible classification and registration mechanism It compiles a map of the domestic AI industry, and assists information service providers with developing more intelligent products and services, in order to expand industrial services and scale, accelerate the application of AI in the industry, and increase industrial value and competitiveness Those that pass the AI technical service capability registration will gain greater creditability as an AI service provider and be connected to the AI HUB aihuborgtw , matching supply and demand, assisting AI service providers with expanding business opportunities, and enhancing their competitiveness in the industry It can also be referenced for eligibility in future government-related guidance programs, and will be actively recommended to AI-related subsidy programs and venture capital platforms to assist enterprise development The Ministry of Digital Affairs Administration for Digital Industries now accepting applications for the first batch of AI technical service capability registration in 2024 and welcomes applicants I Definition of technical scope The definition of AI in the capability registration refers to the realization of simulated human cognition, machine autonomous inference, or knowledge work abilities in specific fields or general fields through new modeling methods, such as machine learning, deep learning, and neural networks This is used as the core business or integrated it into existing industries, software and hardware integration, or consulting service solutions Please note that traditional statistical techniques, such as regression analysis, are not within the scope of this capability registration II Eligibility Software and information service related institutions including for-profit institutions, non-profit institutions, and schools that completed business registration within the territory of the Republic of China in accordance with the law III Application period From now until 1800 on July 19, 2024 Friday IV Application method link wwwcisatwAIloginindexphp before 1800 on July 19, 2024 Friday to apply for a user account and password, fill in basic information online, and download and fill out related documents Upload the electronic documents in 1 to 3 before the deadline that was announced I Application Form and Affidavit for the MODA ADI's 2024 AI Technical Service Capability Registration see Attachment 1 for the formatII Proposal Form for the MODA ADI's 2024 AI Technical Service Capability Registration see Attachment 2 for the formatIII Required attachments of the proposal1 A photocopy of the certificate of company registration or business registration issued by the central competent authority2 The most recent tax bill for profit-seeking enterprise income tax, balance sheets, and statement of comprehensive income, or a photocopy of the approval notification for the declaration of income derived from professional practice3 Resume of full-time employee4 A photocopy of documents proving the performance of AI products or professional services Validity period of performance data 2022-2024 V Application briefing session Taipei session - June 24 Monday 1400-1600Room C206, Taipei Changan Hall, Taiwan Culture and Creative Center The information of Taipei session wwwcisanetorgtwCourseDetail5322 Taichung session - June 26 Wednesday 1400-1600Room 0304, Taichung Startup Hall, Taiwan Culture and Creative Center The information of Taichung session wwwcisanetorgtwCourseDetail5323 Kaohsiung session - June 27 Thursday 1400-1600Room K135, Kaohsiung Xinyi Hall, Taiwan Culture and Creative Center The information of Kaohsiung session wwwcisanetorgtwCourseDetail5324 VI Online consultation meeting An online consultation meeting OnlineTeams meeting the meeting link will be sent two days before the event, registration link wwwcisanetorgtwCourseDetail5321 will be held at 200-600 pm on July 4 Thursday to help companies understand the classification and key points of registering AI technical service capability Each company will have 10 minutes of one-on-one QampA We welcome you to sign up for the event VII Consultation hotline Contact person Mr YehTel 02-25533988 extension 385 Contact Email kevinyehcisanetorgtw

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113th Year Practical Topics: Research on the Application of Large-Scale Language Model Technology for Unstructured Data Understanding

Industry Category Artificial Intelligence Application Service Industry Industrial pain points Processing unstructured data often requires a lot of manual intervention, which can be time-consuming and error-prone Since unstructured data covers a wide range of fields, when processing these data, the model must have cross-domain knowledge integration capabilities and be able to effectively capture contextual semantics to ensure the accuracy of response and analysis In addition, when processing large amounts of data in real time, the model not only needs to have fast computing capabilities, but also needs to maintain a high level of accuracy and response speed Otherwise, the efficiency of industrial decision-making or data analysis may be affected Import AI benefits It can automatically analyze various types of unstructured data, including company documents, news reports, and sentiment analysis of social media, etc, significantly improving the speed and accuracy of data processing Through such automated data processing, enterprises can extract effective insights from huge amounts of information faster, thereby reducing labor consumption and improving the quality and efficiency of decision-making This not only allows companies to respond to market changes more quickly, but also maintains flexibility and decision-making accuracy in a highly competitive environment, further promoting business development In addition, when this technology is applied to research institutions and academia, it can speed up the process of knowledge discovery, literature review and academic question and answer, assisting researchers to quickly screen out useful information, thereby significantly improving research efficiency and result transformation Through such data analysis, researchers can find key research content in a shorter time and further promote academic progress Common AI technologies Open source programs, such as Hugging Face’s Transformer and Meta’s Fairseq

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Year 113 Practical Issues: Policy Explanation Assistant

Industry Artificial Intelligence Application Services Industry Pain Points Many people find it confusing to read policy terms due to the overly professional insurance jargon, making it difficult to understand their true meaning This can lead to erroneous expectations about coverage or compensation conditions, resulting in disputes A lack of proper understanding of key terms can affect insurance decisions, ultimately harming consumer rights。 The lack of transparency between insurance companies and consumers leads to misunderstandings about the policy contents, resulting in misinformation, misinterpretations, and even legal disputes, causing trouble for both insurers and customers。 AI Deployment Benefits The system can instantly parse specific contents within the policy documents, providing clear and accurate explanations, aiding policyholders in quickly understanding relevant term details This functionality significantly reduces the confusion surrounding professional terms and enhances policyholders' comprehension efficiency, enabling them to fully grasp the policy information and make correct insurance decisions For first-time policy buyers or those unfamiliar with clauses, such systems can significantly improve their experience and strengthen their trust in insurance products。 Moreover, the system offers personalized policy explanations and recommendations based on the policyholder's individual background, needs, and past insurance history This type of personalized service further enhances policyholders' satisfaction and loyalty towards insurance services, making the service more targeted and humanized For example, the system can provide detailed explanations of specific clauses based on the policyholder's specific needs, allowing them to make more informed insurance decisions, thereby strengthening the relationship between insurance companies and policyholders。 Additionally, the system also has the capability to automatically handle a large volume of policyholder inquiries, which not only avoids the risk of human error but also significantly reduces the workload of the insurance company's customer service department Through automated processing, the system can significantly improve the speed and accuracy of inquiries, allowing policyholders to obtain needed information in a short time, reducing communication costs between the insurance company and policyholders This functionality not only enhances service efficiency but also improves the overall quality of service, enabling insurance companies to manage customer needs more effectively。 Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeet cetera。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Year 113 Practical Issues: Writing Assistant Programs for Enterprise Net-Zero and Carbon Inventory Reports

Industry Artificial Intelligence Application Services Industry Pain Points When drafting net-zero and carbon inventory reports, enterprises often encounter cumbersome and challenging processes due to lack of expertise and experience, especially small and medium-sized enterprises that may lack specialized internal resources for handling such professional documents。 Ensuring the content of the reports complies with domestic and international regulatory standards is crucial for enterprises, but often requires additional time and manpower for proofreading and revisions, especially under frequent regulatory updates, which could delay the submission of reports。 Hiring external consultants or professionals to assist in drafting net-zero and carbon inventory reports can improve accuracy but significantly increases costs, posing a major challenge for resource-constrained small and medium enterprises。 The lack of appropriate tools can make the reporting process slower, failing to promptly respond to internal corporate changes or regulatory demands, thereby affecting the enterprise's response speed and overall efficiency。 Benefits of Integrating AI By automating the processing and analysis of a company's carbon emission data, accurate carbon inventory reports can be generated, significantly reducing manpower and time costs, and avoiding errors that might occur during manual processes Such a system makes the data collection and report drafting process more efficient and precise, ensuring every detail meets requirements As system automation increases, enterprises can focus more resources on implementing carbon reduction strategies, thereby improving operational efficiency。 Additionally, the system can monitor carbon emission data in real-time and automatically update report content based on the most recent data, ensuring enterprises are always aware of their emissions status This immediacy allows businesses to quickly adjust strategies, maintain compliance, and respond timely to market changes and policy requirements, thus enhancing their overall flexibility and accuracy in dealing with environmental regulations。 Moreover, the system also possesses the capability to deeply analyze carbon emission risks and provide effective emission reduction suggestions based on the risk analysis, helping enterprises optimize their carbon reduction plans This function not only accelerates the achievement of net-zero carbon goals but also enhances their environmental impact and sustainability capabilities Furthermore, by demonstrating their commitment to innovation, environmental responsibility, and social responsibility through this system, enterprises can effectively enhance their brand image and attract partners and investors who share similar values, injecting more momentum into the company's long-term developmentfigure Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc。 Enhanced Retrieval Generation。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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113th Year Practical Topic: Integration of Generative AI Models with Web Search in Smartphone Native Applications for Q&A

Industry Artificial Intelligence Application Services Industry Pain Points Users often need timely and accurate information in specific contexts, yet current search engines still lack in precision and efficiency, unable to swiftly answer specific questions, leading to delays in information retrieval and impacting user experience。 Users often find it challenging to use QampA apps on mobile devices due to unfriendly interfaces or designs that do not match smartphone usage requirements, hindering seamless information search or QampA。 Companies have high security demands for QampA applications, needing to ensure that sensitive information is not threatened or exposed during the QampA process, maintaining confidentiality and control of information。 AI Benefits By integrating web search technology, users can pose questions using natural language without the need to learn complex commands or operations, significantly enhancing the system's ease of use and friendliness This design reduces the learning curve, allowing users of varied backgrounds to easily start and quickly obtain the necessary information Whether they are professionals familiar with technology or novices with less technical background, all can operate the system smoothly, further enhancing the user experience。 At the same time, the system also has the capability of personalization, able to provide highly personalized answers based on users' historical questions and personal preferences, making each interaction more closely meet the users' needs This personalization not only improves user satisfaction but also deepens the system's understanding of user needs, further increasing user stickiness As the system continues to learn and evolve, it becomes increasingly aligned with users' habits, becoming an indispensable assistant in life and work。 Common AI Technologies Generative Artificial Intelligence, such asOpenAIandGPT、AnthropicofClaudeothers Search Enhanced Generation。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-23」

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Practical Issues in 2024: Development of Automated Annotation Tools & Creation of Edge Computing Datasets

Industry AI Application Services Industry Industry Pain Points Establishing high-quality annotated data requires training of professional annotators and meticulous management, significantly increasing labor costs In situations with limited HR resources, training time and management pressure can affect project schedules, posing challenges to business operations。 During the manual annotation process, subjective biases and inconsistent annotations often occur, which directly impacts the accuracy of the datasets and model creation, posing risks to the effectiveness of future product applications。 The process of annotating large volumes of data is time-consuming, which prolongs project completion timelines and delays product launches, impacting the efficiency of business operations and potentially resulting in a loss of market competitiveness。 For small and medium-sized enterprises, the cost of creating large-scale annotated datasets is prohibitively high, making it difficult to achieve the desired benefits, leading to project implementation challenges or even delays。 Benefits of Implementing AI The development of automated annotation tools can significantly reduce the costs associated with creating annotated datasets, decrease the demand for professional annotators, and shorten the overall development time and expenses Through automation, companies can accelerate their workflow and improve efficiency, thus focusing on the development of core products Automated annotation not only reduces manpower investment but also helps companies complete a large volume of data annotation in a shorter time, further enhancing the speed of development and product launch efficiency。 Moreover, automated annotation technologies effectively avoid human errors and subjective biases, which are crucial for enhancing the consistency and accuracy of annotated data The reliability of the data directly affects the outcomes of model training With precise automated annotation, data quality is strengthened, providing a more stable and high-quality data source for model training Such technological applications not only improve model performance but also ensure the credibility of the results produced during the training process。 Additionally, the system also possesses the capability to quickly process large amounts of data, speeding up the establishment of large-scale datasets, aiding companies in swiftly moving forward with functional development As the speed of dataset creation increases, the effectiveness of model training also improves, accelerating the product launch timeline and enabling companies to be more competitive in the market Such automated annotation tools are not only key to enhancing operational efficiency but also essential for helping companies gain a technological advantage in a data-driven era。 Common AI Technologies Convolutional Neural Networks, architecture likeMask R-CNN、SSD、YOLO。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Year 113 Practical Topics: 2D Virtual Avatars Technology

Industry Artificial Intelligence Application Services Industry Industry Pain Points In digital interactions, the demand for privacy protection among users is increasingly high, particularly in video conferences, online interactions, and social media settings。 Many industries such as news media, customer support, retail, and education face challenges due to the shortage of human resources。 Many current video conferencing and remote tools are too monotonous, lacking in personalized choices and fun elements, which affects the user experience。 Introducing AI Benefits AI provides effective privacy protection solutions that prevent direct exposure of personal images, increasing overall security This technology can replace some physical labor in specific areas, achieving real-time interaction or problem-solving, enhancing service efficiency, and expanding service coverage For example, in online customer service and educational training, virtual avatars can provide immediate responses, reducing reliance on human labor and increasing user satisfaction。 Additionally, the system can dynamically adjust in real-time based on the user's voice, expression, and emotions, allowing virtual avatars to interact more realistically with the user and providing a highly personalized experience By recognizing user emotions and tone, the system can generate corresponding virtual avatar reactions, making the interaction process more natural and smooth This instant feedback function not only tightens the user's bond with the virtual character but also significantly enhances the entertainment and immersion of the interaction, hence providing great potential and attraction for virtual avatars in gaming, entertainment, and business applications。 Common AI Technologies Platforms such as JetstoneLipsThinkVoice emotion recognition technology, face landmarkfacial landmark technology, etc。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Year 113 Practical Issue: True or False National ID Card Recognition

Industry Artificial Intelligence Application Services Industry Pain Points Today, the verification of the authenticity of national ID cards still relies on manual inspection, which is inefficient and prone to human error Due to the advancing technology in ID forgery, even experienced staff may not recognize fraudulent documents in a timely or accurate manner, posing high risks for financial institutions, government entities, and other industries requiring identity verification Additionally, manual recognition is susceptible to fatigue and decreased focus, further reducing the accuracy and reliability of ID verification。 Benefits of AI Integration The system can automatically analyze various details of the ID card, such as watermarks and fonts, and promptly make judgments, significantly reducing the likelihood of errors This technology effectively replaces the traditional manual verification process, saving significant labor resources and greatly enhancing operational efficiency It is particularly well-suited for scenarios requiring large-scale identity verification, such as in banks and government offices Through automation, institutions can verify identities more efficiently, reduce human errors, and further improve the accuracy and reliability of the process。 The system also possesses the capability to learn from a vast amount of data and to grasp the subtle features of ID card designs, thereby enhancing the precision of forgery recognition With multiple training sessions, the system continually optimizes its judgment capabilities, reducing the risk of confusion between genuine and fake IDs, decreasing the chances of misjudgments This is particularly crucial for high-security environments such as financial transactions and immigration control, where the system can confirm the authenticity of an ID card within a few seconds, providing immediate feedback for scenarios requiring rapid verification, thus effectively preventing the use of fake IDs。 Furthermore, the system, by learning from a large dataset, can identify common forgery tactics used in fake ID cards, such as altered images, counterfeit watermarks, and inconsistent fonts, thereby further enhancing the accuracy of ID recognition。 Common AI Technologies Convolutional Neural Networks, structured as followsMask R-CNN、YOLO。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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113th Year Practical Topic: Our Distance from False Information

Industry Artificial Intelligence Application Service Industry Industry Pain Points The rapid spread of counterfeit or incorrect information online, especially in social media or forums, where false messages can spread quickly and continuously evolve If companies rely on manual checking of this information, not only can they not respond in real time, but they may also miss the best opportunity to handle false messages due to insufficient human resources This affects not only corporate image but can also lead to financial losses Therefore, companies need more advanced automated tools or systems to quickly detect and handle this information, maintaining their brand image and reputation。 The spread of false information is not always intentional, some erroneous information may initially just be shared unintentionally, and not verified However, after being forwarded by many people, such information may be deliberately altered or distorted by those with ulterior motives, thus turning into intentionally created false information As false messages present different motives and forms at different stages, traditional monitoring or preventive measures can no longer make rapid and accurate judgments, hence enterprises need multi-level detection tools to tackle these challenges。 Introduction of AI Benefits The system can swiftly compare and inspect information on the Internet or social media, analyzing contents that are suspected to be false messages This technology significantly reduces the time spent on manual checking and can automate the detection of inconsistencies or false statements, making the process of verifying false messages much faster and more accurate Through precise algorithms, the system can promptly detect potential false information, assisting companies to take early measures and reduce the impact of false messages on brand and corporate reputation。 Furthermore, the system can not only analyze the accuracy of message content but also assess the credibility of each message based on historical records and the reliability of the sources This rating mechanism allows companies to quickly assess the risks and impacts of each piece of information, thus making precise responses at the early stages of message dissemination, reducing misguidance to the market and consumers Through this technology, companies and organizations can more effectively counter the spread of misinformation, enhancing information transparency and accuracy, thereby maintaining public trust and minimizing the negative impact of false information on business operations Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc。 Enhanced Retrieval。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-02」

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Issues for the 113th Year: Expanding Applications of Generative AI Models

Industry AI Application Services Industry Industry Pain Points The industry faces innovation or problem-solving delays due to a lack of effective tools and platforms that can efficiently stimulate creativity With the increasing complexity of problems in many professional areas, it becomes challenging to quickly present solutions solely through manual efforts, thus slowing down the innovation process and risking increased competition Additionally, the lack of adequate tools limits cross-disciplinary collaboration, causing innovation delays and an inability to meet market changes AI Integration Benefits In the digital transformation process, generative AI technologies can automatically generate texts, images, or other content, significantly simplifying complex internal workflows and enhancing task efficiency and accuracy This technology helps users solve problems quickly, enabling companies to accomplish more with fewer resources For example, marketing teams can use generative AI to quickly produce targeted ad copies based on market demand, while designers can generate preliminary design drafts, saving substantial time and labor costs, and fostering innovative thinking and creativity development。 Moreover, generative AI technology is not only applicable to marketing and design but can also be widely used in technical research and product optimization fields Through automated content generation, businesses can accelerate technological research and product development processes, effectively enhancing product competitiveness Such applications allow companies to remain agile in the rapidly changing market, adjust strategies on the fly, optimize products, and stand out in a highly competitive environment Generative AI is not just a tool for improving efficiency but also a significant force in driving corporate innovation and accelerating digital transformation Common AI Technologies Platforms, such as MicrosoftCopilot Stack。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Practical Issue for 2024: AI-Assisted Quality Enhancement in Manufacturing

Industry AI Application Services Industry Industry Pain Points Factories require quantitative testing devices to determine the quality of the yield For example, in automated optical inspection, when defects are signaled to exceed standards, multiple departments must collaborate, perform anomaly comparison, and link data for improvement These improvement cycles often suffer from non-transparent, untimely data or lack of experience, making it hard to find the genuine cause for improvement, thus prolonging improvement time and preventing effective loss control in yield rate。 From production equipment information to process yield and quality management, integrating information across multiple departments is necessary for a comprehensive explanation and presentation of the product’s manufacturing data Feedback from the back end to the production side requires cross-departmental communication to identify opportunities for improvement。 AI Benefits AI can effectively integrate data from automated optical inspections, equipment output, machine status handovers, quality anomaly management databases, and shipment quality specifications to create a unique, on-site knowledge base Through this system, users can instantly access key data and receive system-recommended solutions for improvements, thus accelerating the identification and resolution of issues Managers and engineers can use the system’s decision support to quickly identify potential issues in the production process and make more accurate decisions based on recommended improvements, not only enhancing the quality of production but also significantly improving operational efficiency。 The automated analysis and knowledge integration features of the system reduce the time required to troubleshoot, allowing for immediate response to abnormalities in the production process With real-time feedback and data analysis, managers can quickly detect and resolve quality issues, further encouraging quality stability and continuous improvement in manufacturing Such intelligent systems provide businesses with higher quality assurance, while reducing risks and costs during production, ensuring that products meet market demands and maintain a competitive edge Common AI Technologies Generative AI, such asOpenAIofGPT、AnthropicofClaudeand。 Retrieval-enhanced generation 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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