Special Cases

21
2021.1
【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】 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
【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】 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

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Key Topic for 113th Year: Implementation of AI Summary Generation on Government Website Platforms

Industry AI Application Services Industry Industry pain points The vast amount of information on website platforms makes it difficult for users to browse all content comprehensively in limited time, significantly reducing their willingness and efficiency to read Particularly when quick access to key information is necessary, manually filtering and reading each piece is not only time-consuming but also prone to missing important details Moreover, for specific and detailed documents like policy announcements, regulations, or lengthy articles, it's challenging for users to quickly grasp the core content, necessitating more efficient tools to help filter and generate summaries, thereby enhancing the overall user experience and improving data readability Benefits of AI introduction AI-powered summarization technology can automatically generate summaries for various articles or announcements, allowing users to grasp key information without reading through lengthy content This feature is extremely convenient for busy users For example, when users need to quickly understand the key points of government announcements, the system can generate summaries in real time, presenting important content and enabling users to quickly access needed information, thereby saving time on unnecessary details This feature not only enhances the efficiency of information transmission but also makes the user experience on government website platforms more user-friendly Users can rely on the system's summary capabilities to quickly browse and understand the core content of articles, especially when there is a need to review large volumes of data This application of technology significantly increases efficiency in work and decision-making The AI-generated summarization function simplifies and speeds up the organization and transmission of information, ensuring that every user can efficiently access important information on government websites Common AI technologies Generative Artificial Learning, such asOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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AI-Generated Dynamic 119 Duty Scheduling: Key Topic for the Year 113

Industry AI Application Services Industry Industry Pain Points Traditional duty scheduling systems often neglect employee personal needs and preferences, and lack the flexibility to adjust work hours, leading to dissatisfaction and even increased turnover rates This loss of personnel is a severe challenge for management, especially in environments where a long-term stable workforce is necessary Traditional scheduling methods appear rigid and lack the agility to address these issues effectively Due to the inability of the existing scheduling systems to flexibly manage the changes in manpower requirements for different shifts, particularly in emergency services or other high-demand response scenarios, this may lead to a shortage of personnel at crucial times further impacting the speed of response and quality of service Additionally, scheduling needs to consider complex variables like working hours, skill matching, and rest time for each employee, increasing the risk of errors Moreover, in dealing with sudden events or demand changes during holidays, the reaction often lacks speed, making it difficult to adjust manpower effectively This can lead to insufficient or excessive resource allocation and also increases the stress on management At the same time, manual scheduling is time-consuming and prone to errors, thus reducing the efficiency and accuracy of scheduling This places unnecessary burdens on both managers and frontline workers, thereby affecting the precision and timeliness of job execution Benefits of Introducing AI Through an automated scheduling system, the system can perform calculations based on multiple criteria to quickly generate a schedule that meets the needs This greatly reduces the time and cost required for manual scheduling while ensuring each step in the scheduling process adheres to set conditions Furthermore, the system possesses intelligent rule-setting capabilities that allow for fair distribution of duties based on predetermined working hours and break intervals This scheduling method not only enhances employee satisfaction with regards to duty assignments but also significantly reduces dissatisfaction caused by unfair scheduling, thereby further reducing turnover rates For human resource management, a stable scheduling system helps maintain long-term team stability, especially in high-pressure work environments Fair scheduling can greatly improve job satisfaction and reduce turnover Most importantly, the AI-generated dynamic schedule can instantly respond to changes in external conditions, such as unexpected events, holidays, or temporary demand shifts The system can quickly adjust the duty assignments based on these changes, ensuring that personnel resources are optimally allocated, enhancing the flexibility and appropriateness of the schedule Common AI Techniques Random Forest, Support Vector Machines, Long Short-Term Memory networks Generative Artificial Learning, such asOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Practical Issues in 2024: Using Generative AI for Image Search with Text

Industry Artificial Intelligence Application Services Industry Pain Points Currently, retrieving images often requires manually checking each frame to find specific objects or persons, especially in large volumes of images If one needs to find clues within a specific timeframe, it consumes a significant amount of time and manpower, impacting work efficiency and increasing the risks of fatigue and error in the review process Even with the aid of online resources to adjust images, some are blurry or low-resolution, still making it difficult to view clearly This requires reliance on user experience for adjustments, posing challenges in effective resource integration for those without professional experience AI Benefits Introduced Investigators can quickly locate key images related to the case from extensive CCTV and other image data without relying on traditional manual filtering methods The system filters images based on textual descriptions entered by investigators, such as time, location, or character traits, shortening the time of manual review significantly and enhancing the screening efficiency, thereby accelerating the pace of investigations By reducing repetitive and time-consuming tasks, investigators can focus more energy on high-value analysis and reasoning, as well as case solving For example, the system quickly filters out irrelevant images, focusing on key scenes potentially involved in the case, enabling investigators to capture crucial evidence in a short time, thus speeding up the case resolution process Common AI Technologies Convolutional Neural Networks, such asMask R-CNN、SSD、YOLO。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Practical Topic for 2023: CUI-Based Smart Work Order API System

Industry Artificial Intelligence Application Services Industry Pain Points For non-technical personnel, many fields and pieces of information in the work order system may be overly complex, leading to entry errors or comprehension difficulties, which increase hidden costs For example, non-technical personnel may not clearly distinguish the purpose of certain fields, or may fill them out incorrectly, ultimately requiring more time to communicate and correct these errors, potentially delaying the workflow This situation not only reduces work efficiency but can also cause delays in operations Therefore, designing a simple and intuitive interface to help non-technical users operate correctly is a major challenge in system design AI Benefits The system guides and assists users in gradually completing the work order, preventing errors or delays due to unfamiliarity with fields For instance, whenever users enter data, the system automatically provides field prompts and real-time error checks based on user requirements This design not only ensures the accuracy of each step's data but also speeds up the overall work order processing Furthermore, the system can analyze common errors made during the filling process and automatically optimize the filling workflow This continuous improvement mechanism effectively reduces the occurrence of repetitive issues, enhancing the overall user experience For example, when the system detects that certain fields are frequently filled incorrectly, it automatically strengthens the hints for these fields, or directly provides logical default options, making the filling process smoother Common AI Technologies Generative Artificial Intelligence, such asOpenAIandGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Practical Topics for 2024: Mastering Knowledge Learning

Industry Artificial Intelligence Application Services Industry Pain Points In certain industries, the cost and training time for interpreters are often high, which does not always lead to the anticipated benefits Particularly in roles where extensive knowledge transfer is necessary, the process of developing interpreter talent is long and challenging to accelerate As knowledge continues to evolve, the discrepancy between training time and actual productivity widens, making human resource cost investments more challenging for businesses Addressing this issue requires a more flexible interpretative approach to balance cost-effectiveness Currently, companies wish to provide more interactive explanations, allowing customers or employees to receive immediate responses Nevertheless, the difficulty in hiring suitable interpreting personnel, coupled with a high turnover rate among professional talent, frequently puts businesses in a predicament Especially in technical or knowledge-intensive industries, interpreters require not only a high level of expertise but also the ability to rapidly adapt to different demands, which makes finding and retaining such specialized talents extremely challenging Benefits of Introducing AI Through smart design, the system can offer highly personalized interactive QampA based on each visitor's individual needs and interests When visitors ask questions in the exhibit area, the system can instantly retrieve relevant information from the database and provide accurate and timely explanations according to the visitor's queries This approach not only significantly increases visitor engagement but also ensures the delivery of knowledge in a more vivid and specific manner Moreover, this interactive interpretation system supports multilingual functions, allowing visitors from various countries to access information seamlessly and delivering knowledge outputs tailored to different backgrounds, from professional expertise to simple background information This type of personalized interactive QampA system not only enhances visitor engagement but also brings about cost savings in manpower By automating instant response functions, exhibition areas can reduce their reliance on human-guided tours while ensuring that each visitor receives consistent and high-quality explanations This not only makes the transfer of knowledge more efficient but also strengthens management within the exhibition area, ensuring a more comprehensive learning experience for visitors Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeand others Enhanced Retrieval Generation。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Practical Topics for 2024: Location and Intelligent Land Information Query System

Industry Artificial Intelligence Application Services Industry Pain Points Currently, when individuals seek information on land location, announced land value, transactional land price, land usage zoning, and land type, they need to visit various websites This process is cumbersome and requires sufficient internet skills This method of inquiry is particularly inconvenient for those unfamiliar with information systems, especially when needing to synthesize multiple data points for decision-making The potential for confusion and high time costs are significant issues Additionally, as the websites may not synchronize updates, data inconsistencies can arise, further complicating the inquiry process AI Benefits The system possesses rapid analysis capabilities and a proficiency in understanding user queries, precisely extracting relevant data from extensive databases Whether it involves announced land values, land use zoning, or land type, the system provides accurate information swiftly, avoiding the need for individuals to manually search through data For example, when a user inquires about the zoning of a particular land piece, the system can respond instantly, offering comprehensive and detailed land information This design not only saves time but also enhances the accuracy of information retrieval, making the inquiry process more efficient Additionally, users can engage in naturally flowing conversational queries about land prices, with the system providing immediate responses, making the entire inquiry process more humanized and significantly boosting user satisfaction Common AI Technologies Generative Adversarial Learning, such asOpenAIandGPT、AnthropicothersClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Practical Issues in 2024: Budget Management Assistant for Business Units

Industry AI Application Services Industry Pain Points Business units often face staffing shortages, especially in budget management When personnel responsible for budget management resign or hand over their duties, it may lead to lost data or difficulties in getting up to speed, thereby affecting the efficiency of budget control Moreover, budget management involves many items without a systematic record, those taking over can spend a lot of time organizing data or finding historical records Especially during annual budget reviews or allocations, these challenging handovers can cause delays in budget allocation or execution, further impacting the operational efficiency of the business AI Implementation Benefits The system possesses the ability to instantly organize and summarize accounting data, enabling complex budget item analysis to be automated, thus saving extensive manual effort For example, when users need to inquire about the status of a specific budget item, the system can quickly retrieve relevant information, providing real-time responses and making the budget usage transparent Additionally, the system can generate budget reports automatically, allowing detailed comparisons of historical data and current expenses, helping decision-makers quickly grasp the budget execution status and avoiding errors or oversights due to manual operations This automation not only enhances efficiency in budget management but also ensures transparency and accuracy in budget usage Furthermore, the system can provide users with precise analysis of account usage during the budget management process This not only helps users better understand the flow of funds but also enables the management to promptly comprehend the efficiency of resource utilization, thereby making more accurate decisions The system can automatically organize all types of budget item data based on user needs and provide real-time responses to complex queries Such features considerably reduce data processing time, enhancing the workflow's efficiency and accuracy For example, if an anomaly in budget expenditures arises, the system will immediately alert users to review and adjust, thus avoiding the risk of misallocating resources Common AI Technologies Generative Artificial Intelligence,OpenAIofGPT、AnthropicofClaudeamong others 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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2024 Real-World Issue: Virtual Reality Showrooms

Industry Artificial Intelligence Application Services Industry Pain Points In many traditional showrooms, there is a lack of sales professionals who can immediately understand customer needs and tailor their offerings accordingly, especially in integrating virtual and real environments Many companies are unable to provide a seamless transition from physical to virtual showrooms, resulting in incomplete customer experiences, incapability to maintain brand continuity and consistency at various touchpoints, which negatively impacts customer purchase decisions and satisfaction In multilingual markets, the inability to use fluent and professional multilingualism to clearly explain product features and design advantages limits the ability of businesses to effectively reach a broader international customer base, lacking personalized interaction that makes customers feel alienated and makes it difficult to build strong brand loyalty and unforgettable user experiences during the sales process AI Integration Benefits Through the intelligent virtual expert system, the system can instantly simulate professional sales personnel, providing precise and personalized sales recommendations based on each customer's needs This system not only dynamically adjusts content recommendations but also effectively reduces reliance on human resources, especially during busy times or when it is not possible to timely arrange sales staff Virtual experts can provide high-level services on the spot, ensuring that customers receive the information they need and enhancing overall satisfaction This approach not only enhances sales efficiency but also increases customer identification with and trust in the brand through intelligent services, thereby fostering brand loyalty Additionally, the system possesses powerful context tracking capabilities, enabling it to understand and respond to customer needs in real-time By dynamically analyzing the conversational context, the system can adjust its product recommendations or service presentations based on customer reactions, avoiding overly standardized responses This level of personalized interaction not only enhances professionalism but also increases purchase intent, allowing customers to feel the brand's dedication and thoughtful service during the presentation process At the same time, the system also supports multiple languages and can introduce products in a humorous and flexible interactive style, making the product presentation more attractive This feature allows companies to overcome language and cultural barriers, particularly in the international market or multinational exhibitions, helping brands to attract customers from different cultural backgrounds This extends beyond single-language formal communication, using diverse interactive methods in a relaxed atmosphere to deepen customers' understanding of the product and enhance their willingness to purchase Common AI Technologies Generative Artificial Intelligence, such asOpenAIofGPT、AnthropicofClaudeetc 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-02」

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2024 Practical Topic: Deepfake Video Artificial Intelligence Detection Model

Industry Artificial Intelligence Application Services Industry Pain Points Today's image and video production technology has developed highly, making deepfake videos increasingly realistic and difficult for ordinary people to distinguish between real and fake For businesses or media, failing to properly identify these deepfake videos may lead to the spread of misinformation, further affecting credibility and business operations This problem requires an effective solution that can detect deepfake content in real time and prevent its further dissemination, especially in industries involving finance, news, and government agencies where the authenticity of content is crucial to public trust Benefits of Introducing AI The system can effectively identify unnatural facial expressions, image textures, and other subtleties in videos, details that are often hard to detect by human eyes Using artificial intelligence technology, the system can automatically detect potential forged videos This function is undeniably a highly valuable tool for investigative agencies, helping them in subsequent tracking and investigation, significantly improving the detection efficiency, and reducing reliance on human resources Many scam tactics often utilize deepfake technology to create seemingly real videos, making it easier for victims to be deceived Therefore, the application is not limited to investigative work it can also serve as an important tool for fraud awareness education, helping the public recognize and guard against scam tactics Through real-time educational outreach using examples of deepfake videos, the system can help the public more intuitively understand how to identify false videos, which is crucial for enhancing societal security awareness Common AI Technologies Convolutional Neural Networks, Recurrent Neural Networks Technologies, like Microsoft'sVideo Authenticator。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Practical Issues in 2024: Solutions for Detecting Fake News

Industry AI Application Services Industry Industry Pain Points Fake information is often used to manipulate the emotions and choices of voters, not only disrupting the electoral process but also potentially jeopardizing the fairness of democratic systems Especially on social media, fake information can spread quickly, increasing the difficulty of discerning truth from falsehood and thereby affecting voters' decisions The reputational damage caused by fake information to individuals or public figures not only leads to social panic but can also have long-term negative impacts on the individual's career and life The spread of fake information targeting businesses can affect investor confidence and potentially destabilize the market, having a significant impact on the national economy Therefore, timely detection and handling of such information is crucial Benefits of Implementing AI The system, through intelligent analysis algorithms, can process and analyze various types of data in real time, quickly detecting potential false content Thus, automated analysis significantly reduces the reliance on human oversight and enhances the accuracy and efficiency of fake news detection Compared to traditional manual reviews, AI technology can process large volumes of data in a short time, which aids in accelerating the detection process and providing more immediate responses For instance, when there is an attack by numerous fake messages, the system can detect anomalies immediately and respond swiftly to prevent further dissemination of information, effectively saving manpower costs and enhancing the overall response speed Through deep learning and natural language processing technologies, the system can identify subtle linguistic features within fake messages, thereby reducing the rate of false positives and increasing the accuracy of detection This enhancement in contextual understanding allows the system to make more accurate judgments when dealing with complex Chinese messages In addition to handling textual content, AI systems can also address various forms of fake news, particularly in recognizing multimedia messages including images and videos The system can detect fake elements in multimedia content through image recognition technologies and video analysis tools This media flexibility allows the system to handle fake news from different media platforms, whether it involves text, images, or videos, enabling the system to automatically recognize and process them, further enhancing its applicability across different scenarios Common AI Technologies Large language models, such asMediaTek Research Breeze-7B。 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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Practical Issue of 113th Year: Solutions for Non-Player Characters Specific to Custom Taiwanese Figures or Styles

Industry Artificial Intelligence Application Services Industry Pain Points Taiwanese-specific characters or styles often carry local accents or language features, which, when missing in game non-player characters, diminish player immersion and character affinity Due to the neglect of language differences and accent features, characters may not accurately reflect the Taiwanese cultural backdrop, especially in scenarios with specific regional or historical contexts The solution should include technologies capable of precisely simulating Taiwanese accents and language expressions, and through personalized voice modules, enable characters to more truly reflect local culture and style, enhancing the player's experience AI Benefits Introduction Through artificially intelligent voice modules, the system can simulate more authentic Taiwanese local accents, making character voices more realistic and enhancing player or user experience Such designs not only make characters conform more to local culture, but also create a stronger sense of immersion in games or interactive applications, enhancing overall user engagement Using artificial intelligence technology, a character's voice and performance can be instantly adjusted based on the context or user's response, offering a more natural interactive effect This interaction not only enhances the closeness of non-player characters but also allows users to feel deeper emotional connections during interactions, further enhancing the overall experience Whether in museum tours, travel spot narrations, or in educational interactions, such customized non-player characters can provide more personalized and humane services, further enhancing business value and scope of application With these innovative technologies, companies can offer richer and more unique experiences to users, boosting market competitiveness and creating more business opportunities Common AI technologies Deep Learning, architectures like Tacotron2 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-02」

泛加拿大AI戰略產業推動政策_產業聚落
2024 Canada AI Development Policy Research: Canada forms a regional AI ecosystem by creating five industrial super clusters

Source Official website of the Canadian government, summarized by the AI HUBPan-Canadian AI Strategy Industry Promotion Policy Canada's AI-related policies can be traced back to the Pan-Canadian AI Strategy formulated by the Canadian Institute for Advanced Research CIFAR, which was commissioned by the Innovation, Science and Economic Development Canada ISED in 2017 The strategy aims to strengthen the national and regional AI ecosystems and drive Canadarsquos social and economic growth Furthermore, the pan-Canadian AI Strategy mainly targets the characteristics of Canadian industries and focuses on supporting specific industrial clusters to expand its own advantages It can be divided into two stages The first stage from 2017 to 2022 is also called the "Innovation Superclusters Initiative" Innovation, Science and Economic Development Canada ISED has invested a total of 950 million Canadian dollars over five years to fund the development of five superclusters, namely Digital Technology, Protein Industries, Advanced Manufacturing, Scale AI, and Ocean, strengthening regional innovation ecosystems by inviting alliances that lead each industry to lead and invest in proposals It supports new partnerships between large companies, SMEs and industry-related research institutions It hopes to create a shared competitive advantage for the cluster by bridging gaps, integrating advantages, and enhancing attributes At the same time, it uses intellectual property rights to protect clusters, and the government supports the strategic use of intellectual property rights to help the development of enterprises The second phase from 2022 to 2028 is also known as "The Global Innovation Clusters program" The Canadian government allocated another 750 million Canadian dollars to continue to fund the development of industrial clusters and drive Canadarsquos economic innovation and ecosystem development, expanding Canadarsquos global leadership in various fields Focusing on the Scale AI cluster, various vertical fields are integrated with information and communication technologies to establish a smart supply chain The use of AI technology allows companies to immediately understand when and where products are needed, making it faster and easier for companies to contact each other and work together to increase sales In addition to improving the ability of enterprises to adopt new AI technologies, it also supports Canadian SMEs through innovation clusters, attracts other capital investments, and accelerates the research and development of Canadian AI solutions to accelerate the commercialization of AI In short, the Canadian government has been actively promoting the development of the AI industry since 2017, and has formulated a number of policies and regulations to promote the RampD, application, and commercialization of AI technology One of the most representative policies is the "Pan-Canadian AI Strategy," which aims to strengthen the national and regional AI ecosystems and create five superclusters, namely Digital Technology, Protein Industries, Advanced Manufacturing, Scale AI, and Ocean, with AI technology and smart supply chain as the core, Canada is actively promoting cross-industry AI integration nbsp nbsp

歐盟《AI創新套案》概要
2024 EU AI Development Policy Research: The EU provides resources to support the development of AI startups through an innovation sandbox under the regulatory policy of the AI Act

Source The EUrsquos official website, summarized by the AI HUB Project, March 2024Figure nbsp nbspSummary of the EU's "AI Innovation Package" The EU's earliest AI industry promotion policy can be traced back to 2018, when the EU launched the "European Strategy for AI" and proposed the "Coordinated Plan on AI," "General Data Protection Regulation" GDPR, "AI White Paper," and "European Data Strategy" to implement the strategy This established the EU's overall AI development strategic framework In this background, due to the influence of the two major AI powers, the United States and China, at the time, and the fact that the EU hopes to catch up quickly, despite its own AI industry just starting out, the EU proposed the "AI Act" AIA in April 2021 The EU hoped to strengthen the implementation of the EU strategy at the legal level, in order to achieve the goal of leading global AI governance The EU introduced new laws and regulations or updated existing ones on this basis in 2022-2023, such as the "AI Liability Directive AILD," "Product Liability Directive PLD," and "AI Innovation Package," in hopes of supporting industrial development through industry promotion policies that take into account both supervision and the spirit of innovation before the AIA takes effect In addition to regulatory policies, the "AI Innovation Package" launched on January 24, 2023 is the EUrsquos approach to developing the AI industry The package is in response to the initiative proposed by the President of the European Commission in the State of the Union Address on September 13, 2023 In terms of computing power, European supercomputers are provided to innovative European AI startups to train their trustworthy AI models Immediately afterwards, the European Union launched the Large AI Grand Challenge on November 16, 2023, and revised rules of the European High-Performance Computing Joint Undertaking EuroHPC JU to provide AI start-ups with financial support and the opportunity to use the computing power of supercomputers, encouraging European startups with experience in large-scale AI models to participate These startups will be able to use EuroHPC supercomputers to develop large-scale AI models and eventually release open source model results for non-commercial use as a first step to implement the new initiative In addition, it will also assist in upgrading supercomputers, allowing EuroHPC to establish supercomputers hosted in various countries in the EU and connect them to form an efficient supercomputer network, such as Spain's MareNostrum 5 and Luxembourg's MeluXina, which have been used for scientific research in the past, but were not optimized for work related to generative AI models In addition, the upgrade will face GPU procurement issues, and the EU will strengthen procurement and obtain GPU chips in compliance with the Chips Act In terms of data, the policy document "Facilitating the development and innovation of trustworthy AI startups in the EU" was proposed, which will provide high-quality data accumulated in the Common European Data Spaces Project in the past, including health, media, transportation, agriculture, construction, environment, manufacturing, and RampD The data can be combined individually or across fields through business-to-business B2B and business-to-government B2G, in order to drive innovation and achieve the goal of eliminating data localization restrictions across the EU Next, the European Commission will also establish data protection mechanisms for shared data, clarify responsibilities for incorrect data, data loss and revision standards, and assist in resolving disputes Therefore, in summary, the EU adopts a two-pronged approach when it comes to the development of the AI industry On the one hand, it improves the supervision of AI systems from a legal perspective, including AIA for prevention beforehand, and AILD and PLD for remedy afterwards, so that victims can seek compensation with a clear legal basis On the other hand, it actively promotes innovation and supports AI startups that have potential and the overall industry by providing computing resources and high-quality data

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Year 113 Second Phase Artificial Intelligence Technology Services Energy Registration Mechanism Announcement - Now Accepting Submissions!!

The Digital Industry Agency of the Ministry of Digital Development guides the research and development direction of technology and application services for domestic digital service organizations and serves as a proof of performance to enhance the resilience R, integration I, security S, and empowerment of the industry E Capabilities and competitiveness drive the growth of digital economy-related industries Specially planned to establish an energy classification and registration mechanism for artificial intelligence technology service organizations Through a credible classification and registration mechanism, we will take inventory of domestic artificial intelligence AI technology and service energy, build a domestic artificial intelligence industry map, and assist in information services Industry players are improving the AI-based nature of their products and services to expand industrial services and scale, accelerate the introduction of industrial artificial intelligence applications, and enhance industrial value and competitiveness Those who sign up through the energy of artificial intelligence AI technical service organizations can enhance the service credibility of AI industry players, connect with the AI HUB platform, promote supply and demand matching, assist AI industry players in expanding service business opportunities, and enhance industry competitiveness In the future, they can also serve as Qualification reference criteria for government-related mentoring programs, and proactive recommendations to AI-related subsidy programs and venture capital platforms to assist enterprise development The second-tier artificial intelligence technology service agency energy registration of the Digital Industry Agency of the Ministry of Digital Development is accepting applications from now on, and you are welcome to participate 1 Definition of technical categories Artificial intelligence, as defined in this article, refers to the realization of simulated human cognition and machine learning in specific or general fields based on new types of computational modeling methods such as machine learning, deep learning, and neural networks Independent inference or knowledge work capabilities, and use this as core business or integrate 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 energy registration 2 Application qualifications Software, information services and other related institutions including for-profit institutions, non-profit institutions and schools that handle business registration within the territory of the Republic of China in accordance with the law 3 Application time From now until 1800 on September 27, 2020 Friday 4 Application method Please go to Software Association Activity Website, apply for a user account and password, apply for basic information online and download relevant documents After filling in, complete the following 1 3 electronic files before the announced deadline Upload 1 113 Artificial Intelligence Technology Service Organization Energy Registration Application and Cut-off Form of the Digital Industry Administration of the Ministry of Digital Development the format is as shown in Appendix 1 2 1 copy of the energy registration application plan for the 113 Artificial Intelligence Technology Service Agency of the Digital Industry Administration of the Ministry of Digital Development the format is shown in Appendix 2 3 The application plan should be accompanied by attachments 1 A copy of the establishment registration certificate or business registration certificate issued by the central competent authority 2 The most recent income tax payment form, balance sheet and comprehensive income statement for profit-making enterprises or a copy of the notification of approval of declaration of business income 3 Resume of full-time personnel 4 Copies of documents proving performance in the construction of artificial intelligence products or professional services Validity period of performance data 111-113 years 5 Inquiry hotline 02-25533988 extension 385 Mr Ye MingyuanE-mail 「Translated content is generated by ChatGPT and is for reference only Translation date:2024-11-15」

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List of AI Technology Services Institutions Registered for the First Batch in Year 113

「Translated content is generated by ChatGPT and is for reference only Translation date:2024-12-12」

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