What should small and medium-sized businesses or startups that want to export their products do when they lack PR resources, media exposure, and journalist contacts SparkAmplify, a company that builds global market platforms using big data, has created a precise media marketing platform SaaS that aggregates data from over 80,000 global media journalists With AI technology, it analyzes data and generates press releases within 20 minutes, matching them with accurately targeted international journalists to greatly increase exposure and achieve marketing goals internationally SparkAmplify's main service is a brand-media matching marketing SaaS platform Since its launch in 2018, it has continually analyzed international media trends and has already analyzed over 3 million international media reports, helping more than 1,200 companies from 25 countries achieve precise media exposure It has partnerships with major events such as CES and Computex, as well as famous incubation accelerators like TechStars, BootUp, Taiwan's TSS, Garage "Media are searching for news, companies are searching for media" By applying AI data, a balance has been found Jian-Qun Li, founder of SparkAmplify, explains, "From observing the demands of both suppliers and consumers in the media marketing market, there's a rigid demand for a platform that matches 'brands with journalists' based on both parties’ needs" Thus, SparkAmplify utilizes machine learning Logistic Regression algorithms to filter specific categories of news text and uses the LDA topic discovery algorithm to identify the hottest news trends, rolling out the 'AI Exploration of Media Trends' service Generate AI Press Releases in 20 Minutes to Find Suitable Media This system service only requires three major steps to disseminate the products or services of brands, small and medium-sized enterprises, and startups on the international market through international media coverage Step One, Material Preparation SparkAmplify sets up a dedicated brand page where brand managers prepare and upload complete materials including company profile, product names, service features, images, related product diagrams, etc步驟二、品牌故事撰寫：透過專家系統及運用機器學習Logistic Regression邏輯回歸演算法，將特定類別的新聞文本篩選出來，並透過主題探勘演算法LDA，找出最熱門新聞趨勢，系統會自動按結構、格式、片詞、文法、關鍵字等等，在短短20分鐘內自動生成AI新聞稿，再加以人工優化。步驟三、精準推薦：將公司及產品介紹、新聞稿等，媒合國際媒體共8萬名記者，將對的主題推薦到對的記者身上，主動提供記者報導素材，以增加媒體露出及曝光機率。 AI探勘媒體趨勢服務協助品牌公司精準國際曝光 Jian-Qun Li points out that traditional methods of gaining media exposure include holding press conferences or distributing press releases widely However, at international exhibitions, brand owners and small and medium-sized business leaders might not have sufficient PR resources Additionally, understanding industry trends and journalists' reporting preferences poses a significant challenge Aside from the challenges of data collection, extracting meaningful insights and trends can often be ineffective, time-consuming, and labor-intensive The 'AI Media Trend Exploration' technology can effectively and accurately collect data, use text mining and machine learning to unearth underlying information, and, by executing periodically, keep track of market changes to products 鎖定科技新聞領域 協助品牌業者精準曝光 善於資料分析的李健群，運用媒體大數據的分析技術，打造以機器學習進行分析的行銷系統平台，專攻歐美市場數據行銷決策與社群行銷，幫助行銷能力不足的的新創團隊，或有想要獲得國際媒體青睞的品牌業主，能以大數據分析找尋適合投放的媒體。 在AI技術的應用上，安普樂發使用NER命名實體識別技術，Named Entity Recognition技術來增加不同的屬性。例如人、組織、產品等，最後再透過知識圖譜Knowledge Graph建立屬性之間的關係，才能迅速達成預估目標。 由於新聞領域五花八門，包括財經、科技、政治、社會、運動、娛樂、美食、時尚設計等，資料數量眾多，但受限於儲存等資源，無法一一掌握，安普樂發將重點擺在科技新聞領域，與CES、Computex等大型國際科技展緊密結合，提供參展商在公關媒體上操作的資源，爭取國外媒體曝光機會，負責找對的媒體將品牌效益傳達、延伸出去。 三步驟完成媒體精準投放流程 SparkAmplify 商業模式主要為訂閱制，每月收取399美元，透過簡單步驟即可輕鬆完成品牌與媒體的對接服務。至於除了英語之外，未來是否會推出中文服務李健群表示，要跨到落地的語系需要重新建立一套模型，中文又比英文要複雜許多，處理過程要刪除非常多的雜訊。然而，因應中文化的需求日益殷切，未來在資源配置足夠的情況下，有機會也會推出中文服務。 SparkAmplify 團隊 SparkAmplify 創辦人李健群「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Business email fraud causes over ten billion USD in losses annually Business Email Compromise BEC has become a global threat, causing losses of over ten billion dollars every year Criminal organizations target victim companies in various ways, infiltrating and lurking over long periods to steal information, eventually orchestrating scams to induce victims to make payments or transact with the wrong parties In such crimes, key fraudulent emails often appear indistinguishable from ordinary business or personal correspondence because they match business contexts and daily transaction behaviors BEC messages may not even contain URLs or attachments that could be detected by technical security measures, rendering antivirus and click-prevention strategies ineffective With email fraud being rampant, effective prevention of such threats has become a top priority A domestic biomedical manufacturer, one of the government's five major innovation industries and a recipient of the 13th Startup Business Award from the Ministry of Economic Affairs, features high innovation and high risk in the biotechnology sector Handling sensitive information related to new drugs, experimental materials, or market development, and even confidential personal data tied to medical and clinical trials, the company faces substantial threats from BEC scams They hope to leverage AI's interpretation capabilities for more accurate and comprehensive alerts against malicious emails, ultimately enhancing productivity and avoiding scams Utilizing AI recognition to preemptively deter threatening emails, effectively boosting corporate productivity 'Artificial Intelligence-assisted BEC transaction intent perception' is a feature developed by NetzEngine Information Software Co, Ltd and Dupont Digital Security Ltd, capable of identifying emails with transactional intents and incorporating them into NetzEngine's MailGates email behavior analysis module to detect threatening messages and improve the detection accuracy of suspicious threat emails This case uses two functionalities from the aforementioned AI technologies, 'Email Fraud Protection by Mail Header Security Policy' and 'Email Fraud Protection by Email Behavior Analysis Policy' Openfind MailGates' 'Mail Header Security Policy' feature In MailGates' 'Email Fraud Protection' features, the 'Mail Header Security Policy' can be adjusted For example, all emails from hotmailcom must have a correct From header, but the Reply-to header will be blank, this is the correct format for Hotmail emails, the same applies to Gmail If the email comes from another source, however, it should adhere to the filtering rules recommended by MailGates Both From and Reply-to should come from the correct and same domain, otherwise, it is likely to be a fraudulent message The third rule in the image represents all emails from openfindcomtw should use the aforementioned default rules for inspection If an email does not comply with the Mail Header Security Policy as per this setup, users will receive a 'Email Fraud Warning' notification on the subject line, helping to prevent BEC email fraud Openfind MailGates' 'Email Behavior Analysis Policy' feature, can set 'Protection Level' Openfind MailGates' 'Email Behavior Analysis Policy' feature, can set 'Operational Actions' In MailGates' 'Email Fraud Protection' feature, the 'Email Behavior Analysis Policy' can be applied as needed This feature's design and settings might seem complex for average users, who could opt for the 'Intelligent Detection' method instead, simply choosing among 'Loose, Standard, Strict' levels The system will determine the actual settings for these levels based on recently collected feedback data More skilled managers can use the 'Custom' mode, to set all behavior analysis functionalities in detail For example, by listing commonly impersonated domain names under 'Similar External Domains', the system will automatically consider similar but unequal domains, intended to impersonate and deceive users, as higher threat sources This feature allows users to set alerts for such emails, including using title and content warnings, and through behavior analysis, if the system deems the email likely to be a BEC fraud, it will clearly prompt users to be vigilant 'Email Fraud Protection by Mail Header Security Policy' and 'Email Fraud Protection by Email Behavior Analysis Policy' are actually planned and designed functionalities incorporated into the MailGates email protection system, combined with the aforementioned AI research outcomes, all MailGates users will be able to utilize these two functions against BEC scam emails For corporate clients, 'missed threat emails' represent the most significant information security threat and the aspect most needing improvement With the adoption of NetzEngine's BEC smart sensing mechanisms, they can immediately and effectively reduce the threats of BEC scams within the unit, avoiding scams and boosting corporate productivity Comprehensively guarding客户 client email security, expanding the value of the domestic information security industry NetzEngine Information Software Co, Ltd, in collaboration with AI startup Dupont Digital Security Ltd, adopts NLP and more specialized threat analysis technologies, capable of intelligently perceiving emails with transaction intentions and increasing the interception rate of BEC emails This not only enhances client value and maintains their cybersecurity capabilities, but as a leader in domestic email security, Openfind NetzEngine Information will continue to develop solutions for email and messaging communication security In the future, NetzEngine plans to integrate BEC protection and APT sandbox defense technologies, continuously expanding into a derivative product line Advanced Threat Protections By addressing customer needs against the continually increasing cybersecurity threats with a robust and comprehensive protection solution, it aims to deliver greater value to customers, while also expanding the domestic information security industry's problem-solving options and enhancing its value In the current high-end cybersecurity protection market, many clients can only use products from foreign manufacturers These products' designs, usage processes, and, most crucially, the sources and processes of samples or policy settings often do not suit the specific requirements of domestic government agencies or enterprises Therefore, through products and services offered by NetzEngine, enterprises will be helped to safeguard email security「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Life is tough for lettuce village farmers who are at the mercy of the elements Global market trends are volatile In terms of agriculture, it must compete with cities for land and water, and also face other crises, such as mitigating climate change and protecting natural habitats In particular, climate change makes it more difficult for farmers to plan cultivation in traditional ways For organic lettuce exporters, they have to overcome problems such as climate and pests to ensure that the lettuce can meet the standards of overseas customers This is an issue that the industry is facing Difficulties and needs of Taiwan Lettuce Village Although Taiwan Lettuce Village currently uses an internationally certified standard process GGAP for cultivation, and also uses the "Intelligent Agriculture Management System" developed by Info-Link Services for cultivation management, it still faces the dilemma of not being able to control crop yields and quality due to climate abnormalities Efforts to solve pests and production problems in recent years not only consumes labor, but also doubled the use of pesticides However, using cultivation and production models of the past will cause the industry to stagnate or even face elimination Therefore, it hopes to add value through AI, and make the lettuce village can be more information-based, intelligent, analytical, and predictive in cultivation, so as to expand the industryrsquos exports and diversify the industry's development in the future Current Demand of the Lettuce Village The agriculture industry in Taiwan Lettuce Village currently only implements "information management" Even though it has the concept of data application, there are no implementation methods and direction, and fields are still manually inspected and the dosage of pesticides is determined based on experience Since crop production varies due to environmental factors each season, the accumulated temperature conditions required for lettuce growth can be estimated by comparing crop yield and harvest date based on historical meteorological data, thereby establishing an accumulated temperature calculation module to estimate the cultivation schedule, allows the system to automatically analyze and make prediction based on the current temperature and humidity of the overall environment During the crop harvesting period, it assists field personnel in optimizing their work, reducing the need for daily inspections to determine when to schedule the next task Agricultural information system AI allows lettuce to grow smoothly "The stability of the cultivation environment" plays an important role in the growth process of crops Understanding the growing conditions of crops can greatly increase production and maintain a certain level of quality Combined with "smart equipment in the field" and "linebot," field management can be carried out and warnings can be received at any time, allowing managers to respond quickly to reduce potential losses, and assist in disease prevention, growth period, and harvest prediction It can be further integrated with data from the Central Weather Administration to establish a "cultivation database," and conduct agricultural analysis through data collection, such as fertilizer dosage planning, analysis of lettuce growth days in different months, analysis of the quality and weight of lettuce output based on temperature, and even disease prevention predictions Comparison of differences before and after digitization Compiled the cultivation data collected by the Lettuce Village from field equipment and external data , such as temperature, humidity, sunlight, and farmland fertility, and applied the data in four aspects, including 1 establishing crop progress and growth obstacle information, analyzing the temperature ranges that are suitable for growth and hinder growth, importing open data real-time and future weather forecast data to establish forecast standards, and using weather sensing equipment for field monitoring, in order to achieve real-time warning notifications and preventive effects 2 Utilize cultivation data for growth predictions, in order to achieve the goal of estimating harvest date 3 Using mobile phones for weather monitoring achieves the goal of real-time control and adjustment of field operations, allowing Lettuce Village to effectively manage manpower, material costs, and crop quality 4 Compiled farmland fertility data to provide the fertilizer ratios for suitable for farmland and reduce the frequency of fertilization, improving farmland fertility while improving the overall environment Description of Data Applications The system will continue to be optimized and promoted it to more units The "Intelligent Agriculture Big Data Application Platform" allows farmers in Lettuce Village to no longer be limited to the traditional agricultural business model, achieve systematic cultivation and production management, and standardize specifications to improve quality, stabilize output, and reduce labor consumption and material costs Improved pest and disease detection accuracy from 80 to 100 In the future, we hope to increase the accuracy of pest and disease detection, so that farmers can monitor the status of crops in real time, making the system more complete We also hope to apply this system model to more crops, and allow more farmers to consistently grow high-quality crops at low cost through government promotion

Does AI cost a lot Is importing AI time-consuming and labor-intensive How to build consensus within the enterprise and build a solid AI team All the above problems are common problems faced by enterprises in AI digital transformation Huang Mingshi, the founder and CEO of the AI startup Kaiyuan Intelligent Manufacturing Company, provides customized AI solutions on a "subscription basis" so that traditional enterprises that want to undergo digital transformation can quickly introduce AI solutions The term "Lean Production" appeared for the first time in the book "The Machine That Changed the World" in 1990 When it comes to business operations, there is no waste of resources Phenomenon, the process operates smoothly and creates the most profit with the minimum investment AI subscription service solution to quickly assist in importing tools Huang Mingshi graduated from Jiaotong University and received a PhD in Electrical Engineering from Penn State University in the United States He also worked for a start-up company in Silicon Valley for five years He was the chief data scientist and led a team of 10 people to develop multiple AI projects, including real-time image recognition , 5GAI, prediction system for dynamic expansion of cloud resources, etc Huang Mingshi returned to Taiwan to start his own business and established Open Source Intelligent Manufacturing in May 2019 The development direction is to promote AI subscription services He hopes to make AI practical and practical, help small and medium-sized enterprises with AI application needs, and accelerate the cultivation of practical capabilities of AI talents Huang Mingshi believes that the scale of AI project software and hardware equipment, which can easily cost NT2 to 3 million, is an "unbearable burden" for small and medium-sized enterprises with limited funds and human resources However, for those who lack For small and medium-sized enterprises with limited resources, AI can indeed solve the problem of automation, reduce costs and improve efficiency for enterprises in a short period of time, and is an indispensable tool for digital transformation Therefore, Huang Mingshi follows the principle of "lean production" to prepare AI digital transformation tools for small and medium-sized enterprises, using small projects to get started, from corporate health clinics to identify problems, corporate training, consulting to providing AI model solutions Done within a year In the process of project promotion, we assist the company's middle- and high-level managers to empower them and help them understand the company's pain points, what problems AI can help solve, and introduce benefit analysis By then, it will be relatively easy to introduce medium and large-scale AI projects Open Source Intelligent Manufacturing’s customized solution for the legal industry is the development of a “food advertising text recognition and analysis” tool A medium-sized law firm wants to help clients solve the problem of "identifying food advertising violations" According to statistics, there are more than 4,000 cases of advertising violations in the food industry every month The traditional method is to assign 2-3 lawyers to search for exaggerations or claims of efficacy in food advertisements published by major media The cost of each lawyer is calculated at NT5,000-10,000, which means the overall cost is considerable However, by collecting relevant information through the crawler system, and then importing AI technologies such as natural algorithm NLP to develop food advertising text recognition and analysis tools, the recognition rate can reach 90, which can also greatly reduce personnel costs In addition, Kaiyuan Intelligent Manufacturing has successfully applied face recognition to applications such as smart tourism and smart door locks, achieving an accuracy of 95 It has also used graphic recognition to help digital advertisers achieve the function of AI image removal , reducing the time designers spend on repetitive memorization by more than 80 It is worth mentioning that for designers, it often took 2 hours to memorize photos in the past Using the AI model, 1,000 photos can be memorized in 10 seconds, which is amazingly efficient This means that designers do not need to spend too much time memorizing photos, and can use their time to come up with creative ideas When photos are needed, they can use AI technology to quickly memorize them for ordinary consumers, when making presentations When designing PPT, you can also use photos that have been reversed to speed up the presentation production time In the future, it will also be connected to Google Flickr's personal photo album or image search, so that you can directly memorize the required photos and complete the task in one go At this stage, the open source intelligent manufacturing project is cooperating with APP manufacturers to remove the photos and create a free website to benefit more people working in the design industry Open Source Intelligent Manufacturing develops an AI model for hair back removal, the effect is comparable to that of professional designers In the medical industry, Kaiyuan Intelligent Manufacturing has also developed obstetrics and gynecology organ image recognition technology and conducted education and training in schools to help students make correct judgments Kaiyuan Intelligent Manufacturing also cooperates with the Taiwan Suicide Prevention and Control Association to use AI models to find people who are emotionally distressed, have depression, or have negative emotions and comments on online forums such as PTT and social networking sites such as Facebook, and design Prepare a suicide risk assessment and submit relevant information to the Taiwan Society for Suicide Prevention and Control to prevent it before it happens and reduce possible tragedies Four-step import method to complete the goal within one year In order to help small and medium-sized enterprises achieve the goal of AI application through subscription services, Kaiyuan Intelligent Manufacturing hopes to use methods to find enterprise pain points in the shortest time and at the same time enhance the commercial value of AI applications The methods are as follows 1 AI Discovery Workshop Guide companies to explore their needs through workshops or corporate health clinics 2 Enterprise AI training The introduction of AI requires the full support of the company's senior managers and the consensus of all employees to be successful Through a one-month training, it can quickly transform into an AI-empowered enterprise 3 AI consulting What is important is the technical feasibility assessment Not a set of AI solutions can solve any problem Different AI models must be established according to the different needs of the enterprise in order to prescribe the right medicine 4 Subscribe to AI solutions Four steps of import method Huang Mingshi pointed out that the services provided by Kaiyuan Intelligent Manufacturing are not a single solution, but customized AI solutions for enterprises There is no problem with talents such as AI algorithms What is more difficult is how to market this set of consulting services to customers in a simple way Fortunately, Kaiyuan Intelligent Manufacturing has participated in the "AI HUB" and "AI GO" projects of the Industrial Bureau of the Ministry of Economic Affairs Through the method of "industry raising problems and talents solving problems", we can understand the needs of enterprises, solve problems accordingly, and launch customer services Customized AI solutions The open source intelligent manufacturing team won the excellence award in the AI GO problem-solving competition The picture first from the right is the founder Huang Mingshi 「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Avoid unnecessary invasive biopsy examinations, all thanks to the professional judgment of radiologists Medical imaging recognition is a crucial task for radiologists, who must make professional judgments based on patient's examination data Upon identifying a tumor, it must be determined whether it is cancerous feasible methods include 'non-invasive medical imaging' and 'invasive biopsy examination' The advantage of biopsy examinations is that they can provide very accurate diagnoses, however, as they are invasive, doctors and patients will avoid this method if the probability of severe conditions is low One of the responsibilities of radiologists is to provide related professional judgments to aim for the most ideal situation Radiologists are overwhelmed, standards for judging tumor benignity or malignancy fluctuate, exposing a crisis in medical quality With the popularization of medical imaging examinations and the gradual flourishing of preventive medicine concepts, the burden on radiologists has been increasing A single doctor needs to handle multiple patients at once, and under conditions of long working hours and multiple patients, the standard for judging the benignity or malignancy of tumors based on images can fluctuate, resulting in patients not receiving optimal medical quality Tatung Science and Technology National Taiwan University Develops 'AI Medical Imaging Recognition System', Introduced to Medical Institutions, Effectively Enhances Tumor Interpretation Efficiency and Accuracy Tatung World Technology Co, Ltd and the Research Team of the Institute of Biomedical Electronics and Informatics at National Taiwan University jointly developed the 'AI Medical Imaging Recognition System' The trained model can interpret the benignity and malignancy of breast X-rays, with an accuracy rate reaching 85 This system has been introduced to the radiology department of a central medical institution for POC verification, helping to reduce the workload of radiologists and the waiting time for patients' examination reports Breast Tumor AI Interpretation System Diagram In the future, the correlation between the breast imaging report, data system BI-RADS grading, and AI benignmalignant interpretation will also be further defined, transforming the imaging interpretation from a binary system to a probabilistic BI-RADS grading This will assist the institution in establishing a common standard and enhance the efficiency of cooperation across different medical specialties Benefits of Introducing AI Identification System Replicating successful models, laying the foundation for the AI medical imaging big data era The development model of this AI Medical Imaging Identification System can be applied to different types of medical imaging, including computed tomography scans, ultrasound imaging, etc and can integrate natural language processing capabilities with pathology analysis reports, laying the foundation for the AI medical imaging big data era「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Did you know a single golf ball can have up to 28 defect inspections Manually, one can inspect 500 balls in an hour, but with AI, up to 6,000 balls can be inspected in the same time Huiwen Technology has developed AOI Automated Optical Inspection technology that achieves a miss rate of 01, which is ten times better than human inspection Besides the golf ball industry, Huiwen Technology's AOI inspections are also being introduced to the textile industry and others Geng Cheng Lin, the founder and general manager of Huiwen Technology, has been an expert in artificial intelligence AI since 2013, recognizing the future potential and explosive power of deep learning DL and AI-based image recognition AOI has always been a strong demand in the manufacturing industry, mainly to improve product quality for business owners, stabilize the quality of delivered goods, and use data from AOI inspections to improve processes, thus creating a virtuous cycle and further cost reductions Due to uncontrollable factors such as human eye fatigue and inconsistent standards, inspection encounters bottlenecks The limit of human inspection miss rate after training is about 1-2, and the situation worsens over time AOI is a stable and capable of mass inspection device, achieving a miss rate of 01, which is ten times that of human eyes, implying a detection rate of 999 Of course, AOI also results in a 5-10 over-inspection rate, which can be further screened manually With the help of AOI, the burden of quality inspection is reduced, saving a considerable amount of labor time AOI Golf Ball Defect Inspection, Inspection Capacity Increased 12 Times per Hour The first litmus test of Huiwen Technology's AOI technology was on golf balls With their highly reflective, uneven surfaces, golf balls were previously inspected manually for defects A tiny golf ball can have up to 28 defects, and traditionally, only 500 balls could be inspected per hour A major domestic golf ball manufacturer, meeting the demands of Japanese customers, introduced AOI inspection two years ago The high-speed, high-precision AOI system combined with AI deep learning image recognition technology conducts defect detection on golf ball surfaces, fully automates the feeding and outfeed process, replacing manual recognition of missed defects, and can immediately record defect conditions and report back, inspecting up to 200,000 packs of golf balls per year per machine, greatly enhancing customer satisfaction However, this step took Huiwen Technology more than two years Golf Ball AOI Recognition Image Golf Ball AOI Recognition, 28 Surface Defects Unveiled Lin Geng Cheng says, from data assessment and consulting, followed by data organization and tagging, selecting and verifying AI algorithms to AI training services, the golf ball data is like starting from zero, accumulating one by one Thankfully, with full support from golf ball manufacturers, the efforts have finally bore fruit With AI inspection, while manually one might inspect 500 balls in an hour, AI can handle 6,000, achieving effectiveness 12 times greater Unlike other companies, Lin believes that AI needs to delve deep into domains to scrape professional data since only with such domain data can AI perform well Therefore, the company starts from individual projects, rather than setting an AI product from the beginning Without quality data or a focused domain, the best algorithms cannot succeed in AI Over the years, Huiwen Technology has accumulated project experience, gradually developing products while focusing on domain data and providing the latest AI algorithms to customers, growing together, creating a tighter collaboration, which is why, different from external fundraising, Huiwen's investors are customers or partners Evaluation to Official Launch AI Introduction Requires Six Phases The projects undertaken by Huiwen Technology are divided into several phases 1 Evaluation period, 2 Initial Validation POC period, 3 Data Collection period, 4 Repeated Verification period, 5 AI Positive Cycle period, 6 Official Launch The evaluation period involves understanding and assessing the Domain conditions of the demand side beforehand, followed by POC verification, extensive data collection after POC, entering repeated verification stage, and finally allowing AI to enter a positive cycle phase, achieving a certain level of effectiveness before the official launch Generally, a project takes about six months to a year to develop However, with more familiar PCB AOI projects, the first two stages are skipped, starting from data collection, thus significantly reducing the time 'Regardless of this project or others, common questions from customers are 'How much data is enough When will AI learn' Facing such questions, Lin points out that the reasons for these questions are 1 The inexplicability of deep learning technology, as it is a black box 2 Generally, customers lack the concept of AI technology Thus, the company must patiently verify data repeatedly, identify the data needed by AI, accumulate and test it, and clarify and resolve all Domain conditions, which requires a lot of time and patience During the AI introduction process, customers have high expectations of integrating AI services, thinking that it can immediately replace human labor Lin points out that this is not the case the real value of AI lies in accumulating large volumes of high-quality data, which is then transformed and analyzed to establish AI training and verification models to fully address problems generated by manual processes Apart from inspecting golf balls, Huiwen Technology is currently targeting the textile industry for items such as fabric and shoelaces, and many industries have conducted POC trials through Huiwen, including the semiconductor industry, PCB industry, and other traditional industries AOI Fabric Defect Detection, Top Image Shows Before AOI Inspection, Bottom Image Shows After AOI Inspection Lin Geng Cheng indicates that the most difficult aspect of entrepreneurship is nurturing talent and customer recognition customers often demand quick results, not realizing that AI adoption requires data accumulation and repeated verification, processes that cannot show results in less than six months Affected by the COVID-19 pandemic, the trend of globalization and centralization of the manufacturing supply chain has been disrupted, replaced by 'short region' supply chains, suggesting small, beautiful factories will flourish everywhere, potentially bringing new opportunities for AOI Lin notes that high automation indeed offers opportunities for automatic inspection AI, however, relatively high capital investments, including automation equipment, mainframes, GPUs, and sufficient AI maintenance talents, are burdens that small and medium enterprises or small factories cannot bear, requiring government financial resources and input to facilitate smooth transformation「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

IBM's supercomputer Watson can predict when employees are likely to resign, with an accuracy rate of 95, saving IBM up to 300 million a year in retaining employees Moreover, through cloud computing services and modernization, IBM has streamlined 30 of personnel costs, allowing the remaining employees to earn higher salaries and engage in more valuable work So, in Taiwan, how can we ensure that 'employees who stay can receive higher salaries and perform more valuable work' The key lies in the 'competence setting' for each position According to the 'iCAP Competency Development Application Platform' established by the Ministry of Labor's Workforce Development Agency, every position has its main responsibilities, work tasks, behavioral indicators, work outputs, knowledge, skills, and attitudes Only by establishing 'competency' for each position can enterprises effectively apply this in employee recruitment, education and training, and performance management Without this, not knowing what employees should do is like groping in the dark, which can pose risks to business operations Competency Benchmark Example Currently, on the 'iCAP Competency Development Application Platform', there are 872 established competency benchmarks, including 553 items completed by various ministries This includes 253 items from the Ministry of Labor and 66 items from the Ministry of Education If companies want to establish their own 'competency benchmarks', they need to search for reference materials on the 'iCAP Competency Development Application Platform' Suppose a company wants to recruit 'sales' personnel but doesn't know what 'sales personnel' should do they should first search for 'sales personnel' as shown in the figure below Searching for 'sales' on the 'iCAP Competency Development Application Platform' You can find that there are 18 types of sales personnel At this point, the company needs to go through each one, check, read, and organize into the 'competency benchmarks' they need however, if we search what should be a common position in any company, 'general affairs', the result is unexpectedly zero items Searching for 'general affairs' on the 'iCAP Competency Development Application Platform' As seen above, although the 'iCAP Competency Development Application Platform' established by the Ministry of Labor's Workforce Development Agency can solve some of the 'competency benchmarks' for positions, the division of labor within each company is different, and some positions might not be found on the 'iCAP Competency Development Application Platform' Secondly, in small and medium enterprises, there are often 'multi-skilled workers', meaning many job responsibilities are on a single employee For example, in small enterprises with less than 30 people, usually, accounting, general affairs, and HR are handled by the same person If you want to establish competency benchmarks for this person, you have to search separately for 'accounting', 'general affairs', and 'HR', and then integrate these three types of job competencies, which is often time-consuming and ineffective This 'Small and Medium Enterprises AI Competency Evaluation System' aims to let 'people fully utilize their capabilities', by introducing AI to more accurately establish basic competency standards for employees, and to track their competency performance at any time Competency models are all generated and adjusted manually, which is time-consuming A domestic exporter of screws, nuts, fasteners, etc, had all its competency models generated and adjusted manually The execution process was time-consuming and insufficient to meet company needs due to personnel changes, such as previously, Qiao Mai Enterprise had specialized 'production control personnel', but after this personnel resigned, this job had to be done by other employees, meaning other employees' competency models needed to be adjusted immediately Or if the company needed to set up a development department due to future development, but previously no one had relevant experience, not only did they not know how to select from within, but also did not understand how to describe on a recruitment website to find the talent they really wanted Besides, the CEO of this company has always been troubled by internal performance management Due to the lack of precise standards and systems to measure employee performance, the results of each performance assessment did not accurately reflect the true performance of the employees, forming assessment blind spots and unable to identify truly deserving employees Thus, it is hoped that with the AI competency evaluation system, the necessary competencies for the development department can be immediately clarified, as well as how recruitment and performance appraisals should be conducted, so as to effectively solve the pain of unclear responsibilities and inaccurate assessments within the company Thus, its benefits are significant AI Competency System Establishment X Deep Learning This 4-month HR field competency system project has a clear execution direction, but the introduction of explanatory models such as Seq2Seq, Deep Keyphrase Generation, Tf-IDF keyword extraction algorithms, and PageRank are new attempts in the HR field During the process, open-source big data architecture is used for natural language processing to complete Word2Vector and index, and inverted index to establish keyword weight and relevance Due to the inability to process like image data with continuous numbers, it is necessary to simplify the feature values with related keywords such as skills, knowledge, and job categories Basic steps are briefly described as follows 1 Establish a Propagation model using Google's long-used LTR mixed Pointwise recommendation engine 2 months 2 Establish a Back Propagation model 2 months, adjust the hyperparameters of the loss function 3 Adjust the hyperparameters of the CF model 4 Establish a human-machine collaboration mechanism to obtain more data to feed the Model 5 Repeat the above steps During the development process of the competency model, Lianhe Trend Co, Ltd and Weiguang International Information Co, Ltd held multiple discussions, believing that there are interconnections between competencies After establishing the knowledge graph, further upload the competency scale to the Neo4j graph database for processing complex relational data structures with excellent performance Currently, 500 competency scales have been uploaded to the Neo4j relationship analysis platform Using python for wor2vector natural language analysis In addition to describing a position with a tensor after word2vector, finding out the appearance of this position's knowledge graph, according to this knowledge graph, one can understand the relevance between different positions and the similarity performance of their dimensions Finally, this knowledge graph is used to establish the company's 'competency model' and train it with deep learning AI Competency Evaluation System Interface In the future, in addition to establishing their own competency models, companies can also be opened to end-users Individuals can analyze their own competency performance to understand their possibilities for job change and their market value, as well as identify skills needing enhancement If companies respond to this knowledge graph, they can develop cross-industry products in the future 1 Short-term Analyze the competency scales iCAP, iPAS published by the government with natural language and keyword models, and cooperate with unsupervised learning to establish 'Native Competency Base Unit Models' 2 Medium-term Tailor-made exclusive competency models for enterprises Based on the existing 'Native Competency Base Unit Models', experts use supervised learning to train the individual company's 'Distributed Derivative Competency Models' 3 Long-term Establish 'Reinforcement Learning' models, incorporating employee career cognition and planning Competency model recommendations, comparable to professional human resource consultants Through the dynamic learning of the competency knowledge graph through unsupervised learning, individual companies' competency models are quickly established Internal human resources personnel or external professional HR consultants can then use the generated competency models to assess and apply aspects of talent recruitment, competency inventory, performance management, and education and training The system will automatically suggest competencies to be strengthened according to the company's existing job structure, including related knowledge, skills, and attitudes Through the continuous introduction and training of data, the system learns the employer's actual view of the model for that profession and feeds back to the cloud competency scale, completing the dynamic learning of the knowledge graph through transfer learning In the future, it can be comparable to professional HR consultants, thereby rapidly assisting many cross-disciplinary or technologically diverse companies in training employee competencies「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

In the era of AI, whether it's smart manufacturing or smart retail industry applications, the most important first step is data collection, especially on factory machines where thousands of information streams exist It's crucial to define what information is useful and what constitutes useless digital junk from the onset If there is simple, lightweight, and low-cost software, it could help enterprises collect data from machines and analyze and predict to achieve traditional enterprises' digital transformation goals AI Commercialization Insufficient, Main Consideration for Enterprises is Cost-Benefit Located in Xindian, New Taipei City, the headquarters of Beier Electronics Asia Pacific is a technology company committed to industrial automation and high-standard data communication Its main products include Industrial IoT Gateways devices linking two different network systems, transmitting data to other networks with similar functionalities but different structures, War Room Visualization software, HMI, with core technologies being controller communication drivers and visualization software Lee Li-Wei, the deputy director of product management and support at Beier Electronics, who has worked at Advantech and Xinchang Company, and familiar with Industry 40 operations, stated that when assessing whether to introduce AI, the most important consideration is cost and benefit From his observations over the years, the level of AI commercialization is still very insufficient because AI is often customized, not mass-reproducible, hence naturally expensive Only if AI moves to vertical applications and standardizes products, like using AOI for defect inspection or predictive diagnostics for motors or tools, can such AI possibly be commercialized and cost-effective X2 pro提供各種高性能工業用人機介面 At this stage, digital transformation challenges faced by factories include 1 Data collection difficulties 2 Factory equipment needing updates, which consumes time 3 High costs For large enterprises, sufficient funds and human resources allow them to introduce AI and undergo digital transformation projects As for SMEs, limited by resources, the key factor is cost-benefit, determining whether to introduce AI Existing software that assists in data collection and provides decision-making visualization software may meet current practical needs Once business owners see tangible benefits, they can further consider the need and incentives for adopting AI As a professional manufacturer of human-machine interfaces in Europe and America, Beier Electronics provides visualization software that fetches controller data on production lines and machine tools, as well as IT data integration services Using AI technology optimized for productivity and quality management can solve data fetching and integration issues For example, the factory war room displays the day's factory data and even real-time financial reports on a large screen wall Factory decision-makers can then use the integrated information and war analysis converted by visualization software to make decisions about production, marketing, inventory management, and procurement preparations Three major advantages of the war room low cost, easy maintenance, and mass reproducibility Compared to a typical factory war room, Beier Electronics' war room service has three major advantages it offers a low-cost, packaged visualization software it doesn't depend on engineers for maintenance and the war room can be easily commercialized and mass produced, which also accommodates future expansions from automation to IoT devices Lee further explains that AI primarily retrieves data for assessment, unlike automation, which demands real-time reaction It can tolerate a slower data fetching speed, still within milliseconds Human-machine communication doesn't need a special interface, so it can be decoupled from existing controllers without program changes, directly interfacing with existing hardware on the production line to fetch data, using existing software for data reading and analysis, aiding in decision management, and carrying out factory digitization upgrades As for whether Beier Electronics will introduce AI algorithm technology to provide users not only with data collection but also analysis and prediction services Lee stated that Beier Electronics considers three aspects Firstly, data collection is absolutely crucial when introducing AI At the same time, it must be done without increasing costs or changing site equipment for high customer acceptance Secondly, what problems AI aims to solve must be clearly defined Beier Electronics' clientele includes PLC programmable logic controller vendors, including Delta Electronics, Yonghong, Alliance Automation, Shilin Electric, and international giants such as Siemens, Rock weld, and MITSUBISHI However, the ultimate customers are PLC users, covering industries beyond semiconductors, including petrochemicals, 3C manufacturing, automotive manufacturing, power generation, and risk control The domain knowledge covered is extensive Given limited resources, whether to extend services to AI is still under consideration However, if professional specialization can be implemented, Beier Electronics plans to arrange an industrial ecosystem, introducing strategic partners to assist customers toward AIoT goals Deputy Director Lee Li-Wei, Product Management and Support Department at Beier Electronics「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

In response to current trends, digital learning and mobile educational materials have attracted widespread attention With rapid technological advancements, effectively nurturing professionals who can 'adapt to developmental changes' is a critical concern that many businesses continually consider Over recent years, various enterprises have progressively integrated 'digital learning' into employee training programs to enhance educational outcomes, thus bringing 'digital learning' and 'mobile educational materials' into the limelight Outsourced narration is costly and cannot handle large volumes of demand Differences in the digital educational material production process before and after the implementation of the AI voice synthesis system Strategic Breakthrough Corporation of Taiwan has assisted companies in converting many seminars, physical courses, and training events conducted by public sectors into digital materials in the past years However, during the conversion process, it required inviting teachers, finding and renting filming locations, and post-production of recordings and videos During recording, issues such as speakers' nervousness, discomfort in front of cameras, or mispronunciations might lead to poor recording quality or constant retakes Though there was an option to provide customer-specific educational material narration, the outsourcing costs were high and could not handle the demand efficiently Therefore, there was a hope to introduce AI speech synthesis technology and develop an 'Intelligent Voice Synthesis Module' to instantly convert text on slides into natural, human-like voice files, thus saving on narration costs Realistic Intelligent Voice Synthesis Module, providing a diversified selection of voices AI Voice Synthesis Module Illustration Strategic Corporation of Taiwan collaborated with the AI technology team, Magic Cube Digital Ltd, using Tacotron2 combined with WaveNet and Tacotron features Characters are embedded into Mel-scale spectrogram plots, then a modified WaveNet model acting as the vocoder synthesizes waveform in the time domain from these spectrograms, finally developing an MOS Mean Opinion Score for voice quality evaluation that approximates human-like intelligent voice synthesis modules This AI Intelligent Voice Synthesis Module, after being tested by testers using the MOS voice quality evaluation standard, received a score of 43, meeting the initial project target score of 421 and surpassing WaveNet's score of 408, thereby demonstrating exceptional effectiveness AI Intelligent Voice Synthesis Module, reducing costs and increasing profits, will effectively enhance Taiwan's digital learning industry environment Costs have been significantly reduced after the implementation of the AI voice system, and profits have increased relatively This AI Intelligent Voice Synthesis Module not only reduces the cost of producing digital educational materials but also solves the difficulties faced by Taiwan's industry, government, and academia in spreading digital educational materials It can effectively enhance the efficiency of customers in producing digital teaching materials, significantly reduce labor shortages, and cost structural risks, and improve profitability Strategic Corporation of Taiwan will also continue to develop the 'Intelligent Transcription Module' and introduce Robotic Process Automation RPA to replace the current manual processes, such as captioning, dubbing, and file conversion in the production of digital educational materials, assisting in the transformation and enhancement of the domestic digital learning industry「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

A tiny chip capable of driving AI algorithm speeds by nearly a hundredfold, Fansi Data's team is dedicated to software and hardware integration, providing industries including finance, smart healthcare, and smart manufacturing with a cost-effective, high-efficiency way of introducing AI and rapidly undergoing digital transformation In recent years, artificial intelligence has been highly prominent however, practical applications have been limited by high costs The enhancement of 'computing power' is crucial for breaking through the bottlenecks in AI applications Fansi Data's customized chip design and solutions can increase processing performance and effectively reduce costs, making AI applications in finance, healthcare, and manufacturing easy and feasible The company's core service is the high-performance hardware acceleration platform FPGA Fansi Data was founded in October 2018 by a founding team from National Tsing Hua University, National Chiao Tung University, and National Taipei University The company currently has 11 employees, including the chip design director Liu Wenkai from the IC design company Huirong Technology, who leads a 5-person IC design team They spent over a year developing the high-performance hardware acceleration platform FPGA, which became the company's core service Fansi Data integrates software and hardware to develop a high-performance hardware acceleration platform FPGA 'To bring AI to practical implementation, the challenges are cost and real processing situations Purchasing a standard set of NIDIA GPUs is expensive If we can adjust the hardware through customization, producing a setup tailored for use, the costs can be significantly reduced' Said Liao Yanchin, General Manager of Fansi Data, who additionally pointed out that most AI startups currently only have software engineers and lack hardware engineers Fansi Data excels in data handling and softwarehardware integration, has an excellent team, and can efficiently solve data issues while developing softwarehardware solutions tailored to customer needs Financial markets are notoriously fickle, as evidenced by the recent COVID-19 pandemic, which triggered a global stock market crash and was reinforced by program trading, leading to the unprecedented implementation of four trading halts in US stock exchanges within a decade This has significantly raised investors' risk awareness Zheng Zongyi, co-founder of Fansi Data, experienced in financial trading, points out that in financial markets such as stocks, futures, and warrants, 'speed' is often the key to victory Typically, the traditional stock trading process involves financial trading data flowing from the network to the mainframe, processed through combination software, measured in milliseconds ms, 10-3 seconds, with an average transaction completed in 20 ms System transaction processing speed, however, is at the nanosecond level ns, 10-9 seconds, and through the high-performance hardware acceleration platform FPGA, each financial matching transaction can be completed in microseconds, a significant difference that can lead to billions in trading gains or losses, and is a major competitive edge for proprietary traders Financial services in the domain of securities firms' proprietary sections, new types of financial product trading departments, and high-frequency traders or major retail traders In the securities market, market volatility is the result of a tremendous amount of data If the system operates at nanosecond speed, allowing you to see transaction information instantaneously, ahead by 01 seconds, you can make trading decisions before others even see the market data Service areas focus on financial technology and smart manufacturing The risk control systems of bank credit cards can also utilize AI integration acceleration, similar to regulatory technology domains Establishing an AI model can effectively identify risky credit card transactions and provide responses in a very short time, enhancing the security and smoothness of online transactions In the AI credit card risk control system, AI acceleration is also used through software integration Transactions are prevalent, and fraud is common, similar to regulatory technology domains By establishing an AI model, risky credit card transactions can be effectively identified, and responses given in a very short time, enhancing the security and smoothness of online transactions This includes financial transactions and credit card risk identification, all through chip-based transaction data analysis and risk management system direct acceleration calculations Financial transaction information acceleration solution Currently, many financial companies have their own IT departments, including data scientists, big data analysts, and AI algorithm engineers What is Fansi Data's advantage in the financial sector Zheng Zongyi points out that the IT departments in the financial industry are more 'users' of IT, not 'developers' of IT Moreover, understanding IC design involves high costs, and the financial industry does not need to maintain their IC design team The specialization is very clear, as Fansi simply develops models for the financial industry to adopt Considering personal privacy and data security, financial data is sensitive and often not easily accessible Fansi Data, by joining the financial technology innovation park FinTechSpace and with the assistance of the Institute for Information Industry, applies for the real-time transaction data and corporate annual financial statements, historical trading data provided by the digital sandbox, using it to group data, analyze, model, backtest, and propose AI risk warnings and other solutions for abnormal transactions and risk management Besides financial technology, Fansi Data also focuses on AI applications in smart manufacturing, such as developing smart image meter reading through image recognition methods, which can help businesses reduce equipment replacement costs and achieve higher accuracy In the process of customized chip design, data analysis, and softwarehardware integration, Fansi Data encounters difficulties in data and talent acquisition At this stage, through interfacing with the digital sandbox and utilizing resources provided by the financial technology innovation park, AI models are built regarding talent, a lean core team is established, continuously accumulating experience and building a robust entrepreneurial culture to face the ever-growing market demands From left to right Co-founder Zheng Zongyi, General Manager Liao Yanchin, and Chip Design Director Liu Wenkai「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Taiwan is located in the subtropics and has a diverse geographic environment that is very suitable for growing fruit Bananas and pineapples were once extremely popular export commodities that we are proud of However, farmers in consuming countries gradually obtained the excellent seeds of Taiwanrsquos fruits, and were able to grow the same quality fruit but at a more affordable price, causing our fruit exports to face a major crisis At present, although Taiwan's fruits such as mango and guava still have certain competitive advantages, if they fail to make further progress compared with other countries, they will still encounter the same problem over time and cannot be ignored Fruit quality and brand value are the only ways for Taiwan's fruit industry to remain competitive internationally Fruit screening is the main link in fruit production and marketing that determines quality Currently, the industry is highly dependent on aging rural manpower, resulting in rising fruit screening costs due to labor shortage and making it extremely difficult to maintain stable yield Therefore, the automation of fruit screening work has become a very important and urgent issue Professor Chi-Chun Lee at the Department of Electrical Engineering of National Tsing Hua University led a team to develop an automatic fruit screening system that combines cameras, conveyor belts, and AI The system currently has an accuracy reaching 93 One production season can increase the output value of mango by NT17 billion With the gradual development of the AI system, the accuracy is expected to improve in the future, and the same system can also be applied to other fruits, further promoting traceable fruit and driving the technological upgrading of Taiwan's fruit industry Fruit screening relies heavily on scarce manpower, and the aging of the rural population makes the situation even worse Professor Chi-Chun Lee learned about the fruit industryrsquos dilemma from his classmate Yu alias, who had studied together in the United States Yu is the young second-generation successor of one of Taiwan's largest fruit import and export companies According to Yu's observations in the industry over numerous years, Taiwan's fruit production and export usually generated good profits at first, but after fruit farmers in the consuming countries obtained the seeds, they will often attempt to grow the fruit locally to reduce costs and obtain greater profits If Taiwanese fruits cannot surpass the products of fruit farmers in consuming countries in terms of quality or brand value, they will be eliminated because competitors' costs are indeed lower Fruit screening is used to divide fruits according to quality If they cannot pass the minimum specification, they will be discarded as waste products In practice, the work of screening fruits will be carried out by farmers' goods yards and distributor' packaging yards respectively However, if it is not properly handled by the collection freight yards and the packaging yards do not do a good job in sampling in the early stage, it will result in a loss for distributors and cause 30 of AA grade fruits to be eliminated This job relies heavily on experienced fruit screeners More experienced fruit screeners can not only control the quality and reduce the chance of fruit damage in the fruit screening process, but also have the ability to pick out about 10 more A grade fruits, which adds great value What worries the industry is that experienced fruit screeners are gradually decreasing due to the aging population in rural areas, making them a very rare resource Such rare human resources are often in high demand during busy farming periods Farmers or distributors who fail to hire experienced fruit screeners have to settle for less experienced one, taking on the risk of additional losses and paying greater costs The most unfortunate situation suffering a loss of 30 mentioned above Fruit screening is an important process in the later stages of fruit production when packaging and selling Failure to properly control quality will result in huge losses AI is very suitable for assisting in fruit screening, but it is difficult to obtain data sets After understanding Yu's difficulties, Professor Lee found that this was a problem that could be solved using AI - although fruit screening relies heavily on experienced fruit screeners, it is a highly repetitive task Handling repetitive tasks with a large amount of data has always been a strength of AI However, the first problem appeared even before research and development work started Which fruit do we start with First of all, a suitable fruit must reach a certain export volume, and the fruit must still have considerable room for growth For some fruits that lack international competitiveness, such as bananas and pineapples, companies no longer have the ability to invest more funds to purchase equipment, let alone sponsor RampD or assist the RampD team in experiments When you have an idea, you need to pick up the pace and put it into practice as soon as possible Therefore, Irwin mango, which still has a certain advantage in terms of scale, was selected as the first experimental subject of the automatic fruit screening systemThe first step after harvesting mangoes is to screen the fruits for the first time at the goods yard After the fruits are screened, they are sent to the packaging yard for fumigation and disinfection, and preparation for sale or loaded into containers for export However, exporters with a deeper understanding of the target market will have stricter quality requirements and will often screen the fruit again to ensure the quality of the fruit before fumigation at the packaging site Since employees at the goods yard are paid based on the number of mangoes screened rather than on the quality of the mangoes, they focus on quantity when working As a result, to ensure the quality of the selected fruits, the subsequent packaging factory has to screen the fruit again, increasing labor The solution seems simple and clear - A camera, machine conveyor belts for grading and sorting, and an AI that can distinguish the quality of mangoes from their appearance are all that are needed to achieve automatic fruit screening However, the hard part is how can AI distinguish the quality of mangoes Thatrsquos right, you must start by establishing a training data set In order to create the data set, Professor Lee's team established a website that allows anyone to upload photos of mangoes and rate them Once the data sets are refined, they can be used to train AI The fruit screening machine developed by Professor Lee's team uses AI image recognition to select the best looking mangoes The accuracy of the trained AI reaches 93, which can increase the output value by NT17 billion in one season In 2019, the assistance of the Industrial Development Bureau now the Industrial Development Administration of the Ministry of Economic Affairs and AI HUB accelerated the verification of the technology Professor Lee's team accumulated 100,000 entries of data during the 2-month empirical period, and the accuracy of the trained AI reached 93 This is far higher than the manual screening accuracy of 70, resulting in a clear difference in quality In terms of export value, the output value of mango is expected to be increased by NT17 billion in one season It can also reduce labor costs by NT1866 million and avoid the seasonal labor shortage problem mentioned above In addition, since it is no longer necessary to screen the fruit once at the goods yard and packaging yard each, it also reduces losses caused by human error in the fruit screening process When the technology becomes more mature, the same system can be applied to other fruits exported by Taiwan, such as wax apple and guava, in the future, taking Taiwan's fruit industry to the next level Since it is AI, accuracy can be improved through continuous training, and continuous adjustment of algorithms and cooperation with equipment manufacturers can significantly improve production capacity In addition, Professor Lee is also organizing the AI Cup competition with the sponsorship of manufacturers and the government, allowing more teams to use the same data set to continue to develop the algorithm, in hopes of facilitating further cooperation with companies that are interested Irwin mango grade identification system on AI HUB Professor Lee's team hopes to use the power of AI to achieve complete traceability of fruits from production to packaging and transportation, thereby increasing the brand value of Taiwan's fruits Besides hoping to allow Taiwan's fruits to seize a place in the fiercely competitive foreign markets, with high-quality supply, Taiwan's fruits can also shine internationally and become the pride of Taiwan Taiwan's fruits still have certain competitive advantages in the international market, but they also face competitive pressure from fruit farmers in consuming countries as they are exported Easily save NT1866 million per mango season and significantly improve quality nbsp nbsp nbsp nbsp nbsp nbsp

Of the hundreds of activities, which one is your favorite There is a wide variety of activities every day in Taiwan, including forums, exhibitions, lectures, and free experiences Event organizers need to use their own media event official website, Facebook, Instagram, event websites, and pay media for marketing, but often do not know where the target customer is, and cannot accurately estimate the number of attendees Records of people's participation in various activities are used through this "Automated Exhibition Matching System," and the data is analyzed to predict what type of activities users like It automatically matches activities with users to provide fast, easy, and accurate marketing and promotion methods There are many types of activity themes, marketing and advertising costs are high, but results are poor A domestic curation company is working with township offices and tourism service providers in the marketing of rural villages It organizes a variety of activities every year, such as agricultural product exhibitions, rural experiences, parent-child themed experience days, and agricultural specialty product marketing Due to the vastly different characteristics of participants in the activities, which have different themes, effective and accurate market cannot be carried out when promoting the activities, which can easily lead to a significant increase in marketing expenses and low matching rate For example, when the curator organizes an exhibition with 200 booths in the venue, the overall marketing cost is about NT800,000 to NT12 million, in which construction of the official website, marketing on the event website, and text message notifications account for about NT400,000 to NT600,000, but the event's matching rate is less than 20, and precision marketing aimed at the target customer group cannot be carried out After adopting the "Automated Exhibition Matching System," it can automatically select suitable customer groups for push notifications Depending on the scale of the event and the exhibition period, the system rental fee is only about NT200,000 to NT300,000, significantly reducing event promotion costs Precision smart marketing, distributing coupons to target groups The "Automated Exhibition Matching System" currently has an accuracy of 82 and can effectively screen target consumers In terms of module accuracy, large amounts of data and data that no longer contains noise will be used to further improve the accuracy in the future Coupons will be distributed to target groups, so that groups that receive the coupons will actually participate in the event After adding value through AI, the system can replace the manual random distribution of coupons without a specific target The AI module can automatically adjust the weights to more accurately lock on to the target group The weighted formula uses the CRM system of the curation company to output analysis of member behavior in the past In the second stage, the AI weighted formula will be used to find the best calculation formula for different activity categories through automatic correction Service Framework of the Automated Exhibition Matching System During the implementation period, Fengchun Technology was troubled by the problem of "the AI classifier module training and learning not finding the best solution" After discussions with AI engineers, the company found that a shortcoming of "back-propagation neural network" is that it only finds the "local" best solution rather than the "global" best result during learning The goal of improving accuracy can be achieved by increasing the number of training times and adjusting parameters Expand system functions, connect member databases, and conduct behavioral analysis The "Exhibition Event Matching System" mainly provides event organizers, independent curators and the public with event matching Next, the platform functions will be expanded for use by event participants, and the accuracy of the AI classifier will be improved In the future, an ocean culture exhibition will be organized with the ocean industry, and use this system to find ocean culture promoters, connecting and expanding member databases for behavioral analysis