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Category: AI

  • What is Transformer? An easy-to-understand explanation of the AI ​​technology behind ChatGPT and Gemini

    What is Transformer? An easy-to-understand explanation of the AI ​​technology behind ChatGPT and Gemini

    Transformer

    Do you know what a Transformer is? The reason why cutting-edge AI like ChatGPT and Gemini can demonstrate such remarkable performance lies in a technology called the Transformer.

    This article provides a comprehensive explanation of the Transformer, including its overview, mechanism, and key points for utilization. If you are considering implementing AI in your company, please read through to the end.

     

    What is a Transformer?

    A Transformer is a deep learning architecture that serves as the foundation for various AI models, including those used in natural language processing and generative AI. It is a technology proposed in the 2017 paper “Attention is All You Need” published by Google. It enabled large-scale data processing and advanced contextual understanding that were difficult with previous methods.

    Traditional Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) process data sequentially, which posed the challenge of reduced computational efficiency when handling long sentences. In contrast, the Transformer adopts a mechanism called the Self-Attention Mechanism. Its major feature is the ability to efficiently understand the context of an entire sentence by simultaneously considering the relationships between all words.

    As detailed later in this article, many of the cutting-edge AI models emerging today are developed based on this Transformer architecture. Furthermore, recently, Transformers are being utilized not only for text generation but also in various fields such as image analysis and speech recognition, significantly improving the accuracy and processing speed of AI.

    In this way, the Transformer can be considered a next-generation engine that further accelerates the evolution of AI.

     

    Transformer Architecture

    The Transformer architecture is an innovative design that revolutionized the field of natural language processing. At the core of this architecture lies the “Self-Attention Mechanism,” which is the crucial component supporting the Transformer.

    The Self-Attention Mechanism is a process where every word in an input sentence evaluates the relationships and importance with respect to every other word. For example, when processing the sentence “I like apples,” the self-attention mechanism understands that “apples” and “like” are strongly related, while processing other words (like “I” and the punctuation) with appropriate importance based on context. This enables the model to accurately grasp the meaning of the entire sentence.

    The strength of this mechanism lies in its ability to consider the entire sentence holistically and calculate relationships between words, regardless of their position in the sentence. Traditional methods processed text sequentially from left to right or right to left, leading to information degradation, especially in longer sentences.

    However, because the self-attention mechanism computes relationships across the entire sentence at once, it can capture meaning efficiently and accurately even in long texts. Due to this characteristic, the Transformer’s ability to precisely understand the context of long sentences allows it to excel not only in natural language processing but also in a wide range of fields such as translation, question answering, and even image generation and audio analysis.

     

    Components and Mechanism of the Transformer

    The Transformer is broadly composed of two main elements: the Encoder and the Decoder. This section explains these two components and how they work.

    Encoder

    The Encoder in a Transformer is responsible for representing input data (e.g., a sentence) in a meaningful way. This process proceeds through the following three steps:

    1. Input Embedding

    2. Positional Encoding

    3. Self-Attention

    First, the input words or tokens are converted into numerical vectors. These vectors are then processed within the encoder to capture relationships and meanings between words.

    Since word order is crucial for generating natural language, the Transformer adds information about word positions using a mechanism called Positional Encoding, incorporating this positional information into the vectors.

    Subsequently, the Self-Attention mechanism calculates how each word relates to every other word in the sentence. This allows the model to capture semantic connections even between distant words.

    Decoder

    If the Encoder’s role is to understand the input information, the Decoder’s role is to generate output based on that understanding. The Decoder’s mechanism is similar to the Encoder’s but includes additional elements for output generation.

    Here are the three main processing steps in the Decoder:

    1. Self-Attention

    2. Encoder-Decoder Attention

    3. Output Generation

    Like the Encoder, the Decoder also uses Self-Attention, but its key feature is that it only considers already generated output (the partial sentence). This controls the process to prevent future words from influencing the prediction of the current word.

    Next, it incorporates the input information processed by the Encoder through Encoder-Decoder Attention, connecting it with the output being generated. This function enables the Decoder to produce appropriate output while leveraging the meaning of the input data.

    Finally, it predicts the next word based on probability. At this stage, the output is generated one word at a time, with each prediction influencing the generation of the subsequent word.

    By repeating this process, the final output sentence is completed. In this way, the Encoder and Decoder in a Transformer work together seamlessly, achieving a consistent process from input to output.

     

    Benefits of Utilizing a Transformer

    So far, we have detailed the overview and mechanism of the Transformer. But what specific benefits can a company gain by utilizing this technology? This section introduces three representative advantages of using a Transformer.

    High-Precision Translation

    One major benefit of the Transformer is its ability to perform high-precision translation. While traditional models sometimes struggled to accurately capture context, the Transformer’s Self-Attention mechanism allows it to precisely grasp the relationships between words within a sentence.

    For instance, even if the subject is far from other related words, the Transformer can understand the semantic connection, enabling it to return output with a deep understanding of grammar and nuance. As a result, multilingual translation tools utilizing Transformers can generate remarkably natural results.

    Long-Term Memory Capability

    Because the Transformer can accurately capture relationships between distant words, it can handle long sentences and complex contexts without issue. This capability is made possible by the dynamic weighting of information provided by the Self-Attention mechanism.

    When processing long sentences with traditional RNNs, there was a risk of losing important information along the way. The Transformer overcomes this challenge. This allows it not only to understand the overall picture of a text but also to perform long-term memory retention for lengthy documents like scientific papers, processing them without missing key points.

    High Flexibility

    High flexibility is another representative benefit of the Transformer. Transformers can handle a wide variety of tasks, not just translation, but also text generation, text summarization, image captioning, and more.

    This is because the Encoder and Decoder in a Transformer are modularized, and by adjusting the configuration, they can be customized for various applications. It is precisely because of this versatility that many state-of-the-art AI models adopt the Transformer architecture.

     

    Representative Models Developed with Transformers

    Nowadays, Transformers are utilized in various models. This section introduces three representative models developed using Transformer technology.

    ChatGPT

    ChatGPT is an advanced conversational AI developed by OpenAI, characterized by its ability to engage in natural, text-based conversations between humans and AI. It is part of the GPT series based on the Transformer and possesses a wide range of knowledge by pre-training on vast amounts of data.

    The appeal of ChatGPT lies in its ability to handle diverse tasks, including question answering, text generation, and even creative support. Thanks to the Self-Attention mechanism, it accurately understands the input context and generates appropriate responses, making it widely used in various scenarios, from everyday conversation to specialized discussions.

    Gemini

    Gemini is a multimodal AI developed by Google, capable of integrally handling both natural language processing and image recognition. A key feature is that Gemini is also designed based on the Transformer architecture, garnering attention as an evolutionary successor to models like BERT and PaLM.

    A noteworthy point about Gemini is its ability to understand not only natural language but also the content of image data. For example, it can recognize objects in an image and generate explanations in natural language based on that recognition.

    It also possesses advanced conversational and reasoning abilities, making it applicable to a wide range of tasks such as translation, design support, and data analysis. Therefore, Gemini is attracting interest from numerous industries as the next frontier in AI technology.

    For more on Multimodal AI, see the related article: [Link to article on Multimodal AI]

    Vision Transformer (ViT)

    Vision Transformer is a model that brings the strengths of the Transformer architecture to the field of image recognition. Unlike the previously dominant Convolutional Neural Networks (CNNs), ViT divides an image into patches (small regions), treats each patch as a token, and efficiently understands the overall image structure using the Self-Attention mechanism.

    A major characteristic of Vision Transformer is its ability to accurately capture not only the fine details of an image but also the overall context and structure. This allows it to achieve very high accuracy in tasks like object recognition and image classification, leading to its adoption in various fields such as medical image analysis and surveillance video analysis.

    Key Points When Using a Transformer

    While the Transformer is a highly useful architecture, there are several important points to consider when actually using it. This section introduces three key considerations for utilizing Transformers.

    Ensure the Quality and Quantity of Training Data

    When using a Transformer, ensuring the quality and quantity of training data is a critical point. Since Transformers learn from the provided data, poor quality or insufficient data will negatively impact the model’s output.

    Inappropriate data can lead to incorrect predictions and biases, making it essential to thoroughly scrutinize the data used for training and remove noise. Furthermore, collecting data specialized for a particular domain helps the model adapt better to the required tasks.

    Be Mindful of Hardware Resource Limitations

    Transformers consume significant computational resources. Especially when dealing with large-scale models, resource scarcity can become a barrier. For example, insufficient GPU or RAM capacity can lead to slower processing speeds, hindering practical usability.

    Therefore, it’s necessary to implement strategies to reduce resource load, such as adjusting model size or batch size. Utilizing cloud services can be an effective option to overcome hardware limitations, as they allow for flexible resource scaling.

    Perform Appropriate Model Tuning

    Appropriate model tuning is another key point when using a Transformer. While Transformers perform well out-of-the-box, fine-tuning is indispensable for optimizing them for specific tasks.

    For instance, detailed adjustments like choosing the learning rate and optimization algorithm, or using validation data to prevent overfitting, can significantly impact the output generated by the AI. By performing appropriate model tuning, you can maximize the potential of the model.

     

    Conclusion

    This article has provided a comprehensive explanation of the Transformer, covering its overview, mechanism, key points for utilization, and more.

    By utilizing Transformers, companies can enjoy various benefits such as high-precision translation and high flexibility. Revisit this article to solidify your understanding of the Transformer architecture and the important considerations for its use.

     

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  • What is a neural network? Explaining its relationship with machine learning and deep learning, and examples of its use.

    What is a neural network? Explaining its relationship with machine learning and deep learning, and examples of its use.

    neural network

    In recent years, AI has been utilized in various scenes of daily life, and one of the core technologies behind it is the neural network. By understanding neural networks, we can gain a deeper insight into the potential and application scope of AI.

    This article provides a comprehensive explanation of the mechanism of neural networks, their relationship with machine learning and deep learning, and examples of their use. If you are considering implementing AI in your company, please read through to the end.

    What is a Neural Network?

    A neural network is a type of AI technology designed to mimic the neural circuitry of the human brain. Computational units called nodes are arranged in layers, transmitting information to each other to perform complex processing.

    This enables AI to learn patterns from vast amounts of data and perform advanced tasks such as image recognition and voice recognition. While it was difficult for conventional programs to process ambiguous data or make predictions, the advent of neural networks has made these tasks efficient.

    Recently, neural networks have been utilized in various fields such as medicine, finance, and entertainment, attracting attention as a technology that greatly expands the possibilities of AI.

     

    The Mechanism of a Neural Network

    The mechanism of a neural network consists of three elements:

    • Input

    • Propagation

    • Output

    First, data is fed into the input layer, where information such as images or numerical values is represented in numerical form. Subsequently, propagation occurs through intermediate layers called hidden layers, where each node performs weighted calculations.

    The results of these calculations are then transmitted to the next layer, and finally, the output layer generates a result. For example, in image recognition, it outputs a judgment such as “Is this image a dog or a cat?”

    Through these processes, the neural network learns patterns in data and becomes capable of handling complex tasks. Neural networks are a crucial point in AI utilization, so it’s important to understand the general flow of how they work.

     

    Relationship with Machine Learning and Deep Learning

    Terms often confused with neural networks include machine learning and deep learning. Let’s take a closer look at the relationship between each.

    Neural Networks and Machine Learning

    Machine learning is a fundamental AI technology that learns patterns from data to make predictions and classifications, and it is used as a term referring to algorithms in general. On the other hand, a neural network is a specific method within machine learning, possessing a structure designed to mimic the neural circuits of the human brain.

    In other words, a neural network can be considered one of the tools within machine learning. While machine learning includes a wide range of methods, from simple linear models to complex non-linear models, neural networks are particularly well-suited for large-scale data and complex tasks.

    Thus, if machine learning is the broad framework of AI, then neural networks represent a specialized, advanced approach within it. Both machine learning and neural networks are AI technologies, but they are strictly distinguished as different concepts, so it’s important to understand the difference.

    Neural Networks and Deep Learning

    Deep learning can be described as a further evolution of neural networks. Specifically, deep learning is a technology that uses neural networks with many layers to enable learning of more complex and advanced patterns.

    This multi-layered structure has enabled deep learning to achieve dramatic progress in fields such as image recognition, voice recognition, and natural language processing. In other words, deep learning is a deep version of neural networks, built upon the foundation of the neural network framework.

    Understanding this relationship—where neural networks form the basis of deep learning, and deep learning expands its applications—will allow you to grasp their respective roles more clearly.

     

    Learning Patterns of Neural Networks

    How does learning proceed in a neural network? This chapter explains the learning patterns of neural networks by dividing them into two types.

    Supervised Learning

    Supervised learning is the most common method among neural network learning patterns.

    Specifically, it involves giving the model pairs of input data and correct labels (answers), training it to predict the correct output. For example, by showing a photo and telling the AI “this is a dog,” the model learns to judge “this is a dog” when it sees a new photo later.

    Here are examples of supervised learning applications:

    • Image recognition: Identifying animals like cats and dogs, or objects like cars and buildings.

    • Voice recognition: Analyzing human voices and converting them into text.

    • Medical diagnosis: Detecting lesions from X-ray images.

    A major appeal of supervised learning is its ability to achieve high accuracy in tasks where the correct answer is predetermined. However, it’s important to note that it requires the cost of preparing labeled data.

    Unsupervised Learning

    Unsupervised learning is a learning method used when correct labels do not exist.

    In unsupervised learning, the model is given a large amount of data and learns to autonomously find the structure and patterns within that data. Analogous to human learning, it’s similar to “inquiry-based learning” where one grasps characteristics by organizing data on their own, without explicit explanations.

    Here are examples of unsupervised learning applications:

    • Clustering: Grouping customers based on purchasing tendencies.

    • Anomaly detection: Finding outliers that deviate from normal data.

    • Dimensionality reduction: Simplifying high-dimensional data.

    A major advantage of unsupervised learning is its ability to utilize vast amounts of unlabeled data. It proves especially powerful in fields where data labeling is difficult or prior knowledge is limited.

    Thus, supervised learning and unsupervised learning are approaches for different types of problems. Therefore, it is important to choose the optimal learning method according to your company’s objectives and the characteristics of your data.

     

    Representative Types of Neural Networks

    Did you know that even though we simply say “neural network,” there are actually various types? This chapter introduces representative types of neural networks.

    CNN (Convolutional Neural Network)

    CNN, also called a “convolutional neural network,” is a type of neural network specialized for image processing. It primarily extracts features from image data and performs classification and recognition based on those features.

    A key characteristic of CNN is the mechanism called the convolutional layer. The convolutional layer plays the role of extracting features from image data, capturing pixel information locally to extract edges, patterns, and more.

    Typical use cases for CNN include object recognition (classifying cats, dogs, etc.), autonomous driving (recognizing road signs and lanes), and the medical field (detecting lesions from MRI images). As such, CNN is widely used as an indispensable technology in projects handling image data.

    RNN (Recurrent Neural Network)

    RNN, also called a “recurrent neural network,” is a model specialized for learning the order of data or temporal sequences. A major feature of RNN is its recurrent structure, which allows it to memorize past information while making subsequent predictions.

    This mechanism enables RNN to efficiently handle time-series data or sequence data. However, when learning long sequences, the vanishing gradient problem can occur, so caution is needed.

    The vanishing gradient problem refers to an issue where, during the neural network learning process, the gradient (differential value) becomes extremely small when propagating errors to update weights, causing learning to stall as layers become deeper.

    Typical use cases for RNN include voice recognition (analyzing continuous sounds), translation (generating sentences considering word order), stock price prediction, and weather data analysis. Thus, RNN is an effective option for tasks that involve a temporal dimension.

    LSTM (Long Short-Term Memory)

    LSTM is an extended model developed to overcome the vanishing gradient problem, a weakness of RNNs. As mentioned, the vanishing gradient problem is a phenomenon where the gradient carrying the error during learning gradually diminishes as it propagates back through layers, preventing deep layers from learning and hindering model accuracy.

    LSTM maintains an internal memory cell, allowing it to retain information over long periods while discarding unnecessary information as needed. This enables efficient learning even with long sequence data. A key feature of LSTM is its ability to handle more advanced tasks, such as generating text while considering the overall context of a sentence.

    Typical use cases for LSTM include natural language processing (chatbots, text generation), music generation (composing considering melodic structure), and biometric data analysis (analyzing heart rate or brainwave patterns). Thus, LSTM is effective for tasks that require capturing long-term dependencies.

    Autoencoder

    An autoencoder is a neural network specialized for compressing input data and then reconstructing new data based on that compressed representation. Autoencoders are often used primarily to extract essential features of data and are positioned as a type of unsupervised learning.

    A typical autoencoder consists of two components: an “encoder” and a “decoder.” The encoder transforms data into a latent space, and the decoder recreates the input based on that latent representation. A major characteristic of autoencoders is their ability to utilize this mechanism for tasks like data denoising and anomaly detection.

    Use cases for autoencoders include data compression (efficient storage of image data), noise removal (restoring old photos or audio), and anomaly detection (detecting patterns deviating from normal data). Thus, using an autoencoder is a good approach when you want to grasp the characteristics of your data.

    We have introduced four types of neural networks here, but their features and areas of expertise are diverse. Therefore, by appropriately choosing the type of neural network according to the application, you can maximize the effectiveness of AI utilization.

     

    Points to Consider When Using Neural Networks

    While neural networks are indispensable for AI utilization, there are several key points to be aware of when using them. This chapter introduces three important considerations.

    Ensure Data Quality and Quantity

    Neural networks achieve high accuracy by learning patterns from large amounts of data. However, if data is insufficient or contains significant noise, model performance will degrade substantially. Therefore, it is crucial to thoroughly preprocess and clean the data to ensure appropriate quantity and quality.

    Implement Measures to Prevent Overfitting

    A major characteristic of neural networks is their high flexibility. This flexibility can lead to “overfitting,” where the model fits the training data too closely, resulting in poor generalization performance on unseen data. To prevent this problem, techniques like dropout and regularization methods should be employed, and an appropriate model size must be chosen.

    Consider Computational Costs and Resources

    Training neural networks requires significant computational resources, and deep models, in particular, demand substantial time and cost. For efficient model development, careful consideration must be given to selecting the hardware environment (GPUs, TPUs) and potentially implementing distributed learning. It is important to plan proactively to avoid project setbacks due to budget or resource constraints.

     

    Conclusion

    This article has explained the mechanism of neural networks, their relationship with machine learning and deep learning, and examples of their application.

    By utilizing neural networks, companies can leverage them in various business scenarios. Revisit this article to solidify your understanding of the mechanisms and representative types of neural networks.

     

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  • What are the ethical issues in using AI? Explaining specific examples and points that companies should be aware of

    What are the ethical issues in using AI? Explaining specific examples and points that companies should be aware of

    ethical issues in AI

    Rapidly advancing AI technology has made our lives and businesses more convenient and prosperous. However, are you aware that this progress also highlights significant ethical challenges in the use of AI?

    This article provides a comprehensive overview of ethical issues in AI, including case studies and initiatives by private companies, as well as key points to consider to avoid AI ethics problems. If your company is considering implementing AI, please read on.

     

    What are Ethical Issues in AI Utilization?

    Ethical issues in AI utilization refer to the social and moral challenges that arise behind the convenience and efficiency gains brought by AI. For example, bias problems, which occur when AI learns from skewed data, risk leading to unfair judgments based on race or gender.

    Furthermore, the use of facial recognition technology and personal data raises concerns about potential privacy violations. Another representative ethical issue is the lack of clarity regarding accountability for decisions and actions made by AI.

    Thus, in our modern world where AI is widespread, AI ethics has become a critically important challenge. It is clear that the more AI permeates society, the more essential it becomes to address these ethical issues.

     

    Key Problems in AI Ethics

    What specific aspects are considered problematic in AI utilization? This chapter explains the main issues in AI ethics.

    Privacy Violations

    As AI technology evolves, vast amounts of personal data are collected and analyzed, making privacy violations a major concern. For instance, facial recognition technology and tracking via surveillance cameras can threaten individual freedom and privacy in exchange for convenience. Therefore, there is a growing need to establish clear rules for AI utilization, defining the acceptable limits of data collection and use.

    Bias and Discrimination

    Since AI systems learn from data provided by humans, they can directly reflect the biases present in that data. This includes cases where AI makes decisions that disadvantage specific genders or races, thereby exacerbating social inequalities. To solve this problem, it is crucial to improve data quality and develop AI with fairness in mind.

    Lack of Accountability

    The process by which AI arrives at a specific decision is often a “black box,” and this lack of accountability is a significant concern. Especially when AI is involved in critical decisions in fields like medicine or finance, transparency is required so users can trust the outcomes. To ensure this transparency, there is an urgent need to develop technologies that can explain the workings of AI algorithms.

    Unclear Allocation of Responsibility

    When a decision or action by AI causes a problem, the difficulty in pinpointing responsibility is another classic AI ethics issue. For example, if content generated by AI contains misinformation or copyright infringement, multiple parties are involved—platform operators, AI developers, users—but identifying who is responsible is challenging. This points to the need for measures at the national and governmental level, such as legal frameworks and ethical guidelines, to clarify responsibility in AI utilization.

     

    Three Case Studies of AI Ethics Issues

    Recently, AI ethics issues have frequently become major topics of discussion. This chapter introduces three real-world examples of such problems.

    Amazon (Gender Discrimination in Recruitment)

    Previously, Amazon used an AI-powered recruitment system. This system learned from past hiring data to judge candidates’ suitability. However, it was found to favor male candidates and disadvantage female candidates.

    This case demonstrates the risk that AI can perpetuate unfair judgments if its training data contains bias. Although Amazon eventually discontinued the system, it served as a catalyst for renewed focus on the transparency of data and algorithms in AI utilization.

    Samsung Electronics (Privacy Violation)

    In 2023, an incident occurred at Samsung Electronics where employees used an AI tool to process sensitive information, leading to a data leak. The primary cause was believed to be an engineer using ChatGPT to fix source code bugs. During this process, code containing sensitive information was sent to the AI’s servers, resulting in a partial leak of the data.

    This highlights the indispensable need for security measures to protect confidential data when companies utilize AI. Leaking sensitive information can lead to irreparable consequences, such as loss of competitiveness and social trust, making this a critical consideration for any company using AI.

    Tokyo 2020 Olympics (Unclear Responsibility in Traffic Accident)

    The introduction of autonomous vehicles was a major talking point during the Tokyo 2020 Olympics. However, one of these vehicles was involved in a collision with a visually impaired athlete within the athletes’ village.

    Subsequent investigations into the accident revealed an unclear allocation of responsibility among the multiple parties involved, including:

    • The vehicle manufacturer

    • The Olympic organizing committee

    • On-site guides

    This case illustrates the necessity of clarifying responsibility sharing when accidents or problems occur, especially as collaboration between AI and humans becomes more prevalent.

     

    Private Sector Initiatives Addressing AI Ethics

    Many companies are now undertaking various initiatives to tackle AI ethics issues. This chapter introduces some of these efforts.

    Google

    In 2018, Google established three AI principles to guide the integration of AI throughout its operations:

    1. Be bold with innovation.

    2. Develop and deploy AI responsibly.

    3. Work together to advance.

    These principles articulate the company’s key priorities and philosophies regarding AI use. Based on these principles, Google promotes the beneficial use of AI, explicitly stating its responsibility in AI development and deployment while addressing grand themes like economic development and scientific progress. By publicly committing to such high-level principles, Google solidifies its position as a leading company at the forefront of the AI field.

    Microsoft

    Based on the philosophy that “AI should be developed and used based on trust, for the benefit of all people,” Microsoft established six core principles for responsible AI development and use in 2018. These principles include:

    • Fairness

    • Reliability and safety

    • Privacy and security

    • Inclusiveness

    • Transparency

    • Accountability

    Guided by these principles, Microsoft develops AI systems with a strong emphasis on ethical considerations. Furthermore, the company promotes the socially responsible use of AI by focusing on internal ethics education and collaboration with external experts.

    Fujitsu

    Fujitsu has established the “Fujitsu Group AI Commitment” to actively promote the ethical development and use of AI. This commitment clearly outlines several key guidelines, including:

    • Human-centric AI

    • Ensuring accountability and transparency

    • Protecting privacy

    • Ensuring security

    • Maintaining fairness

    Based on this policy, Fujitsu employs a consistently ethics-focused approach, from AI research and development to social implementation. Additionally, the company has established an ethics committee comprising internal and external experts to continuously review and address ethical challenges related to AI.

    OKI Group

    In September 2019, the OKI Group established and published its “OKI Group AI Principles.” These principles define ethical standards for AI development and use, incorporating various elements such as:

    • Ensuring transparency

    • Ensuring accountability

    • Protecting privacy

    • Maintaining fairness

    The company is also strengthening collaboration with various stakeholders to promote the social implementation of AI, driving actions toward achieving human-centric AI development.

     

    Key Points for Companies to Avoid AI Ethics Issues

    When introducing and utilizing AI, companies must be mindful of several points. Finally, based on the discussion so far, this chapter explains key points for companies to avoid AI ethics problems.

    Ensuring Transparency and Accountability

    A crucial point when using AI is to clarify how its algorithms and decision-making processes function. Establishing a system that can explain to users and stakeholders how AI makes decisions builds trust in the AI. If an AI system becomes a black box, it risks unexpected outcomes or misunderstandings, making accountability a key to ethical AI use.

    Eliminating Bias and Ensuring Fairness

    Since AI makes decisions based on training data, biases within that data can be directly reflected in the output. Therefore, when developing AI, companies must take sufficient measures to avoid biased data and unfair judgments. Using diverse datasets and implementing mechanisms to monitor and adjust for biased AI decisions contribute to ensuring fairness and building social trust.

    Strengthening Privacy and Security

    For companies utilizing AI, protecting privacy and strengthening security are indispensable. Especially when handling personal or confidential information, implement robust security measures to protect company data from cyberattacks and unauthorized access. Regularly checking AI systems for vulnerabilities and ensuring data safety can help prevent AI ethics issues before they arise.

     

    Conclusion

    This article has explained AI ethics issues through examples and case studies, introduced initiatives by private companies, and discussed key points for avoiding these problems.

    AI is an extremely useful tool, but its use requires careful consideration of ethical implications. Revisit this article to understand the main problems and specific examples within AI ethics.

     

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  • 7 disadvantages of generative AI? A detailed explanation of specific examples of failure and how to avoid them!

    7 disadvantages of generative AI? A detailed explanation of specific examples of failure and how to avoid them!

    7 disadvantages of generative AI

    Recently, generative AI has been garnering significant attention, with various companies utilizing it to achieve greater operational efficiency and productivity improvements. Generative AI is a very useful tool, but did you know that it also has several disadvantages?

    In this article, we will introduce seven representative disadvantages of generative AI. We will also explain specific failure examples and key points for utilization, so please read through to the end.

     

    What is Generative AI?

    First, let’s explain the basic knowledge about generative AI.

    Generative AI refers to next-generation AI where computer programs have the ability to generate data or information by themselves. While traditional AI primarily focused on finding appropriate answers from pre-learned data, generative AI can create new data or content. Its main characteristic is the ability to create something from nothing.

    Typical generative AI is designed to generate new data or content through iterative learning based on vast training data. Taking image generation AI as an example, by inputting a large number of images of “dogs” or “cats” into the AI, it learns the characteristics of dogs and cats. As a result, when you instruct the AI to generate images of dogs or cats, it will output detailed images capturing those characteristics.

    A famous example of generative AI is OpenAI’s ChatGPT, which can automatically generate new text based on textual data. However, it’s worth noting that ChatGPT’s latest model (as of September 2024), GPT-4o (full name: GPT-4 Omni), is provided as a multimodal model. Multimodal refers to a mechanism that collects, integrates, and processes information from multiple different sources, such as text, audio, and images. The generative AI model “Gemini” announced by Google in December 2023 also supports this multimodal capability.

    Thus, there are AI models capable of automatically generating not only text but also images and videos, and they are used in various scenarios, from content creation for marketing activities to TV commercials. It can be said that generative AI is a powerful tool for companies to achieve operational efficiency, improve productivity, and connect these to business growth.

     

    7 Disadvantages of Generative AI

    This chapter explains seven representative disadvantages of generative AI. Understand the specific details to avoid failure in utilizing generative AI.

    1. Implementation Costs Incurred

    Generally, implementing generative AI involves certain costs. While some generative AI tools are available for free, from the perspectives of security and support, paid services are recommended for business use. At this point, to maximize the effectiveness of tool implementation, it is important to compare multiple services and select a tool that fits your company’s situation. Since the benefits of introducing generative AI are significant, consider the implementation cost as a necessary expense and proactively proceed with the consideration.

    2. Involves Ethical and Social Risks

    Utilizing generative AI involves ethical and social risks. For example, if the AI learns from data containing biases, it may generate content that includes those biases. Furthermore, the generated content could lead to misunderstanding or confusion among people, and in the worst case, could result in serious trouble or a loss of corporate trust. To avoid this, it is important to establish mechanisms for the appropriate use of generative AI across the organization, such as developing policies and rules regarding its use.

    3. Involves Data Security and Privacy Risks

    When using generative AI, the security and privacy protection of the data handled become critical issues. For example, when using large amounts of personal information or confidential data for AI learning, there are risks of unauthorized access to or leakage of that information to the outside. Also, since there is a risk that generative AI might output incorrect data, unintentionally disclosing confidential information, be sure to implement data management and security measures. Many generative AI services offered to enterprises have enhanced security features, allowing for safe use of generative AI. Therefore, carefully selecting a service is a key point when introducing generative AI.

    4. Difficulty Complying with Legal Responsibilities and Regulations

    When using generative AI, issues related to legal responsibilities and regulations cannot be ignored. This is because if the copyright or responsibility for AI-generated content is unclear, it could lead to legal disputes. However, since regulations vary by country and region, complying with all of them without omission is extremely difficult. As global expansion involves such legal risks, collaborating closely with your company’s legal department or external experts while using generative AI is an effective option for avoiding legal troubles.

    5. The AI’s Internal Process Becomes a Black Box

    One disadvantage of generative AI is that its internal process can become a black box. This refers to a state where the process by which the AI judges and generates is complex and difficult for developers and users to understand. If the AI cannot explain why it produced a specific output, its reliability and transparency may be called into question. Especially in fields requiring high specialization and accuracy, such as medicine and law, the risk of misunderstanding or trouble increases due to the AI’s black-box nature. To avoid such black-box situations, it is necessary to develop prompt collections and operation manuals for generative AI and promote appropriate AI use throughout the organization. This helps maintain a certain level of AI accuracy and obtain answers aligned with the user’s intent.

    6. Difficulty Securing Human Resources to Develop Generative AI

    Developing high-precision generative AI requires IT personnel with advanced specialized knowledge. However, Japan faces a chronic shortage of IT human resources, making it difficult to secure talented individuals. Therefore, when considering developing generative AI, in addition to actively recruiting mid-career professionals, also consider training your current employees to become IT personnel. If it’s difficult to conduct education and training in-house, requesting assistance from external experts is also a valid option. Our company, G-gen, offers programs to support the utilization of generative AI. For example, we support the use of Google’s latest generative AI and can handle everything from PoC to production-level implementation, so if you are interested, please feel free to contact us via our inquiry form.

    7. Risk of AI Hallucinations

    AI hallucinations, where the AI generates incorrect information, are another disadvantage of using generative AI. The accuracy of the output generated by AI depends on the quality and reliability of the data. Therefore, training AI models based on reliable information leads to ensuring technical reliability and explainability. We explain AI hallucinations in detail in the article below.
    Related article: Pitfalls of AI Utilization? A Clear Explanation of the Causes and Countermeasures for AI Hallucinations!

    We have introduced seven disadvantages of generative AI. While generative AI is a very useful tool, there are also many points to be cautious about, such as ethical and social risks and the challenge of securing human resources.

    Therefore, when using generative AI, it is important to clarify the purpose and application of its introduction to maximize its effectiveness, while also understanding its disadvantages and establishing mechanisms and rules to avoid various risks. By practicing the correct way to interact with generative AI in this manner, it can become a powerful tool for promoting your company’s productivity improvement and business growth.

     

    Failure Examples of Generative AI Utilization

    So far, we have explained the disadvantages of generative AI. What kind of failures actually occur? This chapter introduces three examples of failures in utilizing generative AI.

    Samsung Electronics (External Leakage of Source Code)

    At Samsung Electronics in South Korea, source code was leaked externally due to the use of generative AI. This incident is said to have been caused by an employee inputting code information into a generative AI, which resulted in data existing on the server being mistakenly shared with external users. Following this external leak, the company established a new policy regarding the use of generative AI. For example, they banned the use of generative AI tools on company devices and, for personal devices, imposed certain restrictions on input data (e.g., not uploading company-related information), taking steps to prevent the recurrence of information leaks.

    Macnica Networks Company (AI Quality Issues)

    Macnica Networks Company, a technology provider within the Macnica Group, embarked on developing a generative AI chatbot aimed at improving employee knowledge and operational efficiency. However, once the chatbot’s operation began, quality issues were discovered, such as the AI responding in English to Japanese questions. Upon investigating the cause, they found various problems, including a lack of mechanisms to verify the accuracy and operation of each component, and a failure to define the details of user business processes targeted by the chatbot. Consequently, the company reviewed its RAG (Retrieval-Augmented Generation) mechanism. By separating the retrieval and generation processes, they were able to obtain more accurate answers than before. RAG is a mechanism for improving the quality and performance of generative AI and is gaining attention primarily in the field of natural language processing. Instead of the AI generating information independently, it searches for relevant information in databases or websites to create answers, resulting in higher accuracy. We explain RAG in detail in the article below.
    What is RAG? A Thorough Explanation of Its Mechanism, Benefits, and Precautions!

    OpenAI (Lawsuit Regarding Copyright)

    When using generative AI in business, it is not uncommon for it to lead to copyright lawsuits. In August 2023, more than ten authors, including George R.R. Martin and John Grisham, filed a lawsuit against OpenAI, claiming that “ChatGPT is using copyrighted material without permission.” Furthermore, in December of the same year, The New York Times sued OpenAI and Microsoft for billions of dollars in damages, alleging “unauthorized use of New York Times articles, depriving them of subscription and advertising revenue opportunities.” As seen, this can sometimes lead to lawsuits, so when using generative AI, be extremely careful about copyright and other rights issues.

     

    Key Points for Avoiding Failure in Generative AI Utilization

    There are several points to keep in mind when utilizing generative AI. Finally, we will introduce key points for successfully utilizing generative AI.

    Clarify the Purpose of AI Utilization

    When introducing generative AI, the first step is to clarify the purpose of its use. Be specific about what you want to achieve and in which business processes you will use the AI. If you use AI with a vague purpose, you may not be able to fully utilize its capabilities, and the cost could be wasted. Therefore, clarifying the purpose of AI use and setting specific, measurable goals is a crucial point.

    Implement Thorough Security Measures

    The data handled by generative AI is a company asset, making thorough security measures mandatory. This is because the data processed by AI often contains confidential information or personal data, requiring robust security to prevent data leaks and unauthorized access. Additionally, since the AI itself could become a target of cyberattacks, it is also important to continuously review and update security measures.

    Be Careful Not to Infringe on Rights

    Regarding content created by generative AI, it is important to pay attention to rights issues such as copyright and trademark rights. Especially when the AI generates content by referencing other works or data, it is necessary to verify whether the final output infringes on the rights of others. Seeking advice from legal experts can help minimize these risks.

    Conduct Employee Education and Skill-Up Training

    To effectively utilize generative AI, employees need to understand the AI’s mechanisms and Operating instructions.. Therefore, it is important to conduct employee education and skill-up training, enabling them to acquire knowledge from basic AI concepts to practical skills. Having personnel proficient in AI within the company allows you to maximize the potential of generative AI and enhance your company’s competitiveness.

    Check the Quality of Training Data

    The accuracy and reliability of generative AI depend heavily on the quality of its training data. If the training data contains biases or errors, the AI’s output will be affected. Therefore, when using generative AI, quality control of the training data is a key point. Implement data selection and cleaning to ensure the AI can learn properly, enabling it to generate accurate and useful content.

    Refine the Content of Prompts

    The content of the “prompt” given to instruct the generative AI is one of the key factors that significantly influences the generated result. Setting specific and clear prompts leads to more accurate outputs. Creating prompts requires trial and error, but since refining them can dramatically improve AI performance, investing sufficient time in crafting them is key to success. Note that “prompt engineering” exists as a technical and academic field for efficiently utilizing AI, so keep that in mind for reference.

     

    Conclusion

    In this article, we introduced seven representative disadvantages of generative AI.

    Generative AI is a very useful tool, but it also has several disadvantages. Understand the key points on what to be cautious about when using it.

    Furthermore, Google Cloud can be an effective tool when utilizing generative AI. Google Cloud is a public cloud service provided by Google, equipped with many generative AI-related services to help companies achieve operational efficiency and productivity improvements. For example, a major feature of Google Cloud is the availability of various generative AI services, such as Conversational AI services like Dialogflow for natural language processing, and Imagen for image processing. Additionally, using Vertex AI allows you to freely customize AI models, enabling flexible AI utilization tailored to your company’s specific situation.

     

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  • How much is the annual income of a machine learning engineer? Introducing annual income situation and salary increase method

    How much is the annual income of a machine learning engineer? Introducing annual income situation and salary increase method

    Annual income of a machine learning engineer?

    Machine learning is used in many places and is one of the technologies that enrich our lives. Along with that, attention is also focused on machine learning engineers who build machine learning. What I am concerned about is the outline of machine learning engineers and the annual income situation. This time, we will introduce the outline of machine learning engineers and the annual income situation.

     

    What kind of profession is a machine learning engineer?


    Due to the rapid development of AI technology and the promotion of digital transformation by companies, the attention of machine learning engineers is increasing. So, first, I will explain about machine learning engineers.

    What is a machine learning engineer?

    A machine learning engineer is a job that specializes in machine learning technology in AI development. Machine learning is one of the technologies required to realize AI, and derives certain rules and rules based on a large amount of data. Machine learning engineers use their knowledge of statistics and mathematics to build machine learning models in order to find certain regularities from large amounts of data.

     

    Job description of a machine learning engineer

    Machine learning engineers are mainly responsible for data analysis, construction, and construction and verification of machine learning models. In particular, building and verifying machine learning models is the most important task. In machine learning, the accumulated data is learned according to the constructed machine learning model. At this time, the learning result makes a big difference depending on the machine learning model selected. Therefore, it is necessary to modify the machine learning model with higher accuracy by repeating the verification.

     

    Differences between AI engineers and data scientists

    Similar occupations to machine learning engineers include AI engineers and data scientists. Since AI engineer is a term that refers to all engineers involved in AI, machine learning engineers are also included in AI engineers. However, some companies make a clear distinction between AI engineers and machine learning engineers. A data scientist is a job that is treated in much the same way as a machine learning engineer when looking at the work content. However, some companies make a clear distinction, so it is necessary to confirm in advance.

     

    Check the annual income of machine learning engineers


    Next, let’s take a look at the annual income of machine learning engineers.

    Domestic average annual income

    According to data from the job search site Indeed, the average annual income of machine learning engineers in Japan is around 6.3 million yen. According to the site, the average annual salary of a general engineer is about 4-5 million yen, so it can be said that machine learning engineers have a high salary level. In recent years, the Ministry of Economy, Trade and Industry has been promoting digital transformation, and the importance of AI is increasing in society as a whole. Therefore, increasing demand is increasing the number of companies offering higher salaries.

     

    Trends in average annual income overseas

    Overseas machine learning engineers are often offered higher annual salaries than domestic ones. According to the data of the American recruiting site “Glassdoor”, the average annual income of machine learning engineers is about 14 million yen. If you are a machine learning engineer and want to achieve a high annual income, it is recommended that you also consider working overseas. However, the level required is likely to be higher than in Japan, so you must improve your skills on a daily basis.

     

    To aim to increase annual income as a machine learning engineer


    In order to increase your annual income as a machine learning engineer, the following points are important.

    • Catch up on the latest technology
    • Learn a programming language
    • Improve project management skills
    • Improve your English
    • Consider changing jobs

     

    Catch up on the latest technology

    As technology advances rapidly in the IT industry, it is important to keep an antenna on and catch up with the latest technology and knowledge. In particular, new information is being transmitted one after another in advanced technologies such as machine learning and AI. There are few human resources who can handle the latest technology and the demand is high, so if you do not miss the catch-up, you will be a valuable human resource.

     

    Learn a programming language

    In order to realize machine learning, it is necessary to code “Python” and “R language”. Therefore, you must acquire the knowledge to code each one. Also, when implementing machine learning, knowledge of specialized libraries and frameworks is also required, so it is good to remember the commonly used “NumPy, Pandas, Tensorflow, Matplotlib” and so on.

     

    Improve project management skills

    If you want to advance your career as an engineer, it is important to improve your project management skills in addition to technology skills. If you gain some experience as an engineer, you will have more opportunities to play an active role not only in the field but also as a team leader. In addition to managing the team, there is a wide range of tasks such as formulating development policies and selecting technologies for solving problems. Managers tend to have higher annual incomes, so think ahead and improve your management skills.

     

    Improve your English

    The fields of machine learning and AI are more advanced overseas than in Japan. Therefore, you have to read foreign documents to get the latest knowledge. At that time, it is important to acquire English proficiency so that you can read overseas documents. By acquiring the latest information, even domestic companies will be able to become valuable human resources.

     

    Consider changing jobs

    After gaining experience as a machine learning engineer, it is a good idea to move to a company with better conditions. Due to the high demand for machine learning engineers themselves, it is possible that you will find a company that will generate a higher annual income for the same job. Furthermore, if you are confident in your language skills such as English, it is also effective to work overseas such as the United States in search of a high income.

     

    To get a satisfying annual income with a machine learning engineer


    The following points are important for a machine learning engineer to earn a satisfactory annual income.

    • Choose a job-based employment company
    • Choose a company that is willing to invest in education
    • Check the personnel evaluation system

    Choose a job-based employment company

    Job-type employment is employment that emphasizes external competitiveness and determines salary based on market value. Since job-based employment focuses on skills and determines salary, it is likely that the salary will be higher than the seniority-based assessment found in many companies.

     

    Choose a company that is willing to invest in education

    It is important for engineers to constantly study and improve their skills. Therefore, by selecting a company with a well-educated environment, you can work while increasing your own market value. Also, a company that is willing to invest in education is a company that understands the value of engineers, so it is highly likely that it will get a proper evaluation. By increasing the market value, you can expect high annual income even when you change jobs.

     

    Check the personnel evaluation system

    In order to obtain a satisfactory annual income, it is important to have an evaluation system that clearly and properly evaluates your contribution. Many companies often carry out personnel evaluations in seniority order. However, in seniority, there is almost no such thing as a sudden promotion or salary increase due to evaluation. Therefore, it is important that the evaluation system only properly evaluates ability.

     

    If you want to touch machine learning, leave it to TRYING

    To become a machine learning engineer, you must acquire knowledge and skills related to machine learning. However, it is difficult to learn machine learning from scratch. Therefore, I would like to recommend two cloud-based business efficiency improvement tools developed by TRYING.

     

    Experience machine learning with the no-code AI tool “UMWELT.”

    UMWELT is a tool that can use AI without programming. There is no need to prepare a special environment for using AI in-house, and AI can be used while minimizing preparation costs such as costs and man-hours. It is equipped with a large number of AI algorithms that help improve work efficiency, and anyone can easily build an AI system by freely combining these algorithms.

     

    Easy automatic shift creation with AI cloud “HR BEST”

    HRBEST is a tool that automates complex shift creation with “combinatorial optimization” technology. Since shifts can be created according to the Labor Standards Law and industry rules, shift creation work for managers, who tend to be personalized, can be easily automated. Since it is a cloud service, it is always possible to respond to sudden shift changes and law revisions, and always keep the latest shift status.

     

    summary

    Machine learning engineers have a higher average annual income than general engineers, and it is expected that the level will continue to rise in the future. Many companies would like to acquire machine learning engineers to improve work efficiency and create businesses. However, it is difficult to hire machine learning engineers, and many companies may be worried that they will not be able to incorporate machine learning.  UMWELT / HRBEST is an AI tool that can be used without the need for an instrument learning engineer. Please take this article as an opportunity to contact TRYING.

     

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  • How much is the annual income of a machine learning engineer? Introducing annual income situation and salary increase method

    How much is the annual income of a machine learning engineer? Introducing annual income situation and salary increase method

    Annual income of a machine learning engineer?

    Machine learning is used in many places and is one of the technologies that enrich our lives. Along with that, attention is also focused on machine learning engineers who build machine learning. What I am concerned about is the outline of machine learning engineers and the annual income situation. This time, we will introduce the outline of machine learning engineers and the annual income situation.

     

    What kind of profession is a machine learning engineer?


    Due to the rapid development of AI technology and the promotion of digital transformation by companies, the attention of machine learning engineers is increasing. So, first, I will explain about machine learning engineers.

    What is a machine learning engineer?

    A machine learning engineer is a job that specializes in machine learning technology in AI development. Machine learning is one of the technologies required to realize AI, and derives certain rules and rules based on a large amount of data. Machine learning engineers use their knowledge of statistics and mathematics to build machine learning models in order to find certain regularities from large amounts of data.

     

    Job description of a machine learning engineer

    Machine learning engineers are mainly responsible for data analysis, construction, and construction and verification of machine learning models. In particular, building and verifying machine learning models is the most important task. In machine learning, the accumulated data is learned according to the constructed machine learning model. At this time, the learning result makes a big difference depending on the machine learning model selected. Therefore, it is necessary to modify the machine learning model with higher accuracy by repeating the verification.

     

    Differences between AI engineers and data scientists

    Similar occupations to machine learning engineers include AI engineers and data scientists. Since AI engineer is a term that refers to all engineers involved in AI, machine learning engineers are also included in AI engineers. However, some companies make a clear distinction between AI engineers and machine learning engineers. A data scientist is a job that is treated in much the same way as a machine learning engineer when looking at the work content. However, some companies make a clear distinction, so it is necessary to confirm in advance.

     

    Check the annual income of machine learning engineers


    Next, let’s take a look at the annual income of machine learning engineers.

    Domestic average annual income

    According to data from the job search site Indeed, the average annual income of machine learning engineers in Japan is around 6.3 million yen. According to the site, the average annual salary of a general engineer is about 4-5 million yen, so it can be said that machine learning engineers have a high salary level. In recent years, the Ministry of Economy, Trade and Industry has been promoting digital transformation, and the importance of AI is increasing in society as a whole. Therefore, increasing demand is increasing the number of companies offering higher salaries.

     

    Trends in average annual income overseas

    Overseas machine learning engineers are often offered higher annual salaries than domestic ones. According to the data of the American recruiting site “Glassdoor”, the average annual income of machine learning engineers is about 14 million yen. If you are a machine learning engineer and want to achieve a high annual income, it is recommended that you also consider working overseas. However, the level required is likely to be higher than in Japan, so you must improve your skills on a daily basis.

     

    To aim to increase annual income as a machine learning engineer


    In order to increase your annual income as a machine learning engineer, the following points are important.

    • Catch up on the latest technology
    • Learn a programming language
    • Improve project management skills
    • Improve your English
    • Consider changing jobs

     

    Catch up on the latest technology

    As technology advances rapidly in the IT industry, it is important to keep an antenna on and catch up with the latest technology and knowledge. In particular, new information is being transmitted one after another in advanced technologies such as machine learning and AI. There are few human resources who can handle the latest technology and the demand is high, so if you do not miss the catch-up, you will be a valuable human resource.

     

    Learn a programming language

    In order to realize machine learning, it is necessary to code “Python” and “R language”. Therefore, you must acquire the knowledge to code each one. Also, when implementing machine learning, knowledge of specialized libraries and frameworks is also required, so it is good to remember the commonly used “NumPy, Pandas, Tensorflow, Matplotlib” and so on.

     

    Improve project management skills

    If you want to advance your career as an engineer, it is important to improve your project management skills in addition to technology skills. If you gain some experience as an engineer, you will have more opportunities to play an active role not only in the field but also as a team leader. In addition to managing the team, there is a wide range of tasks such as formulating development policies and selecting technologies for solving problems. Managers tend to have higher annual incomes, so think ahead and improve your management skills.

     

    Improve your English

    The fields of machine learning and AI are more advanced overseas than in Japan. Therefore, you have to read foreign documents to get the latest knowledge. At that time, it is important to acquire English proficiency so that you can read overseas documents. By acquiring the latest information, even domestic companies will be able to become valuable human resources.

     

    Consider changing jobs

    After gaining experience as a machine learning engineer, it is a good idea to move to a company with better conditions. Due to the high demand for machine learning engineers themselves, it is possible that you will find a company that will generate a higher annual income for the same job. Furthermore, if you are confident in your language skills such as English, it is also effective to work overseas such as the United States in search of a high income.

     

    To get a satisfying annual income with a machine learning engineer


    The following points are important for a machine learning engineer to earn a satisfactory annual income.

    • Choose a job-based employment company
    • Choose a company that is willing to invest in education
    • Check the personnel evaluation system

    Choose a job-based employment company

    Job-type employment is employment that emphasizes external competitiveness and determines salary based on market value. Since job-based employment focuses on skills and determines salary, it is likely that the salary will be higher than the seniority-based assessment found in many companies.

     

    Choose a company that is willing to invest in education

    It is important for engineers to constantly study and improve their skills. Therefore, by selecting a company with a well-educated environment, you can work while increasing your own market value. Also, a company that is willing to invest in education is a company that understands the value of engineers, so it is highly likely that it will get a proper evaluation. By increasing the market value, you can expect high annual income even when you change jobs.

     

    Check the personnel evaluation system

    In order to obtain a satisfactory annual income, it is important to have an evaluation system that clearly and properly evaluates your contribution. Many companies often carry out personnel evaluations in seniority order. However, in seniority, there is almost no such thing as a sudden promotion or salary increase due to evaluation. Therefore, it is important that the evaluation system only properly evaluates ability.

     

    If you want to touch machine learning, leave it to TRYING

    To become a machine learning engineer, you must acquire knowledge and skills related to machine learning. However, it is difficult to learn machine learning from scratch. Therefore, I would like to recommend two cloud-based business efficiency improvement tools developed by TRYING.

     

    Experience machine learning with the no-code AI tool “UMWELT.”

    UMWELT is a tool that can use AI without programming. There is no need to prepare a special environment for using AI in-house, and AI can be used while minimizing preparation costs such as costs and man-hours. It is equipped with a large number of AI algorithms that help improve work efficiency, and anyone can easily build an AI system by freely combining these algorithms.

     

    Easy automatic shift creation with AI cloud “HR BEST”

    HRBEST is a tool that automates complex shift creation with “combinatorial optimization” technology. Since shifts can be created according to the Labor Standards Law and industry rules, shift creation work for managers, who tend to be personalized, can be easily automated. Since it is a cloud service, it is always possible to respond to sudden shift changes and law revisions, and always keep the latest shift status.

     

    summary

    Machine learning engineers have a higher average annual income than general engineers, and it is expected that the level will continue to rise in the future. Many companies would like to acquire machine learning engineers to improve work efficiency and create businesses. However, it is difficult to hire machine learning engineers, and many companies may be worried that they will not be able to incorporate machine learning.  UMWELT / HRBEST is an AI tool that can be used without the need for an instrument learning engineer. Please take this article as an opportunity to contact TRYING.

     

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  • How to succeed in cost reduction by AI? Detailed explanation along with examples!

    How to succeed in cost reduction by AI? Detailed explanation along with examples!

     

    When introducing AI, I think there are many people who want to streamline operations and reduce costs such as labor costs and development costs. There are several tips for successful cost savings with AI. In this article, we will introduce the points and examples of cost reduction by AI.

     

    How to choose AI for cost reduction?


    When introducing AI, many companies set sales increase and cost reduction as improvement goals. I will explain the points to pay attention to when considering AI.

    Select by utilization technology

    Image recognition / image analysis

    Technology for image recognition / image analysis that uses AI to identify what is reflected in an image or video and whether there are any abnormalities on the computer. At the site, there is no need for discriminating by skilled workers and training for new employees, which leads to reduction of labor costs.

    Demand Forecast Demand forecast

    that forecasts purchase volume and sales volume based on past data. It is now an indispensable part of marketing strategy. The greater the fluctuations in the products handled and the balance between supply and demand, the longer it takes to analyze, so AI intervention can significantly reduce work time.

    Data analysis Data analysis using

    AI makes it possible to discover new patterns and acquire knowledge (data mining) from a huge amount of data. In addition to reducing the cost of data analysis, the information obtained can be used for marketing.

    Optimization

    Optimization technology uses data analyzed by AI to derive how to achieve goals under limited conditions. Delivery routes in logistics companies and supply chains in the manufacturing industry can also be streamlined with this optimization technology.

    Building
    a data infrastructure By building a data infrastructure, you can collect, store, analyze, and visualize the accumulated data by integrating it. By consolidating them into one, you can shorten the time required for data analysis and utilization.

    Choose according to your company’s needs

    Business efficiency improvement
    The first keyword that comes to mind when reducing costs is business efficiency improvement. You can expect to improve work efficiency by having AI take over data entry work such as invoices and customer information management.

    Cost reduction

    By entrusting simple work and routine work to AI, it is possible to carry out work with a small number of people, and it is possible to reduce costs such as labor costs.

    Defective product detection and failure prediction
    AI, which has defective product detection and failure prediction functions, is a very useful function for manufacturers, and enables highly reliable work in a short time.

    Maximizing Profit

    If the introduction of AI streamlines operations and reduces labor costs and wasteful operations, it will ultimately be possible to significantly increase profits.

    Service development

    By incorporating AI into services, the range of service development can be expanded and service development with a higher degree of freedom can be performed.

    Decision-making power

    Demand forecasting and data analysis make it easier to determine the direction of new products and services to be developed. Explaining the results of AI will make it easier to obtain internal consent and improve decision-making ability.

    In an external environment where promotion of human resources development
    DX is indispensable, introducing AI in-house will lead to the development of AI human resources in the future.

    Benefits of introducing AI


    Now that we’ve explained how to choose AI, let’s talk about the benefits of introducing AI. If you are worried about the balance between the cost of introducing AI and the improvement of business efficiency, please refer to it.

    Reduction of labor costs and elimination of labor shortages

    With the introduction of AI, machines will automatically process simple tasks and routine tasks that were previously personalized. As a result, even a small number of people will be able to carry out internal operations without delay, and will be the savior of companies with serious labor shortages.

    In addition, mechanization leads to shorter working hours and reduces overtime and holiday allowances. There is an initial cost to introduce AI, but in the long run, it will reduce labor costs and reduce the cost of the entire company.

    Business efficiency and productivity improvement

    AI is by no means all-purpose, and there are some tasks that require human intervention within the company. By leaving the work that can be mechanized to AI, you will have more room and you will be able to concentrate more on the work that is personalized. As a result, work efficiency will be improved and productivity can be expected to improve.

    Data analysis / analysis prediction

    Accumulating and verifying a huge amount of customer data is a very time-consuming task. If it is AI, it can capture and analyze data in a short time. By deepening the depth of deep learning, more accurate analysis prediction can be realized. In addition, data analysis is effective not only when developing products and services, but also as a measure for human resource education and operational efficiency improvement.

    Improved safety

    Automation of safety management work at dangerous work sites leads to ensuring the safety of employees. Specifically, it is possible to quickly and automatically detect worker vital monitoring and dangerous behavior with a camera.

     

    Precautions for introducing AI


    The introduction of AI can be said to have many advantages, but it is not without its disadvantages. Here are two things to keep in mind when considering the introduction of AI.

    There are industries where costs cannot be reduced by AI alone

    While the introduction of AI by companies is progressing, there are some industries where mechanization is difficult. For example, it is difficult to fully automate creative jobs where sensitivity is important, the medical industry where reliability is important, and sales positions. When considering the introduction of AI, it is necessary to carefully consider what kind of department or business the company is likely to use.

    Requires knowledge of risk management

    By introducing and operating AI, various problems such as errors and biases in input data, consideration for privacy, and lack of operational know-how can occur. One of the concerns is the blackboxing of AI, which makes it impossible to visualize what kind of information is accumulated. Companies are required to educate AI personnel who can handle these risks.

     

    6 examples of cost reduction by AI


    The shortcut is to learn from the cases of other companies about specific ways to utilize AI. Here are six examples of successful cost reductions by AI.

    1. Inquiry department

    Many companies, including EC sites and manufacturers, have introduced AI chatbots that can respond to inquiries from users. This not only reduces human costs, but also enables uniform, highly accurate and speedy response. As a result, user satisfaction will improve. In addition, it is effective to improve employee satisfaction by utilizing it internally.

    2. Sales department

    By introducing AI into sales activities, it is possible to extract customers who are likely to make a contract with high probability from data such as customer attributes and purchase history. Personnel who have been doing sales activities randomly can switch to efficient sales activities by referring to the recommended recommendations. By leaving the selection of the approach destination to AI, the person in charge can concentrate on the closing work that is important when the contract is concluded.

    3. Human resources department

    AI can evenly arrange staffing and evaluation, which have tended to be subjective. Employees will be able to quantitatively evaluate their satisfaction with their work, and can expect to maintain their motivation. Furthermore, by having AI set objective recruitment standards, it will be possible to improve the efficiency of recruitment activities, which will lead to a decrease in turnover rate and prevention of mismatches.

    4. Logistics department

    In the logistics industry, the efficiency of operations by AI has been remarkably improved, and the automation of processes such as warehousing / delivery work, order processing, inspection work, and baggage sorting is progressing. In addition, it is possible to optimize the delivery route of the driver, automate the vehicle allocation plan, and detect dangerous driving, which is useful for improving safety as well as improving work efficiency.

    5. Maintenance / maintenance department

    AI plays a major role in the maintenance department from the perspective of accident prevention. It is possible to automate inspection work, which used to require visual confirmation, improve inspection efficiency and improve safety of buildings and equipment that are difficult to inspect. It can contribute not only to accident prevention but also to ensuring the safety of workers, and it is expected that the introduction of AI will accelerate in the future.

    6. Factory department

    It is said that quality tends to differ depending on the career of the worker at the production site of the factory. However, even so, a certain level of quality is guaranteed by AI learning veteran techniques and building a system to support inexperienced workers. It is also effective in ensuring safety in the factory and preventing accidents.

     

    Problems and solutions when introducing AI


    Here, we will introduce the problems and solutions that often occur when thinking about cost reduction by AI.

    What should I start with when introducing AI?

    As explained at the beginning, the introduction of AI can be expected to increase sales and reduce costs.

    In order to realize the effect, first clarify your company’s needs and compare the corresponding software. There are various plans, but I think the best is to start with the basic one, verify the effect, and gradually expand. By accumulating know-how, you can reduce mistakes.

    How to choose with an emphasis on cost reduction?

    If you focus on cost reduction by AI, clarify your needs and then estimate the budget for the expected economic effect. Start experimentally on a small scale first so that you can keep costs down even a little.

    What are the expected AI fields in the future?

    As explained so far, AI has some tasks that can be mechanized and some that cannot. Consider the scope of leaving it to AI and consider introducing it. Future AI trends include the fields of image recognition, natural language processing, and speech recognition. These areas are attracting attention as more useful functions are expected to be developed in the future.

     

    “UMWELT” is recommended for cost reduction by AI tools

    If you want to reduce the introduction cost and reduce your own cost, we recommend using “UMWELT” provided by TRYETING. UMWELT has a lower usage fee level than similar AI systems, and it is relatively easy for small businesses to introduce it.

    It is possible to reduce the number of hours required for work x unit hourly wage, and you can expect an improvement in business productivity when viewed in total.

    No-code AI

    Click here for details

    First of all, easy

    Free consultation

    Summary

    This time, we introduced the key points and examples of cost reduction in our company when introducing AI. If cost reductions are achieved, they should be recognized as useful within the company. If you want to reduce the introduction cost and reduce the total cost, please consider UMWELT.

     

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  • How to succeed in cost reduction by AI? Detailed explanation along with examples!

    How to succeed in cost reduction by AI? Detailed explanation along with examples!

     

    When introducing AI, I think there are many people who want to streamline operations and reduce costs such as labor costs and development costs. There are several tips for successful cost savings with AI. In this article, we will introduce the points and examples of cost reduction by AI.

     

    How to choose AI for cost reduction?


    When introducing AI, many companies set sales increase and cost reduction as improvement goals. I will explain the points to pay attention to when considering AI.

    Select by utilization technology

    Image recognition / image analysis

    Technology for image recognition / image analysis that uses AI to identify what is reflected in an image or video and whether there are any abnormalities on the computer. At the site, there is no need for discriminating by skilled workers and training for new employees, which leads to reduction of labor costs.

    Demand Forecast Demand forecast

    that forecasts purchase volume and sales volume based on past data. It is now an indispensable part of marketing strategy. The greater the fluctuations in the products handled and the balance between supply and demand, the longer it takes to analyze, so AI intervention can significantly reduce work time.

    Data analysis Data analysis using

    AI makes it possible to discover new patterns and acquire knowledge (data mining) from a huge amount of data. In addition to reducing the cost of data analysis, the information obtained can be used for marketing.

    Optimization

    Optimization technology uses data analyzed by AI to derive how to achieve goals under limited conditions. Delivery routes in logistics companies and supply chains in the manufacturing industry can also be streamlined with this optimization technology.

    Building
    a data infrastructure By building a data infrastructure, you can collect, store, analyze, and visualize the accumulated data by integrating it. By consolidating them into one, you can shorten the time required for data analysis and utilization.

    Choose according to your company’s needs

    Business efficiency improvement
    The first keyword that comes to mind when reducing costs is business efficiency improvement. You can expect to improve work efficiency by having AI take over data entry work such as invoices and customer information management.

    Cost reduction

    By entrusting simple work and routine work to AI, it is possible to carry out work with a small number of people, and it is possible to reduce costs such as labor costs.

    Defective product detection and failure prediction
    AI, which has defective product detection and failure prediction functions, is a very useful function for manufacturers, and enables highly reliable work in a short time.

    Maximizing Profit

    If the introduction of AI streamlines operations and reduces labor costs and wasteful operations, it will ultimately be possible to significantly increase profits.

    Service development

    By incorporating AI into services, the range of service development can be expanded and service development with a higher degree of freedom can be performed.

    Decision-making power

    Demand forecasting and data analysis make it easier to determine the direction of new products and services to be developed. Explaining the results of AI will make it easier to obtain internal consent and improve decision-making ability.

    In an external environment where promotion of human resources development
    DX is indispensable, introducing AI in-house will lead to the development of AI human resources in the future.

    Benefits of introducing AI


    Now that we’ve explained how to choose AI, let’s talk about the benefits of introducing AI. If you are worried about the balance between the cost of introducing AI and the improvement of business efficiency, please refer to it.

    Reduction of labor costs and elimination of labor shortages

    With the introduction of AI, machines will automatically process simple tasks and routine tasks that were previously personalized. As a result, even a small number of people will be able to carry out internal operations without delay, and will be the savior of companies with serious labor shortages.

    In addition, mechanization leads to shorter working hours and reduces overtime and holiday allowances. There is an initial cost to introduce AI, but in the long run, it will reduce labor costs and reduce the cost of the entire company.

    Business efficiency and productivity improvement

    AI is by no means all-purpose, and there are some tasks that require human intervention within the company. By leaving the work that can be mechanized to AI, you will have more room and you will be able to concentrate more on the work that is personalized. As a result, work efficiency will be improved and productivity can be expected to improve.

    Data analysis / analysis prediction

    Accumulating and verifying a huge amount of customer data is a very time-consuming task. If it is AI, it can capture and analyze data in a short time. By deepening the depth of deep learning, more accurate analysis prediction can be realized. In addition, data analysis is effective not only when developing products and services, but also as a measure for human resource education and operational efficiency improvement.

    Improved safety

    Automation of safety management work at dangerous work sites leads to ensuring the safety of employees. Specifically, it is possible to quickly and automatically detect worker vital monitoring and dangerous behavior with a camera.

     

    Precautions for introducing AI


    The introduction of AI can be said to have many advantages, but it is not without its disadvantages. Here are two things to keep in mind when considering the introduction of AI.

    There are industries where costs cannot be reduced by AI alone

    While the introduction of AI by companies is progressing, there are some industries where mechanization is difficult. For example, it is difficult to fully automate creative jobs where sensitivity is important, the medical industry where reliability is important, and sales positions. When considering the introduction of AI, it is necessary to carefully consider what kind of department or business the company is likely to use.

    Requires knowledge of risk management

    By introducing and operating AI, various problems such as errors and biases in input data, consideration for privacy, and lack of operational know-how can occur. One of the concerns is the blackboxing of AI, which makes it impossible to visualize what kind of information is accumulated. Companies are required to educate AI personnel who can handle these risks.

     

    6 examples of cost reduction by AI


    The shortcut is to learn from the cases of other companies about specific ways to utilize AI. Here are six examples of successful cost reductions by AI.

    1. Inquiry department

    Many companies, including EC sites and manufacturers, have introduced AI chatbots that can respond to inquiries from users. This not only reduces human costs, but also enables uniform, highly accurate and speedy response. As a result, user satisfaction will improve. In addition, it is effective to improve employee satisfaction by utilizing it internally.

    2. Sales department

    By introducing AI into sales activities, it is possible to extract customers who are likely to make a contract with high probability from data such as customer attributes and purchase history. Personnel who have been doing sales activities randomly can switch to efficient sales activities by referring to the recommended recommendations. By leaving the selection of the approach destination to AI, the person in charge can concentrate on the closing work that is important when the contract is concluded.

    3. Human resources department

    AI can evenly arrange staffing and evaluation, which have tended to be subjective. Employees will be able to quantitatively evaluate their satisfaction with their work, and can expect to maintain their motivation. Furthermore, by having AI set objective recruitment standards, it will be possible to improve the efficiency of recruitment activities, which will lead to a decrease in turnover rate and prevention of mismatches.

    4. Logistics department

    In the logistics industry, the efficiency of operations by AI has been remarkably improved, and the automation of processes such as warehousing / delivery work, order processing, inspection work, and baggage sorting is progressing. In addition, it is possible to optimize the delivery route of the driver, automate the vehicle allocation plan, and detect dangerous driving, which is useful for improving safety as well as improving work efficiency.

    5. Maintenance / maintenance department

    AI plays a major role in the maintenance department from the perspective of accident prevention. It is possible to automate inspection work, which used to require visual confirmation, improve inspection efficiency and improve safety of buildings and equipment that are difficult to inspect. It can contribute not only to accident prevention but also to ensuring the safety of workers, and it is expected that the introduction of AI will accelerate in the future.

    6. Factory department

    It is said that quality tends to differ depending on the career of the worker at the production site of the factory. However, even so, a certain level of quality is guaranteed by AI learning veteran techniques and building a system to support inexperienced workers. It is also effective in ensuring safety in the factory and preventing accidents.

     

    Problems and solutions when introducing AI


    Here, we will introduce the problems and solutions that often occur when thinking about cost reduction by AI.

    What should I start with when introducing AI?

    As explained at the beginning, the introduction of AI can be expected to increase sales and reduce costs.

    In order to realize the effect, first clarify your company’s needs and compare the corresponding software. There are various plans, but I think the best is to start with the basic one, verify the effect, and gradually expand. By accumulating know-how, you can reduce mistakes.

    How to choose with an emphasis on cost reduction?

    If you focus on cost reduction by AI, clarify your needs and then estimate the budget for the expected economic effect. Start experimentally on a small scale first so that you can keep costs down even a little.

    What are the expected AI fields in the future?

    As explained so far, AI has some tasks that can be mechanized and some that cannot. Consider the scope of leaving it to AI and consider introducing it. Future AI trends include the fields of image recognition, natural language processing, and speech recognition. These areas are attracting attention as more useful functions are expected to be developed in the future.

     

    “UMWELT” is recommended for cost reduction by AI tools

    If you want to reduce the introduction cost and reduce your own cost, we recommend using “UMWELT” provided by TRYETING. UMWELT has a lower usage fee level than similar AI systems, and it is relatively easy for small businesses to introduce it.

    It is possible to reduce the number of hours required for work x unit hourly wage, and you can expect an improvement in business productivity when viewed in total.

    No-code AI

    Click here for details

    First of all, easy

    Free consultation

    Summary

    This time, we introduced the key points and examples of cost reduction in our company when introducing AI. If cost reductions are achieved, they should be recognized as useful within the company. If you want to reduce the introduction cost and reduce the total cost, please consider UMWELT.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy

  • What is the impact of AI on the media? Introducing examples of media AI utilization

    What is the impact of AI on the media? Introducing examples of media AI utilization

     

    AI has come to be used in all our daily lives and business scenes. The wave of AI innovation is also coming to the media and content industries. In this article, we will focus on “the impact of AI on the media” and explain its outline and application examples.

    The influence of AI is spreading to the media

     

     

    What is AI?

    AI (Artificial Intelligence) is an artificial reproduction of a part of human intelligence using software. Humans make many guesses and judgments in their lives, but AI can automatically extract and judge features such as patterns and rules for making judgments.

    Impact of AI on the media

    In the days when there was only TV, there was competition for ratings. However, with the advent of YouTube, Netflix, and smartphones, the range of content choices for viewers has greatly expanded. Even in such video distribution services, AI that proposes works that match the tastes of each viewer is used.

    Mediaization of physical stores is also progressing

    Real spaces such as physical stores are also becoming media. For example, the case of purchasing an actual product online after seeing it in a store is a typical example of mediaization. With AI, it will be possible to understand the purchasing consciousness and consumption behavior of users, which will be useful for digitizing real stores.

     

    Types of media that are greatly affected by AI

     


    There are three main types of media that are greatly affected by AI. Here, we will explain the outline and features of each medium.

     

    Mass media

    Mass media is a “mass-oriented” communication medium that sends information to an unspecified number of people, such as tens of thousands to tens of millions of people. The mass media plays multiple roles such as news, commentary / enlightenment, education, entertainment, and advertising, and is also known for its great social influence.

    Web media

    Web media are websites that send out some information on the Internet. It can be accessed not only on a personal computer but also on a terminal that can connect to the Internet, such as a smartphone or tablet.

    Social media

    Social media refers to media that includes social elements such as information dissemination by individuals and connections between individuals. In receiving information, it can also be a sender of information at the same time, and it is a major feature of social media that “diffusion” occurs due to interaction.

    Media AI utilization cases

     


    Vendors serving media agencies are also stepping up their efforts to leverage AI. Here, we will introduce six examples of media AI utilization.

     

    Real-time Japanese conversion system

    The real-time Japanese conversion system is an AI technology mainly used in mass media. TV Asahi, which covers the Kanto area as a broadcast target area, uses AI-OCR to display telops of athlete names in overseas sports broadcasts, and realizes automatic Japanese conversion in real time.

    Fully automatic real-time subtitles

    Internet TV “AbemaTV” uses AI voice recognition technology to develop live broadcast programs that display AI subtitles in real time. In addition, the subtitles sent by broadcasting are saved in the log and can be linked with various recording and broadcasting servers, so in recording and rebroadcasting, subtitles can be sent out with the touch of a button.

    Image recognition

    AI technology is also used for image recognition. A particular topic in image recognition using AI was “SEER” announced by the Facebook research team. SEER is a self-supervised learning technique from unlabeled random image groups on the Internet. It autonomously examines the contents of the dataset and learns in the process, achieving top-level accuracy in tasks such as object detection.

    SNS video collection

    A lot of attention is also being paid to “Newsdeck,” a service that automatically collects images and videos of incidents, accidents, disasters, etc. from the Internet using AI and provides them to the news media with the permission of the poster. Newsdeck collects images and videos related to incidents, accidents, and disasters in real time from various SNS, and AI classifies them into items such as “earthquake,” “traffic accident,” and “fire.” As a result, the labor of the employees in charge of the survey can be reduced, leading to an improvement in labor productivity.

    Recommended engine

    A recommendation engine is software that identifies the right offers, products, and content to website and mobile app users, as well as customers interacting through digital channels, to personalize the customer experience. .. AI technology mainly used in web media is being introduced by major companies such as Amazon and Netflix.

    Chatbot

    A chatbot is a robot program that handles real-time response work on behalf of humans. AI chatbots use AI’s ability to derive correct answers based on past statistical data and provide accurate answers to customer inquiries. In the media industry, Korona-ka has regained attention as a non-contact technology for measures against denseness and leveling of congestion, and the range of utilization has expanded.

    Introducing TRYETING’s AI tools

     

    We recommend the two AI tools developed by TRYETING for corporate personnel who want to utilize AI for internal operations and promote DX conversion. Here, we will introduce the no-code AI tool “UMWELT” and the automatic shift creation AI tool “HRBEST”, the product features of each, and the merits of their introduction.

     

    No-code AI tool “UMWELT”

    The no-code AI tool “UMWELT” is a cloud service that allows you to easily introduce AI without a server by using the existing system as it is. With a large number of proven algorithms, no programming language knowledge or special environment required for AI implementation is required. You can easily build AI just by operating the mouse. In addition, the period until the introduction of AI is 1/4 of the conventional one, and high-speed introduction is realized. Another advantage is that the introduction cost is 1/10 of the conventional cost, which is the lowest level in the industry.

    Shift automatic creation AI tool “HR BEST”

    With the shift creation service “HR BEST” that utilizes AI, it is possible to automatically create the optimum shift by machine learning. Employees submit the “desired date and time of shift” from within the smartphone app, and the shift creator displays the submitted information on the calendar and automatically arranges it. You can also propose “members who are likely to enter the shift” after learning past shift information. The shift table creation work, which was all done manually in the past, can be greatly streamlined.

    Summary

    This time, we have explained the impact of AI on the media, examples of media AI utilization, and recommended AI tools. AI technology is evolving day by day, and will become indispensable for human life and corporate development in various fields in the future. By all means, please refer to this article to deepen your knowledge about AI and use AI for your own business.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy
  • What is the impact of AI on the media? Introducing examples of media AI utilization

    What is the impact of AI on the media? Introducing examples of media AI utilization

     

    AI has come to be used in all our daily lives and business scenes. The wave of AI innovation is also coming to the media and content industries. In this article, we will focus on “the impact of AI on the media” and explain its outline and application examples.

    The influence of AI is spreading to the media

     

     

    What is AI?

    AI (Artificial Intelligence) is an artificial reproduction of a part of human intelligence using software. Humans make many guesses and judgments in their lives, but AI can automatically extract and judge features such as patterns and rules for making judgments.

    Impact of AI on the media

    In the days when there was only TV, there was competition for ratings. However, with the advent of YouTube, Netflix, and smartphones, the range of content choices for viewers has greatly expanded. Even in such video distribution services, AI that proposes works that match the tastes of each viewer is used.

    Mediaization of physical stores is also progressing

    Real spaces such as physical stores are also becoming media. For example, the case of purchasing an actual product online after seeing it in a store is a typical example of mediaization. With AI, it will be possible to understand the purchasing consciousness and consumption behavior of users, which will be useful for digitizing real stores.

     

    Types of media that are greatly affected by AI

     


    There are three main types of media that are greatly affected by AI. Here, we will explain the outline and features of each medium.

     

    Mass media

    Mass media is a “mass-oriented” communication medium that sends information to an unspecified number of people, such as tens of thousands to tens of millions of people. The mass media plays multiple roles such as news, commentary / enlightenment, education, entertainment, and advertising, and is also known for its great social influence.

    Web media

    Web media are websites that send out some information on the Internet. It can be accessed not only on a personal computer but also on a terminal that can connect to the Internet, such as a smartphone or tablet.

    Social media

    Social media refers to media that includes social elements such as information dissemination by individuals and connections between individuals. In receiving information, it can also be a sender of information at the same time, and it is a major feature of social media that “diffusion” occurs due to interaction.

    Media AI utilization cases

     


    Vendors serving media agencies are also stepping up their efforts to leverage AI. Here, we will introduce six examples of media AI utilization.

     

    Real-time Japanese conversion system

    The real-time Japanese conversion system is an AI technology mainly used in mass media. TV Asahi, which covers the Kanto area as a broadcast target area, uses AI-OCR to display telops of athlete names in overseas sports broadcasts, and realizes automatic Japanese conversion in real time.

    Fully automatic real-time subtitles

    Internet TV “AbemaTV” uses AI voice recognition technology to develop live broadcast programs that display AI subtitles in real time. In addition, the subtitles sent by broadcasting are saved in the log and can be linked with various recording and broadcasting servers, so in recording and rebroadcasting, subtitles can be sent out with the touch of a button.

    Image recognition

    AI technology is also used for image recognition. A particular topic in image recognition using AI was “SEER” announced by the Facebook research team. SEER is a self-supervised learning technique from unlabeled random image groups on the Internet. It autonomously examines the contents of the dataset and learns in the process, achieving top-level accuracy in tasks such as object detection.

    SNS video collection

    A lot of attention is also being paid to “Newsdeck,” a service that automatically collects images and videos of incidents, accidents, disasters, etc. from the Internet using AI and provides them to the news media with the permission of the poster. Newsdeck collects images and videos related to incidents, accidents, and disasters in real time from various SNS, and AI classifies them into items such as “earthquake,” “traffic accident,” and “fire.” As a result, the labor of the employees in charge of the survey can be reduced, leading to an improvement in labor productivity.

    Recommended engine

    A recommendation engine is software that identifies the right offers, products, and content to website and mobile app users, as well as customers interacting through digital channels, to personalize the customer experience. .. AI technology mainly used in web media is being introduced by major companies such as Amazon and Netflix.

    Chatbot

    A chatbot is a robot program that handles real-time response work on behalf of humans. AI chatbots use AI’s ability to derive correct answers based on past statistical data and provide accurate answers to customer inquiries. In the media industry, Korona-ka has regained attention as a non-contact technology for measures against denseness and leveling of congestion, and the range of utilization has expanded.

    Introducing TRYETING’s AI tools

     

    We recommend the two AI tools developed by TRYETING for corporate personnel who want to utilize AI for internal operations and promote DX conversion. Here, we will introduce the no-code AI tool “UMWELT” and the automatic shift creation AI tool “HRBEST”, the product features of each, and the merits of their introduction.

     

    No-code AI tool “UMWELT”

    The no-code AI tool “UMWELT” is a cloud service that allows you to easily introduce AI without a server by using the existing system as it is. With a large number of proven algorithms, no programming language knowledge or special environment required for AI implementation is required. You can easily build AI just by operating the mouse. In addition, the period until the introduction of AI is 1/4 of the conventional one, and high-speed introduction is realized. Another advantage is that the introduction cost is 1/10 of the conventional cost, which is the lowest level in the industry.

    Shift automatic creation AI tool “HR BEST”

    With the shift creation service “HR BEST” that utilizes AI, it is possible to automatically create the optimum shift by machine learning. Employees submit the “desired date and time of shift” from within the smartphone app, and the shift creator displays the submitted information on the calendar and automatically arranges it. You can also propose “members who are likely to enter the shift” after learning past shift information. The shift table creation work, which was all done manually in the past, can be greatly streamlined.

    Summary

    This time, we have explained the impact of AI on the media, examples of media AI utilization, and recommended AI tools. AI technology is evolving day by day, and will become indispensable for human life and corporate development in various fields in the future. By all means, please refer to this article to deepen your knowledge about AI and use AI for your own business.

     

    Follow us on Facebook for updates and exclusive content! Click here: Each Techy