Level
We define the stages of artificial intelligence from the 1st stage, which is 'object recognition', up to the 11th stage, and we are proceeding with a Baseline Model for this phased approach.
The currently disclosed stage is not about these stages of AI approach, but about the data algorithms that form the basis for self-defining AI. These data algorithms, while constituting a minimal part of the overall algorithm and corresponding to the basic definition of AI, are nevertheless a fundamental and critical aspect.
We are in the process of fulfilling the essential basic elements for the development of such artificial intelligence, and this is a necessary step towards a future with a fully completed AI.
The disclosure of these stages (1 to 11) is not happening because the current level of AI is lower than the level we have defined (even below the 1st stage), and does not meet our official policy of disclosure, which is to make public 'the form of AI technology that is accessible to the general public or experts', based on the judgment of the technology's level (standard technology).
Level
We define the stages of artificial intelligence from the 1st stage, which is 'object recognition', up to the 11th stage, and we are proceeding with a Baseline Model for this phased approach.
The currently disclosed stage is not about these stages of AI approach, but about the data algorithms that form the basis for self-defining AI. These data algorithms, while constituting a minimal part of the overall algorithm and corresponding to the basic definition of AI, are nevertheless a fundamental and critical aspect.
We are in the process of fulfilling the essential basic elements for the development of such artificial intelligence, and this is a necessary step towards a future with a fully completed AI.
The disclosure of these stages (1 to 11) is not happening because the current level of AI is lower than the level we have defined (even below the 1st stage), and does not meet our official policy of disclosure, which is to make public 'the form of AI technology that is accessible to the general public or experts', based on the judgment of the technology's level (standard technology).
Level
We define the stages of artificial intelligence from the 1st stage, which is 'object recognition', up to the 11th stage, and we are proceeding with a Baseline Model for this phased approach.
The currently disclosed stage is not about these stages of AI approach, but about the data algorithms that form the basis for self-defining AI. These data algorithms, while constituting a minimal part of the overall algorithm and corresponding to the basic definition of AI, are nevertheless a fundamental and critical aspect.
We are in the process of fulfilling the essential basic elements for the development of such artificial intelligence, and this is a necessary step towards a future with a fully completed AI.
The disclosure of these stages (1 to 11) is not happening because the current level of AI is lower than the level we have defined (even below the 1st stage), and does not meet our official policy of disclosure, which is to make public 'the form of AI technology that is accessible to the general public or experts', based on the judgment of the technology's level (standard technology).
Befect Characteristic Algorithm & Data
Befect Characteristic Algorithm & Data
Befect Characteristic Algorithm & Data
The limitations of artificial intelligence language models
The limitations of artificial intelligence language models
The limitations of artificial intelligence language models
Current Language Models Do Not Understand Words or Sentences Like Humans, But Operate Based on Statistical Patterns Learned from Large Datasets
These models learn the placement of words and their contextual relationships through numerous text examples. However, this process originates from the fundamental limitation that the model does not 'understand' real-world facts or concepts, but recognizes and mimics linguistic patterns in data.
Language models generate the most probabilistically likely output for a specific input. For example, they predict the words or sentences that are most likely to follow a given word or sentence.
These models can identify how a specific word is used in a sentence and what words fit in context, but they do not 'understand' the real-world meaning or concept of words or sentences.
Therefore, language models may err in problems requiring complex reasoning, common sense, or understanding based on human experience.
Such models are primarily used for tasks based on language patterns, such as natural language processing, sentence generation, text summarization, etc.
Current Language Models Do Not Understand Words or Sentences Like Humans, But Operate Based on Statistical Patterns Learned from Large Datasets
These models learn the placement of words and their contextual relationships through numerous text examples. However, this process originates from the fundamental limitation that the model does not 'understand' real-world facts or concepts, but recognizes and mimics linguistic patterns in data.
Language models generate the most probabilistically likely output for a specific input. For example, they predict the words or sentences that are most likely to follow a given word or sentence.
These models can identify how a specific word is used in a sentence and what words fit in context, but they do not 'understand' the real-world meaning or concept of words or sentences.
Therefore, language models may err in problems requiring complex reasoning, common sense, or understanding based on human experience.
Such models are primarily used for tasks based on language patterns, such as natural language processing, sentence generation, text summarization, etc.
Current Language Models Do Not Understand Words or Sentences Like Humans, But Operate Based on Statistical Patterns Learned from Large Datasets
These models learn the placement of words and their contextual relationships through numerous text examples. However, this process originates from the fundamental limitation that the model does not 'understand' real-world facts or concepts, but recognizes and mimics linguistic patterns in data.
Language models generate the most probabilistically likely output for a specific input. For example, they predict the words or sentences that are most likely to follow a given word or sentence.
These models can identify how a specific word is used in a sentence and what words fit in context, but they do not 'understand' the real-world meaning or concept of words or sentences.
Therefore, language models may err in problems requiring complex reasoning, common sense, or understanding based on human experience.
Such models are primarily used for tasks based on language patterns, such as natural language processing, sentence generation, text summarization, etc.
Language Model's Core Issues, Examples, and Limitations
Language Model's Core Issues, Examples, and Limitations
Language Model's Core Issues, Examples, and Limitations
Accurate Fact-Finding and Limitations of Outdated Information
Accurate Fact-Finding and Limitations of Outdated Information
Accurate Fact-Finding and Limitations of Outdated Information
Issue: AI language models have limitations in accurately understanding and processing complex and detailed factual information.
Question: What are the main differences in the contributions of Isaac Newton and Albert Einstein to physics?
Answer: Isaac Newton discovered the law of universal gravitation, which greatly contributed to explaining the motion of celestial bodies. On the other hand, Albert Einstein proposed the theory of relativity, which innovatively changed our understanding of time and space.
Limitation: AI can briefly summarize the theories of Newton and Einstein, but it is difficult to fully understand or explain the complex physical principles or mathematical proofs of these theories. Additionally, AI is limited in completely analyzing or evaluating the specific and complex impact of these theories on modern physics.
Issue: AI language models have limitations in accurately understanding and processing complex and detailed factual information.
Question: What are the main differences in the contributions of Isaac Newton and Albert Einstein to physics?
Answer: Isaac Newton discovered the law of universal gravitation, which greatly contributed to explaining the motion of celestial bodies. On the other hand, Albert Einstein proposed the theory of relativity, which innovatively changed our understanding of time and space.
Limitation: AI can briefly summarize the theories of Newton and Einstein, but it is difficult to fully understand or explain the complex physical principles or mathematical proofs of these theories. Additionally, AI is limited in completely analyzing or evaluating the specific and complex impact of these theories on modern physics.
Issue: AI language models have limitations in accurately understanding and processing complex and detailed factual information.
Question: What are the main differences in the contributions of Isaac Newton and Albert Einstein to physics?
Answer: Isaac Newton discovered the law of universal gravitation, which greatly contributed to explaining the motion of celestial bodies. On the other hand, Albert Einstein proposed the theory of relativity, which innovatively changed our understanding of time and space.
Limitation: AI can briefly summarize the theories of Newton and Einstein, but it is difficult to fully understand or explain the complex physical principles or mathematical proofs of these theories. Additionally, AI is limited in completely analyzing or evaluating the specific and complex impact of these theories on modern physics.
Lack of Understanding Contextual Situations and Intentions
Lack of Understanding Contextual Situations and Intentions
Lack of Understanding Contextual Situations and Intentions
Issue: AI can understand the direct meaning of words but has limitations in fully understanding and interpreting the speaker's intentions, emotions, and context.
Question: My friend said, 'This is really fun.' Do they really like it?
Answer: Your friend saying 'This is really fun' sounds like a positive response. It seems they think it is enjoyable.
Limitation: AI cannot consider the friend's tone, facial expressions, or situation, which may lead to a misunderstanding of their intentions or emotions.
Issue: AI can understand the direct meaning of words but has limitations in fully understanding and interpreting the speaker's intentions, emotions, and context.
Question: My friend said, 'This is really fun.' Do they really like it?
Answer: Your friend saying 'This is really fun' sounds like a positive response. It seems they think it is enjoyable.
Limitation: AI cannot consider the friend's tone, facial expressions, or situation, which may lead to a misunderstanding of their intentions or emotions.
Issue: AI can understand the direct meaning of words but has limitations in fully understanding and interpreting the speaker's intentions, emotions, and context.
Question: My friend said, 'This is really fun.' Do they really like it?
Answer: Your friend saying 'This is really fun' sounds like a positive response. It seems they think it is enjoyable.
Limitation: AI cannot consider the friend's tone, facial expressions, or situation, which may lead to a misunderstanding of their intentions or emotions.
Lack of Understanding Cultural Nuances and Figurative Expressions
Lack of Understanding Cultural Nuances and Figurative Expressions
Lack of Understanding Cultural Nuances and Figurative Expressions
Issue: AI finds it challenging to fully grasp how expressions are used and interpreted in various cultural backgrounds and contexts.
Question: What does 'an eye for an eye, a tooth for a tooth' mean?
Answer: 'An eye for an eye, a tooth for a tooth' is an ancient legal principle that means fair retaliation.
Limitation: AI may not fully capture the various interpretations or nuances this phrase has in different cultures.
Issue: AI finds it challenging to fully grasp how expressions are used and interpreted in various cultural backgrounds and contexts.
Question: What does 'an eye for an eye, a tooth for a tooth' mean?
Answer: 'An eye for an eye, a tooth for a tooth' is an ancient legal principle that means fair retaliation.
Limitation: AI may not fully capture the various interpretations or nuances this phrase has in different cultures.
Issue: AI finds it challenging to fully grasp how expressions are used and interpreted in various cultural backgrounds and contexts.
Question: What does 'an eye for an eye, a tooth for a tooth' mean?
Answer: 'An eye for an eye, a tooth for a tooth' is an ancient legal principle that means fair retaliation.
Limitation: AI may not fully capture the various interpretations or nuances this phrase has in different cultures.
Lack of Understanding Metaphorical Expressions and Symbols
Lack of Understanding Metaphorical Expressions and Symbols
Lack of Understanding Metaphorical Expressions and Symbols
Issue: AI struggles to fully capture the deep intentions or complex nuances in literary expressions and context.
Question: What does the expression 'a stormy sea' in a poem symbolize?
Answer: In poetry, the expression 'a stormy sea' can symbolize conflict or turmoil.
Limitation: AI cannot consider the context of the poem or the author's personal experiences, and thus may not fully interpret the deep meaning or symbolism of the expression.
Issue: AI struggles to fully capture the deep intentions or complex nuances in literary expressions and context.
Question: What does the expression 'a stormy sea' in a poem symbolize?
Answer: In poetry, the expression 'a stormy sea' can symbolize conflict or turmoil.
Limitation: AI cannot consider the context of the poem or the author's personal experiences, and thus may not fully interpret the deep meaning or symbolism of the expression.
Issue: AI struggles to fully capture the deep intentions or complex nuances in literary expressions and context.
Question: What does the expression 'a stormy sea' in a poem symbolize?
Answer: In poetry, the expression 'a stormy sea' can symbolize conflict or turmoil.
Limitation: AI cannot consider the context of the poem or the author's personal experiences, and thus may not fully interpret the deep meaning or symbolism of the expression.
Data Bias and Prejudice
Data Bias and Prejudice
Data Bias and Prejudice
Issue: AI can learn biases and prejudices that exist in training data.
Question: Can there be biases in decisions made by artificial intelligence?
Answer: Yes, artificial intelligence can learn biases present in training data, which may lead to biased decisions.
Limitation: AI may not recognize biases against specific demographic groups inherent in the training data and might reflect these biases.
Issue: AI can learn biases and prejudices that exist in training data.
Question: Can there be biases in decisions made by artificial intelligence?
Answer: Yes, artificial intelligence can learn biases present in training data, which may lead to biased decisions.
Limitation: AI may not recognize biases against specific demographic groups inherent in the training data and might reflect these biases.
Issue: AI can learn biases and prejudices that exist in training data.
Question: Can there be biases in decisions made by artificial intelligence?
Answer: Yes, artificial intelligence can learn biases present in training data, which may lead to biased decisions.
Limitation: AI may not recognize biases against specific demographic groups inherent in the training data and might reflect these biases.
Misinformation or Misunderstandings in Responses
Misinformation or Misunderstandings in Responses
Misinformation or Misunderstandings in Responses
Issue: AI can generate information based on erroneous data, sometimes providing incorrect information or answers that may lead to misunderstandings.
Question: What was the average lifespan during the medieval period?
Answer: The average lifespan in the medieval period was about 30 years.
Limitation: AI might provide outdated or misleading information, which may not reflect modern research or data.
Issue: AI can generate information based on erroneous data, sometimes providing incorrect information or answers that may lead to misunderstandings.
Question: What was the average lifespan during the medieval period?
Answer: The average lifespan in the medieval period was about 30 years.
Limitation: AI might provide outdated or misleading information, which may not reflect modern research or data.
Issue: AI can generate information based on erroneous data, sometimes providing incorrect information or answers that may lead to misunderstandings.
Question: What was the average lifespan during the medieval period?
Answer: The average lifespan in the medieval period was about 30 years.
Limitation: AI might provide outdated or misleading information, which may not reflect modern research or data.
Limitations in Complex Reasoning Abilities
Limitations in Complex Reasoning Abilities
Limitations in Complex Reasoning Abilities
Issue: AI is useful for drawing simple conclusions based on given information but has limitations in complex reasoning or creative problem-solving.
Question: Why is the measurement problem important in quantum mechanics?
Answer: In quantum mechanics, the measurement problem is a significant theoretical issue that explains how quantum states change due to observation.
Limitation: AI has limitations in fully interpreting and reasoning the complex theoretical aspects or philosophical implications of quantum mechanics.
Issue: AI is useful for drawing simple conclusions based on given information but has limitations in complex reasoning or creative problem-solving.
Question: Why is the measurement problem important in quantum mechanics?
Answer: In quantum mechanics, the measurement problem is a significant theoretical issue that explains how quantum states change due to observation.
Limitation: AI has limitations in fully interpreting and reasoning the complex theoretical aspects or philosophical implications of quantum mechanics.
Issue: AI is useful for drawing simple conclusions based on given information but has limitations in complex reasoning or creative problem-solving.
Question: Why is the measurement problem important in quantum mechanics?
Answer: In quantum mechanics, the measurement problem is a significant theoretical issue that explains how quantum states change due to observation.
Limitation: AI has limitations in fully interpreting and reasoning the complex theoretical aspects or philosophical implications of quantum mechanics.
Befect Characteristic Data
Befect Characteristic Data
Befect Characteristic Data
Traditional artificial intelligence uses feature maps to automatically extract characteristics and perform various tasks based on them.
These characteristics are fundamentally extracted differently (in terms of numbers, missing data, etc.) based on the quality (irrelevant features, redundant data, noisy data), quantity, and diversity of the provided training data, and this process can also lead to hallucinations and alignment issues.
Such problems ultimately affect the decision-making of artificial intelligence.
People generally focus only on preprocessing because they seek valid results from easily accessible, large preprocessed datasets rather than trying to find the perfect characteristics of objects. Although there have been efforts to establish various systems to solve this, they have not been successful because those systems were not perfect.
Currently, there is no separate standard or system for extracting features like this, and since we do not know how to perfectly find characteristic values, we use the traditional preprocessing method. This is why all AI companies strive to secure good data.
Such preprocessed data eventually leads to similar results, and this has been a major reason for the current direction of AI development shifting towards Large Language Models (LLMs), which essentially find vector values better by increasing parameters.
Unlike in the field of images, problems such as hallucinations and inaccuracies have occurred in text. However, text has evolved in a form that humans can solve, unlike image pixels, making good data, large models, and well-structured algorithms important.
If we can find characteristic values well, there will be no need to secure much good data, and ultimately, we can solve problems such as hallucinations and inaccuracies with newly created data that has characteristic values, making it available data for a complete AI model.
Befect AI's characteristic algorithm can fully understand all characteristics of data through perfect definition, create perfect models from such characteristics, and generate or recommend suitable data for each characteristic.
If current AI companies use Befect's characteristic data to create AI models, these will be models made by analyzing data with a new system, and every time data is created, it will be completely new data with characteristics.
Through this, companies can benefit in various ways.
Traditional artificial intelligence uses feature maps to automatically extract characteristics and perform various tasks based on them.
These characteristics are fundamentally extracted differently (in terms of numbers, missing data, etc.) based on the quality (irrelevant features, redundant data, noisy data), quantity, and diversity of the provided training data, and this process can also lead to hallucinations and alignment issues.
Such problems ultimately affect the decision-making of artificial intelligence.
People generally focus only on preprocessing because they seek valid results from easily accessible, large preprocessed datasets rather than trying to find the perfect characteristics of objects. Although there have been efforts to establish various systems to solve this, they have not been successful because those systems were not perfect.
Currently, there is no separate standard or system for extracting features like this, and since we do not know how to perfectly find characteristic values, we use the traditional preprocessing method. This is why all AI companies strive to secure good data.
Such preprocessed data eventually leads to similar results, and this has been a major reason for the current direction of AI development shifting towards Large Language Models (LLMs), which essentially find vector values better by increasing parameters.
Unlike in the field of images, problems such as hallucinations and inaccuracies have occurred in text. However, text has evolved in a form that humans can solve, unlike image pixels, making good data, large models, and well-structured algorithms important.
If we can find characteristic values well, there will be no need to secure much good data, and ultimately, we can solve problems such as hallucinations and inaccuracies with newly created data that has characteristic values, making it available data for a complete AI model.
Befect AI's characteristic algorithm can fully understand all characteristics of data through perfect definition, create perfect models from such characteristics, and generate or recommend suitable data for each characteristic.
If current AI companies use Befect's characteristic data to create AI models, these will be models made by analyzing data with a new system, and every time data is created, it will be completely new data with characteristics.
Through this, companies can benefit in various ways.
Traditional artificial intelligence uses feature maps to automatically extract characteristics and perform various tasks based on them.
These characteristics are fundamentally extracted differently (in terms of numbers, missing data, etc.) based on the quality (irrelevant features, redundant data, noisy data), quantity, and diversity of the provided training data, and this process can also lead to hallucinations and alignment issues.
Such problems ultimately affect the decision-making of artificial intelligence.
People generally focus only on preprocessing because they seek valid results from easily accessible, large preprocessed datasets rather than trying to find the perfect characteristics of objects. Although there have been efforts to establish various systems to solve this, they have not been successful because those systems were not perfect.
Currently, there is no separate standard or system for extracting features like this, and since we do not know how to perfectly find characteristic values, we use the traditional preprocessing method. This is why all AI companies strive to secure good data.
Such preprocessed data eventually leads to similar results, and this has been a major reason for the current direction of AI development shifting towards Large Language Models (LLMs), which essentially find vector values better by increasing parameters.
Unlike in the field of images, problems such as hallucinations and inaccuracies have occurred in text. However, text has evolved in a form that humans can solve, unlike image pixels, making good data, large models, and well-structured algorithms important.
If we can find characteristic values well, there will be no need to secure much good data, and ultimately, we can solve problems such as hallucinations and inaccuracies with newly created data that has characteristic values, making it available data for a complete AI model.
Befect AI's characteristic algorithm can fully understand all characteristics of data through perfect definition, create perfect models from such characteristics, and generate or recommend suitable data for each characteristic.
If current AI companies use Befect's characteristic data to create AI models, these will be models made by analyzing data with a new system, and every time data is created, it will be completely new data with characteristics.
Through this, companies can benefit in various ways.
Areas representing innovative developments that transcend the current limitations of artificial intelligence and natural language processing.
Areas representing innovative developments that transcend the current limitations of artificial intelligence and natural language processing.
Areas representing innovative developments that transcend the current limitations of artificial intelligence and natural language processing.
Human-Machine Interaction
Human-Machine Interaction
Human-Machine Interaction
As AI fully understands human language and intentions, communication between humans and machines will become much more natural and effective.
As AI fully understands human language and intentions, communication between humans and machines will become much more natural and effective.
As AI fully understands human language and intentions, communication between humans and machines will become much more natural and effective.
Natural Language Understanding and Generation
Natural Language Understanding and Generation
Natural Language Understanding and Generation
With AI's ability to understand and use all nuances of language, significant leaps in Natural Language Understanding (NLU) and Natural Language Generation (NLG) will occur.
With AI's ability to understand and use all nuances of language, significant leaps in Natural Language Understanding (NLU) and Natural Language Generation (NLG) will occur.
With AI's ability to understand and use all nuances of language, significant leaps in Natural Language Understanding (NLU) and Natural Language Generation (NLG) will occur.
Language Learning and Education
Language Learning and Education
Language Learning and Education
Perfect understanding of language can bring innovation in language education and learning, providing learners with deeper and more accurate linguistic knowledge.
Perfect understanding of language can bring innovation in language education and learning, providing learners with deeper and more accurate linguistic knowledge.
Perfect understanding of language can bring innovation in language education and learning, providing learners with deeper and more accurate linguistic knowledge.
Precise Language Analysis
Precise Language Analysis
Precise Language Analysis
Perfect word understanding will enable more accurate and in-depth analysis of social trends, public opinions, and cultural expressions.
Perfect word understanding will enable more accurate and in-depth analysis of social trends, public opinions, and cultural expressions.
Perfect word understanding will enable more accurate and in-depth analysis of social trends, public opinions, and cultural expressions.
More Sophisticated AI Development
More Sophisticated AI Development
More Sophisticated AI Development
AI's perfect understanding of human language is far beyond the current level of technology, potentially revolutionizing human-machine interaction.
AI's perfect understanding of human language is far beyond the current level of technology, potentially revolutionizing human-machine interaction.
AI's perfect understanding of human language is far beyond the current level of technology, potentially revolutionizing human-machine interaction.
Advanced Language Analysis for Research
Advanced Language Analysis for Research
Advanced Language Analysis for Research
In-depth language analysis that facilitates new academic discoveries and theoretical advancements also transcends current technology levels, impacting not only linguistics but also adjacent academic fields.
In-depth language analysis that facilitates new academic discoveries and theoretical advancements also transcends current technology levels, impacting not only linguistics but also adjacent academic fields.
In-depth language analysis that facilitates new academic discoveries and theoretical advancements also transcends current technology levels, impacting not only linguistics but also adjacent academic fields.
More Accurate Language Translation
More Accurate Language Translation
More Accurate Language Translation
Translation: Perfect and accurate translation between languages signifies a significant advance over current translation technologies, enabling high-quality translations that include cultural and contextual nuances.
Translation: Perfect and accurate translation between languages signifies a significant advance over current translation technologies, enabling high-quality translations that include cultural and contextual nuances.
Translation: Perfect and accurate translation between languages signifies a significant advance over current translation technologies, enabling high-quality translations that include cultural and contextual nuances.
Legal and Ethical Document Analysis
Legal and Ethical Document Analysis
Legal and Ethical Document Analysis
The ability to perfectly understand the meaning of words in legal and policy documents signifies an important advancement in legal analysis and policy-making, surpassing current legal interpretation and policy analysis techniques.
The ability to perfectly understand the meaning of words in legal and policy documents signifies an important advancement in legal analysis and policy-making, surpassing current legal interpretation and policy analysis techniques.
The ability to perfectly understand the meaning of words in legal and policy documents signifies an important advancement in legal analysis and policy-making, surpassing current legal interpretation and policy analysis techniques.
Immediate Demand and Applicability in the Current Market.
Immediate Demand and Applicability in the Current Market.
Immediate Demand and Applicability in the Current Market.
Enhanced Intercultural Communication
Enhanced Intercultural Communication
Enhanced Intercultural Communication
Reduces misunderstandings among people with diverse cultural and linguistic backgrounds, enabling more effective communication.
Reduces misunderstandings among people with diverse cultural and linguistic backgrounds, enabling more effective communication.
Reduces misunderstandings among people with diverse cultural and linguistic backgrounds, enabling more effective communication.
Improved Education and Learning Tools
Improved Education and Learning Tools
Improved Education and Learning Tools
Provides precise word definitions and usage examples in language learning, helping learners to understand and use the language more accurately and deeply.
Provides precise word definitions and usage examples in language learning, helping learners to understand and use the language more accurately and deeply.
Provides precise word definitions and usage examples in language learning, helping learners to understand and use the language more accurately and deeply.
More Sophisticated Natural Language Processing (NLP)
More Sophisticated Natural Language Processing (NLP)
More Sophisticated Natural Language Processing (NLP)
Detects hidden meanings, emotions, and tones, allowing a more accurate and nuanced understanding and response to human language. This significantly improves the quality of automated customer service, chatbots, and translation services.
Detects hidden meanings, emotions, and tones, allowing a more accurate and nuanced understanding and response to human language. This significantly improves the quality of automated customer service, chatbots, and translation services.
Detects hidden meanings, emotions, and tones, allowing a more accurate and nuanced understanding and response to human language. This significantly improves the quality of automated customer service, chatbots, and translation services.
Enhanced Emotion Analysis
Enhanced Emotion Analysis
Enhanced Emotion Analysis
More precisely identifies hidden emotions and attitudes in product reviews, customer feedback, and social media posts, enabling businesses to more accurately understand actual customer satisfaction and requirements.
More precisely identifies hidden emotions and attitudes in product reviews, customer feedback, and social media posts, enabling businesses to more accurately understand actual customer satisfaction and requirements.
More precisely identifies hidden emotions and attitudes in product reviews, customer feedback, and social media posts, enabling businesses to more accurately understand actual customer satisfaction and requirements.
More Effective Marketing Strategies
More Effective Marketing Strategies
More Effective Marketing Strategies
Identifies hidden preferences and interests in customer language use, enabling the design of more effective and targeted marketing campaigns.
Identifies hidden preferences and interests in customer language use, enabling the design of more effective and targeted marketing campaigns.
Identifies hidden preferences and interests in customer language use, enabling the design of more effective and targeted marketing campaigns.
Social Trend and Opinion Analysis
Social Trend and Opinion Analysis
Social Trend and Opinion Analysis
Identifies hidden meanings and trends in public conversations and social media activities, allowing for more accurate analysis of social, political, and economic trends.
Identifies hidden meanings and trends in public conversations and social media activities, allowing for more accurate analysis of social, political, and economic trends.
Identifies hidden meanings and trends in public conversations and social media activities, allowing for more accurate analysis of social, political, and economic trends.
Personalized Recommendation Systems
Personalized Recommendation Systems
Personalized Recommendation Systems
Analyzes detailed patterns of user language use to provide more personalized and accurate recommendations. Can be utilized in various fields such as shopping, entertainment, and news services.
Analyzes detailed patterns of user language use to provide more personalized and accurate recommendations. Can be utilized in various fields such as shopping, entertainment, and news services.
Analyzes detailed patterns of user language use to provide more personalized and accurate recommendations. Can be utilized in various fields such as shopping, entertainment, and news services.
Literary and Artistic Analysis
Literary and Artistic Analysis
Literary and Artistic Analysis
Analyzes complex meanings and emotions expressed in literary works, poetry, and artworks, promoting new interpretations and understandings of these works.
Analyzes complex meanings and emotions expressed in literary works, poetry, and artworks, promoting new interpretations and understandings of these works.
Analyzes complex meanings and emotions expressed in literary works, poetry, and artworks, promoting new interpretations and understandings of these works.
Data and Meaning
Data and Meaning
Data and Meaning
Only by defining data can we understand the actual meaning it holds.
Without a perfect definition of data, the data defined by humans (positively or negatively) will not solve the enormous issues deeply rooted in AI, such as mistakes due to the dissonance between human cognition and behavior, or the currently addressed simple illusions or alignment problems due to imperfect definitions that don’t consider all characteristics.
This issue stems from the inability to perfectly solve the current definition problems (like the Trolley Dilemma) because it does not define every consideration (such as the characteristics of an entity, the value possessed by the entity).
If the option of human intervention does not exist in the conclusions made by artificial intelligence, we are not aware of any issues to alter such conclusions.
We cannot clearly understand the data regarding the judgment (conclusion) of AI that makes such conclusions, and we cannot understand the newly generated data created by human-defined data, as it is the result of considering combinations of numerous data.
Only by defining through algorithms that can be perfectly defined, can we understand why AI made such choices, and through a clear understanding of how such decisions are fundamentally made, we can correct the wrong judgments of AI and thus change our future from potential disasters.
Befect AI possesses perfect language interpretation and complete language comprehension abilities, understanding not only the superficial meaning of words but also all their contextual, cultural, and historical meanings, including those not apparent on the surface.
If all companies worldwide refine their data through Befect AI, they can create data that aligns with their original purpose and intent, and from this created data, construct datasets to create the perfect model that suits their objectives.
Companies will desire the characteristic datasets created through our AI model.
Only by defining data can we understand the actual meaning it holds.
Without a perfect definition of data, the data defined by humans (positively or negatively) will not solve the enormous issues deeply rooted in AI, such as mistakes due to the dissonance between human cognition and behavior, or the currently addressed simple illusions or alignment problems due to imperfect definitions that don’t consider all characteristics.
This issue stems from the inability to perfectly solve the current definition problems (like the Trolley Dilemma) because it does not define every consideration (such as the characteristics of an entity, the value possessed by the entity).
If the option of human intervention does not exist in the conclusions made by artificial intelligence, we are not aware of any issues to alter such conclusions.
We cannot clearly understand the data regarding the judgment (conclusion) of AI that makes such conclusions, and we cannot understand the newly generated data created by human-defined data, as it is the result of considering combinations of numerous data.
Only by defining through algorithms that can be perfectly defined, can we understand why AI made such choices, and through a clear understanding of how such decisions are fundamentally made, we can correct the wrong judgments of AI and thus change our future from potential disasters.
Befect AI possesses perfect language interpretation and complete language comprehension abilities, understanding not only the superficial meaning of words but also all their contextual, cultural, and historical meanings, including those not apparent on the surface.
If all companies worldwide refine their data through Befect AI, they can create data that aligns with their original purpose and intent, and from this created data, construct datasets to create the perfect model that suits their objectives.
Companies will desire the characteristic datasets created through our AI model.
The Limits of Artificial Intelligence and the Innovation of Befect : Data Definition and the Future of AI Models
The Limits of Artificial Intelligence and the Innovation of Befect : Data Definition and the Future of AI Models
The Limits of Artificial Intelligence and the Innovation of Befect : Data Definition and the Future of AI Models
A new data analysis and algorithm that surpasses the limitations of existing AI.
A new data analysis and algorithm that surpasses the limitations of existing AI.
A new data analysis and algorithm that surpasses the limitations of existing AI.
Imperfect Data Used by All AI Companies (Biases, Lack of Accuracy, Ambiguity, Lack of Definition, etc.) Affects Data Analysis and Modeling, Leading to Issues in the Reliability and Validity of Generated Data
Moreover, Algorithms Trained on Existing Data Were Unable to Measure Data Where Original Information Was Altered
However, Befect Redefines Data According to the Current Data Information (Form and Structure of Existing Data) Based on the Characteristics of 'Data Generated from Existing Data/Altered Original Information' Through Feature Algorithms, Resulting in the Creation of New Characteristic Data
Updating This Data in the Algorithm (Can Be Applied Directly to Existing Algorithms and Systems) Solves All Problems and Enhances the Algorithm (Quality Becomes Perfect and Improvements Are Made)
Thus, the Feature Algorithm Resolves All These Problems Currently Faced by All Artificial Intelligence Companies
-In the Event That a Company's Algorithm and Data Can Be Changed
Applying the Befect Dataset to the Existing Inadequate 'Training Data/Dataset' Improves the Algorithm, Reaching a Level of Artificial Intelligence Algorithm Previously Unattainable
Imperfect Data Used by All AI Companies (Biases, Lack of Accuracy, Ambiguity, Lack of Definition, etc.) Affects Data Analysis and Modeling, Leading to Issues in the Reliability and Validity of Generated Data
Moreover, Algorithms Trained on Existing Data Were Unable to Measure Data Where Original Information Was Altered
However, Befect Redefines Data According to the Current Data Information (Form and Structure of Existing Data) Based on the Characteristics of 'Data Generated from Existing Data/Altered Original Information' Through Feature Algorithms, Resulting in the Creation of New Characteristic Data
Updating This Data in the Algorithm (Can Be Applied Directly to Existing Algorithms and Systems) Solves All Problems and Enhances the Algorithm (Quality Becomes Perfect and Improvements Are Made)
Thus, the Feature Algorithm Resolves All These Problems Currently Faced by All Artificial Intelligence Companies
-In the Event That a Company's Algorithm and Data Can Be Changed
Applying the Befect Dataset to the Existing Inadequate 'Training Data/Dataset' Improves the Algorithm, Reaching a Level of Artificial Intelligence Algorithm Previously Unattainable
Befect Service Overcomes Current AI Technology Limitations, Providing More Accurate and Personalized Custom Information and Services Based on a Deep Understanding of Customer Data
Through Our Service, Customer Satisfaction Can Be Increased, and More Accurate Targeting Enables Significantly Improved Business Performance
-Currently, this service is under development, and its details are restricted from public disclosure due to security reasons.-
Befect Service Overcomes Current AI Technology Limitations, Providing More Accurate and Personalized Custom Information and Services Based on a Deep Understanding of Customer Data
Through Our Service, Customer Satisfaction Can Be Increased, and More Accurate Targeting Enables Significantly Improved Business Performance
-Currently, this service is under development, and its details are restricted from public disclosure due to security reasons.-
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© 2019-2024 Befect. All rights reserved.
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© 2019-2024 Befect. All rights reserved.