Level
Fundamental Data Algorithms for Self-Defining AI
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.
Technology Accessibility Standards
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).
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.
Language Model's Core Issues, Examples, and Limitations
Accurate Fact-Finding and Limitations of Outdated Information
AI language models have limitations in accurately understanding and processing complex and detailed factual information.
Lack of Understanding Contextual Situations and Intentions
AI can understand the direct meaning of words but has limitations in fully understanding and interpreting the speaker's intentions, emotions, and context.
Lack of Understanding Cultural Nuances and Figurative Expressions
AI finds it challenging to fully grasp how expressions are used and interpreted in various cultural backgrounds and contexts.
Lack of Understanding Metaphorical Expressions and Symbols
AI struggles to fully capture the deep intentions or complex nuances in literary expressions and context.
Data Bias and Prejudice
AI can learn biases and prejudices that exist in training data.
Misinformation or Misunderstandings in Responses
AI can generate information based on erroneous data, sometimes providing incorrect information or answers that may lead to misunderstandings.
Limitations in Complex Reasoning Abilities
AI is useful for drawing simple conclusions based on given information but has limitations in complex reasoning or creative problem-solving.
Befect Characteristic Algorithm
and Characteristic Data
Befect Characteristic Data
Traditional artificial intelligence uses feature maps to automatically extract characteristics and perform various tasks based on them.
These features, biases, and other characteristics are all fundamentally extracted differently (in terms of numerical values, 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.
Data and Meaning
Decisions
AI decisions
DEFINITION
Understanding
of every choice
Beyond Data Issues and Solutions
Only by defining data can we truly understand the actual meaning it holds.
Without a perfect definition of data, the data defined by humans (whether positive or negative) cannot resolve the profound issues deeply rooted in artificial intelligence, such as mistakes due to the dissonance between human cognition and behavior, or imperfect definitions that fail to consider all characteristics. These issues are more significant than the simple illusions or sorting problems that are currently being addressed.
These problems stem from the inability to perfectly solve the current definition issues, such as the Trolley Dilemma. The reason for this is the failure to define all aspects and situations under consideration (such as the characteristics of entities, the values they hold, etc.).
When an AI's conclusion does not involve the option of human input, we are not aware of any problem in changing such a conclusion.
We cannot clearly understand the data behind the AI's decision-making process, as it is new data created by human-defined data. The data generated through the combination of numerous datasets is beyond our complete understanding.
Only by defining through an algorithm capable of perfect definition can we understand why AI makes certain choices. A clear understanding of how such decisions are fundamentally made allows us to correct AI's erroneous judgments and potentially change our future to prevent impending disasters.
Befect AI is designed with the goal of high-level language interpretation and deep language understanding, and is implemented to grasp diverse contextual, cultural, and historical meanings, as well as the deeper meanings behind them.
If all companies worldwide refine their data through Befect AI, they can generate data aligned with their original purpose and intent. From this generated data, they can create datasets and develop unique models that suit their specific objectives.
All companies will desire the characteristic datasets generated through our AI model.
The Limits of Artificial Intelligence and the Innovation of Befect
Data Definition and the Future of AI Models
Next-generation data analysis and algorithms that go beyond traditional AI limitations
Drag to explore the data transformation
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 Features of 'Data Generated from Existing Data/Altered Original Information' Through Characteristic Algorithms, Resulting in the Creation of New Characteristic Data
Applying this data to the algorithm can improve key issues and lead to anticipated enhancements in algorithm performance.
-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