Synthetic Data
Synthetic data generation
Synthetic data generation in deep learning, particularly with Cognitive Ki Large Language Models (LLMs), uses AI to create data that captures the statistical features and patterns of real-world information. This approach helps address issues such as data scarcity, privacy concerns, and bias in training machine learning models.
Cognitive Ki uses deep learning algorithms, including our Large Language Models (Mara Ki and Galen Ki), to generate synthetic data that matches the statistical characteristics of real data. This approach is essential for addressing issues such as data scarcity, privacy risks, and bias in training sets. In Cognitive Ki, synthetic data can encompass text, code, or structured records generated by AI to replicate real data. Consequently, Cognitive Ki synthetic data will play an increasingly important role in model training, surpassing traditional data collection methods across various expert domains.
Synthetic data is artificially generated information not derived from real events. It is primarily used in operational datasets and is created by algorithms. This type of data is essential for validating mathematical models and training deep learning systems. Its main benefit is that it alleviates restrictions associated with regulated or sensitive data. Additionally, synthetic data enables tailored data creation to meet specific requirements, which might otherwise be difficult with real data
Why is synthetic data
Synthetic data benefits businesses by addressing privacy concerns, speeding up product testing, and aiding machine learning training. Privacy laws restrict managing sensitive info, and data leaks can lead to lawsuits and damage reputation. Reducing these risks motivates synthetic data use. For new products, data is scarce, and annotations are costly. Synthetic data overcomes this, enabling quick data generation for reliable machine learning models.
Synthetic data generation
Synthetic data generation involves automatically creating new data through simulations and algorithms, substituting for real-world data. It can be derived from existing datasets or generated when real data is unavailable. This data closely resembles the original, often being nearly identical, and can be produced in any amount, at any time. Although artificial, synthetic data simulates real-world information using mathematical or statistical techniques, making it comparable to data gathered from actual objects, events, or people for AI training.
Predictive text generation
Predictive text generation in Mara Ki and Galen Ki, a Cohnative Ki Large Language Model, is an iterative process that produces responses one token at a time—either a fragment or an entire word. Tokens, represented by numerical IDs from prompts, are ranked by likelihood among about 100,000 tokens. Instead of always choosing the most probable token, the model uses strategies like Top-P sampling or temperature adjustments for more natural, varied responses. Each token is added to the sequence, which is then used to predict subsequent tokens until an end marker or max length is reached.
Real data vs synthetic data
Real data is obtained directly from real-world sources through measurement or collection. It is continuously generated as people use devices like smartphones, laptops, or computers, wear smartwatches, browse websites, or make online purchases. Surveys, both online and offline, can also provide this type of data. In contrast, synthetic data is artificially created in digital environments, designed to mimic the core properties of real data while omitting aspects not based on actual events. Various techniques are available for generating synthetic data, which simplifies acquiring training data for machine learning models. This makes synthetic data a promising alternative to real data, although it does not guarantee solutions to all real-world problems.
Cognitive Ki
Machine learning, a subset of artificial intelligence (AI), uses algorithms and statistical models to help computers learn from data and improve on specific tasks. Its goal is to develop models that recognize patterns and make predictions or decisions without explicit programming.
Cognitive Ki
​Natural Language Processing is a field of artificial intelligence that allows computers to understand, interpret, and generate human language. It is utilized in many technologies, including chatbots and real-time translation systems.
Cognitive Ki
Deep learning is a sophisticated subset of machine learning that utilizes multi-layered neural networks to identify patterns in data.
Characteristics of synthetic data
Data scientists focus on data quality, valuing real data while recognizing its costs and errors. Synthetic data offers a reliable, diverse alternative, aiding scalability and customization while ensuring it doesn't link to real data or contain biases. It's vital for limited or sensitive data, boosting machine learning, especially in banking, healthcare, and pharma. Types include fully or partially synthetic data, applicable to formats such as text or tabular data.
Methods for generating synthetic data
Statistical distribution-based methods generate data by sampling from known distributions such as normal or exponential, depending on the data scientist's understanding of the data’s statistical features. Model-based approaches develop models that replicate observed behaviors to produce new data, frequently employing machine learning, though overfitting—particularly with decision trees—can happen when predicting future data. Hybrid techniques combine statistical and model-based strategies, using partial actual data to guide the creation of synthetic data. Deep learning methods like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) create highly realistic synthetic data: VAEs encode and compress data to maintain quality, while GANs involve two neural networks competing—one generating data and the other discriminating its authenticity—offering feedback to enhance the generator. Data augmentation enlarges existing datasets but is not true synthetic data; it adds new data points, often for purposes like anonymization.