Multi-SigmaBlog
We publish articles with information about Multi-Sigma updates and tips on how to utilize it effectively.
Bringing the Power of AI to R&D
Start with minimal data and cost
No programming required AI analysis platform
Trusted by the World’s Leading Companies
About Multi-Sigma
Multi-Sigma is a no-code AI analysis platform that allows you to perform predictions using deep learning and Bayesian analysis, and optimize multiple objective variables through genetic algorithms without the need for programming, directly from your browser. This platform supports the efficiency of R&D by optimizing various inputs such as material quantity, temperature, and time, and addressing multiple objectives such as quality, cost, and environmental impact using innovative experimental design methods.
Achievements
Cumulative Number of Users
More than 100
More than 100
Awards
4th TCI Venture Award
Excellence Award
35th Small and Medium Enterprise Excellent New Technology and New Product Award
Excellence Award
3rd Ibaraki Innovation Award
Excellence Award
Ecotech Grand Prix 2021
Corporate Award
Multi-Sigma
4Key Points
1
High Accuracy Prediction with Minimal Data
By finely tuning deep learning, it is possible to achieve high-accuracy predictions without overfitting, even with minimal data.
2
Explainable AI
Through factor analysis, it is possible to quantitatively analyze the magnitude and direction of contributions of explanatory variables, making AI, which is often considered a black box, explainable.
3
Multi-objective Optimization
The AI automatically explores conditions that satisfy multiple objectives simultaneously. From the Pareto solution set, it extracts conditions that offer the optimal balance for the customer.
4
Reducing Costs for Safe Implementation and Maintenance Management
Built on the Google Cloud Platform, this data-secure SaaS application eliminates the need for customers to prepare hardware and software. You can perform AI analysis using your usual PC, laptop, or tablet from a web browser.
Solution
Process Improvement
- Optimization of manufacturing conditions
- Reduction of environmental load
Material Development and Drug Discovery
- Optimization of material properties and manufacturing conditions
Optimization of Medical Treatments
- Risk assessment for severe cases
- Proposals for optimal treatment policies tailored to patient conditions
Marketing
- Exploration of optimal product specifications for customer segments
- Exploration of the most desirable customer attributes
Introduction of Functions
Multi-input Multi-objective Analysis
General automated machine learning (AutoML) outputs only take one prediction function, but our application can predict up to 100 objective variables from up to 200 explanatory variables, and it can reverse-analyze the 200 explanatory variables to satisfy the conditions of 100 objective variables simultaneously. This is the world's only application that can do this as of March 2024.
Utilization of Deep Learning Techniques
You can use both data analysis through neural networks and Bayesian optimization, which are the two major techniques in deep learning. It allows for analysis combining a neural network that demonstrates power with relatively well-organized data (about 20 or more) and Bayesian optimization that shows strength in sequential optimization from a minimal amount of data (around 3 to 5).
Predictive AI × Optimization AI
It is possible to simultaneously execute prediction using deep learning and optimization using genetic algorithms in one application. The optimization AI can automatically explore multi-objective optimal solutions while using predictive AI.
Precise Optimization
Utilizing genetic algorithms, it is possible to perform multi-objective optimization. You can set the maximization, minimization, and target value conditions of objective variables as well as the constraint conditions of explanatory variables.
Factor Analysis
Using techniques such as predictive model generation and sensitivity analysis, it is possible to quantitatively evaluate the extent to which each explanatory variable contributes positively or negatively.
Auto-tuning of Hyperparameters
You can automatically tune hyperparameters (parameters that set the behavior of machine learning algorithms), which are considered essential skills for AI engineers. This allows for high-accuracy prediction while controlling overfitting with a single button press.
AI Experimental Design Method
By transitioning from traditional statistical-based experimental design methods to AI-based experimental design methods, you can solve complex interaction problems, improve predictive accuracy, and achieve multi-objective prediction and optimization. This simplifies experimental design and eliminates unnecessary efforts.
Integration of Various Data
You can handle diverse data, such as categorical variables and image data, using image analysis tools and parameter diversity handling tools. This allows for spectrum decomposition and the breakdown of explanatory variables to solve complex problems.
Advanced Data Preprocessing Tools
Along with various preprocessing tools to enhance predictive accuracy, it includes tools to enable high-accuracy prediction even for rare or biased data.