Brainalyzed fills the gap for Enterprise AI in the financial and industry sector. It also allows managers without specific prior knowledge of machine learning to easily collate data from available sources and to create and roll out high-performing AIs for their key use cases. Thanks to the greatly simplified possibilities of data preparation and training, the time from the first idea to the finished AI can be significantly reduced.
Customers using Brainalyzed Insight benefit from the following unique selling points:
• Optimization of inputs, architecture and weightings (holistic hyper parameter optimization)
• Domain specific AI optimization
• Enhanced prediction stability due to AI Swarm
• Best performance over time due to flexible AI Swarm adjustment
• Empowers domain experts without ML knowledge
OPTIMIZATION OF INPUTS AND NETWORK ARCHITECTURE
Traditional learning approaches require that model inputs for the training are fixed in advance. This method requires a hypothesis that a given data parameter has added value for the prediction. However, for complex systems, human intuition is a poor guide, resulting in reduced AI performance due to missing inputs or increased numerical noise from unnecessary data points. Brainalyzed Insight ensures that only the relevant inputs are used in the resulting AI swarm. Furthermore, within the training process, the network architecture is adapted to best fit the prediction problem as well as the data available. This effectively prevents the overfitting of the AI models in cases where only a small data set exists.
ARTIFICIAL SWARM INTELLIGENCE
The actual prediction is based on a whole swarm of AIs that have proven to be optimal in terms of the selected performance parameters during optimization. A performance parameter is a metric that the user can define to distinguish a good model from a bad model. One has the option to define a variety of metrics to describe the prediction problem. For example, the minimization of false positive and false negative rates can be chosen as independent performance parameters for classification. Each member of the AI Swarm is therefore unique in its combination of architecture, input and, of course, neuron weights. This improves prediction stability and diversification.
AI SWARM ADJUSTMENT IN REAL TIME
The operational AI Swarm initially consists of all AI models that are chosen by the user or best represent the selected performance parameters. However, the real-time performance is not only monitored for these initial models, but also for the near-optimal models of the AI Academy. If the prediction performance of the AI Swarm deteriorates, poorly performing models of the initial swarm will be exchanged with those of the AI Academy.
PRE-BUILT USE CASES FOR THE (FINANCIAL) INDUSTRY
Brainalyzed Insight includes pre-built use cases for the (finance) industry, developed in collaboration with industry experts. In this way, we ensure that customers can immediately use the best practice for standard applications and start model training. The final AI Swarms enable the customer to reduce the time for manual data analysis, take decisions based on data, or run processes completely automated.
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