The term Artificial General Intelligence (often abbreviated “AGI”) has no precise definition, but one of the most widely accepted ones is the capacity of an engineered system to display intelligence that is not tied to a highly specific set of tasks or generalize what it has learned, including generalization to contexts qualitatively very different from those it has seen before and take a broad view, interpret its tasks at hand in the context of the world at large and its relation thereto.
In essence, Artificial General Intelligence can be summarized as the ability of an intelligent agent to learn not just to do a highly specialized task but to use the skills it has learned to extract insight from data originating in multiple contexts or domains.
How does DataNeuron achieve Artificial General Intelligence?
The DataNeuron platform displays Artificial General Intelligence as it has the ability to perform well on:
- NLP tasks belonging to multiple domains.
- Text data originating from multiple contexts.
Masterlist: Machine Learning is not binary so we don’t rely on rules or predefined functions, we rely on the simpler structure which is the Masterlist where we allow classes to have overlap. Further, we support taxonomy or hierarchical ontologies on the Masterlist. The platform uses intelligent algorithms to assign paragraphs for each class making the data annotation process automated.
Advanced Masterlist: We are also launching Advanced Masterlist to support subjective labeling of datasets (where clear class distribution is missing).
Apart from the ability to perform auto-annotation on data, the platform also provides complete automation for model training including automatic data processing, feature engineering, model selection, hyperparameter optimization, and cross-validation of results.
The DataNeuron Platform automatically deploys the algorithm and provides APIs which can be integrated to build any application with real-time no-code prediction capabilities. It also provides a continuous feedback and retraining framework for updating the model for achieving the best performance. All these features make it one step closer to achieving Explainable AI.
The DataNeuron platform has produced exceptional results in extremely specialized domains like Document or Text classification in the Tax & Legal, Financial, and Life Sciences use cases, as well as general tasks like Document or Text Clustering in any given context. DataNeuron reduces the time and effort by ~95% required to label and create models, allowing users to extract up to ~99.98% insights. DataNeuron is an Advanced platform for complex data annotations, model training, prediction & lifecycle management. We have achieved a major breakthrough by fully automating data labeling with comparable accuracy to state-of-the-art solutions with just 2% of labeled data when compared to human-in-loop labeling on unseen data.
The impact created by DataNeuron’s General Intelligence
We observe that the DataNeuron platform can decrease the annotation time by up to ~98%. This vastly decreases the time and effort spent annotating huge amounts of data and allows teams to focus more on the task at hand by automating the process of data annotation and easing research.
Additionally, it can also help reduce the SME effort up to 96%, while incurring a fraction of the cost. Our platform also significantly reduces the overall cost of the project, by nearly eradicating the need for data labeling/annotation teams. In some cases, the need for an SME is also diminished as the process of annotation is much simpler and anyone with knowledge of the domain can be able to do it properly unless the project is too complex.
Testing On Various Datasets
How DataNeuron performs for various use cases
The difference in Paragraphs is Annotated. We observe it is possible to reduce annotation effort by up to 96%.
The difference in Time Required. We observe it is possible to reduce time required for annotation by up to 98%.
The above visualizations showcase the platform’s ability to perform extraordinarily in different domains. As opposed to the specialized systems that tend to perform well on only one type of task or domain, the DataNeuron platform breaks boundaries by performing exceptionally for a diversified set of domains.
What does it mean for the Future of AI?
As AI adoption has picked up among enterprises, the need for labelled and structured data has dramatically increased in order to remove the bottleneck in developing the AI solutions.
DataNeuron, powered by a data-centric platform provides a complete end-to-end platform from training to Ensemble Model APIs for faster deployment of AI.
Our research continues to be focused on the area of Artificial General Intelligence and further automation of Data Labeling / Validation and provide better explainability of AI.
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