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Top 6 Ways Developers Can Validate Artificial Intelligence Systems


Top 6 Ways Developers Can Validate Artificial Intelligence Systems


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In 2017, Facebook Artificial Intelligence Research (FAIR) pulled the plug from an AI project when a pair of chatbots started communicating in an unknown language. Researchers were baffled at the machine's ability to invent a language and immediately stalled the project fearing the uncertainties revolving the outcome of their development.

Such incidents, though a few in number, cannot be taken lightly as we move closer to a more machine-dependent world. The question that authorities and even governments institutions need to ask is what is the extent of trust they can place on the technology.



Since the role of AI and ML in our day to- day life is completely unavoidable, what we need is a mechanism to optimise both human and AI outcomes for stronger results. So, here are some of the best practices as suggested by experts in the field to authenticate the machine's actions.

Statistical Method: In a recent study, published in Molecular Informatics, the researchers used the statistical equation to validate AI programme's ability and even find the answer to the question "What is the probability of achieving accuracy greater than 90%?" through an AI system.

"AI can assist us in understanding many phenomena in the world, but for it to properly provide us direction, we must know how to ask the right questions. We must be careful not to overly focus on a single number as a measure of an AI's reliability," he said describing the conclusion of his study.

Holdout Method: It is considered to be the easiest model for evaluation technique. For this, a given label data set is taken and divided into test and training sets. "Then, we fit a model to the training data and predict the labels of the test set. And the fraction of correct predictions constitutes our estimate of the prediction accuracy — we withhold the known test labels during prediction, of course. We really don’t want to train and evaluate our model on the same training dataset (this is called resubstitution evaluation), since it would introduce a very optimistic bias due to overfitting," says the researchers in their paper titled, Model Evaluation, Model Selection, And Algorithm Selection In Machine Learning.

Running AI Model Simulations: One of the best ways to ensure that your system is up and running is by regularly holding an AI testing.

See Also

Cross-Validation: AWS describes cross-validation as a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. It is used to detect overfitting or fluctuations in the training data which is picked up and learned as concepts by the model.

Including Overriding Mechanism: Overriding is an object-oriented programming feature that enables a child class to provide a different implementation for a method that is already defined and/or implemented in its parent class or one of its parent classes. The overridden method in the child class should have the same name, signature, and parameters as the one in its parent class.

Teach And Test Methodology: Recently, Accenture was one of the main players that introduced testing services for artificial intelligence. The Tech and Test methodology for enterprises ensure that AI systems are producing the right decisions. “The adoption of AI is to accelerate businesses see it’s transformational value to power new innovations and growth. As organisations embrace AI, it is critical to find better ways to train and sustain these systems – securely and with quality – to avoid adverse effects on business performance, brand reputation, compliance and humans,” said Bhaskar Ghosh, group chief executive, Accenture Technology Services highlighting the importance of AI validation.



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