The New Corporate Strategy with Artificial Intelligence
Camille Beyrouthy

2021-09-30 23:15:47+00:00

Realizing AI full potential for your institution, requires an innovative integration effort with your company-wide system. The approach for deployment is very similar for any sector you're in and results are short of astonishing if well deployed. This transformation can occur in four different domains:
  1. Depth and Insight : Generate new and deeper insights, faster, and in ways that goes beyond human cognition
  2. Performance : Learn from data and use experiences to improve old outcomes and create new ones over time
  3. Automation: Leverage your current operations autonomous capabilities to transform them through automation
  4. Personal Experience: Enhance the human experiences using systems that predict, sense, learn and move
Our Consulting team is ready to support you to fully realize all benefits from AI integration. We will, first demystify and help your team understand the risks and rewards inherent in AI systems, define the capabilities and requirements needed for your organization to roll and deploy scalable AI systems.

Data Cleanliness and training
The main requirement for successful deployment of an AI system is the training data. In instances of a supervised learning algorithms, the procedures go as follows: The function at its very basis level maps the input to an output given an input-output relationship. It uses labeled training data that forms the overall dataset. ( for example the data could consist of images of objects where every image has a label corresponding to the object, e.g. table, car, building etc.). So it's the training phase out of which the smart algorithm tunes its weights that is most crucial for a success deployment.

Training Bias and
As a concrete example of the importance of the training dataset, was the experiments involving data for Breast Cancer patients: Oncologists pointed to the tumor with an arrow on the image itself. The presence of the arrow became the first determinant of a positive case in rolling the algorithm in the prediction phase. It became essential to run various, meticulously designed checking algorithms that can remove any bias or discrepancies in the training datasets. As shown in this Figure below, this localized tumor showing in the scan, has been pointed out at by the treating physician. in collecting data for the training stage, the algorithm can learn from the presence of the arrow to indicate a tumor.

Temperature and algorithmic parameters

 section 5 input