If your organization continues to rely on inefficient manual processes, can't seem to improve accuracy (too many false positives?) or have yet to see ROI out of your Machine Learning (ML) investments, this webinar is for you. Join us for a discussion on UI design patterns and ML operations required to facilitate users and algorithms learning together. We'll walk through several use cases that demonstrate potential for increasing accuracy and amplifying AI investments.
Machine Learning (ML) and AI are no magic bullet. The real magic comes from humans and algorithms learning from each other to make gains. This collaborative process of incorporating a user’s intuition and interactions in a way that improves both the algorithm’s and the user’s understanding is called “co-learning.” Done well, co-learning solutions can increase accuracy, decrease costs and lower user burden
Key Learning Topics:
- Basic tenants of co-learning systems and how collaboration between humans and algorithms can increase accuracy, efficiency, and user burden
- UI patterns that describe how to best solicit model feedback from users within different contexts such as:
- Recommendations
- Predictions
- Anomaly Detection
- Image Recognition
- Finance: Banking Fraud & Investment Optimization
- How increasing the accuracy of fraud detection systems through the use of user feedback saves money and decreases user remedial task burden
- How increasing the precision of recommendations in alternative instrument trading through the use of user customization reduces irrelevant strategies and increases speed of trades
- Healthcare
- How ingesting and dissecting user feedback on individual claims adjustments increases the speed of claims adjusters' workflows and increases consistency between claims.
- How users pinpointing causes of opioid fraud helps decrease the number of irrelevant investigative cases, increasing the throughput of fraudulent case detection
- Cloud Ops
- How accuracy of anomalous transaction detection increases through the ingestion of user feedback, decreasing user burden of irrelevant recommendations
- Customer 360
- How analyzing user interactions with a customer 360 application increases the overall likelihood of customers converting and increases cross-selling.
If your organization continues to rely on inefficient manual processes, can't seem to improve accuracy (too many false positives?) or have yet to see ROI out of your Machine Learning (ML) investments, this webinar is for you. Join us for a discussion on UI design patterns and ML operations required to facilitate users and algorithms learning together. We'll walk through several use cases that demonstrate potential for increasing accuracy and amplifying AI investments.
Machine Learning (ML) and AI are no magic bullet. The real magic comes from humans and algorithms learning from each other to make gains. This collaborative process of incorporating a user’s intuition and interactions in a way that improves both the algorithm’s and the user’s understanding is called “co-learning.” Done well, co-learning solutions can increase accuracy, decrease costs and lower user burden
Key Learning Topics:
- Basic tenants of co-learning systems and how collaboration between humans and algorithms can increase accuracy, efficiency, and user burden
- UI patterns that describe how to best solicit model feedback from users within different contexts such as:
- Recommendations
- Predictions
- Anomaly Detection
- Image Recognition
- Finance: Banking Fraud & Investment Optimization
- How increasing the accuracy of fraud detection systems through the use of user feedback saves money and decreases user remedial task burden
- How increasing the precision of recommendations in alternative instrument trading through the use of user customization reduces irrelevant strategies and increases speed of trades
- Healthcare
- How ingesting and dissecting user feedback on individual claims adjustments increases the speed of claims adjusters' workflows and increases consistency between claims.
- How users pinpointing causes of opioid fraud helps decrease the number of irrelevant investigative cases, increasing the throughput of fraudulent case detection
- Cloud Ops
- How accuracy of anomalous transaction detection increases through the ingestion of user feedback, decreasing user burden of irrelevant recommendations
- Customer 360
- How analyzing user interactions with a customer 360 application increases the overall likelihood of customers converting and increases cross-selling.
