The document discusses the 5 steps of an AI/ML innovation project: 1. Empathize and analyze to understand user needs and objectives. 2. Define and synthesize insights about key decisions and potential variables and metrics. 3. Ideate potential solutions by choosing a suitable machine learning model. 4. Prototype and tune by creating mockups to study user interactions and identify insights. 5. Test and validate prototypes using user testing and evaluating the machine learning model on test data.

1. A Blokklánc használatának lehetőségei mindennapokban
AI/ML INNOVATION PROJECT
MANAGEMENT
Dr. Trinh Anh Tuan, Head of Corvinus Fintech Center

5. “Despite its name, there is nothing “artificial”
about this technology — it is made by humans,
intended to behave like humans, and affects
humans. So if we want it to play a positive role in
tomorrow’s world, it must be guided by human
concerns.”
Li Fei Fei,
Co-Director of Stanford University’s
Human-Centered AI Institute and
the Stanford Vision and Learning Lab

10. Step 1: Empathize and Analyze
• Design Thinking
Understand and capture the user’s task objectives, operational
requirements and impediments to success; learn as much as possible
about the users for whom you are designing.
• Machine Learning
Capture and prioritize the user’s key decisions; capture the variables
and metrics that might be better predictors of those decisions.

11. Step 2: Define and Synthesize
• Design Thinking – define, document and validate your understanding of
the user’s task objectives, operational requirements and potential
impediments.
• Machine Learning – synthesize your understanding of the decisions (e.g.,
latency, granularity, frequency, governance, sequencing) in order to flesh
out the potential variables and metrics, and assess potential analytic
algorithms and approaches.
• Find or synthesize a dataset based on the problem that is being solved.
• Load data in a suitable place.
• Prepare the data — randomize, visualize to see imbalances or relationships,
preprocess, divide and augment the data to be sent for training.
• Split data into training, evaluation and test.

12. Step 3: Ideate
• Design Thinking: Use the information from the previous stages to
generate ideas and brainstorm as many potential solutions as
possible.
• Machine Learning: In this step choose a model for your specific
problem. Many models have been created for images, sequences
such as text or music, numerical data or text based data. If not you
can even define your own model architecture by adding layers one at
a time until you are happy with your network.

13. Step 4: Prototype and Tune
• Design Thinking – create one or more interactive mockups with which
your key constituents can “play”. Study users’ interactions with the
mockups to see what works and where they struggle. Identify what
additional design guides and/or analytics insights could be provided
to improve the user experience.
• Machine Learning – Identify where analytic insights or
recommendations are needed – and what additional data can be
captured – as the users “play” with the mockups. Explore
opportunities to delivery real-time actionable insights to help “guide”
the user experience. Fail fast, but learn faster! Embrace the “Art of
Failure.”

14. Step 5: Test and Validate
• Design Thinking: We test our prototypes using user testing
techniques to see how well they solve the problem that we initially
analyzed in the previous stages.
• Machine Learning: We test the model using the test dataset which
provides the gold standard used to evaluate the model. Based on the
results of testing and validating we iterate the process of
hyperparameter tuning to improve the accuracy of the model.