#130 Productizing Decisional AI
Subscribe to get the latest
on 2023-03-23 00:00:00 +0000
with Darren W Pulsipher, Matthew Pulsipher,
In this episode Darren interviews his son Matthew Pulsipher about productizing decisional AI. Matthew has recently modernized and product development pipeline to include decisional AI in his product development.
Keywords
#decisionalai #generativeai #machinelearning #datamanagement #people
In this episode, Darren Pulsipher, Chief Solution Architect of Public Sector at Intel, interviews his son Matthew Pulsipher, a Product Manager, about the productization of Decisional AI. Matthew explains that generative AI is built on general datasets and is good for general knowledge questions but lacks predictability and determinism, making it difficult to automate processes. Decisional AI, on the other hand, is simpler in scope but more focused in context, allowing it to make data-driven decisions based on specific company needs. Matthew shares his experience integrating Decisional AI into products and highlights the importance of context in AI.
Type of AI
There are different types of AI, and each has a unique ability to help organizations automate processes, make business decisions and augment human work. Decisional AI is primarily used for making decisions and is based on models generated from previous data. Predictive AI, on the other hand, generates predicted values based on custom models and data sets. Training models is critical to the deployment and implementation of AI solutions. The key is to identify a real problem that is operationally relevant and achievable within a reasonable timeframe.
AI to streamline processes
Artificial intelligence (AI) can be used to streamline decision-making processes in businesses. It is important to scope the AI’s capabilities to a specific set of options, so as not to overwhelm the system and make the decision-making process more efficient. AI is best suited for processes that are repeated, involve data-driven decisions, and require subjective human reviews. For example, a financial institution can use AI to validate driver’s licenses using extracted data. Continuous training through user feedback to the AI can improve its decision-making abilities and eventually replace the need for human review altogether. Ancestry.com’s indexing project as an example of how reinforced learning can reduce the need for human involvement over time.
Human in the Loop
When building a machine learning backend, it’s essential to keep the user’s needs in mind. The goal is to streamline their current process and provide an aid to help them do their job more effectively. To achieve this, it’s crucial to interview and observe users in their current environment to understand their behavior, identify inefficiencies, and document any inferences they make that may not be documented. By doing so, you can curate the data to pull out inferences before sending them to the model, which will result in more accurate results based on replicating human behavior. It’s important to remember that the inferences are often more critical than the raw data, and by understanding the user’s behavior and needs, you can design a better AI product.
Another key factor in deploying AI is the human factor. It’s about establishing the right context when implementing automation to avoid the fear of job loss. How to deal with potential AI/Human problems, such as stakeholders circumventing the AI put in place for them. One solution is to design the interface to make it clear when a user has reviewed a specific data point and provide overrides where needed. Additionally, asking stakeholders about their reasons for bypassing the system can help improve the model and prevent gaming of the API. Ultimately, AI and humans can work together to achieve better results.
AI can take care of easy tasks if it’s well-trained, and humans can learn to leverage AI better over time. Building collaborative interfaces that make AI a team member rather than a cold algorithm, allowing for more natural interactions that can help it learn better. AI will become essential in any job dealing with stakeholders who process human data due to the variability involved.