Combining Human and Machine Intelligence

 Broadly a Good Idea

AI News

Justin Swansburg, DataRobot: On combining human and machine intelligence

By Ryan Daws | October 4, 2022 | TechForge Media

Categories: Artificial Intelligence, Enterprise, Ethics & Society,

Advancements in AI are providing transformational benefits to enterprises, but keeping risks in check and improving consumer sentiment is paramount.

Explainable AI (XAI) is the idea that an AI should always provide reasoning for its decisions in a way that makes it easy for humans to comprehend. XAI helps to build trust and ensures that issues can be more quickly identified before they cause wider damage.

AI News caught up with Justin Swansburg, VP of Americas Data Science Practice at DataRobot, to discuss how the company is driving AI adoption using concepts like XAI to combine the strengths of human and machine intelligence.

AI News: Can you give us a brief overview of DataRobot’s core solutions?

Justin Swansburg: DataRobot’s AI Cloud platform is uniquely built to democratise and accelerate the use of AI while delivering critical insights that drive clear business results. 

DataRobot helps organisations across industries harness the transformational power of AI, from restoring supply chain resiliency to accelerating the treatment and prevention of disease and enhancing patient care to combating the climate crisis.

As one of the most widely deployed and proven AI platforms in the market today, DataRobot AI Cloud brings together a broad range of data, giving businesses comprehensive insights to drive revenue growth, manage operations, and reduce risk.

DataRobot has delivered over 1.4 trillion predictions for customers around the world, including the U.S. Army, CBS Interactive, and CVS.

AN: What is “augmented intelligence” and how does it differ from artificial intelligence?

JS: Artificial intelligence and augmented intelligence share the same objective but have different ways of accomplishing it.

Augmented intelligence brings together qualities of human intuition and experience with the efficiency and power of machine learning. Whereas artificial intelligence is often used as a replacement or substitute for human processes and decision-making.

AN: Do you need machine learning or programming experience to build predictive analytics with DataRobot?  

Data Science Fails If it Looks too Good to be True

Not sure if I completely agree.  Have seen very good results come out of an analytic solution.  I agree that if it makes recommendations very different from current practice, or suggests buying into high risk, depends on unknown future states or or high investments, it deserves very close examination.    But if it simply has different methods, results or valuation.  Why not?  Hype bothers me too, but much value started there.

DSC Podcast

Data Science Fails – If It Looks Too Good To Be True...

You’ve probably seen amazing AI news headlines such as: AI can predict earthquakes. Using just a single heartbeat, an AI achieved 100% accuracy predicting congestive heart failure. AI can diagnose covid19 in seconds from a chest scan. A new marketing model is promising to increase the response rate tenfold. It all seems too good to be true. But as the modern proverb says, “If it seems too good to be true, it probably is”.

In this latest Data Science Central podcast, https://dsc.news/3fhbOt9  we look behind the hype to show whether there is substance to these claims, and then show you how to avoid these types of data science fails.
Speaker: Colin Priest, VP of AI Strategy - DataRobot
Hosted by: Sean Welch, Host and Producer - Data Science Central
https://dsc.news/3fhbOt9

via DataRobot

Using Business Rules and Expertise

Via DSC, what looks to be a good podcast on this topic.  It has been a favorite approach of mine since the beginning.  Narrow machine learning methods can be very valuable, but to deliver them they have to be part of existing or proposed tasks or businesses.  Operationally embedded.   That requires real-life decision rules.  Access information to the podcast at the link below.  More on this topic to follow. 

Data Science Fails: Ignoring Business Rules & Expertise 

Nowadays, we have unprecedented access to data, plus the computing power and advanced algorithms to find correlations. We look at a cautionary case study of a cancer center that embarked on an ambitious plan to use AI to eradicate cancer. When AI is being asked to make decisions with significant consequences, such as life and death healthcare recommendations, it needs to be trustworthy. But if you don't follow best practices, if you don't include the knowledge of subject matter experts, and if you don't enforce business rules, your AI project will not be successful.

In this latest Data Science Central podcast, learn four AI governance practices that can help you achieve AI success.

Speaker: Colin Priest, VP of AI Strategy - DataRobot
Hosted by: Sean Welch, Host and Producer - Data Science Central ....