Part II by the inquisitive lawyer: AI Camp Next Con event takeaways: 1. AI and Health Care: I enjoyed this session a lot by Anitha Kannan. I learned about recency bias amongst doctors: this is where a doctor who has dealt with 10 patients with similar symptoms assumes patient 11 has the same ailment. Anitha's work with Curai aids these doctors to give diagnosis on a particular ailment avoiding bias. However ML is required to ensure that the AI program can detect the right ailment or have the ability to respond "I don't know" when it can't. Jay Baxter explains the modeling engagements in creating & ranking your feed and bias in candidate generation. It looks at 3 things: all tweets, the impressions(likes, RTs) and engagement(how often you have engaged with the user in the past) 3 Testing AI & Bias- this was another interesting one on search queries by Jason Arbon.

We are back with more interviewers. I have Tariq and Jason, nice to have you both. Tariq King: Nice to be here. Jason Arbon: It's very nice to be here. Thank you for inviting me here. So we get both you guys all at once. Jennifer Bonine: And, I heard that both of you like to talk about AI. Jennifer Bonine: Yeah, and you have a new organization that was started. Jennifer Bonine: So if people want to find it they just go to ... Jennifer Bonine: And, what is the intent behind it?

Jennifer Bonine is a VP of global delivery and solutions for tap QA, Inc., a global company that specializes in strategic solutions for businesses. Jennifer began her career in consulting, implementing large ERP solutions. She has held executive level positions leading development, quality assurance and testing, organizational development, and process improvement teams for Fortune 500 companies in several domains. In a recent engagement for one of the world's largest technology companies, Jennifer served as a strategy executive and in corporate marketing for the C-Suite. In her career, she has had several opportunities to build global teams from the ground up and has been fortunate to see how many of the world's top companies operate from the C-Suite viewpoint.

There isn't a lot of information relating to AI and Software testing out there, and most of it is, um, lets just call it'lacking' in technical depth, and this causes even more confusion and angst. Below are some links to help demystify AI / machine learning in the context of software testing. My hope is that folks that care will go through most of the material below and move from being "AI Curious" to "AI Aware". Most of the links below should be accessible to people without a math degree or programming background. My hope is that more people will be able to discern the carnival barkers from the real deals, understand the humble reality of AI today vs the hype, and think of ways they might be able to apply AI to their own software testing problems.

Josiah Renaudin: Welcome back to another TechWell interview. I'm joined by Jason Arbon, the CEO of Appdiff and a speaker at this year's STAR WEST. First, could you tell us a bit about where you worked at before you started Appdiff? Jason Arbon: Hi, Josiah, nice to chat with you again. After college, I started my career at Microsoft doing testing and automation for products like Windows and Bing.

Josiah Renaudin: Welcome back to another TechWell interview. I'm joined by Jason Arbon, the CEO of Appdiff and a speaker at this year's STARWEST. First, could you tell us a bit about where you worked at before you started Appdiff? Jason Arbon: Hi, Josiah, nice to chat with you again. After college, I started my career at Microsoft doing testing and automation for products like Windows and Bing.