Monday, July 18, 2022
HomeFundraisingSynthetic Intelligence: The Hotdog Conundrum

Synthetic Intelligence: The Hotdog Conundrum

HBO’s Silicon Valley is a not-too-far eliminated parody of labor at a small expertise startup. In season 4, Jian Yang creates a machine-learning app referred to as SeeFood. The thought is that the app can interpret a picture taken from a cell phone and let you know, in textual content, what the meals is.

Jian Yang works relentlessly on the app and is immensely proud to show the ultimate product to his housemates. He begins by taking an image of a scorching canine, which the app accurately identifies. Everyone seems to be elated—this would be the subsequent large factor (“We’re going to be wealthy!”).

The twist comes just some moments later when Jian Yang takes an image of a pizza slice: “not scorching canine.” His housemates are crestfallen. It seems that Jian Yang’s synthetic intelligence (AI) app relies on a classification prediction—so it acknowledges every thing as both a scorching canine or not a scorching canine.

Many people have skilled that very same disconnect between expectations and expertise, significantly within the realm of AI. For as many pleasant experiences we’ve had with the expertise (autocomplete in Microsoft Workplace, for instance), we’ve had 10x the frustration in different ventures.

It’s straightforward to overpromise on the capabilities of AI in advertising supplies (“Let’s throw some AI on the drawback!”), resulting in a disconnect between the promise of the expertise and its precise utility to a buyer.

After all, expertise alone doesn’t magically change your outcomes (in some circumstances, it might gradual you down). Know-how is magic when there’s shut alignment between an actual drawback or ache level and the distinctive drawback the expertise is designed to resolve. Folks, course of, and expertise should work together.

It’s important to know what AI is (advertising apart) and what it’s uniquely suited to do when exploring merchandise to learn your nonprofit or college. AI and machine studying predictions are—primarily—a set of algebraic equations that articulate essentially the most possible final result to a really particular query. And the worth of making use of that AI can be decided by how a lot you care in regards to the reply to that query.

Listed here are some finest practices when evaluating merchandise or capabilities on this house:

  • Assess the place your crew is unsure and establish wherein side of the work that uncertainty is concentrated.
  • Spend time brainstorming some particular questions that might be predicted to handle this uncertainty (Is that this a scorching canine?).
  • Articulate how eradicating the uncertainty can be priceless for the group. What influence on the group would you see on account of much less uncertainty on this space?
  • What points of the crew’s workflow might be made quicker/shorter/extra streamlined if that uncertainty was alleviated? Will this liberate their time to deal with extra priceless work?

“Scorching canine” or “not scorching canine” could appear foolish. However what if it wasn’t a scorching canine that Jian Yang’s app recognized—however breast most cancers indicators on an MRI studying? What if synthetic intelligence drove the fee down of early identification to some extent the place it’s accessible to extra individuals, which means we are able to catch extra circumstances in earlier levels?

For an additional useful resource on pondering by means of the potential worth of AI in a particular scenario, try the AI Canvas Framework created by Ajay Agrawal, Joshua Gans, and Avi Goldfarb on Harvard Enterprise Assessment.

AI is nice when its objective aligns with the outcomes our prospects hope to realize. However when it’s not, it’s simply noise. That’s why we’re so excited to be offering industry-specific AI-powered instruments that can create actual influence for our prospects.

In June 2022, we launched Prospect Insights—a brand new software program device inside Blackbaud Raiser’s Edge NXT® that permits social good professionals to entry actionable, AI-powered insights to drive main giving.

At Blackbaud, we’re on a journey to construct clever experiences (inclusive of AI-enabled ones) which are really priceless to our prospects. We have now a crew that deeply understands the social good sector and the issues nonprofits and faculties face day-after-day, and we come to the desk with that in thoughts.

Over the approaching weeks, I’ll share a few of what I’ve realized on my journey into AI over the previous few years, and I hope you’ll be a part of me. Cheers!


Sam Venable serves as supervisor of the info intelligence product administration crew at Blackbaud, the place he has labored since 2018. His crew has been laser-focused on constructing the infrastructure to assist outcomes-driven clever software program options (Prospect Insights™ being the primary product leaning on the expertise).

His skilled focus is on the intersection between narrative and knowledge to drive outcomes. Over the past decade, this intersection has been dramatically influenced by the influence of synthetic intelligence on society. In his free time, he likes to mountain bike, learn, and dabble in private well being experiments validated by biometric knowledge.   



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