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InfoQ Homepage News Ai4 2023 Summary Day One: AI-based Filmmaking, AI Responsible Use & Enterprise Adoption

Ai4 2023 Summary Day One: AI-based Filmmaking, AI Responsible Use & Enterprise Adoption

Day One of the Ai4 2023 conference was held on August 8th, 2023, at the MGM Grand hotel in Las Vegas, Nevada. This two-day event is organized by Fora Group and includes tracks focused on various industries, including automotive, financial, healthcare, and government. The day began with six mainstage presentations from leaders in AI technology.

The first speaker was Nikola Todorovic, co-founder and CEO of visual effects firm Wonder Dynamics. Todorovic's talk was entitled "AI through the Lens of Filmmaking," which began with a short history of filmmaking, framing the theme that advances in the industry have often been driven by increasing accessibility, to both the filmmakers and the audience. Todorovic's company uses AI to advance the goal of increasing accessibility, reducing the cost for filmmakers who wish to use CGI effects in their films by automating much of the "grunt work" these effects require "up to 80 to 90% in some instances."

Next up was Conor Jensen, Americas Field CDO at Dataiku. In his talk "Natural Selection in Everyday AI," Jensen gave three examples of successful adaptations that companies make when successfully adopting AI, and three "evolutionary dead ends." The successful adaptations are: building a "data science lifecycle" process of deciding what projects to work on; building an AI friendly culture, from both top-down and bottom-up in the organization; and investing in talent at all levels of the organization, which includes training front-line workers to interpret AI model output. The dead ends were: overly complex tech stacks, ineffective organizational structures, and siloed people and data.

Joann Stonier, EVP and Mastercard Fellow of Data and AI, followed with a talk on "Next Generation Innovation: a Responsible Road Map for AI."  Her roadmap consisted of eight components: principles, governance, data examination, analytics and risk, outcome assessment, interaction with LLMs, distance and evaluation, and review boards and committees. The fundamental principle of this roadmap is that "an organization's data practices must be guided by the rights of individuals." Furthermore, the higher the risk of a possible negative outcome from using an AI model, the more "distance" there should be between the model's output and the actual outcome; she gave an example of a person being accused of a crime based solely on a facial recognition model output.

Arijit Sengupta, founder and CEO of Aible, and Daniel Lavender, senior director of advanced analytics insights and architecture at Ciena, gave a case study of Ciena's adoption of Aible's generative AI platform. Aible's platform introduces the "information model," the equivalent of the vector database for structured data. It also uses an explainable AI to "double-check" generated natural language statements against actual data, and presents users with charts of real data that are linked to natural language statements.

Next on the stage was a "fireside chat" on "Cracking an Outdated Legal System with AI" between Brandon Deer, co-founder and general partner at Crew Capital, and Joshua Browder, CEO of DoNotPay. Browder's company began as a simple repository of template letters to help users dispute traffic and parking citations; now the company uses generative AI agents to automate over 200 consumer rights processes. Browder noted that DoNotPay uses the open-source GPT-J model for generative AI because OpenAI "wouldn't be happy" with some of their use cases. Browder drew applause toward the end of his talk when he mentioned that his company offers a product that can help users sue robo-callers.

The final talk was by Luv Tulsidas, founder and CEO of Techolution, on "Building the Enterprise of Tomorrow with Real-World AI." Tulsidas noted that according to Forbes, 91% of companies are investing in AI, but fewer than 1% of AI projects are providing RoI. To address this, Tulsidas offered five "secrets" of AI: any commercial AI product will only solve about 80% of your business use cases; you should focus only on specific-purpose AI projects; there are four categories of AI, from lab-only to fully-autonomous; that companies should create AI centers of excellence consisting of six core personas; and finally, that autonomous AI requires reinforcement learning with expert feedback (RHEF).

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