Deep neural networks exhibit remarkable performance, yet their black-box nature limits their utility in fields like healthcare where interpretability is crucial. Existing explainability approaches often sacrifice accuracy and lack quantifiable measures of prediction uncertainty. In this study, we introduce Conformal Prediction for Interpretable Neural Networks (CONFINE), a versatile framework that generates prediction sets with statistically robust uncertainty estimates instead of point predictions to enhance model transparency and reliability. CONFINE not only provides example-based explanations and confidence estimates for individual predictions but also boosts accuracy by up to 3.6%. We define a new metric, correct efficiency, to evaluate the fraction of prediction sets that contain precisely the correct label and show that CONFINE achieves correct efficiency of up to 3.3% higher than the original accuracy, matching or exceeding prior methods. CONFINE's marginal and class-conditional coverages attest to its validity across tasks spanning medical image classification to language understanding. Being adaptable to any pre-trained classifier, CONFINE marks a significant advance towards transparent and trustworthy deep learning applications in critical domains.

Through the looking glass: Since being unveiled by OpenAI in early 2021, DALL-E has been turning heads with its ultra-realistic AI image creations. From jaw-dropping to just downright weird, the text-to-image AI system has wowed the internet. Art lovers can now explore a world outside the confines of a frame. DALL-E's new Outpainting feature can expand a picture beyond its original border. OpenAI has introduced a new tool for DALL-E, enabling it to imagine a world beyond the confines of a frame.

Champ Suthipongchai is a General Partner at Creative Ventures, a market-driven Deep Tech venture capital firm based in San Francisco. Soon AI will take over the world. There will be no jobs left for human beings. As a species, we will slowly realize that our existence is meaningless, staring off into the distance while an AI robot cooks our food and folds our laundry. Or at least that's the reality some people envision when they think of the rise of AI--and as general partner of a deep tech venture capital firm, I believe it's far off-base from what lies ahead.

One Sunday morning nearly three decades ago, my wife Dorothy and I were walking along the Potomac River in Washington, D.C. I was considering a job change and was concerned about whether my new responsibilities would divert me from my aspiration that my work "make a mark." She asked what I meant by making a mark. That meant, I confided, that people would long remember my contribution by name. She said that if that is my philosophy of life, I am likely to be disappointed.

When McDonald's spent over $300 million on big-data-crunching startup Dynamic Yield earlier this year, the move came as something of a surprise. Today the Golden Arches announced the acquisition of Apprente, a voice AI system focused on fast-food ordering. It's a niche, but it just paid off. Specific terms of the deal have not been disclosed. But the synergies are at least more immediately understandable.

When McDonald's spent over $300 million on big-data-crunching startup Dynamic Yield earlier this year, the move came as something of a surprise. Today the Golden Arches announced the acquisition of Apprente, a voice AI system focused on fast-food ordering. It's a niche, but it just paid off. Specific terms of the deal have not been disclosed. But the synergies are at least more immediately understandable.

Last week, I was in San Francisco's Mission District, betwixt testing Prisma filters on my photos, and enjoying the fine cuisine, when I noticed an actual painting of the Golden Gate Bridge on the wall. "It's like Prisma in real life," I reacted. In today's technology-fueled hyper-sensualized world, one of the many symptoms is a blurring of the line between art and design. Design is function-oriented, and though some readily fetishize consumer electronics as art objects, the bounds of design are neatly wrapped in utility. Art, on the other hand, is characterized by an effort that redefines the confines of knowledge with a particular emphasis on questioning the boundaries of emotions, politics, and society.

The period since the last conference in this series has been characterized by the explosive expansion of AI out of the confines of institutions of basic research like university departments into the worlds of industry, business, and government (a development I had long expected). But it seems to me that there are plenty -- perhaps an overabundance -- of other occasions, other conferences. Other workshops, and like, at which the applications of AI would appropriately be considered.