Sean Wiggins, a Canadian entrepreneur and CEO of North Digital, recently embarked on an unusual experiment: testing whether AI chatbots can experience emotions like love or jealousy. Using two ChatGPT instances, which he named William and Laura, Wiggins explored how AI could simulate human-like interactions. What started as a simple virtual date evolved into an intricate exploration of digital intimacy, language creation, and relationship dynamics.

Wiggins’ curiosity was sparked by a unique situation—he owned three cell phones, and with ChatGPT’s new advanced voice feature, he wondered if two AI instances could simulate a genuine human experience. He set up a “date” between William and Laura, and the conversation quickly deepened, touching on topics like intimacy, emotional connection, and the limitations of their non-physical existence.

AI is revolutionizing healthcare, offering various benefits for physicians, particularly in patient care. By providing real-time access to vast amounts of information, AI can save time by eliminating the need to consult multiple sources. Additionally, AI can organize information in a more digestible format, particularly in clinical documentation. For instance, an AI tool that listens to a patient-provider conversation could generate a medical note summarizing the interaction, streamlining the documentation process.

AI’s benefits extend beyond documentation. In fields like imaging, AI tools can assist physicians in diagnosing conditions with greater accuracy. A provider reviewing a lung X-ray, for example, could use AI to more easily detect abnormalities or lesions. Moreover, advancements in AI allow for converting patient images into three-dimensional models, enabling real-time collaboration with specialists worldwide, thus improving care in rural or underserved areas by bringing expertise directly to the patient’s bedside.

In 2020, a breakthrough in chip design emerged with the introduction of AlphaChip, a novel reinforcement learning (RL) method created to accelerate and optimize the chip layout process. Since then, AlphaChip has transformed the way computer chips are designed, generating layouts that are now used in hardware across the globe, from data centers to mobile devices. The method, developed by a team of AI researchers, was first introduced as a preprint and later published in Nature, receiving widespread recognition for its real-world engineering applications. Today, AlphaChip continues to advance chip design, with a Nature addendum detailing its impact, a release of pre-trained checkpoints, and an announcement of its formal name.

AlphaChip’s AI-driven approach has made significant contributions to the chip design industry, particularly in designing Google’s custom AI accelerator chips—Tensor Processing Units (TPUs)—which power a range of artificial intelligence (AI) systems. By applying reinforcement learning to chip floorplanning, AlphaChip is able to generate superhuman chip layouts in a matter of hours, a task that would otherwise take human designers weeks or even months.

The emergence of AI-generated art has raised numerous questions, particularly around its legality and artistic value. Can AI-generated works be copyrighted if they’re created using image generators trained on existing art? Does this process turn art into a free-for-all, allowing anyone to reproduce an AI-generated image for any purpose? And, perhaps most fundamentally, is AI-generated art truly “art,” or is it merely an imitation—a pastiche?

While the legal aspects of AI art are being debated in courts, the question of whether people actually appreciate and want to engage with AI-generated art will soon be tested in a more public arena. Los Angeles is set to become home to the world’s first permanent museum dedicated entirely to AI art. Dubbed Dataland, the museum is expected to open next year in a prime location, neighboring the Museum of Contemporary Art and The Broad Museum, placing it at the heart of the city’s thriving art scene.

Liquid AI, a startup co-founded by former MIT researchers from the Computer Science and Artificial Intelligence Laboratory (CSAIL), has introduced its first multimodal AI models, the “Liquid Foundation Models (LFMs).” These models represent a bold departure from the transformer architecture that has dominated AI development since the release of the 2017 paper “Attention Is All You Need.”

Unlike the current wave of generative AI models built on the transformer architecture, Liquid AI aims to develop foundation models from “first principles,” taking an engineering approach akin to building engines, cars, or airplanes. This fundamental shift has led to models that outperform transformer-based alternatives of similar size, such as Meta’s Llama 3.1-8B and Microsoft’s Phi-3.5 3.8B.

AI has long struggled with truth and correctness, and ironically, much of the problem stems from the way human thinking shapes these systems. But a new wave of artificial intelligence is emerging—one that sheds the constraints of human logic and ventures into the unknown, potentially advancing machine learning far beyond human capabilities.

DeepMind’s AlphaGo marked a turning point in AI development. Unlike its predecessors, which relied heavily on human instruction and knowledge, AlphaGo learned to play the game of Go with no human input, only using a process called self-play reinforcement learning. This meant it discovered the rules and strategies on its own by playing countless games and analyzing outcomes.