Category: AI

In late September, Shield AI co-founder Brandon Tseng confidently asserted that fully autonomous weapons—where AI makes the final decision to kill—would never exist in the U.S. “Congress doesn’t want that. No one wants that,” Tseng told TechCrunch. However, his statement was quickly challenged. Just five days later, Anduril co-founder Palmer Luckey expressed a different perspective, signaling a more nuanced view on autonomous weapons.

Speaking at Pepperdine University, Luckey questioned the opposition to such systems, arguing that in some cases, autonomous technology may offer a moral advantage over current weapons. He raised the example of landmines, which can’t distinguish between a school bus and a military target, as a point of comparison. When asked for further clarification, an Anduril spokesperson noted that Luckey’s concern was more about the potential misuse of AI by bad actors, rather than advocating for fully autonomous lethal systems.

How do machine learning models work? And are they really capable of “thinking” or “reasoning” in the same way humans do? This philosophical and practical question has been the subject of ongoing debate. However, a recent paper titled “Understanding the Limitations of Mathematical Reasoning in Large Language Models,” authored by a team of AI research scientists at Apple, suggests a clear answer: not yet.

The core of the research centers around the difference between symbolic learning and pattern reproduction, and while these concepts are complex, the basic premise is straightforward. To illustrate, imagine a simple math problem:

In the ever-evolving landscape of technology, a groundbreaking frontier is emerging, poised to transform the world in unimaginable ways. This new frontier is Quantum AI—a powerful fusion of quantum computing and artificial intelligence. It’s generating immense excitement across industries like finance and healthcare, and for good reason. Quantum AI has the potential to solve complex problems at speeds that make even today’s most advanced classical computers seem archaic.

What exactly is Quantum AI, and why does it matter? At its core, Quantum AI leverages quantum mechanics to process information in ways that classical computers cannot. Traditional computers rely on bits, which exist as either 0 or 1. In contrast, quantum computers use qubits, which can exist in multiple states simultaneously due to the phenomenon known as superposition. This allows quantum computers to perform calculations exponentially faster than their classical counterparts.

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.

Researchers at MIT have developed a groundbreaking generative AI model that significantly simplifies the process of determining the structures of crystalline materials, such as metals, rocks, and ceramics. Traditionally, scientists have relied on X-ray crystallography to uncover these structures, but this new approach offers a more efficient and versatile alternative. The model has far-reaching implications for industries relying on materials like batteries, magnets, and superconductors.

“Understanding the structure of a material is crucial for virtually any application,” explained Danna Freedman, the Frederick George Keyes Professor of Chemistry at MIT. “It’s fundamental for superconductivity, magnets, photovoltaics—essentially anything that is materials-centric.”

Stockholm-based startup SweGreen is transforming the concept of “farm fresh” by bringing the farm directly into supermarkets. With installations across Sweden and Germany, SweGreen is pioneering a sustainable way to grow crops while simultaneously reducing food waste and carbon emissions.

In the U.S. alone, it’s estimated that 16 billion pounds of food are wasted annually in supermarkets. As much as 30% of the food that reaches store shelves is discarded, often because customers only select the most visually appealing fruits and vegetables. This level of waste, coupled with global challenges like climate change, shrinking agricultural land, and geopolitical tensions, raises concerns about future food security. By 2050, with the world population nearing 10 billion, finding innovative ways to ensure sustainable food production is more urgent than ever.

Purdue University engineers have found that autonomous vehicles (AVs) could soon be powered by advanced chatbots like ChatGPT, enabling them to better understand and respond to passengers’ commands. These chatbots are built on large language models (LLMs), a type of artificial intelligence algorithm designed to interpret natural language and continuously learn from vast amounts of data.

This breakthrough study, published on the preprint server arXiv, is set to be presented at the 27th IEEE International Conference on Intelligent Transportation Systems on September 25. It may be one of the first real-world experiments testing how well AVs can use LLMs to understand and act on passenger instructions.