OpenEye is focused on a scientific approach to AI in drug discovery with models that are accurate and interpretable thus enhancing human intelligence.
Category: robotics/AI – Page 130
Tokyo releases AI-generated video of Mount Fuji erupting
With Mount Fuji 100 kilometers away, the video from the Tokyo Metropolitan Government aims to inform Tokyoites about how an eruption could still seriously impact their lives.
Digital to analog in one smooth step: Device could replace signal modulators in fiber-optic networks
Addressing a major roadblock in next-generation photonic computing and signal processing systems, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have created a device that can bridge digital electronic signals and analog light signals in one fluid step.
Built on chips made out of lithium niobate, the workhorse material of optoelectronics, the new device offers a potential replacement for the ubiquitous but energy-intensive digital-to-analog conversion and electro-optic modulation systems used all over today’s high-speed data networks.
“Optical communication and high-performance computing, including large language models, relies on conversion of massive amounts of data between the electrical domain—used for storage and computation—and the optical domain used for data transfer,” said senior author Marko Lončar, the Tiantsai Lin Professor of Electrical Engineering at SEAS.
The Non-Singular Singularity
Part 1 of the Singularity Series was “Putting Brakes on the Singularity.” That essay looked at how economic and other non-technical factors will slow down the practical effects of AI, and we should question the supposedly immediate move from AGI to SAI (superintelligent AI).
In part 3, I will consider past singularities, different paces for singularities, and the difference between intelligence and speed accelerations.
In part 4, I will follow up by offering alternative models of AI-driven progress.
What comes after agentic AI? This powerful new technology will change everything
Ten years from now, it will be clear that the primary ways we use generative AI circa 2025—rapidly crafting content based on simple instructions and open-ended interactions—were merely building blocks of a technology that will increasingly be built into far more impactful forms.
The real economic effect will come as different modes of generative AI are combined with traditional software logic to drive expensive activities like project management, medical diagnosis, and insurance claims processing in increasingly automated ways.
In my consulting work helping the world’s largest companies design and implement AI solutions, I’m finding that most organizations are still struggling to get substantial value from generative AI applications. As impressive and satisfying as they are, their inherent unpredictability makes it difficult to integrate into the kind of highly standardized business processes that drive the economy.
A look at the next big iteration of the transformative technology.
NVIDIA Unveils ‘Mega’ Omniverse Blueprint for Building Industrial Robot Fleet Digital Twins
According to Gartner, the worldwide end-user spending on all IT products for 2024 was $5 trillion. This industry is built on a computing fabric of electrons, is fully software-defined, accelerated — and now generative AI-enabled. While huge, it’s a fraction of the larger physical industrial market that relies on the movement of atoms. Today’s 10 Read Article
What Comes After the LLM: Human-Centered AI, Spatial Intelligence, and the Future of Practice
In a recent episode of High Signal, we spoke with Dr. Fei-Fei Li about what it really means to build human-centered AI, and where the field might be heading next.
Fei-Fei doesn’t describe AI as a feature or even an industry. She calls it a “civilizational technology”—a force as foundational as electricity or computing itself. This has serious implications for how we design, deploy, and govern AI systems across institutions, economies, and everyday life.
Our conversation was about more than short-term tactics. It was about how foundational assumptions are shifting, around interface, intelligence, and responsibility, and what that means for technical practitioners building real-world systems today.
Spatial computing, wearables and robots: AI’s next frontier
Spatial computing, an emerging 3D-centric computing model, merges AI, computer vision and sensor technologies to create fluid interfaces between the physical and digital. Unlike traditional models, which require people to adapt to screens, spatial computing allows machines to understand human environments and intent through spatial awareness.
Recent trademark filings and product launches show AI companies targeting the physical world with wearables and robots driven by complex spatial computing.