Lifeboat Foundation

Tractor beams make intuitive sense. Matter and energy interact with each other in countless ways throughout the Universe. Magnetism and gravity are both natural forces that can draw objects together, so there’s sort of a precedent.

But engineering an actual tractor beam is something different.

Luwu Dynamics products are quadruped robot dogs with 12 degrees of freedom. They are used for teenagers to learn Artificial Intelligence Programming. They can realize omni-directional movement, six-dimensional attitude control, attitude stability and a variety of motion gait. They are internally equipped with 9-axis IMU, joint position sensor and current sensor to feed back their own attitude, joint angle and torque for internal algorithm and secondary development, It can off-line AI functions such as face recognition, image classification, gesture recognition, speech recognition, audio analysis and target tracking, and supports cross platform graphical and python programming.

An interesting interview conducted at a conference workshop with 3 experts discussing what is AI and how it is impacting the world of scientific publishing.

A recap of a recent SSP webinar on artificial intelligence (AI) and scholarly publishing. How can this set of technologies help or harm scholarly publishing, and what are some current trends? What are the risks of AI, and what should we look out for?

An artificial intelligence dubbed Claude, developed by AI research firm Anthropic, got a “marginal pass” on a recent blindly graded law and economics exam at George Mason University, according to a recent blog post by economics professor Alex Tabarrok.

It’s yet another warning shot that AI is experiencing a moment of explosive growth in capability — and it’s not just OpenAI’s ChatGPT that we have to worry about.

Anthropic — which according to Insider secured funding from disgraced crypto exec Sam Bankman-Fried and his alleged romantic partner, former Alameda Research CEO Caroline Ellison — made a big splash with its new AI earlier this week.

— Two papers show that large language models, including ChatGPT, can pass the USMLE.

Quantum-enhanced single-parameter estimation is an established capability, with non-classical probe states achieving precisions beyond what can be reached by the equivalent classical resources in photonic^1,2,3, trapped-ion^4,5, superconducting⁶ and atomic^7,8 systems. This has paved the way for quantum enhancements in practical sensing applications, from gravitational wave detection⁹ to biological imaging¹⁰. For single-parameter estimation, entangled probe states are sufficient to reach the ultimate allowed precisions. However, for multi-parameter estimation, owing to the possible incompatibility of different observables, entangling resources are also required at the measurement stage. The ultimate attainable limits in quantum multi-parameter estimation are set by the Holevo Cramér–Rao bound (Holevo bound)^11,12. In most practical scenarios, it is not feasible to reach the Holevo bound as this requires a collective measurement on infinitely many copies of the quantum state^13,14,15,16 (see Methods for a rigorous definition of collective measurements). Nevertheless, it is important to develop techniques that will enable the Holevo bound to be approached, given that multi-parameter estimation is fundamentally connected to the uncertainty principle¹⁷ and has many physically motivated applications, including simultaneously estimating phase and phase diffusion^18,19, quantum super-resolution^20,21, estimating the components of a three-dimensional field^22,23 and tracking chemical processes²⁴. Furthermore, as we demonstrate, collective measurements offer an avenue to quantum-enhanced sensing even in the presence of large amounts of decoherence, unlike the use of entangled probe states^25,26.

To date, collective measurements for quantum multi-parameter metrology have been demonstrated exclusively on optical systems^{27,28,29,30,31,32}. Contemporary approaches to collective measurements on optical systems are limited in their scalability: that is, it is difficult to generalize present approaches to measuring many copies of a quantum state simultaneously. The limited gate set available can also make it harder to implement an arbitrary optimal measurement. Indeed, the collective measurements demonstrated so far have all been restricted to measuring two copies of the quantum state and, while quantum enhancement has been observed, have all failed to reach the ultimate theoretical limits on separable measurements^33,34. Thus, there is a pressing need for a more versatile and scalable approach to implementing collective measurements.

In this work, we design and implement theoretically optimal collective measurement circuits on superconducting and trapped-ion platforms. The ease with which these devices can be reprogrammed, the universal gate set available and the number of modes across which entanglement can be generated, ensure that they avoid many of the issues that current optical systems suffer from. Using recently developed error mitigation techniques³⁵ we estimate qubit rotations about the axes of the Bloch sphere with a greater precision than what is allowed by separable measurements on individual qubits. This approach allows us to investigate several interesting physical phenomena: we demonstrate both optimal single-and two-copy collective measurements reaching the theoretical limits^33,34. We also implement a three-copy collective measurement as a first step towards surpassing two-copy measurements. However, due to the circuit complexity, this measurement performs worse than single-copy measurements. We investigate the connection between collective measurements and the uncertainty principle. Using two-copy collective measurements, we experimentally violate a metrological bound based on known, but restrictive uncertainty relations³⁶. Finally, we compare the metrological performance of quantum processors from different platforms, providing an indication of how future quantum metrology networks may look.

It’s not at every university that laser pulses powerful enough to burn paper and skin are sent blazing down a hallway. But that’s what happened in UMD’s Energy Research Facility, an unremarkable looking building on the northeast corner of campus. If you visit the utilitarian white and gray hall now, it seems like any other university hall—as long as you don’t peak behind a cork board and spot the metal plate covering a hole in the wall.

But for a handful of nights in 2021, UMD Physics Professor Howard Milchberg and his colleagues transformed the hallway into a laboratory: The shiny surfaces of the doors and a water fountain were covered to avoid potentially blinding reflections; connecting hallways were blocked off with signs, caution tape and special laser-absorbing black curtains; and scientific equipment and cables inhabited normally open walking space.

As members of the team went about their work, a snapping sound warned of the dangerously powerful path the laser blazed down the hall. Sometimes the beam’s journey ended at a white ceramic block, filling the air with louder pops and a metallic tang. Each night, a researcher sat alone at a computer in the adjacent lab with a walkie-talkie and performed requested adjustments to the laser.

The chipmaker is showcasing how a robotics startup is using its simulation platform to train robots to pick up food like pieces of chicken.

Get ready to be amazed by the next generation of artificial intelligence. In this video, we’ll explore what we know about GPT-4 so far, including facts, rumors, and general expectations for this next-generation AI model. From its capabilities to its potential uses, we’ll take a deep dive into everything you need to know about GPT-4.

🔔 Did you enjoy the content? Subscribe here:
- https://rb.gy/nekyhx.

Continue reading “GPT-4: The AI Breakthrough That Will Change Everything” »