THERE are two kinds of theoretical physicists: those who use the correct equation for calculating distances in space-time, and those who don't. Obviously, I'm being a bit tongue in cheek here, but I'm serious when I say there is a real bifurcation. The two formulations of this equation for distance – what we call the metric – are equivalent and when used correctly will give the same answers for all calculations. But each group has reasons for believing that one is more natural than the other. Particle physicists tend to use one; relativists (people trained in general relativity) tend to use the other.

While ``instruction-tuned" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amounts of diverse and high-quality instruction data (such as ChatGPT and GPT-4). Unfortunately, acquiring high-quality data, especially when it comes to human-written data, can pose significant challenges both in terms of cost and accessibility. Moreover, concerns related to privacy can further limit access to such data, making the process of obtaining it a complex and nuanced undertaking. Consequently, this hinders the generality of the tuned models and may restrict their effectiveness in certain contexts. To tackle this issue, our study introduces a new approach called Federated Instruction Tuning (FedIT), which leverages federated learning (FL) as the learning framework for the instruction tuning of LLMs. This marks the first exploration of FL-based instruction tuning for LLMs. This is especially important since text data is predominantly generated by end users. Therefore, it is imperative to design and adapt FL approaches to effectively leverage these users' diverse instructions stored on local devices, while preserving privacy and ensuring data security. In the current paper, by conducting widely used GPT-4 auto-evaluation, we demonstrate that by exploiting the heterogeneous and diverse sets of instructions on the client's end with the proposed framework FedIT, we improved the performance of LLMs compared to centralized training with only limited local instructions. Further, in this paper, we developed a Github repository named Shepherd. This repository offers a foundational framework for exploring federated fine-tuning of LLMs using heterogeneous instructions across diverse categories.

Once upon a time, in an alternate universe, Isaac Newton and Ada Lovelace were brought together to work on a science invention that would change the world forever. Newton, known for his groundbreaking work in physics and mathematics, and Lovelace, known for her contributions to the field of computer science, were both renowned scientists in their own right. Their collaboration began when Newton was approached by a group of investors who were looking for a way to revolutionize the field of transportation. They wanted to create a machine that could transport people and goods faster and more efficiently than any technology that existed at the time. Newton, who had a deep understanding of the laws of motion and gravity, saw the potential in the project and agreed to work on it.

Isaac Newton (left) and Gottfried Wilhelm Leibniz each independently invented calculus.Credit: Left, DeAgostini/Getty; Right, Lombard/ullstein bild via Getty Midway through Infinite Powers, Steven Strogatz writes that Isaac Newton and Gottfried Wilhelm Leibniz both "died in excruciating pain while suffering from calculi -- a bladder stone for Newton, a kidney stone for Leibniz". It was a cruelly ironic end for the scientists who independently invented calculus: the word comes from the Latin for'small stone', in reference to pebbles once used for counting. Such fascinating anecdotes abound in Infinite Powers. Strogatz, a mathematician working in nonlinear dynamics and complex systems, has written a romp through the history of calculus -- the study of how things change. Starting with the ancient Greeks, the book ends with connections between the field and artificial intelligence and machine learning. Calculus was key to working with Newton's laws of motion, which stimulated the Industrial Revolution.

Students who study calculus are experiencing the fruits of the genius of Isaac Newton (1643–1727). The same is true for introductory courses in physics and differential equations. All are due to Newton, who built the beautiful palace of mathematical physics. The wings of quantum mechanics and relativity were added in the early twentieth century. But Newton had other interests as well.

I have chosen a text of my preferred author and thinker Dr. Eliyahu Goldratt about "Engines of Disharmony". The text is publicly available on the website of Goldratt Consulting [1] in Pdf format. The text represents the foreword of the Japanese version of Eli's book "The Choice". I downloaded the Pdf file and transformed the text into a plain ASCII text. For the analysis, I only used the first four pages of the original document.

It was the childhood home where Sir Issac Newton would go on to split the sun's rays using a prism, altering the way we think about light forever. Now, cutting edge lighting technology based on his discovery has revealed drawings thought to have been hand-carved by the young scientist at Woolsthorpe Manor. Conservation experts found a depiction of a windmill, thought to have been inspired by the building of a nearby mill, etched into walls of the historic building. Lighting technology based on his discovery has revealed drawings thought to have been hand-carved by the young scientist. Newton exerted a profound influence on many aspects of science through his great mastery of precise experiments.

In his report, Von Neumann outlined the architecture of a stored-program digital computer, an ancestor of most computers in use today. But the existence of both the computer and Google can be traced to a much earlier date. It was 150 years ago that George Boole published his literary classic The Laws of Thought, wherein he devised a mathematical language for dealing with mental machinations of logic. It was a symbolic language of thought -- an algebra of logic (algebra is the branch of mathematics that uses letters and other general symbols to represent numbers and quantities in formulas and equations). In doing so, he provided the raw material needed for the design of the modern high-speed computer.