Large Language Models (LLMs) (e.g., ChatGPT) have shown impressive performance in code generation. LLMs take prompts as inputs, and Chain-of-Thought (CoT) prompting is the state-of-the-art prompting technique. CoT prompting asks LLMs first to generate CoTs (i.e., intermediate natural language reasoning steps) and then output the code. However, CoT prompting is designed for natural language generation and has low accuracy in code generation. In this paper, we propose Structured CoTs (SCoTs) and present a novel prompting technique for code generation, named SCoT prompting. Our motivation is source code contains rich structural information and any code can be composed of three program structures (i.e., sequence, branch, and loop structures). Intuitively, structured intermediate reasoning steps make for structured source code. Thus, we ask LLMs to use program structures to build CoTs, obtaining SCoTs. Then, LLMs generate the final code based on SCoTs. Compared to CoT prompting, SCoT prompting explicitly constrains LLMs to think about how to solve requirements from the view of source code and further the performance of LLMs in code generation. We apply SCoT prompting to two LLMs (i.e., ChatGPT and Codex) and evaluate it on three benchmarks (i.e., HumanEval, MBPP, and MBCPP). (1) SCoT prompting outperforms the state-of-the-art baseline - CoT prompting by up to 13.79% in Pass@1. (2) Human evaluation shows human developers prefer programs from SCoT prompting. (3) SCoT prompting is robust to examples and achieves substantial improvements.

This is an Introductory course so please buy it if you are a beginner and you want to know more about how the computer works within. Please go through the free preview video before buying that is the introduction part and others so that you will get an idea about what this course is about. The central processing unit (CPU), input devices, and output devices are the three components that make up the basic structure of a computer system. The Central Processing Unit (CPU) can also be separated into two parts: the control unit (CU) and the arithmetic logic unit (ALU). The basic structure of a computer describes a simple concept: data is entered into the central processing unit using input devices such as a keyboard, mouse, joystick, scanner, secondary storage devices, and so on, and when the central processing unit receives the data from the input devices, it has a pre-programmed set of instructions to follow, and the result of instruction execution is the output.

Image Segmentation is considered a vital task in Computer Vision – along with Object Detection – as it involves understanding what is given in the image at a pixel level. It provides a comprehensive description that includes the information of the object, category, position, and shape of the given image. There are various algorithms for Image Segmentation that have been developed with applications such as scene understanding, medical image analysis, robotics, augmented reality, video surveillance, etc. The advent of Deep Learning in Computer Vision has diversified the capabilities of the existing algorithms and paved the way for new algorithms for pixel-level labeling problems such as Semantic Segmentation. These algorithms learn rich representations for the problem, including automatic pixel labeling of images in an end-to-end fashion.

Happiness in the present is only shattered by comparison with the past. Regression analysis does not require any separate introduction today. In fact, it would be hard to find a field of study that can put a bet and win for not using this technique at least once in their life cycle. There exists a relationship, waiting to be explored by someone through some variant of regression technique. Ever since mathematicians Adrien-Marie Legendre and Carl Friedrich Gauss invented this technique in the early 19th century, the world has been experiencing at least one use case every day; by some human being alive in the world.

As a data scientist, you will likely be asked one day to automate your analysis and port your models to production environments. When that happens you cross the blurry line between data science and software engineering, and become a machine learning engineer. I'd like to share a few tips on how to make that transition as successful as possible. Let's first discuss testing, and let's assume without loss of generality that you develop your machine learning application in R. Just like any other software system, your application needs to be thoroughly tested before being deployed. But how do you ensure that your application will perform as expected when dealing with real data?

In our prior The World's Most Disruptive Technology (That No One Is Talking About) post we portrayed the promise and peril of a potent new gene-altering technology. Since our CRISPR post, we've been tracking another technology development equally deserving of legal scrutiny, due to it's potential to "change everything" according to one prominent thinker who knows a thing about disruptive technology. However, unlike CRISPR's promise to fundamentally alter the basic structures of human life (i.e. Deep dive with me, if you dare, into the subzero world of quantum computers. How Cold Is Quantum Computing? Imagine a computer so powerful it could instantly crack any level of data encryption.

HE FORECASTING of power demand is of crucial importance for the development of modern power systems. The stable and efficient management, scheduling and dispatch in power systems rely heavily on precise forecasting of future loads on various time horizons. In particular, shortterm load forecasting (STLF) focuses on the forecasting of loads from several minutes up to one week into the future [1]. A reliable STLF helps utilities and energy providers deal with the challenges posed by the higher penetration of renewable energies and the development of electricity markets with increasingly complex pricing strategies in future smart grids. Various STLF methods have been proposed by researchers over the years. Some of the models used for STLF include linear or nonparametric regression [2], [3], support vector regression (SVR) [1], [4], autoregressive models [5], fuzzylogic approach [6], etc. Reviews and evaluations of existing methods can be found in [7]-[10]. Building STLF systems with artificial neural networks (ANN) has long been one of the mainstream solutions to this task. As early as 2001, a review paper by Hippert et al. surveyed and examined a collection of papers that had been published between 1991 and 1999, and arrived at the conclusions that most of the proposed models

Scientists have revealed a radical plan to grow miniature Neanderthal'brains' in the lab. A team of researchers who have previously inserted Neanderthal genes into mice and frogs' eggs are now using the technique to understand how humans became'cognitively special' compared to our ancient relatives, according to the Guardian. The lab-grown mini brains will only be about the size of a lentil, and cannot achieve thoughts or feelings – but, by mimicking the basic structure of the developed brain, they could reveal key differences in how the nerve cells function. A team of researchers who have previously inserted Neanderthal genes into mice and frogs' eggs are now using the technique to understand how humans became'cognitively special' compared to our ancient relatives, according to the Guardian. The work is led by Professor Svante Pääbo, director of the genetics department at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, who previously unraveled the Neanderthal genome, the Guardian reports.

The average single-family home takes roughly seven months to complete, but MIT researchers have developed a cutting-edge system that does the job in less than a day. The team has designed an autonomous robot capable of 3D printing the basic structure of an entire building. Equipped with a precision-motion robotic arm and powered by solar panels, the machine sprays an insulation foam mold on the ground and then fills it with concrete – it completed the walls of a 50-foot-diameter, 12-foot-high dome in 14 hours. MIT has designed an autonomous robot capable of 3D printing the basic structure of an entire building. MIT's system is a massive robotic arm attached to a track vehicle.

Bits and pieces of random parts don't join together automatically to form machines that do a job, but in case of life, it seems like that's not the case. Randomly generated traits are propagated if they are found fit for the environment. But how can this process of creation be visualized? For us to observe this change in natural environment, it takes observations of millions of generations of offspring. But luckily for us, computers can do the job quickly, for us to awe to this simple process which created all the glory of the natural world.