Analyzing development projects is crucial for understanding donors aid strategies, recipients priorities, and to assess development finance capacity to adress development issues by on-the-ground actions. In this area, the Organisation for Economic Co-operation and Developments (OECD) Creditor Reporting System (CRS) dataset is a reference data source. This dataset provides a vast collection of project narratives from various sectors (approximately 5 million projects). While the OECD CRS provides a rich source of information on development strategies, it falls short in informing project purposes due to its reporting process based on donors self-declared main objectives and pre-defined industrial sectors. This research employs a novel approach that combines Machine Learning (ML) techniques, specifically Natural Language Processing (NLP), an innovative Python topic modeling technique called BERTopic, to categorise (cluster) and label development projects based on their narrative descriptions. By revealing existing yet hidden topics of development finance, this application of artificial intelligence enables a better understanding of donor priorities and overall development funding and provides methods to analyse public and private projects narratives.

This story originally appeared on WIRED Japan and has been translated from Japanese. A project to develop autonomous vehicles for self-driving taxis has begun in earnest in Japan. The plan put forward by Tier IV, a startup specializing in autonomous-driving technology, has been selected for a demonstration project by the Japanese Ministry of Economy, Trade, and Industry. Now, a prototype development project has officially begun. Tier IV became known for developing open-source self-driving software and conducting demonstrations of self-driving taxis in May and June in Odaiba, an entertainment district of Tokyo.

An increasing number of software companies have already realized the importance of storing project-related data as valuable sources of information for training prediction models. Such kind of modeling opens the door for the implementation of tailored strategies to increase the accuracy in effort estimation of whole teams of engineers. In this article we review the most recent machine learning approaches used to estimate software development efforts for both, non-agile and agile methodologies. We analyze the benefits of adopting an agile methodology in terms of effort estimation possibilities, such as the modeling of programming patterns and misestimation patterns by individual engineers. We conclude with an analysis of current and future trends, regarding software effort estimation through data-driven predictive models.

Collaboration on complex development projects almost always presents challenges. For traditional software projects, these challenges are well known, and over the years a number of approaches to addressing them have evolved. But as machine learning (ML) becomes an essential component of more and more systems, it poses a new set of challenges to development teams. Chief among these challenges is getting data scientists (who employ an experimental approach to system model development) and software developers (who rely on the discipline imposed by software engineering principles) to work harmoniously. In this SEI blog post, which is adapted from a recently published paper to which I contributed, I highlight the findings of a study on which I teamed up with colleagues Nadia Nahar (who led this work as part of her PhD studies at Carnegie Mellon University and Christian Kästner (also from Carnegie Mellon University) and Shurui Zhou (of the University of Toronto).The study sought to identify collaboration challenges common to the development of ML-enabled systems.

VR and AI (virtual reality and artificial reality) are not new inventions. VR uses technology to create simulated environments into which we can immerse ourselves, whereas AI seeks to empower technical gadgets with the understanding and awareness of a responsive creature. Recently, significant improvements have been made to enhance VR and AI and integrate them to develop a unified type of technology with virtually limitless possibilities. Both VR and AI have a growing market; a Grand View Research study claims that by 2025, "the worldwide artificial intelligence market size is estimated to reach USD 390.9 billion." Between 2019 and 2025, the market is expected to grow at a CAGR of 46.2 percent.

Python is a very highly rated programming language, even if we only count the possibilities through it. When hiring Python developers, you will have to be very specific, as, for such development languages, core expertise in the subject matters remains essential. Using Python, you can do almost anything, and it is far more capable in those terms, compared to other languages. This can act as a double-edged sword to many people who are just looking for Python developers, as their project might miss the fit Python developers. This is basically due to so many applications that Python serves a purpose for, and to find those who can be fit your project needs more bifurcation of skills.

A new way of using machine learning to examine satellite images could help people around the world. More than 700 imaging satellites orbit the earth, but only governments and companies with wealth and expertise can access the data they produce. Now, researchers said in a recent paper that they have invented a machine learning system using low-cost, easy-to-use technology that could bring satellite analytical power to researchers and governments worldwide. "To plan infrastructure like roads and bridges or to target food aid, we need to know where people live and what their needs are," Jonathan Proctor, a co-author of the paper, told Lifewire in an email interview. "Satellite imagery and machine learning can help measure socio-economic conditions in places where other measurements are insufficient."

Description Python has a simple syntax that makes it suitable for learning programming as a first language. The learning curve is smoother than other languages such as Java, which quickly requires learning about Object Oriented Programming or C/C that requires to understand pointers. Still, it's possible to learn about OOP or functional programming in Python when the time comes. Python provides a well-furnished standard library and many external libraries are available. This allows to quickly develop concrete applications.

AI (Artificial Intelligence) was created by writing numerous lines of code, now AI has the capability to code with ease. Sounds unreal, but it's true. Nowadays, coders and even many companies are using AI to help humans in the software development process. Now, software developers can not only use AI to write and review codes but also test software, find bugs and optimize development projects. AI not only will help the new generation of developers learn to code easily, but also help companies to deploy software and apps efficiently.

The massive inflow of data in recent years, the growth of powerful processing and affordable data storage has given wheels to machine learning. Machine learning is an advanced technology that helps machines to learn from data. The performance of the solution solely depends on the data it is fed with. Earlier, the data flow was comparatively less. Henceforth, it took many years for machine learning technology to mature and come out rock headed in the market.