If you have been using twitter recently I bet that from the last 10 tweets 5 of them are linked to AI and the rise of chatGPT. Looking at those tweets AI tools are going to change the world as we know it. This is even more significant in the case of Education and in particular in Higher Education where most of the traditional methods of assessments are based on essay that consists on pieces of written work where students have to answer questions in a specific number of words. A lot of messages that I hear from institutions, and normally from traditional institutions, is that we need to ban chatGPT as this is a danger and a temptation to student to cheat and create this contractual cheating situation were students are submitting work that is not they original work. I think that this view is completely wrong and it is just a way to avoid the problem without thinking first why there is a problem and second what is actually AI and the possible benefits that can bring to education, learning outcomes and yes to assessments.

Ready to build the future with Deep Neural Networks? Stand on the shoulder of TensorFlow and Keras for Machine Learning.

Microsoft shares rose 12.4% last week on word of the release of Copilot, a generative artificial intelligence tool to be integrated into its Office suite. That's huge news from the tech giant because it hands AI to the masses via highly familiar, everyday interfaces such as Word, Excel, PowerPoint and Outlook. Copilot will swiftly bring significant efficiencies and improvements to common tasks including email, analyses, business cases, presentations and performance reviews. With streamlined workflows and automated administrative functions, companies will be challenged to rethink business models, talent needs and resource usage. Very quickly, it will spur creativity, shorten work timelines and improve results.

In this paper, we present a demo of an intelligent personal agent called Hey Dona (or just Dona) with virtual voice assistance in student course registration. It is a deployed project in the theme of AI for education. In this digital age with a myriad of smart devices, users often delegate tasks to agents. While pointing and clicking supersedes the erstwhile command-typing, modern devices allow users to speak commands for agents to execute tasks, enhancing speed and convenience. In line with this progress, Dona is an intelligent agent catering to student needs by automated, voice-operated course registration, spanning a multitude of accents, entailing task planning optimization, with some language translation as needed. Dona accepts voice input by microphone (Bluetooth, wired microphone), converts human voice to computer understandable language, performs query processing as per user commands, connects with the Web to search for answers, models task dependencies, imbibes quality control, and conveys output by speaking to users as well as displaying text, thus enabling human-AI interaction by speech cum text. It is meant to work seamlessly on desktops, smartphones etc. and in indoor as well as outdoor settings. To the best of our knowledge, Dona is among the first of its kind as an intelligent personal agent for voice assistance in student course registration. Due to its ubiquitous access for educational needs, Dona directly impacts AI for education. It makes a broader impact on smart city characteristics of smart living and smart people due to its contributions to providing benefits for new ways of living and assisting 21st century education, respectively.

Simultaneous machine translation, which aims at a real-time translation, is useful in many live scenarios but very challenging due to the trade-off between accuracy and latency. To achieve the balance for both, the model needs to wait for appropriate streaming text (READ policy) and then generates its translation (WRITE policy). However, WRITE policies of previous work either are specific to the method itself due to the end-to-end training or suffer from the input mismatch between training and decoding for the non-end-to-end training. Therefore, it is essential to learn a generic and better WRITE policy for simultaneous machine translation. Inspired by strategies utilized by human interpreters and "wait" policies, we propose a novel adaptive prefix-to-prefix training policy called LEAPT, which allows our machine translation model to learn how to translate source sentence prefixes and make use of the future context. Experiments show that our proposed methods greatly outperform competitive baselines and achieve promising results.

TensorFlow is by far, the most popular library for deep learning. Backed by Google, it is a solid investment of your time and efforts if you want to succeed in the area of machine learning and AI. The issue most people face is that getting started with Tensorflow guides usually delve too deeply into unnecessary mathematics. That is where this course comes in. While some theory is important, a lot of it is just not needed when you're just getting started!

Drexel University has recently launched a new Artificial Intelligence and Machine Learning (AI/ML) learning group. This initiative is designed to help students, faculty, and staff gain the skills necessary to develop AI/ML applications for research and industry. The goal of this program is to create an environment where people can come together to learn about the latest advancements in AI/ML technology while also gaining hands-on experience with real-world projects. The Drexel AI/ML learning group will be led by Dr. Yaser Abu-Mostafa, Professor of Electrical Engineering at Caltech, who brings decades of experience in machine learning research and teaching. He will be joined by other experts from academia as well as industry professionals who have expertise in various aspects of artificial intelligence and machine learning development.

Programming is one aspect of computer science and software engineering. The primary goal of this course is to build a solid foundation of programming knowledge and skills. With what learned in this course, the students should find it is easier to learn more advanced concepts in computer science. Not everyone will be or want to be a software engineer, however, this course can help them realize how a problem can be solved by using computer program; how Python can help scientists and engineers improve their productivity. Believe or not, software developers usually join a product development from the very beginning to the very end.

Welcome back to our DeepStream tutorial series! In the last blog, we covered the basics of DeepStream and how to get it up and running on your machine. Now it's time to dive a little deeper into the world of DeepStream and see what it can do. GStreamer is a powerful open-source multimedia framework that helps you build audio and video processing pipelines. And when it comes to DeepStream, GStreamer pipelines are kind of a big deal. They're the driving force behind DeepStream, and they're what allow you to process video streams in real-time.

Computer vision is a field of artificial intelligence that has become increasingly popular in recent years. It involves teaching machines to understand and interpret visual information, and it has many applications in areas like facial recognition, self-driving cars, and medical imaging. If you're interested in learning computer vision from scratch, here's a step-by-step guide that includes algorithms to get you started. Python is a widely used programming language in the field of computer vision. It's easy to learn, and there are many libraries available that make it easy to perform complex tasks.