A new institute dedicated to teaching Artificial Intelligence (AI) applications to university students has been launched in Abu Dhabi on Monday (July 15). This is the first-of-its-kind-institute in the UAE will also train government and industries in AI science and applications. With a Dh160 million five-year-fund for AI projects, Khalifa University of Science and Technology launched the Artificial Intelligence and Intelligent Systems Institute (AI Institute) which will focus on AI, data science, robotics, next generation networks, semiconductor technologies and cybersecurity. The AI Institute will bring all the university's research in robotics, artificial intelligence (AI), cyber-security, data science and information and communication technologies under a single umbrella. "Khalifa University's AI Institute, a single umbrella that gathers activities of six research centres, reflects our commitment to research in next generation digital technologies that are priority areas for the UAE's economy," Dr Arif Sultan Al Hammadi, executive vice-president of Khalifa University of Science and Technology said during the launch of the AI Institute.
Lately, I've been working on a couple of scenarios that have reminded me of the importance of feature extraction in deep learning models. As a result, I would like to summarize some ideas I've outlined before about some of the principles of knowledge quality in deep learning and model and the applicability of representation learning to those scenarios. Understanding the characteristics of input datasets is an essential capability of machine learning algorithms. Given a specific input, machine learning models need to infer specific features about the data in order to perform some target actions. Representation learning or feature learning is the subdiscipline of the machine learning space that deals with extracting features or understanding the representation of a dataset.
Machine Learning is a fast growing, rapidly advancing field that touches nearly everyone's lives. There has recently been an explosion of successful machine learning applications - in everything from voice recognition to text analysis to deeper insights for researchers. While common and frequently talked about, most people have only a vague concept of how machine learning actually works. In this tutorial, Dr. Artemy Kolchinsky and Dr. Brendan Tracey outline exactly what it is that makes machine learning so special in an accessible way. The principles of training and generalization in machine learning are explained with ample metaphors and visual intuitions, an extended analysis of machine learning in games provides a thorough example, and a closer look at the deep neural nets that are the core of successful machine learning.
KHARAGPUR: Researchers at IIT Kharagpur have evolved an Artificial Intelligence-aided method to automate the reading of legal case judgments, the premier institute said in a statement on Friday. The researchers from IIT Kharagpur's Computer Science and Engineering department have developed two deep neural models to understand the rhetorical roles of sentences in a legal case judgment, which could prove phenomenal in India where AI is yet to sufficiently penetrate the legal field. The country uses a Common Law system that prioritises the doctrine of legal precedent over statutory law, and where legal documents are often written in an unstructured way. "Taking 50 judgments from the Supreme Court of India, we segmented these by first labelling sentences with the help of three senior law students from IIT Kharagpur's Rajiv Gandhi School of Intellectual Property Law, then performing extensive analysis of the human-assigned labels and developing a high quality gold standard corpus to train the machine to carry out the task," explained research lead Professor Saptarshi Ghosh. Unlike earlier attempts which required substantial human intervention, the neural methods used by Ghosh's team enables automatic learning of the features, given sufficient amount of data, and can be used across multiple legal domains.
Classification is a two-step process, learning step and prediction step, in machine learning. In the learning step, the model is developed based on given training data. In the prediction step, the model is used to predict the response for given data. Decision Tree is one of the easiest and popular classification algorithms to understand and interpret. Decision Tree algorithm belongs to the family of supervised learning algorithms.
Finnish technology firm Reaktor and the University of Helsinki joined forces to educate people on AI for free. The institutions combined to develop an online course to teach the basics of AI to anyone interested in the technology. Reaktor and the University also challenged organizations to train their staff in AI, so far over 200 organisations have pledged to do so – including banks, telecoms, and healthcare organizations. Almost 90 000 students have signed up for the course since it began in May. While popular with Finns, the course is already seeing strong demand globally, attracting students from over 80 different countries.
Python has massive applications in Artificial Intelligence (AI) applications, data science, Machine Learning (ML) and data analytics, US-based online education company according to Coursera. The top 10 list of courses, such as "Programming for Everybody," Python Data Structures," Python for Data Science and AI," has been dominated by python. Python has a lot of advantages. One of them is that it is extremely easy getting started with. It offers a lot of flexibility.
As AI has permeated our lives most innovatively, the interest and investment are expected to grow further to drive the innovation engine across all sectors of society. Such large scale investments by government or private firms will create a long-appraised impact on society and its citizens. AI researches powered by such investment will help root-out the societally relevant problems to engage people in creating a more diverse workforce to better tackle the problems. The report "A 20-Year Community Roadmap for Artificial Intelligence Research in the US" highlights six significant areas where AI research is likely to impact in the next 20 years. In the near term, chronic health conditions like diabetes, cancer, and heart and neurological diseases are likely to benefit most from new applications of AI, according to a survey of healthcare professionals.
At Mawson, we partner with exceptionally talented founders to build AI startups that redefine industries. We are always on the lookout for outstanding technical talent and entrepreneurs, who like us at Mawson, believe building companies is not a job, but a way of life. If you're curious about what the journey of a Machine Learning Engineer at Mawson is like, we invite you to step into the shoes of one as you continue to read on… Congratulations, welcome to the Mawson family, where you've joined the team along with several other new Machine Learning Engineers. The fact that you've made it this far already is an achievement, as we've specifically selected individuals who are exceptional in Math and Programming. Our journey starts by advancing your Deep Learning skills.
Any high school student would guess there is a cosine involved when they see an integral of a sine. Regardless of whether the person understands the thought process behind these functions, it does the job for them. This intuition behind calculus is rarely explored. Though Newton and Leibnitz developed advanced mathematics to solve real-world problems, today most of the schools teach differential equations through semantics. The linguistic appeal of mathematics might get grades in high school, but in the world of research, this is hysterical.