In this work, we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of deep learning chatbots for task classification. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transformation-based learning approaches for Natural Language Processing. Human beings are asked to paraphrase commands and questions for task identification for further execution of a machine. The commands and questions are split into training and validation sets. A total of 483 responses were recorded. Secondly, the training set is paraphrased by the T5 model in order to augment it with further data. Seven state-of-the-art transformer-based text classification algorithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet) are benchmarked for both sets after fine-tuning on the training data for two epochs. We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4.01%. The best result was the RoBERTa model trained on T5 augmented data which achieved 98.96% classification accuracy. Finally, we found that an ensemble of the five best-performing transformer models via Logistic Regression of output label predictions led to an accuracy of 99.59% on the dataset of human responses. A highly-performing model allows the intelligent system to interpret human commands at the social-interaction level through a chatbot-like interface (e.g. "Robot, can we have a conversation?") and allows for better accessibility to AI by non-technical users.

Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of (connected) triples. Two main challenges still face the current crop of encoder-decoder-based methods, especially on small subgraphs: (1) low diversity and poor fluency due to the limited information contained in the subgraphs, and (2) semantic drift due to the decoder's oblivion of the semantics of the answer entity. We propose an innovative knowledge-enriched, type-constrained and grammar-guided KBQG model, named KTG, to addresses the above challenges. In our model, the encoder is equipped with auxiliary information from the KB, and the decoder is constrained with word types during QG. Specifically, entity domain and description, as well as relation hierarchy information are considered to construct question contexts, while a conditional copy mechanism is incorporated to modulate question semantics according to current word types. Besides, a novel reward function featuring grammatical similarity is designed to improve both generative richness and syntactic correctness via reinforcement learning. Extensive experiments show that our proposed model outperforms existing methods by a significant margin on two widely-used benchmark datasets SimpleQuestion and PathQuestion.

AI (Artificial Intelligence) governance is about evaluating and monitoring algorithms for effectiveness, risk, bias and ROI (Return On Investment). But there is a problem: Often not enough attention is paid to this part of the AI process. "AI projects are rarely coordinated across a company and data science teams are often isolated from application development," said Mike Beckley, who is the CTO of Appian. "And now regulators are starting to ask questions businesses don't know how to answer." Keep in mind that AI introduces unique problems.

My opinions are my own. An introduction to the harm that ML systems cause and to the power imbalance that exists between ML system developers and ML system participants …and 10 concrete ways for machine learning practitioners to help build fairer ML systems. Image description: Photo of Black Lives Matter protesters in Washington, D.C. -- 2 signs say "Black Lives Matter" and "White Silence is Violence." Machine learning systems are increasingly used as tools of oppression. All too often, they're used in high-stakes processes without participants' consent and with no reasonable opportunity for participants to contest the system's decisions -- like when risk assessment systems are used by child welfare services to identify at-risk children; when a machine learning (or "ML") model decides who sees which online ads for employment, housing, or credit opportunities; or when facial recognition systems are used to surveil neighborhoods where Black and Brown people live. In reality though, machine learning systems reflect the beliefs and biases of those who design and develop them.

A new software platform, created by two University of Toronto alumni, aims to make virtual classrooms more functional by providing real-time feedback and specific insights into how student understanding of mathematics is changing. Last March, Nived Kollanthara, an alumnus of the Faculty of Applied Science & Engineering, was living in New York City, where he volunteered part-time at a shelter, helping kids with their math homework. When the pandemic hit, he realized right away the impact it would have. "The kids I work with need extra, individual attention to help them succeed in the classroom," Kollanthara says. "I was worried about how they would be getting that in a remote environment."

The National Educational Policy (NEP) 2020 was introduced this year. One of the highlights of NEP has been the emphasis on introducing artificial intelligence, machine learning, and related technologies into the curriculum. When we think of exposing children to technology-based learning, there is often a certain amount of scepticism involved. Parents and teachers are often torn between the question -- how soon is too soon. However, it doesn't need to be as intimidating as it is made out to be.

The grading process is a method of assigning a grade to an assignment submitted by a student. From high school to college students, the need for grading assignments is quite evident. The graded assignments help students understand the quality of their submitted work. The manual process of grading involves checking each assignment and assigning a grade to it. The quality of the written work and the substantiation of the assignment's core topic are vital factors for grading the paper.

From morning to night we are surrounded by computers and can't imagine ourselves without it. Online games and puzzles have replaced our old board games and OTTS has changed our entertainment consuming habits. Pandemic has changed a lot of existing jobs and creates a lot of new jobs that we have never thought will ever exist. The coming age is going to be completely dynamic and our current curriculum was not enough to prepare our students for the coming market. The National Education Policy (NEP), has introduced Coding at a young age to enhance the exposure to technology and create a path to a new world of innovation and creativity.

Nearly a decade ago, back in 2011, when I had just completed the 4th semester of my Computer Science & Engineering Degree course, I found that even though I had fared well in all my exams, my practical knowledge in this field was (in the words of Lord Kelvin -- the famous mathematical physicist)"of a meager and unsatisfactory kind". This was due to the fact that, aside from a handful of really great courses, the majority of my coursework relied on rote-learning and the competitive pursuit of grades instead of practical knowledge. I had joined the field of Computer Science to satisfy my childhood dream of working with computers, but I found I was still far from my dream of understanding and creating software with my computer. In this dismal state, I spent the beginning of my semester-break searching for a motivation in the online universe. After Googling for a short while, I stumbled upon Mehran Sahami's CS106A video lectures on Stanford's Youtube channel, and thus began my tryst with online education.

Created by The Click Reader Preview this Udemy Course - GET COUPON CODE Learn how to build Machine Learning projects in this TensorFlow Course created by The Click Reader. In this course, you will be learning about Scalar as well as Tensors and how to create them using TensorFlow. You will also be learning how to perform various kinds of Tensor operations for manipulating and changing tensor values. You will be performing a total of three Machine Learning projects while learning through this TensorFlow full course: 1. Linear Regression from Scratch You will be learning how to create a Linear Regression model from scratch using TensorFlow. You will be preparing the data, building the model architecture as well as training the model using a custom-made loss function as well as an optimizer.