In open-domain conversational systems, it is important but challenging to leverage background knowledge. We can use the incorporation of knowledge to make the generation of dialogue controllable, and can generate more diverse sentences that contain real knowledge. In this paper, we combine the knowledge bases and pre-training model to propose a knowledge-driven conversation system. The system includes modules such as dialogue topic prediction, knowledge matching and dialogue generation. Based on this system, we study the performance factors that maybe affect the generation of knowledge-driven dialogue: topic coarse recall algorithm, number of knowledge choices, generation model choices, etc., and finally made the system reach state-of-the-art. These experimental results will provide some guiding significance for the future research of this task. As far as we know, this is the first work to study and analyze the effects of the related factors.

Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of disturbances, and environmental conditions. Since state-of-the-art attitude estimators do not generalize well over these characteristics, their parameters must be tuned for the individual motion characteristics and circumstances. We propose RIANN, a real-time-capable neural network for robust IMU-based attitude estimation, which generalizes well across different motion dynamics, environments, and sampling rates, without the need for application-specific adaptations. We exploit two publicly available datasets for the method development and the training, and we add four completely different datasets for evaluation of the trained neural network in three different test scenarios with varying practical relevance. Results show that RIANN performs at least as well as state-of-the-art attitude estimation filters and outperforms them in several cases, even if the filter is tuned on the very same test dataset itself while RIANN has never seen data from that dataset, from the specific application, the same sensor hardware, or the same sampling frequency before. RIANN is expected to enable plug-and-play solutions in numerous applications, especially when accuracy is crucial but no ground-truth data is available for tuning or when motion and disturbance characteristics are uncertain. We made RIANN publicly available.

This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!

This Course covered all the topics you need to know to master yourself in NLP (Natural language Processing) Using Python in Artificial Intelligence(AI). Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. NLTK is a leading platform for building Python programs to work with human language data.NLTK, the most widely-mentioned Natural Language Processing(NLP) library for Python. And we also know that a word can behave both as a verb, noun and any pos, according to the situation of word in sentence.

Welcome to our April 2021 monthly digest where you can catch up with any AIhub stories you may have missed, get the low-down on recent conferences and events, and much more. In this edition we cover a diverse range of topics including AI ethics, education, music, GPT-Neo, and Westworld. Marija Slavkovik wrote this very interesting retrospective on the AAAI symposium on implementing AI ethics. The aim of the symposium was to "facilitate a deeper discussion on how intelligence, agency, and ethics may intermingle in organizations and in software implementations." Another ethics conference on the horizon is the AAAI/ACM conference on artificial intelligence, ethics, and society, scheduled for 19-21 May.

One of my first assignments as a new electrical engineering graduate was to diagnose and troubleshoot an out-of-service induction motor. The first step was to exam the symptoms of the fault and determine the root cause of failure. In order to do this, I required a specific diagnostic tool and a detailed testing procedure. Unfortunately, the diagnostic tool was unavailable in the short-term and the testing procedure required a minimum of 3 weeks to execute. I continue to think about this scenario and I classify it as an opportunity to improve the current standards for motor diagnostics and repair.

Palantir could kick off the adoption of AI and Blockchain across industry, by enabling organizations to create digital twins that these technologies can be deployed on, completely changing the way companies function. I have been eyeing Palantir recently. As with other tech companies, I find that the time that I spend working with different technologies has helped me understand what Palantir is all about very quickly. You see, we hear the tech buzzwords "AI" and "blockchain" a lot, but there are a lot of questions about how these technologies are going to drive the GDP needle. In the remainder of this post, I am going to breakdown how I believe Palantir could very well kick start the generalized adoption of AI and blockchain technologies across industry, resulting in better overall business performance for its customers and ultimately, in a solid business for itself.

Last week, I taught a cybersecurity course at the University of Oxford case. I felt that this is significant because typically the problem domain of AI and cybersecurity is mostly an Anomaly detection or a Signature detection problem. Also, most of the times, cybersecurity professionals use specific tools such as splunk or darktrace(which we cover in our course) – but these threats and their mitigations are very new. Hence, they need exploring from first principles/research. Thus, we can cover newer threats such as adversarial attacks(making modifications to input data to force machine-learning algorithms to behave in ways they're not supposed to).

Deep neural networks excel at finding patterns in datasets too vast for the human brain to pick apart. That ability has made deep learning indispensable to just about anyone who deals with data. This year, the MIT Quest for Intelligence and the MIT-IBM Watson AI Lab sponsored 17 undergraduates to work with faculty on yearlong research projects through MIT's Advanced Undergraduate Research Opportunities Program (SuperUROP). Students got to explore AI applications in climate science, finance, cybersecurity, and natural language processing, among other fields. And faculty got to work with students from outside their departments, an experience they describe in glowing terms.

House Republicans are moving closer to ousting Conference Chair Liz Cheney (R-Wyo.) What we're hearing: Most members recognize Cheney can't be succeeded by a white man, given their top two leaders -- House Minority Leader Kevin McCarthy (R-Calif.)