Welcome to our February 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we cover our latest New voices in AI interview, hear from a NeurIPS award winner, and get stuck into AAAI 2022. You may have seen the launch of our new series last month. In the latest episode, Isabel Cachola talks about how she got into AI and her work on interpretability of NLP models. In this interview, Bernard Koch tells us about research that won him, and co-authors Emily Denton, Alex Hanna and Jacob Foster, a best paper prize at NeurIPS 2021.

I took a year off Medium and never posted a single article on computer vision. Looking back, the last article I posted here was December 12, 2020 and boy was it a long a time ago. The truth is that I was torn between my game project and also a million other things like engineering studies, competitions and projects, internships and my time in self-learning computer vision skills. But, it was a great year nonetheless and I have to say, I am proud of myself that I am still able to continue to learn about computer vision and even got an internship in this field. Needless to say, I learnt a lot from the job and here's the breakdown.

This comprehensive machine learning course includes over75 HD videolectureswith over 11 hours of video content. Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?! You came to the right place! Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale! The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020.

This is a must read for any practitioner of RL. The book is divided into 3 parts and I would strongly recommend reading through Parts I and II. The sections marked with (*) can be skipped in first reading. And if you click on this, you will see the links of python and Matlab implementations of the examples and exercises contained in the book.

Inside of the development studios of San Francisco-based Dexterity, Inc. there is a robot arm that stands as tall as a human. It is placed between a conveyor belt and several wooden pallets, all of which are typical of most warehouse packing facilities. But this is no typical warehouse facility. This is the location where most warehouse packing facilities would have teams of people manually picking up boxes from the conveyor belt and carefully placing them onto the pallets for wrapping and shipping, but there are no such people here. Instead, as the packages come down the belt, this robot recognizes them, picks them up, and then deposits them onto the target pallet with a gentle touch.

The US scientific workforce is not representative of the population. Barriers to entry and participation have been well-studied; however, few have examined the effect of these disparities on the advancement of science. Furthermore, most studies have looked at either race or gender, failing to account for the intersection of these variables. Our analysis utilizes millions of scientific papers to study the relationship between scientists and the science they produce. We find a strong relationship between the characteristics of scientists and their research topics, suggesting that diversity changes the scientific portfolio with consequences for career advancement for minoritized individuals. Science policies should consider this relationship to increase equitable participation in the scientific workforce and thereby improve the robustness of science. The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact. We find homophily between identities and topic, suggesting a relationship between diversity in the scientific workforce and expansion of the knowledge base.

Data science is one of the most important and in-demand skills in 2022. If you're looking to learn data science, you're in luck! There are plenty of great resources available online, including DataCamp, Coursera, and Udacity. But if you're looking for a more informal and entertaining learning experience, Youtube might be the right place for you. In this post, we will list 7 of my favorite Data Science Youtubers who offer free learning content.

Practical tools for clustering streaming data must be fast enough to handle the arrival rate of the observations. Typically, they also must adapt on the fly to possible lack of stationarity; i.e., the data statistics may be time-dependent due to various forms of drifts, changes in the number of clusters, etc. The Dirichlet Process Mixture Model (DPMM), whose Bayesian nonparametric nature allows it to adapt its complexity to the data, seems a natural choice for the streaming-data case. In its classical formulation, however, the DPMM cannot capture common types of drifts in the data statistics. Moreover, and regardless of that limitation, existing methods for online DPMM inference are too slow to handle rapid data streams. In this work we propose adapting both the DPMM and a known DPMM sampling-based non-streaming inference method for streaming-data clustering. We demonstrate the utility of the proposed method on several challenging settings, where it obtains state-of-the-art results while being on par with other methods in terms of speed.

Machine Learning applications are everywhere nowadays from Google Translate and NLP API,to Recommendation Systems used by YouTube,Netflix and Amazon,Udemy and more. As we have come to know, data science and machine learning is quite important to the success of any business and sector- so what does it take to build machine learning systems that works? In performing machine learning and data science projects, the normal workflow is that you have a problem you want to solve, hence you perform data collection,data preparation,feature engineering,model building and evaluation and then you deploy your model. However that is not all there is, there is a lot more to this life cycle. In this course we will be introducing to you some extra things that is not covered in most machine learning courses - such as working with pipelines specifically Scikit-learn pipelines, Spark Pipelines,etc and working with imbalanced dataset,etc We will also explore other ML frameworks beyond Scikit-learn,Tensorflow or Pytorch such as TuriCreate, Creme for online machine learning and more.

Whether you like it or not, Artificial intelligence (AI) and its subcategory Machine learning (ML), are already an important part of our everyday lives. From using Google maps, navigating social media or even passing an exam at university, ML is changing how we learn, communicate and do business. But what is ML exactly and should we be worried or optimistic about the future? In this article we err on the side of optimism and explore in detail the impact ML is having on education, and where things might be heading in the future. To comprehend what Machine learning actually is, we first need to understand the broader category of artificial intelligence.