Brad Efron, in his lecture at the occasion of receiving the International Prize in Statistics, brought up some fascinating thoughts on "prediction, estimation and attribution", with particular attention to the new "wide data era" which has entered statistics and data science more generally (Efron, 2019, 2020). Looking back almost 20 years ago, there has been a huge development in statistics since Leo Breiman's article "Statistical Modeling: The Two Cultures" (Breiman, 2001). Even more broadly, data science has become an emerging new field and profession. It deals with information extraction from data, often in close proximity with other sciences. Its historical roots are in statistics, and statistical "critical" thinking plays an ever important role in inference from data to models and prediction. There are many interesting facets of this broad topic, see for example David Donoho's "50 years of Data Science" (Donoho, 2017) or Bin Yu's "Veridical Data Science" (Yu and Kumbier, 2020). Efron (2019, 2020) has formulated intriguing ideas on "prediction, estimation and attribution". We are presenting here a few additional considerations on the topic, as outlined in the following Sections 1.1 and 1.2.

Maria Viego and Cooper Glynn were thriving at their elementary schools. Maria, 10, adored the special certificates she earned volunteering to read to second-graders. Cooper, 9, loved being with his friends and how his teacher incorporated the video game Minecraft into lessons. But when their campuses shut down amid the COVID-19 pandemic, their experiences diverged dramatically. Maria is a student in the Coachella Valley Unified School District, where 90% of the children are from low-income families. She didn't have a computer, so she and her mother tried using a cellphone to access her online class, but the connection kept dropping, and they gave up after a week. She did worksheets until June, when she at last received a computer, but struggled to understand the work. Now, as school starts again online, she has told her mother she's frustrated and worried.

After multiple online meetings and virtual conversations, I've learned there are many ways people are dealing with suddenly working from home. I would categorize a really low desire as, "I don't want to start anything new, let's just try to get through this." And a really high desire as, "I have more free time than I used to, I should learn something new!" If and when you are looking to learn new things, I've compiled a list of deep learning resources. Below is a range of deep learning resources that can take anywhere from 5 minutes to 3 hours depending on what you're looking for.

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—Rebecca Bayeck and Azure Stewart “Artificial Intelligence and Archives” was the inaugural webinar of the series on Emerging Technologies, Big Data & Archives, organized by CLIR postdocs Rebecca Y. Bayeck of the Schomburg Center for Research in Black Culture and Azure Stewart of New York University. With the emergence of new technologies and big data, the processing and preservation of data has changed and will continue to change. As in other domains (e.g., health, video games), artificial intelligence (AI) is increasingly reshaping the way we process, interact with, and think about archives. Consequently, in the age of big data, archives are not just “a collection of historical records relating to a place, organization, or family” (Cambridge Dictionary Online). Today, archives also include all types of digital data—including social media data—and algorithms. Archivists are therefore called on to preserve and process data as they are being created, which requires understanding AI languages, processes, and practices for the creation and protection of data/records now for the future. In this webinar, our speaker Dr. Anthea Seles, from the International Council on Archives (ICA), discussed AI in archival spaces: its uses, application, and the role archivists should play to become critical voices in AI discussions. Two hours were not enough to address all the questions raised by the 280 attendees. As a follow up to the webinar, we have thematically organized and addressed the unanswered questions and present them here. Artificial Intelligence in Archives How much has AI penetrated archives in the developing world? I would say [this has been] limited, if at all. I think the main issue is that these technologies are being applied in the assessment of development initiatives like Sustainable Development Goals (SDGs). Increasingly there are many projects focusing on artificial intelligence and human rights, for example the University of Essex Human Rights, Big Data and Technology Project, and it is becoming a concern for organisations like Amnesty International. Who already has the best AI for archives today, according to ICA regulation, that we can adopt? There is no commercial provider that works specifically on archival questions. I think you can use off-the-shelf eDiscovery software, but you need to have a basic understanding of what the technology is doing in order to measure your precision and recall.  Artificial Intelligence Tools Will governments and big corporations use artificial intelligence as a tool to centralize information in future? Potentially. I think there is some thinking about this coming out of the records management community, but I still believe it is about balancing the strengths of the tool with the continuing need for human intervention. The question is, when will the human be needed? And what can the tool be trusted to do with minimum supervision? How do we ensure a continuous feedback loop to identify records of long-term value as information creation changes?  What tools were you using for the file analysis and visualization in this presentation? The screen shots are only example photos, they are not from any of the tools we used. We looked at several eDiscovery tools with different algorithms (e.g., Latent Semantic Indexing, Latent Dirichlet Allocation). These are bog standard machine learning applications that have been around for a while, and we chose to go down that road to see what we could get in off-the-shelf commercial software packages. So, is there a way to write a script to avoid metadata corruption and alteration? There are tools now you can use that will preserve the integrity of the metadata when you move material from one system or file to another. I think for historical metadata alteration/corruption it is a question of how we explain this to users and how this might affect different access methods like visualisation.  Will the International Council on Archives provide training on artificial intelligence and machine learning? Not yet, but I’m open to suggestions. [We are] currently speaking with different stakeholders and maybe we can hold a hackathon at the Abu Dhabi Congress.  Access to Archives Will the course Managing Digital Archives be accessible online? The managing digital archives course is organized by the ICA and will be accessible online in fall 2020. Please check the ICA website or social media channels (Twitter and Facebook) for more information. What are some of the practices in the UK National Archives and government on managing structured data as records? How does the UK identify, capture, manage, and apply retention and disposition to data (both transactional applications and analytical ones)? There are no published policies on identification of datasets that I can see and would suggest you contact either the record copying or the UK government web archive records unit to see if anything more substantive has been developed. What is your suggestion for keeping physical records for posterity and authentication? Records should always be maintained in the format in which they are created. The belief in scanning paper records and destroying them in order to save space and save on storage costs is a false economy. The level at which you should be scanning that material and the amount of metadata that should be captured to maintain it over time is very high. Also, you need to take into account computer storage costs, and whether you can afford the costs of digital preservation software, which all begins to add up. One must also take into account the active management of these authentic digital surrogates by digital preservation specialists. Furthermore, if you have a paper management problem and you don’t take that into account when you move into the digital environment you are then transferring an analog integrity issue into a digital integrity/authenticity issue. Digital will not solve integrity issues; in my opinion it will magnify them. Artificial Intelligence and Society In Brazil, we are concerned with the problem of the spread and political use of misinformation (fake news). How can archivists with algorithm training provide reliable research insights to fight against this historical problem? At this point, I couldn’t honestly provide you with an answer but Read More

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PyTorch has rapidly become one of the most transformative frameworks in the field of Deep Learning. Since its release, PyTorch has completely changed the landscape in the field of deep learning due to its flexibility, and how easy it is to use when building Deep Learning models. Deep Learning jobs command some of the highest salaries in the development world. This course is meant to take you from the complete basics, to building state-of-the art Deep Learning and Computer Vision applications with PyTorch. With over 44000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.

Description: In this project, you will learn how to build a convolutional neural network using Google TensorFlow. You will do the visualization of images using training, providing input images, losses, and distributions of activations and gradients. You will learn to break each image into manageable tiles and input them to the convolutional neural network for the desired result. Description: In this project, by understanding the customer needs, you will be able to offer the right services through Artificial Intelligence chatbots. You will learn how to create the right artificial neural network with the right amount of layers to ensure that the customer queries are comprehensible to the Artificial Intelligence chatbot.

Data science interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to ensure you are ready for back-to-back questions on statistics, programming and machine learning. Before we get started, there's one tip I'd like to share. I've noticed that there are several types of data science interviews that companies conduct. Some data science interviews are very product and metric driven.

AI is an enabler in transforming healthcare delivery in terms of treatment modalities and their outcomes, electronic health records-based prediction, diagnosis and prognosis and precision medicine. This course will introduce you to the cutting edge advances in AI concerning healthcare by exploiting deep learning architectures. The course aims to provide students from diverse backgrounds with both conceptual understanding and technical grounding of leading research on AI in healthcare.