Artificial intelligence, 21st century competences, and socio-emotional learning in education: More than high-risk? [Scholarly Article - European Journal of Education, October 2022]

Title:

Artificial intelligence, 21st century competences, and socio-emotional learning in education: More than high-risk?

 

Author:

Ilkka Tuomi 

Published:

European Journal of Education, 7 October 2022

https://onlinelibrary.wiley.com/doi/full/10.1111/ejed.12531

Abstract:

Over the last two decades, 21st century competences and socio-emotional skills have become a major focus in educational policy. In this article, skills for the 21st century, soft skills, as well as social and emotional skills, are contextualised in the context of technological change, machine learning, and the ethics of artificial intelligence. The use of data-driven AI technologies to model and measure these skills—in this article defined as non-epistemic competence components—can lead to major social challenges that have important implications for educational policies and practices. A moratorium on the use of data on these competence components in machine learning systems is proposed until the society-wide impact is better understood.

University of Innsbruck, Austria - Reading old handwriting with an AI platform [Tech Xplore, October 2022]

Title:

Reading old handwriting with an AI platform

 

Author:

Christian Flatz, University of Innsbruck

 

Published:

Tech Xplore, 11 October 2022

https://techxplore.com/news/2022-10-ai-platform.html

 

From the article:

Using artificial intelligence, computers can decipher handwritten texts and make them readable for everyone. The Transkribus platform, co-developed at the University of Innsbruck, Austria, makes this technology available to scholars and the general public. An ever-growing group of people are using Transkribus to research their family history. Recently, users from all over the world met in Innsbruck.

 

Note from blog owner:

The author of this article also mentions that "a recent study by the University of Edinburgh revealed that more than 400 scientific publications have now been produced with the help of Transkribus." 

ALSO SEE

Title:

Machine learning and big data are unlocking Europe's archives

By: 

Horizon Magazine, Fintan Burke, Horizon: The EU Research & Innovation Magazine

Published:

Tech Xplore, 11 December 2020

https://techxplore.com/news/2020-12-machine-big-europe-archives.html

Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education [Scholarly Article - Data, August 2022]

Title:

Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education

 

Authors:

Joanna Alvarado-Uribe, Paola Mejía-Almada, Ana Luisa Masetto Herrera, Roland Molontay, Isabel Hilliger, Vinayak Hegde, José Enrique Montemayor Gallegos, Renato Armando Ramírez Díaz & Hector G. Ceballos

 

Published:

Data, 7(9), 25 August 2022

https://www.mdpi.com/2306-5729/7/9/119

Abstract:

High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify students at risk and understand the main factors of dropping out. Together with the emergence of efficient computational methods, the rich data accumulated in educational administrative systems have opened novel approaches to promote student persistence. In order to support research related to preventing student dropout, a dataset has been gathered and curated from Tecnologico de Monterrey students, consisting of 50 variables and 143,326 records. The dataset contains non-identifiable information of 121,584 High School and Undergraduate students belonging to the seven admission cohorts from August–December 2014 to 2020, covering two educational models. The variables included in this dataset consider factors mentioned in the literature, such as sociodemographic and academic information related to the student, as well as institution-specific variables, such as student life. This dataset provides researchers with the opportunity to test different types of models for dropout prediction, so as to inform timely interventions to support at-risk students.

 

Yann LeCun has a bold new vision for the future of AI [MIT Technology Review, June 2022]

Title:

Yann LeCun has a bold new vision for the future of AI

 

Authors:

Melissa Heikkilä & Will Douglas Heavenarchive

 

Published:

MIT Technology Review, 24 June 2022

https://www.technologyreview.com/2022/06/24/1054817/yann-lecun-bold-new-vision-future-ai-deep-learning-meta/

 

From the article:

One of the godfathers of deep learning pulls together old ideas to sketch out a fresh path for AI, but raises as many questions as he answers.

ALSO SEE

LeCun,Yann (2022). A Path Towards Autonomous Machine Intelligence. OpenReview, 27 June 2022.

https://openreview.net/forum?id=BZ5a1r-kVsf

Abstract:

How could machines learn as efficiently as humans and animals?  How could machines learn to reason and plan?  How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons?  This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning.

 

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review [Scholarly Article - Agronomy, March 2022]

Title:

Challenges to Use Machine Learning in Agricultural Big Data: A Systematic Literature Review

 

Authors:

Ania Cravero1, Sebastian Pardo1, Samuel Sepúlveda1 & Lilia Muñoz2

 

1 Department of Computer Science and Informatics, Center for Software Engineering Studies, Universidad de La Frontera, Temuco 4780000, Chile 

 

2 Faculty of Computer Systems Engineering, Universidad Tecnológica de Panamá, Panama City 32401, Panama

 

Published:

Agronomy, 21 March 2022

https://www.mdpi.com/2073-4395/12/3/748

 

Abstract:

Agricultural Big Data is a set of technologies that allows responding to the challenges of the new data era. In conjunction with machine learning, farmers can use data to address different problems such as farmers' decision-making, crops, weeds, animal research, land, food availability and security, weather, and climate change. The purpose of this paper is to synthesize the evidence regarding the challenges involved in implementing machine learning in Agricultural Big Data. We conducted a Systematic Literature Review applying the PRISMA protocol. This review includes 30 papers, published from 2015 to 2020. We develop a framework that summarizes the main challenges encountered, the use of machine learning techniques, as well as the main technologies used. A major challenge is the design of Agricultural Big Data architectures, due to the need to modify the set of technologies adapting the machine learning techniques, as the volume of data increases.

Cedars-Sinai Medical Center - AI may detect earliest signs of pancreatic cancer [Medical Xpress, April 2022]

Title:

AI may detect earliest signs of pancreatic cancer

 

By:

Cedars-Sinai Medical Center, United States of America

 

Published:

Medical Xpress, 26 April 2022

https://medicalxpress.com/news/2022-04-ai-earliest-pancreatic-cancer.html

 

From the article:

An artificial intelligence (AI) tool developed by Cedars-Sinai investigators accurately predicted who would develop pancreatic cancer based on what their CT scan images looked like years prior to being diagnosed with the disease. The findings, which may help prevent death through early detection of one of the most challenging cancers to treat, are published in the journal Cancer Biomarkers. 

 

Enhanced Robots as Tools for Assisting Agricultural Engineering Students’ Development [Scholarly Article - Electronics, 2022]

Title:

Agricultural Engineering Students’ Development 

 

Authors:

Dimitrios Loukatos, Maria Kondoyanni, Ioannis-Vasileios Kyrtopoulos & Konstantinos G. Arvanitis

 

Published:

Electronics, 1 March 2022

https://www.mdpi.com/2079-9292/11/5/755

 

Abstract:

Inevitably, the rapid growth of the electronics industry and the wide availability of tailored programming tools and support are accelerating the digital transformation of the agricultural sector. The latter transformation seems to foster the hopes for tackling the depletion and degradation of natural resources and increasing productivity in order to cover the needs of Earth’s continuously growing population. Consequently, people getting involved with modern agriculture, from farmers to students, should become familiar with and be able to use and improve the innovative systems making the scene. At this point, the contribution of the STEM educational practices in demystifying new areas, especially in primary and secondary education, is remarkable and thus welcome, but things become quite uncertain when trying to discover efficient practices for higher education, and students of agricultural engineering are not an exception. Indeed, university students are not all newcomers to STEM and ask for real-world experiences that better prepare them for their professional careers. Trying to bridge the gap, this work highlights good practices during the various implementation stages of electric robotic ground vehicles that can serve realistic agricultural tasks. Several innovative parts, such as credit card-sized systems, AI-capable modules, smartphones, GPS, solar panels, and network transceivers are properly combined with electromechanical components and recycled materials to deliver technically and educationally meaningful results.

Neural networks overtake humans in Gran Turismo racing game [Nature, February 2022]

Title:   

Neural networks overtake humans in Gran Turismo racing game  

 

Author: 

J. Christian Gerdes  

 

Published:    

Nature, 9 February 2022 

https://www.nature.com/articles/d41586-022-00304-2  

 

From the article: 

Driving a racing car requires a tremendous amount of skill. Now, artificial intelligence has challenged the idea that this skill is exclusive to humans — and it might even change the way automated vehicles are designed.

 

AFRICA - 5,000 PhD scholars to meet Africa’s growing AI needs [University World News, February 2022]

Title: 

5,000 PhD scholars to meet Africa’s growing AI needs  

 

Author: 

Eve Ruwoko  

 

Published: 

University World News, 1 February 2022 

https://www.universityworldnews.com/post.php?story=20220130114337493   

 

From the article: 

If Africa wants to tap into the benefits of the digital economy to address the United Nations’ Sustainable Development Goals (SDG), at least 5,000 PhD scholars in the areas of artificial intelligence (AI) and machine learning must be cultivated over the next five years, according to Professor Tom Ogada, executive director of the African Centre for Technology Studies (ACTS).

 

Researchers Create a Camera the Size of a Salt Grain Using Neural Nano-Optics [NVIDIA.DEVELOPER, December 2021]

Title:

Researchers Create a Camera the Size of a Salt Grain Using Neural Nano-Optics

 

Author:

Michelle Horton

 

Published:

NVIDIA.DEVELOPER, 9 December 2021

https://developer.nvidia.com/blog/using-neural-nano-optics-researchers-create-a-camera-the-size-of-a-salt-grain/

 

From the article:

A team of researchers from Princeton and the University of Washington created a new camera that captures stunning images and measures in at only a half-millimeter—the size of a coarse grain of salt. 

 

The new study, published in Nature Communications, outlines the use of optical metasurfaces with machine learning to produce high-quality color imagery, with a wide field of view. The device could be used across industries ranging from robotics to most notably the medical field, to help with disease diagnosis.

 

Hebrew University of Jerusalem, Israel - New Study Finds a Single Neuron Is a Surprisingly Complex Little Computer

Title:

New Study Finds a Single Neuron Is a Surprisingly Complex Little Computer

 

Author:

Jason Dorrier

 

Published:

SingularityHub, 12 September 2021

https://singularityhub.com/2021/09/12/new-study-finds-a-single-neuron-is-a-surprisingly-complex-little-computer/

 

From the article:

Scientists know biological neurons are more complex than the artificial neurons employed in deep learning algorithms, but it’s an open question just how much more complex.  

 

In a fascinating paper published recently in the journal Neuron, a team of researchers from the Hebrew University of Jerusalem tried to get us a little closer to an answer. While they expected the results would show biological neurons are more complex—they were surprised at just how much more complex they actually are.

 

Charles University, Czech Republic - New AI Algorithm Unlocks RAPID High-Resolution Color 3D Printing [3D Printing Industry, June 2021]

Title:

New AI Algorithm Unlocks RAPID High-Resolution Color 3D Printing

 

Author:

Paul Hanaphy

 

Published:

3D Printing Industry, 4 June 2021

https://3dprintingindustry.com/news/new-ai-algorithm-unlocks-rapid-high-resolution-color-3d-printing-191112/

 

From the article:

Researchers from Charles University’s Computer Graphics Group (CGG) have developed a machine learning (ML)-based technique that could help unlock the potential of high fidelity color 3D printing.

 

University of Gothenburg - New Deep Learning AI Tool Can Revolutionize Microscopy [SciTechDaily, 14 April 2021]

Title:

New deep learning AI tool can revolutionize microscopy

By:

University of Gothenburg

Published:

SciTechDaily, 14 April 2021

https://scitechdaily.com/new-deep-learning-ai-tool-can-revolutionize-microscopy/

From the article:

An AI tool developed at the University of Gothenburg offers new opportunities for analyzing images taken with microscopes. A study shows that the tool, which has already received international recognition, can fundamentally change microscopy and pave the way for new discoveries and areas of use within both research and industry.

MIT machine learning models find gaps in coverage by Moderna, Pfizer, other Warp Speed COVID-19 vaccines [ZDNet, 2 December 2020]

Title:

MIT machine learning models find gaps in coverage by Moderna, Pfizer, other Warp Speed COVID-19 vaccines

 

Author:

Tiernan Ray

 

Published:

ZDNet, 2 December 2020

https://www.zdnet.com/article/mit-machine-learning-models-find-gaps-in-coverage-by-moderna-pfizer-other-warp-speed-covid-19-vaccines/

 

From the article:

Vaccines to block COVID-19 that are in development by Moderna, Pfizer, AstraZeneca and others, and that are currently in Phase III clinical trials, may not do as well covering people of Black or Asian genetic ancestry as they do for white people, a study released Wednesday by the Massachusetts Institute of Technology indicated.

The study was published Thursday in the scholarly journal Cell Systems.

Massachusetts Institute of Technology (MIT) & Harvard University - Using machine learning to track the pandemic’s impact on mental health

Title:

Using machine learning to track the pandemic’s impact on mental health

 

Author:

Anne Trafton

 

Published:

MIT News, 5 November 2020

https://news.mit.edu/2020/covid-19-impact-mental-health-1105

 

From the article:

Textual analysis of social media posts finds users’ anxiety and suicide-risk levels are rising, among other negative trends.

Can We Trust AI Doctors? Google Health and Academics Battle It Out

Title:

Can We Trust AI Doctors? Google Health and Academics Battle It Out

Author:

Shelly Fan

Published:

SingularityHub, 20 October 2020

https://singularityhub.com/2020/10/20/can-we-trust-ai-doctors-google-health-and-academics-battle-it-out/

 

From the article:

"Machine learning is taking medical diagnosis by storm. From eye disease, breast and other cancers, to more amorphous neurological disorders, AI is routinely matching physician performance, if not beating them outright.  

 

Yet how much can we take those results at face value? When it comes to life and death decisions, when can we put our full trust in enigmatic algorithms—“black boxes” that even their creators cannot fully explain or understand? The problem gets more complex as medical AI crosses multiple disciplines and developers, including both academic and industry powerhouses such as Google, Amazon, or Apple, with disparate incentives.  

 

This week, the two sides battled it out in a heated duel in one of the most prestigious science journals, Nature. On one side are prominent AI researchers at the Princess Margaret Cancer Centre, University of Toronto, Stanford University, Johns Hopkins, Harvard, MIT, and others. On the other side is the titan Google Health."

Massachusetts Institute of Technology (MIT) - Machine learning uncovers potential new TB drugs

Title:

Machine learning uncovers potential new TB drugs

 

Author:

Anne Trafton, Massachusetts Institute of Technology (MIT)

Published:

Tech Xplore, 15 October 2020

https://techxplore.com/news/2020-10-machine-uncovers-potential-tb-drugs.html

From the article:

Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability.

Using this new approach, which allows computer models to account for uncertainty in the data they're analyzing, the MIT team identified several promising compounds that target a protein required by the bacteria that cause tuberculosis.

See also the scholarly article related to this TechXplore article:

 

Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design

by Brian Hie, Bryan D. Bryson & Bonnie A. Berger

Cell Systems, 15 October 2020

DOI: https://doi.org/10.1016/j.cels.2020.09.007

Purdue University - Machine learning model helps characterize compounds for drug discovery

Title: 

Machine learning model helps characterize compounds for drug discovery

By:

Purdue University

Published:

Phys.org, 4 October 2020

https://phys.org/news/2020-10-machine-characterize-compounds-drug-discovery.html

From the article:

Purdue University innovators have created a new method of applying machine learning concepts to the tandem mass spectrometry process to improve the flow of information in the development of new drugs. Their work is published in Chemical Science.

University of Tokyo - Future mental health care may include diagnosis via brain scan and computer algorithm - Computer IDs differences in brains of patients with schizophrenia or autism

Title:
Future mental health care may include diagnosis via brain scan and computer algorithm

Source:
University of Tokyo

Published:
Newswise, 17 August 2020
https://www.newswise.com/articles/future-mental-health-care-may-include-diagnosis-via-brain-scan-and-computer-algorithm

From the article:
Most of modern medicine has physical tests or objective techniques to define much of what ails us. Yet, there is currently no blood or genetic test, or impartial procedure that can definitively diagnose a mental illness, and certainly none to distinguish between different psychiatric disorders with similar symptoms. Experts at the University of Tokyo are combining machine learning with brain imaging tools to redefine the standard for diagnosing mental illnesses.

How Close Are Computers to Automating Mathematical Reasoning? AI tools are shaping next-generation theorem provers, and with them the relationship between math and machine

Title:
How Close Are Computers to Automating Mathematical Reasoning?

Author:
Stephen Ornes

Published:
Quanta magazine, 27 August 2020
https://www.quantamagazine.org/how-close-are-computers-to-automating-mathematical-reasoning-20200827/

From the article:
A formidable open challenge in the field asks how much proof-making can actually be automated: Can a system generate an interesting conjecture and prove it in a way that people understand? A slew of recent advances from labs around the world suggests ways that artificial intelligence tools may answer that question.