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Japan's Fugaku supercomputer goes fully live to aid COVID-19 research

The Japan Times

Kobe – Japan's Fugaku supercomputer, the world's fastest in terms of computing speed, went into full operation Tuesday, earlier than initially scheduled, in the hope that it can be used for research related to the novel coronavirus. The supercomputer, named after an alternative word for Mount Fuji, became partially operational in April last year to visualize how droplets that could carry the virus spread from the mouth and to help explore possible treatments for COVID-19. "I hope Fugaku will be cherished by the people as it can do what its predecessor K couldn't, including artificial intelligence (applications) and big data analytics," said Hiroshi Matsumoto, president of the Riken research institute that developed the machine, in a ceremony held at the Riken Center for Computational Science in Kobe, where it is installed. Fugaku, which can perform over 442 quadrillion computations per second, was originally scheduled to start operating fully in the fiscal year from April. It will eventually be used in fields such as climate and artificial intelligence applications, and will be used in more than 100 projects, according to state-sponsored Riken. The supercomputer, which was developed jointly with Fujitsu Ltd., was ranked the world's fastest for computing speed in the twice-yearly U.S.-European TOP500 project for the first time in June, and retained the top spot in November.


Adam Bry and Hayk Martiros's talk – Skydio Autonomy: Research in Robust Visual Navigation and Real-Time 3D Reconstruction (with video)

Robohub

In the last online technical talk, Adam Bry and Hayk Martiros from Skydio explained how their company tackles real-world issues when it comes to drone flying. Skydio is the leading US drone company and the world leader in autonomous flight. Our drones are used for everything from capturing amazing video, to inspecting bridges, to tracking progress on construction sites. At the core of our products is a vision-based autonomy system with seven years of development at Skydio, drawing on decades of academic research. This system pushes the state of the art in deep learning, geometric computer vision, motion planning, and control with a particular focus on real-world robustness.


Monitoring the climate crisis with AI, satellites and drones – a workshop at NeurIPS2020

AIHub

As part of the workshop programme at NeurIPS2020, Climate Change AI (CCAI) held an all-day session on "Tackling climate change with machine learning". You can watch the talks from this side event in full in a recording provided by CCAI. In this workshop, the speakers, from both industry and academia, discuss how artificial intelligence and remote sensing can be used to monitor global carbon impact. They also consider trust and accountability issues relating to governments, companies, and international projects. You can find out more about this event, and the main workshop, here.


Best Master's Programs in Machine Learning (ML) for 2021

#artificialintelligence

Considering various factors such as the research areas, research focus, courses offered, duration of the program, location of the university, honors, awards, and job prospects, we came up with the best universities to help you in your choosing process. This article is most suited for individuals who'd like to pursue a master's degree with a focus on machine learning and need some guidance on their decision making. Feel free to jump to the end if you are only looking for the university names. Note: The universities mentioned below are in no particular order.


Best Public Datasets for Machine Learning and Data Science

#artificialintelligence

This resource is continuously updated. If you know any other suitable and open datasets, please let us know by emailing us at pub@towardsai.net or by dropping a comment below. Google Dataset Search: Similar to how Google Scholar works, Dataset Search lets you find datasets wherever they are hosted, whether it's a publisher's site, a digital library, or an author's web page. It's a phenomenal dataset finder, and it contains over 25 million datasets. Kaggle: Kaggle provides a vast container of datasets, sufficient for the enthusiast to the expert.


Driving Efficiency with MLOps

#artificialintelligence

The advancements in machine learning has more and more enterprises turning towards the insights provided by it. Data scientists are busy creating and fine-tuning machine learning models for tasks ranging from recommending music to detecting fraud. Here's what a machine learning model lifecycle looks like: According to Wikipedia, "MLOps ('Machine Learning' 'Operations') is a practice for collaboration and communication between data scientists and operations professionals to help manage the production ML lifecycle. MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics." So, is MLOps just another fancy name for DevOps?


Python Code Assistant Powered by GPT-3

#artificialintelligence

GPT-3 from OpenAI has captured public attention unlike any other AI model in the 21st century. The sheer flexibility of the model in performing a series of generalized tasks with near-human efficiency and accuracy is what makes it so exciting. It has created a paradigm shift in the world of Natural Language Processing(NLP), where till now the models were trained based on the ungenralized approach to excel at one or two tasks. GPT-3 is trained by OpenAI with a generalized approach on a massive scale involving 175 billion parameters which allows it to mimic functionalities of the human brain (like GPT-3 is capable of generating text that is surprisingly human-like after only being fed a few examples of the task you want it to do). Like a human brain GPT-3 is able to learn and do things with few shots of training unlike the conventional way of training an NLP model over a large corpus, which is both difficult and time-consuming.


The AI Monthly Top 3 -- February 2021

#artificialintelligence

Odei Garcia-Garin et al. from the University of Barcelona have developed a deep learning-based algorithm able to detect and quantify floating garbage from aerial images. They also made a web-oriented application allowing users to identify these garbages, called floating marine macro-litter, or FMML, within images of the sea surface.


Practical Guide: Build and Deploy a Machine Learning Web App

#artificialintelligence

This is a guide for a simple pipeline of a machine learning project. For this course, our target is to create a web app that will take as input a CSV file of flower attributes (sepal length/width and petal length/width) and returns a CSV file with the predictions (Setosa Versicolour Virginica). I know that you want to skip this step but don't. This will organize your packages and you will know exactly the packages you need to run your code incase we want to share it with someone else. Trust me, this is crucial.


The Future of AI Innovation

#artificialintelligence

After rapid growth over the past few years, artificial intelligence has become one of the biggest focuses of enterprises. Well, what has made it so hot? With AI, we can design systems that learn and adapt to all the new data we collect. Just a few years ago, AI seemed to be impossible. But now, it's quickly becoming necessary and expected.