As Japan faces a fresh wave of coronavirus infections and the government readies itself to declare a state of emergency, medical staff say a shortage of beds and a rise in cases linked to hospitals are pushing Tokyo's medical system to the brink of collapse. The crisis has already arrived at Eiju General Hospital, a pink, 10-story building in central Tokyo that has reported 140 cases of COVID-19 in the past two weeks. Of those, at least 44 are doctors, nurses and other medical staff. On a recent weekday, the glass doors of Eiju General were plastered with posters saying the hospital was closed until further notice. More than 60 patients with the virus are still being treated inside.

A reminder to those who are working at home: You might want to turn your Amazon or Google smart home speaker them off, or at the very least, mute the microphone. What most people forget is that Alexa and the Google Assistant are always listening. Sure, they only come to life after you utter "Alexa" or "Hey, Google," but what happens when you slip those words in the middle of sentences? Amazon and Google record every interaction, even if you don't ask a specific question, and the recordings are stored on Amazon and Google servers. Sometimes the speakers are awakened with words that they mistake for the wake words.

ECMWF is organising a series of seminars given by international experts to explore aspects of the use of machine learning in weather prediction and climate studies. The first will take place on 28 April and will be live-streamed. Sherman Lo and Ritabrata Dutta from the University of Warwick will present a statistical methodology to predict precipitation at 0.1 resolution using lower-resolution model fields of air temperature, geopotential, specific humidity, total column water vapour and wind velocity. On 9 June, Annalisa Bracco from the School of Earth and Atmospheric Sciences at the Georgia Institute of Technology will talk about spatiotemporal complexity and time-dependent networks in mid- to late Holocene simulations. In subsequent seminars, Maxime Taillardat (Météo-France) will present examples of operational ensemble post-processing using machine learning; Alberto Arribas (UK Met Office) will talk about work at the Met Office Informatics Lab; and Nal Kalchbrenner (Google) will talk about now-casting applications at Google.

Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of "The XL-Files," a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990.

There's no denying that artificial intelligence is having a huge impact on our … "And the reason for that is AI models are generally machine learning …

This post was published on April 1st, 2020, and should not be taken too seriously. You use new formulas, you gather insights, you vote and you have action points. Sometimes you start to regret being just a human. What if you could process incoming requests in parallel? And provide always most accurate responses?

According to Deltec Bank, Bahamas – "Artificial intelligence and big data can be combined to create powerful predictive machine learning models that can be used for predicting risks associated with loan default, market crash, customer churn, fraudulent transactions, money laundering to name the few." Big Data is referred to as the huge amount of abundant data that is getting generated due to the digitalization of the economy. Whereas, artificial intelligence in the field of making computers make decisions without explicitly programmed, usually with the help of machine learning techniques. Big Data and AI actually complement each other because machine learning models require data, in some cases a huge amount of data to create accurate modes. In this post, we will see how the finance and banking industry is leveraging both Big Data and AI to their advantage.

Several recent papers have investigated similar ideas as this project; however, none of them captured the specific intent I was aiming for and so I ended up taking inspiration from these models but going in a new general direction. Specifically, I wanted to be able to generate objects from at least 10 different categories (the papers below capture only 2–3) and I wanted to develop the model architecture with the capacity to extend to unlabelled 3D shape data. To produce an encoded knowledge base for this design space I chose to use the PartNet database (a subset of ShapeNet) which has 30k densely annotated 3D models across 24 categories. From these annotations and heuristics on the models, I made simplified text descriptions. From the 3D models, I created 3D voxel volumes (voxels are like pixels in 3D) to represent the model in a way that could then be fed into a neural network architecture.

Ensuring we have access to healthy and tasty food for the future means lots of people are working hard across food industry supply chains on a global scale. Agtech, food origins, alternative proteins, native foods, food waste, personal food choices, and more are up for discussion. Prof Andy Lowe examines the solutions being put in place today to put tomorrow's meal on your table.