Every day, Massachusetts seventh-grader Kaylyn Wilson takes a break from doing homework online and opens an app on her phone for a half-hour foreign language lesson. "The boy has three green bikes and an egg," the 12-year-old announced to her family in French at the start of her third week using the mobile app from Rosetta Stone, the language-learning software giant. Wilson doesn't yet need to study a language for credit. But during the school shutdowns to contain the coronavirus, her father saw Rosetta Stone advertise free accounts for students – an offer other language-learning software companies have made as well. Wilson decided to give it a go.

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.

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.

Huawei Cloud joins in the fight against COVID-19 using technology that includes cloud and artificial intelligence (AI). The company crafted an international action plan which will allow collaborators to use AI and cloud services for free. "Huawei Cloud has been working with partners in China to use innovative technologies such as cloud and AI to fight the pandemic and has accumulated practical experience with AI-assisted CT scan analysis, drug discovery, online education, and telecommuting technologies, " said Deng Tao, president of Huawei Cloud Global Market. "Now, we are launching this international action plan to share our practical experience with the international market. We will make every effort to leverage technology to help our customers around the world cope with the challenges faced in the midst of this crisis."

Since the corona erupted into our world, research institutes and governments have released many databases publicly to allow research groups (and independent individuals) to analyze the data around the corona's spread. These databases are scattered under numerous initiatives and sources. The purpose of this blog is to organize all the major open databases and data initiatives around the world. Feel free to add it in the comments or through this form. In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19).

Machine learning uses algorithms to determine if specific activities from consumers seem out of character when compared to previous spending habits. Some individuals see the advancement of artificial intelligence as an indispensable technology that the banking sector can utilize to generate new revenue streams. Others look at AI as an existential menace to the very existence of jobs. When up to 1.2 million employment opportunities could get lost due to the automation and self-regulation capabilities that AI software provides, then it is a topic that must be taken earnestly. Artificial intelligence might seem like another marketing buzzword today, much like the notion of Big Data was back in the early 2010s.

In a paper published on the preprint server Arxiv.org this week, IBM researchers describe SeizureNet, a machine learning framework that learns the features of seizures to classify various types. They say that it achieves state-of-the-art classification accuracy on a popular data set, and that it helps to improve the classification accuracy of smaller networks for applications with low memory and faster inference. If the claims stand up to academic scrutiny, the framework could, for instance, help the over 3.4 million people with epilepsy better understand the factors that trigger their seizures. The World Health Organization estimates that up to 70% of people living with epilepsy could live seizure-free if properly diagnosed and treated. SeizureNet is a machine learning framework consisting of individual classifiers (specifically convolutional neural networks) that learn the features of electroencephalograms (EEGs) -- i.e., tests that evaluate the electrical activity in the brain -- to predict seizure types.

The figure above illustrates the method:(a) Goal-conditioned RL often fails to reach distant goals, but can successfully reach the goal if starting nearby (inside the green region). Reinforcement learning (RL) has seen a lot of progress over the past few years, tackling increasingly complex tasks. Much of this progress has been enabled by combining existing RL algorithms with powerful function approximators (i.e., neural networks). Function approximators have enabled researchers to apply RL to tasks with high-dimensional inputs without hand-crafting representations, distance metrics, or low-level controllers. However, function approximators have not come for free, and their cost is reflected in notoriously challenging optimization: deep RL algorithms are famously unstable and sensitive to hyperparameters.