Either way, it's clear where her former colleagues stand on the matter. "This has become a matter of public concern," continues the open letter, "and there needs to be public accountability to ensure any trust in Google Research going forward." Time will tell whether or not Google heeds the call.

BEGIN ARTICLE PREVIEW: Download our Mobile App The components of time-series can be as complex and sophisticated as the data itself. With every passing second, the data obtained multiplies and modelling becomes tricky. For instance, social media platforms, the data handling chores get worse with their increasing popularity. Twitter stores 1.5 petabytes of logical time series data and handles 25K query requests per minute. There are more critical applications of time series modelling, such as IoT and on various edge devices. Sensors of smart buildings, factories, power plants, and data centres generate vast amounts of multivariate time series data. Conventional anomaly detection methods are inadequate due to the dynamic complexities of these systems. Today, most of the state-of-the-art methods aim to leverage deep learning for time-series modelling. In this article, we take a look at a few of the top works on deep learning base time series that have been

"Computers are able to see, hear and learn. The biggest advancement that software technology has achieved in the last decade has to be the concept of artificial intelligence. Today, we encounter the perks of artificial intelligence at almost every step in our day-to-day life and also, in professional scenario. As per research, $28.5 billion has been already allocated to artificial intelligence worldwide during the first quarter of 2019. The monetary and human investment in artificial intelligence proves that it forms a core part of future technologies.

Conversational AI has fundamentally changed how companies think of customer service. According to a 2019 Gartner report Evolving IVRs to Conversational Platforms -- Critical Leadership Issues, "by 2025, 30% of major enterprises will have selected a single, enterprise-wide, conversational platform that is leveraged as a front end by business applications, both for customer service and to improve employee effectiveness." Relying on representatives to respond to all inbound requests can become costly if not impossible. Some companies have learned this the hard way during the pandemic. Today, customers are almost always greeted with automated, but too many simple customer requests are still being rerouted to a representative.

The DIY spirit is alive and well these days, especially with individuals who have a technical mind. Regardless of age, these people enjoy working on and completing projects -- they just need the resources, tools and a bit of direction to make it happen. If you have a DIYer in your life, you'll want to check out this roundup of six DIY kits that make excellent gifts for the holidays. Circuit Scribe's conductive ink pen, sweet magnetic modules and plain old printer paper allow kids to merge their creativity with science as they build exciting circuits. By placing paper over the steel sheet included in the kit, your child turns the paper into a base for blinking lights, beeping buzzers and whirling motors. Alongside all of the essential materials, this kit comes with an easy-to-follow instructional manual booklet and workbook to guide your children through the process of learning about circuits and switches.

Tesla might have hiked the price of Full Self-Driving for many customers, but others are getting a break. Electrek has discovered that Tesla quietly cut the price of a Full Self-Driving upgrade to $5,000, a $1,000 drop, for customers who bought Enhanced Autopilot. That's still not a trivial purchase, but it might be easier to justify if you're pining for Navigate on Autopilot, Autopark and other FSD perks. The company hasn't explained the decision and isn't expected to respond to requests for comment after reportedly dissolving its PR department. FSD is still expensive for many customers at $10,000 for both new buyers and many after-the-fact upgrades.

The cool part about this approach is that we have the freedom to generate training examples for different fonts, ligatures, text colors, background colors. This is very useful if we want to avoid the model overfitting during the training (so that the model could generalize well to unseen real-world examples instead of failing once the background shade is changed for a bit). It is also possible to generate a variety of link types like http://, http://, ftp://, tcp:// etc. Otherwise, it might be hard to find enough real-world examples of this kind of links for training. Another benefit of this approach is that we could generate as many training examples as we want. We're not limited to the number of pages of the printed book we've found for the dataset.

“Building the New Reality” Who We Are: We are Africa’s FIRST finance and technology talent accelerator for women! Yielding Accomplished African Women aims at erecting and cultivating the largest community of African female developers and financial analysts who are passionate about using STEM to revolutionize Africa and beyond. We are creating this online community for African women across the continent. Yaa W. is introducing Africa's FIRST Machine Learning conference for Young Women. Yielding Accomplished African Women (Yaa W.) presents “Solving the Algorithm: Women in Machine Learning Conference." According to the United Nations Development Program, 66% of sub-saharan African women work in informal labor markets and in the age of technology many of these jobs may be lost in the future due to automation. This fully funded conference will be an opportunity to equip African Women with the tools and skills needed to be leaders in this emerging field. Participants will enjoy a day immersive experience with: - Inspiring keynotes - Machine learning tutorials - Networking with Google employees - Community building exercises with other women in tech - Professional development training & More........ Final Deadline - December 12th 11:59PM GMT

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications.

Lyft describes'Flyte as a "structured and distributed platform for concurrent, scalable, and maintainable machine learning workflows." 'Flyte' is built to en-power and speedup machine learning models and data orchestration to be compatible with the latest products and applications. Flyte comes with Flytekit -- a Python SDK to develop applications on Flyte to allow contributors to provide rapid integrations with new services or systems. Apart from Flytekit, 'Flyte' also provides backend plugins which can be used to create and manage Kubernetes resources, including CRDs like Spark-on-k8s, or any remote system like Amazon Sagemaker, Qubole, BigQuery, and more.