Collaborating Authors


Artificial Intelligence In Digital Marketing 2020


Artificial Intelligence In Digital Marketing 2020 Artificial intelligence has already made a huge difference in how brands interact with consumers and how marketing strategies are managed. In such a rapidly changing environment, it's difficult to predict what the future holds, but there are certainly some clues to what we might expect in the coming year. Being smart in business means knowing what's just around the corner. It means thinking ahead and preparing for inevitable changes that will impact the way business is conducted. This is what allows a business to be resilient and to thrive in a changing environment.

Machine Learning Summits '20 - Home


If you are in the ML/AI field, and are interested in enhancing your skills, while networking and learning from Google's ML/AI experts, this is the event for you! Attendees should have prior knowledge and experience with Machine Learning and/or AI technologies. We want to create a learning journey for developers around Google's ML content - from data to decisions.

Future Tense Newsletter: Make the Future Great Again


Politics are in the air, like that ominous reddish glow suffocating much of the West in recent weeks on account of all those tragic wild fires. This coming week we get our first presidential debate. A chance for Donald Trump and Joe Biden to shake hands and have a respectful, reasoned exchange of views on the future of the unfairly maligned Section 230 of the Communications Decency Act; the need to reform the Stored Communications Act; the wisdom of replicating Europe's General Data Privacy Regulation; the merits of taking antitrust action against Google for its manipulation of search results or against Amazon for its treatment of third-party sellers on its platform. Maybe we will even see the candidates reflect humbly on humanity's place in the universe, in light of the breaking news from Venus. The debate will probably be all tense, no future--maybe not as heated as a debate between 2016 Lindsey Graham and 2020 Lindsey Graham, but close.

Top Technologies To Achieve Security And Privacy Of Sensitive Data


Companies today are leveraging more and more of user data to build models that improve their products and user experience. Companies are looking to measure user sentiments to develop products as per their need. However, this predictive capability using data can be harmful to individuals who wish to protect their privacy. Building data models using sensitive personal data can undermine the privacy of users and can also cause damage to a person if the data gets leaked or misused. A simple solution that companies have employed for years is data anonymisation by removing personally identifiable information in datasets.

Geoff Hinton And His Team File A Patent For Capsule Neural Networks


"According to the filing, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks." Looks like Google won't be stopping its infamous patenting spree anytime soon. Earlier this month, Google filed a patent for capsule networks. Turing award recipient and Google researcher Geoff Hinton was named amongst the list of inventors in the filing. According to the patent filed, the inventors claimed that capsule networks can be used in place of conventional convolutional neural networks for traditional computer vision applications. Capsule networks are aimed at alleviating the extra dimensionality which surfaces with a convolutional neural network.

Google launches AI lip-sync challenge


Google has launched a lip-sync challenge for anyone who cares to participate. Those interested can visit the site Google has set up and test their lip-synching skills. The challenge is being run by Google's AI Experiments group--the same group behind Google's Pixel device. The purpose of the challenge is to allow Google to use real singers to teach its AI system (which the company is calling simply LipSync by YouTube) how to read lips. Google hopes to use the AI system as part of an effort to develop applications for people with speaking disabilities such as ALS.

Most Useful C/C++ ML Libraries Every Data Scientist Should Know


C is ideal for dynamic load balancing, adaptive caching, and developing large big data frameworks, and libraries. Google's MapReduce, MongoDB, most of the deep learning libraries listed below have been implemented using C . Scylla known for its ultra-low latency and extremely high throughput is coded using C acts as a replacement to Apache Cassandra and Amazon DynamoDB. With some of the unique advantages of C as a programming language, (including memory management, performance characteristics, and systems programming), it definitely serves as one of the most efficient tools for developing fast scalable Data Science and Big Data libraries. Further, Julia (a compiled and interactive language – developed from MIT) is emerging as a potential competitor to Python in the field of scientific computing and data processing. Its fast processing speed, parallelism, static along with dynamic typing and C bindings for plugging in libraries, has eased the job for developers/data scientists to integrate and use C as a data science and big data library.

AI Remote Learning for Professionals


The way we work has changed and it's continuing to change. People are working remotely while being part of their team irrespective of the location. With this change, traditional training methods being restrictive and costly have become less relevant. One of the challenges faced by teachers is to provide customized learning catering to the needs of every student. As different students have different requirements, even teaching one student is an arduous task as the teacher is challenged to find the right curriculum to meet their requirements.

4 No-Code Machine Learning platforms you should use in 2020


Artificial Intelligence (AI) and Machine Learning (ML) are among the most sought after tech skills by companies around the world. There is also a surge in no-code AI platforms. As more and more businesses are looking to leverage the power of AI, companies are accelerating the adoption of these technologies. Building solutions with these technologies require a sound experience and expertise in programming languages, however, there are some no-code visual drag-and-drop tools available to build ML solutions. Now it is an independent macOS application that comes with a bunch of pre-trained model templates.

Debugging Incidents in Google's Distributed Systems

Communications of the ACM

Google has published two books about Site Reliability Engineering (SRE) principles, best practices, and practical applications.1,2 In the heat of the moment when handling a production incident, however, a team's actual response and debugging approaches often differ from ideal best practices. This article covers the outcomes of research performed in 2019 on how engineers at Google debug production issues, including the types of tools, high-level strategies, and low-level tasks that engineers use in varying combinations to debug effectively. It examines the research approach used to capture data, summarizing the common engineering journeys for production investigations and sharing examples of how experts debug complex distributed systems. Finally, the article extends the Google specifics of this research to provide some practical strategies that you can apply in your organization. As this study began, its focus was on developing an empirical understanding of the debugging process, with the overarching goal of creating optimal product solutions that met the needs of Google engineers. We wanted to capture the data that engineers need when debugging, when they need it, the communication process among the teams involved, and the types of mitigations that are successful.