MOUNTAIN VIEW, CALIFORNIA -- In a move that is completely unsurprising to many, Google's AI research division has announced that they are issuing PhD degrees to select employees. Industry research organizations like Google Brain, DeepMind, and FAIR are well known as heavy hitters in the artificial intelligence research community, publishing as many papers (if not more) as academic institutions like Stanford, Berkeley, and MIT. Many top professors from academia have migrated over to industry research labs as well, sacrificing the security of academic tenure for fat stacks of money. Although Google has previously experimented with research residencies, this is the first time that they have issued postgraduate degrees. According to a representative, the tech giant decided to issue PhDs in order to attract scarce AI talent.

About the author: Hidalgo earned a B.S. in Electrical Engineering and an M.S. in Computer Science, with a concentration in AI, from Stanford University. As a master student, Hidalgo researched artificial intelligence and was the teacher assistant for Andrew Ng's Machine Learning Course and Stefano Ermon's Probabilistic Graphical Models Course. He also holds an M.B.A. from the Stanford Graduate School of Business.

This extensive course leads you through a complete range of software skills and languages, skilling you up to be an incredibly on-demand developer. The combination of being able to create full-stack websites AND machine learning and AI models is very rare – something referred to as a unicorn. This is exactly what you will be able to do by the end of this course. Whether you're looking to get into a high paying job in tech, aspiring to build a portfolio so that you can land remote contracts and work from the beach, or you're looking to grow your tech start-up, this course will be essential to set you up with the skills and knowledge to develop you into a unicorn. This course will fill all the gaps in between.

For Telangana, information technology (IT) plays a significant role in its GDP. Overall services continue to contribute the maximum – around 55 per cent – of which IT contributes about 40 per cet in the state's GDP. Now, with emerging technologies such as AI and ML playing a defining role, the state is looking to leverage these frontier technologies to strengthen public service delivery and tech innovation. In an exclusive interview with ETGovernment's Mohd Ujaley, Jayesh Ranjan, principal secretary - IT, Electronics & Communication Department said: "Emerging Technologies is the one area where we are making real advancement. In Telangana, these frontier technologies are being explored by different government departments to improve governance, public service delivery and creation of new jobs."

Artificial intelligence can help us to solve some of society's most difficult challenges and create a safer, healthier and more prosperous world for all. I've already shared the exciting possibilities in the fields of healthcare and agriculture in previous posts. But there may be no area where the possibilities are more interesting – or more important – than Education and skills. From personalized learning that takes advantage of AI to adapt teaching methods and materials to the needs of individual students, to automated grading that frees teachers from the drudgery of assessing tests so they have more time to work with students, to intelligent systems that are transforming how learners find and interact with information, the opportunities to improve Education outcomes and accessibility will be truly transformational. There are many classrooms around the world where educators teach very diverse groups of students from different cultures, who speak multiple languages.

The following is adapted from a joint release from the MIT Press and the Harvard Data Science Initiative. The MIT Press and the Harvard Data Science Initiative (HDSI) have announced the launch of the Harvard Data Science Review (HDSR). The open-access journal, published by MIT Press and hosted online via the multimedia platform PubPub, an initiative of the MIT Knowledge Futures group, will feature leading global thinkers in the burgeoning field of data science, making research, educational resources, and commentary accessible to academics, professionals, and the interested public. With demand for data scientists booming, HDSR will provide a centralized, authoritative, and peer-reviewed publishing community to service the growing profession. The first issue features articles on topics ranging from authorship attribution of John Lennon-Paul McCartney songs to machine learning models for predicting drug approvals to artificial intelligence (AI).

Gaurav Malhotra joins Scott Hanselman to show how you can run your Azure Machine Learning (AML) service pipelines as a step in your Azure Data Factory (ADF) pipelines. This enables you to run your machine learning models with data from multiple sources (85 data connectors supported in ADF). This seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.

In recent years, deep learning models have shown great potential in source code modeling and analysis. Generally, deep learning-based approaches are problem-specific and data-hungry. A challenging issue of these approaches is that they require training from starch for a different related problem. In this work, we propose a transfer learning-based approach that significantly improves the performance of deep learning-based source code models. In contrast to traditional learning paradigms, transfer learning can transfer the knowledge learned in solving one problem into another related problem. First, we present two recurrent neural network-based models RNN and GRU for the purpose of transfer learning in the domain of source code modeling. Next, via transfer learning, these pre-trained (RNN and GRU) models are used as feature extractors. Then, these extracted features are combined into attention learner for different downstream tasks. The attention learner leverages from the learned knowledge of pre-trained models and fine-tunes them for a specific downstream task. We evaluate the performance of the proposed approach with extensive experiments with the source code suggestion task. The results indicate that the proposed approach outperforms the state-of-the-art models in terms of accuracy, precision, recall, and F-measure without training the models from scratch.

--Smart and agile drones are fast becoming ubiquitous at the edge of the cloud. The usage of these drones are constrained by their limited power and compute capability. In this paper, we present a Transfer Learning (TL) based approach to reduce on-board computation required to train a deep neural network for autonomous navigation via Deep Reinforcement Learning for a target algorithmic performance. A library of 3D realistic meta-environments is manually designed using Unreal Gaming Engine and the network is trained end-to- end. These trained meta-weights are then used as initializers to the network in a test environment and fine-tuned for the last few fully connected layers. V ariation in drone dynamics and environmental characteristics is carried out to show robustness of the approach. Using NVIDIA GPU profiler it was shown that the energy consumption and training latency is reduced by 3.7x and 1.8x respectively without significant degradation in the performance in terms of average distance traveled before crash i.e. The approach is also tested on a real environment using DJI T ello drone and similar results were reported. The video of the drone with proposed approach will be uploaded to Y ouTube. VER the past decade, Unmanned aerial vehicle (UA V) are emerging as a new form of IoT devices being used in varied applications such as reconnaissance, surveying, rescuing and mapping. Irrespective of the application, navigating autonomously is one of the key desirable features of UA Vs both indoors and outdoors.

What does it take to make the university the best learning experience in the lifecycle of one's education? Higher education is all about developing skills, exploring new theories, and applying them to the actualities of real life. Throughout this journey, students are encouraged to stay on top of their workload, study, and complete assessments all while simultaneously leading a healthy, active, and balanced social life. The essential materials relied on at university include books, books, and more books. As we move into an age of digitalization of practically everything, there is a reason to believe that the existing higher education model should too be digitalized to allow for an enhanced university experience.