Paul Hildreth looked at images from security cameras set up at schools in Fulton County, Georgia. He began watching a video of a woman walking inside one of the school buildings. The top of her clothing was bright yellow. Hildreth used his computer's artificial intelligence, or AI system to find other images of the woman. The system put the pictures together in a video that showed where she currently was, where she had been and what she was doing.

The training and development of your workforce is vital to the achievement of digital transformation success for businesses. And today, more and more businesses are leveraging e-learning to educate their employees. The advantages for businesses using online learning platforms as opposed to traditional training methods are bountiful. First, it lowers business costs since one training session can be delivered to multiple people. Second, topics can be broken down into bite-sized chunks, meaning that employees do not need to spend lengthy periods of time away from their desks.

'For species like chimpanzees, which have complex social lives and live for many years, getting snapshots of their behaviour from short-term field research can only tell us so much,' says Dan Schofield, researcher and DPhil student at Oxford University's Primate Models Lab, School of Anthropology. 'By harnessing the power of machine learning to unlock large video archives, it makes it feasible to measure behaviour over the long term, for example observing how the social interactions of a group change over several generations.' The computer model was trained using over 10 million images from Kyoto University's Primate Research Institute (PRI) video archive of wild chimpanzees in Guinea, West Africa. The new software is the first to continuously track and recognise individuals in a wide range of poses, performing with high accuracy in difficult conditions such as low lighting, poor image quality and motion blur. 'Access to this large video archive has allowed us to use cutting edge deep neural networks to train models at a scale that was previously not possible,' says Arsha Nagrani, co-author of the study and DPhil student at the Department of Engineering Science, University of Oxford.

The goal of data science teams are to build and deploy high impact models. Data scientists prefer to focus on building algorithms, while data engineers focus on performance and productionizing machine learning. Kubernetes is an orchestration platform that can be deployed anywhere and can serve any kind of machine and deep learning environment. Kubernetes is a great tool for data scientists to use to stay productive and for data engineers to get production-ready results. In this free workshop you'll learn how to build your own Kubernetes to use in your next machine learning pipeline.

-- The increasing inclusion of Machine Learning (ML) models in safety critical systems like autonomous cars have led to the development of multiple model-based ML testing techniques. One common denominator of these testing techniques is their assumption that training programs are adequate and bug-free. These techniques only focus on assessing the performance of the constructed model using manually labeled data or automatically generated data. However, their assumptions about the training program are not always true as training programs can contain inconsistencies and bugs. In this paper, we examine training issues in ML programs and propose a catalog of verification routines that can be used to detect the identified issues, automatically. We implemented the routines in a T ensorflow-based library named TFCheck. Using TFCheck, practitioners can detect the aforementioned issues automatically. T o assess the effectiveness of TFCheck, we conducted a case study with real-world, mutants, and synthetic training programs. Results show that TFCheck can successfully detect training issues in ML code implementations. I. INTRODUCTION Nowadays, software applications powered by Machine Learning (ML) are increasingly being deployed in safety-critical systems such as self-driving cars or aircraft collision-avoidance systems. Therefore, their reliability is now of paramount importance. Recently, researchers have proposed many testing approaches to help improve the reliability of ML applications [1].

Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simplify the operations for compute acceleration. Recently, researches on DNN quantization develop from inference to training, laying a foundation for the online training on accelerators. However, existing schemes leaving batch normalization (BN) untouched during training are mostly incomplete quantization that still adopts high precision FP in some parts of the data paths. Currently, there is no solution that can use only low bit-width INT data during the whole training process of large-scale DNNs with acceptable accuracy. In this work, through decomposing all the computation steps in DNNs and fusing three special quantization functions to satisfy the different precision requirements, we propose a unified complete quantization framework termed as "WAGEUBN" to quantize DNNs involving all data paths including W (Weights), A (Activation), G (Gradient), E (Error), U (Update), and BN. Moreover, the Momentum optimizer is also quantized to realize a completely quantized framework. Experiments on ResNet18/34/50 models demonstrate that WAGEUBN can achieve competitive accuracy on ImageNet dataset. For the first time, the study of quantization in large-scale DNNs is advanced to the full 8-bit INT level. In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency. Our throughout quantization framework has great potential for future efficient portable devices with online learning ability.

An artificial intelligence system has made history by being the first to pass an eighth grade science test with flying colors. According to The New York Times, researchers at the Allen Institute for Artificial Intelligence in Seattle Washington have cracked the code for test-taking computers. Its system, called Aristo, received a 90 percent score on an eighth-grade science test and passed with an 80 percent grade on a 12th-grade exam. An AI passed an eighth grade science exam with flying colors, marking a first for the technology. Scientists four years ago failed to get AI to achieve a passing grade.

Bengaluru: A team of post-graduate students and teachers from the Electrical and Electronics Engineering (EEE) department of the Indian Institute of Technology (IIT) Guwahati, said they are developing an Artificial Intelligence (AI)-enabled chatbot to teach and support first year EEE students. Dubbed'ALBELA', the AI chatbot is capable of addressing the queries of the nearly 850 EEE students at the institute, the institution said in a statement on Tuesday. Professor Praveen Kumar of the EEE department said a dedicated team of seven research scholars has been developing the chatbot for the last seven months. "Earlier we did the trial runs of the chabot, and started using it from this academic session onwards," he said, adding that IBM has extended support to this project. The institute says the AI chatbot is trained to answer queries on topics relevant to the students.

There are 700 million people globally who learn music. An estimated 25-30 million people spend $100 annually on music lessons, from countries across the world with per-capita income higher than $15k. There is no real-time feedback during such practice sessions. This builds bad habits and hard to correct. Kena's goal is to enable real-time practice feedback and dynamic paths practice based on real-time diagnostics.

The individual filling this position will be responsible for leading the modernization of our teaching of machine learning, including developing new online and technology-assisted materials to improve educational outcomes and to extend our reach. They will work closely with the department head and other faculty to develop a strategic plan for taking advantage of new online and technology-assisted educational options over the coming decade. They will also be responsible for teaching classes and overseeing aspects of the educational program, e.g., admissions to our Ph.D. and Masters programs and advising undergraduate students minoring in Machine Learning. Candidates should have a Ph.D. with deep expertise in machine learning, and background of demonstrated excellence and dedication to teaching. Candidates must be prepared to teach extensive lecture courses at the advanced undergraduate and graduate level, and also be prepared to work with the existing faculty of the department to establish, improve, and standardize the curriculum.