This will enable more people in your organization to leverage machine learning and most importantly allow domain experts to rapidly prototype ML solutions and validate their hypothesis before involving data scientists. If you are an experienced data scientist, automated ML will let you improve productivity and save time by eliminating the need to manually perform the tedious and repetitive tasks of feature engineering, algorithm selection and hyperparameter tuning. You can even start by generating a model with automated ML as a starting point and tune it further. Organizations can also use automated ML to benchmark their models. Many Fortune 500 customers are benefiting from using automated ML. These include a global oil & refinery enterprise that's using automated ML to forecast reservoir production and a medical devices company that's using automated ML for predictive maintenance. Automated ML also powers Microsoft Power BI's AI capabilities, where business analysts can build machine learning models without writing a single line of code. Azure Machine Learning service's automated ML capability is based on a breakthrough from our Microsoft Research division and different from competing solutions in the market. The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous space of possible machine learning pipelines intelligently and efficiently.

Julia emerged in 2018 as one of the fastest-growing programming languages, prized for its approach of combining the strengths of several major languages. Having recently hit version 1.0, those behind Julia now have ambitions for it to become the language of choice in the field of machine learning (ML). Helping realise that goal is Flux, a machine-learning software library for Julia that's designed to make ML code easier to write, to simplify the training process, and to offer certain performance benefits over rival frameworks on hardware accelerators such as GPUs and Google's TPUs [Tensor Processing Units]. Today the Python and R languages typically dominate machine learning, with Python still the fastest-growing programming language in terms of developer popularity, driven in large part by the strength of its machine-learning frameworks and libraries. In comparison, only a relatively small proportion of developers use the fledgling Julia.

Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at all times. This setup, however, is rarely encountered in practical applications. Therefore we investigate how to transform continual learning to an online setup. We develop a system that keeps on learning over time in a streaming fashion, with data distributions gradually changing and without the notion of separate tasks. To this end, we build on the work on Memory Aware Synapses, and show how this method can be made online by providing a protocol to decide i) when to update the importance weights, ii) which data to use to update them, and iii) how to accumulate the importance weights at each update step. Experimental results show the validity of the approach in the context of two applications: (self-)supervised learning of a face recognition model by watching soap series and learning a robot to avoid collisions.

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.

Why guess what will happen tomorrow, the next day, or further into the future? Put your computer to work and allow it to do the predicting. Machine learning allows computers to learn from data and make predictions based on that, saving you the headache. With the Machine Learning Master Class Bundle, you can learn all you need to know about this technology, such as data visualization with Python, R programming, game development, and more. The following eight courses are included in this bundle.

Artificial intelligence systems and creators now need direct intervention by governments and human rights organizations, according to a recent report from researchers at Google, Microsoft and others at AI Now. In the report (PDF) published this week, the New York University-based organization (with Microsoft Research and Google-associated members) indicates that AI-based tools are being used with little regard for possible ill effects it can have on the general public. It would've been a different story if it was happening in controlled, regulated environment here and there; instead these untested, undocumented AI systems are being put to work in places where they can severely affect millions of people. These systems are causing real damage, and not only are there no systems in place to stop them, but few to even track and quantify that harm. Think border patrol, entire school districts and police departments, and so on.

A collaboration between Cornell University and r4 Technologies, a Connecticut-based artificial intelligence company, will develop and apply artificial intelligence solutions to structural challenges that have hindered growth and modernization, and will train a new generation of students to thrive in a data-driven world. The Cornell-r4 Applied AI initiative, launched Dec. 6, will bring together cross-disciplinary scholars and industry experts to apply AI, data science, advanced math, and leading-edge technology to help solve business and societal problems. The initiative will also focus on developing new courses that bring AI and data science to more students across the university. "Cornell University believes in the vast potential of applied AI to achieve breakthrough solutions in business and society," said Greg Morrisett, dean of Computing and Information Science (CIS) and co-chair of the new initiative. "Our partnership with r4 Technologies puts us at the leading edge of academic discovery across crucial disciplines such as statistics, machine learning, and optimization."

Traditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators across robotic agents or across time. We propose using variational inference to learn an explicit latent representation of unknown environment properties that accelerates learning and facilitates generalization on novel environments at test time. We use Online Bayesian Inference of these learned latents to rapidly adapt online to changes in environments without retaining large replay buffers of recent data. Combined with a neural network ensemble that models dynamics and captures uncertainty over dynamics, our approach demonstrates positive transfer during training and online adaptation on the continuous control task HalfCheetah.

Faces play a magnificent role in human robot interaction, as they do in our daily life. The inherent ability of the human mind facilitates us to recognize a person by exploiting various challenges such as bad illumination, occlusions, pose variation etc. which are involved in face recognition. But it is a very complex task in nature to identify a human face by humanoid robots. The recent literatures on face biometric recognition are extremely rich in its application on structured environment for solving human identification problem. But the application of face biometric on mobile robotics is limited for its inability to produce accurate identification in uneven circumstances. The existing face recognition problem has been tackled with our proposed component based fragmented face recognition framework. The proposed framework uses only a subset of the full face such as eyes, nose and mouth to recognize a person. It's less searching cost, encouraging accuracy and ability to handle various challenges of face recognition offers its applicability on humanoid robots. The second problem in face recognition is the face spoofing, in which a face recognition system is not able to distinguish between a person and an imposter (photo/video of the genuine user). The problem will become more detrimental when robots are used as an authenticator. A depth analysis method has been investigated in our research work to test the liveness of imposters to discriminate them from the legitimate users. The implication of the previous earned techniques has been used with respect to criminal identification with NAO robot. An eyewitness can interact with NAO through a user interface. NAO asks several questions about the suspect, such as age, height, her/his facial shape and size etc., and then making a guess about her/his face.

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.