This work presents an architecture that generates curiosity-driven goal-directed exploration behaviours for an image sensor of a microfarming robot. A combination of deep neural networks for offline unsupervised learning of low-dimensional features from images, and of online learning of shallow neural networks representing the inverse and forward kinematics of the system have been used. The artificial curiosity system assigns interest values to a set of pre-defined goals, and drives the exploration towards those that are expected to maximise the learning progress. We propose the integration of an episodic memory in intrinsic motivation systems to face catastrophic forgetting issues, typically experienced when performing online updates of artificial neural networks. Our results show that adopting an episodic memory system not only prevents the computational models from quickly forgetting knowledge that has been previously acquired, but also provides new avenues for modulating the balance between plasticity and stability of the models.

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions that MNMT research might take. This paper is aimed towards both, beginners and experts in NMT. We hope this paper will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.

The stakes are higher than ever now for AI and security. Following Sepideh Seifzadeh's keynote on managing the AI lifecycle, Dawn Song of the University of California's BAIR Lab took the stage to discuss an important but often overlooked component of the AI lifecycle: Security, specifically with deep learning, and how the stakes are becoming higher as AI becomes more intelligent. We often hear about various security concerns in the news, seemingly every day in regards to a hack affecting our personal information. Though, now both the hackers themselves and AI systems are becoming significantly more sophisticated, and data scientists are tasked with finding new ways to mitigate, prevent, and remedy these advancements. "Our current framework is insufficient for protecting data rights and privacy," she said.

It helps you keep pace with innovations in deep learning algorithms and neural network processing, and meet the diverse processing, memory, and connectivity requirements across mobile, IoT, data center, automotive, and digital home applications. Our specialized processors are fully programmable and configurable IP cores, helping you manage massive and changing compute requirements for machine learning and deep learning. Efficient memory IP supports different memory constraints including bandwidth, capacity, and cache coherency. We also provide reliable connectivity to CMOS image sensors, microphones, and motion sensors for embedded vision, natural language understanding, and context awareness.

Deep learning models have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications. Nevertheless, they are consistently utilized in many applications, consequential to humans' lives, mostly because of their better performance. Therefore, there is a great need for computational methods that can explain, audit, and debug such models. Here, we use flip points to accomplish these goals for deep learning models with continuous output scores (e.g., computed by softmax), used in social applications. A flip point is any point that lies on the boundary between two output classes: e.g. for a model with a binary yes/no output, a flip point is any input that generates equal scores for "yes" and "no".

One and two are pre-trained deep learning models, meaning that they are provided to you as-is by OpenCV. The Movidius NCS will perform inference using each of these models. The third recognizer model is not a form of deep learning. Rather, it is our SVM machine learning face recognition model. The RPi CPU will have to handle making face recognition predictions using it. We also load our label encoder which holds the names of the people our model can recognize (Line 42). Let's initialize our video stream: Line 47 initializes and starts our VideoStream object. We wait for the camera sensor to warm up on Line 48. Line 51 initializes our FPS counter for benchmarking purposes.

A lot of money goes into artificial intelligence research, and advocates of the technology have praised it as a way to revolutionize health care. The global market for AI in health care is expected to rise from $1.3 billion in 2019 to $10 billion by 2024, according to investment bank Morgan Stanley. Researchers recently published new findings in the Lancet Digital Health Journal that concluded AI is on par with medical professionals in identifying diseases. However, in order to really tap into how AI can improve health care, scientists concluded more research is needed. The research centered around something called deep learning, which uses algorithms, data and computing to emulate human intelligence.

This past semester we have been collaborating on a machine learning Capstone Project with Columbia University's Master of Science in Applied Analytics: capstone projects are applied and experimental projects where students take what they have learned throughout the course of their graduate program and apply it to examine a specific area of study. Capstone projects are specifically designed to encourage students to think critically, solve challenging data science problems, and develop analytical skills. Two group of students built an end-to-end data science solution using Azure Machine Learning to accurately forecast sales. Azure Machine Learning is a cloud-based environment that you can use to train, deploy, automate, manage, and track ML models. Azure Machine Learning can be used for any kind of machine learning, from classical machine learning to deep learning, supervised, and unsupervised learning.

One of the fastest-growing areas of research in machine learning is the area of meta-learning. Meta-learning, in the machine learning context, is the use of machine learning algorithms to assist in the training and optimization of other machine learning models. As meta-learning is becoming more and more popular and more meta-learning techniques are being developed, it's beneficial to have an understanding of what meta-learning is and to have a sense of the various ways it can be applied. Let's examine the ideas behind meta-learning, types of meta-learning, as well as some of the ways meta-learning can be used. The term meta-learning was coined by Donald Maudsley to describe a process by which people begin to shape what they learn, becoming "increasingly in control of habits of perception, inquiry, learning, and growth that they have internalized".

Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are examples of AI-based applications that affect us on a daily basis. Deep learning has been systematically improving the state of the art in areas such as speech recognition, autonomous driving, machine translation, and visual object recognition. However, the reasons why deep learning works so spectacularly well are not yet fully understood. Paul Dirac, one of the fathers of quantum mechanics and arguably the greatest English physicist since Sir Isaac Newton, once remarked that progress in physics using the "method of mathematical reason" would There are many examples in history where purely abstract mathematical concepts eventually led to powerful applications way beyond the context in which they were developed.