-- DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems. Using the platform, we demonstrate how a 1/18th scale car can learn to drive autonomously using RL with a monocular camera. It is trained in simulation with no additional tuning in physical world and demonstrates: 1) formulation and solution of a robust reinforcement learning algorithm, 2) narrowing the reality gap through joint perception and dynamics, 3) distributed on-demand compute architecture for training optimal policies, and 4) a robust evaluation method to identify when to stop training. It is the first successful large-scale deployment of deep reinforcement learning on a robotic control agent that uses only raw camera images as observations and a model-free learning method to perform robust path planning. Due to high sample complexity and safety requirements, it is common to train the RL agent in simulation [1], [5], [17]. To reduce training time and encourage exploration, the agent is usually trained with distributed rollouts [18], [19], [20], [21]. For a successful transfer to the real world, researchers use calibration [2], [22], domain randomization [23], [24], [25], [12], fine tuning with real world data [9], and learn features from a combination of simulation and real data [26], [27]. To experiment with robotic reinforcement learning, one needs to have expertise in many areas, access to a physical robot, an accurate robot model for simulations, a distributed training mechanism and customizability of the training procedure such as modifying the neural network and the loss function or introducing noise. For the uninitiated, dealing with this complexity is daunting and dissuades adoption. As a result, much of prior work is limited to a single robot [1], [23], [28] or a few robots [16]. We reduce the learning curve and alleviate development effort with DeepRacer.

Social bias in machine learning has drawn significant attention, with work ranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias. In natural language processing, gender bias has been shown to exist in context-free word embeddings. Recently, contextual word representations have outperformed word embeddings in several downstream NLP tasks. These word representations are conditioned on their context within a sentence, and can also be used to encode the entire sentence. In this paper, we analyze the extent to which state-of-the-art models for contextual word representations, such as BERT and GPT-2, encode biases with respect to gender, race, and intersectional identities. Towards this, we propose assessing bias at the contextual word level. This novel approach captures the contextual effects of bias missing in context-free word embeddings, yet avoids confounding effects that underestimate bias at the sentence encoding level. We demonstrate evidence of bias at the corpus level, find varying evidence of bias in embedding association tests, show in particular that racial bias is strongly encoded in contextual word models, and observe that bias effects for intersectional minorities are exacerbated beyond their constituent minority identities. Further, evaluating bias effects at the contextual word level captures biases that are not captured at the sentence level, confirming the need for our novel approach.

Professional software developers spend a significant amount of time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new deep learning architecture, called Graph2Diff, for automatically localizing and fixing build errors. We represent source code, build configuration files, and compiler diagnostic messages as a graph, and then use a Graph Neural Network model to predict a diff. A diff specifies how to modify the code's abstract syntax tree, represented in the neural network as a sequence of tokens and of pointers to code locations. Our network is an instance of a more general abstraction that we call Graph2Tocopo, which is potentially useful in any development tool for predicting source code changes. We evaluate the model on a dataset of over 500k real build errors and their resolutions from professional developers. Compared to the approach of DeepDelta (Mesbah et al., 2019), our approach tackles the harder task of predicting a more precise diff but still achieves over double the accuracy.

Despite outstanding contribution to the significant progress of Artificial Intelligence (AI), deep learning models remain mostly black boxes, which are extremely weak in explainability of the reasoning process and prediction results. Explainability is not only a gateway between AI and society but also a powerful tool to detect flaws in the model and biases in the data. Local Interpretable Model-agnostic Explanation (LIME) is a recent approach that uses a linear regression model to form a local explanation for the individual prediction result. However, being so restricted and usually oversimplifying the relationships, linear models fail in situations where nonlinear associations and interactions exist among features and prediction results. This paper proposes an extended Decision Tree-based LIME (TLIME) approach, which uses a decision tree model to form an interpretable representation that is locally faithful to the original model. The new approach can capture nonlinear interactions among features in the data and creates plausible explanations. Various experiments show that the TLIME explanation of multiple blackbox models can achieve more reliable performance in terms of understandability, fidelity, and efficiency.

Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a deep-learning model trained to map projection radiographs of a patient to the corresponding 3D anatomy can subsequently generate volumetric tomographic X-ray images of the patient from a single projection view. We demonstrate the feasibility of the approach with upper-abdomen, lung, and head-and-neck computed tomography scans from three patients. Volumetric reconstruction via deep learning could be useful in image-guided interventional procedures such as radiation therapy and needle biopsy, and might help simplify the hardware of tomographic imaging systems.

Deep-learning based daily financial predictors. The program is aimed to provide trending predictors for major stocks and ETFs. There are nine combinations of trending (Up, Flat, Down) and Volatility (Stable, Decreasing, Increasing), based on which optimal allocations are suggested for individual asset. Please become a patron to receive prompt updates.

Object detection problems pose several unique obstacles beyond what is required for image classification. Five such challenges are reviewed in this post along with researchers' efforts to overcome these complications. The field of computer vision has experienced substantial progress recently owing largely to advances in deep learning, specifically convolutional neural nets (CNNs). Image classification, where a computer classifies or assigns labels to an image based on its content, can often see great results simply by leveraging pre-trained neural nets and fine-tuning the last few throughput layers. Classifying and finding an unknown number of individual objects within an image, however, was considered an extremely difficult problem only a few years ago.

Arguably, one of the biggest debates happening in data science in 2019 is the need for AI explainability. The ability to interpret machine learning models is turning out to be a defining factor for the acceptance of statistical models for driving business decisions. Enterprise stakeholders are demanding transparency in how and why these algorithms are making specific predictions. A firm understanding of any inherent bias in machine learning keeps boiling up to the top of requirements for data science teams. As a result, many top vendors in the big data ecosystem are launching new tools to take a stab at resolving the challenge of opening the AI "black box." Some organizations have taken the plunge into AI even with the realization that their algorithm's decisions can't be explained.

They had the freedom to use different software platforms including machine–learning, deep learning and block chain to build solutions.

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