Cognex Corporation introduces the In-Sight D900 embedded vision system. The In-Sight D900 features Cognex's ViDi deep learning software inside an In-Sight industrial-grade smart camera. The system is designed to solve applications including, optical character recognition (OCR), assembly verification, and defect detection. The In-Sight D900 system, which can be setup using a small number of image samples, leverages Cognex's spreadsheet platform and does not require a PC or deep learning expertise to deploy. The In-Sight D900 is ideal for automating complex inspection applications across a range of industries including automotive, consumer electronics, consumer products, packaging, food and beverage, medical devices, and logistics.

Battlesnake is an AI competition in which you build AI-powered snakes. Battlesnake's rules are similar to the traditional snakes game. Your goal is to be the last surviving snake when competing against other snakes. Developers of all levels build snakes using techniques ranging from unique heuristic-based strategies to state-of-the-art deep reinforcement learning (RL) algorithms. You can use the SageMaker Battlesnake Starter Pack to build your own snake and compete in the Battlesnake arena.

As deep learning algorithms and artificial intelligence applications become more widely used, concern has been growing about their ethical implications. In particular, questions have emerged both about the trustworthiness of AI algorithms and the risks of relying on them for automated decision-making. How can technologists, regulators, and the general public be confident that AI algorithms are designed and used in ways that are safe and consistent with our values? Although numerous studies have proposed ethical considerations for artificial intelligence, there is currently no standardized framework for putting those values into effect. A new working paper from the AI Ethics Impact Group (AIEIG), however, seeks to change that.

As cancer cells spread in a culture dish, Guillaume Jacquemet is watching. The cell movements hold clues to how drugs or gene variants might affect the spread of tumours in the body, and he is tracking the nucleus of each cell in frame after frame of time-lapse microscopy films. But because he has generated about 500 films, each with 120 frames and 200–300 cells per frame, that analysis is challenging to say the least. "If I had to do the tracking manually, it would be impossible," says Jacquemet, a cell biologist at Åbo Akademi University in Turku, Finland. So he has trained a machine to spot the nuclei instead.

First, we're going to take a look at the tensor object type. Then we'll have a graphical understanding of TensorFlow to define computations. Finally, we'll run the graphs with sessions, showing how to substitute intermediate values. In TensorFlow, data isn't stored as integers, floats, or strings. These values are encapsulated in an object called a tensor, a fancy term for multidimensional arrays.

Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly understood. This paper investigates how different normalization techniques affect the performance of neural ODEs. Particularly, we show that it is possible to achieve 93% accuracy in the CIFAR-10 classification task, and to the best of our knowledge, this is the highest reported accuracy among neural ODEs tested on this problem.

Neural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence. However, existing studies on neural NLG are primarily focused on surface-level realizations with limited emphasis on logical inference, an important aspect of human thinking and language. In this paper, we suggest a new NLG task where a model is tasked with generating natural language statements that can be \emph{logically entailed} by the facts in an open-domain semi-structured table. To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset \cite{chen2019tabfact} featured with a wide range of logical/symbolic inferences as our testbed, and propose new automatic metrics to evaluate the fidelity of generation models w.r.t.\ logical inference. The new task poses challenges to the existing monotonic generation frameworks due to the mismatch between sequence order and logical order. In our experiments, we comprehensively survey different generation architectures (LSTM, Transformer, Pre-Trained LM) trained with different algorithms (RL, Adversarial Training, Coarse-to-Fine) on the dataset and made following observations: 1) Pre-Trained LM can significantly boost both the fluency and logical fidelity metrics, 2) RL and Adversarial Training are trading fluency for fidelity, 3) Coarse-to-Fine generation can help partially alleviate the fidelity issue while maintaining high language fluency. The code and data are available at \url{https://github.com/wenhuchen/LogicNLG}.

Following the latest release of the programming language Python, an updated version of the Python package PyTorch is now available. PyTorch is designed to provide Tensor computation and deep neural networks. PyTorch 1.5 features new and updated libraries as well as new API additions and improvements. A highlight of the release is that the C frontend API is now stable and at parity with Python. This includes 100% coverage and docs for C torch::nn module/functional, C__ optimizers that behave the same as the Python equivalent; and the ability to use tensor.index({Slice(),

This great cheatsheet from Datacamp is going to be extremely useful for any people learning Python for data science. All the basic commands, from list manipulation to numpy arrays are there. Keras is a great and easy-to-use deep learning library for Python. It is easier to get started in deep neural networks with Keras, rather than it is with Tensorflow directly. This cheatsheet contains some quick recipes to create the most basic neural network types.

Not unlike the land of the blind where the one eyed man is king, AI has achieved royalty status in both corporate and technology landscapes where any enterprise worth its salt is racing to embrace the "AI first" mindset in its business strategy. While manual processing has been relegated to the sidelines, proficiency in AI and ML has become the prized skill. AI has literally transformed the way we look at work at the fundamental level. This includes intelligent automation, transaction processing at multiples of the speed of humans, enhanced accuracy of up to 85% in business operations, and even suggested decision-making for managers. But this is just the tip of the iceberg.