Best performing nuclear segmentation methods are based on deep learning algorithms that require a large amount of annotated data. However, collecting annotations for nuclear segmentation is a very labor-intensive and time-consuming task. Thereby, providing a tool that can facilitate and speed up this procedure is very demanding. Here we propose a simple yet efficient framework based on convolutional neural networks, named NuClick, which can precisely segment nuclei boundaries by accepting a single point position (or click) inside each nucleus. Based on the clicked positions, inclusion and exclusion maps are generated which comprise 2D Gaussian distributions centered on those positions. These maps serve as guiding signals for the network as they are concatenated to the input image. The inclusion map focuses on the desired nucleus while the exclusion map indicates neighboring nuclei and improve the results of segmentation in scenes with nuclei clutter. The NuClick not only facilitates collecting more annotation from unseen data but also leads to superior segmentation output for deep models. It is also worth mentioning that an instance segmentation model trained on NuClick generated labels was able to rank first in LYON19 challenge.

How to improve the efficiency while preventing deadlocks is the core issue in designing AGV systems. In this paper, we propose an approach to tackle this problem. The proposed approach includes a traditional AGV scheduling algorithm, which aims at solving deadlock problems, and an artificial neural network based component, which predict future tasks of the AGV system, and make decisions on whether to send an AGV to the predicted starting location of the upcoming task, so as to save the time of waiting for an AGV to go to there first when the upcoming task is created. Simulation results show that the proposed method significantly improves the efficiency as against traditional method, up to 20% to 30%. Index T erms --Automated guided vehicles, efficiency improvement, deep learning, LSTM.

The paradigm of pretrained deep learning models has recently emerged in artificial intelligence practice, allowing deployment in numerous societal settings with limited computational resources, but also embedding biases and enabling unintended negative uses. In this paper, we treat pretrained models as objects of study and discuss the ethical impacts of their sociological position. We discuss how pretrained models are developed and compared under the common task framework, but that this may make self-regulation inadequate. Further how pretrained models may have a performative effect on society that exacerbates biases. We then discuss how pretrained models move through actor networks as a kind of computationally immutable mobile, but that users also act as agents of technological change by reinterpreting them via fine-tuning and transfer. We further discuss how users may use pretrained models in malicious ways, drawing a novel connection between the responsible innovation and user-centered innovation literatures. We close by discussing how this sociological understanding of pretrained models can inform AI governance frameworks for fairness, accountability, and transparency.

The paper discusses a pooling mechanism to induce subsam-pling in graph structured data and introduces it as a component of a graph convolutional neural network. The pooling mechanism builds on the Non-Negative Matrix Factorization (NMF) of a matrix representing node adjacency and node similarity as adaptively obtained through the vertices embedding learned by the model. Such mechanism is applied to obtain an incrementally coarser graph where nodes are adaptively pooled into communities based on the outcomes of the nonnegative factorization. The empirical analysis on graph classification benchmarks shows how such coarsening process yields significant improvements in the predictive performance of the model with respect to its non-pooled counterpart.

The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account of maximizing returns. Therefore, this paper proposes a deep reinforcement learning based trading agent that can manage the portfolio considering not only profit maximization but also risk restraint. We also propose a new target policy to allow the trading agent to learn to prefer low-risk actions. The new target policy can be reflected in the update by adjusting the greediness for the optimal action through the hyper parameter. The proposed trading agent verifies the performance through the data of the cryptocurrency market. The Cryptocurrency market is the best test-ground for testing our trading agents because of the huge amount of data accumulated every minute and the market volatility is extremely large. As a experimental result, during the test period, our agents achieved a return of 1800% and provided the least risky investment strategy among the existing methods. And, another experiment shows that the agent can maintain robust generalized performance even if market volatility is large or training period is short.

Machine learning and deep learning are still in the experimental stage of development. However, machine learning capabilities are already routinely incorporated in software for both personal and business use. You can find good machine learning projects everywhere – from home and office automation tools through industrial equipment to mobile devices. Machine learning ideas drive mostly projects aimed at the development of smart algorithms like artificial intelligence. Even so, do not make the mistake of referring to a machine learning project as "artificial intelligence".

OpenAI, the artificial intelligence firm that Elon Musk founded then later departed, just released a stronger version of its "conversational" text-writing AI system. When OpenAI first released the algorithm, dubbed GPT-2, back in February, the company declared that it was too dangerous to release to the public, instead opting to share a watered-down version. Now, OpenAI announced that it's sharing a new version that's six times as robust as the original -- while keeping an eye out to make sure people don't misuse it. In the past, OpenAI expressed concerns that its AI could be used to flood the internet with fake news and propaganda. The first model had notable flaws and telltale signs that its output was machine-written.

If your organization is considering the use of AI, we invite you to check out this two-part series that defines the six common AI buzzwords that are the cornerstones of artificial intelligence: machine learning, neural networks, deep learning, natural language processing, Turing Tests, and algorithm vs. heuristic. We hope this knowledge will help you easily navigate AI conversations. Have you ever watched a documentary on Netflix, only to have the system suggest eight more documentaries when you were done? It's essentially a computer program that learns from experience. Every time a user interacts with the program, it uses the feedback it receives to more accurately predict future behaviors. In theory, each interaction should yield improved results.

I have been frequently asked about the tools for the Machine Learnign projects There are lot of them on the market so in my newest post you will find my view on them. I would like to start my first Machine Learning project. But I do not have tools. What are the tools I could use? I will give you some hints and advices based on the toolbox I use.

AI is the future, and it's also the past. Not just in the sense of having been developed in previous years and decades, but also in the sense of being capable of recreating human history. This power was highlighted vividly by a study published at the end of August by researchers from University College London and Duke University, who managed to use artificial intelligence to create separate representations of two images that had been painted on both sides of a single panel. More specifically, they used X-ray imaging techniques to produce a combined representation of the outer panels of the famous 15th Century Ghent Altarpiece painting. Because the resulting image was a combination of two images superimposed on each other, it was previously hard to analyze.