Hive is a full-stack AI company providing solutions in computer vision and deep learning-based industry-specific use-cases. Hive is focused on AI powering solutions and data labelling. Hive has four specialized operations in the business: Hive Data, HivePredict, Hive Media, Spaces by Hive. Hive's deep learning platform helps companies monitor the machine learning workflow. Hive offers pre-trained models as well as custom model development services to help customers' with their own use cases.

Last week, Alphabet subsidiary, DeepMind open-sourced Lab2D, which the researchers explained to be a scalable environment simulator for artificial intelligence research, helping them to create 2D environments for AI and ML research. Researchers claim that it facilitates researcher-led experimentation with environment design while also helping them understand the influence of environments in multi-agent reinforcement learning. While it was built with the specific needs of multi-agent deep reinforcement learning researchers in mind, it can be used beyond that particular subfield. In this article, we take a deeper look into what DeepMind Lab2D is all about and how it can help AI researchers. As researchers explain in the paper, DeepMind Lab2D (or "DMLab2D" for short) is a platform for the creation of two-dimensional, layered, discrete "grid-world" environments, in which the pieces -- which can be compared to chess pieces on a chessboard -- move around. This system is particularly tailored for multi-agent reinforcement learning.

The innovation of GPT-3 and advancements in facial recognition technology, brain chips, chatbots, self-driving cars, drones, as well as robotics have marked 2020 as the year of artificial intelligence. However, similar to any other technologies, AI also came with its challenges. From biasness to inaccuracy, AI has proved its immaturity in many cases. As a matter of fact, prominent tech leaders, researchers as well as scientists, like Elon Musk, Yann LeCun, as well as Bill Gates have continuously warned the industry about the hype AI has created and the consequences it can bring if not appropriately handled by the tech giants. Such critical judgements came from the many instances where AI failed to demonstrate its value to the industry.

Undoubtedly, one of the artificial intelligence models that have left its mark on the last period is GPT-3, in other words, Generative Pre-trained Transformer, Productive Pre-Processed Transformer 3 model in Turkish. GPT-3 was developed by OpenAI which is called an artificial intelligence R&D company that includes computer experts and investors such as Elon Musk, CEO of companies such as SpaceX Tesla, Sam Altman, known for her initiatives Loopt, Y Combinator, and Ilya Sutskever, one of the inventors of software and networks such as AlexNet, AlphaGo, TensorFlow, carries out projects and R & D studies in many groundbreaking areas, especially artificial intelligence. GPT-3 is defined as an autoregression language model that uses the deep learning method to produce content similar to texts and graphics are written and created by humans. It is stated that the system that processes data with "1.5" billion parameters in its previous version, GPT-2, will perform analysis with 175 billion parameters in GPT-3, so it can produce very advanced content. However, it is also stated that artificial intelligence that can produce such high quality and qualified content has many risks and can cause many problems.

Despite the challenges of 2020, the AI research community produced a number of meaningful technical breakthroughs. GPT-3 by OpenAI may be the most famous, but there are definitely many other research papers worth your attention. For example, teams from Google introduced a revolutionary chatbot, Meena, and EfficientDet object detectors in image recognition. Researchers from Yale introduced a novel AdaBelief optimizer that combines many benefits of existing optimization methods. OpenAI researchers demonstrated how deep reinforcement learning techniques can achieve superhuman performance in Dota 2. To help you catch up on essential reading, we've summarized 10 important machine learning research papers from 2020. These papers will give you a broad overview of AI research advancements this year. Of course, there are many more breakthrough papers worth reading as well.

Artificial Intelligence (AI)-powered pathology is a revolutionary step in the world of digital pathology and shows great promise to increase both diagnosis accuracy and efficiency. However, defocus and motion blur can obscure tissue or cell characteristics hence compromising AI algorithms'accuracy and robustness in analyzing the images. In this paper, we demonstrate a deep-learning-based approach that can alleviate the defocus and motion blur of a microscopic image and output a sharper and cleaner image with retrieved fine details without prior knowledge of the blur type, blur extent and pathological stain. In this approach, a deep learning classifier is first trained to identify the image blur type. Then, two encoder-decoder networks are trained and used alone or in combination to deblur the input image.

COVID-19 pandemic has affected the entire world. Many people lost their jobs, kids stay at home, and the economic crisis is disastrous. The question of "how will the world be after COVID-19" is of high interest. Many futurists predict a different world, where we should rethink public spaces and believes that the memory of the COVID-19 lockdown will remain for a long time (Del Bello, 2020). This information presents a sad situation, where the COVID-19 continues to spreads with tragic death cases.

MIT researchers claim that deep learning neural networks need better uncertainty analysis to reduce errors. "Deep evidential regression" reduces uncertainty after only one pass on a network, greatly reducing time and memory. This could help mitigate problems in medical diagnoses, autonomous driving, and much more.

As demands for AI applications grow, we've seen a lot of effort put by companies to build their Machine Learning Engineering (MLE) tools tailored for their needs. There are just so many challenges faced by industries in regards to having a well-designed environment for their Machine Learning (ML) lifecycle: building, deploying, and managing ML models in production. This post will cover two papers, explaining MLE practices from two of the leading tech companies: Google and Microsoft. Adding a little bit of context, this article is part of a graduate-level course at Columbia University: COMS6998 Practical Deep Learning System Performance taught by Prof. Parijat Dube who also works at IBM New York as Research Staff Member. The first section will present a paper from Google and will touch on the building part of an ML lifecycle.

This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. If I wanted to learn deep learning with Python again, I would probably start with PyTorch, an open-source library developed by Facebook's AI Research Lab that is powerful, easy to learn, and very versatile. When it comes to training material, however, PyTorch lags behind TensorFlow, Google's flagship deep learning library. There are fewer books on PyTorch than TensorFlow, and even fewer online courses. Among them is Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann, three engineers who have contributed to the project and have extensive experience developing deep learning solutions.