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.

The most frightening potential use is disinformation. Imagine a computer that can create unlimited amounts of false information or fake photos and you can't distinguish between what's real and fabricated. We hear all the time about technology that writes like people or converses like us, or makes realistic computer-generated human faces or faked videos. Repeatedly since the 1950s scientists have produced technologies that seemed as if we were close to mimicking human intelligence. Each time we were nowhere close.

GPT-3, the latest incarnation of artificially intelligent natural-language systems, knows how to write -- and write and write and write. For a taste of what it can (and cannot) do, here are three examples of its verbosity. In each case, we gave the system a short prompt (in italics) and let it roll. First we asked it to write about itself. Then, playing off a suggestion from a start-up called Sudowrite, which has spent months testing GPT-3, we asked the system to write a Modern Love column.

So far, their impact on real-world technology has been small. But GPT-3 -- which learned from a far larger collection of online text than previous systems -- opens the door to a wide range of new possibilities, such as software that can speed the development of new smartphone apps, or chatbots that can converse in far more human ways than past technologies. As software designers, entrepreneurs, pundits and artists explore this system, each new experiment stokes an already heated debate over how powerful this breed of technology will ultimately be. While some say it may be a path toward truly intelligent machines, others argue that these experiments, while endlessly fascinating, are also misleading. "It is very fluent," said Mark Riedl, a professor and researcher at the Georgia Institute of Technology.

This is a great time to be alive. The only problem is that, in the next five years, A.I. will replace millions of jobs. By 2020, over five million jobs will be lost, and the number of jobs will continue to increase with each passing year. By 2030, 50 percent of jobs will be lost. However, there will be a silver lining.

This paper presents a new approach to visual zero-shot slot filling. The approach extends previous approaches by reformulating the slot filling task as Question Answering. Slot tags are converted to rich natural language questions that capture the semantics of visual information and lexical text on the GUI screen. These questions are paired with the user's utterance and slots are extracted from the utterance using a state-of-the-art ALBERT-based Question Answering system trained on the Stanford Question Answering dataset (SQuaD2). An approach to further refine the model with multi-task training is presented. The multi-task approach facilitates the incorporation of a large number of successive refinements and transfer learning across similar tasks. A new Visual Slot dataset and a visual extension of the popular ATIS dataset is introduced to support research and experimentation on visual slot filling. Results show F1 scores between 0.52 and 0.60 on the Visual Slot and ATIS datasets with no training data (zero-shot).

Artificial intelligence (AI) coding can be used to improve medical websites in various ways, from custom personalised content presentations to the integration of unique medical AI features. These include medical appointment scheduling software, healthcare cost estimators, design medical website, prescribe medication, and answer questions. By developing more targeted and custom AI solutions, some artificial intelligence healthcare platforms aim to usher in the more widespread adoption of medical AI technologies, benefitting organisations, practices, clinicians, and patients alike. AI medical websites can use AI tools to present targeted information specific to the consumer. AI medical websites use IP addresses to locate the user and present information specific to physicians and practices local to their area.

Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state of the art for scalable object detection.

Demis Hassabis, co-founder and CEO, DeepMind, says: 'I'm delighted to expand our support of AI research at Oxford with the DeepMind Professorship of Artificial Intelligence. I look forward to seeing who the University appoints and where they decide to focus their research with the support of Oxford's world-class AI research community.'

DeepMind this week open-sourced Lab2D, a software system designed to support the creation of 2D environments for AI and machine learning research. The Alphabet subsidiary says that Lab2D was built with the needs of deep reinforcement learning researchers in mind, but that it can be useful beyond that particular subfield of machine learning. The DeepMind team behind Lab2D makes the case that 2D environments are inherently easier to understand than 3D ones at little loss of expressiveness. Even a game as simple as Pong, which essentially consists of three moving rectangles on a black background, can capture something fundamental about the real game of table tennis, the researchers assert. This abstraction ostensibly makes it easier to capture the essence of problems and concepts in AI. "Rich complexity along numerous dimensions can be studied in 2D just as readily as in 3D, if not more so … In addition, 2D worlds are significantly less resource-intensive to run, and typically do not require any specialized hardware (like GPUs) to attain reasonable performance," the researchers continued in their paper describing Lab2D. "2D worlds have been successfully used to study problems as diverse as social complexity, navigation, imperfect information, abstract reasoning, exploration, and many more."