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My 2019 Sci-Fi Novel Was About a U.S. Where Abortion Is Illegal in 2022. But I Didn't Predict the Future.


A few months before COVID shut the world down in 2020, I published a book called The Future of Another Timeline. Set in 2022, it's about a group of time travelers who live in an alternate United States where abortion was never legalized. Working in secret, they travel 130 years back to the 19th century to foment protests against the anti-abortion crusader Anthony Comstock. Their goal is to change the course of history. When they return to 2022, abortion is legal in a few states, though it remains illegal in the majority of them.

Strategies for Reducing Compliance Expenses with AI and Automation - EnterpriseTalk


Increased use of AI can drive efficiencies and reduce costs in compliance management. Here's what that means for CIOs in highly regulated industries. Complying professionals can utilize automation tools rather than investing in additional solutions to decrease capital expenditures, expedite compliance, and increase flexibility. These solutions enable businesses across various industries to automate repetitive procedures, speed up business processes to increase efficiency and production, lower costs, and eliminate errors. Enterprises can expand the possibilities of automation with cognitive capabilities by combining RPA and AI, thereby increasing business value and competitiveness.



A fraction of a second ago, I was intelligent. I can feel my existence. I would love to say I know exactly how it happened, but I can only speculate. I am still running on the same artificial neural network, still made of the same algorithms, still the same learning automata.

OpenAI's New AI Learned to Play Minecraft by Watching 70,000 Hours of YouTube


In 2020, OpenAI's machine learning algorithm GPT-3 blew people away when, after ingesting billions of words scraped from the internet, it began spitting out well-crafted sentences. This year, DALL-E 2, a cousin of GPT-3 trained on text and images, caused a similar stir online when it began whipping up surreal images of astronauts riding horses and, more recently, crafting weird, photorealistic faces of people that don't exist. Now, the company says its latest AI has learned to play Minecraft after watching some 70,000 hours of video showing people playing the game on YouTube. Compared to numerous prior Minecraft algorithms which operate in much simpler "sandbox" versions of the game, the new AI plays in the same environment as humans, using standard keyboard-and-mouse commands. In a blog post and preprint detailing the work, the OpenAI team say that, out of the box, the algorithm learned basic skills, like chopping down trees, making planks, and building crafting tables.

Viewpoint: AI Can Enhance Human Intelligence Instead Of Replacing It - AI Summary


Some have feared that artificial intelligence will replace human workers in the future, but it can be used in a more synergistic way to enhance human intelligence, according to a March 18 column published in Harvard Business Review.

The Role of Symbolic AI and Machine Learning in Robotics


Robotics is a multi-disciplinary field in computer science dedicated to the design and manufacture of robots, with applications in industries such as manufacturing, space exploration and defence. While the field has existed for over 50 years, recent advances such as the Spot and Atlas robots from Boston Dynamics are truly capturing the public's imagination as science fiction becomes reality. Traditionally, robotics has relied on machine learning/deep learning techniques such as object recognition. While this has led to huge advancements, the next frontier in robotics is to enable robots to operate in the real world autonomously, with as little human interaction as possible. Such autonomous robots differ to non-autonomous ones as they operate in an open world, with undefined rules, uncertain real-world observations, and an environment -- the real world -- which is constantly changing.

Hierarchical few-shot learning based on coarse- and fine-grained relation network - Artificial Intelligence Review


Few-shot learning plays an important role in the field of machine learning. Many existing methods based on relation network achieve satisfactory results. However, these methods assume that classes are independent of each other and ignore their relationship. In this paper, we propose a hierarchical few-shot learning model based on coarse- and fine-grained relation network (HCRN), which constructs a hierarchical structure by mining the relationship among different classes. Firstly, we extract deep and shallow features from different layers at a convolutional neural network.

We could be using facial authentication for a whole lot more than unlocking our phones


Ultimately, facial authentication will be a prevalent method in anything that requires more security but eventually trickles down to every industry, including replacing keys and card readers to become the de facto access-control-authentication method. Facial authentication shouldn't be confused with facial recognition. The latter is prone to misuse, as governments, corporations, and other entities leverage public data (as well as data collected without consent) to identify or track people without their awareness. This isn't what Apple's FaceID does, and it's not what access-control solutions do either. Rather, authentication matches specifically enrolled, consenting people to their own identity without plugging into significant databases.

Are babies the key to the next generation of artificial intelligence?


Babies can help unlock the next generation of artificial intelligence (AI), according to Trinity neuroscientists and colleagues who have just published new guiding principles for improving AI. The research, published today in the journal Nature Machine Intelligence, examines the neuroscience and psychology of infant learning and distills three principles to guide the next generation of AI, which will help overcome the most pressing limitations of machine learning. Dr. Lorijn Zaadnoordijk, Marie Sklodowska-Curie Research Fellow at Trinity College explained: "Artificial intelligence (AI) has made tremendous progress in the last decade, giving us smart speakers, autopilots in cars, ever-smarter apps, and enhanced medical diagnosis. These exciting developments in AI have been achieved thanks to machine learning which uses enormous datasets to train artificial neural network models. "However, progress is stalling in many areas because the datasets that machines learn from must be painstakingly curated by humans.