This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. The human hand is one of the fascinating creations of nature, and one of the highly sought goals of artificial intelligence and robotics researchers. A robotic hand that could manipulate objects as we do would be enormously useful in factories, warehouses, offices, and homes. Yet despite tremendous progress in the field, research on robotics hands remains extremely expensive and limited to a few very wealthy companies and research labs. Now, new research promises to make robotics research available to resource-constrained organizations.

Greek literature has, for centuries, served as a paradigm and model for literary writing all over Europe. The oldest surviving texts of Classical Greek literature - texts such as the Iliad, the Odyssey, and the works of Hesiod - are epic poems that all share the same metre: hexameter. They are written in an artificial language that has never been spoken in everyday life and owes its origin and many of its peculiarities to the nature of metrically bound language (Meister (1921)). Comprehensive hexameter annotation is, therefore, crucial for large-scale and data-driven investigations into some of the linguistic features of Ancient Greek epic language. Furthermore, it may provide additional criteria for the evaluation of Homer's repeated verses, the so-called iterata. Within Classical Philology, controversy around the nature of the Homeric repetitions started in 1840, and it remained one of the central research questions in the field for a long period of time (see Strasser (1984), pp.

Over the years, artificial intelligence has amazed everyone with numerous breakthroughs, and this year it was no different. The whole year, we witnessed awe-inspiring innovations in reinforcement learning, neural networks, among others. Tech companies from across the world benchmarked various leaps in artificial intelligence to further eliminated the doubts people had about achieving true AI. As a chronicler of the technological progress in the space of analytics, artificial intelligence, data science and big data, among others, Analytics India Magazine was on top of every jaw-dropping development. We bring to you the top 7 amazing AI advancements that changed the world forever.

OpenAI, Elon Musk's artificial intelligence laboratory in Sand Francisco, introduced Dactyl in 2018. The robot hands tagged as the "spinner" has five fingers just like the human hands. In 2019, the Technological University of Munich in Berlin introduced H-1: a robot with sensors like human skin. Each day, we are getting closer to the future where robots live with but not replace humans. We have available sensors in the market mass-produced for different uses.

Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group's robotics division says Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik's cube one-handed. OpenAI sees the feat as a leap forward both for the dexterity of robotic appendages and its own AI software, which allows Dactyl to learn new tasks using virtual simulations before it is presented with a real, physical challenge to overcome. In a demonstration video showcasing Dactyl's new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle.

Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group's robotics division says that Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik's cube one-handed. In a demonstration video showcasing Dactyl's new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle. It's somewhat unsettling to see in action, if only because the movements look noticeably less fluid than human ones and especially disjointed when compared to the blinding speed and raw dexterity on display when a human speedcuber solves the cube in a matter of seconds.

Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group's robotics division says Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik's cube one-handed. OpenAI sees the feat as a leap forward both for the dexterity of robotic appendages and its own AI software, which allows Dactyl to learn new tasks using virtual simulations before it is presented with a real, physical challenge to overcome. In a demonstration video showcasing Dactyl's new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle.

For engineers at the Elon Musk-founded nonprofit OpenAI, this presented both a challenge and an opportunity. How could their researchers use artificial intelligence to teach a robot to manipulate objects as artfully as a human? Usually, when teaching an AI to control a physical robot, scientists tend to come up against the same problems. Training is often done using reinforcement learning; a method where the AI learns through a process of trial and error. But this requires a lot of time, usually amounting to years of experience.

OpenAI has trained a human-like robot hand to manipulate physical objects with unprecedented dexterity. Their system, called Dactyl, is trained entirely in simulation and transfers its knowledge to reality, adapting to real-world physics. Dactyl learns from scratch using the same general-purpose reinforcement learning algorithm and code as OpenAI Five. The results show that it's possible to train agents in simulation and have them solve real-world tasks, without physically-accurate modeling of the world. Dactyl is a system for manipulating objects using a Shadow Dexterous Hand.

AI researchers have demonstrated a self-teaching algorithm that gives a robot hand remarkable new dexterity. Their creation taught itself to manipulate a cube with uncanny skill by practicing for the equivalent of a hundred years inside a computer simulation (though only a few days in real time). The robotic hand is still nowhere near as agile as a human one, and far too clumsy to be deployed in a factory or a warehouse. Even so, the research shows the potential for machine learning to unlock new robotic capabilities. It also suggests that someday robots might teach themselves new skills inside virtual worlds, which could greatly speed up the process of programming or training them.