Goto

Collaborating Authors

Go


AI-bank of the future: Can banks meet the AI challenge?

#artificialintelligence

In 2016, AlphaGo, a machine, defeated 18-time world champion Lee Sedol at the game of Go, a complex board game requiring intuition, imagination, and strategic thinking--abilities long considered distinctly human. Since then, artificial intelligence (AI) technologies have advanced even further, 1 1. AI can be defined as the ability of a machine to perform cognitive functions associated with human minds (e.g., perceiving, reasoning, learning, and problem solving). It includes various capabilities, such as machine learning, facial recognition, computer vision, smart robotics, virtual agents, and autonomous vehicles. See "Global AI Survey: AI proves its worth, but few scale impact," November 2019, McKinsey.com.


Humans Versus Artificial Intelligence

#artificialintelligence

The complexity of the human brain, in relation to that of other species, is one of the biggest wonders in science. Today, neural networks compete alongside us with processing powers that can calculate 200 million potential outcomes per second. With the data trail we feed into it as we go through our lives, and the vast data sets scientists are training machines on, they are learning to compete with the greatest human minds at our own games. In this story we explore how humans compare with machines in games, healthcare, art and emotional intelligence. AlphaGo, DeepMind's Go playing AI, has been dubbed "The AI that has nothing to learn from humans". Go is a complex strategy game over 3000 years old, with 10170 different board configurations.


We're entering the AI twilight zone between narrow and general AI

#artificialintelligence

With recent advances, the tech industry is leaving the confines of narrow artificial intelligence (AI) and entering a twilight zone, an ill-defined area between narrow and general AI. To date, all the capabilities attributed to machine learning and AI have been in the category of narrow AI. No matter how sophisticated – from insurance rating to fraud detection to manufacturing quality control and aerial dogfights or even aiding with nuclear fission research – each algorithm has only been able to meet a single purpose. This means a couple of things: 1) an algorithm designed to do one thing (say, identify objects) cannot be used for anything else (play a video game, for example), and 2) anything one algorithm "learns" cannot be effectively transferred to another algorithm designed to fulfill a different specific purpose. For example, AlphaGO, the algorithm that outperformed the human world champion at the game of Go, cannot play other games, despite those games being much simpler.


Autonomous robot plays with NanoLEGO

ScienceDaily > Robotics Research

Rapid prototyping, the fast and cost-effective production of prototypes or models -- better known as 3D printing -- has long since established itself as an important tool for industry. "If this concept could be transferred to the nanoscale to allow individual molecules to be specifically put together or separated again just like LEGO bricks, the possibilities would be almost endless, given that there are around 1060 conceivable types of molecule," explains Dr. Christian Wagner, head of the ERC working group on molecular manipulation at Forschungszentrum Jülich. There is one problem, however. Although the scanning tunnelling microscope is a useful tool for shifting individual molecules back and forth, a special custom "recipe" is always required in order to guide the tip of the microscope to arrange molecules spatially in a targeted manner. This recipe can neither be calculated, nor deduced by intuition -- the mechanics on the nanoscale are simply too variable and complex.


Autonomous robot plays with NanoLEGO: Scientists are developing an autonomous artificial intelligence system that can selectively grip and move individual molecules

#artificialintelligence

Rapid prototyping, the fast and cost-effective production of prototypes or models -- better known as 3D printing -- has long since established itself as an important tool for industry. "If this concept could be transferred to the nanoscale to allow individual molecules to be specifically put together or separated again just like LEGO bricks, the possibilities would be almost endless, given that there are around 1060 conceivable types of molecule," explains Dr. Christian Wagner, head of the ERC working group on molecular manipulation at Forschungszentrum Jülich. There is one problem, however. Although the scanning tunnelling microscope is a useful tool for shifting individual molecules back and forth, a special custom "recipe" is always required in order to guide the tip of the microscope to arrange molecules spatially in a targeted manner. This recipe can neither be calculated, nor deduced by intuition -- the mechanics on the nanoscale are simply too variable and complex.


The Danger of Humanizing Algorithms

#artificialintelligence

To many, 2016 marked the year when artificial intelligence (AI) came of age. AlphaGo triumphed against the world's best human Go players, demonstrating the almost inexhaustible potential of artificial intelligence. Programs playing board games with superhuman skills like AlphaGo or AlphaZero have created unparalleled hype surrounding AI, and this has only been fueled by big data availability. In this context, it is not surprising that the public, business, and scientific interest in machine learning are unchecked. These programs can go further than beating a human player, going so far as to invent new and ingenious gameplay.


Why some artificial intelligence is smart until it's dumb

#artificialintelligence

Starfleet's star android, Lt. Commander Data, has been enlisted by his renegade android "brother" Lore to join a rebellion against humankind -- much to the consternation of Jean-Luc Picard, captain of the USS Enterprise. "The reign of biological life-forms is coming to an end," Lore tells Picard. "You, Picard, and those like you, are obsolete." In real life, the era of smart machines has already arrived. They haven't completely taken over the world yet, but they're off to a good start. "Machine learning" -- a sort of concrete subfield within the more nebulous quest for artificial intelligence -- has invaded numerous fields of human endeavor, from medical diagnosis to searching for new subatomic particles.


Will Reinforcement Learning Pave the Way for Accessible True Artificial Intelligence? - KDnuggets

#artificialintelligence

Reinforcement learning (RL) has received a massive boost in attention recently. Thanks to impressive projects such as DeepMind's AlphaGo and AlphaGo Zero, which beat the world's best players in the strategy board game "Go", RL has garnered extensive news coverage. Just recently, RL was used to compete with the world's top e-sports players in the real-time strategy video game StarCraft II. Python Machine Learning, Third Edition covers the essential concepts of RL, starting from its foundations, and how RL can support decision making in complex environments. The book discusses agent-environment interactions and Markov decision processes (MDP), and considers three main approaches for solving RL problems: dynamic programming, MC learning, and TD learning. It discusses how the dynamic programming algorithm assumes that the full knowledge of environment dynamics is available, an assumption that is not typically true for most real-world problems.


How Managers Can Enable AI Talent in Organizations

#artificialintelligence

Recent progress on the technical side of machine learning, particularly within deep learning, has followed an accelerating trend of businesses adopting AI technologies into their processes and workflows in the past decade.1 Some of these advances, such as Google DeepMind's AlphaGo and OpenAI's GPT-2 and GPT-3 models, have demonstrated expert-level performance in domains previously held up as examples of areas where bots would be incapable of challenging human abilities.2 With respect to business outcomes, most of the exciting developments involve using deep learning for supervised learning problems. Supervised learning is a form of machine learning where you have input and output variables and use an algorithm to learn the function that relates input to output. The algorithm is "supervised" because it learns from training data where input and output are known in advance.


Quantum version of the ancient game of Go could be ultimate AI test

New Scientist

A new version of the ancient Chinese board game Go that uses quantum entanglement to add an element of randomness could make it a tougher test for artificial intelligences than regular board games. "Board games have long been good test beds for AI because these games provide closed worlds with specific and simple rules," says Xian-Min Jin at Shanghai Jiao Tong University in China. In Go, players take turns to place a stone on a board, trying to surround and capture the opponent's stones.