In the last few years, many companies have embraced innovation methodologies, from design thinking to agile organizations. Most of these are outstanding contributions to the innovation space. However, it is also true that many organizations fail at putting these methodologies into practice.
Contrary to popular belief, sometimes the largest barrier is not technological or operational, but a question of having the wrong mindset: when an organization is relatively successful, the right thing to do is to keep improving what worked in the past, thinking incrementally as a general rule. In contrast, C-level executives should “upgrade” their operating system and leave room for “10 × thinking”, a mental model based on improving a relevant problem by a factor of 10, rather than by 10%. Read More
Daily Archives: April 10, 2019
AI 100: The Artificial Intelligence Startups Redefining Industries
The most promising 100 AI startups working across the artificial intelligence value chain, from hardware and data infrastructure to industrial applications.
CB Insights’ third annual cohort of AI 100 startups is a list of 100 of the most promising private companies providing hardware and data infrastructure for AI applications, optimizing machine learning workflows, and applying AI across a variety of major industries. Read More
Andrew Ng’s AI Transformation Playbook shares key lessons
Co-founder of Google Brain and former Chief Scientist at Baidu, Andrew Ng, has unveiled an AI Transformation Playbook. The guide to successfully adopting AI in enterprise draws on insights gained from leading AI teams at Google and Baidu.
The AI Transformation Playbook is distributed freely on the Landing AI website. Under Ng’s leadership as Chairman and CEO, Landing AI helps enterprises develop and execute cohesive AI strategies.Five key steps form the backbone of the guide:
Execute pilot projects to gain momentum
Build an in-house AI team
Provide broad AI training
Develop an AI strategy
Develop internal and external communications
Read More
Mastering the game of Go without human knowledge
A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo. Read More
AlphaGo Zero Explained In One Diagram
Human-level control through deep reinforcement learning
The theory of reinforcement learning provides a normative account1, deeply rooted in psychological2 and neuroscientific3 perspectives on animal behaviour, of how agents may optimize their control of an environment.To use reinforcement learning successfully insituations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations.Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems4,5, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms3. While reinforcement learning agents have achieved some successes in a variety of domains6–8, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks9–11 to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games12. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games,using the same algorithm, network architecture and hyper-parameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks. Read More
A general reinforcement learning algorithm that masters chess, shogi and Go through self-play
The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently
achieved superhuman performance in the game of Go by reinforcement learning from self play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games. Starting from random play and given no domain knowledge except the game rules, AlphaZero convincingly defeated a world champion program in the games of chess and shogi (Japanese chess) as well as Go. Read More
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case. Read More
Rick's Cafe AI 