Artificial Intelligence Emerging Technology – An Inside Look at a Startup Key Player
Artificial Intelligence Emerging Technology Report
More than 80,600 startups in the Artificial Intelligence emerging technology space.
Artificial Intelligence Emerging Technology Startup #1
Let’s take a deep look into a startup key player: DeepMind
What does DeepMind hold in the Artificial Intelligence emerging technology space?
- 33 conference data sources
- 9 scientific journal publications
- 647 news publications
“Think about Watson, the computer that won Jeopardy!, Deep Blue, the computer that defeated a world champion chess player, and Google DeepMind’s AlphaGo program, which defeated a South Korean master in the board game Go. AI is not science fiction anymore — it is everywhere.”
“Sure, exactly. I want to create an advantage for my country in artificial intelligence, directly. And that’s why we have these announcements made by Facebook, Google, Samsung, IBM, DeepMind, Fujitsu who choose Paris to create AI labs and research centers: this is very important to me. Second, I want my country to be part of the revolution that AI will trigger in mobility, energy, defense, finance, healthcare and so on. Because it will create value as well. Third, I want AI to be totally federalized. Why? Because AI is about disruption and dealing with impacts of disruption.”
Emmanuel Macron, the president of France
Scientific Journal Publications
The CFR+ algorithm for solving imperfect information games is a variant of the popular CFR algorithm, with faster empirical performance on a range of problems. It was introduced with a theoretical upper bound on solution error, but subsequent work showed an error in one step of the proof. We provide updated proofs to recover the original bound.
Journal: JAIR – Journal of Artificial Intelligence Research
In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks. However, both paradigms have strengths and weaknesses, and a significant challenge for the field today is to effect a reconciliation. A central tenet of the symbolic paradigm is that intelligence results from the manipulation of abstract compositional representations whose elements stand for objects and relations. If this is correct, then a key objective for deep learning is to develop architectures capable of discovering objects and relations in raw data, and learning how to represent them in ways that are useful for downstream processing. This short review highlights recent progress in this direction.
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