DeepMind was established in London by Demis Hassabis, Shane Legg and Mustafa Suleyman in 2011. Major venture capital firms Horizons Ventures and Founders Fund have invested in the company, as well as entrepreneurs Scott Banister and Elon Musk. Jaan Tallinn was an early investor and an advisor to the company. In 2014, DeepMind received the "Company of the Year" award by Cambridge Computer Laboratory. Also on 26 January 2014 Google announced that it had agreed to take over DeepMind Technologies.

Infusion's VP, Technology, Sheldon Fernandez, examines exciting developments in Deep Learning and the impact of these technologies in the mobile sphere! In the last couple years, a revolution in Artificial Intelligence has captured the public imagination. Google and Facebook are designing systems that understand images well enough to describe them in words. Like all frightening technology revolutions, however, empowerment comes from understanding.

We analyze the associations between top Data Science tools, Commercial vs Free/Open Source, rank tools on R vs Python bias, find tools more associated with Big Data, those more associated with Deep Learning, and uncover strong regional differences.

In Mountain View, California, a legion of competitors is vying for first place in the race to develop a self-sufficient form of artificial intelligence (AI). While self-driving cars are fast defining the future of transportation, and AI gaming systems are becoming a reality, the idea of a truly autonomous AI is often tied to the promise of computers capable of tackling the complexities of language. Growth had previously been stunted due to the primitive processing powers of these machines. However, recent advancements at Google headquarters in Silicon Valley have helped researchers take a step towards a linguistically adept AI. Google Brain, a research project exploring deep learning (a branch of machine learning), presented a paper earlier this month at the International Conference on Learning Representations, detailing the methods employed to teach its AI how to better communicate with language.

Deep learning is rapidly'eating' artificial intelligence. But let's not mistake this ascendant form of artificial intelligence for anything more than it really is. The famous author Arthur C. Clarke wrote, "Any sufficiently advanced technology is indistinguishable from magic." And deep learning is certainly an advanced technology--it can identify objects and faces in photos, recognize spoken words, translate from one language to another, and even beat the top humans at the ancient game of Go. Oren Etzioni is the CEO of the Allen Institute for Artificial Intelligence and a computer scientist at the University of Washington.

One of the most acknowledged theories about human learning was first published in 1995 and it states that learning is the product of two complementary learning systems. The theory is developed from David Marr, a British computational neuroscientist, research and it claims that the first system gradually acquires knowledge and skills from exposure to experiences, while the second stores specific experiences for effective integration into the first system. The second system e.g. is required for learning new things that could contradict already acquired knowledge. Demis Hassabis, Google DeepMind co-founder and also a co-author on the Review says: "In my view the extended version of the complementary learning systems theory is likely to continue to provide a framework for future research, not only in neuroscience but also in the quest to develop Artificial General Intelligence, our goal at Google DeepMind."

Recent breakthroughs in creating artificial systems that outplay humans in a diverse array of challenging games have their roots in neural networks inspired by information processing in the brain. In a Review published June 14 in Trends in Cognitive Sciences, researchers from Google DeepMind and Stanford University update a theory originally developed to explain how humans and other animals learn - and highlight its potential importance as a framework to guide the development of agents with artificial intelligence. First published in 1995 (Psychol Rev., 102(3):419-57), the theory states that learning is the product of two complementary learning systems. The first system gradually acquires knowledge and skills from exposure to experiences, and the second stores specific experiences so that these can be replayed to allow their effective integration into the first system. The paper built on an earlier theory by influential British computational neuroscientist David Marr and on then-recent discoveries in neural network learning methods.

I believe that there exist classic deep learning papers which are worth reading regardless of their applications. Rather than providing overwhelming amount of papers, I would like to provide a curated list of the classic deep learning papers which can be considered as must-reads in some area. Please read the contributing guide before you make a pull request. Distinguished deep learning researchers who have published 3 ( 6) papers which are on the awesome list (The papers in Hardware / Software, Papers Worth Reading, Classic Papers sections are excluded in counting.) Thank you for all your contributions.

Tech giants are making acquisitions in the AI space, and an increasing number of startups are working with the technology. Speaking at The Europas, a European startup conference held in London today, John Henderson, principal at Whitestar Capital, spoke about the overwhelming competition in the space and noted the need for young tech firms to stand out from the crowd. "When it comes to investing in AI startups what we look for and what is hard to find is defensibility," he said. DeepMind – acquired by Google in 2014 – Henderson argued was "a one-off". "It [DeepMind] was acquired for the research talent. Startups out there need to think about AI as an enabling technology or a platform, as opposed to every startup building their own AI technology," added Henderson.

In this post you will discover how you can check-point your deep learning models during training in Python using the Keras library. When training deep learning models, the checkpoint is the weights of the model. Checkpointing is setup to save the network weights only when there is an improvement in classification accuracy on the validation dataset (monitor'val_acc' and mode'max'). In this post you have discovered the importance of checkpointing deep learning models for long training runs.