This story was first published at The Aleph Report. If you want to read the latest reports, please subscribe to our newsletter and our Twitter. Those that follow technology closely have noticed a significant trend in the field, China. The more you read, the more you encounter increasing coverage on China's tech dealings. In the last few weeks, we've seen Tencent's market capital surpass that of Facebook; Venture Capital activity reach US VC levels; LinkedIn's Chinese rival MaiMai, outperform LinkedIn in the country and the amount of Chinese Women in Tech pass that of the US.

Martin Welker is the chief executive of Axonic. Even with the support of AI frameworks like TensorFlow or OpenAI, artificial intelligence still requires deep knowledge and understanding compared to a mainstream web developer. If you have built a working prototype, you are probably the smartest guy in the room. Congratulations, you are a member of a very exclusive club. With Kaggle, you can even earn decent money by solving real-world projects. All in all, it is an excellent position to be in, but is it enough to build a business? You can not change market mechanics, after all.

Artificial Intelligence has come a long way from the pages of science fiction to the toolkit of engineers, helping solve numerous real-life problems. Advances in Deep Learning has led to remarkable progress in Computer Vision and Natural Language Processing. This paper tries to analyse the impact of Artificial Intelligence on enterprise mobility and applications. Will it really change the way enterprises use and manage mobility?

What is machine learning / ai? How to learn machine learning in practice? What are recurrent neural networks ( rnn), what are long short term neural networks ( lstm) and how do the work? Neural Networks (often referred to as deep learning) in their differnt forms are particular interesting. But there are a few questions.

AlphaGo defeated Go world champion Sedol in the first of five games of the Go contest.

In this video from CSCS-ICS-DADSi Summer School, Atilim Güneş Baydin presents: Deep Learning and Automatic Differentiation from Theano to PyTorch. Inquisitive minds want to know what causes the universe to expand, how M-theory binds the smallest of the small particles or how social dynamics can lead to revolutions. In recent centuries, developments in science and technology brought us closer to explore the expanding universe, discover unknown particles like bosons or find out how and why a society interacts and reacts. To explain the fascinating phenomena of nature, Natural scientists develop complex'mechanistic models' of deterministic or stochastic nature. But the hard question is how to choose the best model for our data or how to calibrate the model given the data. The way that statisticians answer these questions is with Approximate Bayesian Computation (ABC), which we learn on the first day of the summer school and which we combine with High Performance Computing.

A year ago, Gartner, the world's leading research and advisory company, named artificial intelligence (AI), machine learning (ML), and conversational systems three of the top strategic tech trends for 2017. In May last year, SAP launched the SAP Leonardo Machine Learning portfolio at SAPPHIRE NOW in Orlando, Florida, and thus demonstrated that it's on the pulse of innovation. Today, it is about time to sum up the latest developments and give an outlook on the potential of intelligent technologies. Deep learning, neural networks, and natural language processing elevated ML to new levels. Thanks to mature ML algorithms, higher processing power, and the availability of huge data sets, machines are becoming intelligent and able to process unstructured data, like pictures, text, or spoken language – often even on a superhuman level. Additionally, deep learning is now stable enough to potentially establish ML as a standard commodity across businesses worldwide.

MAgent is a research platform for many-agent reinforcement learning. Unlike previous research platforms that focus on reinforcement learning research with a single agent or only few agents, MAgent aims at supporting reinforcement learning research that scales up from hundreds to millions of agents. MAgent supports Linux and OS X running Python 2.7 or python 3. We make no assumptions about the structure of your agents. You can write rule-based algorithms or use deep learning frameworks. The training time of following tasks is about 1 day on a GTX1080-Ti card.

If you missed that thread and there's something you want to mention, post it and I'll put it up. Lots of categories didn't have an entry. You can also make a category yourself. "and we all realized what a pain in the ass Tensorflow was and how it didn't need to be that way. In the academic community, it certainly to me feels like pytorch has become the dominant framework (probably not backed up by actual stats... But my school's CV research lab has certainly switched over)" I might have nominated distill.pub

The growth of artificial intelligence and machine learning is picking up pace, and those who thought adoption was a few more years away are finding the reality much different. AI is already embedded in our everyday lives, and forward-thinking marketers are embracing machine learning technology efficiency to automate and scale their marketing programs. However, little has been mentioned of deep learning, the gem in the AI armory that provides even more powerful insights to marketers. In my last article, I focused on AI applications and explained why not showing up in 2018 without an AI system or application in your martech stack could leave CMOs lagging. Taking that premise a step further, not utilizing deep learning technology means that marketers are losing out on essential insights that I predict will fuel marketing technology development in 2018, 2019 and beyond.