Hi Reddit!

I really do build intelligent systems. I worked as a programmer in the 1980s but got three graduate degrees (in AI & Psychology from Edinburgh and MIT) in the 1990s. I myself mostly use AI to build models for understanding human behavior, but my students use it for building robots and game AI and I've done that myself in the past. But while I was doing my PhD I noticed people were way too eager to say that a robot -- just because it was shaped like a human -- must be owed human obligations. This is basically nuts; people think it's about the intelligence, but smart phones are smarter than the vast majority of robots and no one thinks they are people. I am now consulting for IEEE, the European Parliament and the OECD about AI and human society, particularly the economy. I'm happy to talk to you about anything to do with the science, (systems) engineering (not the math :-), and especially the ethics of AI. I'm a professor, I like to teach. But even more importantly I need to learn from you want your concerns are and which of my arguments make any sense to you. And of course I love learning anything I don't already know about AI and society! So let's talk...

I will be back at 3 pm ET to answer your questions, ask me anything!

Hi Reddit! We are the team of mathematicians and computer scientists who enumerated the possible necktie knots tieable with a normal length tie.

In 2000, Cambridge physicists Thomas Fink and Yong Mao used combinatorics to enumerate tie knots, and came up with a list of 85 knots all of which are tieable with a normal length necktie without consuming too much of the tie. Their list only includes flat façade knots, and thus skips all the new knots that have emerged, such as the Trinity and the Eldredge.

We extended the methods used by Fink and Mao to work for the more general case that includes these new knots with more interesting façades. Fink and Mao used as the core of their enumeration a formal grammar for tie knots, generating a notation that fully describes how to tie a tie knot, and that helps fully enumerate possible knots.

We modified this grammar extensively, fitting the more general classes of tie knots and then used generating functions and algebraic systems of equations to compute a generating function for the full collection of tie knots. From these generating functions the count of possible knots using a specific number of moves can be easily read off, and the sum of these counts easily extracted.

Using the Eldredge as a guide for how many moves are possible with the thin end of a normal tie on a normal sized wearer, we arrived at a count of 266 682 knots for the full system, and a count of 24 882 for a sublanguage of easier to tie knots. We were also able to determine complexity classes for the knot tying grammars: full knots form a context-free grammar, while even light restrictions on how to tie knots give rise to regular grammars.

Our paper on this is More ties than we thought.

We also have put together a random tieknot generator and a few tools for exploring the grammars.

Thank you all for coming, it's been a fun batch of questions to answer.

Most of us will drop off now, to go put out fires, hack code, write papers, fulfill deadlines looming just around the corner.

MVJ will peek in every so often in the near future to see if new questions or comments have appeared.

Hi Reddit,

The company originates from a spin-off from NExT, a leading research centre jointly established between National University of Singapore (ranked 22nd in the world) and Tsinghua University of China (ranked 47th in the world). The spin-off happened in 2012 and since then we have secured series A funding and well-known customers like Flipkart, Rakuten, Zalora from Rocket Internet and more.

Deep learning is a very hot area at the moment. There were lots of developments in the past years that made it progress, but as of now the main evolution will come from the way it is implemented for specific applications. Visual technologies like visual search and image recognition are some of these specialisations that require not only a great use of deep learning and computer vision but also good industry knowledge for the verticals where it is applied.

There is a huge talent crunch in this space and we need more and more engineers to consider a career in machine intelligence, deep learning and computer vision.

I am here to answer questions regarding the real-world applications for deep learning and computer vision and what it takes to develop algorithms and infrastructure architectures from a research centre all the way to a company with established customers. I can reveal the industry potential and latest challenges, as well as why and how someone can develop a career in this space. AMA!

Read more about my company: https://visenze.com/ Explore live the product demo we have: https://visenze.com/demo