Becoming a Fool – A Much Better View

Wake Up My Faith

As our culture of Christian-y leaders, icons, and unapproachable gurus of influence continue to graduate disciples who are more interested in platform building than bending their knees, we’ve inadvertently, and ironically buried a generation of young hopefuls in a canyon of “well done my good and famous servant” darkness. The good news, however, is that it only takes a flicker of light to conquer a canyon of darkness.

I was recently blessed with one the most encouraging emails I’ve ever received. And it came from one of the wisest, kindest, and youngest people I’ve had the privilege of being connected with on this seven year journey of living by faith, and writing about the results. Jordan S. is a seventeen year old high school student, who has been on quite a journey of her own. As a result, this bright young light has been used by God to not only…

View original post 1,533 more words

Unity / C# : Pedestrian Simulation

Alex Brinkley

For me AI has always been an interesting topic to read about. I first thought about AI in video games when I was a boy playing “007 GoldenEye” on the N64. I somehow always managed to set the alarm off on the first level “Dam”, and then watched as a horde of guards made their way through the tunnel to kill me. “It’s pretty cool the way they know where you are and hunt you down”, I always said to my dad. AI is just pretty cool in general. AI for video games and simulations alike.

When I was tasked to create a Pedestrian Simulation in Unity, I was pretty chuffed as I could finally look deeper into how it’s created. Having never used Unity before, learning how to use it just added to the excitement.

After days and days of trial and error, Unity tutorials and watching Pedestrian simulations…

View original post 322 more words

Python : Traffic AI

Alex Brinkley

Shortly after completing the Pedestrian Simulation in Unity found here, I received another task that required me to design and implement a Traffic Simulation inside of Maya using Python. The focus was on six driver behaviors, in which we had to put into the traffic simulation.

  • Emergency stop
  • Panic
  • Spin out of control and crash
  • Swerve
  • Recover from stop
  • Drive around obstacle

Having had a series of lecturers on Artificial Intelligence within games, the first implementation technique that sprung to mind was using a Finite State Machine which would easily be able to handle the different behaviors we were tasked with creating. Knowing this, I began researching into the world of State Machines to gain a deeper knowledge into the way they work.

Through my research, I came across a series of Python videos by Trevor Payne in which he talks through different aspects of coding in…

View original post 474 more words

Dwelling in Possibility

Otium

Epistemic Status: Intuitive, Casual

One of the things I’ve noticed in people who are farther along in business or management than I am, usually men with a “leaderly” mien, is a certain comfort with uncertainty or imperfection.

They can act relaxed even when their personal understanding of a situation is vague, when the future is uncertain, when the optimal outcome is unlikely.  This doesn’t mean they’re not motivated to get things done.  But they’re cool with a world in which a lot of things remain nebulous and unresolved at any given moment.

They’re able to produce low-detail, high-level, positive patter for a general audience.  They’re able to remain skeptical, expecting that most new ideas won’t work, without seeming sad about that.

Talking to someone like that, it feels like a smooth layer of butter has been spread over the world, where everything is pretty much normal and fine most of…

View original post 1,646 more words

I tried Haskell for 5 years and here’s how it was

Meta Rabbit

One blogpost style which I find almost completely useless is “I tried Programming Language X for 5 days and here’s how it was.” Most of the time, the first impression is superficial discussing syntax and whether you could get Hello World to run.

This blogpost is I tried Haskell for 5 years and here’s how it was.

In the last few years, I have been (with others) developing ngless, a domain specific language and interpreter for next-generation sequencing. For partly accidental reasons, the interpreter is written in Haskell. Even though I kept using other languages (most Python and C++), I have now used Haskell quite extensively for a serious, medium-sized project (11,270 lines of code). Here are some scattered notes on Haskell:

There is a learning curve

Haskell is a different type of language. It takes a while to fully get used to it if you’re coming from…

View original post 959 more words

Carrot & Stick: that doesn’t work on Programmers!

Experiments

'A raise might destroy their initiative. The good old carrot and stick bonus keeps them focused.'

Carrot & Stick – by definition “a policy of offering a combination of rewards and punishment to induce behaviour”, perhaps the oldest methodology for motivation in organisations doesn’t work for tech companies. There are many reasons for that, I will discuss them later. Any engineering solution just can’t follow this kind of rigid methodology to motivate their employees. Still, I can see many software companies here in India and elsewhere following it blindly.

It was a great talk – ‘The Puzzle of Motivation‘ by Dan Pink. The quotes from the talk:

There is a mismatch between what science knowsand what business does.And what worries me, as we stand here in the rubbleof the economic collapse,is that too many organizations are making their decisions,their policies about talent and people,based on assumptions that are outdated,unexamined,and rooted more in folklore than in science.And if…

View original post 327 more words

Unsupervised learning of 3D structure from images

the morning paper

Unsupervised learning of 3D structure from images

Unsupervised learning of 3D structure from images Rezende et al. (Google DeepMind) NIPS,2016

Earlier this week we looked at how deep nets can learn intuitive physics given an input of objects and the relations between them. If only there was some way to look at a 2D scene (e.g., an image from a camera) and build a 3D model of the objects in it and their relationships… Today’s paper choice is a big step in that direction, learning the 3D structure of objects from 2D observations.

The 2D projection of a scene is a complex function of the attributes and positions of the camera, lights and objects that make up the scene. If endowed with 3D understanding agents can abstract away from this complexity to form stable disentangled representations, e.g., recognizing that a chair is a chair whether seen from above or from…

View original post 774 more words