Ai

Thinking about #machinelearning? It will be helpful to understand some numerical computations and concepts that affect the #ML algorithm. One might not interact with these directly, but we surely can feel the effect. The things you need to think about are: 1. Overflow and underflow - thinking of them as rounding up or down errors that shift the functions enough, and compounded across the iterations cam be devastating. Of course can also easily get to division by zero. ...

I know I have had to explain this a lot in most #AI related conversations that I have had - and lately those have been quite a lot. In my experience, most people use these terms interchangeably when they are meaning one over the other. Whilst they all are (inter)related and one might help trigger the other, they are still fundamentally different and at some point, it is good to understand the differences. I like the image below ( source ) that whilst on one hand is showing a time graph, the correlation between them and how one is a subset of the other is what is interesting. ...

There of course are many, but for someone coming from computer science, and, software engineering, where the environment is relatively clean and certain (deterministic), it usually is a leap to understand that Machine Learning (and other elements of #AI) are not. Machine learning, is based on probability theory and deals with stochastic (non-deterministic) elements all the time. Nearly all activities in machine learning, require the ability to factor and more importantly, represent and reason with uncertainty. ...

When it comes to AI and the limits of human kind, what better example that shows the art of the possible than what Microsoft is doing with special awareness and HoloLens and other sensors. And not only can this replay time and allow you to have a ’living memory’ but it also is mobile. ...

Of course you heard of Neural Networks! In the context of #AI they are all the buzz of course. You might have heard of some such as DFF (Deep Feed Forward) or RNN (Recurrent neural networks)? Or perhaps you meant Recursive neural networks? Irrespective, it can be quite messy as you can see below and it would be somewhat important to have some understanding of the differences. And in case you are thinking, well what good or use is all this? Here is one example ( MarI/O - Machine Learning for Video Games) that shows how a computer learned to play Mario using DeepMind and a Neural network. ...

Continuing the previous #ArtificialIntelligence theme. Wanted to see what and how does Amazon’s rekognition work and different from the #AI offerings from the others, such as Microsoft. Here is a #ProjectMurphy image’s confidence score. I am glad to see that there is a 99% confidence that this is a person. Object and Scene detection The request POST is quite simple: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 { "method": "POST", "path": "/", "region": "us-west-2", "headers": { "Content-Type": "application/x-amz-json-1.1", "X-Amz-Date": "Thu, 01 Dec 2016 22:21:01 GMT", "X-Amz-Target": "com.amazonaws.rekognitionservice.RekognitionService.DetectLabels" }, "contentString": { "Attributes": [ "ALL" ], "Image": { "Bytes": "..." } } } And so is the response: ...

So, been spending a lot of time recently around many things related to Artificial Intelligence (#AI). More on that some day. :) Was curious about yesterdays Amazon’s announcement to jump on this bandwagon. Of course Microsoft and others have been there. I don’t know to what extend has Amazon been working on this, but given Alexa has been out for a couple of years, I know they have had rich pickings of tuning this further. ...