In a previous post we saw basic object recognition in images using Google's TensorFlow library from Smalltalk. This post will walk you step by step through the process of using a pre-trained model to detect objects in an image. It may also catch your attention that we are doing this from VASmalltalk rather than Python. Check out the previous post to see why I believe Smalltalk could be a great choice for doing Machine Learning. We provide a collection of detection models pre-trained on the COCO dataset, the Kitti dataset, the Open Images dataset, the AVA v2.1 dataset and the iNaturalist Species Detection Dataset.
AI is a huge technology. That's why a lot of developers simply don't know how to get started. Also, personally, I've met a bunch of people who have no coding background whatsoever, yet they want to learn artificial intelligence. Most aspiring AI developers wonder: what languages are needed to create an AI algorithm? So, I've decided to draw up a list of programming languages my friends-developers use to create AIs.
U.S.A Abstract PIE is an experimental personal information environment implemented in Smalltalk that uses a description language to support the interactive development of programs. PIE contains a network of nodes, each of which can be assigned several perspectives. Each perspective describes a different aspect of the program structure represented by the node, and provides specialized actions from that point of view. Contracts can be created that monitor nodes describing different parts of a program's description. Contractual agreements are expressible as formal constraints, or, to make the system failsoft, as English text interpretable by the user. Contexts and layers are used to represent alternative designs for programs described in the network.
Ruby inherits characteristics from various languages--Lisp, Smalltalk, C, and Perl, to name a few. Metaprogramming comes from Lisp (and Smalltalk). It's a bit like magic, which makes something astonishing possible. There are two kinds of magic: white magic, which does good things, and black magic, which can do nasty things. If you discipline yourself, you can do good things, such as enhancing the language without tweaking its syntax by macros or enabling internal domain-specific languages.