AI
Programming Languages of the Future!
An assortment of AI madness that is shaping up the world into the future!
DeepCoder
“ We develop a first line of attack for solving programming competition-style problems from input-output examples using deep learning. The approach is to train a neural network to predict properties of the program that generated the outputs from the inputs. We use the neural network's predictions to augment search techniques from the programming languages community, including enumerative search and an SMT-based solver. Empirically, we show that our approach leads to an order of magnitude speedup over the strong non-augmented baselines and a Recurrent Neural Network approach, and that we are able to solve problems of difficulty comparable to the simplest problems on programming competition websites.”
Link
Facebook researchers were able to reduce the time to train the ImageNet-1k dataset of over 1.2 million images from multiple days to one hour with leading classification accuracy. The team achieved this result with Caffe2 and the Gloo library for collective communication — both available on GitHub — and Big Basin, Facebook's next-generation GPU server, the design for which was contributed to the Open Compute Project earlier this year.
Similar ideas have been used for planning in game environments but have never been applied to language because the number of possible actions is much higher. To improve efficiency, the researchers first generated a smaller set of candidate utterances to say, and then for each of these, they repeatedly simulated the complete future of the dialog in order to estimate how successful they were. The prediction accuracy of this model is high enough that the technique dramatically improved negotiation tactics in the following areas:
Negotiating harder: The new agents held longer conversations with humans, in turn accepting deals less quickly. While people can sometimes walk away with no deal, the model in this experiment negotiates until it achieves a successful outcome.
Intelligent maneuvers: There were cases where agents initially feigned interest in a valueless item, only to later “compromise” by conceding it — an effective negotiating tactic that people use regularly. This behavior was not programmed by the researchers but was discovered by the bot as a method for trying to achieve its goals.
Producing novel sentences: Although neural models are prone to repeating sentences from training data, this work showed the models are capable of generalizing when necessary.
Paradigms
of AI Programming Source Code
This
page is the index for the Lisp source code files for the book
Paradigms of Artificial Intelligence Programming. The code is offered
as open source freeware under this license. You can browse all the
files in this directory.
Installation Instructions
Download
the file paip.zip and unzip it. You
must have a lisp compiler/interpreter. To
test all the code, start lisp and do the following at the interactive
prompt:
(load
"auxfns.lisp") (requires
"examples") (do-examples
:all)
This
should print out a long list of inputs and outputs, and the last
output should be the total number of errors. If all goes well, this
should be "0".
Use
To use the code, edit any of
the files or add new files. You will always have to do (load
"auxfns.lisp") first, and you will typically have to do
(requires "file"), for various instances of file that you
want to use.
The function "requires" is used for a
primitive form of control over what files require other files to be
loaded first. If "requires" does not work properly on your
system you may have to alter its definition, in the file
"auxfns.lisp". For more complicated use of these files, you
should follow the guidelines for organizing files explained in
Chapter 24.
The function do-examples, which takes as an
argument either :all or a chapter number or a list of chapter
numbers, can be used to see examples of the use of various functions.
For example, (do-examples 1) shows the examples from chapter 1.
The
Files
The index below gives the chapter in the book, file name,
and short description for each file.
CH
Filename
Description
-
README.html
This
file: explanation and index
-
examples.lisp
A
list of example inputs taken from the book
-
tutor.lisp
An
interpreter for running the examples
24
loop.lisp
Load
this first if your Lisp doesn't support ANSI LOOP
-
auxfns.lisp
Auxiliary
functions; load this before anything else
1
intro.lisp
A
few simple definitions
2
simple.lisp
Random
sentence generator (two versions)
3
overview.lisp
14
versions of LENGTH and other examples
4
gps1.lisp
Simple
version of General Problem Solver
4
gps.lisp
Final
version of General Problem Solver
5
eliza1.lisp
Basic
version of Eliza program
5
eliza.lisp
Eliza
with more rules; different reader
6
patmatch.lisp
Pattern
Matching Utility
6
eliza-pm.lisp
Version
of Eliza using utilities
6
search.lisp
Search
Utility
6
gps-srch.lisp
Version
of GPS using the search utility
7
student.lisp
The
Student Program
8
macsyma.lisp
The
Macsyma Program
8
macsymar.lisp
Simplification
and integration rules for Macsyma
9-10
(no
files; important functions in auxfns.lisp
11
unify.lisp
Unification
functions
11
prolog1.lisp
First
version of Prolog interpreter
11
prolog.lisp
Final
version of Prolog interpreter
12
prologc1.lisp
First
version of Prolog compiler
12
prologc2.lisp
Second
version of Prolog compiler
12
prologc.lisp
Final
version of Prolog compiler
12
prologcp.lisp
Primitives
for Prolog compiler
13
clos.lisp
Some
object-oriented and CLOS code
14
krep1.lisp
Knowledge
Representation code: first version
14
krep2.lisp
Knowledge
Representation code with conjunctions
14
krep.lisp
Final
KR code: worlds and attached functions
15
cmacsyma.lisp
Efficient
Macsyma with canonical form
16
mycin.lisp
The
Emycin expert system shell
16
mycin-r.lisp
Some
rules for a medical application of emycin
17
waltz.lisp
A
Line-Labeling program using the Waltz algorithm
18
othello.lisp
The
Othello playing program and some strategies
18
othello2.lisp
Additional
strategies for Othello
18
edge-tab.lisp
Edge
table for Iago strategy
19
syntax1.lisp
Syntactic
Parser
19
syntax2.lisp
Syntactic
Parser with semantics
19
syntax3.lisp
Syntactic
Parser with semantics and preferences
20
unifgram.lisp
Unification
Parser
21
grammar.lisp
Comprehensive
grammar of English
21
lexicon.lisp
Sample
Lexicon of English
22
interp1.lisp
Scheme
interpreter, including version with macros
22
interp2.lisp
A
tail recurive Scheme interpreter
22
interp3.lisp
A
Scheme interpreter that handles call/cc
23
compile1.lisp
Simple
Scheme compiler
23
compile2.lisp
Compiler
with tail recursion and primitives
23
compile3.lisp
Compiler
with peephole optimizer
23
compopt.lisp
Peephole
optimizers for compile3.lisp
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
GitHub
An open-source software library for Machine Intelligence
https://www.tensorflow.org/
Programming Languages For (AI):
LISP:
In
the 1970s and 1980s, Lisp was the best developed and most widely used
language that offered the following set of features:
- Easy dynamic creation of new objects, with automatic garbage collection,
- A library of collection types, including dynamically-sized lists and hashtables,
- A development cycle that allows interactive evaluation of expressions and re-compilation of functions or files while the program is running,
- Well-developed compilers that could generate efficient code,
- A macro system that let developers create a domain-specific level of abstraction on which to build the next level.
These
five features are valuable for programming in general, but especially
for exploratory problems where the solution is not clear at the
onset; thus Lisp was a great choice for AI research. Over the
years, these features started migrating into other languages, and
Lisp no longer had a unique position; today, (5) is the only
remaining feature in which Lisp excels compared to other languages.
Lisp
fares well in the AI field because of its excellent prototyping
capabilities and its support for symbolic expressions. It's a
powerful programming language and is used in major AI projects, such
as Macsyma, DART, and CYC. There are many dialects of Lisp in use
today, among which are Common Lisp, Scheme, and Clojure.
The
Lisp language is mostly used in the Machine Learning/ ILP sub-field
because of its usability and symbolic structure. Peter Norvig, the
famous computer scientist who works extensively in the AI field, and
also the writer of the famous AI book, “Artificial Intelligence: A
modern approach,” explains why Lisp is one of the top programming
languages for AI development in a
Quora
answer.
PROLOG:
Prolog
is a high-level programming language based on formal logic. Unlike
traditional programming languages that are based on performing
sequences of commands, Prolog is based on defining and then solving
logical formulas. Prolog is sometimes called a declarative
language
or
a rule-based
language
because
its programs consist of a list of facts and rules. Prolog is used
widely for artificial intelligence applications, particularly expert
systems.
Prolog
stands alongside Lisp when it comes to usefulness and usability.
According to the literature,
Prolog
Programming for Artificial Intelligence,
Prolog is one of those programming languagesfor some basic
mechanisms, which can be extremely useful for AI programming. For
example, it offers pattern matching, automatic backtracking, and
tree-based data structuring mechanisms. Combining these mechanisms
provides a flexible framework to work with.
Prolog
is extensively used in expert systems for AI and is also useful for
working on medical projects.
Java:
Java
uses several ideas from Lisp, most notably garbage collection. Its
portability makes it desirable for just about any application, and it
has a decent set of built-in types. Java is still not as high-level
as Lisp or Prolog, and not as fast as C.
Java
is also a great choice. It is an object-oriented programming language
that focuses on providing all the high-level features needed to work
on AI projects, it's portable, and it offers in-built garbage
collection. The Java community is also a plus point as there will be
someone to help you with your queries and problems.
Java
is also a good choice as it offers an easy way to code algorithms,
and AI is full of algorithms, be they search algorithms, natural
language processing algorithms or neural networks. Not to mention
that Java also allows for scalability, which is a must-have feature
for AI projects.
Python:
Python
is the preferred choice of many to start with artificial intelligence
because Python is one of the easiest and the fastest programming
language out there, Mostly AI developers suggest Python for
Artificial Intelligence development.
Python
is one of the most widely used programming languages in the AI field
of Artificial Intelligence thanks to its simplicity. It can
seamlessly be used with the data structures and other frequently used
AI algorithms.
The
choice of Python for AI projects also stems from the fact that there
are plenty of useful libraries that can be used in AI. For example,
Numpy
offers
scientific computation capability,
Scypy
for
advanced computing and
Pybrain
for
machine learning in Python.
Haskell AI:
Most
of the major algorithms are already available via cabal.
Additionally, Haskell has CUDA bindings and is compiled to bytecode,
and because it’s stateless and functional, programs can easily be
executed on multiple CPUs in the cloud. So overall it’s an
excellent language for AI development.
C++
C++
is the fastest programming language in the world. Its ability to talk
at the hardware level enables developers to improve their program
execution time. C++ is extremely useful for AI projects, which are
time-sensitive. Search engines, for example, can utilize C++
extensively.
In
AI, C++ can be used for statistical AI techniques like those found in
neural networks. Algorithms can also be written extensively in the
C++ for speed execution, and AI in games is mostly coded in C++ for
faster execution and response time.
Modules
Each time your bot responds, that reply corresponds with a "Module".
Collectively, a series of modules create and define the flow of conversation. It is possible to create complex flows of conversation through the use of Connections.
There are several types of modules, each of which have slightly different behavior. The types of modules you choose to utilize depends on the type of bot you're creating, and the data you would like to gather.
Module Types
When you click the "Add Module" button for your bot, you will see a list of module types. Below is our current set of offered module types, with a description and example of usage.
The simplest of modules, bot statement is best for situations when you have no expectation of what the user's response might be. Think of it as a free-form text field.
Intended for situations where a number of defined options or answers exist to a given question.
NumPy
NumPy
is the fundamental package for scientific computing with Python. It
contains among other things:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
Besides
its obvious scientific uses, NumPy can also be used as an efficient
multi-dimensional container of generic data. Arbitrary data-types can
be defined. This allows NumPy to seamlessly and speedily integrate
with a wide variety of databases.
For
more information on the SciPy Stack (for which NumPy provides the
fundamental array data structure), see
scipy.org.
SciPy
(pronounced
“Sigh Pie”) is a Python-based ecosystem of open-source software
for mathematics, science, and engineering. In particular, these are
some of the core packages:
NumPy
Base
N-dimensional array package
SciPy library
Fundamental
library for scientific computing
Matplotlib
Comprehensive
2D Plotting
IPython
Enhanced
Interactive Console
Sympy
Symbolic
mathematics
pandas
Data
structures & analysis
PyBrain
is a modular Machine Learning Library for Python. Its goal is to
offer flexible, easy-to-use yet still powerful algorithms for Machine
Learning Tasks and a variety of predefined environments to test and
compare your algorithms.
PyBrain
is short for Python-Based Reinforcement Learning, Artificial
Intelligence and Neural Network Library. In fact, we came up with the
name first and later reverse-engineered this quite descriptive
"Backronym".
How is PyBrain different?
While
there are a few machine learning libraries out there, PyBrain aims to
be a very easy-to-use modular library that can be used by entry-level
students but still offers the flexibility and algorithms for
state-of-the-art research. We are constantly working on more and
faster algorithms, developing new environments and improving
usability.
What PyBrain can do
PyBrain,
as its written-out name already suggests, contains algorithms for
neural networks, for reinforcement learning (and the combination of
the two), for unsupervised learning, and evolution. Since most of the
current problems deal with continuous state and action spaces,
function approximators (like neural networks) must be used to cope
with the large dimensionality. Our library is built around neural
networks in the kernel and all of the training methods accept a
neural network as the to-be-trained instance. This makes PyBrain a
powerful tool for real-life tasks.
Using PyBrain
PyBrain
is open source and free to use for everyone (it is licensed under the
BSD
Software Licence).
Just download it and start using the algorithms and modules in your
own project or have a look at the provided tutorials and examples. If
you use PyBrain for your research, we kindly ask you to cite us in
your publications. Use the reference below or import this
bibtex
reference.
You
will also have no problems learning Python for AI as there are tons
of resources available online.
Other
AI Languages
AIML:
( "Artificial Intelligence Markup Language") a XML dialect
with A.L.I.C.E.-type chatterbots.
Smalltalk
used
for simulations, neural networks, machine learning and genetic
algorithms; a form of object-oriented programming using message
passing.
IPL: first language developed for artificial intelligence. Includes features intended to support programs that perform general problem solving like in lists, associations, schemas , dynamic memory allocation, data types, recursion, associative retrieval, functions as arguments, generators, & cooperative multitasking.
Haskell
another
programming language for AI. Lazy evaluation and the list and LogicT
monads make it easy to express non-deterministic algorithms. Infinite
data structures for search trees. The language's features enable a
compositional way of expressing the algorithms.
POP-11 a reflective, incrementally compiled programming language with many of the features of an interpreted language; a core language of Poplog programming environment developed originally by University of Sussex, and recently in the School of Computer Science at the University of Birmingham - often used to introduce symbolic programming techniques to programmers of more conventional languages like Pascal, who find POP syntax more familiar than that of Lisp. POP-11's features is that it supports first-class functions.
POP-11 a reflective, incrementally compiled programming language with many of the features of an interpreted language; a core language of Poplog programming environment developed originally by University of Sussex, and recently in the School of Computer Science at the University of Birmingham - often used to introduce symbolic programming techniques to programmers of more conventional languages like Pascal, who find POP syntax more familiar than that of Lisp. POP-11's features is that it supports first-class functions.
Wolfram
Language
includes
a wide range of integrated machine learning capabilities, from highly
automated functions like Predict and Classify to functions based on
specific methods and diagnostics. The functions work on many types of
data, including numerical, categorical, time series, textual, and
image.
STRIPS
a
language for expressing automated planning problem instances. It
expresses an initial state, the goal states, and a set of actions.
For each action preconditions and postconditions are specified.
Planner a hybrid between procedural and logical languages. It gives a procedural interpretation to logical sentences where implications are interpreted with pattern-directed inference.
MATLAB Perl
