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The JF-17 Thunder has become a popular platform due to the successful development milestones it has achieved under the leadership of the Pakistan Air Force (PAF) who is actually the main driving force behind the aircraft’s continuous research and development as a result of operational necessity. We should in fact not look at the JF-17 Thunder as a Chinese product, but rather look at it as a Product of Pakistan. China views the JF-17 (or better known in China as the CAC FC-1 Xiaolong) as a ‘low-end’ export fighter to Pakistan, but it is Pakistan who is actually developing the JF-17 platform into a very formidable 4th generation + fighter. Does it compare to the SAAB JAS 39 Gripen C/D? From a quality over lifetime perspective, no, but from a missions capability perspective, yes (referring to the JF-17 Thunder Block III). But, it was never designed to directly compete against Western platforms for it was designed from the onset as a ‘lower end market alternative’ to offer emerging- and/or arms restricted economies some form of modern fighter capability at a fraction of the cost of purchasing modern Western fighters. Looking back at the original PAF requirements (if they had their way), they would have purchased the latest F-16V Block 70 from the US to complement their existing F-16 fleet, but with the ongoing US Congress restrictions blocking arms sales to Pakistan due to Pakistan’s continuation of their nuclear weapons program (as countermeasure for the Indian nuclear weapons program), Pakistan had to seriously look at alternatives to counter future threats of (for instance) India possibly operating F-16V Block 70 on their doorstep. This led to the hastily commissioned JF-17 Thunder fighter program as a joint venture with China, who was the only feasible development partner to design and build a new 4th generation fighter in as short a time possible, while keeping program development costs to the absolute minimum (around US$ 500 million, which is extremely low compared to the development costs involving Western aircraft development).Looking at the JF-17 Thunder since its first introduction into the PAF as the JF-17 Block I, it has had some mixed results. Again, we need to understand the development approach followed by the Pakistan Air Force trying to keep development costs low, and at the same time meeting critical operational performance requirements. To understand this from a development context, the JF-17 Block I is what would be referred to as the XDM (Experimental Development Model). The JF-17 Block II is the upgraded version of the Block I, which would be referred to as the ADM (Advanced Development Model), and the JF-17 Block III is the approved production model (the final product conforming to initial design requirements). What is referred to now as the JF-17 Block III is what the PAF originally wanted from the onset but knowing how long the development timeline can take, along with constantly increasing costs (based on lessons learnt from the PAF Mirage III ROSE upgrades), the PAF decided on combining the JF-17 development phases with operational frontline use. Where a traditional development approach entails developing different versions subject to various ‘testing’ regimes over a project’s lifetime until the perfect production version is achieved, the PAF just went all in and combined the whole development phase with operational use, testing each platform version under real operational conditions while at the same time enabling ground crews to acquire the technical capabilities to support these platforms. To date (what really makes this platform fascinating), is that it has already accumulated around 20,000 hours flying operational sorties within the War in North-West Pakistan combatting Islamic extremist groups, using Block I and Block II platforms. Based on limited data, there are also sufficient reasons to believe that two IAF aircraft (1 x Mig 21, 1 x Su-30MKI) were shot down by two PAF JF-17 Block II fighters on 27 Feb 2019. Now, this can be considered quite remarkable for an aircraft that is still technically under development. Looking at the future, when the first Block III platforms enter operational service, the whole induction process will be extremely simple and less time consuming, allowing for gradual withdrawal of Block I platforms for upgrading to the Block III standard (followed by Block II).One of the design limitations of the JF-17 is its current choice of powerplant. The decision to use the RD-93 on Block I + II was just a safeguard to ensure the program did not fail as a result of an engine still under development (the Chinese developed WS-13). Luckily due to the simple modular design of the JF-17 platform, upgrading to another engine (such as WS-13 or alternatively the RD-33MKM) will be much simpler than with many other aircraft. Looking at the whole JF-17 Thunder development program, it is actually quite ingenious, and I think that Pakistan can be congratulated on a job well done, and I think Western militaries can learn quite a lot from the Pakistan approach to complex arms development, and how to keep development costs down to the bare minimum. The main lesson we can learn from this is that the JF-17 program is successful because the Pakistan government was 100% committed from the onset to achieve success as a matter of necessity. Also, the JF-17 Thunder program was directly managed by the PAF with no private involvement. Initially I was somewhat ignorant about the JF-17 Thunder during the initial stages of the program, and it was only after a few ‘coffee table discussions’ with a few outstanding individuals involved with the Pakistan Armed Forces, that I truly realised the growing capabilities of the JF-17, especially the PAF thinking behind the program. I was initially more impressed with the Mirage III ROSE program, and what Pakistan achieved with extending the life-span of their ageing Mirage III fleet (currently still the back-bone of the PAF). However, the ROSE program is as far as you can go with the Mirage III, and it has become too costly to continue operating the Mirage III. The PAF is not the only country facing the dilemma of replacing ageing fighter fleets with a modern design solution, and I have come to believe the JF-17 Thunder (as supplied via Pakistan), is a suitable platform for many Air Forces around the world being excluded in some way from purchasing modern hardware without operational use restrictions. The JF-17 is an affordable solution for many MENA, African and South American Air Forces seeking budget friendly options, and current Chinese armaments and avionics have proved very capable, with improved long-term support.To end my discussion around the JF-17, I quote the words of a Pakistani official involved with the program: “We know the present version JF-17 does not compete one-on-one with the latest F-16V Block 70, but for the price of one F-16V Block 70 we can purchase multiple JF-17 Thunder Block IIIs. With the correct armament, two JF-17 Block III against one F-16V turns the odds against the F-16 in battle”. Based on the last 19 x F-16V Block 70 sale to Bahrain in September 2017, the total package unit price for the F-16V Block 70 came down to around US$ 146 million each, whereas the average unit price for a JF-17 Block III is estimated to be between US$ 35 - 45 million each. In light of these costs, the PAF approach to the F-16V threat dilemma from a JF-17 perspective makes totally sense, while having an aircraft in inventory that is built, supported and upgraded domestically, already well established within the PAF frontline structures, with little to no operational use restrictions from major component suppliers.Now, looking at the WS-13E engine, within its current phase of development (as publicly advertised – China being quite good at intentionally spreading misinformation to the point where you cannot believe a word they say), it has a shorter lifespan than the Russian RD-93/RD-33 range of engines. However, I do believe that it is not long before China will actually achieve the point where that engine will equal, and maybe improve, on current RD-93 performance. Also, there is a rumour within Chinese defence circles that they have already resolved most of the performance issues pertaining the WS-13E, but they are holding back until they have ridden themselves of all current RD-93 stock which currently stands at less than 150 units remaining (None of the current PLAAF aircraft are equipped with the RD-93, therefore the reason why there is no PLAAF demand for it). China also does not plan on purchasing more RD-93 engines but intends to phase in the WS-13E after the last RD-93 is sold off. Current performance projections indicate that the WS-13E would push the payload for the JF-17 Block III beyond 5 tons due to an increase in thrust. But, looking at a WS-13E engine with even reduced lifespan (as claimed), in many a developing country a guaranteed supply of cheaper ‘reduced lifespan engines’ is better than a ‘time expired engine which cannot be repaired or replaced’ as applicable to engines originating from Europe and especially the US (you know the old saying: ‘In the land of the blind, one eye is king’). South Africa, for example, has learnt that lesson many times during the era of sanctions with the French SNECMA, and it was also because of the US Pratt and Whitney turbofan engine in the original Israeli Lavi why South Africa was eventually excluded from that program due to US enforced restrictions (Israel eventually killing off the Lavi program in favour of the F-16I as a result of political influence). Russian engines are available, but at a very high premium. For many African air forces with limited budgets, flying moderate hours over lifetime to limit cost of operation, the WS-13E will suffice. Based on experiences in Africa, a characteristic of Chinese arms sales is that they will sell anything to anyone, but sometimes you have to acknowledge the opportunities and associated advantages relating to their manner of doing business in terms of guaranteeing sustainable supplies when traditional friends become your greatest limitation (looking at the current state of world affairs and rising global economic uncertainty).

Many companies have used Haskell for a range of projects, including:ABN AMRO Amsterdam, The NetherlandsABN AMRO is an international bank headquartered in Amsterdam. For its investment banking activities it needs to measure the counterparty risk on portfolios of financial derivatives.ABN AMRO's CUFP talk.Aetion Technologies LLC, Columbus, OhioAetion was a defense contractor in operation from 1999 to 2011, whose applications use artificial intelligence. Rapidly changing priorities make it important to minimize the code impact of changes, which suits Haskell well. Aetion developed three main projects in Haskell, all successful. Haskell's concise code was perhaps most important for rewriting: it made it practicable to throw away old code occasionally. DSELs allowed the AI to be specified very declaratively.Aetion's CUFP talk.Alcatel-LucentA consortium of groups, including Alcatel-Lucent, have used Haskell to prototype narrowband software radio systems, running in (soft) real-time.Alcatel-Lucent's CUFP talkAllston TradingHeadquartered in Chicago, Illinois, Allston Trading, LLC is a premier high frequency market maker in over 40 financial exchanges, in 20 countries, and in nearly every conceivable product class. Allston makes some use of Haskell for their trading infrastructure.Alpha Heavy IndustriesAlpha Heavy Industries is an alternative asset manager dedicated to producing superior returns through quantitative methods. They use Haskell as their primary implementation language.Amgen Thousand Oaks, CaliforniaAmgen is a human therapeutics company in the biotechnology industry. Amgen pioneered the development of novel products based on advances in recombinant DNA and molecular biology and launched the biotechnology industry’s first blockbuster medicines. Amgen uses Haskell;To rapidly build software to implement mathematical models and other complex, mathematically oriented applicationsProvide a more mathematically rigorous validation of softwareTo break developers out of their software development rut by giving them a new way to think about software.Amgen's CUFP talk.Ansemond LLC"Find It! Keep It! is a Mac Web Browser that lets you keep the pages you visit in a database. A list of these pages is shown in the 'database view'. "Antiope Fair Haven, New JerseyAntiope Associates provides custom solutions for wireless communication and networking problems. Our team has expertise in all aspects of wireless system design, from the physical and protocol layers to complex networked applications. Antiope Associates relies on a number of advanced techniques to ensure that the communication systems we design are reliable and free from error. We use custom simulation tools developed in Haskell, to model our hardware designs..Antiope's CUFP talk.AT&THaskell is being used in the Network Security division to automate processing of internet abuse complaints. Haskell has allowed us to easily meet very tight deadlines with reliable results.Bank of America Merril LynchHaskell is being used for backend data transformation and loading.Barclays Capital Quantitative Analytics GroupBarclays Capital's Quantitative Analytics group is using Haskell to develop an embedded domain-specific functional language (called FPF) which is used to specify exotic equity derivatives. These derivatives, which are naturally best described in terms of mathematical functions, and constructed compositionally, map well to being expressed in an embedded functional language. This language is now regularly being used by people who had no previous functional language experience.Simon Frankau et al's JFP paper on their use of HaskellRead their 2013 job advertisementBAE SystemsAs part of the SAFE project, BAE has built a collection of compilers, interpreters, simulators, and EDSLs almost entirely in Haskell.CUFP 2013 talkBazQux ReaderBazQux Reader is a commercial RSS reader. Its feeds and comments crawler and a part of web-server are implemented in Haskell.BetterBetter, formerly known as Erudify, is a learning company built around the mission of making people better. We are an unusual mix of a software company, a consulting firm, and a creative agency. This tight integration enables us to deliver innovative, high-quality courses to our customers. Founded in 2012, Better is based in Zurich, Switzerland and New York, USA. Better is fully invested in Haskell; Most parts of our back-end system (web-servers and learning logic) are written in Haskell. Haskell is also used in most parts of our front-end system.bCODE Pty Ltd Sydney AustraliabCode Pty Ltd is a small venture capital-funded startup using Ocaml and a bit of Haskell in Sydney Australia.Bdellium Hawaii, United StatesBdellium develops software systems that enable companies in the financial industry to deliver new customer services that grow their business. Bdellium uses Haskell for heavy lifting analysis in back end infrastructure.Bluespec, Inc. Waltham, MassachusettsDeveloping a modern integrated circuit (ASIC or FPGA) is an enormously expensive process involving specification, modeling (to choose and fix the architecture), design (to describe what will become silicon) and verification (to ensure that it meets the specs), all before actually committing anything to silicon (where the cost of a failure can be tens of millions of dollars). Bluespec, Inc. is a three year-old company that provides language facilities, methodologies, and tools for this purpose, within the framework of the IEEE standard languages SystemVerilog and SystemC, but borrowing ideas heavily from Term Rewriting Systems and functional programming languages like Haskell. In this talk, after a brief technical overview to set the context, we will describe our tactics and strategies, and the challenges we face, in introducing declarative programming ideas into this field, both externally (convincing customers about the value of these ideas) and internally (using Haskell for our tool implementation).Bluespec's CUFP talk.BumpBump use a Haskell-based server, Angel, for process supervisor for all their backend systems, and for other infrastructure tasks.Haskell at BumpCapital IQWe have been using functional programming here at S&P Capital IQ in Scala, Haskell, and our homegrown reporting language Ermine, since 2008 for financial analytics.Capital IQ's CUFP 2013 talkChordifyChordify is a free online music service that transforms music, from YouTube, Deezer, SoundCloud or uploaded files, into chords. There's an ICFP experience report explaining how Haskell is used for this: José Pedro Magalhães and W. Bas de Haas. Functional Modelling of Musical Harmony: an Experience Report. In Proceedings of the 16th ACM SIGPLAN International Conference on Functional Programming (ICFP'11), pp. 156–162, ACM, 2011.Circos Brand Karma SingaporeBrand Karma provides services to brand owners to measure online sentiments towards their brands. Haskell is used in building parts of the product, specifically for back-end job scheduling and brand matching.CircuitHubCircuitHub aims to be the AWS for manufacturing, enabling hardware companies and makers to instantly quote designs and scale from prototype to production. We are also proud to host a large collection of open hardware designs. CircuitHub uses Haskell for our core services and algorithms.Credit Suisse Global Modeling and Analytics Group London, UK; New York City, New YorkGMAG, the quantitative modeling group at Credit Suisse, has been using Haskell for various projects since the beginning of 2006, with the twin aims of improving the productivity of modelers and making it easier for other people within the bank to use GMAG models. Current projects include: Further work on tools for checking, manipulating and transforming spreadsheets; a domain-specific language embedded in Haskell for implementing reusable components that can be compiled into various target forms (see the video presentation: Paradise, a DSEL for Derivatives Pricing).Credit Suisse's CUFP talk.DetexifyDetexify is an online handwriting recognition system, whose backend is written in Haskell.FynderFynder is an online booking platform. We use Haskell and clojurescript, all stitched together with nixosSee more in their original job posting.Deutsche Bank Equity Proprietary Trading, Directional Credit TradingThe Directional Credit Trading group uses Haskell as the primary implementation language for all its software infrastructure.Deutsche Bank's CUFP talk.Eaton Cleveland, OhioDesign and verification of hydraulic hybrid vehicle systemsEaton's CUFP talkEaton's experiences using a Haskell DSL[Ericsson AB]Ericsson uses Haskell for the implementation of Feldspar, an EDSL for digital signal processing algorithms.Ericsson's Feldspar compilerextensiblNew Zealand-based company. Provides a variety of software development, consulting, operational support services worldwide. Both Haskell and Ur/Web are actively used for commercial projects.FacebookFacebook uses some Haskell internally for tools. lex-pass is a tool for programmatically manipulating a PHP code base via Haskell.Facebook's CUFP talkFacebook's HaXL system is open sourceFactis ResearchFactis research, located in Freiburg, Germany, develops reliable and user-friendly mobile solutions. Our client software runs under J2ME, Symbian, iPhone OS, Android, and Blackberry. The server components are implemented in Python and Haskell. We are actively using Haskell for a number of projects, most of which are released under an open-source license.Factis' HCAR submissionfortytools gmbhLocated in Hamburg, Germany, we are developing web-based productivity tools for invoicing, customer management, resource scheduling and time tracking. While using Javascript for building rich frontend application in the browser, we use Haskell to implement the REST backends. Additionally, we do occasional project/client work as well.Oh, and of course we develop and maintain Hayoo! :)Functor AB, Stockholm, SwedenFunctor AB offers new tools for ground-breaking static analysis with pre-test case generation of programs to eliminate defects and bugs in software very early in development. Functor collaborates with the JET fusion reactor run by EFDA CCFE. JET is currently the largest reactor in the world of its kind. At Functor, almost all development is done in Haskell but also to some extent also C and Scala.See more in the Functor AB job advertisementFunktionale Programmierung Dr. Heinrich Hördegen, Munich, GermanyWe develop software prototypes according to the Pareto principle: After spending only 20 percent of budget, we aim to provide already 80 percent of the software's functionality. We can realize this by constructing a 2080-software-prototype that we can further develop into a full-fledged solution...Galois, Inc Portland, OregonGalois designs and develops high confidence software for critical applications. Our innovative approach to software development provides high levels of assurance, yet its scalability enables us to address the most complex problems. We have successfully engineered projects under contract for corporations and government clients in the demanding application areas of security, information assurance and cryptography.Galois' 2007 CUFP talkGalois' 2011 CUFP talkGalois' retrospective on 10 years of industrial Haskell useGoogleHaskell is used on a small number of internal projects in Google, for internal IT infrastructure support, and the open-source Ganeti project. Ganeti is a tool for managing clusters of virtual servers built on top of Xen and KVM.Google's ICFP 2010 experience report on HaskellVideo from ICFP Project Ganeti at GoogleGlydeGlyde uses OCaml and Haskell for a few projects. Glyde uses Haskell for our client-side template source-to-source translator, which converts HAML-like view templates into JS code.Group CommerceGroup Commerce uses Haskell to drive the main component of their advertising infrastructure: a Snap Framework based web server. Haskell enabled quicker development, higher reliability, and better maintainability than other languages, without having to sacrifice performance.HasuraHasura is a BaaS/PaaS focussed on keeping things DRY and letting you write custom code with the tools you love. We're building a micro-service platform christened Instant APIs for web & mobile apps (alpha release scheduled in summer 2015), and we used Haskell as the core programming language to build it.Humane SoftwareWe develop enterprise systems with de-coupled, asynchronous Haskell backends and Javascript UIs.For our current customer, an Internet connectivity provider, we wrote a solution for monitoring multiple remote machines and analyzing gigabytes of traffic samples. Haskell proved an excellent tool for the job. We were able to replace legacy systems in a granular, piece-by-piece manner, while delivering new features.Hustler Turf Equipment Hesston, KansasDesigns, builds, and sells lawn mowers. We use quite a bit of Haskell, especially as a "glue language" for tying together data from different manufacturing-related systems. We also use it for some web apps that are deployed to our dealer network. There are also some uses for it doing sysadmin automation, such as adding/removing people from LDAP servers and the likeiba Consulting Gesellschaft - Intelligent business architecture for you. Leipzig, Germanyiba CG develops software for large companies:risk analysis and reporting solution for power supply company;contract management, assert management, booking and budgeting software for one of the worldwide leading accounting firm.IMVU, IncIMVU, Inc. is a social entertainment company connecting users through 3D avatar-based experiences. See the blog article What it's like to use HaskellInformatik Consulting Systems AGICS AG developed a simulation and testing tool which based on a DSL (Domain Specific Language). The DSL is used for the description of architecture and behavior of distributed system components (event/message based, reactive). The compiler was written in Haskell (with target language Ada). The test system is used in some industrial projects.IntelIntel has developed a Haskell compiler as part of their research on multicore parallelism at scale.Read the Intel Research paper on compilerIVU Traffic Technologies AGThe rostering group at IVU Traffic Technologies AG has been using Haskell to check rosters for compliance with EC regulations. Our implementation is based on an embedded DSL to combine the regulation’s single rules into a solver that not only decides on instances but, in the case of a faulty roster, finds an interpretation of the roster that is “favorable” in the sense that the error messages it entails are “helpful” in leading the dispatcher to the resolution of the issue at hand. The solver is both reliable (due to strong static typing and referential transparency — we have not experienced a failure in three years) and efficient (due to constraint propagation, a custom search strategy, and lazy evaluation). Our EC 561/2006 component is part of the IVU.crew software suite and as such is in wide-spread use all over Europe, both in planning and dispatch. So the next time you enter a regional bus, chances are that the driver’s roster was checked by Haskell.JanRainJanRain uses Haskell for network and web software. Read more about Haskell at JanRain and in theirtech talk at Galois. JanRain's "Capture" user API product is built on Haskell's Snap webframework.See Janrain's technical talk about their use of SnapJoyride LaboratoriesJoyride Laboratories is an independent game development studio, founded in 2009 by Florian Hofer and Sönke Hahn. Their first game, "Nikki and the Robots" was released in 2011.Keera StudiosKeera Studios Ltd is a European game development studio that develops mobile, desktop and web apps.Games: The Android game Magic Cookies! was written in Haskell and released in 2015. Other games include Haskanoid, now being developed for Android, and a multi-platform Graphic Adventure library and engine with Android support and an IDE.Reactive Programming and GUIs: Keera Studios is also the maintainer of Keera Hails, an Open-Source reactive rapid application development framework, which has been used in Gale IDE and other desktop applications. Backends exist for Gtk+, Qt, Wx, Android's native GUI toolkit and Web DOM via GHCJS. Keera Posture is an open-source posture monitor written in Haskell using Keera Hails and Gtk+.Web: Keera Studios also develops web applications in Yesod.See the Facebook page for details on Android games and ongoing development.LinkqloLinkqlo Inc is a Palo Alto-based technology startup that is building a pioneering mobile community to connect people with better fitting clothes. We’re solving an industry-wide pain point for both consumers and fashion brands in retail shopping, sizing and fitting, just like Paypal took on the online payment challenge in 1999. We started deploying Haskell as the backend language recently in August 2015, in an effort to eventually replace all PHP endpoint APIs with Haskell ones.Linkqlo's iOS app from App StoreLinspireLinspire, Inc. has used functional programming since its inception in 2001, beginning with extensive use of O'Caml, with a steady shift to Haskell as its implementations and libraries have matured. Hardware detection, software packaging and CGI web page generation are all areas where we have used functional programming extensively. Haskell's feature set lets us replace much of our use of little languages (e.g., bash or awk) and two-level languages (C or C++ bound to an interpreted language), allowing for faster development, better code sharing and ultimately faster implementations. Above all, we value static type checking for minimizing runtime errors in applications that run in unknown environments and for wrapping legacy programs in strongly typed functions to ensure that we pass valid arguments.Linspire's CUFP talkLinspire's experience report on using functional programming to manage a Linux distributionLumiGuideLumiGuide is an innovative software company which specialises in smart parking and guidance systems for both bicycles and cars. LumiGuide developed and installed the P-route Bicycle system for the City of Utrecht in 2015. This system guides cyclists via digital, street-level displays to available parking space in a number of parking facilities in the city centre. Utrecht is the first city in the world that has a system like this. The detection technology is based on optical sensors which are independent of the bicycle stands. The sensors are mounted to the ceiling in indoor facilities and mounted to poles in outdoor facilities. Every minute, one sensor detects 40 to 60 parking places at the same time in either single- or two-tier bicycle stands as well as (stand-less) free parking places. Bicycles that exceed the maximum parking duration ('orphaned' bicycles) are also detected and the system will automatically keep a log of pictures of the orphaned bicycle which can be used as evidence when the orphaned bicycle is removed by a facility operator. The usage of the facility can be monitored with web-based control software. LumiGuide also develops the indoor and outdoor digital displays which can be controlled using the web-based control software. We are extensively using Haskell and NixOS.MicrosoftMicrosoft uses Haskell for its production serialization system, Bond. Bond is broadly used at Microsoft in high scale services. Microsoft Research has, separately, been a key sponsor of Haskell development since the late 1990s.MITREMITRE uses Haskell for, amongst other things, the analysis of cryptographic protocols.The New York TimesA team at the New York Times used Haskell's parallel array library to process images from 2013 New York Fashion Week. Haskell was chosen based on its fast numerical arrays packages, and ease of parallelization.Model analysisHaskell in the NewsroomNICTANICTA has used Haskell as part of a project to verify the L4 microkernel.Read the Dr. Dobbs article on using Haskell and formal methods to verify a kernelNRAONRAO has used Haskell to implement the core science algorithms for the Robert C. Byrd Green Bank Telescope (GBT) Dynamic Scheduling System (DSS).Source code available on GitHub.NS Solutions(NSSOL) Tokyo, JapanNS Solutions has employed Haskell since 2008 to develop its software packages including "BancMeasure", a mark-to-market accounting software package for financial institutions, "BancMeasure for IFRS" and "Mamecif", a data analysis package. "BancMeasure" and "Mamecif" are registered trademarks of NS Solutions Corporation in JAPAN.NVIDIAAt NVIDIA, we have a handful of in-house tools that are written in HaskellOpenomyOpenomy's API v2.0 is developed in Haskell, using the HAppS web platform.OblomovOblomov Systems is a one-person software company based in Utrecht, The Netherlands. Founded in 2009, Oblomov has since then been working on a number of Haskell-related projects. The main focus lies on web-applications and (web-based) editors. Haskell has turned out to be extremely useful for implementing web servers that communicate with JavaScript clients or iPhone apps.Oblomov's HCAR submission.Patch-Tag: hosting for DarcsNeed somewhere to put your Darcs code? Try us. Patch-Tag is built with happstack, the continuation of the project formerly known as HAppS.Peerium, Inc Cambridge, MassachusettsAt Peerium, we're striving to bring a new level of quality and efficiency to online communication and collaboration within virtual communities, social networks, and business environments. We believe that a new environment that supports the effortless sharing of both information and software will enable a level of online cooperation far beyond current Web-based technologies -- modern programming techniques will enable the creation of more robust and more powerful programs within these environments. To this end, we're building a new software platform for direct, real-time communication and collaboration within graphically rich environments. Peerium is located in the heart of Harvard Square in Cambridge, Massachusetts.PlanIt9PlanIt9 is a Yesod-based web application for defining, planning, scheduling and tracking tasks. It's designed to be fast, simple, collaborative and cost effective. We're currently signing up users for our beta program.PlumlifePlum is replacing light switches with Lightpads; a capacitive touch dimmer that is internet connected, clusters with other Lightpads in the home for group control... Haskell composes our cloud services and Erlang is used for the embedded software in the Lightpads (hot-code reloading, easy node clustering, etc...). ... We use Haskell extensively for all of our cloud services software at Plumlife ... Amazing language and ecosystem.Qualcomm, IncQualcomm uses Haskell to generate Lua bindings to the BREW platformSQreamAt SQream, we use Haskell for a large part of our code. We use Haskell for the compiler, which takes SQL statements and turns them into low level instructions for the high performance CUDA runtime. We also use Haskell for rapid prototyping and for many auxiliary utilities.Parallel Scientific, Boulder, Colorado.We are using Haskell to develop an ultra-scalable high-availability resource management system for big clusters (millions of nodes). A key element of the design is to provide scalable and reliable mechanisms for communicating failures and coordinating recovery transitions.See Parallel Scientific's CUFP talkRenaissaince Computing Institute, Chapel Hill, North CarolinaThe Renaissance Computing Institute (RENCI), a multi-institutional organization, brings together multidisciplinary experts and advanced technological capabilities to address pressing research issues and to find solutions to complex problems that affect the quality of life in North Carolina, our nation and the world. Research scientists at RENCI have used Haskell for a number of projects, including The Big Board.RENCI's CUFP talk.SamplecountSamplecount develops mobile, location-aware sound and music applications. They are currently using Haskell for prototyping their server-side soundscape streaming components and as a cross-platform build tool for their mobile applications and frameworks.Sankel Software Albuquerque, New MexicoSankel Software has been using Haskell since 2002 for both prototyping and deployment for technologies ranging from CAD/CAM to gaming and computer animation. We specialize in the development of user-friendly, large, long-term applications that solve difficult and conceptually intricate problems.ScriveScrive is a service for e-signing tenders, contracts, and other documents. We help our clients close deals faster, decrease their administrative burden, and improve their customers’ experience.Siemens Convergence Creators GmbH AustriaSiemens CVC uses Haskell since a few years in the space domain. Starting with small tools like data conversion and automation of scripting tasks over installers we use Haskell currently for Space Protocol Proxies to allow connect different space systems (e.g. Cortex to NCTRS or SLE to NCTRS with COP-1 handling). The main use is currently a Simulator implemented in Haskell which handles parts of NCTRS (or SSB), the ground station and parts of the satellite to be able to make closed-loop tests for the SCOS-2000 based Mission Control System. It is in use for testing and debugging of the Mission Control System and for checking implementation of new features. It has served for various, currently active missions and also is in use for some missions to come.Signali Portland, OregonSignali Corp is a new custom hardware design company. Our chief products are custom IP cores targeted for embedded DSP and cryptographic applications. Our specialty is the design and implementation of computationally intensive, complex algorithms. The interfaces to each core are modular and can be very efficiently modified for your specific application. System-level integration and validation is crucial and is the majority of investment in a product.Soostone New York, NYSoostone is an advanced analytics technology provider specializing in algorithmic optimization opportunities in marketing, pricing, advertising, sales and product management. As the preferred language, Haskell is used intensively at Soostone in numerous applications including customized machine learning algorithms, models/simulations, real-time decision-making engines, DSL/EDSLs, web applications and high volume APIs.Standard CharteredStandard Chartered has a large group using Haskell for all aspects of its wholesale banking business.Starling Software Tokyo, JapanStarling Software are developing a commercial automated options trading system in Haskell, and are migrating other parts of their software suite to Haskell.Starling Software's experience building real time trading systems in HaskellSensor Sense Nijmegen, The NetherlandsSensor Sense is offering high technology systems for gas measurements in the ppbv down to pptvrange. We use Haskell for the embedded control software of our trace gas detectors.For more information see Senor Sense's position advertisementSilk Amsterdam, The NetherlandsSilk investigates and develops new ways of creating and consuming online content. Their Silkapplication makes it easy to filter and visualize large amounts of information.Silk's blog on why they use HaskellA review of SilkSkedge Meskedge.me is an online scheduling platform that allows businesses to completely automate the process of making appointments, such as customer visits, job interviews, and tutoring sessions.See more in their CUFP talkSee their 2014 job advertisementSuite SolutionsSuite Solutions provides products and solutions in support of large sets of technical documentation based on DITA for general technical documentation, and other more specialized XML and SGML formats for specific industries such as the aerospace industry. Many of Suite Solutions' products and solutions, such as the featured products SuiteHelp and SuiteShare, are written in Haskell.SumAll New York, New YorkSumAll aggregates various public streams of data such as various social network data into useful analytics, reports and insights. We are in process of rewriting our entire data-processing backend in Haskell. What attracted us to the language is its disciplined and uncompromising approach to solving hard problems and managing complexity. We truly believe that the language and ecosystem is ready for prime time and will give us competitive advantage in the industry.Tabula.comTabula is a privately held fabless semiconductor company developing 3-D Programmable Logic Devices. Haskell is used for internal compiler toolchains related to hardware design.Tsuru Capital Tokyo, JapanTsuru Capital is operating an automated options trading system written in Haskell.Tsuru Capital's HCAR submissionTupil Utrecht, The NetherlandsTupil is a Dutch company that built software for clients, written in Haskell. Tupil used Haskell for the speed in development and resulting software quality. The company is founded by Chris Eidhof and Eelco Lempsink. Currently they build iPhone/iPad applications in Objective-C.Tupil's experience building commercial web apps in HaskellWagon San Francisco, CaliforniaWagon is a modern SQL editor: a better way for analysts and engineers to write queries, visualize results, and share data & charts.We’re a team of functional programmers writing apps and services in Haskell (and Javascript). We love to teach and learn functional programming; our team is humble, hard working, and fun. Read our engineering blog to learn more about our stack, how we combine Haskell, React, and Electron, and what it’s like working at a Haskell-powered startup.We're hiring Haskell engineers based in San Francisco, learn more about the roles and our team!WeedreporterPage on weedreporter.com is a news site in the up and coming cannabis industry, featuring news stories from around the world and USA. This includes news stories about legalization and medical Marijuana. The site is built using Haskell and Postgres. Haskell has allowed us to build a site with fast load times.

Python For Artificial IntelligencePython is widely used for artificial intelligence, with packages for a number of applications including General AI, Machine Learning 40, Natural Language Processing and Neural Networks.Choosing a programming language for your AI project depends heavily on the sub-field. So before you pick up a programming language, ensure that it can be utilized extensively and not partially. Above all these programming languages, Python is slowly making its way to the top as it is viable to use for most of the AI subfields. Lisp and Prolog have always been there and are still being used extensively by certain groups as they are more productive with them. Java and C++ are also still very useful because of the benefits they offer.The practice of AI and Machine Learning in python is as given below,Creating AI Using Python Is Easier Than You ThinkYou may be interested in what’s going on in AI sphere, main development stages, achievements, results, and products to use. There are hundreds of free sources and tutorials describing using Python for AI. However, there is no need to waste your time looking through them. Here is a detailed guide with all points you need to know before building artificial intelligence using Python.1. What Languages Are Used for Building AI?LISP is one of the most popular languages for creating AI. Its best features include garbage collection, uniform syntax, dynamic typing, and interactive environment. LISP code is written is s-expressions and consists of lists.Another widely popular AI programming language is Prolog. The best thing about this language is a built-in unifier. Its main disadvantage is that this language is difficult to learn.C/C++ is used for building simple AI in a short period of time. Java is not as fast as C but its portability and built-in types make Java a choice of many developers. And finally, there is Python. As developers state, Python is similar to Lisp. It’s one of the most popular AI languages. Why is it so? Why do developers code AI with Python? Let’s check it out.2. Why Do People Choose Python?Python was created at the end of 1980s. Its implementation started in 1989. Python’s philosophy is very interesting as it includes several aphorisms. Explicit rather than implicit, simple rather than complex. Python creators value beautiful design and look. They prefer complex to complicated. And what’s even more important, they state that readability counts. Python has a clean grammar and syntax. It’s natural and fluent. As Python’s developers state, the language’s goal is to be cool in use. Being named after Monty Python, a British comedy group, the language has a playful approach to many tutorials and other materials.Developers state that they enjoy the variety and quality of Python’s features. Though it’s not the perfect scientific programming language, its features are efficient:Data structuresClassesFlexible function calling syntaxIteratorsNested functionsKitchen-sink-included standard libraryGreat scientific librariesCool open source libraries (Numpy, Cython, IPython, MatPlotLib)Other features developers like about Python are as following: the holistic language design, thought out syntax, language interoperability, balance of high-level and low-level programming, documentation generation system, modular programming, correct data structures, numerous libraries, and testing frameworks. One of the disadvantages is the need of programmers to be good at MATLAB, as it’s common in general scientific coding. That’s why many devs publish open research code in MATLAB.If comparing to other OOP languages, Python is relatively easy to learn. It has a bunch of image intensive libraries: VTK, Maya 3D Visualization Toolkits, Scientific Python, Numeric Python, Python Imaging Library, etc. These tools are perfect for numeric and scientific applications.Python is used everywhere and by everyone: in simple terminal commands, in vitally important scientific projects, and in big enterprise apps. This language is well designed and fast. It’s scalable, open source, and portable.3. How to Build AI Using Python?The first step is to get started. Though it sounds a bit stressful and hard, you should understand that building AI in Python will take some time. The amount of time needed depends on your motivation, skills, the level of programming experience, etc.In order to build AI with Python, you need to have some base understanding of this language. This is not just a popular general purpose programming language. It’s also widely used for machine learning and computing. First of all, install Python. You may do that installing Anaconda, the open source analytics platform. Including the needed packages for machine learning, NumPy, scikit-learn, iPython Notebook, and matplotlib.If you are searching for some materials on how to boost your Python skills quicker, check out the following books:Python The Hard WayGoogle Developers Python CourseAn Introduction to Python for Scientific ComputingLearn X in Y MinutesIf you’ve already got enough experience of programming using Python, you should still check out Python documentation from time to time.The next step is to boost your machine learning skills. Of course, it’s almost impossible to reach the ultimate understanding of machine learning in a short period of time. Unless you are a genius or a machine like IBM Watson. That’s why it’s better to start with gaining basic machine learning knowledge or improving its level with a help of the following courses: Andrew Ng’s Machine Learning Course, Tom Mitchell Machine Learning Lectures, etc. Everything you need is the basic understanding of machine learning theoretical aspects.When talking about Python, I’ve already mentioned scientific libraries. These Python libraries will be useful when you build AI. For example, you will use NumPy as a container of generic data. Containing an N-dimensional array object, tools for integrating C/C++ code, Fourier transform, random number capabilities, and other functions, NumPy will be one of the most useful packages for your scientific computing.Another important tool is pandas, an open source library that provides users with easy-to-use data structures and analytic tools for Python. Matplotlib is another service you will like. It’s a 2D plotting library that creates publication quality figures . Among the best matplotlib advantages is the availability of 6 graphical users interface toolkits, web application servers, and Python scripts. Scikit-learn is an efficient tool for data analysis. It’s open source and commercially usable. It’s the most popular general purpose machine learning library.After you work with scikit-learn, you may take programming AI using Python to the next level and explore k-means clustering.You should also read about decision trees, continuous numeric prediction, logistic regression, etc. If you want to learn more about Python in AI, read about a deep learning framework Caffee and a Python library Theano.There are Python AI libraries: AIMA, pyDatalog, SimpleAI, EasyAi, etc. There are also Python libraries for machine learning: PyBrain, MDP, scikit, PyML. If you’re searching for natural language and text processing libraries, check out NLTK.As you see, the importance of Python for AI is obvious. Any machine learning project will benefit from using Python. As AI needs a lot of research, programming artificial intelligence using Python is efficient – you may validate almost every idea with up to thirty code lines.4. How to Create a Chatbot Using Python?If you read the Letzgro blog often, you know that we love creating awesome apps and programs that help our clients change their lives and businesses in particular. Chatbots are our new love. Chatbots are the new beginning. Chatbots are the new apps. I can continue it for ages. However, everything you should know is that chatbots are new online assistants that provide different services via chatting.For example, there is Hi Poncho! that tells people the weather forecast. There is The Spring chatbot that allows people choose shoes and clothes while chatting. There is CNN chatbot, a chatbot that orders flowers. Recently, our developers have built a chatbot that facilitates work of an entrepreneur who promotes tattoo artists via Instagram. Isn’t that cool? A chatbot may be used in every sphere, business, and every environment.Chatbots are a type of AI. To be more specific, chatbots are ANI, artificial narrow intelligence. They are not as clever as humans. Besides, chatbots can carry out a limited amount of tasks. Nevertheless, these functions still make our lives easier. That’s why so many entrepreneurs are thinking about bringing chatbots to their sites. There are many ways to do that. You may use different languages and approaches. You may build chatbots with a professional software development company. You may also build it using Python. Here is a short guide how to do that.If you want to create artificial intelligence chatbots in Python, you’ll need AIML package (Artificial Intelligence Markup Language). First of all, create a standard startup file with on pattern. Load aiml b. Add random responses that make a dialog interesting. Now to write your own AIML file, browse for some files you already may use. For example, search among AIML files from the Alice Bot website. Enter Python.When you create the startup file, it will then serve as a separate entity. Thus, you may have more AIML files without source code modifications. The program will start learning when there are many AIML files. Speed up the brain load. Add Python commands. So that’s an introduction to how you can make artificial intelligence using Python.Why Python?Choosing a programming language for your AI project depends heavily on the sub-field. So before you pick up a programming language, ensure that it can be utilized extensively and not partially. Above all these programming languages, Python is slowly making its way to the top as it is viable to use for most of the AI subfields. Lisp and Prolog have always been there and are still being used extensively by certain groups as they are more productive with them. Java and C++ are also still very useful because of the benefits they offer.The engineering team responsible for the initial prototyping was mainly Artificial Intelligence oriented. It consisted of professionals, graduated from the Maastricht University, most of them quite theoretical and with little to no actual programming experience. The project itself required a highly object oriented Programming Language able to deliver the features we required for our prototyping but also was easy to learn because of the lack of coding experience. Python was chosen because it matched the criteria set by the project while still easy to learn.Although we considered Python perfect for the prototype phase, we anticipated that ultimately most of the prototypes would have to be rewritten into C and C++. However during the prototyping and alpha phase Python turned out to be more qualified for the job than initially expected i.e. Python turned out to be a true problem solver. At present most of the original Python code is still untouched or replaced by new Python code.Python has been used in artificial intelligence projects[137][138][139][140]. As a scripting language with modular architecture, simple syntax and rich text processing tools, Python is often used for natural language processing.[141](Source: https://www.google.co.in/search?q=python+for+artificial+intelligence&lite=0&sa=X&ved=0ahUKEwju_LK84b7YAhUM148KHbIaDaUQhEEIWg )General AIAIMA - Python implementation of algorithms from Russell and Norvig's 'Artificial Intelligence: A Modern Approach'pyDatalog - Logic Programming engine in PythonSimpleAI - Python implementation of many of the artificial intelligence algorithms described on the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well documented and tested library.EasyAI - Simple Python engine for two-players games with AI (Negamax, transposition tables, game solving).Machine LearningTensorFlow, an open-source software library for machine learning.pyTorch, an open-source Tensors and Dynamic neural networks in PythonGraphLab Create - An end-to-end Machine Learning platform with a Python front-end and C++ core. It allows you to do data engineering, build ML models, and deploy them. Key design principles: out-of-core computation, fast and robust learning algorithms, easy-to-use Python API, and fast deployment of arbitrary Python objects.Feature Forge - A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.Orange - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Extensions for bioinformatics and text mining. Packed with features for data analytics.PyBrain - 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.PyML - PyML is an interactive object oriented framework for machine learning written in Python. PyML focuses on SVMs and other kernel methods. It is supported on Linux and Mac OS X.MlPy - mlpy makes extensive use of NumPy to provide fast N-dimensional array manipulation and easy integration of C code. The GNU Scientific Library ( GSL) is also required. It provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification, regression and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping.Milk - Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems.scikit-learn - scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.Shogun - The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) . It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art OCAS, Liblinear, LibSVM, SVMLight, SVMLin and GPDT. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels. SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave and Python and is proudly released as Machine Learning Open Source SoftwareMDP-Toolkit - Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user’s perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer’s perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.LibSVM - LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. A Python interface is available by by default.Weka - Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. See here for a tutorial on using Weka from jython.Monte - Monte (python) is a Python framework for building gradient based learning machines, like neural networks, conditional random fields, logistic regression, etc. Monte contains modules (that hold parameters, a cost-function and a gradient-function) and trainers (that can adapt a module's parameters by minimizing its cost-function on training data).SOM - Self-Organizing Maps is a form of machine learning technique which employs unsupervised learning. It means that you don't need to explicitly tell the SOM about what to learn in the input data. It automatically learns the patterns in input data and organizes the data into different groups.Yalign - Yalign is a friendly tool for extracting parallel sentences from comparable corpora..Natural Language & Text ProcessingNLTK - Open source Python modules, linguistic data and documentation for research and development in natural language processing and text analytics, with distributions for Windows, Mac OSX and Linux.gensim - Gensim is a Python framework designed to automatically extract semantic topics from documents, as naturally and painlessly as possible. Gensim aims at processing raw, unstructured digital texts (“plain text”). The unsupervised algorithms in gensim, such as Latent Semantic Analysis, Latent Dirichlet Allocation or Random Projections, discover hidden (latent) semantic structure, based on word co-occurrence patterns within a corpus of training documents. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation, and queried for topical similarity against other documents and so on.Quepy - A python framework to transform natural language questions to queries in a database query language.Neural Networksneurolab - Neurolab is a simple and powerful Neural Network Library for Python. Contains based neural networks, train algorithms and flexible framework to create and explore other networks. It has the following features: pure python + numpy; API like Neural Network Toolbox (NNT) from MATLAB; interface to use train algorithms from scipy.optimize; flexible network configurations and learning algorithms; and a variety of supported types of Artificial Neural Network and learning algorithms.ffnet - ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient (also parallel) training tools, network export to fortran code.FANN - Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to more than 15 programming languages are available. An easy to read introduction article and a reference manual accompanies the library with examples and recommendations on how to use the library. Several graphical user interfaces are also available for the library.bpnn.py - Written by Neil Schemenauer, http://bpnn.py is used by an IBM article entitled "An introduction to neural networks".PyAnn - A Python framework to build artificial neural networkspyrenn - pyrenn is a recurrent neural network toolbox for python (and matlab). It is written in pure python and numpy and allows to create a wide range of (recurrent) neural network configurations for system identification. It is easy to use, well documented and comes with several examples.An Overview of Python Deep Learning FrameworksTags: Deep Learning, Keras, Neural Networks, Python, TensorFlow, Theano, TorchRead this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.Anaconda: The Most Popular Data Science PlatformI recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years. The library I recommended in July 2014, pylearn2, is no longer actively developed or maintained, but a whole host of deep learning libraries have sprung up to take its place. Each has its own strengths and weaknesses. We’ve used most of the technologies on this list in production or development at indico, but for the few that we haven’t, I’ll pull from the experiences of others to help give a clear, comprehensive picture of the Python deep learning ecosystem of 2017.In particular, we’ll be looking at:TheanoLasagneBlocksTensorFlowKerasMXNetPyTorchTheanoDescription:Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It works with GPUs and performs efficient symbolic differentiation.Documentation:http://deeplearning.net/software/theano/Summary:Theano is the numerical computing workhorse that powers many of the other deep learning frameworks on our list. It was built by Frédéric Bastien and the excellent research team behind the University of Montreal’s lab, MILA. Its API is quite low level, and in order to write effective Theano you need to be quite familiar with the algorithms that are hidden away behind the scenes in other frameworks. Theano is a go-to library if you have substantial academic machine learning expertise, are looking for very fine grained control of your models, or want to implement a novel or unusual model. In general, Theano trades ease of use for flexibility.Pros:FlexiblePerformant if used properlyCons:Substantial learning curveLower level APICompiling complex symbolic graphs can be slowResources:Installation guideOfficial Theano tutorialTheano slideshow and practice exercisesFrom linear regression to CNNs with TheanoIntroduction to Deep Learning with Python & Theano (MNIST video tutorial)LasagneDescription:Lightweight library for building and training neural networks in Theano.Documentation:http://lasagne.readthedocs.org/Summary:Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning. It’s written and maintained primarily by Sander Dieleman, a current DeepMind research scientist. Instead of specifying network models in terms of function relationships between symbolic variables, Lasagne allows users to think at the Layerlevel, offering building blocks like “Conv2DLayer” and “DropoutLayer” for users to work with. Lasagne requires little sacrifice in terms of flexibility while providing a wealth of common components to help with layer definition, layer initialization, model regularization, model monitoring, and model training.Pros:Still very flexibleHigher layer of abstraction than TheanoDocs and code contain an assortment of pasta punsCons:Smaller communityResources:Official GitHub pageOfficial installation guideOfficial Lasagne tutorialExample Lasagne codeBlocksDescription:A Theano framework for building and training neural networks.Documentation:http://blocks.readthedocs.io/en/latest/Summary:Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano. It’s written by the University of Montreal’s lab, MILA — some of the same folks who contributed to the building of Theano and its first high level interface to neural network definitions, the deceased PyLearn2. It’s a bit more flexible than Lasagne at the cost of having a slightly more difficult learning curve to use effectively. Among other things, Blocks has excellent support for recurrent neural network architectures, so it’s worth a look if you’re interested in exploring that genre of model. Alongside TensorFlow, Blocks is the library of choice for many of the APIs we’ve deployed to production at indico.Pros:Still very flexibleHigher layer of abstraction than TheanoVery well testedCons:Substantial learning curveSmaller communityResources:Official installation guideArxiv paper on the design of the Blocks libraryA reddit discussion on the differences between Blocks and LasagneBlock’s sister library for data pipelines, FuelTensorFlowDescription:An open source software library for numerical computation using data flow graphs.Documentation:https://www.tensorflow.org/api_docs/python/Summary:TensorFlow is a blend between lower level, symbolic computation libraries like Theano, and higher level, network specification libraries like Blocks and Lasagne. Although it’s the newest member of the Python deep learning library collection, it likely has garnered the largest active community because it’s backed by the Google Brain team. It offers support for running machine learning models across multiple GPUs, provides utilities for efficient data pipelining, and has built-in modules for the inspection, visualization, and serialization of models. More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list. The community seems to agree that although TensorFlow has its shortcomings, the sheer size of its community and the massive amount of momentum behind the project mean that learning TensorFlow is a safe bet. Consequently, TensorFlow is our deep learning library of choice today at indico.Pros:Backed by software giant GoogleVery large communityLow level and high level interfaces to network trainingFaster model compilation than Theano-based optionsClean multi-GPU supportCons:Initially slower at many benchmarks than Theano-based options, although Tensorflow is catching up.RNN support is still outclassed by TheanoResources:Official TensorFlow websiteDownload and setup guideindico’s take on TensorFlowA collection of TensorFlow tutorialsA Udacity machine learning course taught using TensorFlowTensorFlow MNIST tutorialTensorFlow data inputKerasDescription:Deep learning library for Python. Convnets, recurrent neural networks, and more. Runs on Theano or TensorFlow.Documentation:https://keras.io/Summary:Keras is probably the highest level, most user friendly library of the bunch. It’s written and maintained by Francis Chollet, another member of the Google Brain team. It allows users to choose whether the models they build are executed on Theano’s or TensorFlow’s symbolic graph. Keras’ user interface is Torch-inspired, so if you have prior experience with machine learning in Lua, Keras is definitely worth a look. Thanks in part to excellent documentation and its relative ease of use, the Keras community is quite large and very active. Recently, the TensorFlow team announced plans to ship with Keras support built in, so soon Keras will be a subset of the TensorFlow project.Pros:Your choice of a Theano or TensorFlow backendIntuitive, high level interfaceEasier learning curveCons:Less flexible, more prescriptive than other optionsResources:Official installation guideKeras users Google groupRepository of Keras examplesInstructions for using Keras with DockerRepository of Keras tutorials by application areaMXNetDescription:MXNet is a deep learning framework designed for both efficiency and flexibility.Documentation:http://mxnet.io/api/python/index.html#python-api-referenceSummary:MXNet is Amazon’s library of choice for deep learning, and is perhaps the most performant library of the bunch. It has a data flow graph similar to Theano and TensorFlow, offers good support for multi-GPU configurations, has higher level model building blocks similar to that of Lasagne and Blocks, and can run on just about any hardware you can imagine (including mobile phones). Python support is just the tip of the iceberg — MXNet also offers interfaces to R, Julia, C++, Scala, Matlab, and Javascript. Choose MXNet if you’re looking for performance that’s second to none, but you must be willing to deal with a few of MXNet’s quirks to get you there.Pros:Blazing fast benchmarksExtremely flexibleCons:Smallest communitySteeper learning curve than TheanoResources:Official getting started guideindico’s intro to MXNetRepository of MXNet examplesAmazon’s CTO’s take on MXNetMXNet Arxiv paperPyTorchDescription:Tensors and dynamic neural networks in Python with strong GPU acceleration.Documentation:http://pytorch.org/docs/Summary:Released just over a week ago, PyTorch is the new kid on the block in our list of deep learning frameworks for Python. It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives. The jury is still out on what role PyTorch will play in the Python deep learning ecosystem, but all signs point to PyTorch being a very respectable alternative to the other frameworks on our list.Pros:Organizational backing from FacebookClean support for dynamic graphsBlend of high level and low level APIsCons:Much less mature than alternatives (in their own words — “We are in an early-release Beta. Expect some adventures.”)Limited references / resources outside of the official documentationResources:Official PyTorch homepagePyTorch twitter feedRepository of PyTorch examplesRepository of PyTorch tutorials(Source: KDnuggets By Madison May, indico.)Python isn't only used in AI field but it is also widely used in many different top fields everywhere in the world. For e.g., such as Data Science, Web development(GUI), etc. ( Refer: List of Python software - Wikipedia )15 Python Libraries for Data ScienceIf you’ve read our introduction to Python, you already know that it’s one of the most widely used programming languages today, celebrated for its efficiency and code readability. As a programming language for data science, Python represents a compromise between R, which is heavily focused on data analysis and visualization, and Java, which forms the backbone of many large-scale applications. This flexibility means that Python can act as a single tool that brings together your entire workflow.Python is often the choice for developers who need to apply statistical techniques or data analysis in their work, or for data scientists whose tasks need to be integrated with web apps or production environments. In particular, Python really shines in the field of machine learning. Its combination of machine learning libraries and flexibility makes Python uniquely well-suited to developing sophisticated models and prediction engines that plug directly into production systems.One of Python’s greatest assets is its extensive set of libraries. Libraries are sets of routines and functions that are written in a given language. A robust set of libraries can make it easier for developers to perform complex tasks without rewriting many lines of code. In this article, we’ll introduce you to some of the libraries that have helped make Python the most popular language for data science in Stack Overflow’s 2016 developer poll.BASIC LIBRARIES FOR DATA SCIENCEThese are the basic libraries that transform Python from a general purpose programming language into a powerful and robust tool for data analysis and visualization. Sometimes called the SciPy Stack, they’re the foundation that the more specialized tools are built on.NumPy is the foundational library for scientific computing in Python, and many of the libraries on this list use NumPy arrays as their basic inputs and outputs. In short, NumPy introduces objects for multidimensional arrays and matrices, as well as routines that allow developers to perform advanced mathematical and statistical functions on those arrays with as little code as possible.SciPy builds on NumPy by adding a collection of algorithms and high-level commands for manipulating and visualizing data. This package includes functions for computing integrals numerically, solving differential equations, optimization, and more.Pandas adds data structures and tools that are designed for practical data analysis in finance, statistics, social sciences, and engineering. Pandas works well with incomplete, messy, and unlabeled data (i.e., the kind of data you’re likely to encounter in the real world), and provides tools for shaping, merging, reshaping, and slicing datasets.IPython extends the functionality of Python’s interactive interpreter with a souped-up interactive shell that adds introspection, rich media, shell syntax, tab completion, and command history retrieval. It also acts as an embeddable interpreter for your programs that can be really useful for debugging. If you’ve ever used Mathematica or MATLAB, you should feel comfortable with IPython.matplotlib is the standard Python library for creating 2D plots and graphs. It’s pretty low-level, meaning it requires more commands to generate nice-looking graphs and figures than with some more advanced libraries. However, the flip side of that is flexibility. With enough commands, you can make just about any kind of graph you want with matplotlib.Libraries for Machine LearningMachine learning sits at the intersection of Artificial Intelligence and statistical analysis. By training computers with sets of real-world data, we’re able to create algorithms that make more accurate and sophisticated predictions, whether we’re talking about getting better driving directions or building computers that can identify landmarks just from looking at pictures. The following libraries give Python the ability to tackle a number of machine learning tasks, from performing basic regressions to training complex neural networks.scikit-learn builds on NumPy and SciPy by adding a set of algorithms for common machine learning and data mining tasks, including clustering, regression, and classification. As a library, scikit-learn has a lot going for it. Its tools are well-documented and its contributors include many machine learning experts. What’s more, it’s a very curated library, meaning developers won’t have to choose between different versions of the same algorithm. Its power and ease of use make it popular with a lot of data-heavy startups, including Evernote, OKCupid, Spotify, and Birchbox.Theano uses NumPy-like syntax to optimize and evaluate mathematical expressions. What sets Theano apart is that it takes advantage of the computer’s GPU in order to make data-intensive calculations up to 100x faster than the CPU alone. Theano’s speed makes it especially valuable for deep learning and other computationally complex tasks.TensorFlow is another high-profile entrant into machine learning, developed by Google as an open-source successor to DistBelief, their previous framework for training neural networks. TensorFlow uses a system of multi-layered nodes that allow you to quickly set up, train, and deploy artificial neural networks with large datasets. It’s what allows Google to identify objects in photos or understand spoken words in its voice-recognition app.Libraries for Data Mining and Natural Language ProcessingWhat if your business doesn’t have the luxury of accessing massive datasets? For many businesses, the data they need isn’t something that can be passively gathered—it has to be extracted either from documents or webpages. The following tools are designed for a variety of related tasks, from mining valuable information from websites to turning natural language into data you can use.Scrapy is an aptly named library for creating spider bots to systematically crawl the web and extract structured data like prices, contact info, and URLs. Originally designed for web scraping, Scrapy can also extract data from APIs.NLTK is a set of libraries designed for Natural Language Processing (NLP). NLTK’s basic functions allow you to tag text, identify named entities, and display parse trees, which are like sentence diagrams that reveal parts of speech and dependencies. From there, you can do more complicated things like sentiment analysis and automatic summarization. It also comes with an entire book’s worth of material about analyzing text with NLTK.Pattern combines the functionality of Scrapy and NLTK in a massive library designed to serve as an out-of-the-box solution for web mining, NLP, machine learning, and network analysis. Its tools include a web crawler; APIs for Google, Twitter, and Wikipedia; and text-analysis algorithms like parse trees and sentiment analysis that can be performed with just a few lines of code.Libraries for Plotting and VisualizationsThe best and most sophisticated analysis is meaningless if you can’t communicate it to other people. These libraries build on matplotlib to enable you to easily create more visually compelling and sophisticated graphs, charts, and maps, no matter what kind of analysis you’re trying to do.Seaborn is a popular visualization library that builds on matplotlib’s foundation. The first thing you’ll notice about Seaborn is that its default styles are much more sophisticated than matplotlib’s. Beyond that, Seaborn is a higher-level library, meaning it’s easier to generate certain kinds of plots, including heat maps, time series, and violin plots.Bokeh makes interactive, zoomable plots in modern web browsers using JavaScript widgets. Another nice feature of Bokeh is that it comes with three levels of interface, from high-level abstractions that allow you to quickly generate complex plots, to a low-level view that offers maximum flexibility to app developers.Basemap adds support for simple maps to matplotlib by taking matplotlib’s coordinates and applying them to more than 25 different projections. The library Folium further builds on Basemap and allows for the creation of interactive web maps, similar to the JavaScript widgets created by Bokeh.NetworkX allows you to create and analyze graphs and networks. It’s designed to work with both standard and nonstandard data formats, which makes it especially efficient and scalable. All this makes NetworkX especially well suited to analyzing complex social networks.These libraries are just a small sample of the tools available to Python developers. If you’re ready to get your data science initiative up and running, you’re going to need the right team. Find a developer who knows the tools and techniques of statistical analysis, or a data scientist with the development skills to work in a production environment. Explore data scientists on Upwork, or learn more about the basics of Big Data.(Source: Upwork)Artificial intelligence researchers have developed several specialized programming languages for artificial intelligence: Some other languages that are being used for Artificial Intelligence as follows,AIML (meaning "Artificial Intelligence Markup Language")[1] is an XML dialect[2]for use with A.L.I.C.E.-type chatterbots.IPL[3] was the first language developed for artificial intelligence. It includes features intended to support programs that could perform general problem solving, such as lists, associations, schemas (frames), dynamic memory allocation, data types, recursion, associative retrieval, functions as arguments, generators (streams), and cooperative multitasking.Lisp[4] is a practical mathematical notation for computer programs based on lambda calculus. Linked lists are one of the Lisp language's major data structures, and Lisp source code is itself made up of lists. As a result, Lisp programs can manipulate source code as a data structure, giving rise to the macro systems that allow programmers to create new syntax or even new domain-specific programming languages embedded in Lisp. There are many dialects of Lisp in use today, among which are Common Lisp, Scheme, and Clojure.Smalltalk has been used extensively for simulations, neural networks, machine learning and genetic algorithms. It implements the purest and most elegant form of object-oriented programming using message passing.Prolog[5][6] is a declarative language where programs are expressed in terms of relations, and execution occurs by running queries over these relations. Prolog is particularly useful for symbolic reasoning, database and language parsing applications. Prolog is widely used in AI today.STRIPS is 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 (what must be established before the action is performed) and postconditions (what is established after the action is performed) are specified.Planner is a hybrid between procedural and logical languages. It gives a procedural interpretation to logical sentences where implications are interpreted with pattern-directed inference.POP-11 is a reflective, incrementally compiledprogramming language with many of the features of an interpreted language. It is the core language of the Poplog programming environment developed originally by the University of Sussex, and recently in the School of Computer Science at the University of Birminghamwhich hosts the Poplog website, It is 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. One of POP-11's features is that it supports first-class functions.Haskell is also a very good programming language for AI. Lazy evaluation and the list and LogicT monads make it easy to express non-deterministic algorithms, which is often the case. Infinite data structures are great for search trees. The language's features enable a compositional way of expressing the algorithms. The only drawback is that working with graphs is a bit harder at first because of purity.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.[8]C++ (2011 onwards)MATLABPerlJulia (programming language), e.g. for machine learning, using native or non-native libraries.