How Artificial Intelligence Works & How To Implement It

How artificial intelligence works
Artificial intelligence has many applications in business

The world of business and tech advancement knows of very few concepts that are as astonishing as artificial intelligence.

However, it’s hard to grasp the importance and potentials of AI tech idea – no matter how excited – if one can hardly understand its basics:

  • How does artificial intelligence work?
  • What kinds exist?
  • How can it be made and how long does it take?
  • And are there any presets that can be used as a shortcut to the end goal?

These questions needed answering – and that’s just what we did.

With a better understanding of the AI technical nitty-gritty and equipped with useful resources, you should be on your way towards making your AI ambitions happen.

Let’s dig in!

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How Artificial Intelligence Works And The Basics Of AI Tech

As a branch of computer science that strives to simulate and replicate human intelligence – and it’s this burning ambition that makes it challenging to define this field universally.

Yes – AI tech is making machines intelligent. But what makes a machine intelligent? How does it transpire?

Artificial intelligence combines vast amounts of data with fast processes and superior algorithms. This makes it possible for systems to learn from patterns, automatically,

Stuart Russel and Peter Norvig have tried answering this in their book Artificial Intelligence: A Modern Approach by stating that AI tech is “the study of agents that receive percepts from the environment and perform actions.”

The four approaches to AI tech, as defined by them, are:

  • Thinking humanly
  • Thinking rationally
  • Acting humanly
  • Acting rationally
Three basic AI concepts provide a further categorization of artificial intelligence

Arising from those approaches, artificial intelligence most frequently gets categorized into these concepts:

1. Artificial Narrow Intelligence (ANI)

A branch of AI that excels in performing singular tasks by replicating human intelligence, and AI’s basic concept. This type of knowledge is found in speech recognition systems and voice assistants.

2. Artificial General Intelligence (AGI)

AI whose purpose is general and whose efficiency can be applied to diverse tasks. This type of artificial intelligence can improve itself by learning and is the closest to the human brain in terms of capacities.

3. Artificial Super Intelligence (ASI)

Exceeding human intelligence, this AI concept is way more sophisticated than any artificial intelligence system or a human brain. The main trait of ASI is that it can contemplate about abstractions of which humans are unable to think. Its neural network exceeds that of humans’ billions of neurons.

What Types Of Artificial Intelligence Are There?

Some of the major subfields of AI tech, that also present methodological and theoretical grounds, are:

  • Neural networks: units that are interconnected and provide machines with learning ability by processing info gathered from external inputs.
  • Machine learning: uses neural networks, physics and stats to find insights and learn from them without being programmed for the ability to make conclusions.
  • Cognitive computing: human-like interaction with machines whose ultimate goal is to simulate human processes by interpreting speech and images.
  • Natural Language Processing (NLP): machine’s ability to analyze, comprehend and even recreate human language and speech.
  • Deep learning: a higher form of machine learning that uses computing power to learn intricate patterns in significant amounts of data. Image and speech recognition arises from this.
These artificial intelligence subfields create the backbone of every type of AI system.

Born from these major concepts are the four main types of artificial intelligence, as defined by Michigan State University’s Arend Hintze (in this article, we will look into and discuss three of these as they are of relevance to our topic):

1. Reactive machines

These are the most basic types of AI systems that can’t form or use past experiences that they’d use to make future decisions.

A typical example of a reactive machine is Deep Blue, IBM’s supercomputer that rose to prominence when it beat chess world champion Garry Kasparov in the 1990s. The computer identifies chess pieces and knows how to move every one of them. It can also make predictions about an opponent’s move, based on that knowledge.

2. Limited memory

Artificial systems of this type have the ability to look past the present moment and learning from experiences. An excellent example of these systems is self-driving cars.

By learning from observing cars’ direction and speed over a prolonged period of time, they add the obtained knowledge to their own preprogrammed understanding of the world that also includes traffic lights and other vital elements of driving.

3. Theory of mind

AI tech systems in this category can form their own representations of the world and the entities that make it. Theory in mind is just that, understanding that living entities in the world have thoughts and emotions and that their behavior is impacted by them. Artificial intelligence systems that have this ability can understand motivations and intentions and are the foundation of the next-gen of artificial intelligence machines.

Have a look at this great video explaining the basics of self-learning AI, as evidenced by Google Deep Mind:

What AI Tech And Languages Are Used For Coding Artificial Intelligence?

According to Existek, five major coding languages used in AI development are:

  • C++
  • Java
  • Lisp
  • Python
  • Prolog

These are the most agile, capabilities-rich languages that are the backbone of any software or app catering to the business use of AI tech. Each, of course, has certain drawbacks and advantages when it comes to coding AI tech- choosing one over the other mainly depends on the functionalities you’d like your AI system to have.

1. C++

C++ is excellent for resolving complex AI problems and finding permanent solutions for them. This program is packed with tools for programming and library functions that enable AI systems to reach their full potential.

As it provides full support for object-oriented principles, it has shown itself useful with processing organized data.

However, C++’s weakness is that multitasking isn’t its forte. Also, it is suitable for the implementation of the base of specific systems and algorithms. New developers may have a hard time coding AF with this because this program is prominently in favor of the bottom-up approach.

2. Java

Among Java’s advantages is the fact that this language is very easy to implement on different platforms. Its simplistic use has made it very powerful and multi-purposeful – it even makes debugging easy.

Moreover, Java has an automatic memory manager that makes the work of a developer easier.

Among downsides is the fact that Java is slow – slower than C++, for instance, as its execution can be kind of sluggish and takes more response time. Despite it being portable, that won’t help on older platforms, where Java requires software and hardware changes for it to work properly.

3. Lisp

Lisp is quite fast and very efficient – especially in coding, where it is supported by compilers instead of interpreters. This AI tech programming language has one great advantage over the others, which is – it has an automatic memory manager invented for it.

However, Lisp is a rarity – there aren’t many developers acquainted with programming in this language.

4. Python

Widely used in AI tech coding, Python has a vast diversity of tools and libraries and it supports testing of algorithms, without there being a need to implement them first.

This program can increase the overall productivity of a developer by being supportive of object-oriented design. Also, this program is fast – faster, in fact, than Java and C++.

If we were to name a few downsides, then it might be the difficulty of developers accommodating to the new syntax for AI tech programming. Also, it works with the help of an interpreter. That makes execution slower in artificial intelligence development, compared to that of C++ and Java. One big obstacle is coding AI tech for mobile computing, as its language makes it unstable for mobile.

5. Prolog

With its list of essential tree-based data structures, Prolog has certain advantages over the other programs on this list, such as high efficiency for quick prototyping for AI programs to be released.

Prolog hasn’t been standardized yet. This in regard to some features being different in implementation, which makes developers need to do some extra work.

How Long Does It Take To Code And Program AI?

Of course, the question of how long does it take to code for artificial intelligence depends on obvious factors:

  • Your level of expertise
  • The breadth and level of AI complexity
  • The purpose of the AI system
  • The language used

Linear algebra, matrix algebra, variable calculus, statistics and more fields is what one would need to have a basic understanding of before tackling the AI coding.

Taking all the prerequisite knowledge into account, Hackernoon estimates that it would take two hours for Data Scientists, Software Engineer and Domain Experts to leverage AI and go from a working idea to a “dirty prototype.”

For the “ordinary Joe” the forecast is, of course, radically less optimistic.

According to Mike West, “Machine Learning Evangelist”, for entry-level AI coders and programmers, studying 3-4 hours a day could attain them the required knowledge of AI coding in less than a year.

ai-tools-programs-frameworks
Certain AI tools harness the full potential of this technology while open-source templates make coding easy.

5 Pre-Made AI Tech Tools, Frameworks And Templates

Everyone willing to dab into AI bandwagon asked themselves the same question: is there some open-source, free AI tech templates to be used?

Luckily – yes! And, thankfully, they are great time-savers, too.

Microsoft has released Power Platform – a sturdy “one-size-fits-all” solution, comprised of Microsoft Power BI, PowerApps and Flow, that “enables non-technical users to create and automate workflows that span applications without help from developers.”

There are numerous other open-source artificial intelligence projects as well, such as:

  • ACT-R
  • DeepDetect
  • Theano
  • DeepMind Labs
  • Numenta
  • Aerosolve
  • Dlib
  • Encog
  • And more!

DigitalSilk takes a hard, abrasive look into the following 5 artificial intelligence tools and frameworks that are suggested by OpenSourceForU.

1. TensorFlow

An open-source software library that was developed and used by Google Brain Team researchers.

TensorFlow has a flexible architecture allowing developers to “deploy computation to one or more CPUs in a desktop, server or mobile device with a single API”, although this library itself provides multiple APIs.

The lowest-tier API gives total programming control, while the higher-level APIs alleviate repetitive tasks that need to be undertaken among different users.

2. Caffe

Caffe is a framework for creating deep learning systems. It was made to be speedy and modular, as well as very expressive in technical terms.

It was developed by Berkeley AI research team and its primary focus are networks applied to computer vision. By design, this tool buoys innovation and application because its models are configured without hard coding.

It also fosters active development, quick research experiments and industry deployment (Caffee can process 60 million pictures at a daily level). It has a big community of users as it backs academic research, startups, and applications in multimedia.

Check out this link for a full-day crash course!

3. Neuroph

This open-source tool is used for creating artificial neural networks, exclusively for Java programs.

It contains Java’s class library (with a very small number of basic classes) and with its easyNeurons tool, it can facilitate the creation and training of neural networks. Its GUI neural network editor is very convenient and handy and developers can use it to create their own neural network components.

The neural networks conceived through Neuroph have artificial neuron layers, neuron connections, transfer function, input function, learning rule and so on. Also, this tool has its own support for image recognition.

4. Apache SystemML 

This framework develops systems that are capable of machine learning using Big Data. SystemML was created by IBM and it’s renowned for its flexibility and scalability.

SystemML allows multiple execution modes, customization of algorithms and optimization based on data and cluster characteristics. The additional levels of deep learning deployed with this system include GPU capabilities, importing and running neural networks, and so on.

5. Torch

An open-source machine learning library based on LuaJIT programming language. It boasts a big number of algorithms and flexible tensors for indexing, resizing, cloning, sharing storage, and more.

With a top-notch interface, linear algebra routines, neural network models, efficient GPU support and embeddable nature, Torch is used by the Facebook AI Research Group, IBM and Yandex, among others.

Its subset, PyTorch, is an open-source machine learning library for Python and can be used for natural language processing.

AI Tech Use For Creating Chatbots

According to resources such as edureka and ChatBotsMagazine, you can use the above frameworks such as TensorFlow to build fully functioning AI chatbots.

They can be interacted with via voice or text functions and are used in retail customer service or for troubleshooting technical issues the most.

ChatBotsMagazine lists four priority steps when setting out to create a chatbot:

  • Identify the opportunities for an AI chatbot
  • Understand its goals
  • Design a conversation
  • Build it using frameworks or non-coding platforms

Some of the most advanced platforms that are designed to help developers build chatbots are:

Also, have a look at this helpful video by edureka on how to create an AI chatbot using TensorFlow (that we listed as one of the preferred methods of AI-building)

How long does it take? RubyGarage argues that, depending on the scope of work, creating a chatbot can take from 40 to 160 hours of work.

AI Tech Use For Creating Speech Recognition

TowardsDataScience’s Admond Lee understands that it is the speed that will make voice and speech-enabled products like Google Alexa the thing of the future.

He gives a compelling case in favor of creating your own voice-controlled AI tech with the help of the aforementioned Python programming language and Google Speech Recognition API.

He even provided the source code that can be found HERE!

Noting that there are other Speech Recognition APIs out there, there are also valuable, such as:

…Lee argues that Google API has a default key that is hard-coded into the SpeechRecognition library.

How much time does it take? According to Nickolay Shmyrev, CMUSphinx developer, coding a speech recognition system can take a few months.

You can follow Lee’s thorough instruction on building a voice recognition AI almost from scratch at this location.

Have a look at his own video showcasing the voice recognition demo he built on his own:

Conclusion

All in all, artificial intelligence has the power to engage customers, improve user experience, and ultimately help businesses boost their revenue while integrating the latest technology.

The best overall AI solutions are built using the following programming languages:

  • C++
  • Java
  • Lisp
  • Python
  • Prolog

But pre-existing platforms can help to enact artificial intelligence solutions for businesses. For instance, successful chatbots can be built using:

  • Botsify
  • Chatfuel
  • Chattypeople
  • Motion AI
  • BeepBoop
  • Botkit
  • Converse.io
  • Octane.ai

And speech recognition can be created with the following programs:

  • Microsoft Bing Speech
  • Houndify
  • IBM Speech to Text
  • Wit.ai

Whether you decide to utilize chatbots, voice recognition, or incorporate a different form of AI, artificial intelligence has the power to put your brand ahead of the competition and build a repeat customer base that will rely on you for years to come.

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