Introduction to Artificial Intelligence Programming
Artificial Intelligence (AI) holds a rather special place in today’s tech environment. While many have imagined the many shapes and forms it can take in the future, AI is now a reality. The technological advances related to AI are vast and great, with so much more still left to be discovered.
Artificial Intelligence or AI is sometimes called machine learning. Now, you may wonder: how is machine learning different from artificial intelligence? The main purpose of AI is to develop machines or computer systems that show degrees of human intelligence. As defined by Wikipedia, AI research is defined as ”the study of intelligent agents: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.” Machine learning uses statistical techniques to enable computers to “learn” using data, without having to be explicitly programmed to do so.
AI focuses on some of these human intelligence traits: self-awareness, learning, deduction, decision-making, recognition, reasoning, and more.
There are four majorly recognized types of AI in the field:
- Reactive machines: Considered the most basic form of AI since it acts as a direct result of what it sees. It is incapable of storing memories or past experiences. A well-known example of a reactive machine is Deep Blue, IBM's supercomputer that plays chess. In summary, Deep Blue is a machine that is capable of analyzing over 200 million chess moves per second, predicting the moves of its opponent and deciding the optimal one for itself. This exemplifies how the machine perceives the world directing and acts on what it sees.
- Limited memory: This type of AI takes past information into account to make informed decisions and execute actions accordingly. An example of this type of AI is self-driving cars as they take speed, direction, traffic, roads, and more, into consideration to decide when to change lanes or avoid hitting a nearby car. This sentient behavior only has enough memory or experiences to make adequate decisions, but it doesn’t store every interaction or representation ever experienced like a human brain would.
- Theory of mind: This third type of AI is a distinct separation from the previous AI examples. Theory of mind is the next generation of intelligence as it is far more advanced because it understands thoughts and emotions, it deals with feelings, motives, and intentions, and can interact in a social environment. A Theory of mind AI representation has yet to be fully developed.
- Self-awareness: The final and most futuristic type of AI where machines are aware of themselves and are conscious. While these types of intelligent artificial beings are far from being created, AI research efforts are made toward comprehending memory, learning, and decision making based on past experiences for these hyper-intelligent and sentient future machines.
Now that you have an understanding of what AI is and its different types, the next step is to dive into Artificial Intelligence Programming languages and framework to build these AI technologies. While there are many languages available for AI Programming, developers must focus on what technology suits their needs. Let’s take a look at the available AI Programming Languages.
How to create Artificial Intelligence?
Almost every industry can benefit from Artificial Intelligence Programming. Whether it’s a sophisticated bot, a chess game, or a self-driving car, AI is here to blow our minds.
With many tech giants leveraging AI related technology such as deep learning and machine learning, developers can’t help but wonder: How to create Artificial Intelligence successfully?
Some of the main components to develop an AI solution involve AI Programming Languages and Artificial Intelligence Tools and Frameworks. Next in this article, we will talk about a few of these AI resources and how they compare to one another.
AI Programming Languages
This is a list of some of the most prominent AI Programming Languages available that answer how to program artificial intelligence and help you decide what is the best programming language for artificial intelligence:
- Python: a vast majority of developers show a preference for the multi-paradigm Python programming language since it’s got libraries that are better suited for machine learning. Its syntax, support for the object-oriented paradigm, and versatility also play a big role in its popularity. Python, one of the best AI programming language, nurtures machine learning and is significantly less complex than C++ or Java. It is oftentimes used in platforms such as Linux, Windows, Mac OS, and UNIX.
- C++: C++ is one of the most suitable programming languages for artificial intelligence for complex AI problems. It’s considered one of the fastest programming languages for artificial intelligence with a rich offering in library functions and programming tools on many hardware and software platforms. It is ideal for fast execution projects and it allows an extensive use of algorithms that support object-oriented principles for better-organized data.
- Java: this multi-paradigm language follows the Write Once/Run Anywhere (WORA) principle and is object-oriented. Java uses virtual machines to work on different platforms. It is easy to use and debug and has an automatic memory manager that makes any developer’s life easier.
- LISP: one of the oldest programming languages for artificial intelligence that has become strong and dynamic over the years. It is flexible and efficient, which makes it an ideal option for developers who want to develop a prototype quickly. One of LISP’s biggest advantage is its syntax which is simple and intuitive.
- PROLOG: this primary computer language has mechanisms and a declarative language that contains facts and rules to dictate for coding an AI. In PROLOG, a program is a set of predicates which define the relationships between arguments and are used for programming.
- IPL: it’s the first language developed for AI programming. It includes features to support programs that are able to problem-solve with lists, associations, schemas, data types, arguments, and cooperative multitasking. Many of its features are not leverages and incorporated into LISP.
- R: a programming language and free software environment that is mainly used for new AI development, including statistical computations, numerical analysis, Bayesian inference, neural networks and machine learning.
Artificial Intelligence Tools and Frameworks
Next, we detail a compilation of some of the most prominent Artificial Intelligence Tools and Frameworks:
- TensorFlow: this open source software library is a robust artificial intelligence tool that supports deep learning. Descendant from the Google family, it is considered a top tool for statistic program development as it provides machine models that can be trained to match user preferences. In this framework, the central unit of data is called a tensor, which is a set of primitive values that can be transformed into any number of dimensions. It uses the Python programming language.
- Caffe: this tool is a powerful, deep learning framework that is fast and efficient for research and industry deployment. It is loaded with a set of trained neural networks and is known for its image processing capabilities, as it can process over 60 million images per day.
- Apache SystemML: created by IBM, it is a top-level machine learning technology that uses big data, and is well-known for its flexibility and scalability. It uses multiple execution modes such as Spark MLContext, Spark Batch, Hadoop Batch, Standalone, and the Java Machine Learning Connector (JMLC).
- Microsoft Cognitive Toolkit: this is Microsoft’s response to Google’s TensorFlow. It is one of the fastest deep-learning frameworks with C”/C++/Python interface support. It supports distributed learning and modularization via learning algorithms and data readers.
- Open Neural Networks: a high-performing library that is open source and uses neural networks, which are one of the biggest research areas of machine learning. It’s written in C++ and also uses data mining methods embedded in other APIs for predictive analytic tasks. It is designed to learn from datasets and mathematical models.
- Torch: machine learning library used for scientific and numerical operations. It is open source and based on the Lua programming language. This framework is easy to use and efficient due to its fast scripting language, that helps developers use neural networks and optimization libraries.
- Keras: written in Python, this is an open source library designed to serve as an interface with a high abstraction level for easy configuration of neural networks. It is user-friendly and extensible, making it easy to work with TensorFlow or Microsoft Cognitive Toolkit.
- Amazon Machine Learning: this set of tools and wizards helps create machine learning models for developers of all skill levels. It can be connected to data stored in AWS, Amazon S3, or Amazon Redshift and the data is encrypted for maximum security. Additionally, it offers tools for data analysis and comprehension and is able to integrate with major datasets.
With many more tools and frameworks available, developers need to identify which one suits their AI needs best to make the best use for deep learning and machine learning projects.
AI is embedded in some of the most remarkable and everyday solutions out there. Its applications in the world range from Google’s AI powered predictions, ridesharing in apps such as Uber and Lyft, AI autopilots for commercial flights, and much more. These AI solutions pave way to future discoverments and the road ahead for AI.
Artificial Intelligence solutions have a profound effect on many fields including gaming, natural language processing, expert systems, vision systems, speech recognition, handwriting recognition, intelligent robots, and more. To understand more about the impact of these AI solutions, we must tally the Artificial Intelligence pros and cons in general.
Artificial Intelligence Pros and Cons
This list includes some of the most notorious artificial intelligence pros and cons:
- Daily tasks: Artificial Intelligence is capable of minimizing human effort when it comes to mundane, everyday tasks. Automated reasoning, learning, and perception are a common trait in agents such as Siri or Alexa that help end users with multiple activities.
- Error reduction: computers with artificial intelligence are designed and programmed to avoid mistakes, achieve accuracy, and process data in an error-free method. For example, AI in the aerospace industry is carefully programmed to be completely accurate for high-stake space exploration projects.
- Exploration: AI machines are capable of performing tasks that may put humans at risk. Ocean exploration or space exploration projects are prime examples of these exploration tasks.
- Increased accuracy: AI technology is embedded with highly capable programming to avoid errors and eliminate human and system inaccuracy.
- Medical application: medical research, diagnosis and surgery fields benefit greatly from AI solutions. As previously stated, AI is recognized for its precision and increased accuracy, which is highly beneficial to any medical application.
- Continuous, repetitive activities: AI systems are capable of performing continuous, repetitive activities with no need for breaks or rest time.
- High cost: Artificial intelligence is expensive. The cost of research, maintenance and repair grow high as more sophisticated systems are developed.
- No creativity: So far, AI machines are unable of developing a creative thought or decision. Instead, it is intelligent enough to take multiple variables and data to deliver the best possible outcome. This loss of creativity hinders AI exploitation to its full extent.
- Human unemployment: heavy application AI may reduce the need of human involvement, nearly eliminating the need for human jobs related to the tasks AI machines perform. For example, the displacement of assembly line jobs is decreasing manual labor on a daily basis.
Artificial Intelligence Security and the future of AI programming
AI’s impact on society is beneficial so far, which motivates research, development, and investment in many fields to carry on discovering more potential.
This growth also worries researchers and industry experts as many questions about what will happen when an AI system is superior to humans in all cognitive tasks? Important names such as Elon Musk, Bill Gates and Stephen Hawking have been known for their highly-publicized opinions on how worrisome and dangerous AI can be for humanity.
Artificial Intelligence Security and Safety is a big concern when it comes to the future of AI. Because AI has the potential to surpass human intelligence, there is no roadmap to how these hyper-intelligent machines will behave.
Artificial Intelligence Security is of the utmost importance to secure the system’s capabilities and to ensure they stay bound to a comfortable limit. The Artificial Intelligence Security field is growing its resources to continue to understand the impact of AI in today’s world and the future.
Could a machine ever pass as human? Humanity has been dreaming of a hyper-intelligent machine for some time now and it feels as if the AI community is now closer than ever before.
This pivotal moment comes with a complete realization of what it actually means to be self-aware. Do we know, to a full extent, what it means to be fully aware of oneself? This question is what prevents AI research from moving forward as the question is rather complex.
The future of AI and AI programming is vast and endless in possibilities. And we are here for the ride to see where it leads. Currently, with so many beneficial applications and uses of AI, it is important for businesses to leverage and understand this powerful technology branch.
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