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AIWIS Review: get ahead of the game with NASA technology

· Software and Tool

Artificial intelligence (AI) has gradually been making its way into business software and will continue to for the foreseeable future. These intelligent applications (AIWIS Review) have incorporated machine and deep learning algorithms into their everyday functionality to better automate tasks for the user. Automating these processes saves the user time and energy, makes their job simpler, and allows employees to work more efficiently and productively. While there are some that believe AI is out to replace their jobs, they will be pleasantly surprised that, in most cases, this is a false assumption. Instead, the application of AI will simply make their jobs easier. For the developers interested in building their own intelligent applications, the AI platforms, machine learning algorithms, and deep learning libraries and frameworks used to create such functionality are found in the following subcategories:

Popular Artificial Intelligence Software Categories

AI Platforms

For developers tryings to build their own intelligent applications on top of a platform, AI platforms are the ideal solution. Like a standard application platform, these tools often provide drag-and-drop functionality with pre-built algorithms and code frameworks to assist in building the application from scratch. The difference between AI platforms and cloud platforms as a service (PaaS) products is the former provide the ability to add in machine and deep learning libraries and frameworks when constructing the application. AI platforms ultimately give applications an intelligent edge. AI platforms are a mix of open-source and proprietary products, meaning they make possible the creation of an intelligent application with little overhead. However, for those without sufficient development knowledge, these platforms may prove to be challenging, even with the inclusion of drag-and-drop functionality for beginners.

Popular AI Platforms products used by Artificial Intelligence professionals

Deep Learning:

Deep learning algorithms differ from machine learning algorithms specifically because they use artificial neural networks to make their predictions and decisions, and do not necessarily require human training. With artificial neural networks, elaborate algorithms can make decisions in a similar way as the human brain. However, the decisions are made on a smaller scale because replicating the amount of neural connections in the human brain is currently impossible. Deep learning can be broken down into the subcategories of image recognition (computer vision), natural language processing (NLP) , and voice recognition . Image recognition algorithms allow applications to learn specific images pixel by pixel; the most common usage of an image recognition algorithm may be Facebook’s ability to recognize the faces of your friends when tagging them in a photo. NLP has the ability to consume human language in its natural form, which allows a machine to easily understand simple commands and speech by the user. NLP is widely used in applications like iPhone’s Siri or Microsoft’s Cortana in Windows products. Each of these subcategories utilize artificial neural networks and rely on the networks’ deep layers of neural connections for an increased level of learning.

While each of these AI solutions or algorithms offer very advanced capabilities, the commonality of their usage is only increasing. Soon, all applications will contain some form of machine or deep learning, so those interested in developing their own application may find knowledge of these libraries and frameworks somewhat of a necessity.

Popular Deep Learning products used by Artificial Intelligence professionals

Machine Learning

The machine learning algorithm category consists of a broad range of libraries and frameworks that can perform a variety of machine learning tasks when correctly implemented. When embedded into software, these predominantly open-source algorithms allow applications to make decisions and predictions based entirely on data. These algorithms learn, often using supervised or reinforcement learning, based on the data sets presented to them for consumption. These styles of machine learning do require some element of human training. There are a number of different machine learning algorithm types, including association rule learning, Bayesian networks, and clustering and decision tree learning, among many others. The ability to connect machine learning algorithms to data sources to use them when building intelligent applications requires a high level of development skill and technical knowledge.

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