Artificial intelligence (AI) technology simulates human intelligence using computer systems powered by advanced machine learning algorithms. AI technology can perform many functions that previously could only be performed by humans - including speech recognition, natural language processing, and computer vision.
AI development leverages programming skills, data science skills, massive datasets, and specialized hardware, to enable machines to simulate human cognitive tasks. Most current AI solutions are considered Narrow AI, because they can perform only specific functions. Multiple organizations are working on applications of General-Purpose AI which can rival human cognitive capabilities for any task.
Two key concepts in AI technology are machine learning and deep learning:
In this article:
Artificial intelligence allows computer programs to think and learn like humans. AI generally refers to any problem or task that would normally require human intelligence to handle.
AI applications offer huge advantages, revolutionizing many professional sectors. These include:
Machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning. The two may seem similar because both serve to facilitate artificial learning, but there are distinct differences in the type of learning and the results.
Machine learning involves using Bayesian techniques for pattern recognition and learning. It consists of algorithms that use data to learn and make predictions. Machine learning enables machines to classify data, extract patterns from data, and optimize a specific utility function.
Regular software code uses given input to generate a program code-specific output. Machine learning algorithms use data to generate statistical code—a machine learning model. The model outputs a result according to a pattern detected from previous input (unsupervised learning) or output (supervised learning). The model's accuracy relies on the quantity and quality of the historical data.
Deep learning involves layering algorithms to facilitate an improved understanding of data. This sub-field of machine learning employs layers of non-linear algorithms to create distributed representations that interact according to a series of factors. It is not limited by basic regression that must create an explainable set of relationships.
Deep learning algorithms use large sets of training data to identify relationships between elements, such as shapes, words, and colors. These relationships help deep learning algorithms to create predictions. Deep learning algorithms can identify many relationships, including relationships that humans may miss, and make predictions or interpretations of highly complex data.
Computer vision enables computers and systems to see and understand observed inputs. It is a subfield of AI focused on enabling artificial sight in machines. It involves training machines to recognize and derive meaning from visual inputs like digital images and videos. Based on this information, machines can take action and make recommendations.
Computer vision works similarly to human vision. Instead of using retinas, a visual cortex, and optic nerves, machines use cameras, algorithms, and data to perform vision functions. For example, computer vision enables machines to distinguish between objects, calculate the distance between them, and determine if the objects are moving.
Learn more in the detailed guide to deep learning for computer vision
Natural language processing (NLP) enables computers and systems to understand text and speech. It is a subfield of AI that trains machines to process human language in various forms, including text and voice data, and derive meaning, including intent and sentiment, from this input.
NLP involves using computational linguistics (rule-based modeling of human language) alongside machine learning, deep learning, and statistical models. Computer programs powered by NLP can translate texts from and to various languages, quickly summarize big data in real-time, and respond to spoken commands.
There are several common ways to deploy AI algorithms - via cloud-based platforms, at the edge, and via the Internet of Things (IoT).
Artificial intelligence (AI) helps automate regular tasks in IT infrastructure, increasing productivity. Combining AI with cloud computing produces a flexible network that can hold extensive data and continuously improve. Leading cloud providers offer AI tools for enterprises.
Benefits of AI in the cloud include:
Edge AI is a paradigm for creating AI workflows that span both centralized data centers and devices deployed near people and physical things (at the edge). This is in contrast to the common approach of developing and running AI applications entirely in the cloud. It is also different from traditional AI development, where organizations create AI algorithms and deploy them on centralized servers—in edge AI, algorithms are deployed directly on edge devices.
In an Edge AI deployment model, each edge device has its own local AI functionality, and usually stores a relevant part of the dataset. The edge device can still access cloud services for certain functions, but is able to perform most functions independently, with very low latency.
Edge AI has tremendous potential to enhance the functionality of devices like phones, autonomous vehicles and robots. By pushing AI to these edge devices, AI innovation can be used more efficiently, with lower latency, reduced storage costs, and improved security.
Learn more in the detailed guide to Edge AI
Artificial Intelligence for IoT (AIoT) combines artificial intelligence (AI) technologies with Internet of Things (IoT) infrastructure to enable more efficient IoT operations, improve human-machine interactions, and improve data management. AI can be used to turn IoT data into actionable information, improve decision-making processes, and lay the groundwork for new technologies such as IoT data as a service (IDaaS).
AIoT allows AI to add value to IoT, through machine learning capabilities, while the IoT adds value to AI through connectivity, signals and real time data exchange. As IoT networks proliferate across major industries, more and more human-centric, machine-generated unstructured data will emerge. AIoT can power data analytics solutions that derive value from IoT-generated data.
With AIoT, artificial intelligence is embedded in infrastructure components deployed on IoT devices, which are plugged into the IoT network. It then uses APIs to extend interoperability between components at the device, software, and platform levels.
A rich ecosystem has developed that enables organizations to develop and release AI solutions. This ecosystem includes development frameworks that make it easier to construct and train complex AI models, specialized hardware that can accelerate AI computations, and high performance computing (HPC) systems that can be used to run large-scale computations in parallel.
Machine learning involves using complex algorithms. Machine learning frameworks offer interfaces, tools, and libraries that simplify the machine learning process.
TensorFlow is a popular open source machine learning platform. In 2007, the Google Brain team launched the TensorFlow library. It has since matured into an end-to-end platform that supports training, data preparation, model serving, and feature engineering.
TensorFlow supports the following:
You can deploy models trained on TensorFlow on desktops, edge computing devices, microcontrollers, and browsers.
PyTorch is an open source machine learning framework based on Torch, a framework for running fast computations originally written in C. It was developed at Facebook AI and Research lab (FAIR) to provide flexibility, stability, and modularity for production deployment.
PyTorch offers a Python interface as well as a C++ interface. The Python interface is generally considered more accessible and user-friendly for Python developers. In 2018, Facebook merged PyTorch with the Convolutional Architecture for Fast Feature Embedding (Caffe2) framework.
Deeplearning4j offers a set of tools designed to natively run deep learning on the Java Virtual Machine (JVM). It is supported commercially by Skymind and developed by machine learning developers based in San Francisco. In 2017, it was donated to the Eclipse Foundation.
Here are key features:
You can use Deeplearning4j to perform linear algebra as well as matrix manipulation for training and inference.
Scikit-learn is an open source machine learning framework available as a Python library, developed in 2007 as a Google Summer of Code project by David Cournapeau. It supports supervised and unsupervised learning algorithms, including manifold learning, Gaussian mixture models, clustering, principal component analysis (PCA), outlier detection, and biclustering.
The library is built on top of an open source scientific toolkit called SciPy. The toolkit uses Matplotlib for visualization, NumPy for mathematical calculations, SymPy for algebra capabilities, and Pandas for data manipulation. Scikit-learn extends SciPy's capabilities through modeling and learning capabilities.
Deep learning models require training a neural network to perform cognitive tasks. Neural network training usually involves large data sets containing thousands of inputs, with millions of network parameters learning from the data. A graphics processing unit (GPU) can help handle this computationally intensive process.
GPUs are dedicated microprocessors that perform multiple simultaneous calculations, accelerating the DL training process. A GPU contains hundreds or even thousands of cores, which can divide calculations into different threads. GPUs have much higher memory bandwidth than CPUs.
Options for incorporating GPUs into a deep learning implementation include:
Learn more in the detailed guides to:
Deep learning projects often use multiple GPUs to train models. Deep learning calculations are easy to parallelize, significantly reducing the training time. Many, if not most, DL projects are only feasible with multiple GPUs, as they would take too long to train otherwise.
Multi-GPU deployments run deep learning experiments on a cluster of GPUs, providing the advantage of parallelism. Multiple GPUs are accessible as a single pool of resources, supporting faster and larger experiments than single-GPU-based deployments.
Learn more in the detailed guide to multi-GPU
DL workstations are dedicated computers or servers that support computationally intensive deep learning workloads. They provide higher performance than traditional workstations, powered by multiple GPUs.
In recent years, demand for AI and data science has ballooned, with the market expanding to off products for handling massive data sets and complex DL workflows. Data science projects often involve security concerns, such as maintaining data privacy, making it infeasible to run such projects in the cloud.
The need for secure, specialized AI has created a growing selection of AI workstations that run on-premises. These dedicated machines can handle compute-heavy AI workloads while leveraging the security of the local data center.
High performance computing (HPC) systems provide extensive processing power and perform large numbers of complex computations. An HPC system typically consists of multiple machines, called nodes, in a cluster. HPC clusters use parallel processing to process distributed workloads. An HPC system usually contains 16-64 nodes with at least CPUs for each node.
HPC offers increased storage and memory in addition to higher and faster processing. HPC devices often use GPUs and FPGAs to achieve higher processing power. HPC is useful for AI and deep learning in several ways:
Learn more in the detailed guide to HPC Clusters
Machine learning operations (MLOps) is a methodology that streamlines the entire machine learning cycle. It aims to facilitate quicker development and deployment of high-quality machine learning and AI solutions.
MLOps promotes collaboration between machine learning engineers, data scientists, and IT experts. It involves implementing continuous integration and deployment (CI/CD) practices alongside monitoring, governance, and validation of ML models.
Learn more in the detailed guides to MLOps
AIOps stands for artificial intelligence for IT operations. It involves using machine learning and AI to automate, centralize, and streamline IT operations. AIOps is typically delivered through a platform that employs analytics, big data, and machine learning capabilities.
AIOps platforms provide a centralized location for all your IT operations needs. It facilitates more efficient IT operations by eliminating the use of disparate tools. By using AIOps technology, IT teams can quickly and proactively respond to events such as outages and slowdowns.
Here are the core capabilities of AIOps:
Synthetic data is generated artificially by machine learning algorithms. It mimics the statistical properties of real-world data without using identifying properties like names and personal details.
Synthetic data is an alternative data source that ensures sensitive and personal data remains protected while ensuring AI and machine learning have enough data to generate usable outcomes.
Explainable artificial intelligence is a process and technology that makes it possible for humans to understand why AI algorithms arrived at a certain decision or output. The goal is to improve trust in AI systems and make them more transparent to their human operators and users.
Explainable AI can provide information such as a description of the AI model’s function, possible biases, accuracy, and fairness. It is becoming a critical element needed to deploy models to production, in order to build confidence with customers and end-users. Explainable AI is also important to ensure an organization is practicing AI responsibly, and is becoming a requirement of some compliance standards and data protection regulations.
Beyond its importance for an AI algorithm’s users, explainable AI can also help data scientists and machine learning engineers identify if an AI system is working properly, gain more visibility over its daily operations, and troubleshoot problems when they occur.
Learn more in the detailed guide to Explainable AI
A machine learning engineer (ML engineer) builds and designs AI systems to automate predictive models. It involves designing and creating AI algorithms with capabilities to learn and make predictions. Machine learning engineers need to assess, analyze, and organize massive volumes of data while running tests and optimizing machine learning models and algorithms.
ML engineers often work together as a data science team collaborating with other experts such as data scientists, data analysts, data architects, data engineers, and administrators. This team may also communicate with other personnel, such as software development, sales or web development, and IT.
Learn more in the detailed guide to machine learning engineering
Data scientists work with big data, gathering and analyzing sets of unstructured and structured data from various sources, such as social media feeds, emails, and smart devices. Data scientists use computer science, mathematics, and statistics to process, analyze, and model data. Next, they interpret the results to create actionable plans for organizations.
Data scientists employ technological and social science skills to find trends and manage data. They uncover solutions to business challenges by using industry knowledge, skepticism of existing assumptions, and contextual understanding.
Data engineers design and build systems for data collection, storage, and analysis. They work in various settings to build systems that collect, manage, and convert raw data into meaningful information. Data scientists and business analysts interpret this data.
Data engineers aim to make data accessible, helping organizations use data to assess and optimize performance. Data engineering is a broad field with applications in numerous industries.
Self-driving cars and other autonomous vehicles are powered by AI-based vehicle frameworks. The technology applies neural networks on big data from image recognition systems to assemble vehicle frameworks that can drive autonomously. That data typically includes images from cameras, and the neural networks attempt to recognize and distinguish between traffic signals, checks, trees, pedestrians, road signs, and other objects within a random driving environment.
The classification of the development stages up to the self-driving vehicle comes from The Society of Automotive Engineers (SAE) classifies six development stages building up to fully self-driving vehicles. Each stage describes the extent of automation and the driver tasks handled by the vehicle.
Here are the development stages:
UEBA technology employs machine learning to analyze massive amounts of data and determine patterns of normal human and machine behavior. It helps create a baseline of normal behavior within a specific digital environment or network and then detect anomalies. Once the technology establishes models of typical and atypical behavior, machine learning can further support the following:
Learn more in the detailed guide to User and Entity Behavior Analytics (UEBA)
Machine learning powers automated security testing processes that identify potential weaknesses and flaws during software development. This process runs across the entire development cycle to ensure productivity and efficiency. It helps catch errors and flaws in early phases and prevents them from negatively impacting the release schedule.
For example, fuzz testing (fuzzing) can automatically identify coding errors and security loopholes. This automated software testing technique randomly feeds unexpected and invalid inputs and data into a program.
Fuzzing involves feeding massive amounts of random data, called fuzz, into the tested program until it gets breached or crashes. The process also uses a tool called fuzzer to identify the potential causes of a detected vulnerability.
Learn more in the detailed guide to fuzzing and fuzz testing
With the proliferation of rich media on websites and social networks, image and video editing are increasingly common operations performed by organizations and individuals everywhere. Traditionally, these were time consuming manual operations, but many image and video editing tasks can be performed by AI algorithms with superior performance to humans.
AI algorithms can analyze photos and make intelligent predictions about how to edit, adjust or enhance them. This can eliminate manual tasks and save time and costs for producers of content. For large media organizations, this can generate major cost savings and enable more agile content production processes.
With the help of AI, organizations can create more personalized videos to increase engagement. AI-driven video applications give end-users powerful functionality like the ability to search through video for key moments, and automatically produce professional video footage with only a few clicks.
Learn more in the detailed guide to automated image editing
Conversational AI technology enables machines to mimic human interactions by understanding user input and generating a human-like response. This technology powers technologies such as virtual agents and chatbots that users can talk to.
It involves using big data, machine learning, and natural language processing (NLP) to imitate human interactions, recognize text and speech inputs, translate the input’s meaning across multiple languages, and generate human-like responses.
Collaborative robots (cobots) perform actions in collaboration with human workers. AI technology automates the functionality of cobots, and machine vision technology enables them to see the environment.
Cobots include safety mechanisms like padded joints and force limiters. Additionally, cobots use safety shut-offs to perform quality assurance, machine tending, and packaging. It ensures the cobot does not require much space to work or puts people at risk.
Run:ai automates resource management and orchestration for AI infrastructure. With Run:ai, you can automatically run as many compute intensive experiments as needed.
Here are some of the capabilities you gain when using Run:ai:
Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run:ai GPU virtualization platform
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