Why Is AI Important?

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:

• Automated repetitive learning—AI typically handles high volumes of frequent, repetitive tasks rather than simply automating manual tasks. These computerized tasks are reliable and can process large amounts of data without fatigue. Most AI systems require a human to set up and manage them.
• Progressive learning—algorithms that consume data and can progressively program themselves. They can identify patterns and acquire more accurate skills over time. For example, algorithms can learn to play chess or recommend suitable products to online customers, adapting to new inputs. 
• Multi-layered data analysis—neural networks have multiple hidden layers to analyze deep data, enabling the creation of tools such as AI-based fraud detection systems. The availability of big data and improved computing power enable deep learning models to train directly on huge datasets. ‍
• Fast decision-making—AI-based technologies can make decisions and perform actions faster than humans. Humans tend to analyze multiple emotional and practical factors when making decisions, while AI quickly analyzes data in a structured way to deliver fast results.

‍Learn more in our detailed guide to Machine Learning for business

Machine Learning vs Deep Learning

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.

What Is Machine Learning?

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. 

What Is Deep Learning?

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.

What Is Computer Vision?

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

What Is Natural Language Processing (NLP)?

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.

AI Deployment Models

There are several common ways to deploy AI algorithms - via cloud-based platforms, at the edge, and via the Internet of Things (IoT).

AI in the Cloud

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:

• Reduced costs—cloud computing eliminates the cost of maintaining AI infrastructure, allowing businesses to access AI tools on a pay-per-use basis. 
• Automated tasks—AI-based cloud services can perform repetitive tasks that require more intelligence and complexity than traditionally automated tasks. Automation boosts productivity while reducing the burden on the human workforce.
• Enhanced security—AI helps secure data and applications in the cloud, providing powerful tools for tracking, analyzing, and addressing security issues. For example, behavioral analytics can identify anomalous behavior and alert security teams.
• Data-based insights—AI detects patterns in large volumes of data to provide IT personnel with deeper insights into recent and historical trends. The fast, accurate insights allow teams to address issues quickly.
• Enhanced management capabilities—AI can process, structure, and manage data to streamline the management of supply chain, marketing, and other business data.

Edge AI

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

AI for the Internet of Things (IoT)

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.

AI Infrastructure

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 Frameworks

Machine learning involves using complex algorithms. Machine learning frameworks offer interfaces, tools, and libraries that simplify the machine learning process.

TensorFlow

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 run TensorFlow on standard CPUs, as well as on specialized AI accelerators like GPUs and TPUs. 
• TensorFlow is available on macOS, 64-bit Linux, and Windows.
• TensorFlow supports various mobile computing platforms, including iOS and Android. 

You can deploy models trained on TensorFlow on desktops, edge computing devices, microcontrollers, and browsers. 

PyTorch

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.

Eclipse Deeplearning4j

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:

• The Deeplearning4j library is compatible with Scala and Clojure. It includes an n-dimensional array class with ND4J that enables scientific computing in Java and Scala. 
• Deeplearning4j integrates with Apache Hadoop and Apache Spark to support clustering and distributed training.
• Deeplearning4j integrates with NVIDIA CUDA runtime to enable distributed training and GPU operations across multiple GPUs.

You can use Deeplearning4j to perform linear algebra as well as matrix manipulation for training and inference.

Scikit-learn

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.

GPUs for Deep Learning

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: 

• Consumer GPUs—suitable for small-scale projects, offering an affordable way to supplement an existing DL system to build or test models at a low level. Examples include NVIDIA Titan V (12-32GB memory, 110-125 teraflops performance), NVIDIA Titan RTX (24GB memory, 130 teraflops performance), and NVIDIA GeForce RTX 2080 Ti (11GB memory, 120 teraflops performance).
• Data center GPUs—suitable for standard DL implementations in production, including large-scale projects with higher performance such as data analytics and HPC. Examples include NVIDIA A100 (40GB memory, 624 teraflops performance), NVIDIA v100 (32GB memory, 149 teraflops performance), NVIDIA Tesla P100 (16GB memory, 21 teraflops performance), NVIDIA Tesla K80 (24GB memory, 8.73 teraflops performance).

Learn more in the detailed guides to:

• GPUs for deep learning
• CUDA NVIDIA
• NVIDIA A100

Multi-GPU Parallelization

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

Deep Learning Workstations

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.

HPC for AI

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:

• Specialized processors—GPUs can better process AI algorithms than CPUs.
• Processing speed — parallel processing accelerates computations to reduce training and experiment times.
• Data volume—extensive storage and memory resources support the processing of large data volumes, improving AI model accuracy.
• Workload distribution—distributing workloads across computing resources enables more efficient resource utilization. 
• Cost-effectiveness—a cloud-based HPC system can be a more cost-effective way to leverage HPC for AI, with pay-per-use pricing.‍

Learn more in the detailed guide to HPC Clusters

Trends Driving the Future of AI Development

MLOps

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

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:

• Data collection and aggregation—AIOps technology collects and aggregates the massive volumes of operations data generated across IT infrastructure components, performance-monitoring tools, and applications.
• Intelligence and insights—AIOps platforms analyze the collected data and distinguish between false positives to true events and patterns related to system performance and availability issues.‍
• Root cause diagnosis and reporting—once the AIOps platform determines the root cause of an issue, it provides the information to IT for rapid response. Some platforms can automatically resolve specific issues without any human intervention.

AutoML

AutoML is a way of automating the end-to-end process of applying machine learning to real-world problems. It has been identified as a key trend driving the future of AI development.

Traditionally, building a machine learning model required a deep understanding of the mathematical principles behind machine learning algorithms. However, with AutoML, even non-experts can build machine learning models. It automates the process of training and tuning a large selection of candidate models and selecting the best one for the task at hand.

‍Learn more in our detailed guide to AutoML

Synthetic Data

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.

‍Learn more in the detailed guide to synthetic data

Explainable AI

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

Who Is Building AI Technology? AI Organizational Roles

Machine Learning Engineer

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 Scientist

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 Engineer

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.

How Is AI Technology Used? Example Applications

Autonomous Vehicles

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:

• Stage 1: No automation - the most basic development stage has no automation. For example, an ordinary car where the driver controls everything.
• Stage 2: Driver assistance - the automation provides longitudinal or latitudinal control but not both. An example of this is adaptive cruise control, which automatically controls the driving speed but requires the driver to steer the vehicle.
• Stage 3: Partial driving automation - the vehicle can simultaneously automate longitudinal and latitudinal tasks but only in limited contexts, requiring the driver’s supervision. Examples include General Motors Super Cruise and Nissan Pro Pilot Assist.
• Stage 4: Conditional driving automation - the level of automation requires significant technological advances, including limited operational design domain (ODD) and object and event detection and response (OEDR) capabilities. ODD refers to the operating conditions that a system can support (i.e., lighting or environmental characteristics), while OEDR detects and responds to objects and events immediately impacting the driving task. At this stage, the vehicle can perform tasks under certain conditions without the driver’s supervision, although the driver is still responsible for emergency scenarios. 
• Stage 5: High driving automation - the system has a fallback mechanism to handle emergencies without human supervision. The driver becomes like a passenger and doesn’t have to concentrate on driving tasks. However, the ODD capabilities remain limited to specific environmental and weather conditions, while the driver can control the vehicle during emergencies.
• Stage 6: Full driving automation - the system is fully autonomous with unrestricted ODD. The vehicle can operate autonomously regardless of weather and environmental conditions, with no requirement for a driver. There are no real-world examples of fully automated vehicles, but they are likely to emerge soon. 

User and Entity Behavior Analytics (UEBA)

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:

• Threat detection—UEBA uses machine learning to determine whether an atypical behavior indicates a real threat. It can identify potential threats and attacks often missed by traditional antivirus designed to detect known threats. UEBA analyzes various behavioral patterns, and detects threats such as lateral movement and insider threats.
• Threat prioritization—once threats are identified, machine learning helps UEBA solutions determine the threat level of a given threat and apply a risk score. This information can help ensure response is initiated quickly during high-risk incidents.

‍Learn more in the detailed guide to User and Entity Behavior Analytics (UEBA)

Automated Security Testing

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

Automated Image and Video Editing

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 guides to:

• Image editing
• Image datasets

Conversational AI

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

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.

Customer Success

AI technology also plays a pivotal role in enhancing customer success. Today's companies are using AI to offer personalized experiences, make accurate product recommendations, and provide fast and efficient customer service.

AI-powered chatbots, for instance, are used to handle customer inquiries and complaints. These chatbots can understand and respond to customer queries in real time, providing instant support and freeing up human agents to handle more complex issues.

AI technology is also used to predict customer behavior and preferences. Based on a customer's past behavior and interactions with a company, AI can predict what products or services the customer might be interested in. This enables companies to make personalized product recommendations, enhancing customer satisfaction and increasing sales.

Learn more in the detailed guides to:

• Customer success
• Customer success KPI

Customer Intelligence

AI technology is also used in customer intelligence. This is the process of collecting and analyzing customer data to gain insights into their behavior and preferences.

AI technology can analyze large amounts of customer data and extract meaningful insights. It uses machine learning algorithms to analyze the data and identify patterns and trends. This helps businesses to understand their customers better and make informed decisions.

Moreover, AI technology in customer intelligence allows businesses to predict future behavior of customers. This helps them to plan their marketing strategies and improve their products or services.

Learn more in the detailed guide to customer intelligence

Customer Journey and Experience

AI technology has also revolutionized how businesses understand and influence the customer journey. It's used to analyze customer behavior, understand their needs, and provide personalized experiences. This not only improves customer satisfaction but also boosts business growth.

One of the ways AI technology is used in customer journeys is through chatbots. These AI-powered bots can handle customer queries round the clock, provide instant responses, and ensure customer issues are resolved promptly. They can also analyze customer interactions to understand their preferences and make personalized recommendations.

Furthermore, AI technology is used to analyze customer behavior and predict their likelihood of churning. It uses machine learning algorithms to analyze customer data and identify patterns that could indicate potential churn. This allows businesses to take proactive measures to retain their customers.

Learn more in the detailed guides to:

• Customer journey
• Customer churn

AI Infrastructure Virtualization with Run:ai

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: 

• Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
• No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
• A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.

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