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 perform human cognitive tasks.
Two key concepts in AI technology are machine learning and deep learning:
Machine learning involves training models to make accurate classifications and predictions according to input data.
Deep learning is a subset of machine learning algorithms that use artificial neural networks, inspired by the structure of the human brain, which enable computer systems to perform complex, unstructured cognitive tasks.
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.
Multilayered 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.
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 nonlinear algorithms to create distributed representations that interact according to a series of factors. It’s 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.
The pros and cons of AI
Advantages of AI
Artificial intelligence offers numerous benefits that are transforming multiple industries:
Increased efficiency: AI can handle tasks at a scale and speed unattainable by humans. Automated systems can operate continuously, processing large volumes of data or performing routine tasks without fatigue. This leads to significant improvements in operational efficiency across industries, from manufacturing to customer service.
Cost reduction: AI reduces the need for human intervention in repetitive and labor-intensive tasks, which can lower operational costs. Over time, AI systems may replace manual labor for certain jobs, leading to cost savings in payroll, training, and management.
Enhanced accuracy: Machine learning models, especially deep learning models, can achieve high accuracy in areas such as medical diagnosis, fraud detection, and predictive maintenance. By analyzing vast amounts of data, AI can uncover subtle patterns and anomalies, making decisions based on data that would be impossible for humans to process manually.
Scalability: AI solutions can easily scale to meet demand, adapting to process larger datasets or manage more transactions without requiring significant changes to underlying systems. This makes AI particularly valuable in industries where rapid growth or variability in workload is common.
Innovation and new capabilities: AI opens up new possibilities for innovation, enabling breakthroughs in areas like autonomous driving, personalized medicine, and smart infrastructure. By automating complex decision-making processes, AI helps develop products and services that previously seemed out of reach.
Disadvantages of AI
Despite its advantages, AI comes with notable challenges and risks. These range from economic concerns like job displacement to ethical and security issues, raising important questions about its broader impact on society.
Implementation costs: Developing and deploying AI systems requires significant financial investment in hardware, software, and talent. While AI can save costs in the long run, the initial setup is often prohibitively expensive for small and medium-sized enterprises, limiting accessibility.
Bias and fairness issues: AI systems learn from the data they are trained on, and if this data contains biases, the AI may reinforce or even amplify these biases. This can lead to unfair outcomes in critical areas like hiring, law enforcement, and credit scoring, where biased algorithms can disproportionately affect certain demographic groups.
Lack of transparency: Many AI models, particularly deep learning algorithms, function as “black boxes” with decision-making processes that are not easily understood by humans. This lack of transparency can be problematic, especially in fields like healthcare and finance, where understanding how decisions are made is crucial.
Ethical and security concerns: The growing use of AI raises ethical issues, such as the potential for misuse in areas like surveillance, social engineering, or personal data manipulation. Additionally, as AI systems become more prevalent, they become targets for cyberattacks, potentially compromising sensitive data or causing significant damage.
Narrow AI vs. General AI
Narrow AI
Narrow AI, also known as weak AI, refers to systems that are programmed to carry out a specific set of tasks. These systems operate within a narrowly defined domain and are designed to follow rules or algorithms to achieve highly specialized objectives. Examples include AI models used for facial recognition, spam filtering, or game-playing algorithms like those in chess or Go.
Although these systems can outperform humans in their specific areas, they lack the ability to generalize knowledge beyond their training. Weak AI systems don’t possess consciousness or self-awareness — they simply process data and generate outputs based on predefined patterns and inputs.
General AI
General AI, also known as strong AI, and in its extreme form called Artificial General Intelligence (AGI), envisions machines that can perform any intellectual task that a human being can, with the ability to reason, plan, learn, and understand across a wide range of subjects.
General AI is not limited to predefined tasks but can adapt and apply knowledge in unfamiliar situations, just as humans can. It has the capacity for abstract thinking and emotional understanding, enabling it to navigate complex, unpredictable environments and solve problems without needing explicit instructions.
While weak AI is already integrated into many aspects of modern life, strong AI is in its infancy. Advanced large language models (LLMs) like OpenAI GPT-4, Claude Sonnet 3.5, and Meta LLaMA 3.1 are showing the first signs of general intelligence applicable to a wide range of use cases. These models are already revolutionizing industries and impacting society by performing tasks that require creativity, critical thinking, and independent decision-making. However, this also raises significant ethical concerns about control, security, and the potential risks associated with machines that can operate autonomously.
What is computer vision?
Computer vision enables computers and systems to see and understand observed inputs. It’s 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.
Natural language processing (NLP) enables computers and systems to understand text and speech. It’s 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.
Traditional NLP involved computational linguistics (rule-based modeling of human language) alongside classical machine learning and statistical models. Computer programs powered by traditional NLP can translate texts from and to various languages, quickly summarize big data in real time, and respond to spoken commands.
Modern NLP is based on the Transformer architecture, commonly taking the form of LLMs. These are advanced AI systems that can understand natural language commands, analyze long texts and multimodal inputs, and generate realistic text, code, and audiovisual artifacts.
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
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 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’s 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 can 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.
AI for IoT (AIoT) combines 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 that 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.
AI open source projects
Open source projects have contributed to the advancement of AI technology by making sophisticated tools and frameworks accessible to a broad audience. These projects promote innovation, collaboration, and transparency in AI development, providing resources for researchers, developers, and organizations.
Some open source AI projects provide powerful tools and frameworks that enable the development and deployment of advanced AI solutions. By leveraging these resources, developers and organizations can accelerate innovation and contribute to the ongoing evolution of artificial intelligence.
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’s 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 its 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.
Databricks
Databricks is a unified analytics platform powered by Apache Spark. It is designed to simplify the process of building, training, and deploying machine learning models at scale. Databricks combines data engineering, data science, and AI in one platform to provide a collaborative environment for data teams to work together.
With Databricks, you can manage the entire machine learning lifecycle from data preparation to model training and deployment. It allows you to build machine learning models using popular libraries such as TensorFlow and PyTorch, and scale them effortlessly with Spark. It also offers MLflow, an open-source platform to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment.
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).
Datacenter 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).
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.
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 datasets 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.
The first step in training an AI model is gathering a large and diverse dataset relevant to the task at hand. This dataset should include various examples and scenarios the model is expected to handle. For instance, training an image recognition model would require a vast collection of labeled images representing different objects or scenes.
The quality and quantity of data significantly influence the model’s performance. Data can be collected from various sources such as publicly available datasets, web scraping, or through sensors and other IoT devices. Ensuring data diversity is crucial for the model to generalize well to new, unseen data. For example, in natural language processing (NLP), data collection may involve scraping text from websites, aggregating tweets, or collecting transcriptions of spoken language.
2. Data preprocessing
Once the data is collected, it needs to be cleaned and preprocessed. This involves handling missing values, normalizing data, and transforming it into a format suitable for the model. In the case of image data, preprocessing may include resizing images, augmenting them to create more training examples, and converting them into numerical arrays.
Data preprocessing also involves feature extraction and engineering, which are crucial for enhancing model performance. For numerical data, this may include scaling features to a similar range, converting categorical variables into numerical representations, and dealing with outliers. For text data, preprocessing may involve tokenization, removing stop words, stemming, and lemmatization.
3. Model selection
Choosing the appropriate model architecture is crucial. Depending on the problem, different models may be more suitable. For example, convolutional neural networks (CNNs) are commonly used for image processing tasks, while Transformers are more effective for textual data and NLP tasks.
The selection process involves understanding the problem domain, the nature of the data, and the specific requirements of the task. Simple linear models might suffice for straightforward regression tasks, whereas more complex problems, such as natural language understanding or image recognition, may require deep neural networks. Additionally, exploring state-of-the-art models and frameworks through literature review and benchmarking against standard datasets can guide model selection.
4. Training
Training involves feeding the preprocessed data into the model and adjusting the model’s parameters to minimize error. This is typically done using optimization algorithms such as gradient descent. The training process is iterative, with the model learning from the data over multiple epochs, where each epoch represents a complete pass through the entire training dataset.
During training, the model’s weights are updated iteratively to reduce the difference between predicted and actual values, often measured by a loss function. Monitoring metrics such as loss and accuracy across training and validation datasets helps in tracking progress and identifying potential overfitting or underfitting issues. Techniques like data augmentation and dropout are used to improve generalization.
5. Hyperparameter tuning
Hyperparameters are settings that govern the training process and model architecture, such as learning rate, batch size, number of layers, and number of neurons per layer. Hyperparameter tuning involves systematically adjusting these settings to find the combination that results in the best model performance. Techniques for hyperparameter tuning include grid search, random search, and more advanced methods like Bayesian optimization. Effective hyperparameter tuning can significantly improve model accuracy and generalizability.
Hyperparameter tuning is often computationally expensive, requiring extensive experimentation. Automated machine learning (AutoML) tools can assist by conducting extensive searches and optimizing hyperparameters more efficiently. Cross-validation techniques are commonly used to evaluate different hyperparameter combinations, ensuring the selected settings perform well on unseen data.
After training, the model is evaluated using a separate validation dataset to assess its performance. Key metrics such as accuracy, precision, recall, and F1 score are calculated to determine how well the model performs on unseen data. This step helps in identifying issues like overfitting, where the model performs well on training data but poorly on validation data.
Evaluation involves both qualitative and quantitative assessment. Confusion matrices, ROC curves, and precision-recall curves provide detailed insights into model performance. Evaluating the model on different segments of the data can reveal biases or weaknesses, guiding further refinements. For example, in NLP tasks, human evaluation may complement automated metrics to assess the quality of text generation or translation.
7. Fine-tuning
Based on the evaluation results, the model is fine-tuned to improve its performance. This may involve adjusting hyperparameters, modifying the model architecture, or using techniques like regularization to prevent overfitting. Fine-tuning is an iterative process that continues until the model achieves satisfactory performance.
Fine-tuning can also involve transfer learning, where a pre-trained model is adapted to a new but related task. This approach leverages the knowledge gained from large-scale datasets and can significantly reduce the amount of data and training time required. Regular retraining with new data helps maintain model relevance and performance over time.
8. Deployment and monitoring
Once the model is trained and fine-tuned, it can be deployed in a production environment. Continuous monitoring is essential to ensure the model remains accurate over time, especially as new data becomes available. Retraining the model periodically with new data helps maintain its performance and adaptability to changing conditions.
Deployment involves integrating the model into an application or service where it can make real-time predictions or decisions. Monitoring tools track the model’s performance in production, detecting drifts in data patterns and model accuracy. Implementing feedback loops allows for automated retraining, ensuring the model adapts to new data and evolving requirements.
Training AI models is a complex process that requires careful planning, execution, and ongoing refinement. By following these steps, practitioners can develop robust and effective AI systems capable of solving a wide range of problems.
Trends driving the future of AI development
Generative AI
Generative AI refers to artificial intelligence algorithms that can create new content. This content can range from text to images, music, and even video. These algorithms are capable of learning the patterns and structures of their training data and then generating similar but original pieces of content.
Generative AI has numerous applications. In content creation, it can produce artwork, music, and writing, transforming industries such as entertainment and marketing. In data augmentation, generative AI creates synthetic data to enhance training datasets, improving model accuracy, especially when real data is limited.
In product design, generative design algorithms explore possible configurations to optimize designs. In medical imaging, generative AI enhances images and generates synthetic data for research and training. In gaming and virtual reality, it creates realistic characters and environments, enhancing the user experience.
Generative AI offers benefits such as fostering creativity and innovation, automating content creation to save time and reduce costs, and personalizing content to improve user engagement. However, it also faces challenges like ensuring quality and coherence of generated content, addressing ethical concerns such as deepfakes and copyright issues, and managing the high computational resources required for training large models.
Large language models (LLMs) are a new type of machine learning architecture, based on the Transformer model that revolutionized the field of natural language processing. Trained on vast amounts of text data, LLMs can generate human-like text and code that is coherent and contextually relevant.
The evolution of LLMs has been driven by advances in machine learning and the availability of massive amounts of text data. Over the past few years, these models have grown increasingly sophisticated, capable of generating content that is remarkably human-like in its coherence and relevance.
LLMs open up new possibilities for NLP and human-machine interaction in general. They can be used in a wide range of applications, from chatbots and virtual assistants to content generation and translation. At the same time, the rapid advancement of LLMs has raised serious concerns about AI safety. Many experts believe that AI systems could be used by bad actors or cybercriminals, or may eventually represent a larger threat to human society.
Explainable AI 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.
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.
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’s 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 nonexperts 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.
The AI organizational roles building AI technology
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.
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.
Example applications for AI technology
AI code assistants
AI technology is transforming the way developers work. There are numerous AI tools available that streamline the development process. For example, AI-powered code generation tools can automate the coding process, reducing the time and effort required. These tools can generate code by completing existing code, or based on natural language prompts, making it easier for developers to create complex applications.
AI code assistants like Tabnine can also assist in debugging, identifying errors and suggesting solutions, and can help developers refactor existing applications. New uses are constantly emerging, such as planning (i.e., asking general coding questions or better understanding code in an existing project), explaining code, creating tests, creating documentation, generating high-quality code by leveraging and enforcing your best practices and standards, and implementing and validating Jira issues.
AI for cybersecurity
AI technology is significantly enhancing cybersecurity measures, offering advanced solutions to protect systems and data from cyber threats. AI can analyze vast amounts of data to detect anomalies and identify potential security breaches in real time. Machine learning algorithms learn from historical data to recognize patterns associated with various types of cyber attacks, enabling quicker and more accurate threat detection.
One key application of AI in cybersecurity is in the development of intelligent threat detection systems. These systems can automatically monitor network traffic, identify suspicious activities, and issue alerts before an attack can cause damage. AI can also help in predictive analysis, forecasting potential vulnerabilities and threats based on emerging trends.
AI-driven tools are also used for automated incident response. In the event of a detected threat, these tools can execute predefined protocols to mitigate the risk, such as isolating affected systems or blocking malicious IP addresses. This automation speeds up response times and reduces the burden on human security teams.
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 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.
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.
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.
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.
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.
Customer sentiment analysis involves using AI to determine the emotional tone behind a series of words used by customers in text communications. This technology helps companies understand how customers feel about their products, services, or brand.
Sentiment analysis leverages natural language processing (NLP) and machine learning techniques to analyze textual data. The process typically involves data collection from various sources like reviews, social media, emails, and surveys. This is followed by text preprocessing to remove noise and standardize the format, including tokenization, stop-word removal, and stemming.
Enhancing developer productivity with Tabnine’s AI code assistant
Tabnine is the AI code assistant that you control — helping development teams of every size use AI to accelerate and simplify the software development process without sacrificing privacy, security, or compliance. Tabnine boosts engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to your team. It’s trusted by more than 1 million developers across thousands of organizations.
Key features
Best-in-class AI code generation: Let Tabnine’s AI coding assistant streamline AI code generation and automate mundane tasks so you can spend more time on the work you love. Get accurate and personalized code completions. Add comments and other natural language prompts in-line or via chat and Tabnine will automatically convert them into code.
Supports all popular languages and IDEs: Tabnine supports more than 80 programming languages and frameworks such as Python, Java, JavaScript, C, C++, Go, and more. Tabnine is easy to integrate with popular development environments, with plugins available for VS Code, the JetBrains family of IDEs (e.g., IntelliJ, Android Studio), Visual Studio, and Eclipse.
Protection from IP issues: Tabnine has trained its proprietary models (Tabnine Protected for Chat, and the universal model for code completion) exclusively on permissively licensed code. This ensures that the recommendations from Tabnine never match any proprietary code and removes any concerns around legal risks associated with accepting the code suggestions. Tabnine is transparent about the data used to train our proprietary model and shares it with customers under NDA. Additionally, Tabnine offers an IP indemnification to enterprise users for peace of mind.
Tabnine Chat: Tabnine includes Tabnine Chat, the enterprise-grade, code-centric chat application that allows developers to interact with AI models using natural language. It supports numerous use cases such as planning (i.e., asking general coding questions or better understanding code in an existing project), code generation, explaining code, creating tests, fixing code, creating documentation, and maintaining code.
AI personalized to you: In AI, context is everything. To increase the effectiveness of AI code assistants, it’s imperative to provide contextual awareness to the LLMs so that they can understand the subtle nuances that make a developer and organization unique. Tabnine leverages locally available data in the developer’s IDE to provide more accurate and relevant results. This includes: – Runtime errors – Imported libraries – Other open files – Current files – Compile / syntax errors – Noncode sources of information – Current selected code – Connected repositories – Conversation history – Git history – Project metadata and other project files
Personalized AI recommendations based on awareness of a developer’s IDE are accepted 40% more often than AI suggestions generated without these integrations. Developers can connect Tabnine to their organization code repos (e.g., GitHub, GitLab, Bitbucket) to gain global context. Tabnine also offers model customization — you can fine-tune Tabnine’s proprietary model using your own code to create a custom model. Model customization is extremely valuable when you have code in a bespoke programming language or a language that’s underrepresented in the training data set, such as System Verilog.
AI Code Review Agent: Tabnine AI Code Review Agent enables customers to codify their institutional knowledge (e.g., accepted standards for software development, unique best practices, or corporate policies) into rules that can be applied in code review at the pull request or in the IDE. You provide the parameters you’d like to see your code comply with via plain language (no complex setup required) and Tabnine converts this into a set of comprehensive rules (also reviewable via plain language). When developers create a pull request, the Code Review Agent checks the code and information in the pull request against that set of rules. If the code doesn’t conform to your expectations in any way, the agent flags it to the code reviewer and provides guidance and suggested edits to fix the issue.
Switchable model selection: Access new state-of-the-art models in Tabnine Chat as soon as they become available. You can choose from Tabnine Protected, Tabnine + Mistral, GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo, Codestral, Claude3, and Cohere’s Command R. You’re not locked into any one of these models and can switch instantly between models for specific projects, use cases, or to meet the requirements of specific teams.
Tabnine AI agents for Atlassian Jira: Jira Implementation Agent and Jira Validation Agent. With just a single click, Tabnine can implement a Jira issue, generating code from the requirements outlined in those issues. In addition to generating code for issues, you can also use Tabnine on either human- or AI-generated code to validate and review your implementation. The Jira Validation Agent will verify that your code accurately captures the requirements outlined in the Jira issue, offering guidance and code suggestions if it doesn’t.
Total deployment flexibility: Tabnine offers its customers numerous deployment options. Customers can consume Tabnine as a secure SaaS offering (in a multitenant environment or a single-tenant environment) or do a fully private installation (on-premises or on VPC) to ensure that their code stays in the boundaries of their corporate network and isn’t shared with any external party.
Enterprise-grade security: Tabnine offers key compliances like SOC 2 Type 2, GDPR, and ISO 9001 to ensure the security and privacy of your data.
Onboard onto projects in minutes: The Code Explorer agent for Tabnine helps developers ramp on a new project faster. For developers who are new to an organization or existing developers who are new to a project, Code Explorer provides a comprehensive overview of key project elements, including runnable scripts, dependencies, and overall structure to help them get up to speed effortlessly.
Plan your approach to development tasks: Ask Tabnine coding questions, learn how things work in your specific project, and get solutions and references relevant to your workspace. You can also use Tabnine to search your codebase. For example, if you were planning an approach to fixing errors in your log files, you can ask Tabnine to “find the errors in these log files,” and then prompt it to “generate and ASCII table showing the errors and their locations.” Then you could move on to fixing the errors by asking Tabnine to “provide solutions to fix and resolve these errors.”
Natural language code generation: Use natural language to generate code based on your design specs. Create software components, features, functionality, and more. As you continue coding, Tabnine will also provide in-line code completions, offering real-time, context-aware suggestions that seamlessly blend with your coding style. Tabnine can also support high-complexity tasks. For example, if you needed to create a function to parse an array and return specific values if criteria were found, you can use natural language to describe your requirements and prompt the AI agent to generate code matching those requirements. With Tabnine, you could also use “@” mentions to tag elements in the workspace to instruct the AI to generate code with specific context taken into account.
Unit test generation: Ask Tabnine to create tests for a specific function or code in your project, and get back the actual test cases, implementation, and assertion. Tabnine takes in the context from existing tests in your project and codebase to suggest tests that align with your project’s testing framework and variables.
Error fixing: Select or reference code with an error and Tabnine will recommend fixes. As your tools identify errors within your code and error notifications emerge in the problems tab or in-line using colored indicators, simply click on the error indicator and prompt Tabnine to suggest a fix. Rapidly accelerate error fixing without leaving your IDE and try multiple solutions to fix your errors. You can even use Tabnine to help resolve security issues identified by tools like Snyk.
AI documentation generation: Generate documentation for specific sections of your code to enhance readability and make it easy for other team members to understand. As you write code, use Tabnine to generate documentation including format documentation of classes and functions, comments, and in-line docs. Tabnine will generate standardized documentation, enhancing the readability of your code with every function, method, class, or line as needed clearly documented in a standardized format that’s easy to understand. If you want docs written in a specific format, you can even prompt Tabnine to do so. If you’re already using a documentation (format for example Google Java Style Guide), Tabnine will pick up on that and automatically generate documentation in your code that matches the context of existing documentation.
Code explanations: Tabnine Chat can provide you with an explanation for a block of existing code, which is especially useful when reading a new codebase or reading legacy code in languages you don’t know as well. This AI chat function allows for a pair programming experience, where the AI contributes to coding tasks, making it easier to work with unfamiliar codebases, frameworks, APIs, and languages.
Maintain and improve existing code: In addition to writing new code, Tabnine can help you change the existing code by adding functionality, refactoring, or fixing specific code with contextually relevant recommendations.
