Artificial intelligence (AI) is playing increasingly vital roles in business. Its widespread adoption and ability to enhance efficiency and accuracy has led to numerous benefits for both companies and their customers.
This glossary provides precise definitions for a wide range of AI topics that you may find in business, from foundational principles like machine learning and neural networks to advanced topics like generative adversarial networks and AI governance.
Adversarial attack
An adversarial attack is a technique used to deceive artificial intelligence (AI) models by introducing maliciously crafted inputs designed to cause the model to make errors. These attacks expose vulnerabilities in AI systems and can lead to incorrect predictions or classifications.
AI governance
AI governance refers to the policies, regulations, and frameworks established to ensure the ethical and responsible development, deployment, and use of artificial intelligence technologies. It encompasses issues like transparency, accountability, fairness, and privacy to mitigate risks and maximize benefits of AI systems.
Artificial intelligence
Artificial intelligence (AI) refers to computer systems and technologies capable of performing tasks that traditionally require human intelligence, as well as the field of computer science focused on creating these systems. While computers reach their outputs very differently from people (and are based on probability, not reasoning), the results of their programming resemble reasoning, problem-solving, and perception. These systems also have the ability to process and analyze language, images, and sound.
Artificial general intelligence (AGI)
Artificial general intelligence (AGI) refers to a hypothetical type of AI technology that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to human intelligence. Unlike narrow AI, which is designed for specific tasks, AGI aims to perform any intellectual task that a human can.
Automatic speech recognition
Automatic speech recognition (ASR) is a technology that enables computers to process and analyze human speech by converting spoken language into text. In the context of AI, ASR systems use machine learning algorithms and neural networks to analyze audio signals, identify linguistic patterns, and accurately transcribe spoken words into written text.
Backpropagation
Backpropagation is a training algorithm for artificial neural networks that measures how changes in the network’s internal connections affect its overall accuracy. It does this by tracing errors backward through the network. This process allows the computer program to gradually adjust these connections, improving its performance over time.
Bias
Bias in AI has both a colloquial and a technical meaning. Colloquially, bias refers to systematic errors in the outputs of machine learning models caused by prejudiced assumptions or flawed training data. These biases can lead to unfair or inaccurate results, affecting the reliability and ethical implications of AI systems. The more technical meaning of bias refers to a parameter given a node in a neural net, capable of shifting the node’s activation function left or right. Bias works in conjunction with weighting, enabling a model to better fit the data.
Chaining
Chaining in AI refers to the process of linking together multiple logical statements or rules to derive a conclusion or solve a problem. This technique is used in expert systems and rule-based AI to perform artificial reasoning, where forward chaining starts with known facts and applies rules to infer new facts, and backward chaining starts with a goal and works backward to determine the necessary conditions to achieve it.
Classification
Classification is a type of supervised machine learning in which the model is trained to categorize data into predefined classes or labels. It’s commonly used in applications such as email filtering, medical diagnosis, and image recognition.
Clustering
Clustering is an unsupervised learning technique in machine learning used to group similar data points together based on their characteristics. It’s commonly used for exploratory data analysis, pattern recognition, and anomaly detection.
Computer vision
Computer vision is a field of artificial intelligence that enables machines to interpret and make decisions based on visual data. It involves tasks such as image recognition, object detection, and image segmentation, allowing computers to process and analyze visual information.
Confusion matrix
A confusion matrix is a table used to evaluate the performance of a classification model by comparing the predicted labels with the actual labels. It displays the true positives, true negatives, false positives, and false negatives, providing insight into the model’s accuracy, precision, recall, and overall performance.
Conversational AI
Conversational AI refers to technologies that enable machines to engage in human-like dialogue, processing, analyzing, and responding to natural language input. It encompasses chatbots and virtual assistants, which use natural language processing (NLP) to facilitate interactions and provide services or information.
Cross-validation
Cross-validation is a technique used to assess the performance and generalizability of a machine learning model by dividing the data into multiple subsets. The model is trained on some subsets and tested on others, ensuring that it performs well on unseen data and reducing the risk of overfitting.
Data mining
Data mining is the process of discovering patterns, correlations, and insights from large datasets using statistical, machine learning, and computational techniques. It’s commonly used to extract valuable information for decision-making in fields such as marketing, finance, and healthcare.
Decision trees
Decision trees are a type of supervised learning algorithm used for classification and regression tasks, where data is continuously split according to certain parameters. Each node represents a decision based on an attribute, and each branch represents the outcome, making it easy to interpret and visualize the decision-making process.
Deep learning
Deep learning is a subset of machine learning that uses artificial neural networks with many layers (hence “deep”) to model complex patterns in data. It excels in tasks such as image and speech recognition, natural language processing, and game playing, where large amounts of data and computational power are required.
Edge AI
Edge AI refers to the deployment of artificial intelligence algorithms directly on devices at the edge of the network, rather than in a centralized cloud. This approach reduces latency, enhances privacy, and allows for real-time data processing and decision-making in applications such as autonomous vehicles, smart cameras, and IoT devices.
Explainable AI (XAI)
Explainable AI (XAI) refers to methods and techniques that make the outcomes of AI models understandable and interpretable to humans. It aims to provide transparency in AI decision-making processes, allowing users to comprehend how and why specific decisions or predictions are made, which is crucial for trust, accountability, and regulatory compliance.
Feature engineering
Feature engineering is the process of selecting, transforming, and creating new features from raw data to improve the performance of machine learning models. It involves techniques such as scaling, encoding, and combining existing features to provide better input representations for the learning algorithms.
Few-shot learning
Few-shot learning is a machine learning approach in which models are trained to make accurate predictions with very few labeled training examples. This technique is particularly useful in scenarios in which collecting large amounts of labeled data is impractical or expensive.
Generative adversarial network (GAN)
A generative adversarial network (GAN) consists of two neural networks, a generator and a discriminator, that compete against each other to create realistic data samples. By implementing this adversarial process, GANs improve their robustness and quality by training the generator to produce more convincing fake data and the discriminator to better detect these fakes.
Generative AI
Generative AI refers to algorithms (and larger systems built from these algorithms) that create new data or content, such as text, images, or audio, by learning patterns from existing data. These algorithms can be used in applications like content creation, data augmentation, and enhancing the capabilities of AI systems with minimal input data.
Generative pretrained transformer (GPT)
A generative pretrained transformer is a type of large language model that generates human-like text based on input prompts. It uses a transformer architecture and is pretrained on vast amounts of text data, making it capable of performing tasks such as text generation, translation, summarization, and more. OpenAI’s ChatGPT is an example of this AI technology.
Gradient descent
Gradient descent is an optimization algorithm used to minimize the cost or loss function in machine learning models by iteratively adjusting the model’s parameters. By calculating the gradient of the cost function, it determines the direction to update the parameters to reduce errors and improve the model’s performance.
Hallucination
Hallucination in AI refers to instances where a generative model, such as a language model or image generator, produces outputs that are not grounded in the input data or reality. These outputs can be nonsensical, inaccurate, or completely fabricated, highlighting challenges in ensuring the reliability of AI-generated content.
Hidden layers
Hidden layers are layers of neurons in a neural network that exist between the input and output layers. They process input data through weights and activation functions to detect complex patterns and features in the data.
Human-in-the-loop (HITL)
Human-in-the-loop (HITL) refers to systems where human feedback and interaction is integrated into the training, operation, or refinement of AI models. This approach enhances the accuracy and reliability of AI by incorporating human judgment and expertise, especially in complex or ambiguous tasks.
Hyperparameters
Hyperparameters are configurations used to control the training process of machine learning models. Unlike model parameters, which are derived from the training data during the learning phase, hyperparameters are set before training begins and remain constant throughout the training process. They play a critical role in determining the performance and efficiency of the model, including defining aspects of the model’s architecture.
Image segmentation
Image segmentation is a computer vision technique that involves dividing an image into multiple segments or regions to simplify its analysis. This process allows for the identification and classification of objects within the image, making it useful in applications such as medical imaging, autonomous driving, and object detection.
Knowledge graph
A knowledge graph is a structured representation of information that uses nodes to represent entities and edges to depict relationships between them. It’s used in AI to enhance search, recommendation systems, and data integration by providing a semantic understanding of the data and its interconnections.
Large language model (LLM)
A large language model (LLM) is an AI model, typically based on transformer architecture, that is trained on vast amounts of text data to understand and generate human language. These models can perform a wide range of tasks, such as text generation, translation, and question answering, but they can also produce outputs that include hallucinations.
Machine learning
Machine learning is a subset of artificial intelligence that involves training algorithms to learn patterns from data and make decisions or predictions without being explicitly programmed. This field encompasses various techniques, including supervised learning, unsupervised learning, and reinforcement learning, and can also involve concepts like meta-learning, latency, and cross-validation.
Model
A model is a mathematical representation created by training an algorithm on a dataset to make predictions or decisions. The model’s performance can be evaluated and optimized through techniques like cross-validation, and it can be affected by issues such as model drift and underfitting.
Machine learning operations (MLOps)
Machine learning operations (MLOps) is the practice of managing the end-to-end life cycle of machine learning models, from development and deployment to monitoring and maintenance. It combines principles from DevOps and data engineering to ensure reliable, scalable, and efficient production of machine learning applications.
Named entity recognition (NER)
Named entity recognition (NER) is a natural language processing technique that identifies and classifies named entities in text, such as names of people, organizations, locations, dates, and other specific terms. This technique is widely used in information extraction, search engines, and text analysis to enhance the understanding and organization of textual data.
Natural language generation (NLG)
Natural language generation (NLG) is a subfield of artificial intelligence that focuses on generating coherent and contextually relevant human language text from structured data. NLG systems are used in applications like automated report writing, content creation, and conversational agents to transform data into readable and meaningful narratives.
Natural language processing (NLP)
Natural language processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. It involves enabling computers to interpret and generate human language, encompassing tasks such as text analysis, translation, sentiment analysis, and speech recognition.
Neural network
A neural network is a computational model inspired by the human brain, consisting of interconnected nodes (known as neurons) that process data in layers. Neural networks are fundamental to deep learning and are used for tasks such as image recognition, speech processing, and natural language understanding.
One-shot learning
One-shot learning is a machine learning technique where a model learns to recognize or classify objects from a single example or very few examples. This approach is particularly useful in scenarios where acquiring large amounts of labeled training data is difficult or impractical.
Overfitting
Overfitting occurs when a machine learning model learns the training data too well, capturing noise and details that do not generalize to new data. This results in high accuracy on the training set but poor performance on unseen data, indicating that the model is too complex for the given problem.
Predictive analytics
Predictive analytics involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events. This approach is commonly used in fields like finance, marketing, and healthcare, leveraging data science and time series analysis to forecast trends and outcomes.
Prompt engineering
Prompt engineering is the practice of designing and refining prompts to elicit the desired responses from language models and other AI systems. This technique is crucial for improving the performance and accuracy of AI applications, such as chatbots and automated writing tools.
Python
Python is a high-level, interpreted programming language known for its simplicity and readability, making it popular for developing AI and machine learning applications. It offers extensive libraries and frameworks, such as TensorFlow, PyTorch, and scikit-learn, that support data analysis, visualization, and model building.
Real data
Real data refers to actual, unmodified data collected from real-world sources, as opposed to synthetic or simulated data. It’s crucial for training and validating machine learning models to ensure they perform accurately in practical, real-world scenarios.
Regression
Regression is a statistical method used in machine learning to model and analyze the relationships between variables. It predicts a continuous output based on one or more input features.
Reinforcement learning
Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions and receiving rewards or penalties based on the outcomes. This approach, which can involve human-in-the-loop interactions, is commonly used in applications such as robotics, gaming, and autonomous systems to optimize behavior over time.
Responsible AI
Responsible AI refers to the development and deployment of artificial intelligence systems in a manner that is ethical, transparent, and accountable. It involves incorporating human-in-the-loop mechanisms to ensure fairness, prevent biases, and protect privacy, ultimately aiming to benefit society while minimizing harm.
Self-attention mechanism
The self-attention mechanism is a process in machine learning models, particularly in transformers, that allows the model to weigh the importance of different parts of the input data relative to each other. It helps the model focus on relevant information by assigning attention scores to each input element.
Semi-supervised learning
Semi-supervised learning is a machine learning technique that combines a small amount of labeled data with a large amount of unlabeled data during training. This approach can significantly improve learning accuracy and efficiency, leveraging the abundance of unlabeled data while minimizing the need for extensive labeling efforts.
Supervised learning
Supervised learning is a type of machine learning where models are trained on labeled data, meaning that each training example includes input data and the corresponding correct output. This method is used for tasks such as classification and regression, and it can incorporate techniques like semi-supervised learning and unsupervised learning to enhance performance.
Synthetic data
Synthetic data is artificially generated data that mimics real-world data, often created using generative models like GANs. It’s used to augment training datasets, helping to improve model performance and address privacy concerns by reducing the reliance on real data.
Training data
Training data is the dataset used to train a machine learning model, consisting of input-output pairs that the model learns from. The quality and quantity of training data are critical for the model’s performance. The data often includes labeled examples to guide the learning process, with validation data used to evaluate the model’s accuracy during training.
Transfer learning
Transfer learning is a machine learning technique where a model developed for a particular task is reused as the starting point for a model on a different but related task. This approach leverages the knowledge gained from the initial training, enabling effective learning with less data and computation, and can incorporate methods like few-shot learning and fine-tuning.
Transformer
A transformer is a type of neural network architecture that uses self-attention mechanisms to process input sequences, making it highly effective for tasks involving natural language processing. Transformers enable models like GPT to understand context and relationships in data, allowing for accurate text generation, translation, and other language-related tasks.
Text-to-speech (TTS)
Text-to-speech (TTS) is a technology that converts written text into spoken words using synthetic voice generation. It’s used in applications such as virtual assistants, accessibility tools for the visually impaired, and automated customer service systems to provide audible information based on textual input.
Underfitting
Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data, resulting in poor performance on both the training and testing datasets. This indicates that the model has not “learned” enough from the data and needs to be more complex to improve its accuracy.
Unsupervised learning
Unsupervised learning is a type of machine learning where models are trained on data without labeled outputs. The model identifies patterns and structures within the data to group similar items or discover hidden relationships.
Zero-shot learning
Zero-shot learning is a machine learning technique where a model can make predictions about classes or tasks it has never seen during training. It leverages prior knowledge and relationships between known and unknown tasks to generalize and perform effectively without requiring labeled examples for the new classes.
This article originally appeared on Upwork.com and was syndicated by MediaFeed.org.
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AI Isn’t Synonymous with Efficiency: 4 Ways to Conquer the AI Productivity Paradox
The possible future promised by generative AI is compelling: work can be done faster, knowledge work can be democratized, and the workforce has access to an always-on collaborator.
Business leaders have recognized this potential in generative AI, and they’re embracing it wholeheartedly. The latest research—surveying 2,500 C-suite executives, full-time employees, and freelancers—shows that 85% of leaders are already mandating or encouraging their employees’ use of AI tools. And 96% of executives expect AI to improve overall productivity.
So why, then, are nearly two-thirds of employees struggling more than ever to meet their productivity goals?
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Since OpenAI released ChatGPT to the mainstream in late 2022, there’s been an ongoing public conversation about generative AI and efficiency.
The two are often discussed as if they’re the same thing—as if generative AI equals efficiency. But this isn’t true.
Yes, generative AI can support and enhance processes for better efficiency, but the technology by itself isn’t a magic pill. It can’t create efficiency where there isn’t any to begin with—or replace the strategic guidance of a leader who recognizes the nuances of a particular context.
If you try to insert AI into inefficient processes, you run the risk of making work harder on your team members. In this scenario, AI can wind up exacerbating, not reducing, friction points while employees struggle to meet higher productivity demands. It’s no surprise that workers often feel frustrated or resistant to new technologies that impose higher productivity demands. Without system-wide changes and opportunities for upskilling, these advancements can feel overwhelming and unattainable.
It’s this very productivity paradox that’s led 77% of full-time employees to feel like their workload is heavier after the introduction of AI.
For many, AI isn’t helping; it’s hurting. But that doesn’t have to be your reality.
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Increasingly, leaders are being asked to supply their employees with the tools and resources they need to do great work while also supporting their well-being. This much is apparent—our research shows that 84% of C-suite leaders highlight the importance of employee well-being over productivity at work.
But with 71% of full-time employees reporting a sense of burnout, something’s getting lost in translation.
By looking to work innovators for inspiration, we can better learn how to close the gap between leaders’ goals and employee experiences.
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Work innovators are high-performing companies typically led by action-oriented leaders. They share several attributes, including a willingness to use new technology throughout their organizations.
But work innovators aren’t just introducing a new tool and asking employees to use it. The leaders at these companies are actively adjusting their existing strategy with AI in mind—and training their teams on how to use generative AI tools for peak efficiency.
In fact, work innovators are 3.8 times more likely than their counterparts to have a well-defined generative AI strategy. They’re also 1.9 times more likely to have a generative AI training program in place.
As a result, 47% of work innovator companies had already incorporated generative AI into their daily operations by Q4 2023—making them early to figure out the secrets to successful AI adoption without burnout.
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By adopting the mindset of a work innovator, you can help to improve the rollout of generative AI tools at your own company and ultimately reduce the likelihood of added stress or burnout across your teams.
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The “jagged technological frontier” is a term used to describe the impact of AI on knowledge worker productivity and quality. It refers to the fluctuation between tasks that can be sped up with the use of AI versus other tasks that are slowed down by the introduction of AI.
Understanding this difference in your own operations can help you truly maximize your teams’ efficiency. Rather than requiring your employees to use AI on tasks better suited to a human, you can create clear delineation between:
- Tasks to be fully handled by generative AI
- Tasks to be done by employees with the help of AI
- Tasks to be executed entirely by employees or flexible talent, without AI
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In some instances an AI tool can be helpful, even with minimal training. This can include:
- AI acting as a companion tool during the information gathering and research process
- AI serving as a virtual collaborator, including organizing team members’ input and scanning data for repeating patterns
- AI supporting coaching and training efforts—such as by reviewing a junior developer’s code for errors
In all of these instances, AI isn’t replacing a human task. Workers are still going about their normal processes; they’re just using AI to help them find or sort information—like a more robust version of a web browser or Excel formula.
But when AI is used to fully automate certain tasks, or serve as a new step in an existing process, more preparation, strategy adjustment, and training is required to make the deployment smooth. This could involve:
- Reworking processes to create steps specifically for AI use
- Updating policies about sharing of information with third-party tools
- Gradually introducing generative AI into non-critical parts of workflows before expanding its use
- Implementing mandatory or encouraged training programs to help employees understand how to best use the AI tools at their disposal
- Co-creating new productivity goals with the input of your employees who will be using the tools
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As part of your evaluation process, you may discover that you simply can’t meet increased productivity goals with your existing full-time headcount alone. Rather than relying fully on AI to boost productivity, consider bringing in flexible talent as well.
Once you establish your company’s internal “jagged edge”—which tasks can be outsourced to AI, and which must remain with humans—you can then bring in flexible talent with specific skill sets to fill gaps.
This is another work innovator attribute. Innovative companies use flexible talent models to respond to changing demands and have established processes for smooth knowledge transfer between flexible talent, full-time employees, and generative AI.
You don’t have to choose between hiring more full-time team members or attempting to force certain tasks through generative AI processes. Flexible talent can fill skill and headcount gaps as they happen, giving you the ability to scale up and down as needed.
You can also turn to flexible talent for as-needed consulting opportunities. This is particularly useful when planning how to integrate AI into your operations without overwhelming workers.
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Ultimately, using AI like a true work innovator can mean you have to shift from a mindset of doing more with less to doing more with … more.
This doesn’t mean you have to expand your full-time headcount or invest large sums into multiple AI solutions. Instead, you can retain your existing employees, tap flexible talent to fill strategic skill gaps, get help selecting the best generative AI solution for your needs, and bring in experts to train your team in AI best practices.
By taking the time to lay the groundwork for successful and efficient AI use now, you can begin scaling up your output over time—and outpace your competitors who haven’t yet figured out the secret to true AI productivity.
This article originally appeared on Upwork.com Resource Center (Upwork is a company that helps businesses find talent and people find work) and was syndicated by MediaFeed.org.
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