Tag Archives: difference


Narrow artificial intelligence (AI) refers to AI systems that are designed and trained to perform a specific task, such as playing chess, driving a car, answering customer service queries or detecting spam emails. In contrast, general artificial intelligence (AGI) describes a hypothetical AI system that demonstrates human-level intelligence and mental flexibility across a broad range of cognitive tasks and environments. Such a system does not currently exist.

Narrow AI is also known as weak AI, specific AI or single-task AI. These systems are focused on narrowly defined tasks and they are not designed to be flexible or adaptable. They are programmed to perform predetermined functions and do not have a general understanding of the world or the capability to transfer their knowledge to new problem domains. Examples of narrow AI include algorithms developed for image recognition, machine translation, self-driving vehicles and conversational assistants like Siri or Alexa. These systems excel at their specialized functions but lack the broader general reasoning abilities of humans.

Narrow AI systems are created using techniques of artificial intelligence like machine learning, deep learning or computer vision. They are given vast amounts of example inputs to learn from, known as training data, which helps them perform their designated tasks with increasing accuracy. Their capabilities are limited to what they have been explicitly programmed or trained for. They do not have a general, robust understanding of language, common sense reasoning or contextual pragmatics like humans do. If the input or environment changes in unexpected ways, their performance can deteriorate rapidly since they lack flexibility.

Some key characteristics of narrow AI systems include:

They are focused on a narrow, well-defined task like classification, prediction or optimization.

Their intelligence is limited to the specific problem domain they were created for.

They lack general problem-solving skills and an understanding of abstract concepts.

Reprising the same task in a new context or domain beyond their training scope is challenging.

They have little to no capability of self-modification or learning new skills independently without reprogramming.

Their behavior is limited to what their creators explicitly specified during development.

General artificial intelligence, on the other hand, aims to develop systems that can perform any intellectual task that a human can. A true AGI would have a wide range of mental abilities such as natural language processing, common sense reasoning, strategic planning, situational adaptation and the capability to autonomously acquire new skills through self-learning. Some key hypothetical properties of such a system include:

It would have human-level intelligence across diverse domains rather than being narrow in scope.

Its core algorithms and training methodology would allow continuous open-ended learning from both structured and unstructured data, much like human learning.

It would demonstrate understanding, not just performance, and be capable of knowledge representation, inference and abstract thought.

It could transfer or generalize its skills and problem-solving approaches to entirely new situations, analogous to human creativity and flexibility.

Self-awareness and consciousness may emerge from sufficiently advanced general reasoning capabilities.

Capable of human-level communication through natural language dialogue rather than predefined responses.

Able to plan extended sequences of goals and accomplish complex real-world tasks without being explicitly programmed.

Despite several decades of research, scientists have not achieved anything close to general human-level intelligence so far. The sheer complexity and open-ended nature of human cognition present immense scientific challenges to artificial general intelligence. Most experts believe true strong AGI is still many years away, if achievable at all given our current understanding of intelligence. Research into more general and scalable machine learning algorithms is bringing us incrementally closer.

While narrow AI is already widely commercialized, AGI would require enormous computational resources and exponentially more advanced machine learning techniques that are still in early research stages. Narrow AI systems are limited but very useful for improving specific application domains like entertainment, customer service, transportation etc. General intelligence remains a distant goal though catalysts like advanced neural networks, increasingly large datasets and continued Moore’s Law scaling of computing power provide hope that it may eventually become possible to develop an artificial general intelligence as powerful as the human mind. There are also open questions about the control and safety of super-intelligent machines which present research challenges of their own.

Narrow AI and general AI represent two points on a spectrum of machine intelligence. While narrow AI already delivers substantial economic and quality of life benefits through focused applications, general artificial intelligence aiming to match human mental versatility continues to be an ambitious long term research goal.Future generations of increasingly general and scalable machine learning may potentially bring us closer to strong AGI, but its feasibility and timeline remain uncertain given our incomplete understanding of intelligence itself.


Capstone projects are culminating academic experiences that students undertake at the end of their studies. These projects allow students to demonstrate their knowledge and skills by undertaking an independent research or design project. When conducting research or evaluation for a capstone project, students will typically gather both quantitative and qualitative data.

Quantitative data refers to any data that is in numerical form such as statistics, percentages, counts, rankings, scales, etc. Quantitative data is based on measurable factors that can be analyzed using statistical techniques. Some examples of quantitative data that may be collected for a capstone project include:

Survey results containing closed-ended questions where respondents select from preset answer choices and their selections are counted. The surveys would provide numerical data on frequencies of responses, average scores on rating scales, percentages agreeing or disagreeing with statements, etc.

Results from psychological or skills tests given to participants where their performance or ability levels are measured by number or score.

Financial or accounting data such as sales figures, costs, profits/losses, budget amounts, inventory levels that are expressed numerically.

Counts or frequencies of behavioral events observed through methods like timed sampling or duration recording where the instances of behaviors can be quantified.

Content analysis results where the frequency of certain words, themes or concepts in textual materials are counted to provide numerical data.

Numerical ratings, rankings or scale responses from areas like job performance reviews, usability testing, customer satisfaction levels, or ratings of product qualities that are amenable to statistical analyses.

The advantage of quantitative data for capstone projects is that it lends itself well to statistical analysis methods. Quantitative data allows for comparisons and correlations to be made statistically between variables. It can be easily summarized, aggregated and used to test hypotheses. Large amounts of standardized quantitative data also facilitate generalization of results to wider populations. On its own quantitative data does not reveal the contextual factors, personal perspectives or experiences behind the numbers.

In contrast, qualitative data refers to non-numerical data that is contextual, descriptive and explanatory in nature. Some common sources of qualitative data for capstone projects include:

Responses to open-ended questions in interviews, focus groups, surveys or questionnaires where participants are free to express opinions, experiences and perspectives in their own words.

Field notes and observations recorded through methods like participant observation where behaviors and interactions are described narratively in context rather than through numerical coding.

Case studies, stories, narratives or examples provided by participants to illustrate certain topics or experiences.

Images, videos, documents, or artifacts that require descriptive interpretation and analysis rather than quantitative measurements.

Transcripts from interviews and focus groups where meanings, themes and patterns are identified through examination of word usages, repetitions, metaphors and concepts.

The advantage of qualitative data is that it provides rich descriptive details on topics that are difficult to extract or capture through purely quantitative methods. Qualitative data helps give meaning to the numbers by revealing contextual factors, personal perspectives, experiences and detailed descriptions that lie behind people’s behaviors and responses. It is especially useful for exploring new topics where the important variables are not yet known.

Qualitative data alone does not lend itself to generalization in the same way quantitative data does since a relatively small number of participants are involved. It also requires more time and resources to analyze since data cannot be as easily aggregated, compared or statistically tested. Researcher subjectivity also comes more into play during qualitative analysis and interpretation.

Most capstone projects will incorporate both quantitative and qualitative methods to take advantage of their respective strengths and to gain a more complete perspective on the topic under study. For example, a quantitative survey may be administered to gather statistics followed by interviews to provide context and explanation behind the numbers. Or observational data coded numerically may be augmented with field notes to add descriptive detail. The quantitative and qualitative data are then integrated during analysis and discussion to draw meaningful conclusions.

Incorporating both types of complementary data helps offset the weaknesses inherent when using only one approach and provides methodological triangulation. This mixed methods approach is considered ideal for capstone projects as it presents a more robust and complete understanding of the research problem or program/product evaluation compared to what a single quantitative or qualitative method could achieve alone given the limitations of each. Both quantitative and qualitative data have important and distinct roles to play in capstone research depending on the research questions being addressed.


Analytical thinking and critical thinking are often used interchangeably, but they are different higher-order thinking skills. While related, each style of thinking has its own distinct approach and produces different types of insights and outcomes. Understanding the distinction is important, as applying the wrong type of thinking could lead to flawed or incomplete analyses, ideas, decisions, etc.

Analytical thinking primarily involves taking something apart methodically and systematically to examine its component pieces or parts. The goal is to understand how the parts relate to and contribute to the whole and to one another. An analytical thinker focuses on breaking down the individual elements or structure of something to gain a better understanding of its construction and operation. Analytical thinking is objective, logical, and oriented towards problem-solving. It relies on facts, evidence, and data to draw conclusions.

An analytical thinker may ask questions like:

  • What are the key elements or components that make up this topic/idea/problem?
  • How do the individual parts relate to and interact with each other?
  • What is the internal structure or organization that ties all the pieces together?
  • How does changing one part impact or influence the other parts/the whole?
  • What patterns or relationships exist among the various elements?
  • What models or frameworks can I use to explain how it works?

Analytical thinking is useful for understanding complex topics/issues, diagnosing problems, evaluating alternatives, comparing options, reverse engineering systems, rationally weighing facts, and making objective decisions. It is evidence-based, seeks explanations, and aims to arrive at well-supported conclusions.

On the other hand, critical thinking involves evaluating or analyzing information carefully and logically, especially before making a judgment. Whereas analytical thinking primarily focuses on taking something apart, critical thinking focuses on examination and evaluation. A critical thinker questions assumptions or viewpoints and assesses the strengths and weaknesses of an argument or concept.

A critical thinker may ask questions like:

  • What viewpoints, assumptions, or beliefs underlie this perspective/argument/conclusion?
  • What are the key strengths and limitations of this perspective?
  • How sound is the reasoning and evidence provided? What flaws exist?
  • What alternative viewpoints should also be considered?
  • What implications or consequences does adopting this perspective have?
  • How might cultural, social, or political biases shape this perspective?
  • How would other informed people evaluate this argument or conclusion?

Critical thinking is more interpretive, inquisitive, and reflective. It challenges surface-level conclusions by examining deeper validity, reliability, and soundness issues. The aim is to develop a well-reasoned, independent, and overall objective judgement. While analytical thinking can identify flaws or gaps, critical thinking pushes further to question underlying implications.

Some key differences between analytical and critical thinking include:

Focus – Analytical thinking primarily focuses on taking something apart, while critical thinking focuses on examination and evaluation.

Approach – Analytical thinking is more objective/systematic, while critical thinking is more interpretive/questioning.

Motivation – Analytical thinking aims to understand how something works, while critical thinking aims to assess quality/validity before making a judgment.

Perspective – Analytical thinking examines individual parts/structure, while critical thinking considers multiple perspectives and validity beyond the surface.

Role of assumptions – Analytical thinking accepts the framework/perspectives given, while critical thinking questions underlying assumptions/biases.

Outcome – Analytical thinking arrives at conclusions about how something functions, while critical thinking forms an independent reasoned perspective/judgment.

Relationship to evidence – Analytical thinking relies on facts/data provided, while critical thinking scrutinizes how evidence supports conclusions drawn.

Both analytical and critical thinking are important skills with practical applications to academic study, research, problem-solving, decision-making, and more. Using them together is often ideal, as analytical thinking can expose gaps/issues that then need the deeper examination of critical thinking. Developing proficiency in both can strengthen one’s ability to process complex topics across a wide range of domains. The key distinction is how each approach differs in its focus, motivation, and outcome. Understanding these differences is vital for applying the right type of thinking appropriately and avoiding logical fallacies.

Analytical thinking systematically breaks down a topic into constituent parts to understand structure and function, while critical thinking evaluates perspectives, assumptions, and evidence to form a well-justified viewpoint or judgment. Both skills are essential for dissecting multifaceted topics or problems, though their goals and methods differ in important ways. Mastering both requires ongoing practice, experience applying them across disciplines, and reflecting on how to combine their strengths effectively.


Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are two popular generative models in deep learning that are capable of generating new data instances, such as images, that plausibly could have been drawn from the original data distribution. There are some key differences in how they work and what types of problems they are best suited for.

GANs are based on a game-theoretic framework where there are two competing neural networks – a generator and a discriminator. The generator produces synthetic data instances that are meant to fool the discriminator into thinking they are real (coming from the original training data distribution). The discriminator is trained to detect synthetic data from the generator versus real data. Through this adversarial game, the generator is incentivized to produce synthetic data that is indistinguishable from real data. The goal is for the generator to eventually learn the true data distribution well enough to fool even a discriminator that has also been optimized.

VAEs, on the other hand, are based on a probabilistic framework that leverages variational inference. VAEs consist of an encoder network that learns an underlying latent representation of the data, and a decoder network that learns to reconstruct the original data from this latent representation. To ensure the latent space accurately captures the underlying structure of the data, a regularization term is added based on latent space density estimation. This forces the latent representation to follow a prior conditional Gaussian distribution (typically standard normal). During training, VAEs optimize both the reconstruction loss as well as the KL divergence loss between the posterior and the prior on the latent space.

Some key differences between GANs and VAEs include:

Model architecture: GANs consist of separate generator and discriminator networks that compete against each other in a two-player mini-max game. VAEs consist of an encoder-decoder model trained using variational inference to maximize a variational lower bound.

Training objectives: GAN generators are trained to minimize log(1 – D(G(z))) to fool the discriminator, while discriminators minimize log(D(x)) + log(1 – D(G(z))) to detect real vs. fake. VAEs are trained to maximize the evidence lower bound (ELBO) which consists of reconstruction loss – KL divergence loss.

Latent space: GANs do not explicitly learn a latent space and conditioning must be done by manipulating latent vectors directly. VAEs learn an explicitly conditioned latent space through the encoder that can be sampled from or interpolated in.

Mode dropping: Due to only playing an adversarial game, GANs more easily suffer from mode dropping where certain modes in the data are not captured by the generator. VAEs directly regularize the latent space to mitigate this.

Stability: GAN training is notoriously unstable and difficult, often not converging or convergence to degenerate solutions. VAE training is much more stable via standard backpropagation and regularization.

Evaluation: It is difficult to formally evaluate GANs since their goal is to match the data distribution rather than just minimize a cost function. VAEs can be directly evaluated via reconstruction error and their latent space density.

Applications: GANs tend to produce higher resolution, sharper images but struggle with complex, multimodal data. VAEs work better on more structured data like text where their probabilistic framework is advantageous.

To summarize some key differences:

GANs rely on an adversarial game between generator and discriminator while VAEs employ variational autoencoding.
GANs do not explicitly learn a latent space while VAEs do.
VAE training directly optimizes a regularized objective function while GAN training is notoriously unstable.
GANs can generate higher resolution images but struggle more with multimodal data; VAEs work better on structured data.

Overall, GANs and VAEs both allow modeling generative processes and generating new synthetic data instances, but have different underlying frameworks, objectives, strengths, and weaknesses. The choice between them depends heavily on the characteristics of the data and objectives of the task at hand. GANs often work best for high-resolution image synthesis while VAEs excel at structured data modeling due to their stronger inductive biases. A combination of the two approaches may also be beneficial in some cases.


Traditionally, most healthcare systems in the United States have utilized a volume-based payment model. In this model, medical providers such as physicians and hospitals are paid based on the volume of services they provide, meaning the more tests, procedures, and services delivered, the more revenue they generate. The volume-based payment model incentivizes providers to focus on the quantity of care delivered rather than the quality or outcomes of that care. This is because their compensation is directly tied to how many patients they see and treatments they perform.

There are some flaws in the volume-based payment approach. It does not reward providers for keeping patients healthy or helping them manage chronic conditions. The incentives are to perform more procedures and services, not necessarily to provide the most effective and efficient care. This can lead to overutilization and unnecessary, low-value care that drives up costs. It also makes the healthcare system treatment-focused rather than outcomes-focused. Under a volume-based model, there is no financial incentive for coordination across care settings or investing in preventative care.

In contrast, value-based payment models aim to shift the focus from service volume to value and quality of care. Under these models, providers are paid or rewarded based on patient health outcomes rather than fee-for-service volume. The goal is to tie part of provider compensation to overall performance and quality metrics rather than individual services. Examples of value-based models include bundled payments, episodic payments, pay for performance, and global budgets.

With bundled payments, providers receive a single payment to cover all services needed for a clinical episode of care such as a surgical procedure, from pre-operative consultations through post-acute rehabilitation. This motivates care coordination and efficiency. Episodic payments cover services over a set period of time, again emphasizing coordination across settings. Pay for performance programs reward or penalize providers financially based on achievement of targeted clinical quality and efficiency goals. Global budgets set an overall spending limit for a provider group and allow flexibility in how funds are allocated.

The fundamental difference is that value-based models incentivize providers to allocate resources based on the value and outcomes of care rather than attempting to maximize service volumes. For example, these models reward preventative care, chronic disease management, integrated care teams, and using the most cost-effective treatment when clinically appropriate. They also make providers responsible for total cost of care rather than individual services.

This shift in incentives better aligns provider compensation with goals of lowering costs, improving population health outcomes, care coordination, and quality. Studies comparing cost growth in regions transitioning to alternative payment models versus remaining fee-for-service show potential savings from value-based models. Costs generally rise more slowly under bundled payments compared to traditional fee-for-service. Global budgets and population-based payments also correlate with reduced healthcare spending growth.

Fully transitioning from volume-based fee-for-service is challenging for a variety of reasons. Measuring and defining appropriate quality metrics is complex, and desired outcomes may take years to be evident. Providers face financial risk if they cannot control total spending for a patient cohort. Administrative and data infrastructure is needed to support care coordination and performance tracking across settings. Adoption of value-based models also requires willingness of providers, payers and patients to embrace change from traditional fee-for-service. So while value-based care offers benefits, success depends upon overcoming economical, technological and behavioral hurdles to implementation.

Value-based payment models aim to shift the healthcare system’s orientation from volume-driven fee-for-service to a quality and value-focused system. By structuring compensation around outcomes rather than service volume, these models change the incentives in ways that better support care coordination, prevention, affordability and overall patient wellness. While transitioning from traditional payment approaches poses implementation challenges, the potential for improved health and reduced costs make value-based payment reform a strategic national priority according to many healthcare experts.