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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.


Proof-of-work is the decentralized consensus mechanism that underpins public blockchain networks like Bitcoin and Ethereum. It allows for all participants in the network to agree on the validity of transactions and maintain an immutable record of those transactions without relying on a centralized authority.

The core idea behind proof-of-work is that participants in the network, called miners, must expend computing power to find a solution to a complex cryptographic puzzle. This puzzle requires miners to vary a piece of data called a “nonce” until the cryptographic hash of the block header results in a value lower than the current network difficulty target. Finding this proof-of-work requires a massive amount of computing power and attempts. Only when a miner finds a valid solution can they propose the next block to be added to the blockchain and claim the block reward.

By requiring miners to expend resources (electricity and specialized computer hardware) to participate in consensus, proof-of-work achieves several important properties. First, it prevents Sybil attacks where a single malicious actor could take over the network by creating multiple fake nodes. Obtaining a 51% hashrate on a proof-of-work blockchain requires an enormous amount of specialized mining equipment, making these attacks prohibitively expensive.

Second, it provides a decentralized and random mechanism for selecting which miner gets to propose the next block. Whoever finds the proof-of-work first gets to build the next block and claim rewards. This randomness helps ensure no single entity can control block production. Third, it allows nodes in the network to easily verify the proof-of-work without needing to do the complex calculation themselves. Verifying a block only requires checking the hash is below the target.

The amount of computing power needed to find a proof-of-work and add a new block to the blockchain translates directly to security for the network. As more mining power (known as hashrate) is directed at a blockchain, it becomes exponentially more difficult and expensive to conduct a 51% attack. Both the Bitcoin and Ethereum networks now have more computing power directed at them than most supercomputers, providing immense security through their accumulated proof-of-work.

For a blockchain following the proof-of-work mechanism, the rate at which new blocks can be added is limited by the difficulty adjustment algorithm. This algorithm aims to keep the average block generation time around a target value (e.g. 10 minutes for Bitcoin) by adjusting the difficulty up or down based on the hashrate present on the network. If too much new mining power joins and blocks are being found too quickly, the difficulty will increase to slow block times back to the target rate.

Likewise, if older mining hardware is removed from the network causing block times to slow, the difficulty is decreased to regain the target block time. This dynamic difficulty adjustment helps a proof-of-work blockchain maintain decentralized consensus even as exponential amounts of computing power are directed towards mining over time. It ensures the block generation rate remains stable despite massive changes in overall hashrate.

While proof-of-work secures blockchains through resource expenditure, it is also criticized for its massive energy consumption as the total hashrate dedicated to chains like Bitcoin and Ethereum continues to grow. Estimates suggest the Bitcoin network alone now consumes around 91 terawatt-hours of electricity per year, more than some medium-sized countries. This environmental impact has led researchers and other blockchain communities to explore alternative consensus mechanisms that aim to achieve security without high computational resource usage like proof-of-stake.

Nonetheless, proof-of-work has remained the primary choice for securing public blockchains since it was introduced in the original Bitcoin whitepaper. Over a decade since Bitcoin’s inception, no blockchain at scale has been proven secure without either proof-of-work or a hybrid consensus model. The combinations of randomness, difficulty adjustment, and resource expenditure provide an effective, if energy-intensive, method for distributed ledgers to reach consensus in an open and decentralized manner without a centralized operator. For many, the trade-offs in security and decentralization are worthwhile given present technological limitations.

Proof-of-work leverages economic incentives and massive resource expenditure to randomly select miners to propose and verify new blocks in a public blockchain. By requiring miners to find solutions to complex cryptographic puzzles, it provides crucial security properties for open networks like resistance to Sybil attacks and a random/decentralized consensus mechanism. This comes at the cost of high energy usage, but no superior alternative has been proven at scale yet for public, permissionless blockchains. For its groundbreaking introduction of a working decentralized consensus algorithm, proof-of-work remains the preeminent choice today despite improvements being explored.


Mentors play a vital role in guiding students through the capstone project process from start to finish. A capstone project is meant to be a culminating academic experience that allows students to apply the knowledge and skills they have developed throughout their studies. It is usually a large research or design project that demonstrates a student’s proficiency in their field before they graduate. Due to the complex and extensive nature of capstone projects, students need expert guidance every step of the way to ensure success. This is where mentors come in.

Capstone mentors act as advisors, consultants, coaches and supporters for students as they plan out, research, design and complete their capstone projects. The first major role of a mentor is to help students generate good project ideas that are feasible and will allow them to showcase their expertise. Mentors will ask probing questions to get students thinking about problems or issues within their field of study that could be addressed through original research or design work. They provide input on narrowing broad topic areas down to specific, manageable project scopes that fit within timeline and resource constraints. Once students have selected an idea, mentors work with them to clearly define deliverables, outcomes and evaluation criteria for a successful project.

With the project aim established, mentors then guide students through conducting a comprehensive literature review. They ensure students are exploring all relevant prior studies, theories and approaches within the field related to their project topic. Mentors point students towards appropriate research databases, journals and other scholarly sources. They also teach students how to analyze and synthesize the literature to identify gaps, opportunities and a focused research question or design problem statement. Students learn from their mentors how to structure a literature review chapter for inclusion in their final written report.

When it comes to the methodology or project plan chapter, mentors play a pivotal role in helping students determine the most rigorous and appropriate research design, data collection and analysis techniques for their projects given the questions being investigated or problems being addressed. They scrutinize proposed methodologies to catch any flaws or limitations in reasoning early on and push students to consider additional options that may provide richer insights. Mentors also connect students with necessary experts, committees, tools or facilities required for special data collection and ensure all ethical guidelines are followed.

During the active project implementation phase, mentors check in regularly with students through one-on-one meetings. They troubleshoot any issues encountered, offer fresh perspectives when problems arise and keep projects moving forward according to schedule. Mentors lend an extra set of experienced hands to help process complex quantitative data, read drafts of qualitative interview transcripts or review prototype designs. They teach students how to manage their time efficiently on long duration projects. Mentors connect students to relevant research groups and conferences to present early findings and get constructive feedback to strengthen their work.

For the results and discussion chapters of capstone reports, mentors guide students through analyzing their compiled data with appropriate statistical or qualitative methods based on the project design. They coach students not just in terms reporting objective results but also crafting insightful discussions that interpret what the results mean within the broader literature and theoretical frameworks. Mentors emphasize tying findings back to the original problem statement or research question and drawing meaningful conclusions. They push students to consider limitations and implications of their work along with recommendations for future research and applications.

Mentors review multiple drafts of students’ complete written reports and provide detailed feedback for improvements. They ensure all required elements including abstracts, TOCs and formatting guidelines are properly addressed based on the standards of their program or discipline. For projects with major design artifacts or prototypes, mentors will review final specs, demo the deliverables and offer mentees advice before public presentations or defense. Through it all, mentors encourage and motivate students to help them reach high quality final outcomes from which they can learn and be proud.

Capstone mentors play an integral role across all phases of the capstone project process from initial topic selection through completion. They provide expert guidance, oversight and quality control to help challenged students apply both their acquired disciplinary skills and new independent research skills. Mentors scaffold the learning experience, catching mistakes early and pushing for excellence. Their developmental coaching style equips students not just to successfully finish their current projects but leaves them prepared to be independent problem-solvers in future academic or professional contexts. The role of the capstone mentor is vital for facilitating impactful culminating experiences that truly demonstrate students’ readiness for the next steps after undergraduate study.


Developing a social media platform from scratch is an extremely ambitious capstone project that presents numerous technical challenges. Some of the key technical challenges involved include:

Building scalable infrastructure: A social media platform needs to be architected in a highly scalable way so that it can support thousands or millions of users without performance degradation as the user base grows over time. This requires building the backend infrastructure on cloud platforms using microservices architecture, distributed databases, caching, load balancing, auto-scaling etc. Ensuring the database, APIs and other components can scale horizontally as traffic increases is a major undertaking.

Implementing a responsive frontend: The frontend for a social media site needs to be highly responsive and optimized for different devices/screen sizes. This requires developing responsive designs using frameworks like React or Angular along with techniques like progressive enhancement/progressive rendering, lazy loading, image optimization etc. Ensuring good performance across a wide range of devices and browsers adds complexity.

Securing user data: A social network will store a lot of sensitive user data like profiles, posts, messages etc. This data needs to be stored and transmitted securely. This requires implementing best practices for security like encryption of sensitive data, secure access mechanisms, input validation, defending against injection attacks, DDoS mitigation techniques etc. Data privacy and regulatory compliance for storing user data also adds overhead.

Developing core features: Building the basic building blocks of a social network like user profiles, posts, comments, messages, notifications, search, friends/followers functionality involves a lot of development work. This requires designing and developing complex data structures and algorithms to efficiently store and retrieve social graphs and activity streams. Features like decentralized identity, digital wallet/payments also require specialized expertise.

Building engagement tools: Social media platforms often have advanced engagement and recommendation systems to keep users engaged. This includes Activity/News feeds that select relevant personalized content, search ranking, hashtag/topic suggestions, friend/group suggestions, notifications etc. Developing predictive models and running A/B tests for features impacts complexity significantly.

Integrating third party services: Reliance on external third party services is necessary for key functions like user authentication/authorization, payments, messaging, media storage etc. Integrating with services like Google/FB login, PayPal, AWS S3 increases dependencies and vendor lock-in risks. Managing these third party services comes with its own management overheads.

Testing at scale: Exhaustive testing is critical but difficult for social platforms due to the complex interactions and network effects involved. Testing core functions, regression testing after changes, A/B testing, stress/load testing, accessibility testing needs specialized tools and expertise to ensure high reliability. Significant effort is needed to test at scale across various configuration before product launch.

Community management: Building a user-base from scratch andseeding initial engagement/network effects is a major challenge. This requires strategies around viral growth hacks, promotions, customer support bandwidth etc. Moderating a live community with user generated content also requires content policy infrastructure and human oversight.

Monetization challenges: Social platforms require monetization strategies to be economically sustainable. This involves designing revenue models around areas like ads/sponsorships, freemium features, paid tiers, in-app purchases etc. Integrating these models while ensuring they don’t degrade the user experience takes significant effort. Analytics are also needed to optimize monetization.

As can be seen from above, developing a social media platform involves overcoming immense technical challenges across infrastructure, development, data security, community growth, testing, and monetization. Given the complexity, undertaking such an ambitious project would require a dedicated multidisciplinary team working over multiple iterations. Delivering core minimum viable functionality within the constraints of a typical capstone project timeline would still be extremely challenging. Shortcuts would have to be taken that impact the stability, scalability and long term sustainability of such a platform. Therefore, developing a fully-fledged social network could be an over-ambitious goal for a single capstone project.


Documentation is essential for ensuring the capstone project work is well recorded and can be understood by others. It provides a record of the process that was undertaken to complete the project from concept to execution. Thorough documentation demonstrates the research, planning, methodology, outputs and results of the project work. It allows others to understand the thought process and technical details of how and why certain decisions were made. Documentation serves several important purposes for a capstone project:

It acts as an historical record of the full scope of work so future readers have context on the project background, goals, development and outcomes. This is important for project replication or building upon the work in the future.

Documentation helps demonstrate the complex problem solving and analytical thinking undertaken during the project. It conveys the process of investigating challenges, weighing design options, testing solutions and improving based on results. This showcases the higher-level skills developed through the capstone experience.

Maintaining documentation throughout the project allows for periodic review of progress and course corrections if needed. It supports ongoing planning, monitoring and evaluation of whether project aims are being successfully achieved.

The documentation provides raw materials, notes, data collection instruments and interim or failed results for inclusion in a final capstone report or thesis. This evidences the breadth and depth of effort.

Thorough documentation facilitates supervisor/advisor oversight and guidance. It allows them to understand project progress, provide timely feedback and ensure the work remains on track to meet requirements.

Documentation acts as a reference guide for how to replicate processes, techniques or solutions developed through the project. This reference aspect supports knowledge sharing and application of lessons learned to future initiatives.

Documentation materials may be included as appendices or supplemental files in the final capstone submission. This enrichment enhances understanding of the full scope and process behind the reported results.

Documentation sets the stage for potential publication, presentation or further development of project insights and outcomes. It preserves intellectual property and attributions should any aspects warrant continued research, commercialization or application post-capstone.

Presentation of the capstone work is also critical for effectively communicating the project experience and outcomes to others. Presentation allows the student to tell the full story of their capstone journey in a compelling format and have their work evaluated based on how clearly and convincingly they are able to convey it. The presentation provides an opportunity to:

Synthesize and highlight the most important aspects of documentation in a summative manner using visual and oral presentation tools. This distills down copious notes and materials into a clear narrative.

Demonstrate public speaking, presentation development and delivery skills learned through completion of the extensive capstone project. Concisely sharing findings lends itself well to showcasing communication talents.

Stimulate interest and engage audience members by painting a picture of the motivation, aims and significance of the work in a memorable format. Storytelling abilities are emphasized.

Provide a question and answer period where deeper understanding, remaining questions and next steps can be explored interactively. This facilitates two-way knowledge exchange.

Receive valuable feedback on the merits and limitations of approaches, outcomes, analyses as well as on the presentation style itself. Suggestions for improvement are garnered.

Express passion, confidence and mastery over the topic after investing major effort into planning and implementing the capstone study. Presentation validates competence.

Formally report conclusions, implications, lessons learned and impact made through completion of the project. Persuasiveness of arguments is tested.

Allow work to be critiqued by the broader community of peers, faculty and industry partners. Increased exposure for potential applications results.

Thorough documentation accompanied by an effective presentation is vital for demonstrating full achievement and sharing the fruits of capstone projects. Together, they support evaluating comprehensive understanding, application of knowledge and communication skills developed through this culminating undergraduate experience. Proper attention to documentation and presentation ensures maximum learning and future impact from the capstone work.