Most drones don’t have radars. They merely have positions that they change based on fully autonomous decisions or provided by a controller. In the former case, the waypoints and trajectory determine the flight path, and each drone independently tries to minimize the errors in deviations from the flight path while aligning its path using the least squares method. The selection of waypoints and the velocity and ETA at each waypoint is determined for each unit in a UAV swarm with ability to make up delays or adjust ETAs using conditional probability between past and next waypoint while choosing a path of least resistance or conflict between the two. Usually, a formation, say matrix, already spreads out the units, and its center of mass is used to calculate the progress on the flight path for the formation. This article discusses a novel approach to minimize conflicts and adhere to the path of least resistance.

For example, to transform between an “Abreast” and a “Diamond” formation, any technique must demonstrate efficiency in minimizing transformation distance and maintaining formation coherence. Similarly, to transform matrix formation to flying linearly under a bridge between its piers, any technique must demonstrate a consensus based on pre-determined order.

The approach included here defines a drone formation state with six parameters: time, 3D positions, yaw angle (heading), and velocity. For a formation to be considered coherent, all drones must share the same heading and speed while maintaining relative positions—essential for realistic aerial maneuvers.

The transformation itself consists of two steps: location assignment and path programming. First, to determine which drone should move to which position in the new formation, the Hungarian algorithm, a centralized optimization method is used or in its absence the information about the greatest common denominator for volume between two waypoints determines the number of multiple simultaneous paths to choose and the matrix model is used to assign the positions for the drones to the nearest path. If there is only one path and no centralized controller, the units use the Paxos algorithm for coming to a consensus on the linear order. This first step evaluates the cost of moving each drone to each new position by considering spatial displacement, heading change, and velocity difference. This ensures that the assignment minimizes overall disruption and maneuvering effort.

Second, each drone calculates its own flight path to the newly assigned position using a Dubins path model, which generates the shortest possible route under a minimum turning radius constraint—a requirement for fixed-wing drones that can’t make sharp turns or hover. Positions alone do not guarantee compliance, and the velocity adjustments for each unit must also be layered over the transition. The adjustment of velocities follows a Bayesian conditional probability along the associated path for the unit. This involves computing acceleration and deceleration phases to fine-tune the duration and dynamics of the transition with error corrections against deviations.

Overall, this provides a cohesive framework for in-flight drone formation reconfiguration that balances centralized planning with distributed execution. By coding the physical constraints and states for each unit and classifying the adherence, outliers can be handled by rotating them with other units for a smooth overall progression for the formation and overcoming environmental factors such as turbulence with error corrections.

Lastly, a simple Hungarian algorithm application demonstration with sample code to determine the position allocation in formation transformation.

#! /usr/bin/python

# pip install hungarian-algorithm

from hungarian_algorithm import algorithm

import numpy as np

# Source: drones in a 3×3 grid on Z=0 plane

source_positions = [

    (x, y, 0)

    for y in range(3)

    for x in range(3)

]

# Target: drones in a single horizontal line (linear flight path), spaced 10 units apart

target_positions = [

    (i * 10, 0, 0) for i in range(9)

]

# Compute cost matrix (Euclidean distance)

cost_matrix = [

    [

        np.linalg.norm(np.array(src) - np.array(dst))

        for dst in target_positions

    ]

    for src in source_positions

]

# Run Hungarian Algorithm to get minimum-cost assignment

assignment = algorithm.find_matching(cost_matrix, matching_type='min')

# Report matched pairs

for src_idx, dst_idx in enumerate(assignment):

    print(f"Drone {src_idx} → Target Position {dst_idx}: {target_positions[dst_idx]}")

 This is a summary of the book titled “Applying AI in Learning and Development: From Platforms to Performance” written by Josh Cavalier and published by ATD (Association for Talent Development) in 2025. This book examines how learning and development (L\&D) professionals can use artificial intelligence thoughtfully to improve both learning efficiency and organizational performance. Rather than presenting AI as a replacement for human expertise, the book positions it as a partner that can handle routine, data-intensive tasks while allowing L\&D professionals to focus on strategy, analysis, and design.

Cavalier begins by showing how AI can streamline common instructional design activities. Tasks such as transcribing interviews, summarizing discussions, or generating draft materials—once time-consuming—can be completed quickly with AI support. As these efficiencies increase, the role of the L\&D professional evolves. The book introduces the idea of the human–machine performance analyst (HMPA), a role in which practitioners use judgment, contextual knowledge, and empathy to interpret data and shape learning interventions, while AI supports content creation and analysis. An example illustrates this shift: when compliance incidents continued despite high course completion rates, an L\&D professional used AI-generated data as a starting point but relied on interviews and observation to identify the real issue—irrelevant training. Redesigning the program into role-specific scenarios led to a measurable reduction in incidents.

Throughout the book, he emphasizes that the core skills of L\&D—understanding how people learn, connecting learning to performance, and aligning learning with business outcomes—remain unchanged. What has changed is the set of tools available and the scope of influence L\&D can have across an organization. He encourages teams to begin experimenting with AI in small, low-risk ways, such as using meeting assistants to capture action items or deploying chatbots to answer frequently asked learner questions. Progress should be tracked, lessons documented, and experimentation treated as part of normal professional growth rather than a one-time initiative.

A significant portion of the book focuses on assessing an organization’s current relationship with AI. He outlines several common patterns, ranging from informal individual experimentation to full organizational integration. In some organizations, employees use external AI tools without guidance, increasing the risk of data exposure. Others hesitate to act at all, stalled by concerns about privacy, bias, or regulation. Still others implement AI unevenly, creating silos where some departments benefit while others are left behind. The most mature organizations, by contrast, provide approved tools, clear policies, and role-specific training that allow AI to be used consistently and responsibly. Understanding where an organization falls along this spectrum helps L\&D leaders determine realistic next steps.

From there, the book argues that successful AI adoption depends less on choosing a particular tool and more on establishing a strong foundation. AI initiatives should be explicitly tied to business goals such as faster onboarding, improved compliance, or better customer service, with clear explanations of how time or costs will be saved. Small pilots and case studies can demonstrate value and reduce resistance, especially when results are communicated through concrete comparisons rather than abstract claims.

He places strong emphasis on governance. While many L\&D professionals already experiment with AI, far fewer feel confident about using it ethically. An effective AI policy, he argues, must address data privacy, security, regulatory compliance, and bias. Policies should specify which tools are approved, what information can be shared with them, and where human review is required. The book uses the well-known example of Amazon’s abandoned résumé-screening system to illustrate how biased training data can produce discriminatory outcomes. To mitigate these risks, he recommends close collaboration with legal, HR, and cybersecurity teams, as well as processes that allow learners and employees to flag AI-generated content for review.

When it comes to technology selection, the book encourages L\&D leaders to advocate for platforms that support both learning and broader business needs. Many organizations are moving away from standalone learning management systems toward integrated human capital management platforms that combine learning, skills tracking, performance management, and workforce planning. He suggests defining what the organization wants AI to accomplish over the next six to twelve months and evaluating vendors against practical criteria such as transparency, system integration, usability, analytics, scalability, support, security, and return on investment. The goal is not to adopt the most advanced system available, but to choose the one that fits the organization’s context and constraints.

The book also provides detailed guidance on working effectively with generative AI. Cavalier stresses that output quality depends heavily on prompt quality. Clear instructions, explicit constraints, and well-defined criteria produce more useful results than vague requests. He recommends treating prompts as reusable assets by developing templates and maintaining a shared prompt library that documents use cases, tested models, and variations. Chaining prompts within a single session—moving from objectives to outlines to scripts, for example, can also improve coherence. Despite these efficiencies, the book repeatedly underscores the importance of human oversight to ensure accuracy, relevance, and alignment with learning goals.

In its final section, the book explores the use of AI agents to personalize learning at scale. Unlike traditional automated systems, these agents can reason, adapt, and make recommendations based on learner data, such as skill gaps, goals, and performance trends. Examples show how personalized recommendations can increase engagement and motivation. However, he is careful to frame AI agents as collaborators rather than autonomous decision-makers. He advocates for models in which AI proposes learning paths or resources, while human coaches or managers remain involved in reflection and decision-making. Implementing these systems requires careful attention to data quality, accessibility, integration with existing platforms, and iterative testing with pilot groups.

Overall, Applying AI in Learning and Development presents AI not as a disruptive force to be feared or a shortcut to be exploited, but as a tool that amplifies the strategic role of L&D. By combining experimentation with governance, efficiency with human judgment, and technology with organizational context, he argues that L&D professionals can use AI to deliver learning that is both more personalized and more closely tied to real performance outcomes.

 3756. Concatenate Non-Zero Digits and Multiply by Sum II

You are given a string s of length m consisting of digits. You are also given a 2D integer array queries, where queries[i] = [li, ri].

For each queries[i], extract the substring s[li..ri]. Then, perform the following:

Form a new integer x by concatenating all the non-zero digits from the substring in their original order. If there are no non-zero digits, x = 0.

Let sum be the sum of digits in x. The answer is x * sum.

Return an array of integers answer where answer[i] is the answer to the ith query.

Since the answers may be very large, return them modulo 109 + 7.

Example 1:

Input: s = "10203004", queries = [[0,7],[1,3],[4,6]]

Output: [12340, 4, 9]

Explanation:

s[0..7] = "10203004"

x = 1234

sum = 1 + 2 + 3 + 4 = 10

Therefore, answer is 1234 * 10 = 12340.

s[1..3] = "020"

x = 2

sum = 2

Therefore, the answer is 2 * 2 = 4.

s[4..6] = "300"

x = 3

sum = 3

Therefore, the answer is 3 * 3 = 9.

Example 2:

Input: s = "1000", queries = [[0,3],[1,1]]

Output: [1, 0]

Explanation:

s[0..3] = "1000"

x = 1

sum = 1

Therefore, the answer is 1 * 1 = 1.

s[1..1] = "0"

x = 0

sum = 0

Therefore, the answer is 0 * 0 = 0.

Example 3:

Input: s = "9876543210", queries = [[0,9]]

Output: [444444137]

Explanation:

s[0..9] = "9876543210"

x = 987654321

sum = 9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1 = 45

Therefore, the answer is 987654321 * 45 = 44444444445.

We return 44444444445 modulo (109 + 7) = 444444137.

Constraints:

1 <= m == s.length <= 105

s consists of digits only.

1 <= queries.length <= 105

queries[i] = [li, ri]

0 <= li <= ri < m

 Solution:

class Solution {

    public int[] sumAndMultiply(String s, int[][] queries) {

        int[] answers = new int[queries.length];

        for (int i = 0; i < queries.length; i++){

            long x = 0;

            long sum = 0;

            String sub = s.substring(queries[i][0], queries[i][1]+1);

            for (int j = 0; j < sub.length(); j++) {

                int numericValue = Character.getNumericValue(sub.charAt(j));

                if (numericValue != 0) {

                    x = x * 10 + numericValue;

                    sum += numericValue;

                }

            }

            answers[i] = (int) ((x * sum) % (Math.pow(10, 9) + 7));

        }

        return answers;

    }

}

 This is a summary of a book titled “The Datapreneurs: The Promise of AI and the Creators Building Our Future” written by Steve Hamm and Bob Muglia and published by Peakpoint Press, 2023. This book examines how artificial intelligence and data-driven systems are reshaping the economy, technology, and society. The authors argue that the world is entering a period in which intelligence, labor, and energy—the three foundational forces of the modern economy—are all becoming cheaper due to technological advances. Artificial intelligence, particularly the development of artificial general intelligence and the possibility of artificial superintelligence, has the potential to add intelligence to nearly every device and application. At the same time, progress in renewable and advanced energy technologies may reduce the cost of electricity, while robotics could significantly lower the cost of certain kinds of labor. Together, these shifts point toward a profound economic transformation.

The authors suggest that within the next decade, many of the AI assistants people interact with daily could surpass the level of median human intelligence. As these systems evolve through successive generations, they may become capable of artificial superintelligence, potentially exceeding the combined intellectual capacity of humanity. This development could trigger what has often been described as a technological singularity, a moment when technological progress accelerates beyond human prediction or control. Such a shift could compress centuries of scientific and economic advancement into a much shorter time span, creating opportunities to address persistent global challenges such as climate change, disease, and poverty. However, the authors emphasize that these outcomes are not guaranteed and depend heavily on how humans choose to guide and govern intelligent machines.

The authors delve into need for ethics and values to shape the relationship between humans and machines. They contrast optimistic visions of a future characterized by abundance and ease with darker, more dystopian possibilities in which powerful machines generate fear or inequality. To avoid harmful outcomes, they argue for the creation of a new social contract that defines how intelligent systems should behave once they exceed human capabilities. Because advanced machines will increasingly make decisions and take actions independently, the values embedded in their design will play a decisive role in shaping their impact. Establishing ethical frameworks is therefore not an abstract concern but a practical necessity for long-term human and machine collaboration.

The book places current developments in artificial intelligence within a longer historical context by tracing the evolution of data management technologies. Relational databases are presented as a foundational breakthrough that made modern data-driven computing possible. Earlier systems relied on rigid hierarchical or network-based structures that were difficult to update and scale. The relational model, developed by IBM researcher Ted Codd in 1970, introduced a more flexible way to organize data, allowing relationships to be defined mathematically rather than hard-coded into applications. The introduction of SQL and the commercialization of relational databases by companies such as IBM, Oracle, and Sybase helped make data more accessible and adaptable for organizations of all sizes.

Microsoft’s role in expanding access to data management is highlighted as a key moment in the democratization of computing. The company’s emphasis on making information readily available, combined with the release of more affordable and user-friendly database systems such as SQL Server 7.0, lowered barriers for smaller businesses that previously lacked access to enterprise-level data tools. By reducing costs and simplifying maintenance, Microsoft helped bring advanced data processing capabilities beyond large corporations and into the broader economy.

As data volumes grew, the book explains, new infrastructure became necessary to support machine learning and AI systems. Cloud-based data platforms and pipelines now allow organizations to store, process, and move massive amounts of structured and unstructured data. These pipelines function as connective tissue, transferring data into centralized repositories where it can be used to train AI systems. In this framework, future AI assistants will increasingly learn from data warehouses and data lakes, drawing insights from continuous streams of information rather than static datasets.

The authors also describe the emergence of data applications, which differ from traditional software by responding directly to changes in data rather than user commands. Powered by relational knowledge graphs and predictive models, these systems can automate routine decisions and actions. As a result, many administrative tasks may be handled by machines, allowing people to focus on analysis, strategy, and creative problem-solving. This shift extends to autonomous systems such as drones and self-driving vehicles, which require databases capable of synchronizing data rapidly across networks to ensure safety and coordination.

The book further explores the importance of programming languages in the evolving data ecosystem, particularly the rise of Julia. Designed to address inefficiencies in data science workflows, Julia enables high-performance computing without requiring developers to rewrite code in lower-level languages. Its support for automatic differentiation makes it well suited for building predictive models and neural networks, and it is already being used in fields ranging from finance to climate science.

Finally, the authors turn to foundation models, large-scale AI systems trained on vast datasets that exhibit emergent capabilities. These models can be adapted for a wide range of tasks, from writing text to generating images and assisting with software development. Powered by neural networks, such systems can sense, learn, reason, plan, adapt, and act with increasing autonomy. As these capabilities advance, the authors argue that computer scientists and society as a whole must prepare for a future in which machines generate long-term plans and predictions. The book concludes that while superintelligent systems hold enormous promise, their impact will ultimately depend on the values and responsibilities humans choose to embed within them.

 While operational and analytical data gets rigorous treatment in terms of the pillars of good architecture such as purview, privacy, security, governance, encryption at rest and in transit, aging, tiering and such others, DevOps tasks comprising Extract-Transform-Load, backup/restore and such others, is often brushed aside but never eliminated for the convenience they provide. This is inclusive of the vast vector stores that have now become central to building contextual copilots in many scenarios.

One of the tools to empower access of data for purposes other than transactional or analytics is the ability to connect to it with a client native to the store where the data resides. Even if the store is in the cloud, data plane access is usually independent of the control plane command-line interfaces. This calls for a creating a custom image that can be used on any compute to spin up a container with ability to access the vectors. For example, this Dockerfile installs clients:

FROM python:3.13-latest-dev

USER root

RUN apt-get update && \

    apt-get install -y ksh \

    ldap-utils \

    mysql-client \

    vim \

    wget \

    curl \

    libdbd-mysql-perl \

    libcurl4-openssl-dev \

    rsync \

    libev4 \

    tzdata \

    jq \

    pigz \

    python3-minimal \

    python3-pip && \

    apt-get clean && \

    rm -rf /var/lib/apt/lists/* && \

    pip3 install s3cmd

RUN apk add --no-cache mariadb mariadb-client

RUN pip install azure-storage-blob requests

RUN pip install requests

WORKDIR /app

COPY custom_installs.py .

RUN mysqldump --version

RUN mysql --version

ENTRYPOINT ["python", "custom_installs.py"]

 This is a summary of the book titled “Technology for Good: How Nonprofit Leaders Are Using Software and Data to Solve Our Most Pressing Social Problems” written by Jim Fruchterman and published by MIT Press, 2025. This book piques my interest because bad ideas need to be abandoned fast and both startups and non-profits struggle with that until it becomes critical. In this book, the author explores why high-growth, profit-driven start-ups can and nonprofit technology ventures cannot. While the popular imagination tends to focus on for-profit start-ups capable of viral success and massive wealth creation, Fruchterman argues that nonprofit tech start-ups play an equally important role in shaping the future, particularly when it comes to addressing entrenched social problems. Drawing on his experience as a social entrepreneur, he offers a practical guide to building social enterprises, noting that while nonprofit and for-profit start-ups face similar challenges in developing ideas and raising capital, nonprofits benefit from a crucial advantage. Because they are not beholden to investors seeking financial returns, nonprofit founders have greater freedom to prioritize impact over profit.

Nonprofit organizations are chronically behind the technology curve. Tight budgets and donor expectations often leave charities and public agencies relying on outdated hardware and software, sometimes lagging a decade or more behind current standards. Although technology is essential to modern organizational effectiveness, donors frequently view technology spending as overhead rather than as a core part of the mission. Fruchterman challenges this mindset and emphasizes that the most effective way for nonprofits to modernize is often by adapting widely used, standard platforms rather than attempting to build custom solutions from scratch. Tools such as Microsoft Office or Slack can meet many needs, and large technology companies frequently offer discounted pricing to nonprofits, often coordinated through organizations like TechSoup Global. While custom software development is sometimes necessary, it is usually more cost-effective to purchase existing solutions, provided the organization has enough technical expertise to manage vendor relationships and protect its interests. In rare cases, nonprofits even form specifically to create technology that the commercial market has failed to address.

Fruchterman is particularly critical of the nonprofit sector’s tendency to incubate ill-fated technological innovations. Unlike the for-profit world, where start-ups are encouraged to test ideas quickly, gather feedback, and abandon bad concepts early, nonprofit leaders often cling to flawed ideas for too long. One common mistake is the assumption that every organization needs a mobile app simply because apps are ubiquitous in everyday life. In reality, most users do not want more apps, and many nonprofit apps fail to gain traction. The author also cautions against rushing into experimental or heavily hyped technologies. Blockchain, for example, attracted significant attention after the success of Bitcoin, leading many donors and nonprofits to assume it could be easily repurposed for social good. In practice, blockchain initiatives have often failed to deliver meaningful benefits, as illustrated by costly implementations that outweighed their promised savings. Fruchterman urges social leaders to remain skeptical and clear-eyed, especially when technologies are promoted by those more focused on ideology than sound technical design.

Despite these pitfalls, the book makes a strong case that thoughtfully deployed technology can dramatically increase the social sector’s impact. While for-profit companies often aim to eliminate human interaction through automation, nonprofits tend to emphasize person-to-person relationships. Fruchterman argues that technology should not replace human connection in the social sector, but rather support it, particularly by improving efficiency for frontline workers. When those closest to the people being served can work more effectively, the organization’s overall impact is amplified. He also highlights the potential of delivering well-designed tools directly to communities themselves.

One illustrative example is Medic, a social organization that builds tools specifically for community health workers. By replacing paper forms with digital data and linking frontline workers to local health systems, Medic created an app that succeeded precisely because it was narrowly targeted and deeply practical. Although most nonprofit apps add little value, Medic’s tool stands out because it was designed for a clearly defined audience and addressed real operational needs. The result was improved outcomes in areas such as maternal health, disease treatment, and vaccination tracking.

Fruchterman also challenges conventional nonprofit strategic planning. He argues that long-term strategic plans are often too rigid to survive in a rapidly changing world, a lesson underscored by the COVID-19 pandemic, which rendered many carefully crafted plans irrelevant almost overnight. Instead of producing static documents, nonprofits should adopt a more agile approach to strategy that remains grounded in mission while allowing for rapid adaptation. This means focusing on the organization’s core objectives—the “what”—rather than locking into specific tactics—the “how.” By collecting real-time data and learning continuously from results, nonprofits can test assumptions, adjust programs, and respond more effectively to changing conditions.

The book devotes significant attention to artificial intelligence, emphasizing both its promise and its limitations. Fruchterman stresses that AI systems are only as good as the data used to train them, and that bias is an unavoidable risk when datasets are incomplete or unrepresentative. Because many AI tools are developed primarily in English and rely on mainstream data sources, they often overlook the poor and underserved populations that nonprofits aim to support. The author illustrates this problem with examples of biased facial recognition systems that perform poorly on women and people of color due to skewed training data. Such cases underscore the importance of diverse development teams and careful scrutiny when deploying AI in social contexts.

Another key distinction Fruchterman draws is between the goals of nonprofit and for-profit start-ups. While commercial tech ventures are often driven by the promise of wealth, nonprofit start-ups exist to serve people who cannot pay for services. As a result, financial success is defined not by profits but by impact and sustainability. Although the motivations differ, the basic phases of launching a start-up are similar, beginning with exploration and user research, followed by development, growth, and eventual maturity. Throughout these stages, nonprofit founders must be disciplined about testing ideas, releasing imperfect products, and learning from feedback.

Funding and talent emerge as persistent challenges for nonprofit tech start-ups. Fruchterman estimates that early-stage funding typically ranges from modest six-figure sums to around a million dollars for more ambitious projects, with founders often contributing unpaid labor in the beginning. Philanthropic foundations, fellowship programs, accelerators, government agencies, and corporate social good initiatives all play important roles in supporting these ventures. Unlike for-profit start-ups, nonprofits aim simply to break even while maximizing the number of people they help. Although nonprofits cannot compete with the salaries offered by commercial tech firms, they can attract professionals motivated by purpose rather than profit, particularly when expectations around compensation are addressed transparently from the outset.

Fruchterman argues that social entrepreneurs should prioritize empowering communities and individuals rather than imposing top-down solutions. Access to healthcare, education, capital, and inclusion can transform lives, and technology can be a powerful enabler when used responsibly. He encourages nonprofit leaders to embrace data collection and cloud-based tools while remaining transparent about how data is used and firmly committed to protecting it from exploitation. The book closes with a call to use AI and other emerging technologies for good, capturing efficiency gains without surrendering human judgment or ethical responsibility. Fruchterman has a long career in social entrepreneurship and open-source development that gives authenticity to his message that when technology is guided by mission, humility and respect for the people it serves, it can become a powerful force for positive social change.

 While operational and analytical data gets rigorous treatment in terms of the pillars of good architecture such as purview, privacy, security, governance, encryption at rest and in transit, aging, tiering and such others, DevOps tasks comprising Extract-Transform-Load, backup/restore and such others, is often brushed aside but never eliminated for the convenience they provide. This is inclusive of the vast vector stores that have now become central to building contextual copilots in many scenarios.

One of the tools to empower access of data for purposes other than transactional or analytics is the ability to connect to it with a client native to the store where the data resides. Even if the store is in the cloud, data plane access is usually independent of the control plane command-line interfaces. This calls for a creating a custom image that can be used on any compute to spin up a container with ability to access the vectors. For example, this Dockerfile installs clients:

FROM python:3.13-latest-dev

USER root

RUN apt-get update && \

    apt-get install -y ksh \

    ldap-utils \

    mysql-client \

    vim \

    wget \

    curl \

    libdbd-mysql-perl \

    libcurl4-openssl-dev \

    rsync \

    libev4 \

    tzdata \

    jq \

    pigz \

    python3-minimal \

    python3-pip && \

    apt-get clean && \

    rm -rf /var/lib/apt/lists/* && \

    pip3 install s3cmd

RUN apk add --no-cache mariadb mariadb-client

RUN pip install azure-storage-blob requests

RUN pip install requests

WORKDIR /app

COPY custom_installs.py .

RUN mysqldump --version

RUN mysql --version

ENTRYPOINT ["python", "custom_installs.py"]

 

This is a summary of the book titled “How the Future Works: Leading Flexible Teams To Do The Best Work of Their Lives” written by Brian Elliott, Sheela Subramanian and Helen Kupp and published by Wiley, 2022. In this book, the authors examine one of the most profound transformations in modern business: the rapid and irreversible shift toward flexible work. Written in the aftermath of the COVID-19 pandemic, the book argues that what began as an emergency response has evolved into a durable and preferable way of working—one that challenges long-held assumptions about productivity, leadership, and the role of the traditional office.

Before the pandemic, flexible work arrangements were rare and often reserved for elite performers. Most organizations relied on physical offices, fixed schedules, and direct supervision as the foundation of productivity. Many leaders believed that innovation depended on employees sharing the same space, learning through proximity, and being visibly present. The idea of managing a distributed workforce seemed risky, if not impossible. Yet when offices abruptly closed in 2019, companies had no choice but to test those assumptions at scale.

What followed surprised many executives. Productivity did not collapse; in many cases, it increased. Employees reported greater autonomy, improved focus, and stronger work–life balance. Creativity and innovation continued, and in some organizations even flourished. As the authors note, flexibility turned into a powerful advantage in recruiting and retaining talent, particularly in a highly competitive labor market. The authors conclude that a full return to rigid, office-centered work is both unlikely and undesirable.

Central to the book’s argument is the idea that traditional measures of productivity were flawed long before remote work became common. Managers once relied on visible activity—attendance, desk time, and “management by walking around”—as proxies for performance. These methods fail in distributed environments and, more importantly, never truly measured the quality or impact of work in the first place. Seeing employees at their desks does not reveal whether they are engaged, effective, or producing meaningful outcomes.

To help organizations adapt, the authors outline seven interrelated steps for retrofitting companies for the future of work. The first is to operate according to a clear and shared set of principles. Because flexibility introduces complexity and uncertainty, principles act as a compass for decision-making. Rather than imposing uniform rules, leaders should prioritize team-level autonomy, recognize that different functions require different approaches, and adopt a digital-first mindset that treats remote participation as the default rather than the exception.

Principles alone, however, are not enough. Organizations must also establish behavioral guidelines that translate values into everyday practices. These “guardrails” ensure fairness and prevent the emergence of “faux flexibility,” where policies appear progressive but still constrain employee autonomy. Examples such as Slack’s “one dials in, all dial in” rule demonstrate how simple norms can reinforce inclusion and equity across hybrid teams.

A defining theme of the book is collaboration rather than control. The authors caution against top-down mandates and instead encourage leaders to co-create flexible work policies with employees. Teams that are already working effectively should be studied and learned from, and flexibility should be formalized through team-level agreements that clarify expectations around schedules, communication, accountability, and relationships. This participatory approach builds trust and ensures that flexibility works for both individuals and the organization.

Because no universal blueprint exists, experimentation is essential. Leaders must accept uncertainty, support pilot programs, and view trial and error not as failure but as learning. Over time, patterns emerge that reveal what truly supports performance and well-being. The authors emphasize that there is no perfect data point or benchmark—only continuous improvement guided by experience and feedback.

The book also challenges the belief that culture depends on physical proximity. While companies once invested heavily in office campuses, the authors argue that connection and belonging can be cultivated virtually—and sometimes more inclusively than before. Research cited in the book links flexibility to stronger feelings of belonging, higher job satisfaction, and improved well-being, undermining the assumption that creativity depends on shared physical space.

Leadership, however, must evolve. The shift to flexible work has exposed weaknesses in managers who rely on control rather than trust. The authors advocate developing managers as coaches—leaders who communicate clearly, show empathy, and focus on outcomes instead of activity. Training initiatives like Slack’s “Base Camp” illustrate how organizations can intentionally build these capabilities.

The authors contrast two management paths: the “doom loop” of constant surveillance and the “boom loop” of trust and accountability. Excessive monitoring erodes morale, increases anxiety, and drives attrition, while goal-based management fosters engagement and performance. Tools such as the RACI matrix help organizations track progress without resorting to intrusive oversight, reinforcing the principle that results—not hours—matter most.

Flexibility is not a temporary accommodation but a defining feature of modern work. Employees want and need it, and organizations that embrace it thoughtfully gain a lasting competitive advantage. While flexibility is not a cure-all, the authors argue it is a decisive step toward healthier, more resilient, and more human workplaces when implemented with intention and trust.

#codingexercise:CodingExercise-02-12-2026

 In continuation of previous posts of exemplary video analysis stacks on AWS, we focus on Azure today. The most explicit lineage of “well architected” drone and video analytics on Azure starts with Live Video Analytics on IoT Edge and evolves into more general edge to cloud platforms like Edge Video Services. Live Video Analytics (LVA) was introduced as a hybrid platform that captures, records, and analyzes live video at the edge, then publishes both video and analytics to Azure services. It is deliberately pluggable: we wire in our own models—Cognitive Services containers, custom models trained in Azure Machine Learning, or open source ML—without having to build the media pipeline ourself. Operational excellence is baked into that design: the media graph abstraction gives us declarative topologies and instances, so we can version, deploy, and monitor pipelines as code, while IoT Hub and the Azure IoT SDKs provide a consistent control plane for configuration, health, and updates across fleets of edge devices. (LVA)

Reliability and performance efficiency in LVA come from pushing the latency sensitive work—frame capture, initial inference, event generation—onto IoT Edge devices, while using cloud services like Event Hubs, Time Series Insights, and other analytics backends for aggregation and visualization. The edge module runs on Linux x86 64 hardware and can be combined with Stream Analytics on IoT Edge to react to analytics events in real time, for example raising alerts when certain objects are detected above a probability threshold. That split honors the reliability pillar by isolating local decision making from cloud connectivity, and it improves performance efficiency by avoiding round trips to the cloud for every frame. At the same time, Azure Monitor and Application Insights provide the observability layer—metrics, logs, and traces across IoT Hub, edge modules, and downstream services—so operators can detect regressions, tune graph topologies, and automate remediation in line with the operational excellence pillar.

Edge Video Services (EVS) takes those ideas and generalizes them into a reference architecture for high density video analytics across a two or three layer edge hierarchy. In EVS, an IoT Edge device on premises ingests camera feeds and runs an EVS client container that fans frames out to specialized video ML containers such as NVIDIA Triton Inference Server, Microsoft Rocket, or Intel OpenVINO Model Server. A network edge tier—typically AKS running in Azure public MEC—provides heavier compute with GPUs and low latency connectivity back to the on prem edge. This cascaded pipeline is a direct expression of the performance efficiency and cost optimization pillars: lightweight filtering and pre processing happen close to the cameras, while more expensive models and multi stream correlation are centralized on shared GPU clusters, avoiding over provisioning at either layer. Reliability is addressed through Kubernetes based orchestration, multi node clusters at the network edge, and the ability to re route workloads across the hierarchy if a node fails. (EVS)

From a sustainability and cost perspective, both LVA and EVS lean heavily on managed services and right sized compute. In LVA style deployments, only the necessary analytics results and selected clips are shipped to the cloud, with raw video often retained locally or in tiered storage, reducing bandwidth and storage overhead. EVS goes further by explicitly partitioning workloads so that GPU intensive inference runs on shared AKS clusters in MEC locations, improving utilization and reducing the number of always on, underused GPU nodes. This aligns with Azure’s sustainability guidance: use managed services where possible, aggressively manage data lifecycles, and concentrate specialized hardware in shared, high utilization pools rather than scattering it across many small sites.

When we compare these drone and video centric stacks to more generic ingestion and analytics patterns on Azure, the performance story is less about raw maximum throughput and more about how that throughput is shaped. Event Hubs and IoT Hub are documented to handle millions of events per second across partitions, and AKS hosted Kafka or custom gRPC ingestion services can be scaled horizontally to similar levels; those patterns are typically used for logs, telemetry, and clickstreams where each event is small and homogeneous. In LVA and EVS, the “events” are derived from high bandwidth video streams, so the architectures focus on early reduction—frame sampling, on edge inference, event extraction—before feeding Event Hubs, Time Series Insights, or downstream databases. In practice, that means we inherit the same proven ingestion envelopes and scaling knobs as other well architected Azure stacks, but wrapped in domain specific primitives: media graphs, edge hierarchies, GPU aware scheduling, and hybrid edge cloud control planes that are tuned for drone and camera workloads rather than generic telemetry.

 In the previous post, the Well-Architected pillars are woven directly into the way the stack ingests, analyzes, and serves video from large fleets of IoT devices. At the operational excellence layer, the architecture leans on API Gateway, Lambda, and Step Functions as the control plane for all asynchronous workflows. These services provide end to end tracing of requests as they move through ingestion, indexing, search, and alerting, so operators can see exactly where latency or failures occur and then automate remediation. The result is an operations model where deployments, rollbacks, and workflow changes are expressed as code, and observability is built into the fabric of the system rather than bolted on later. AWS

Reliability and performance efficiency are largely delivered through serverless and on demand primitives. Lambda functions form the core processing tier, inheriting multi AZ redundancy, automatic scaling, and built in fault tolerance, so the video analytics pipeline can absorb bursty workloads—such as many cameras or drones triggering events at once—without explicit capacity planning. Kinesis Video Streams, Kinesis Data Streams, and DynamoDB are configured in on demand modes, allowing ingest and metadata operations to scale with traffic while avoiding the idle capacity that plagues fixed size clusters. This mirrors the broader AWS streaming reference architectures, where Kinesis Data Streams is positioned to handle “hundreds of gigabytes of data per second from hundreds of thousands of sources,” with features like enhanced fan out providing each consumer up to (2,\text{MB/s}) per shard for low latency fan out at scale. AWS aws.amazon.com

Cost optimization and sustainability in the video analysis guidance are treated as first class design constraints rather than afterthoughts. Data retention is explicitly tiered: 90 days for Kinesis Video Streams, 7 days for Kinesis Data Streams, and 30 days for OpenSearch Service, with hot to warm transitions after 30 minutes. That lifecycle design keeps only the most valuable slices of video and metadata in high cost, low latency storage, while older data is either aged out or moved to cheaper tiers. Combined with Lambda’s pay per use model and the shared, managed infrastructure of Kinesis, OpenSearch Service, and S3, the architecture minimizes always on resources and therefore both spend and energy footprint. This aligns directly with the Well Architected sustainability pillar, which emphasizes managed services, automatic scaling, and aggressive data lifecycle policies to reduce the total resources required for a workload. AWS Protera Technologies

When we compare this video analysis stack to other well architected ingestion and analytics patterns on AWS—such as the generic streaming data analytics reference architectures built around Kinesis Data Streams, Amazon MSK, and Managed Service for Apache Flink—the main difference is not in raw throughput but in workload specialization. The streaming reference designs show that Kinesis Data Streams can scale from a few MB/s per shard up to hundreds of MB/s per stream, while MSK clusters can be sized to ingest on the order of (200,\text{MB/s}) and read (400,\text{MB/s}) with appropriate broker classes and partitioning. pages.awscloud.com AWS Documentation Those architectures are optimized for generic event streams—logs, clickstreams, IoT telemetry—where we often trade richer per event processing for extreme fan in and fan out. The video analysis guidance, by contrast, wraps those same primitives in a domain specific pattern: Kinesis Video Streams for media ingest, OpenSearch for indexed search over events and clips, and Lambda driven workflows tuned for video centric operations like clip extraction, event correlation, and fleet wide search. In practice, that means we inherit the same proven performance envelope and scaling characteristics as the broader streaming patterns, but expressed through a solution that is already aligned with the operational excellence, reliability, cost, and sustainability expectations of a production grade video analytics service.

 AWS and DVSA:

A number of efforts in both industry and academia have attempted to build drone‑video analytics pipelines on AWS, and while none mirror the full spatial‑temporal, agentic‑reasoning architecture of your platform, several come close in spirit. One of the most visible industry examples is Amazon’s own reference implementation for real‑time drone‑video ingestion and object detection. This solution uses Amazon Kinesis Video Streams for live ingestion, a streaming proxy on EC2 to convert RTMP feeds, and an automated frame‑extraction workflow that stores images in S3 before invoking Lambda functions for analysis. The Lambda layer then applies Amazon Rekognition—either with built‑in detectors or custom Rekognition Custom Labels models—to identify objects of interest and trigger alerts through SNS. The entire system is packaged as a CDK deployment, emphasizing reproducibility and infrastructure‑as‑code, and demonstrates how AWS primitives can be orchestrated into a functional, cloud‑native drone‑video analytics pipeline. Github

AWS has also published a broader architectural pattern under the banner of “Video Analysis as a Service,” which generalizes these ideas for fleets of IoT video devices, including drones. This guidance describes a scalable, multi‑tenant architecture that supports real‑time event processing, centralized dashboards, and advanced search across large video corpora. It highlights the use of API Gateway, Lambda, and Step Functions for operational observability, IAM‑scoped permissions for secure access control, and AWS IoT Core Credential Provider for rotating temporary credentials at the edge. Although not drone‑specific, the architecture is clearly designed to support drone‑like workloads where video streams must be ingested, indexed, analyzed, and queried at scale. AWS

Together, these efforts illustrate how AWS has historically approached drone‑video analytics: by leaning heavily on managed ingestion (Kinesis Video Streams), serverless processing (Lambda), and turnkey vision APIs (Rekognition). They provide a useful contrast to your own platform, which treats drone video as a continuous spatial‑temporal signal and integrates vision‑LLMs, agentic retrieval, and benchmarking frameworks. The AWS examples show the industry’s earlier emphasis on event‑driven object detection rather than the richer semantic, temporal, and reasoning‑oriented analytics your system is now pushing forward.

References: CodingChallenge-02-10-2026.docx

 Integration of DVSA

The development of spatial-temporal analysis for first-person-view (FPV) drone imagery has evolved significantly, influenced by the constraints of onboard computing, the advancement of cloud platforms, and the availability of reliable geolocation. Initially, FPV feeds were treated as isolated images, with lightweight detectors operating on the drone or a nearby ground station. These systems could identify objects or hazards in real time but lacked temporal memory. Without stable geolocation, insights were fleeting, and analytics could not form a coherent understanding of the environment.

The transition began when public-cloud-based drone analytics platforms, initially designed for mapping and photogrammetry, started offering APIs for video ingestion, event streaming, and asynchronous model execution. This enabled FPV feeds to be streamed into cloud pipelines, overcoming edge compute limitations. This advancement marked the beginning of spatial-temporal reasoning: object tracks persisted across frames, motion vectors were aggregated into behavioral patterns, and detections could be anchored to cloud-generated orthomosaics or 3D models. However, the spatial dimension's fidelity remained inconsistent due to GNSS drift, multipath interference, and urban canyons, complicating the alignment of FPV video with ground truth, especially during fast or close-to-structure flights.

GEODNET introduced a decentralized, globally distributed RTK corrections network, providing centimeter-level positioning to everyday drone operators. With stable, high-precision geolocation, the cloud analytics layer gained a reliable spatial backbone. Temporal reasoning, enhanced by transformer-based video models, could now be integrated with precise coordinates, treating FPV footage as a moving sensor within a geospatial frame. This enabled richer analysis forms: temporal queries on site evolution, spatial queries retrieving events within a defined region, and hybrid queries combining both.

As cloud platforms matured, they began supporting vector search, event catalogs, and time-indexed metadata stores. FPV video could be segmented semantically, each tagged with geospatial coordinates, timestamps, and embeddings from vision-language models. This allowed operators to ask natural-language questions and receive results grounded in both space and time. GEODNET's corrections ensured alignment with real-world coordinates, even in challenging environments.

Recent advancements have moved towards agentic, closed-loop systems. FPV drones stream video to the cloud, where spatial-temporal analytics run continuously, generating insights that flow back to the drone in real time. The drone adjusts its path, revisits anomalies, or expands its search pattern based on cloud-derived reasoning. GEODNET's stable positioning ensures reliable feedback loops, enabling precise revisits and consistent temporal comparisons. In this architecture, FPV imagery becomes a live, geospatially anchored narrative of the environment, enriched by cloud intelligence and grounded by decentralized GNSS infrastructure.

The evolution of FPV analytics into truly spatial-temporal systems was driven by scalable reasoning from public-cloud platforms and trustworthy positioning from GEODNET. Together, they transformed raw video into a structured, queryable, and temporally coherent source of insight, setting the stage for the next generation of autonomous aerial intelligence.

Earlier spatial-temporal analysis pipelines' limitations are evident when compared to a system designed from first principles to treat drone video as a high-dimensional, continuously evolving signal. Our platform departs from historical approaches by treating time as a primary computation axis, allowing for rigorous modeling of persistence, causality, and scene evolution. This integration of detection, tracking, and indexing components into a unified spatial-temporal substrate results in a qualitatively different analytical capability.

Object tracks become stable, queryable entities embedded in a vectorized environment representation, supporting advanced reasoning tasks such as identifying latent behavioral patterns, detecting deviations from learned temporal baselines, or correlating motion signatures across flights and locations. The platform's geospatial grounding, enhanced by GEODNET's corrections, integrates positional data directly into feature extraction and embedding stages, producing embeddings that are both semantic and geospatial.

The platform emphasizes agentic retrieval and closed-loop reasoning, transforming the drone from a passive collector into an adaptive observer. Temporal anomalies trigger targeted re-inspection, semantic uncertainty prompts viewpoint adjustments, and long-horizon reasoning models synthesize multi-flight evidence to refine hypotheses. This results in a more efficient and scientifically grounded sensing loop.

Benchmarking-driven design principles, adapted from reproducible evaluation frameworks like TPC-H, expose the performance of spatial-temporal analytics to systematic scrutiny. Standardized workloads, cost-normalized metrics, and scenario-driven evaluation suites allow for comprehensive performance measurement, positioning the platform as a reference point for the field.

The integration of multimodal vector search and vision-language reasoning enables open-ended queries combining spatial constraints, temporal windows, and semantic intent. This redefinition of FPV video as a dynamic, geospatially grounded dataset marks a substantive advancement over prior attempts, setting a new trajectory for spatial-temporal drone analytics.

This is a summary of the book titled “Work Without Jobs: How to Reboot Your Organization’s Work Operating System” written by Ravin Jesuthasan and John W. Boudreau and published by MIT Press in 2022. The modern workplace is undergoing a profound transformation, driven by rapid technological advancement and shifting expectations around how work should be organized whether it be with ownership in demarcated roles or shared contributions to a workflow. The authors build on the premise that the traditional job-centered model can no longer keep pace with this change. Instead of treating work as a fixed set of duties assigned to static roles, they propose a radical shift: breaking work down into its component tasks and reassembling it in more flexible, dynamic ways.

According to the authors, organizations have long relied on “constructed” jobs—formal descriptions that bundle skills, responsibilities, pay structures and performance measures into tidy packages. But as automation, artificial intelligence and the gig economy reshape the labor landscape, these rigid constructs increasingly hinder progress. They advocate for “deconstruction,” a process of stripping jobs down to the tasks and capabilities they truly require. From there, organizations can “reconstruct” work in ways that better align with workers’ strengths, available technologies and emerging strategic priorities.

This shift represents more than a structural change; it is a reimagining of the workplace operating system itself. Just as computers run on different operating systems, organizations rely on unwritten systems that define hierarchies, job titles, and even how they interface with unions and social institutions. But the old system—built around full-time employees holding stable roles—is becoming obsolete. Talent now flows freely across organizational boundaries, and work increasingly blends contributions from full-time employees, contractors, gig workers and AI-driven processes.

To navigate this transition, They recommend treating work design as an ongoing experiment. Instead of large-scale, top-down restructuring, leaders should test small changes that reveal better ways of organizing tasks and deploying resources. Early experiments might involve redistributing tasks among employees, augmenting work with automation or tapping into external talent pools. These incremental steps can lead to a “return on improved performance,” where efficiencies gained from better task alignment generate compounding value.

Examples like Genentech illustrate the power of deconstruction in practice. By creating personas that represent archetypes of workers suited to certain tasks, the company freed employees to work more flexibly and attracted new talent seeking adaptable roles. Other organizations, such as agricultural co-op Tree Top, have used automation to handle repetitive tasks, allowing human workers to focus on more complex, variable work.

This reimagined operating system also expands the ways organizations engage talent. Beyond traditional hiring, leaders can explore options such as talent exchanges with other firms, gig work platforms, innovation partnerships with universities, crowdsourcing initiatives and internal talent marketplaces that let employees pursue projects outside their formal roles. As workers progress through their careers, they will increasingly be defined by the skills and capabilities they develop rather than by tenure or conventional degrees. Stackable credentials and modular learning pathways will further support this fluidity.

In such an environment, organizations must embrace a culture of continuous reinvention. Rather than relying on fixed job descriptions, leaders must constantly adjust workflows, coordinate cross-functional teams and foster organizational agility. As automation and AI take on more tasks, work will evolve daily—becoming slightly more automated, adaptive and collaborative over time. Teams will need to shed outdated routines and embrace perpetual upgrades similar to those common in the tech world.

Leadership itself will be reshaped by this evolution. Executives and managers will see their own roles deconstructed and redesigned as they move toward more fluid, project-based forms of leadership. They will establish strategic guardrails while enabling employees to form and reform agile teams as needed. With blurred boundaries between roles, managers must excel at human-centered leadership, guiding teams through constant change and integrating human and technological contributions seamlessly.

They emphasize that work will become increasingly social rather than transactional. Even independent contractors and gig workers develop psychological ties to the organizations they serve. Leaders can strengthen these connections by fostering supportive, inclusive cultures that value emotional well-being, diversity and open communication. As networks of gig workers and task-based contributors grow, organizations will need new ways to recognize collaboration, protect worker welfare and understand the informal social structures that drive value creation.

Clinging to job-based models limits organizations’ ability to harness both human and automated potential. By adopting new work operating systems grounded in flexibility, inclusion and continuous reinvention, companies can become more adaptive, empowering and future-ready—and workers can thrive in more meaningful, dynamic and socially connected ways.

 This is a summary of the book titled “Building Ontologies with Basic Formal Ontology” written by Robert Arp, Andrew Spear and Barry Smith and published by MIT Press, 2015. Modern scientific research is producing data at a pace and scale that far exceed the capacities of traditional analytical methods. This transformation is especially visible in the life sciences, where advances such as high‑throughput gene sequencing and multidimensional imaging generate vast amounts of information every day. As researchers confront this deluge of data, the question of how to store, integrate, interpret, and share it efficiently has become increasingly urgent. Robert Arp, Andrew Spear, and Barry Smith address this challenge, presenting ontology as a powerful solution for achieving interoperability, accessibility, and coherence across scientific domains. 

Ontologies, as the authors explain, emerge from philosophy’s long tradition of studying what exists and how different entities relate to one another. In contemporary scientific and computational contexts, an ontology functions as a representational structure—essentially, a taxonomy—designed to categorize and relate types of entities according to their defining characteristics. A classic example is the familiar biological hierarchy that starts with broad categories such as “vertebrate animals” and branches into more specific groups such as mammals, reptiles, primates, and snakes. Such structured classification enables scientists to clarify how individual items fit within broader categories, enhancing clarity and communication. 

This philosophical grounding underlies the ontology’s central purpose: representing reality as faithfully as possible. Ontological realism—the idea that the categories and relations described in an ontology correspond to entities in the real world—plays an important role here. For instance, the classification “mammal” is not a linguistic convenience but a label for a genuine biological class of organisms. Ontologies used in applied fields such as biomedical informatics depend on this realism, enabling researchers to use consistent terminology and shared conceptual frameworks across diverse technological platforms. 

The authors distinguish among different kinds of ontologies, showing how they operate at varying levels of specificity. A general ontology might describe broad types of organisms, while a domain ontology focuses on particular systems or phenomena—such as the human heart, with its chambers, valves, and functions. Domain ontologies are indispensable for specialized research areas, but they also risk creating isolated conceptual systems that do not integrate well with each other. To avoid this fragmentation, the authors emphasize the importance of beginning every ontology with universal, top‑level categories that provide a common foundation for more specific structures. This top‑down approach improves interoperability and supports scientific collaboration across disciplines. 

Designing an effective ontology also requires adherence to several foundational principles. Ontologists must assume the existence of real-world entities, acknowledge the complexity of systems, recognize the limitations of scientific theories, and strive to represent reality as accurately as possible given current knowledge. They must also design ontologies so that entities at various levels of granularity—from broad categories to fine distinctions—can be represented. Because science evolves, ontologies must remain flexible, open to revision as new discoveries emerge. 

Basic Formal Ontology (BFO) framework distinguishes between continuants and occurrents. Continuants are entities that persist over time while retaining their identity—like a human being or a piece of fruit—even though their parts may change. Occurrents, by contrast, are processes or events unfolding in time, such as infections or biological functions. These two types of entities require different representational strategies, and BFO provides the conceptual tools to integrate both within a single coherent ontology. 

The relationships among entities are equally crucial. Ontologies go beyond hierarchical classification, incorporating relationships among universals, between universals and particulars, and among individual entities. These relational structures reflect the complexity of scientific reality—for example, the shared atomic composition of different organisms or the dependence of certain qualities on larger structures. 

An ontology must become a practical tool—not just a conceptual model but a computer‑implementable artifact. Using tools such as the Protégé ontology editor and the Web Ontology Language (OWL), ontologists translate conceptual structures into software systems capable of supporting large‑scale data analysis and knowledge integration. These digital ontologies already underpin major scientific efforts in fields ranging from cell biology to mental health research. 

Through their systematic exposition, Arp, Spear, and Smith demonstrate that ontologies, when properly constructed, serve as vital infrastructure for modern science. They provide the shared language and structure necessary to manage overwhelming volumes of data, bridge disciplinary divides, and ensure that scientific knowledge remains coherent, accessible, and continually adaptable.