Author Archives: Evelina Rosser

HOW CAN GOVERNMENTS ENSURE THAT AUTOMATION DOES NOT WORSEN EXISTING SOCIOECONOMIC INEQUALITIES

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As automation increasingly disrupts labor markets and the workforce, governments must implement proactive policies to ensure that the benefits of technological progress are shared broadly across society. If left unaddressed, automation has the potential to exacerbate socioeconomic divides and inequality by primarily affecting lower-skilled jobs and helping those with higher skills, more education, and greater wealth. Through implementing a robust and multifaceted policy approach, governments can help manage this transition and prevent automation from disproportionately harming disadvantaged populations.

One of the most important steps governments must take is to significantly invest in vocational education, job training programs, and lifelong learning opportunities. As automation eliminates many routine tasks, reskilling and upskilling large swaths of the workforce will be essential for allowing people to gain the new skills needed for jobs less susceptible to replacement by machines. Beyond just allocating funding, governments should work with employers, unions, community colleges, and universities to design comprehensive training programs tailored towards developing skills matching those forecasted to be in growing demand as work becomes more non-routine and interactive. Subsidizing such programs, especially for disadvantaged groups, can help prevent barriers that may hinder workers’ ability to transition into new occupations and fields.

Governments also need to modernize their social safety net programs and labor policies to provide a robust support structure given the potential mass displacements of workers. This includes expanding and reforming unemployment insurance programs to provide more coverage, for longer periods of time, and make eligibility requirements more flexible given the possibility of permanent job losses, rather than temporary layoffs, due to automation. Active labor market policies could assist the unemployed, such as job search assistance programs, wage subsidies for jobseekers, public sector hiring and community benefit programs. Advancing universal basic income proposals is another option some argue could help address issues of job insecurity and inequality in an automated future. Beyond cash transfers, targeted social programs may also be needed to support vulnerable populations disproportionately impacted.

To complement these efforts, governments must implement new policies that foster business investment and job creation in sectors with growth opportunities amidst automation. This involves everything from tailoring tax incentives for R&D targeting certain fields to strategic public investments in scientific research, high-tech infrastructure, and other areas aligned with developing technologies like AI, biotech, green energy and more. Streamlining regulations and creating specialized industry zones can also attract private capital towards expanding employment opportunities. Similarly, placing conditions on subsidies or tax breaks for automating companies to retrain displaced workers or implement hiring quotas could help address the challenge in a balanced manner.

In addition to active labor market and social policies, governments need to consider reforming education at all levels to better prepare citizens for the skills demands of tomorrow. K-12 education systems should integrate more STEM, computational thinking and social-emotional skills from an early age. Meanwhile, higher education requires reforms like subsidizing vocational programs, making public colleges tuition-free, and incentivizing curriculums aligned with emerging fields. Lifelong learning opportunities beyond initial schooling also need empowerment through options like subsidized online course platforms, skills certification programs and learning accounts workers can draw from over their careers.

Governments have a role to play in shaping how automation is developed and deployed to maximize its benefits for society. “Automation with a human touch” should be a guiding principle. This involves everything from supporting interdisciplinary research at the intersection of technology and jobs to establishing ethics review boards and human-centered design standards for AI. It may even require interventions like mandating reasonable retraining periods before large-scale layoffs due to automation or requiring “human in the loop” oversight for algorithms affecting people’s lives. The goal is for technological progress and job disruption to be managed inclusively and cooperatively between workers, companies and policymakers.

By comprehensively investing in workforce reskilling, strengthening social safety nets, fostering new job growth, reforming education and helping guide more responsible technology development, governments have the means to ensure automation lifts society as a whole rather than leaving many behind. It will require a level of proactivity, coordination and innovation from policymakers not seen to date. If done right, an automated future could be one with broadly shared increases in living standards, leisure and quality of life – but left unmanaged it risks greater inequality, insecurity and societal problems. With the right balanced policies, the benefits of automation can be maximized while the costs are minimized.

WHAT ARE SOME IMPORTANT FACTORS TO CONSIDER WHEN CONDUCTING INDEPENDENT RESEARCH FOR A CAPSTONE PROJECT

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Determine a clear research question or topic area to guide your work. Your research should have a focused question that can be reasonably addressed within the scope and timeframe of your project. Coming up with an too broad or unclear question will make your research difficult to manage and complete successfully. Choose a topic that is interesting to you and that has enough supporting research and data available to draw meaningful conclusions.

Develop a comprehensive research plan. Your plan should include determining relevant keywords and databases to search for literature and research on your topic, establishing a realistic timeline to keep your research on track, outlining an annotated bibliography to organize sources, and drafting a methodology section describing how you will conduct your own research if applicable. The research plan will help ensure your research process is strategic and moves systematically toward completing your objectives.

Thoroughly research published literature and existing studies on your topic. Research published studies, reports, reviews, and other materials that relate to your research question or area of focus to gain a deep understanding of what is already known on the topic and what gaps exist in the current body of research. Make sure to research materials from credible peer-reviewed academic journals, reputable research organizations, and expert authors. Your literature review will form the basis of knowledge for your own research.

Evaluate sources for relevance and credibility. Not all published materials will be equally applicable or trustworthy related to your research question. It’s important to carefully evaluate sources based on their relevance to your specific topic, date of publication to ensure timeliness, methodology rigor if describing a study, author credentials and affiliation, publisher or host, and other factors that speak to the thoroughness and credibility of the information. Lower quality or outdated sources should not be included in your review.

Consider ethics in your research. Any research, especially when involving human subjects, requires a consideration of ethics. You need to ensure your study adheres to ethical standards relating to issues like informed consent, privacy, data transparency, minimizing harm, research integrity, and others. For research requiring human participation, plan to gain necessary approvals from your institution’s IRB. Your research design and processes should demonstrate an attention to conducting ethically sound work.

Apply rigorous research methods as needed. Beyond an extensive literature review, your project may entail collecting and analyzing your own primary data using accepted methods for your field. Make sure to employ research methodologies that are well designed, implemented systematically and consistently, and documented thoroughly enough that your work can be replicated. The credibility and strength of your conclusions depend greatly on the rigor of your research procedures and analyses.

Consider limitations and implications. No study is perfect, so it’s important to openly acknowledge limitations in your research design, methods employed, data available, and other potential sources of bias or imprecision. Your findings should also be discussed in the context of their real-world implications, applications, areas for further research, and how they address your original research question. Contemplating limitations and implications lend depth to your analysis and demonstrate your research integrity.

Develop organized and clear documentation of your work. Your final paper or written report needs to follow accepted reporting guidelines for your area of research and clearly communicate the purpose, methods, findings and conclusions of your study or project. Your documentation includes elements like an abstract, introduction, background literature review, methodology, analyses, implications, limitations and references. Organizing your documentation in a format aligned to expectations in your field enhances readability and rigor.

Present findings to relevant audiences as applicable. Consider presenting a summary or poster of your capstone project findings at a local or regional conference in your field. This allows you to receive feedback on your work, share your contributions with your professional network, and begin developing presentation skills. Oral defense of your completed work to capstone committee members is another common presentation format. Presenting heightens the impact and rigor of your overall project experience.

Conducting an independent and high quality capstone research project requires careful planning, execution of rigorous research methods, systematic documentation of your work following accepted standards, consideration of ethics, and evaluation of findings. Approaching your project with an attention to these key factors helps ensure credible, well-supported outcomes and strengthens the experience. The resulting research demonstrates higher order communication, critical thinking and problem solving skills valued by graduate programs and employers.

CAN YOU PROVIDE MORE INFORMATION ON THE SAFETY MEASURES IN PLACE FOR SELF DRIVING CARS

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Self-driving cars have the potential to significantly reduce traffic accidents caused by human error, which account for over 90% of all accidents according to the National Highway Traffic Safety Administration. For autonomous vehicles to be deployed safely on public roads, robust safety measures need to be in place. Vehicle manufacturers and researchers are taking safety very seriously and implementing redundant systems to minimize risks.

One of the most important safety aspects of self-driving car design is sensors and perception. Autonomous vehicles use cameras, lidar, radar and ultrasonic sensors to perceive the environment around the vehicle in all directions at once. These sensors provide a 360 degree awareness that humans cannot match. Relying on any single sensor could potentially lead to accidents if it fails or is disrupted. Therefore, multiple redundant sensors are used so that the vehicle can still drive safely even if one or more sensors experience an outage. For example, a vehicle may use four long range lidars, six cameras, twelve short-range ultrasonic sensors and four radars to observe the surroundings. The data from these diverse sensors is cross-checked against each other in real-time to build a confident understanding of the environment.

In addition to using multiple sensors, self-driving systems employ sensor fusion, which is the process of combining data from different sensors to achieve more accurate and consistent information. Sensor fusion algorithms reconcile data discrepancies from sensors and compensate for individual sensor limitations. This reduces the chances of accidents from undetected objects. Advanced neural networks are being developed to further improve sensor fusion capabilities over time via machine learning. Strong sensor coverage and fusion are vital to safely navigating complex road situations and avoiding collisions.

Once perceptions are obtained from sensors, the self-driving software (the “brain” of the vehicle) must make intelligent decisions quickly. This decision making component is another focus for safety. Researchers are developing models with built-in conservatism that prioritize avoiding risks over optimal route planning. obstacle avoidance maneuvers are chosen only after extensive validation testing shows they will minimize harm. The software also continuously monitors itself and runs simulations to ensure it is still operating as intended, with safeties that can stop the vehicle if any issues are suspected. Over-the-air updates further enhance safety as new situations are learned.

To account for any possible software or hardware faults that could lead to hazards, self-driving cars employ an entirely redundant autonomous driving software stack which is completely independent from the primary stack. This ensures that even a full failure in one stack would not cause loss of vehicle control. The redundant stack will be able to brake or change lanes if needed. There is always a fully functional human-operable primary driving mode available to fall back on. Drivers can also be remotely monitored and vehicles can be remotely stopped if any serious issues are detected during operation.

Self-driving cars are also designed with security in mind. Vehicle networks and software are tested to robustly resist hacking attempts and malicious code. Regular security updates further strengthen the systems over time. Driving data is also carefully managed to protect passenger privacy while still enabling ongoing learning and improvement of the technology. Strong cybersecurity is a fundamental part of ensuring safe adoption of autonomous vehicles on public roads.

Perhaps most significantly, self-driving companies extensively test vehicles under diverse conditions before deployment using simulation and millions of real-world miles. This gradual approach to introduction allows them to identify and address issues well before the public uses the technology. The testing process involves not just logging miles, but also performing edge case simulations, software and hardware-in-the-loop testing, redundant system checks and ongoing validation of operational design domain assumptions. Only once companies have achieved an exceptionally high level of safety are autonomous vehicles operated without a human safety driver behind the wheel or on public roads. Testing is core to the safety-first approach taken by researchers.

Through this multifaceted approach with redundant sensors and software, ongoing validation, security safeguards and meticulous testing prior to deployment, researchers are working to ensure self-driving cars can operate safely on public roads and avoid accidents even under complex conditions involving environmental changes, anomalies and unpredictable situations. While continued progress is still needed, the safety measures now in place have already brought autonomous vehicles much closer to matching and exceeding human levels of safety – paving the way for eventually preventing many of the tens of thousands of traffic fatalities caused by human mistakes each year. With appropriate oversight and care for safety remaining the top priority, self-driving cars have great potential to save lives.

HOW CAN THE APP ENSURE THAT THE INFORMATION REMAINS UP TO DATE AND RELEVANT

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A key challenge for any app is maintaining up-to-date and relevant information over time as the broader context changes. Here are some strategies an app can employ:

Establish Processes and Policies for Regular Updates

The foundation is setting clear internal processes and policies for routinely reviewing and updating content. The app developers should determine reasonable timeframes for updates (e.g. weekly, monthly) based on the type of information and how rapidly it is likely to change. They should also establish guidelines for what merits an update and when to retire outdated content. Having documented processes makes it more systemic rather than ad hoc.

Leverage User Feedback Mechanisms

Apps should incorporate ways for users to easily provide feedback, including a comments section on articles or the ability to flag content as outdated. This allows users themselves to help identify where information needs refreshing. Developers can then prioritize updating based on user input. It also encourages a two-way dialogue where users feel heard. Analytics on user behavior like page views can also point to content in need of freshening.

Monitor External Data Sources and Events

Much information is derived from or impacted by external data sources, news outlets, organizations, or current events. The app needs processes to routinely check these external sources for new developments and changes. For time-sensitive topics, this may mean daily monitoring. Designated staff can be tasked with following relevant hashtags or tracking government, industry or community sources. Alerts can also be set up through tools that monitor for updates to online documents or databases the app utilizes.

Conduct Periodic Content Audits

In addition to reacting to updates, the app should periodically audit all existing content to proactively identify information that is no longer accurate or complete. Again, newer articles may need more frequent review than older steady content. Staff can be assigned different sections to evaluate with specific criteria or rubrics based on the type of material. Outdated factual details, obsolete statistics, incomplete topics and redundant pages can then be prioritized for fixes.

Maintain Transparency in Versioning

When content is updated, the app should clearly note what was changed and when through embedded editorial notes, history tracking or versioning. This maintains transparency about the living, evolving nature of information. It reassures users that staying current is a priority and that they can trust the resource. It also provides accountability and documentation if questions ever arise about what information was present at a given time in the past.

Solicit Input from Subject Matter Experts

For topics requiring specialized expertise, the app can develop relationships with outside experts who are actively working in the field. These experts can be periodically consulted or asked to review sections to ensure accuracy from an authoritative perspective. Some may even be willing to contribute new material as their work advances. Their expert feedback helps validate if the right information is being conveyed or flag need for improvements.

Analyze Traffic and Engagement Over Time

It is also telling to analyze how users are engaging with different pages or sections over extended time periods. Static or declining traffic could mean the information is no longer compelling and warrants freshening. In contrast, consistently popular pages may simply need minor routine updates. These analytics help continuously refine editorial priorities and resource allocation for maintenance.

Provide Context on Information Staleness

For articles and pages that cannot be freshly updated with the latest intel in real-time due to limits in staff or resources, the app should provide clear labeling on the intended freshness or publication date. Users thus have appropriate expectations on the timeframe of the information presented. Perhaps an obvious “Last Updated in 2018” note for example, to acknowledge the content reflects that point in time.

Consider Outsourcing Select Maintenance

If updating major sections requires deep subject matter expertise that exceeds in-house resources, the app could potentially outsource some content development or auditing to specialized independent contractors. This helps supplement internal capacities and tap relevant skills more efficiently for the most knowledge-intensive content areas. Contracts would need clear expectations set around deliverables, timeline and quality standards.

Solicit User-Generated Updates

In a more collaborative approach, the app may allow registered users meeting certain qualifications to directly propose or submit minor updates and corrections that are then vetted by editors before publication. This crowdsources some maintenance work from the user community while still ensuring editorial oversight. Policies would be required around transparency, review processes, and third party content disclaimers.

Through proactive planning and leveraging both internal workflows with external monitoring, feedback and expertise, an app can systemically work to evolve its information landscape and maintain up-to-date relevance over the long run. Regularly reviewing content and refining processes based on usage insights also helps optimize how well the content serves its audiences.

CAN YOU EXPLAIN THE TECHNICAL CHALLENGES INVOLVED IN DEVELOPING A SOCIAL MEDIA PLATFORM AS A CAPSTONE PROJECT

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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.