StealthGPT outputs

Adopting a four-day work week isn't just a nice treat for workers. It's a proven productivity move that many companies should be embracing. For a long time, too many companies have confused commitment with long hours. But in practice, extended work days lead to burnout, daydreaming, and busywork. A four-day week can increase productivity and employee satisfaction, and even increase output. A four-day week would be more productive because employees will feel a sense of urgency to manage their time more effectively. If they only have four days to get things done, they'll be more likely to focus on high-priority tasks and skip unnecessary meetings. Employees would have to stop spending so many hours going back and forth on decisions, scheduling multiple status meetings to go over something that could have been resolved in a quick email, or waiting for someone else to take action. It forces everyone to examine why they are spending so much time on low value activities and eliminate those inefficiencies in order to get the necessary work done. A four-day week would also increase employee productivity because productivity is closely related to adequate rest. Fatigued employees make more mistakes, aren't as creative and need more time to finish tasks, especially those that require mental acumen, such as office jobs. Giving workers an extra day to recharge and handle their personal lives will help them come to work the next day feeling more rested and ready to take on their responsibilities. Studies have proven that employees who feel more rested are more engaged and motivated. The result is often higher quality of work even though fewer hours are being put in to the task. Critics of the four-day week may say that it may reduce availability for customer service, decrease collaboration, or increase the workload of employees. These are certainly good reasons to question the four-day week. That being said, they are also good reasons to examine how the four-day week may be implemented and how to deal with the potential issues that arise from it. Employees may have to stagger their days to make sure the company is running at full capacity at all times. Communication needs to be clearer and deadlines more concrete. The quality and value of work being produced must also become more important than the hours spent doing the work. If it takes some trial and error to figure out how to manage this type of schedule and the industry demands that workers be available all day, then rotating days would be the most feasible way to implement a four-day week. Finally, a four-day work week might also increase productivity by allowing companies to attract and retain high-quality employees. Many people have more control over their work-life balance when they have a shorter work week. This can be one of the biggest benefits that a company can offer its workers, since people with more balance tend not to have as many problems and are more productive. Additionally, high employee turnover decreases productivity and increases the cost of hiring and training new staff. Retention increases productivity by maintaining experienced workers. The four-day work week is a good productivity strategy because it forces workers to focus on the most important things while eliminating the things that do nothing to advance goals. It increases productivity by reducing employee burnout and increasing the value of time by using it more efficiently. While the four-day week isn't without its challenges, it will likely be more effective in the long run. What counts is not how many hours people are working but how hard and well.

10 Tips for Your First 10K You’ve registered for your very first 10K! Congrats! You might be a 5K vet looking to increase your distance or a new runner looking to set your first distance goal, either way, 6.2 miles is a great place to start. A little preparation and the right mindset can help you enjoy your first race (yes, you can actually have fun!) Here’s our list of 10 tips to help you crush your 10K debut. Stick to a simple training plan Don’t just wing it. Start with a simple 8 to 10 week training program to work on your endurance. Be sure to incorporate a weekly easy-run and longer run with rest days to keep you injury-free. Don’t rush your pace Don’t be like most runners and run too fast, too soon. Most of your training runs should be comfortable with the speed and distance to add in as the program progresses. Go get fitted for new running shoes You might not need any fancy equipment, but make time to get fitted for a pair of running shoes that’s right for you. Break in your clothes for race-day Don’t wear anything you’ve never worn before on race day—not a single sock. Be sure to try on your entire outfit for each practice run to ensure you don’t get any unexpected chafing or blisters. Be mindful about your nutrition For a 10K, you probably don’t need energy gels or complicated nutrition, but be sure to eat well before your training runs. Try a carb-based meal or snack and see if your stomach enjoys it. Stay hydrated Keep up your water intake during each run and on off-days, especially when you’ve trained on a hot day. If you want to stay hydrated on race day, have a little bit of water at water stops along your route, but don’t over hydrate. Strength train Don’t underestimate the importance of strength training. A few exercises like squats, lunges, planks and calf raises can help you to stay strong on hills and prevent injury. Work on your pacing strategy Be sure to run slower than you expect to on race day. You’ll probably find it challenging to go out at your goal pace because the excitement and adrenaline will make you want to sprint. It’s important to stay at your goal pace the entire race so you don’t burn out in the last miles. Get prepared the night before You can do a few things the night before to make the morning a little less frantic: lay out your clothes for race day, tape your bib to your shirt, charge your watch, and make note of race day details. Trust future you, you’ll thank you. Make memories First time racing 10K is a big deal, so make it a day to enjoy! High five your spectators, thank the volunteers and focus less on the clock. Try to finish the race smiling. Maybe even have fun and set a new goal of another 10K or 5K!

[Your Name] [Your Phone Number] | [Your Email Address] | [Your LinkedIn/Portfolio URL] [Date] [Hiring Manager Name or "Hiring Team"] [Company Name] [Company Address] Dear [Hiring Manager Name / Hiring Team], As a data professional with more than six years of experience turning data into decision-making strategies, I'm writing to apply for the Data Scientist job at [Company Name]. With your recent focus on [name a company's project, product, or area of interest] in mind, I'm thrilled to use my expertise in predictive modeling, machine learning, and leading across teams to help take [Company Name] data projects to the next level. I'm a Data Scientist at [Most Recent/Current Company], where I build and manage the complete machine learning pipelines used to drive revenue. In a recent project, I built a customer churn model using gradient-boosted decision tree methods (e.g., XGBoost) that was coupled with extensive feature engineering and deployed in conjunction with marketing and product for a customer-facing CRM. The model resulted in a 14% reduction in churn, generating approximately $1.2M in annual revenue. I'm well-versed in the entire data stack, from Python, SQL, and R to deploying models in a cloud infrastructure such as AWS or GCP. I'm proud of my ability to make a bridge between technical execution and business strategy. I regularly distill complicated statistical results into stories and visuals that resonate with executive audiences. I've optimized recommendation models, built sophisticated AB tests, and led other junior analysts in order to ensure that data solutions that are scalable for both the business and the customers. I find [Company Name]'s belief in [state something the company cares about or values, e.g., data-driven innovation / ethical, scalable AI] inspiring, and I'd welcome the chance to partner with you to apply my methodological approach and collaborative approach to [Company Name] in order to transform data into a competitive edge. I appreciate your attention to this letter. I'd appreciate the chance to further explore how my experience as a mid-career professional and my passion for data science would translate to [Company Name]'s continued success. Sincerely, [Your Name]

Subject: Update: Moving our annual review and comp cycle to April Hi everyone, As we look for ways to continue improving and supporting your growth at the company, I'm writing to share an update to our performance review timeline. We are moving our annual review and comp cycle from December to April. The reason we are making this change is to decouple the cycle from end-of-year. We all know that December is a month typically rife with wrapups, deadlines, and the holidays. Moving this to April enables our team members to dedicate the time, energy and focus to a review cycle which is more conducive to self-reflection and career development conversations. As this is an extension of the cycle, the next cycle begins in April [year]. Any merit increases or promotions for this cycle will take effect May [year]. As this is an extended cycle period from your last review, all employees will be eligible for a pro-rated comp adjustment plan to ensure no one is affected by a delayed cycle. More details will be shared from HR shortly, and FAQs will be circulated. I believe this move will lead to more meaningful and less stressful development conversations while aligning the cycle with our Q1 planning. Please join a Q&A session this Friday at [time] to ask any questions. Otherwise, feel free to reach out to me or HR with any questions. Thank you for understanding and as always, for doing great work! Best, [Name] [Title]

A five-course, eight-person party is the best party, really. It's fancy but it's not so many guests that you'll be stuck in the kitchen all night. Here is what you need to throw the five-course dinner party. The Five Courses Start with something small, flavorful and light; it can be a fig and goat cheese crostini. Next, something light and refreshing; think chilled cucumber gazpacho or heirloom tomato salad. Third, something a bit more flavorful; go for something light and easy to prepare, such as scallops or a very small portion of wild mushroom risotto. The main course should be the big, bold moment of the meal; roasted beef tenderloin or pan-roasted duck breast with seasonal veg. Save dessert for last; try something decadent yet not heavy (lemon tarts, chocolate pot de crème). The Prep Forty-eight hours before guests arrive, buy all ingredients and wine (aim to have at least one bottle of wine per guest). Twenty-four hours before, set table; prep all vegetables, sauces and the dessert. Day of, only do last-minute prep (cooking and plating the meal). The kitchen should be clean before guests arrive. Setting the Table Eight is the right number for a dinner party; it allows you to fit them all around your table and everyone is able to see and hear each other, which is good for keeping conversation lively. Use a simple tablecloth, candles and place settings. (Make sure your candles are unscented so guests aren't hindered by the smell while you are trying to eat the food). Set out eight cloth napkins. If you can, make sure the silverware and glassware for eight are polished before the night. Serving and Timing A good five-course dinner should take about two and a half to three hours. Serve courses 20 or 30 minutes apart so diners are able to digest, enjoy themselves (and their wine) and talk with each other. Pick your main course wisely (one that will rest well and isn't cooking right before they arrive is ideal) so you don't have to be in the kitchen. When it comes time to clear plates and dishes, have a partner or close friend at the dinner ready to help you clear dishes so your counter doesn't stay cluttered with dirty ones. Once you've done the prep and planned out a menu, you won't be a chef. You will be the life of your own party. Cheers!

Generative AI is one of the biggest changes to knowledge work in many decades. Generative AI can do work like writing code, research summaries, emails, etc. Generative AI can also write code, which means we now have machines that may be able to write code better than us, or at the very least as good as us (and probably better). There is an active field of literature examining the effects of generative AI on knowledge workers. Some key studies in the area include Brynjolfsson, Li, and Raymond (2023), Noy and Zhang (2023), which show that generative AI significantly increases the productivity of knowledge workers, perhaps in an unprecedently broad scope, which may lead to the reduction of productivity dispersion between workers.

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Control Every Dollar of Your Monthly Budget Without the Hassle Living on a limited budget doesn’t have to be stressful. BudgetEase allows you to plan, track and stretch the budget with tools to manage your money for the real world. No matter what you have to fit into your monthly budget – rent, food, bills, credit cards or even emergency situations – BudgetEase helps you know what income you have, what expenditures you have, and what you have available to spend. Set Your Monthly Budget Enter your income, expenses, savings goals and categories of spending to set a budget each month. BudgetEase tells you exactly how much is available for basic expenses, extra expenses and savings each month so you can make spending decisions with confidence. Track Your Expenditures Enter how much you’ve spent for each category, like food, gas, phone, internet, rent, credit card and more. BudgetEase shows you a clear picture of how much you’ve spent in each category to help you identify problem spending and change your habits before the end of the month. Keep Track of Your Bills Never miss another payment. Your bills are all in one place for easy access, and BudgetEase will let you know which are due so you don’t miss a payment. Make Every Dollar Count When you have a limited budget, every dollar is important. Use BudgetEase to determine what is a basic expense, plan for upcoming expenses and determine where you can spend less to help your limited budget go further. A Limited-Budget Budgeting Solution for People Who Have Limited Budgets A simple and straightforward way to set a budget each month to help you make better budgeting decisions, month after month. BudgetEase lets you start living on a budget today, and grow your budget better day by day. Sign up for BudgetEase today, it’s free and easy to start managing your monthly budget.

Computational neuroscience is an appealing field of study for me because it addresses the issues that most fascinate me: the nature of consciousness, the mathematical analysis of complex systems, and the ways we use technological means to explore humanity. The brain is, in the first place, a biological organ, but its capacity for information processing—its abilities to learn, to adjust, to forecast, to imagine—raises questions about how to model it in a quantitative way. To me, computational neuroscience provides just the necessary instruments to explore these issues: it helps us to tie experimental observations to formal representations of the brain that are testable and predictive. I have always been intrigued by biological systems on the one hand and by mathematical reasoning on the other. I have been drawn to the nervous system for the richness and variety of its connections and for its ability to give rise to the sensations we have, the movements we execute, the memories we retain. And I have been drawn to models in the same spirit: I have enjoyed discovering regularities in the universe, expressing those regularities as formal structures, and using those structures as a means to understand and manipulate our world. For me, computational neuroscience is a marriage of these two sets of interests. It lets me pose scientific questions about the mind, while also using mathematical and computational tools—learning algorithms, stochastic processes, dynamical systems theory, statistical modeling, computer simulation—to answer them. Specifically, I am interested in exploring the role of the brain’s circuitry for the purposes of learning and decision-making. These are functions that are crucial to understanding the mind; they are also the functions that are often impaired in mental illness and that can be disrupted by neurodegenerative diseases. By studying computational neuroscience I believe I can learn how to use computational tools to bridge different scales of explanation, from neurons and synapses to thoughts and actions, and I am confident in its potential to provide insight into both biological and artificial intelligence. What draws me to computational neuroscience is that it is a discipline that requires both disciplined mathematical reasoning and creative insight; you have to be comfortable with the formalism, but you also have to figure out how best to characterize the complexity of the mind. I would like to master the methods and tools that will allow me to examine the data, build a useful model, and validate that model rigorously. I would also like to apply those skills to research that has a bearing on real-world problems: improving our understanding and treatment of neurological diseases, building better prosthetic interfaces with the brain, and perhaps even improving our technologies by building more life-like artificial systems. I hope to study computational neuroscience, then, because I hope to enter a field that is demanding, multi-disciplinary, and fundamental to our technological and scientific progress. I am motivated by the opportunity to advance our knowledge of the human mind using the language of computation.

Fyi – We will move our Annual Review Cycle to April Team, Please be advised that we are changing our Annual Review Cycle from December to April, starting this year. The Annual Review Cycle has traditionally been held in December but this year will be April. This will allow the following benefits: Our review process will coincide with the beginning of our fiscal planning cycle, providing more time to set future goals. It will provide us a little bit of a breather from the holidays and year-end deadlines. It will allow a little more time for everyone to take a look at their whole year’s accomplishments and not get bogged down in time pressure to meet year-end deliverables. Better connects us with the next year’s objectives. This next formal review will happen in April instead of this December. For this transition year, this means a longer Annual Review cycle but in the future, each year’s reviews will be conducted in April. Please continue to have your goals and priorities discussions with your manager on an ongoing basis. Informal feedback conversations should also continue to take place throughout the year as well, this only applies to the formal annual review cycle. The updated timeline will be shared by HR in the coming weeks with key dates and timelines for the annual review cycle including when self-assessments and manager reviews will be due. If you have any questions/concerns, please feel free to reach out to your manager or to HR directly. Thank you and please be patient as we make this change.

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10 Tips for Running Your First 10k Congratulations! So you’ve officially signed up for your first 10k? That’s awesome. Double the distance of a 5k can feel a little intimidating, but you’re going to be just fine. Crossing that finish line will be one of the most satisfying feelings in your life. And that transition from: “Why on earth did I do this?” to “I crushed this!” could be significantly smoother with these 10 extremely simple tips for your first 10k. Don’t buy new running shoes the week before your race. No really. Race day is not the time to introduce your feet to new running shoes. Stick with what you’re used to, otherwise your feet will hate you on race day. Follow a training plan! Don’t think you can just show up on race day. Go find a super simple 8-to-10-week 10k plan and stick to it. This is not the time to run super fast, just follow the plan consistently. Pace yourself. When you line up behind the starting line you will be so gung-ho ready to go. Hold back though. Slow down. The race starts slower than you think it does. This will come back to haunt you at kilometer 8. Eat for your training runs, but not the same stuff that you eat on race day. This should never apply to the morning of a race. Find out what’s going to work best for your gut in training and stick to it. Most people seem to eat bananas and peanut butter, or a piece of toast before a race. Walk when you have to! You’re not going to die. You don’t have to run through the water stations. You can walk up the hill. Run in your outfit the week before the race. Go out and test your race outfit to make sure everything works, that nothing rubs against you, or rides up, or does anything that makes you feel weird. Drink the water the week before. A common myth in endurance sports is that if you drink the whole bottle of water the morning of a race you’ll feel fine on the race day. If you do, your guts are going to feel weird, and you’ll end up running to the bathroom. Drink water in the days leading up to a race, and not so much on race day. Know the course map. The 10k course is a long way, and you’re not going to want to run the whole course thinking “I’m running a long way!” You want to break the course up, and one way to do that is to memorize where the hills are, or where the turn-offs are to break up the run. Have an epic 10k playlist. When your legs get super heavy on race day, listening to music that pumps you up can get you through the run. Go pick some songs that make you go “let’s go!” Smirk for the camera. You are amazing that you completed a 10k! Look out for the photographers and smile for them!

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Five-Course Dinner Party for Eight Two Weeks Before Mail your invitations, noting the time and date, and ask about dietary restrictions. Choose a menu that works with the weather or a topic. Make sure it’s got rich, heavy foods, and lighter options. Write a list of what you’ll need to shop for, grouping it by pantry, grocery store, and foods you should order beforehand (e.g., rare meat, cheese, etc.). Planning Your Menu Typical five-course meal: Appetizer: Small bites with lots of flavor—soup shooters, canapés, etc. First course: Salad or something light. Second course: Fish or lighter meat. Main: Roast, braised meat, or complex vegetarian dish. Dessert: Something you can make ahead. One Week Before Determine the menu and how much you need. At a rough estimate, 6–8 oz of meat or fish per person for the main course is good for ordering special products. Plan your table, and if you need to order, do so now. If you have 10 guests, you should have 5 or 5 matching sets of plates, glasses, and cutlery to serve 5 courses, or plan to use the same plate for more than one course. Two to Three Days Before Buy non-perishables and foods you can freeze. Prepare things you can make before the day. Set your table so you’re not worried about that day-of. The Day Before Prep any produce and marinades you need. Prepare stocks or sauces. Chill wines. Write your time of day to make sure you get things ready in time for what you’re going to eat (working backwards from dinner). The Day Morning: Finish shopping and prep any things that aren’t time-sensitive. Afternoon: Cook dishes that take a long time—braises, roasts. Set your serving plates and serving utensils for each course. Two hours before: Take your shower and dress. Prepare any last-minute food for cooking. Chill the white wine; take reds off the cork so they have time to breathe. Half an hour before: Light the candles, start some music, and put the final touches on the amuse-bouche. Serving the Food Serve the amuse-bouche before drinks for people arriving. Have 15–20 minutes between each course to give your guests time to talk. Clear the dishes after each one, but don’t push the guests. If possible, get a helper (or hire one) to do the dishes; if you don’t, try to get people to help you clear the dishes. Good Advice Prepare as much as you can. Prepare 3–5 courses ahead. Don’t make too much. One complex dish is fine; other things you want to keep it easy. If you’re going with wine, you can pair 2 courses for 1 wine. Have a spare option (bread, cheese, salad) for any kitchen accidents. It all comes down to how you prep for the meal. The majority of the work should be done ahead, so you can relax when your guests arrive and have a great time.

Dear Hiring Manager, I’m writing to express my enthusiasm for the Mid-Career Data Scientist opening. Over the course of my career, I’ve gained experience in using data to provide meaningful business insights. My skills span statistical analysis, machine learning, data engineering, and collaboration with diverse teams. I’m seeking an environment where data is leveraged to drive both business performance and strategic decisions, improve the customer experience, and enable more informed decision-making. As a lead data scientist in my current organization, I’m responsible for overseeing all phases of data science projects, from conceptualization to production, and model performance evaluation. I’ve designed and built a number of machine learning models for predictive customer segmentation, churn mitigation, demand planning, and operational efficiency. These efforts have included utilizing technologies like Python, SQL, scikit-learn, pandas, and cloud platforms. For example, I recently developed a machine learning approach that identified high-risk customer segments for intervention and resulted in improved renewal rates. I’ve worked closely with members of our product, marketing, engineering, and leadership teams to develop robust data science products and services that are not only technically sound, but also easy to understand and aligned with business goals. My technical work is backed by strong problem-solving abilities and excellent written and oral communication skills with both technical and business stakeholders alike. I believe in approaching data projects by identifying the right questions to answer based on an understanding of business needs, and conveying results in a way that enables informed and action-oriented decisions. Whether it’s developing intuitive dashboards, detailing the trade-offs in my proposed models, or outlining future actions based on experiments, I prioritize clarity, utility, and collaboration at every stage. My professional background covers many core data science competencies including: supervised and unsupervised learning A/B testing feature engineering data visualization model evaluation data pipelines I like to work with big, messy datasets and enjoy the process of uncovering new insights that can lead to business enhancements and opportunities. I’m also passionate about responsible data science practices including model transparency, equity and fairness, privacy, and reproducibility. I’m interested in this opportunity because it would enable me to build on my background of experience as a practitioner and developer of data science products, and continue to expand my knowledge and skills in a dynamic, collaborative environment. I’m confident that my technical acumen, business awareness, and communication abilities would allow me to provide strong, impactful value to your data science team. Thanks so much for reviewing my application. I would love to talk more about how my knowledge, experience and values fit your team and objectives. I look forward to hearing from you. Sincerely, [Your Name]

The Four-Day Work Week: Why Working Less is Actually Working Better The 40-hour work week is so engrained in modern culture that it is often thought of as “common sense.” For the past century, the four-day week has been the cornerstone of the global workforce, with workers putting in 8 hours a day, 5 days a week, as standard practice. As with many traditions that we follow without questioning, this schedule dates back to the Industrial Revolution. Back then, working fewer hours could have resulted in significant lost output, given that it was a physical economy. But the modern workforce now relies more on our mental and creative capital. With all this knowledge in mind, it is clear that we must reconsider the standard work week and move toward the 4-day, 32-hour work week. It is not a sacrifice of worker time, but an advancement of worker efficiency. For starters, adopting a four-day week actually makes your workforce more efficient and productive. This is because of Parkinson’s Law: work expands to fill the time available to it. In short, if you schedule 8 hours for a task, it will take 8 hours, even if that task only needs to take 4. This also means that workers will often “fake” being busy, doing meaningless tasks to fill up their time, and suffering through an afternoon slump. However, a condensed schedule forces workers to prioritize the tasks that are truly productive, while eliminating useless meetings and mindless internet surfing that have become so commonplace among the modern worker. The workers will still do the same amount of work, but they will be doing it more efficiently. Adopting a 4-day work week also helps reduce one of the biggest productivity-killers in the modern age: burnout. In a highly demanding work environment, it is inevitable that workers will be tired, and this exhaustion will lead to poor productivity, absenteeism, and mistakes. When employees are given a 3-day weekend, they will be given a chance to reenergize themselves and spend time with their families and friends. It will have a positive impact on their mental and physical health, which means they will be better equipped to handle their workload during the work week and produce high-quality work during this time. If it is given to the employees, a 3-day weekend is a much more efficient means of combating burnout than a one-week vacation. Finally, the benefits of a four-day week are supported by scientific data. The largest trial of 4-day work weeks was conducted in the U.K in 2022, with hundreds of businesses participating in the trial. After six months, 92% of businesses chose to remain on a 4-day schedule, and revenues were up 1.4% on average, compared to previous months. Employee sickness days and turnover also plummeted in the 6 month trial. With all this in mind, it is clear that the 40-hour work week is outdated. The standard schedule was not designed to help employees achieve their maximum productivity, but rather to maximize employee output, which is not what a productive work environment looks like.

Investing in public art alongside public infrastructure is not a false choice between aesthetics and essential services like roads, water, or transit; rather, it is an opportunity to design for vibrant and resilient communities. Although there are many other needs that must be met in order for our societies to flourish, including roads, water, or public transit, these are basic civic requirements, not luxuries. Art, though, helps turn an area or place into a home or community; therefore, public art and public infrastructure go hand-in-hand. By treating these two as partners, we can create an inclusive public realm. In many cities across the country, this idea is captured with a "Percent-for-Art" ordinance that requires cities to allocate a small amount (usually 1 to 2%) of all new capital improvements budget to public art. This way, art is not seen as something optional and only funded with surplus or leftover funds, but rather it is seen as an integral part of public infrastructure. For example, when a city builds a new bridge for transit purposes, the funding for public art means that bridge will become a work of art. Or when a city builds a new retention pond for flood control, that project can also include a public space for the community with local artists creating murals to enhance that public space. Public art also provides an immediate and long-term economic impact. A city can expect that by having a robust public art program and funding for public art it can expect the following results: increased civic pride and community cohesion reduced incidences of graffiti improved community public safety through the arts and placemaking increased tourism and local business sales If a city needs to choose between spending its budget dollars on public infrastructure and public art, the most reasonable and productive response is to invest in projects and programs that provide multiple public benefits while also leveraging public-private partnerships to extend a city’s public dollars to include a robust public art program. If a city requires new developers to donate a percentage of their new buildings to fund public art, or encourages corporations to help fund new public art projects, these new programs will not require the city to have to delay the fixing of roads and potholes or the replacement of sewer lines. A city that spends all its public dollars on infrastructure is merely a place where people survive; but, a city that invests both in public art and public infrastructure is a city that people want to live and grow old in.

Introduction The exponential growth of generative AI (GenAI), sparked by the global launch of large language models (LLMs) such as GPT-4, represents a seismic shift in our contemporary socio-technical landscape. Previous automation efforts had focused on lower-level manual or cognitive tasks, and knowledge work—defined by creative problem solving, divergent thought, and intellectual output—was previously thought to be immune to it. Yet, the ability of GenAI to summarize, generate text, write code, and answer questions has removed the wall that historically protected knowledge work from automation. As such, a growing research agenda has emerged to determine the impact of GenAI on knowledge work. Research on the impact of GenAI on knowledge work focuses on two competing narratives: empowerment and replacement. Those who see GenAI as a tool of empowerment view it as an "intelligence partner," or "co-pilot" that augments the capabilities of professionals. The assumption is that GenAI can free up professionals to focus on their most valuable work while automating lower-value, more mundane tasks. Indeed, research suggests that GenAI can lead to large improvements in the speed and quality of knowledge worker output, especially for junior workers across a variety of domains, from software development to management consulting. On the other hand, critics express fears that reliance on GenAI can lead to deskilling, cognitive degradation, and the loss of autonomy. The "black-box" nature of GenAI, and resulting risks such as hallucinations and algorithmic bias, are often cited in these concerns. This literature review explores how GenAI impacts knowledge work. It starts by reviewing what knowledge work is and the mechanisms by which GenAI can disrupt knowledge work. Next, this review examines research on the impacts of GenAI on knowledge workers before concluding with the implications for practice.

Cover Letter To the Hiring Team, I would like to apply for the Data Scientist opening at your company. I have more than six years' experience turning data into business insights and I look forward to contributing my experience to your team. Currently, I work as a Senior Data Analyst at [Company Name] where I worked on projects that brought back 23% of the lost clients through data analysis using the random forest and gradient boosting methods. The analysis I did predicted that customers would leave 45 days ahead of their departure, which allowed me to take actions to minimize those losses, which resulted in annual savings of $1.2M dollars. I am skilled in Python, R, SQL, and cloud computing (AWS and Azure). I can do all steps of the Data Science cycle, ranging from collecting data to the monitoring of data. I am also good at explaining complex results to people who are not experts. I can present the results of the data science project to the top management and get them to agree to changes on the product. My interest in [Company Name] comes from the belief in the impact of data science in [the industry you work in or the problem you solve]. Your initiative in [name of the project] is consistent with my personal motivation to use quantitative methods in solving problems. I look forward to taking part in projects related to [technology or methodology in the job description]. I have always tried to help junior analysts and help establish a data culture within a firm. In [previous company], I implemented code reviews which reduced deployment errors by 30 percent and helped model reproduce the analysis more reliably. I think the ability to work and lead a team is as important as technical skills, if not more. I am ready to accept both the creative and methodical challenges that come with data science. I also enjoy working in places that value development and learning. The opportunity to join [Company Name] would be a welcome progression for me because I can apply what I have learned to your needs while growing as a person and a professional. Thank you for considering my application. I would like to meet to share my experience with data science, stakeholder management, and team leadership and explore how they can benefit your firm. I look forward to discussing this with you. Sincerely, [Your Name] Be sure to fill in the details in brackets on a company, job description, and your experience, which can improve the quality of the letter.

FYI: Annual Review Cycle moving to April. Team, Just to let you know, we will be transitioning our annual review cycle from December to April effective this year. This will ensure our performance process is more measured and well thought out. December is such a busy time for everyone: end of year deadline, holiday leave, planning, holidays and other obligations all make December not the best time to do annual reviews. Moving to April will give managers and employees more time to have thoughtful discussions about performance. This will affect annual performance reviews, goal-setting discussions and compensation discussions in our review. We will provide more details about dates and timelines when we’re ready. Regular reviews and conversations continue. Managers may use upcoming 1-1s to discuss performance, development and goals. We will continue to encourage ongoing performance conversations throughout the year to ensure that performance discussions are transparent, ongoing and beneficial to both the company and the employee. This is a change for us. We’ll do our best to make it work well for everyone. Please contact your manager or HR if you have questions about how this may impact your goal setting, reviews or plans. Thanks for your understanding and flexibility. Regards, [Your Name]

We should be mindful of the difference between public art and infrastructure while acknowledging that both contribute to the quality of life, yet play different roles that may or may not overlap. We need safe and well-maintained roads, water systems, accessible sidewalks, public transit, schools, etc. If these things are broken, they should be funded first because they can be issues of public health, safety, and the economy. However, that does not mean public art is frivolous and has no civic value. Public art can be used in many ways to connect people to their neighborhoods; murals, sculptures, and performance spaces can help foster community pride, attract tourists and talent to the city, support local artists, and help public spaces feel safe and welcoming. Public art can even enhance infrastructure; think of a more aesthetically designed light rail station, or bridges that feature local artwork, or murals along public parks and walking paths. We need to avoid a conversation where it is a fight between art and infrastructure because that is not an accurate description of the problem. The solution is more nuanced. The budget should be clear and understandable to local communities, and public infrastructure needs should be identified first. There are limits to how much a city can spend on arts funding. A model to consider might be that only a small percentage of a city's infrastructure budget can be allocated to public art, but that money should only be spent after basic infrastructure needs have been met. It also helps to seek additional sources of funding to finance public art outside of public budgets, such as grants, private philanthropy, public-private partnerships (often with local not-for-profit arts organizations), or developer impact fees. A community needs both well-functioning infrastructure and meaningful spaces and places. Good infrastructure allows us to function safely each day; public art is a tool to engage and connect to our shared spaces. The funding for art should be used to create meaningful art for the local community that is inclusive of diverse backgrounds. Public art funding should not only serve wealthy or tourist-driven parts of the community but should be invested in a way that is beneficial to the community at large. Public art can be particularly impactful in neighborhoods that have been marginalized, so there should be equitable ways of distributing arts funding across the city, and local residents should be given a voice in what is funded.

Personal Statement: Computational Neuroscience My interest in neuroscience began during the summer of my high school education. My grandmother began to experience the first signs of Alzheimer's Disease, and it was truly a terrifying experience watching the person that I knew fade away. As her mind broke down, the neurons within it began to change, forming new and different networks in their attempt to recover. How does the human mind emerge from the firing of neurons? What happens when this intricate and complex machine malfunctions? These are the questions that have guided my academic career. My academic journey started with biology, followed by mathematics, but my desire to truly understand the brain led me to the field of computational neuroscience, which applies mathematical analysis and rigorous quantitative models to understand how neuronal circuits process and store information. As a student at Duke, I became fascinated with the potential of modeling neuronal circuits with mathematics, and how one might test different hypotheses by comparing the results of such computational models to experimental findings. While in graduate school, I was able to work in a computational neuroscience lab developing computational models, and this truly opened my mind to the potential of the field, specifically in applying modeling and simulations to help understand neurodegenerative diseases and psychiatric conditions. I developed a computational model that simulates spike-timing-dependent plasticity within a neural network and watched as "neuronal" connections grew and adjusted based on the inputs. This showed the beauty of mathematics, not just in helping us understand the behavior and dynamics of the brain, but in being a language to explain the very logic of thought. I believe it is essential that computational models and mathematical analysis be applied in a clinical setting, a research area currently known as computational psychiatry. With a more complete mathematical understanding of the processes involved in the development and progression of neurodegenerative diseases, and how neural networks are able to process and store information, we may be able to develop improved therapies and treatments to combat and possibly even cure such diseases. A graduate program in computational neuroscience will allow me to gain experience in machine learning, dynamical systems theory, and statistical data modeling, as well as work in a research environment with like-minded students. I am particularly drawn to your program because of the translational aspect, applying the results of basic scientific research and theoretical models directly to practical and clinical applications, and I look forward to the opportunity of pursuing this research as part of your graduate program in the near future. I truly believe in the power of applying rigorous quantitative analysis and biological insight, and in a more complete mathematical understanding of the mechanisms of neurodegenerative disease and psychiatric conditions, and am eager to help discover what the human brain has yet to reveal.

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The Universal Basic Income Debate: Human Needs vs. Economic Constraints GENEVA — Universal Basic Income (UBI) has gone from concept to global experiment as nations worldwide have struggled with inequality and looming robot jobs. While UBI programs have been beneficial to participants, they have proven to be financially and economically damaging and may not be practical for a sustainable system. Local UBI experiments like the ones in Stockton, CA and Helsinki, Finland have shown positive results for the people who receive payments, such as money with no stipulations, from the government. Results have included improved mental health, lower stress due to money and more stable housing situations. Contrary to popular opinion, money has not proven to make people lazy. The participants of the various local experiments have maintained their jobs, and in the case of Stockton, actually found employment in greater numbers than the test group. The data showed people who received payments are better able to find jobs and are able to focus on family and education. However, experts stress the major problems with UBI that could lead to its ultimate failure, especially issues of scaling. "The main cost benefit of UBI is the efficacy," said Dr. Aris Vance, a public finance expert at Horizon Institute. "In order to be effective, payments must be large. However, in order to pay for those payments, the tax burden on the population must be equally large, risking high levels of inflation." Additionally, the temporary nature of the local experiments creates a problem. Two years in a UBI program will not provide people the same results as if they could be permanently supported by a UBI system. For example, short term payments like the local programs have proven to be beneficial, while people who may have to rely on payments permanently could actually stop working and lower the tax base needed to run such a system. There is also the issue of how a UBI program will work. In order to implement a UBI program a local community would likely have to eliminate welfare systems like welfare payments and disability. These programs are often the best suited for those who cannot find or hold employment, and a flat income may not provide the same amount of help to these groups. While the trials of local UBI experiments have proven their potential, it is still too early to say if they would have the potential to be beneficial at the national scale. Many countries are beginning to look to guaranteed basic income programs that give money to lower income people.

10 Tips For Your First 10K You signed up for your first 10K! Awesome. Excited? Maybe a little scared? Maybe both. If I had known this before I ran my first 10K, I wish someone would have told me this… Don't get new shoes the week of the race I know how great new sneakers feel and they are SO much fun to run in but don't wear them on race day! You need to get your shoes run in before you have to wear them for 6.2 miles. Try your race morning breakfast in advance You need to know what you can eat that morning before you wake up. Nobody wants to try a new breakfast on race morning and have it sit weird in your stomach. Run slower It is super easy to run too fast early in a race especially when you're in all those runners and adrenaline is pumping. Slow down the first mile or two and you will be SO much happier at mile 4. Learn the course If you're able to run the course before the race you should do it. Knowing the hills and the fast flats will give you an advantage and help you mentally. Train, train, train 3-4 runs a week is enough. Start drinking a few days before Chugging a gallon of water in the hour before you run isn't going to do it. Start days prior. Know a mantra This one was so good for me to have. When I was tired and it wasn't a great day, I had one of my go-to's like "strong" "good job" or "almost done" and it gave me strength when I was tired. Dress like it's 20 degrees hotter than it is You're about to run 6 miles. It gets cold at the start line but you will warm up once you start running. Don't dress for where you are standing and wait for the race to start. Don't compare yourself to others Some people will run faster and some will walk. Don't compare yourself. This is your run and your finish. Don't get caught up in your time It's your first 10k! Just finish it and celebrate the fact you're going to have a new medal.

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Public art and infrastructure spending get set up as if they are in conflict, or as if one is significantly more valuable than the other. The latter, however, is missing the point of public art. And, in a budgeting process, it is certainly not appropriate to cut back on infrastructure in order to fund art. The latter, however, is not as straightforward as it sounds and it is not as obvious as one would expect. We can talk here of two general categories. There’s public infrastructure—roads, drainage, utilities, and the like. There is no community without these basics. If they do not work, then people have a really hard time and, perhaps even worse, are put at serious risk of bodily harm or death. No budgeting process should consider infrastructure secondary and a place for cutbacks. On the other hand, public art is more than just a nice-to-have, even though it has nothing directly to do with getting the lights on or keeping the water running. Public art is about aesthetics, economic development, tourism and community health. People need it in their lives in some way, but it should not detract from the basics, and should never compete with it. The question is not, then, how to set the two against each other or how one or the other might be prioritized at the expense of the other, but how they can each find appropriate balance within a community budgeting process. One way is to have a percent-for-art program, whereby the community allocates 1-2 percent of the money spent on public infrastructure toward that same work. This way you have artistic elements on the new bridge rather than the old one; you have a sculpture that can be sat on that is not a bench, but has the purpose of seating; you have an art-filled bus shelter. Another way to do it is through having separate funding sources. Public art doesn’t compete with public infrastructure if they have no common money, whether public funding or private philanthropy. Another way to balance these is to understand during a fiscal shortfall which must have precedence and prioritize that. A community may prioritize critical infrastructure spending first, while treating arts funding as a discretionary item that, like many discretionary items, is scaled back but not eliminated. And lastly, communities may choose to have an advisory committee to determine priorities that reflect the values of community stakeholders. Ultimately, communities must have both infrastructure and public art. Neither one makes a place more livable by itself, and neither should be compromised for the other. The budgeting process must be flexible enough to allow for both.

The Four-Day Work Week is Real Work Strategy The five-day, 40-hour work week is a concept that, remarkably, has remained mostly unaltered since it was conceived in the first half of the twentieth century, despite a sea change in the ways we work. It is time to acknowledge that the four-day work week is much more than an employee benefit, it’s a real productivity strategy that can benefit businesses, workers and society. The common criticism of the four-day work week is that reducing the number of hours worked means less will get done. But the research doesn’t back this up. Multiple pilots conducted by businesses and governments across the globe have shown productivity can stay the same or even increase when the work week is shortened. When Microsoft Japan switched to a four-day work week for a month in 2019 it found a massive 40 per cent boost to productivity. A study in Iceland of 2,500 workers — roughly 1 per cent of the country’s population — found that in almost every business productivity was either the same or better than the five-day work week. These aren’t isolated examples; this research has been repeated by businesses and governments across a wide variety of sectors and in countries across the world. How this works is because when time isn’t scarce, work is often expanded to fit around the work day. In longer work weeks, employees stretch work out to fill their working time. Meetings are scheduled that could be conducted via email. Fatigue increases throughout the week as productivity declines. Compressing the work week can force organizations to get rid of these inefficiencies. If your employees have one fewer day to work they’re far more likely to finish tasks quickly, meetings are shorter and you’ve got to be far more effective with your time. Employees are also likely to be more focused. We want time off, so we work to keep that time off. In the same vein, the four-day work week helps tackle the costly issue of employee burn out. A major benefit of the four-day work week is that it offers more time off. In addition to having more time for yourself, this can allow employees to complete non-work-related chores, spend more time with friends and family or just have more time to relax and rest before starting the week again. This can help to address one of the most costly costs of working longer hours which is employees suffering from burnout, making more mistakes, calling in sick and leaving your business. The four-day work week doesn’t just improve efficiency within individual businesses, it has a positive impact on society as well. Fewer commuting days mean fewer carbon emissions. And more time off means more time for families and leisure, and less time commuting — which can increase mental health and reduce the likelihood of burn-out — which are all crucial factors that lead to better productivity. The four-day work week is not a fringe concept or a radical experiment. It’s a real productivity strategy. If businesses can reduce the amount of time they work, but still maintain their current levels of output, while workers become happier, more relaxed and better rested, then the four-day work week makes for a lot more productive business model.

The introduction of generative artificial intelligence (AI)—specifically large language and multimodal models—into the practice of knowledge work, which entails the generation, understanding, communication, and application of knowledge, has been swift. Unlike previous technological revolutions in automation that mainly impacted routine manual jobs and back-office functions, generative AI can generate text, code, images, summaries, analyses, and recommendations, thereby automating tasks in professional, higher-skill fields. This has led to considerable research attention in information systems, management, labor economics, education, and organization research. There is a significant amount of literature that focuses on the opportunities that generative AI presents to knowledge workers. Research and field experiments find some evidence that generative AI tools can save time writing, coding, researching, customer support, and decision support, for example, in particular settings and for particular levels of task complexity, but more generally for moderately complex knowledge tasks. Generative AI could aid in composing documents, understanding information, brainstorming, troubleshooting code, and translating information to different audiences. This could lead to changes that could potentially enable professionals to devote more of their time and expertise to judgment and critical thinking, creative expression, persuasion, and decision-making. The technology could also potentially help bridge the gap in the performance of novice and expert workers by providing the support and guidance (e.g., in the form of examples and templates) that less experienced individuals might lack or require expert help to acquire. However, there are also numerous examples in the literature that demonstrate the limitations and risks associated with generative AI. The technology could generate factual errors or false or biased information, for example, or provide misleadingly convincing results with poor underlying reasoning, potentially with few outward signs of error. This would lead to new requirements for the verification and scrutiny of information. Moreover, knowledge work is likely not to be entirely automated, but rather may become a task to provide oversight (i.e., to check the quality of AI-generated material), create prompts, ensure quality, and determine ethical issues, with potentially more of a need for domain expertise. There could be risks in deskilling, dependence on the technology, intellectual property concerns, data privacy issues, responsibility of output, and the transparency of generative AI. Such issues could be particularly impactful in law, health care, financial management, journalism, education, and government, where erroneous information could have serious social and professional repercussions. Generative AI may also have different organizational implications for knowledge workers. For example, it could be the case that generative AI is used to support and empower workers in the workplace. In this regard, there are a number of organizational and managerial factors and contextual settings, including workplace norms and management approaches, information infrastructure and regulations, skills of workers, that affect the nature of change with generative AI. Thus, it is plausible that the technology has heterogeneous effects across sectors and occupations; generative AI is unlikely to be universally transformative, given that the nature and extent of change depends on the task environment, the degree of knowledge-worker control over workflow, the institutional context, and the training of the worker. This review paper contributes to the understanding of how the introduction of generative AI to the practice of knowledge work by reviewing and synthesizing studies of the generative AI technology, including the literature on productivity, changes in skills and knowledge work, organizational adoption, and ethical issues. The review paper provides an understanding of generative AI to knowledge work, situating it within the broader discussion of automation and professional work and helping to identify both the opportunities and challenges of this novel technology.

If you want to throw a five-course dinner party, you probably think it's a crazy, unachievable idea. But, with some careful planning, a five-course dinner party can be fun and manageable. You want to relax and have a good evening while everything goes as scheduled. The food has to go great together, and everything needs to flow in a specific order. You want your guests to leave full, and not have an upset stomach from eating too much. Choose dishes that will not all need to be made at the same time. For example: serve appetizers that can be made beforehand (crudités, smoked salmon with dill crème fraiche or crostini). Serve a soup that can be kept simmering in a pot on the stove. Serve something baked, roasted, grilled, or fried for the main course. Choose a small cheese course and a simple dessert (tarts, mousse, or panna cotta, for instance). Think about how much food is appropriate to give. It is a five-course meal, so each dish should be modest in size. You should serve your guests enough to satisfy them, not make them feel like they have eaten 3 days worth of food all in one night. Make sure to cook a little more than you think you need in case someone needs to go back for more or something got spilled on it, but no more. Plan your prep work. The appetizers and dessert can be made up to one day ahead. Chop up all the vegetables and marinate any meat or chicken, up to one day ahead. Make all the sauces and dressings you can the day before. Buy the bread two days ahead. Drink can be bought two days in advance, unless you are serving fresh juices. Set the table. You will need enough plates for at least three courses, so if you don't have enough you will need to quickly re-plate the courses so the guests can have a new clean plate for every dish. You don't need to have real fine china, it is okay to just have a basic plate to each place. Make sure you have enough glasses, napkins, and silverware. Put some pretty napkin holders down, and flowers or candles on the table in the middle. Drinks. Set out drinks you have purchased beforehand. Serve 1-2 alcoholic drinks and 1-2 non-alcoholic drinks. If guests are coming over early, you might want to serve a welcome drink or a starter drink (a wine, champagne or a spritzer for a good effect), so while everyone is drinking it you can get the first appetizer out. When dinner guests arrive, you can set out their drinks, pour them a glass or serve them the drink they ordered before serving the first course. Serve your meals and let people take as much of it as they want, you don't have to worry about how they take too much food, as long as they finish before the next course is served, they'll be okay. Try to eat at least 30 minutes a course (the main course can take longer, as much as 40-45 minutes), as you don't want your guests to be feeling overwhelmed and uncomfortable, so the meal can last 1 hour and 30 minutes or so. You might want to pre-plate the courses so it is easier on you to serve it to guests, it can be elegant too. Let your guests help! If one of your dinner party guests offers to pour you a glass of wine or clear off a plate, let him or her do so. You don't need it to be perfect, it just has to be warm, welcoming, organized and a nice time. If you keep it relaxed and well-timed, your dinner guests will as well.

Pilot programs in cities reveal benefits and drawbacks of cash grants As universal basic income programs are tested in a growing number of cities, early findings are beginning to inform a key public policy discussion: whether unconditional cash transfers might alleviate financial hardship without generating new financial or political problems. Universal basic income, or UBI, generally refers to regular payments of cash given to residents with minimal conditions. Though the term "universal" implies such a program would benefit everyone, nearly every experiment so far has been smaller and more targeted. Municipalities in the U.S., Canada, Europe and Africa have made monthly payments to low-income adults, unemployed people, young adults and people at risk of becoming homeless. The proponents of the pilots maintain that direct payments of cash are one of the simplest forms of aid in the world for those struggling with higher expenses. Participants of many of these experiments have reported spending the cash on rent, grocery bills, utility costs, transport, child care and payments on past debt. They note that, unlike social welfare programs that are highly bureaucratic, cash transfers give people the opportunity to decide exactly how to best use their money. "People know exactly what their own household needs," said a researcher who studies such policies. "That is one of the primary advantages of cash. It's treating people as decision-makers, not as customers of a system." Other pilots have reported benefits to people's mental health, financial well-being and work-related behaviors. Some participants were better able to keep jobs, find better work, pay unexpected expenses that would have caused a financial crisis otherwise. But the programs also come with limitations. Perhaps the most important is money. A nationwide UBI could potentially cost trillions in taxpayer funding in the US alone, especially as payments are typically sized with poverty in mind. Policymakers who would decide whether to pursue it would have to determine how to pay for it, which could require tax increases, cuts to existing social welfare programs or new government borrowing. Critics of large-scale UBI initiatives also caution that small-scale pilots are not necessarily a good guide for what would happen if a UBI were put on a permanent and large basis. Pilots tend to have a limited population and are short-term programs. They are also usually funded by non-government sources like foundations. A larger scale UBI could have a variety of impacts in the economy on the labor market, wages, inflation, government budgets and other economic variables that would not necessarily be visible in a pilot program. Another concern is targeting. A universal UBI eliminates some bureaucratic hurdles and does not come with any stigma attached, but it also benefits people who do not need help. More limited and targeted cash transfers could potentially be less expensive and more targeted on fighting poverty, but may require additional eligibility criteria and increase bureaucratic burdens in ways that could exclude some individuals who need aid. How it interacts with social welfare benefits is also a question. A UBI program could reduce the amount of cash that recipients receive through existing programs, meaning they could see less benefit from a UBI than expected if they receive less through other channels, or they might not be able to receive both benefits at the same time. And, of course, putting the two programs together could make the cost of UBI even higher. While the pilots in the past few years have not closed the debate, they certainly have changed it, as questions around the efficacy and benefits of cash have shifted towards the magnitude of the benefit, and its impact on different demographics and over longer periods of time, including the total cost of such a program.

I’ve always wanted to understand how our brain operates: how can billions of neurons that have noise and fire sparsely work together to produce consciousness, memory, and human actions? The structure and the physical functions of the brain are studied in anatomy and physiology, and we study how our behavior is produced. But we need to study the mathematics to understand how our brain works and produces consciousness, memory, and human behavior. If we view our brain as the most advanced computing machinery in the known world, then the key is how it processes information. I became interested in computational neuroscience when I was an undergraduate student. Neural network modeling was one of my favorite classes, which taught me that many cognitive functions could be formulated as problems of computing and information. I learned many of the important functions of the brain, including how the brain processes external stimuli and makes memories, through computational techniques and math. This motivated me to take more classes on linear algebra, probability, and machine learning, and to apply them to study how neurons work and connect with each other. I am attracted to your program for a number of reasons. I hope to work on building better computational models that reflect the real physical systems of neural circuits and neurons instead of just using existing data from experiments. I want to use these models to understand and solve certain neurological problems such as disorders in cognition and mental diseases such as epilepsy and schizophrenia. When neurological diseases occur, the neural circuits and the way information is exchanged in the brain become corrupted or broken. We may not see what is happening in the circuits, because the signals in the brain may not be obvious. In these cases, it is important to understand how to repair these circuits. In a computer simulation, we may be able to identify which part of the system causes the disease and use it to treat it. Furthermore, a good computer simulation of neural circuits may help us to develop better machine learning algorithms. Machine learning is an important technology that can learn from large amounts of data, and many of these technologies are inspired by how the neural circuits in our brain work. If we learn better how neural circuits work and why the human brain can learn so well with energy efficiency, we may be able to design our own more effective learning algorithms. The research you do is very interesting and important. I hope to contribute my knowledge to your research program, to improve the state of the field, and understand how our brain functions. I want to continue to be a researcher at the intersection of computational and biological sciences. It is a good fit for the career path I have chosen and it is one of the best programs to prepare me. I am excited to further pursue a computational neuroscience degree at your school, to improve my theoretical skills, and to learn more about the neuroscience and machine learning algorithms.

UBI Trials Show Success, but Uncertainty Grows Universal Basic Income Trials Yield Positive Results, But Questions of Cost and Scale Loom Universal Basic Income Trials Yield Positive Results, But Questions of Cost and Scale Loom