Scam shopping websites are common around the holidays and cybersecurity researchers have spotted examples masquerading as LL Bean, Wayfair, and others.

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Score deeply discounted floor coverings from some of my favorite brands — Kelly Clarkson, Magnolia Home by Joanna Gaines and Loloi.

These are the Wayfair Way Day rug deals I'm eyeing as an interior designer — save up to 80% off

Wayfair’s Way Day sale is here — get in on huge deals from brands like Shark, Bissell, Staub and more.

Way Day sale 2024: Get up to 80% off on top brands this weekend

Wayfair’s Way Day 2024 sale has discounts up to 80% off on outdoor furniture, home decor, kitchen appliances and way more.

Wayfair Way Day 2024: 20 best deals | CNN Underscored

Wayfair’s largest sale of the year, Way Day, is finally here and the event includes markdowns across kitchenware, outdoor seating, home decor, furniture and more. Browse deals up to 80 percent off on popular brands like Le Creuset, KitchenAid, Joss &amp; Main, and Shark.

The 64 Best Wayfair Way Day Deals That Can’t Be Missed—Save Up to 80%

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Way Day 2024: How to find best deals on Wayfair

Shop the best Way Day couch deals on Wayfair, including velvet couches, loveseats, sleeper sofas, cloud couches and more, during Way Day from May 4-6, 2024.

16 Best Way Day Couch Deals 2024 — Best Sofas on Sale at Wayfair

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6 Early Deals to Shop Ahead of Wayfair's Way Day Sale | Us Weekly

As spring blossoms and the outdoors beckons, Way Day—an eagerly awaited annual sales event by Wayfair—promises irresistible discounts on a variety of items, including a vast selection of outdoor furniture.

Way Day deals: The best deals on outdoor furniture on Wayfair

Wayfair Way Day 2024 is happening May 4 to May 6. Here&#x27;s what you need to know and the best deals on furniture, mattresses, and patio sets for up to 80% off.

Wayfair Way Day Sale 2024: Everything to Know About the Big Sale

Wayfair

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Optimizing Tech Design Meetings with Structured Ticket Templates

Optimaliseer tegniese ontwerpvergaderings met gestruktureerde kaartjiesjablone

Аптымізацыя сустрэч па тэхнічным дызайне з дапамогай структураваных шаблонаў білетаў

Optimalizace schůzek technického designu pomocí strukturovaných šablon vstupenek

Optimización de reuniones de diseño tecnológico con plantillas de tickets estructuradas

構造化されたチケット テンプレートを使用して技術設計会議を最適化する

ტექნიკური დიზაინის შეხვედრების ოპტიმიზაცია ბილეთების სტრუქტურირებული შაბლონებით

Оптимизирање на состаноци за технолошки дизајн со структурни шаблони за билети

संरचित टिकट टेम्प्लेटहरूको साथ टेक डिजाइन बैठकहरू अनुकूलन गर्दै

Optimaliseren van technische ontwerpvergaderingen met gestructureerde ticketsjablonen

Go kaonafatša Dikopano tša Moralo wa Theknolotši ka Dithempleite tša Thekethe tše di Rulagantšwego

د جوړښت شوي ټکټ ټیمپلیټونو سره د ټیک ډیزاین غونډې اصلاح کول

Ukuthuthukisa Imihlangano Yedizayini Yezobuchwepheshe Ngezifanekiso Zamathikithi Ahleliwe

I was listening to the podcast this morning featuring Wayfair’s founders and found a tremendous amount of value and parallels that can benefit software startups — particularly bootstrapped companies. How I Built This Wayfair didn’t start selling a plethora of home decor and furniture from day one. In fact, they started with a single item. Wayfair’s founders, and , noticed brick-and-mortar retail stores weren’t able to supply consumers with the selection most people hoped for. By trying to offer large retailers were only able to offer a couple choices of each item. Steve Niraj everything, Steve and Niraj saw this as an opportunity to choose one specific item and offer the largest selection of it. To be spot to buy whatever it was. the Rather than choosing an item they were interested in, they methodically researched various options by looking at search queries to see what consumers wanted. And as it would be, at the time, consumers wanted TV stands and speaker racks. By focusing just on stands and racks, they were able to be the best at it (have the greatest selection, rank at the top of Yahoo/Google searches) and carve out a sizable market share. Once their first site was a success, Steve and Niraj replicated the same approach with TV wall mounts, grandfather clocks, dinner plates, and many other products until they eventually combined them all into Wayfair. In the tech world, we often talk about MVPs or minimum viable products. Starting with just a single product and being the best at it was Wayfair’s version of an MVP. Many founders get this wrong, me included. Many founders look at their competition and say, “hmm, Ashley’s Furniture sells TV stands, couches, ottomans, tables, dressers, beds, etc… therefore, in order to compete, I need to sell all of these items.” Being impossible to build a complete product/store from day one, they do a half-assed job and eventually go out of business because they’re trying to compete with some giant that has been around for years. Instead, find your niche. Find one area where you can really excel — where you can be the best option out there. Your value prop will be clearer, your site will rank better on Google, and your project will be manageable in size. Once you’ve established dominance with the small niche you started with, you can begin to carve into the larger market.

What you can Learn from Wayfair — the online furniture retailer generating $5B

Before diving in, I want to clarify that I’m not a project or product manager. As a senior engineer, I do engage in planning and prioritization, but I’m not a SCRUM master or a certified PMP. My insights are drawn purely from my experiences as a developer, not formal training in project management. Over the past 15 years, I’ve worked with various teams and managers, but one consistent truth stands out: time is our most valuable asset. To maintain a proper work-life balance, managing and protecting that time effectively is essential. While project management has long sought to move away from time-based estimates, I believe that, as an engineer, these estimates are crucial for shielding me from the burden of long hours. The real issues we need to address are feature creep and poor estimation practices. Let’s explore why the industry is moving away from time-based estimates, and then I’ll explain why I think they can be effective when used correctly. Why Not Time-Based Estimates? Developers and project managers often have reservations about time-based estimates for several reasons: Uncertainty: Software development is unpredictable. Changing requirements, technical challenges, and team dynamics can all impact task durations.


Pressure: Time estimates can create pressure to meet deadlines, potentially leading to rushed work and compromised quality.


Miscommunication: Estimates may be misunderstood or misrepresented, resulting in unrealistic expectations from stakeholders.


Focus on Speed: Time estimates can shift the emphasis from quality and creativity to merely getting the job done.


Variability: Team members work at different paces and have varying levels of experience, complicating the creation of consistent estimates.


Retrospective Accountability: Consistently inaccurate estimates can lead to blame games, hindering learning and improvement.


Overemphasis on Metrics: A strong focus on time estimates can cultivate a culture more concerned with metrics than with outcomes and customer satisfaction. Pro-Time-Based Arguments Upon examining these concerns, I find flaws in many of them. It seems that the common objections to time-based estimates stem from poor estimation practices or poorly planned deadlines. Issues like uncertainty, pressure, and retrospective accountability often arise from inadequate processes. Miscommunication is a separate issue altogether, and variability and an overemphasis on metrics relate more to planning and culture than the estimation process itself. The real challenge appears to be that many development managers or teams lack the skills or maturity for effective time-based estimation, rather than time-based estimates being inherently flawed. Why Are Time-Based Estimates Important? Time is a precious resource. We have twenty-four hours in a day, typically allocating around eight for sleep and another eight for work. This leaves us with limited time for commuting, family, and personal interests. Many of us find that maintaining a work-life balance requires strict adherence to an eight-hour workday. However, employers often have their own agendas and expect a consistent flow of completed tasks that can often force employees beyond their expected eight hours. So, how do we demonstrate productivity while protecting our time? For me, time-based estimation is the answer. At Wayfair, we refined our sprint process into something remarkable. Every sprint was well-sized, our team was engaged and productive, and we rarely had to work extra hours to meet deadlines. Our project manager established an efficient and informed approach that allowed us to function smoothly, grounded in time-based estimation. Here’s how we did it: Technically, we used “Story Points” based on “days,” allowing for splitting into two-hour increments (0.25 pts). Days were assigned Fibonacci numbers (1, 2, 3, 5), and anything beyond five needed to be broken into smaller tickets. The estimation process included a weekly meeting (or biweekly if necessary) where an engineer familiar with a ticket presented it to the team. We discussed potential solutions, identified unknowns, and noted any external influences impacting our estimates. After the discussion, we would vote on how many “days” the ticket should take, considering developers’ varying levels of experience. If estimates differed, we opted for the largest or most common estimate based on the spread. If we couldn’t reach a consensus, we would discuss until the team reached a satisfactory conclusion. Crucially, everyone had a voice in sizing the tickets, fostering a collaborative atmosphere. The next step involved assessing developer capacity and ensuring our sprints aligned with that capacity. With our tickets focused on expected completion times, we established capacity as follows: Junior Dev: 7 days of work per sprint
Mid/Senior Dev: 8 days of work per sprint
Leads: 6 days of work per sprint These figures are adjusted for workload and vacations. Once we determined each developer's capacity, we held a grooming session to refine the upcoming sprint. By this point, all tickets were estimated, and developer capacity was clear. As an example, suppose we had 27 tickets totaling 56 points, and our team of six developers (two junior, three senior, one lead) had 47 points available. This discrepancy meant we needed to remove nine days of work. Our project manager would then prioritize which less critical tickets to drop, ensuring that developers championed any tickets relevant to their work to prevent important tasks from being sidelined. By the end of this session, we could confidently move forward with a manageable sprint. Conclusion Time is paramount. As a developer who values a healthy work-life balance, I aim to complete my tasks within a 40-hour week, and I need my manager to recognize my efforts. Moving away from time-based estimates can create a situation where developers are expected to work extra hours to complete their tasks, as there’s no reliable method to size the workload effectively. By committing to a robust process that amplifies every developer’s voice and incorporates time as a core element, we can ensure that everyone’s time is respected while still delivering results. This approach helps avoid issues like sprints bleeding into one another or failing to meet deadlines, ensuring work is completed on schedule. Of course, no system is perfect. There will inevitably be tickets that need to carry over, and adjustments may be necessary over time. Nevertheless, this approach provided me with the best working environment and estimation/planning process I have ever experienced. Before diving in, I want to clarify that I’m not a project or product manager. As a senior engineer, I do engage in planning and prioritization, but I’m not a SCRUM master or a certified PMP. My insights are drawn purely from my experiences as a developer, not formal training in project management. Over the past 15 years, I’ve worked with various teams and managers, but one consistent truth stands out: time is our most valuable asset. To maintain a proper work-life balance, managing and protecting that time effectively is essential. While project management has long sought to move away from time-based estimates, I believe that, as an engineer, these estimates are crucial for shielding me from the burden of long hours. The real issues we need to address are feature creep and poor estimation practices. Let’s explore why the industry is moving away from time-based estimates, and then I’ll explain why I think they can be effective when used correctly. Why Not Time-Based Estimates? Developers and project managers often have reservations about time-based estimates for several reasons: Uncertainty: Software development is unpredictable. Changing requirements, technical challenges, and team dynamics can all impact task durations. Pressure: Time estimates can create pressure to meet deadlines, potentially leading to rushed work and compromised quality. Miscommunication: Estimates may be misunderstood or misrepresented, resulting in unrealistic expectations from stakeholders. Focus on Speed: Time estimates can shift the emphasis from quality and creativity to merely getting the job done. Variability: Team members work at different paces and have varying levels of experience, complicating the creation of consistent estimates. Retrospective Accountability: Consistently inaccurate estimates can lead to blame games, hindering learning and improvement. Overemphasis on Metrics: A strong focus on time estimates can cultivate a culture more concerned with metrics than with outcomes and customer satisfaction. Uncertainty: Software development is unpredictable. Changing requirements, technical challenges, and team dynamics can all impact task durations. Uncertainty : Software development is unpredictable. Changing requirements, technical challenges, and team dynamics can all impact task durations. Uncertainty Pressure: Time estimates can create pressure to meet deadlines, potentially leading to rushed work and compromised quality. Pressure : Time estimates can create pressure to meet deadlines, potentially leading to rushed work and compromised quality. Pressure Miscommunication: Estimates may be misunderstood or misrepresented, resulting in unrealistic expectations from stakeholders. Miscommunication : Estimates may be misunderstood or misrepresented, resulting in unrealistic expectations from stakeholders. Miscommunication Focus on Speed: Time estimates can shift the emphasis from quality and creativity to merely getting the job done. Focus on Speed : Time estimates can shift the emphasis from quality and creativity to merely getting the job done. Focus on Speed Variability: Team members work at different paces and have varying levels of experience, complicating the creation of consistent estimates. Variability : Team members work at different paces and have varying levels of experience, complicating the creation of consistent estimates. Variability Retrospective Accountability: Consistently inaccurate estimates can lead to blame games, hindering learning and improvement. Retrospective Accountability : Consistently inaccurate estimates can lead to blame games, hindering learning and improvement. Retrospective Accountability Overemphasis on Metrics: A strong focus on time estimates can cultivate a culture more concerned with metrics than with outcomes and customer satisfaction. Overemphasis on Metrics : A strong focus on time estimates can cultivate a culture more concerned with metrics than with outcomes and customer satisfaction. Overemphasis on Metrics Pro-Time-Based Arguments Upon examining these concerns, I find flaws in many of them. It seems that the common objections to time-based estimates stem from poor estimation practices or poorly planned deadlines. Issues like uncertainty, pressure, and retrospective accountability often arise from inadequate processes. Miscommunication is a separate issue altogether, and variability and an overemphasis on metrics relate more to planning and culture than the estimation process itself. The real challenge appears to be that many development managers or teams lack the skills or maturity for effective time-based estimation, rather than time-based estimates being inherently flawed. Why Are Time-Based Estimates Important? Time is a precious resource. We have twenty-four hours in a day, typically allocating around eight for sleep and another eight for work. This leaves us with limited time for commuting, family, and personal interests. Many of us find that maintaining a work-life balance requires strict adherence to an eight-hour workday. However, employers often have their own agendas and expect a consistent flow of completed tasks that can often force employees beyond their expected eight hours. So, how do we demonstrate productivity while protecting our time? For me, time-based estimation is the answer. At Wayfair, we refined our sprint process into something remarkable. Every sprint was well-sized, our team was engaged and productive, and we rarely had to work extra hours to meet deadlines. Our project manager established an efficient and informed approach that allowed us to function smoothly, grounded in time-based estimation. Here’s how we did it: Technically, we used “Story Points” based on “days,” allowing for splitting into two-hour increments (0.25 pts). Days were assigned Fibonacci numbers (1, 2, 3, 5), and anything beyond five needed to be broken into smaller tickets. The estimation process included a weekly meeting (or biweekly if necessary) where an engineer familiar with a ticket presented it to the team. We discussed potential solutions, identified unknowns, and noted any external influences impacting our estimates. After the discussion, we would vote on how many “days” the ticket should take, considering developers’ varying levels of experience. If estimates differed, we opted for the largest or most common estimate based on the spread. If we couldn’t reach a consensus, we would discuss until the team reached a satisfactory conclusion. Crucially, everyone had a voice in sizing the tickets, fostering a collaborative atmosphere. The next step involved assessing developer capacity and ensuring our sprints aligned with that capacity. With our tickets focused on expected completion times, we established capacity as follows: Junior Dev: 7 days of work per sprint Mid/Senior Dev: 8 days of work per sprint Leads: 6 days of work per sprint Junior Dev: 7 days of work per sprint Mid/Senior Dev: 8 days of work per sprint Leads: 6 days of work per sprint These figures are adjusted for workload and vacations. Once we determined each developer's capacity, we held a grooming session to refine the upcoming sprint. By this point, all tickets were estimated, and developer capacity was clear. As an example, suppose we had 27 tickets totaling 56 points, and our team of six developers (two junior, three senior, one lead) had 47 points available. This discrepancy meant we needed to remove nine days of work. Our project manager would then prioritize which less critical tickets to drop, ensuring that developers championed any tickets relevant to their work to prevent important tasks from being sidelined. By the end of this session, we could confidently move forward with a manageable sprint. Conclusion Time is paramount. As a developer who values a healthy work-life balance, I aim to complete my tasks within a 40-hour week, and I need my manager to recognize my efforts. Moving away from time-based estimates can create a situation where developers are expected to work extra hours to complete their tasks, as there’s no reliable method to size the workload effectively. By committing to a robust process that amplifies every developer’s voice and incorporates time as a core element, we can ensure that everyone’s time is respected while still delivering results. This approach helps avoid issues like sprints bleeding into one another or failing to meet deadlines, ensuring work is completed on schedule. Of course, no system is perfect. There will inevitably be tickets that need to carry over, and adjustments may be necessary over time. Nevertheless, this approach provided me with the best working environment and estimation/planning process I have ever experienced.

A Developer's Perspective on Time-Based Estimates

Across 2022 and 2023, hundreds of workers lost their jobs in a post-pandemic environment which saw tech companies tighten their belts in response to changing customer demand, rising inflation, and a spate of over-hiring during the Covid-19 crisis. In fact, according to data from the redundancy tracking website , more than 420,000 workers lost their jobs across that period. Towards the end of last year, things seemed to settle––but now, are back. Layoffs.fyi layoffs Almost 50,000 people have been laid off so far this year, with Salesforce, SAP, eBay, Google, PayPal, Amazon, Wayfair, and Vroom accounting for a significant slice of the pie. Microsoft also made 1,900 redundancies in its 22,000-person gaming division, which accounts for 8.64% of its staff. Opinions are divided as to why companies are once more cutting thousands of jobs, but continued efforts to rationalize cost bases are likely to be part of the picture. Additionally, high-interest rates and the rise of AI are two other factors that may be contributing to job cuts. Fears and worries post-layoff If you’ve just been told your job is being axed, the reasons why may provide scant comfort. There are a whole host of worries and fears that come with redundancy, and you may worry about money or the effect it may have on your career. You may also experience psychological issues, because losing a job can lead to depression and anxiety, for example. Some people can feel shame or failure, and worry about their futures. Minding your mental health around a layoff is crucial, and it is also key to understand that this isn’t a reflection on you or your abilities, and it is rarely personal. Additionally, many people experience at least one redundancy during their working lives, so try to keep this in mind as you navigate the first few days after you get the news. That’s one very important factor to be aware of, but here’s what else you can do to rebound after you get bad news. Look at your money situation Finances can cause huge stress during a redundancy, so assess your financial situation and create a budget to manage your expenses during the transition period. This may involve cutting back on non-essential spending, applying for social security support, or exploring other options for financial assistance, such as a mortgage holiday if needed. Refine your resume Take some time to polish your resume, adding any new experience, skills, and professional certifications. Also, update your professional social networks you’re on. Specifically on your professional social presence, make sure you set yourself to “open to work” as you’ll have a better chance of being contacted by recruiters looking to hire for your skill set. Networking works Thanks to referral bonuses, it’s now far easier to leverage your contacts to boost your job search. Reach out to those on your professional network, including former colleagues, mentors, and industry contacts, to inform them of your situation and ask about potential job opportunities or referrals. Begin looking for a job Start actively searching for new job opportunities through . Decide what roles and areas you’d like to work for, and consider broadening your search to include industries or roles outside of your previous experience if necessary. online job boards Recent data from Glassdoor indicates that tech candidates should look to non-tech competitors who are also hiring for their skills. That’s because many tech candidates gravitate towards “traditional” tech firms, so non-traditional companies will often pay more to attract the best talent. Be patient It can take a lot of time, effort, and patience to get a new job. To help, think about new skills development. Enhance your existing skills and qualifications through online courses, workshops, or certifications that can make you more competitive in the job market. If you’re just starting on your new job hunt, then the is a great place to begin. It contains thousands of open roles, like these three. HackerNoon Job Board Whisker, formerly known as AutoPets, is the maker of Litter-Robot, Feeder-Robot, and Litterbox.com. The will be responsible for ongoing AWS IoT development, integrations with third-party IoT products, co-management of an agency partner, system debugging and more. You will also provide support to the mobile application team when developing new interfaces and debugging issues. Senior Software Engineer In Washington, the MetroStar is seeking a to develop web applications using .NET / C# / Entity Framework / MVC / RESTFul APIs as well as establish and maintain a high level of client trust and confidence with your software development skills, and creatively think out of the box to help out with troubleshooting issues and providing innovative solutions that fit customers' needs. Full-Stack (.NET) Developer Or, discover this job at G-Research in Dallas. You’ll join the Infrastructure Development team, providing tooling to maintain and develop the smooth running of the company’s IaaS platform and allow other software development teams to leverage it to its full potential. Software Engineer Accelerate your career in tech today via the HackerNoon Job Board By Kirstie McDermott

Tech Layoffs Are Back: Here’s How To Rebound if You’ve Just Been Laid Off

This paper is under CC BY 4.0 DEED license. available on arxiv Authors: (1) Ehsan Toreini, University of Surrey, UK; (2) Maryam Mehrnezhad, Royal Holloway University of London; (3) Aad Van Moorsel, Birmingham University. Table of Links Abstract & Introduction Background and Related Work FaaS Architecture Implementation and Performance Analysis Conclusion Acknowledgment and References 2 Background and Related Work One of the benefits of auditing ML-based products relates to trust. Trust and trustworthiness (in socio-technical terms) are complicated matters. Toreini et. al [32] proposed a framework for trustworthiness technologies in AI–solutions based on existing social frameworks on trust (i.e. demonstration of Ability, Benevolence and Integrity, a.k.a. ABI and ABI+ frameworks) and technological trustworthiness [30]. They comprehensively reviewed the policy documents on regulating AI and the existing technical literature and derived any ML–based Table 1: Features of FaaS and comparison with other privacy–oriented fair ML proposals (support: full: ✓, partial: ✙, none: ✗) solution needs to demonstrate fairness, explainability, auditability, and safety and security to establish social trust. When using AI solutions, one cannot be assured of the fairness of such systems without trusting the reputation of the technology provider (e.g., datasets and ML models). It is commonly believed that leading tech companies do not make mistake in their implementation [8]; however, in practice, we often witness that such products indeed suffer from bias in ML [28, 23]. 2.1 Fairness Metrics There exist several fairness definitions in the literature. Designing a fair algorithm requires measuring and assessment of fairness. Researchers have worked on formalising fairness for a long time. Narayanan [24] lists at least 21 different fairness definitions in the literature and this number is growing, e.g., [5, 6]. Fairness is typically expressed as discrimination in relation to data features. These features for which discrimination may happen are known as Protected Attributes (PAs) or sensitive attributes. These include, but are not limited to, ethnicity, gender, age, scholarity, nationality, religion and socio-economic group. The majority of fairness definitions expresses fairness in relation to PAs. In this paper, we consider Group Fairness, which refers to a family of definitions, all of which consider the performance of a model on the population groups level. The fairness definitions in this group are focused on keeping decisions consistent across groups and are relevant to both disparate treatment and disparate impact notions, as defined in [9, 15]. In this paper, we will focus on the computations based on the above three fairness metrics. For this computation, the auditor requires to have access to the three pieces of information for each elements in the dataset: (1) the sensitive group membership (binary value for A demonstrating if a sample does or does not belong to a group with PAs) (2) the actual labelling of the sample (binary value for Y ) (3) the predicted label of the sample (binary value for Yˆ ). The ML system transfers this information for each sample from their test set. Then, the auditor uses this information to compute the above fairness metrics. Note that while we consider the above metrics for our protocol and proof-ofconcept implementation in next sections, our core architecture is independent of metrics, and the metric set can be replaced by other metrics too (Fig. 1). 2.2 Auditing ML Models for Fairness The existing research in fair ML normally assumes the computation of the fairness metric to be done locally by the ML system, with full access to the data, including the private attributes [15, 6, 5]. However, there is a lack of verifiability and independence in these approaches which will not necessarily lead to trustworthiness. To increase trust in the ML products, the providers might make the trained model self–explaining (aka transparent or explainable). There is also the transparent–by–design approach [12, 2, 34]. While this approach has its benefits, it is both model–specific and scenario–specific [25]; thus it cannot be generalised. There is also no trusted authority to verify such claims and explanations. Moreover, in reality, the trained model, datasets and feature extraction mechanisms are company assets. Once exposed, it can make them vulnerable to the competitors. Another approach to provide transparency to the fairness implementation comes through the black–box auditing, also known as adhoc [12, 22, 26]. In this way, the model is trained and audited for different purposes [1]. This solution is similar to tax auditing and financial ledgers where accountants verify and ensure these calculations are legitimate.However, unlike the well–established body of certifications and qualifications for accountants in tax auditing and financial ledgers; there does not exist any established processes and resources for fairness computation in AI and ML. The concept of a service that calculates fairness has been proposed before, e.g., in [33]. The authors introduced an architecture to delegate the computation of fairness to a trusted third party that acts as a guarantor of its algorithmic fairness. In this model, the fairness service is trusted both by the ML system and the other stakeholders (e.g. users and activists). In particular, the ML system must trust the service to maintain the privacy of data and secrecy of its model, whilst revealing to the trusted third party the algorithm outcome, sensitive input data and even inner parameters of the model. This is a big assumption to trust that the third party would not misuse the information and hence the leakage of data and model information is not a threat. To address these limitations, Kilbertdus et al. [19] proposed a system known as ‘blind justice’, which utilises multi–party computation protocols to enforce fairness into the ML model. Their proposal considers three groups of participants: User (data owner), Model (ML model owner) and the Regulator (that enforces a fairness metric). These three groups collaborate with each in order to train a fair ML model using a federated learning approach [35]. The outcome is a fair model that is trained with the participation of these three groups in a privacy-preserving way. They only provide a limited degree of verifiability in which the trained model is cryptographically certified after training and each of the participants can make sure if the algorithm has not been modified. It should be noted that since they operate in the training stage of the ML pipeline, their approach is highly dependent on the implementation details of the ML model itself. Jagielski et al. [17] proposed a differential privacy approach in order to train a fair model. Similarly, Hu et al. [16] used a distributed approach to fair learning with only demographic information. Segal et al. [29] used similar cryptographic primitives but took a more holistic approach towards the computation and verification of fairness. They proposed a data-centric approach in which the verifier challenges a trained model via an encrypted and digitally certified dataset using merkle tree and other cryptographic primitives. Furthermore, the regulator will certify the model is fair based on the data received from the clients and a set of dataset provided to the model. Their approach does not provide universal verifiability as the regulator is the only party involved in the computation of fairness. More recently, Park et al. [27] proposed a Trusted Execution Environment (TEE) for the secure computation of fairness. Their proposal requires special hardware components which are cryptographically secure and provide enough guarantees and verification for the correct execution of the code. The previous research generally has integrated fairness into their ML algorithms; therefore, such algorithms should be redesigned to use another fairness metric set. As it can be seen in Table 1, FaaS is the only work which is independent of the ML model and fairness metric with universal verifiability, and hence, can be used as a service.

Background and Related Work on Privacy-preserving Computation of Fairness for ML Systems 

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86 Stories To Learn About Startup

Let's learn about via these 26 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Meetings /Learn Repo 1. Discover the Truth About Time Management When you’re in a serious discussion with people who are beside you, make sure you turn off your cell phone. 2. Forget What You See on TV: Good Ideas Don’t Come From Brainstorming The reality of brainstorming sessions is far from the idealized scenes made to entertain us. 3. Make Time for Making: 7 Ways Builders Control Their Days You have many good intentions at the start of the day, but before you know it, one request comes on top of another and you have a day full of meetings. 4. 5 Ways AI Can Improve Your Next Meeting How to use AI to improve your meetings: Five ways to improve your team meetings through the use of AI meeting assistants 5. How to Behave during Video Calls: 9 Rules for Communicating with Colleagues These days most companies have asked their employees to stay at home and change their office work for a remote one. In such conditions, the issues of organizing video calls have become especially relevant. It seems that online conferences for dozens and even hundreds of people are a step towards a digital future without borders when people can work from anywhere in the world and get together for negotiations literally by click. But in reality, video calls are often not as good as you might imagine. Someone cannot install the necessary program, someone is nervous  because of the relatives in the background, and someone appears at an important conference call in his pajamas and using the nickname “DimonKiller666”. We have put together some simple rules for making video calls that will help make them comfortable for yourself and your colleagues. 6. Synchronous Communication is the New Cocaine in Silicon Valley This blog from Paul Graham beautifully articulates why meetings kill productivity for people in the "maker's schedule": 7. 3 Solutions for Sharing and Managing Your Zoom Recordings Recorded a Zoom meeting, now what? How do I efficiently share it with people around my organization? How do I restrict access to such a recording? I want my marketing team to only view this recording so how do I segregate access? 8. Daily Standup Meeting Is Wasting Your Time Here is how I saved my team 2200 hours annually not to do it. 9. There Are Only 5 Meetings: Standup, Demo, Plan, Reflect, and All-Hands There are too many agile meetings that tech teams use. Save by adopting the essential types of agile meetings I use on my teams. 10. 4 Zoom Tweaks To Improve The Quality of Your WFH Meetings The Coronavirus outbreak is no less than a fortune for Zoom. The cloud meetings app has seen enormous growth that roots from the disruption of normal routine due to lockdowns everywhere. From the surge of Zoom app downloads worldwide to the company almost doubling its annual revenue, 2020 may have a bad start for most of us but for Zoom it’s their heydays. 11. How to Do Meetings You Don't Hate Meetings keep increasing in length and frequency, to the point where executives spend around 23 hours a week in them. 12. The Joys and Sorrows of Corporate Meetings When was the last time you felt like bolting out of the conference room either bored to death or incredible frustrated and feeling like your time was carelessly and disrespectfully wasted? We bet it was as close as yesterday. What, is happening now? Well, get out now! 13. 10 Tips For Securing Zoom Meetings Nowadays, remote work has become widespread with the effect of coronavirus. Although online meetings provide great convenience, it brings with it large cyber risks. Zoom is often preferred especially for online meetings. It is very important to take measures against cyber attacks for online meetings. In this process, I prepared some precautions with the experiences I gained while using Zoom. Here are some precautions for cyber attacks. 14. Meeting Room Booking System: How to Manage It Efficiently In the present world, the work environment is characterized by any organization that is at a fast pace and has a large amount of exchangeable information. According to a research study, an employee spends their time in about 50 meetings per month on average. No wonder meeting rooms are completely booked up, and generally, an organization needs to have pre-bookings. 15. We Just Had Our First Virtual Reality Status Meeting Just about everyone who can is working from home now. And while we're lucky to work in an industry that already works from home regularly and easily, there are still some aspects of being in an office that are difficult to replace. One of them is the stand-up meeting. 16. 8 Tips To Reinvent Your Project Meetings And Win In a typical project meeting most attendees do not contribute but attend just to hear the conversation. A large amount of developer time is wasted. This drains resources from the project and can create a scheduling nightmare. 17. How to Lead More Productive Meetings When it comes to scheduling meetings, I am a firm believer in Maker’s schedule vs Manager’s schedule. In his essay, Paul Graham touches upon the topic of optimizing the number of meetings. In this article, I would like to talk about another related aspect of meetings, i.e. how to optimize the meetings themselves by establishing good meeting etiquettes. 18. 3 Ways to Share and Manage Zoom Recordings Recorded a Zoom meeting, now what? How do I efficiently share it with people around my organization? How do I restrict access to such a recording? I want my marketing team to only view this recording so how do I segregate access? 19. Successfully Change Collaborative Meetings With These Five Proven Methods of Facilitation The five methods of facilitation I use to create collaborative meetings from over a decade of experience as a software engineering leader. 20. Top 10 Questions Managers are asking during 1:1 Meetings I love 1:1 meetings. It’s a chance to swap feedback and make sure our company is working. It’s also one of the reasons we built SoapBox bot — a bot to help people have productive 1:1 and team meetings. The reaction has been overwhelming: Managers from over 500 of the best companies in the world (Adobe, Intercom, Wayfair, etc…) are now using it with their teams. 21. Just Your Average HackerNoon Product Meeting Notes [Feb 2022] This Slogging thread by andemosa, richard-kubina, David and Kien occurred in HackerNoon's official #meeting-recap channel, and has been edited for readability. 22. How To Use the "10/50/99" Approach to Give Feedback And how it reduces the odds you frustrate your team to the brink of leaving 23. How Live Streaming Is Helping Businesses During The Coronavirus Crisis? The World Health Organization (WHO) declared COVID-19 as a global pandemic, which means that it is an infectious disease passing from person to person in many parts of the world at the same time. The maximum cases have been reported in China where it originated and has more than 245,463 confirmed cases across the world. While China has quickly coped with the situation and reported zero new locally transmitted cases, Italy has become the epicenter with over 41,035 cases. As Governments have halted flights from affected countries, shut down businesses and entire cities, people are being insisted to stay at home to contain the spread. 24. End The "Not Another Meeting" Discussions: There's A Better Way To Hold Efficient Meetings It turns out there is a way to hold better meetings. If you want them to be more efficient and productive, there are a few simple things your meetings need to b 25. 5 Benefits of Hosting Online Webinars Webinars can help your search engine ranking, promote sales, reinforce your authority, and more. 26. Should You Enable Your Camera on Zoom Calls? I have a simple question: 📽 Should you enable your camera on Zoom calls? There is a lot of evolving etiquette around this. A #slogging water-cooler style story Thank you for checking out the 26 most read stories about Meetings on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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Smartphones have become integral to modern life since the launch of Apple’s first iPhone in 2007. They hold much of our personal information and give us access to services and apps like email, mobile banking, and social media. Years later, arrived in our smartphones. The technology, which refers to computer systems or machines that perform tasks that usually require human intelligence, has since advanced. AI is now powered by a variety of applications, including: artificial intelligence (AI) Machine learning (ML) algorithms detect patterns in data to make predictions and improve over time based on new inputs. Natural language processing (NLP) analyses and generates human language. Computer vision identifies and interprets visual inputs like images and video. Robotics integrates AI to make automated machines that can move and interact with the physical world. It’s no wonder that technology companies across the globe are heavily investing in AI, some to specifically improve a consumer’s smartphone experience through virtual assistants and other services. Here are : some numbers Google has invested $30.7 billion in AI to improve its search engine and virtual assistant, Google Assistant. Facebook has invested $22.1 billion to improve the social media platform’s user experience (UX). Amazon has invested $10 billion to improve its delivery services and voice assistant, Alexa. Microsoft has invested $10 billion to improve its products and services, including voice recognition. Let’s look at how our smartphones already integrate AI. Virtual assistants Apple pioneered mobile virtual assistants when it launched Siri in 2011. Afterward, Google Assistant, Huawei’s Celia, Samsung’s Bixby, and others joined the competition. They rely on natural language processing and machine learning, two subsets of AI, to improve performance over time. A virtual assistant, also called an AI assistant or digital assistant, can understand human voice commands, engage in two-way conversations with a user, and complete day-to-day tasks, including making a phone call, adding an event to your calendar, or checking the weather. Virtual assistants are primarily available on smartphones and home automation devices (such as Amazon’s Alexa). However, Samsung’s Bixby is also incorporated into some smart refrigerators. You can search the entire internet by making a voice command to your virtual assistant, and search engines offer better results because they understand natural language, not just keywords. Facial Recognition Facial recognition, also called facial detection and Face ID, uses computer vision to find and identify human faces in digital images and video. It is now commonly used to unlock your smartphone, access locked apps such as mobile banking, and make digital wallet payments. Facial recognition first became available on Samsung phones as early as 2011. However, it was not very secure as it used 2D images, so a printed photo of your face or even one displayed on another phone could trick the technology. Later, Apple took over the technology with the launch of the iPhone X in 2017. Face ID works by illuminating your face with infrared light and projecting 30,000 infrared points onto your face to build a detailed 3D map of your features. Apple said: “The probability that a random person in the population could look at your iPhone or iPad Pro and unlock it using Face ID is approximately 1 in 1,000,000 with a single enrolled appearance.” By 2024, biometric facial recognition hardware will be deployed on over mobile devices, accounting for 90% of smartphones. 800 million Recommendations and Suggestions AI consistently analyses your smartphone usage, browsing history, and other data and insights to predict content that might be of interest to you or to recommend apps, products, and more. App suggestions are another advancement but aren’t as widely used yet. Both Apple and Google continue investigating how their operating systems can guess why you’ve unlocked your smartphone from factors including the time of day and location. For example, if you’ve plugged in or connected your earphones/headphones, your smartphone might suggest opening Spotify, Apple Music, or Audible. Another instance is if it’s 8 a.m. on a weekday and you’re at home, your smartphone might suggest Google Maps or Apple Maps, predicting that you want to know the traffic on your route to work. Augmented Reality Augmented reality (AR) is software that combines a real-world environment with digital overlays to offer an interactive experience. AR powers several mobile apps, including games such as Pokemon Go - where an avatar is placed on a map of the player’s real-world surroundings - and furniture shopping apps such as Wayfair - which lets you visualize new furniture in a physical room with their ‘View in Room’ feature. Photography Many recent smartphones, such as the and Pixel 6 Pro, now have AI cameras that use tools to combine the capabilities of a professional camera with features that were previously only available in editing software. Here are some examples: Google Pixel 6 Optical flow technology rapidly snaps multiple pictures and then uses AI software to combine them into a single image without blurring. AI powers Pixel’s Night Sight feature, where photos shot in low light can still look bright and clear. AI software in Pixel phones can also fix the exposure of high-contrast photos, where shadows make one part of the image look dark, and bright light makes another part look washed out. Image Search Computer vision identifies and interprets visual inputs like images and video and can, therefore, offer image search. You can use the ASOS app to search for an item of clothing or pair of shoes you want or the Amazon app to search for a product using an image from your camera or camera roll of a similar product. Whilst Image Search on the Google app can be used the same (searching a wide range of online clothing retailers), it can also be used to find the original artist of an image or where a household product is from. Predictive Text A smartphone keyboard can use natural language processing to learn from your writing style over time and offer next-word suggestions, making typing faster and easier. Compared to other aspects of AI, predictive text is straightforward because the rules of a language are universal. If you type “Are” into a message or search box, the chances are the next word will be “you” or “there’ - and then there are only a limited number of words that suitably follow. Wrapping Up AI technology and applications make our smartphones more responsive and human-like. Virtual assistants, predictive text, facial recognition, personalized recommendations and suggestions, and other AI-powered features enhance how we interact with our devices. , an author and internationally recognized AI expert, said: “ ” Bernard Marr The future of the smartphone is all about AI. AI is allowing our phones to become more personalized and more helpful. At the same time, AI is helping to solve some of the biggest problems with smartphones today – like battery life.

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