The company is still well positioned to be the market leader in global luxury e-commerce, but new owner Coupang has its work cut out for it, writes Imran Amed.

A Letter to Farfetch’s New Owner | BoF

Coupang purchased Farfetch for $500 million USD last month.

Farfetch CEO José Neves Steps Down Following Coupang Acquisition

Farfetch founder José Neves is stepping down from the business following its £400m rescue by Korean ecommerce company Coupang.

Farfetch founder steps down following Coupang takeover - Retail Gazette

Founder José Neves and eight other C-suite executives are departing the luxury marketplace, which faces an uncertain future under its new owner.

Can Farfetch Survive Coupang’s Shock Therapy? | BoF

Breaking news and real-time stock market updates from Seeking Alpha. Check out the latest investing news and financial headlines.

Farfetch CEO and founder resigns as company braces for job cuts (FTCHF)

Fashion Industry News, Designers, Runway Shows, Style Advice

Farfetch CEO José Neves Steps Down

José Neves is stepping down as Farfetch CEO amid Coupang's management shakeup which has also seen Browns chief Elizabeth Von Der Goltz exit.

José Neves Quits as CEO of Farfetch In Management Shakeup at Coupang

Jose Neves‘s departure is part of a wider reshuffle of the company’s top management

Farfetch founder ousted after failed US float triggered takeover

The founder of the luxury fashion retailer Farfetch has been ousted weeks after its $500m (£400m) rescue by a Korean internet shopping giant.

Kering has terminated its contract with Farfetch, preventing the listing of its portfolio brands on the marketplace for orders shipped directly from the brands' respective warehouses.

Kering pulls its brands from Farfetch | Retail News UK

Farfetch

farfetch

The corporate world is often compared to a marathon - steady, structured, and predictable. In contrast, start-ups can feel like a series of sprints, full of direction changes. Transitioning from the structured environment of the corporate world to the dynamic fast-paced start-up ecosystem can be daunting but incredibly rewarding. It requires a shift in mindset, a willingness to step out of your comfort zone, and the courage to embrace uncertainty. Gaby Haffner, currently the Head of Europe at Baret Scholars and former Founder of Ameli Health, has successfully navigated this transition. As a former Strategy Consultant and Investment Banker turned start-up founder. From her early roles at Bank of America Merrill Lynch to her time at Monitor Deloitte and EY, where she focused on tech and consumer companies, Gaby's experiences can offer lessons for aspiring entrepreneurs. Here, Gaby shares five actionable insights from her transition from corporate to entrepreneurship. Lesson 1: Focus on Execution Over Perfection In start-ups, speed and execution often matter more than meticulous planning. "Real-world feedback provides insights no amount of theoretical planning can match," Gaby notes. This lesson became especially clear when she founded Ameli Health, a luxury prescription skincare platform. Gaby launched a proprietary platform that integrated doctor-patient matching with pharmacy supply chains in a remarkably short timeframe. She moved fast, gathered feedback, and pivoted based on real-time market reactions. This experience contrasted sharply with her corporate experience at EY and Monitor Deloitte, where processes were more structured and detailed. "A good idea is only as valuable as the ability to execute it," Gaby emphasizes. This ability of hers has also been crucial at Baret Scholars, where she is part of the founding team who are building a pioneering education company. Lesson 2: Embrace Uncertainty Entrepreneurship demands a strong appetite for risk. "In corporate environments, things are often predictable, but in the start-up world, you need to be comfortable with uncertainty", Gaby explains. This shift means learning to thrive in ambiguity. As a founder, you're responsible for everything, from product development to finances, often pushing you well beyond your comfort zone. During her time at Ameli Health, Gaby had to quickly become proficient in areas outside her expertise, such as digital marketing and product management. This adaptability has been invaluable at Baret Scholars, where she successfully leads the digital marketing efforts. Her advice: “Growth happens in the unknown”. Lesson 3: Adopt a Growth Mindset Unlike the scarcity-driven, zero-sum thinking that often dominates corporate environments, start-ups require a growth mindset - the belief that abilities can be developed through dedication and effort. "A growth mindset allows you to embrace challenges and setbacks as opportunities for learning and growth," Gaby emphasizes. Gaby honed this mindset during her time at Farfetch, where she worked on improving revenue strategies for high-end luxury fashion clients. These experiences translated directly into her role at Baret Scholars, where she continues to apply growth-oriented strategies to drive business development. Lesson 4: Prepare for the Emotional Rollercoaster "Entrepreneurship is an emotional rollercoaster," Gaby admits. "There are extreme highs and crushing lows. You need a high pain threshold and a deep passion for what you're building to push through the tough times". In corporate roles, setbacks are often cushioned by larger organizations, but in start-ups, every challenge can feel personal. Her experience founding Ameli Health taught Gaby the resilience needed to endure the ups and downs of entrepreneurship. This emotional resilience has been crucial to her role at Baret Scholars, where she manages the pressures of scaling a start-up. Lesson 5: Constantly Seek Critique In start-ups, mediocrity is not an option. "Whatever you create must be far superior to what's already out there," Gaby emphasizes. Constantly seeking feedback from trusted sources and iterating based on that critique is key to refining a high-value product or service. At Baret Scholars, Gaby implemented a fast feedback loop early on, speaking directly to customers to refine the program’s structure. This adaptability has allowed the company to evolve its offering and offer a truly innovative product. “Listening to the market and improving based on feedback has been essential to our growth,” she notes. Gaby’s Thoughts on Entrepreneurship Gaby believes that start-ups are society’s true source of innovation and disruption. As a mentor at MassChallenge and for Fitzwilliam College, University of Cambridge’s entrepreneurship competition, she dedicates her time to supporting early-stage start-ups. The corporate world is often compared to a marathon - steady, structured, and predictable. In contrast, start-ups can feel like a series of sprints, full of direction changes. Transitioning from the structured environment of the corporate world to the dynamic fast-paced start-up ecosystem can be daunting but incredibly rewarding. It requires a shift in mindset, a willingness to step out of your comfort zone, and the courage to embrace uncertainty. Gaby Haffner , currently the Head of Europe at Baret Scholars and former Founder of Ameli Health, has successfully navigated this transition. As a former Strategy Consultant and Investment Banker turned start-up founder. From her early roles at Bank of America Merrill Lynch to her time at Monitor Deloitte and EY, where she focused on tech and consumer companies, Gaby's experiences can offer lessons for aspiring entrepreneurs. Gaby Haffner Gaby Haffner Here, Gaby shares five actionable insights from her transition from corporate to entrepreneurship. Lesson 1: Focus on Execution Over Perfection In start-ups, speed and execution often matter more than meticulous planning. "Real-world feedback provides insights no amount of theoretical planning can match," Gaby notes. This lesson became especially clear when she founded Ameli Health, a luxury prescription skincare platform. Gaby launched a proprietary platform that integrated doctor-patient matching with pharmacy supply chains in a remarkably short timeframe. She moved fast, gathered feedback, and pivoted based on real-time market reactions. This experience contrasted sharply with her corporate experience at EY and Monitor Deloitte, where processes were more structured and detailed. "A good idea is only as valuable as the ability to execute it," Gaby emphasizes. This ability of hers has also been crucial at Baret Scholars, where she is part of the founding team who are building a pioneering education company. Lesson 2: Embrace Uncertainty Entrepreneurship demands a strong appetite for risk. "In corporate environments, things are often predictable, but in the start-up world, you need to be comfortable with uncertainty", Gaby explains. This shift means learning to thrive in ambiguity. As a founder, you're responsible for everything, from product development to finances, often pushing you well beyond your comfort zone. During her time at Ameli Health, Gaby had to quickly become proficient in areas outside her expertise, such as digital marketing and product management. This adaptability has been invaluable at Baret Scholars, where she successfully leads the digital marketing efforts. Her advice: “Growth happens in the unknown”. Lesson 3: Adopt a Growth Mindset Unlike the scarcity-driven, zero-sum thinking that often dominates corporate environments, start-ups require a growth mindset - the belief that abilities can be developed through dedication and effort. "A growth mindset allows you to embrace challenges and setbacks as opportunities for learning and growth," Gaby emphasizes. Gaby honed this mindset during her time at Farfetch, where she worked on improving revenue strategies for high-end luxury fashion clients. These experiences translated directly into her role at Baret Scholars, where she continues to apply growth-oriented strategies to drive business development. Lesson 4: Prepare for the Emotional Rollercoaster "Entrepreneurship is an emotional rollercoaster," Gaby admits. "There are extreme highs and crushing lows. You need a high pain threshold and a deep passion for what you're building to push through the tough times". In corporate roles, setbacks are often cushioned by larger organizations, but in start-ups, every challenge can feel personal. Her experience founding Ameli Health taught Gaby the resilience needed to endure the ups and downs of entrepreneurship. This emotional resilience has been crucial to her role at Baret Scholars, where she manages the pressures of scaling a start-up. Lesson 5: Constantly Seek Critique In start-ups, mediocrity is not an option. "Whatever you create must be far superior to what's already out there," Gaby emphasizes. Constantly seeking feedback from trusted sources and iterating based on that critique is key to refining a high-value product or service. At Baret Scholars, Gaby implemented a fast feedback loop early on, speaking directly to customers to refine the program’s structure. This adaptability has allowed the company to evolve its offering and offer a truly innovative product. “Listening to the market and improving based on feedback has been essential to our growth,” she notes. Gaby’s Thoughts on Entrepreneurship Gaby believes that start-ups are society’s true source of innovation and disruption. As a mentor at MassChallenge and for Fitzwilliam College, University of Cambridge’s entrepreneurship competition, she dedicates her time to supporting early-stage start-ups.

Five Lessons for Transitioning From Corporate to Start-up with Gaby Haffner

Pet lekcija za prelazak iz korporativnog u start-up sa Gaby Haffner

Cinco lecciones para la transición de una empresa a una startup con Gaby Haffner

Gaby Haffner-ekin korporaziotik hasierara igarotzeko bost ikasgai

پنج درس برای انتقال از شرکت به راه اندازی با گبی هافنر

Gaby Haffnerin viisi oppituntia siirtymiseen yritystoiminnasta startup-yritykseen

कॉरपोरेट से स्टार्ट-अप में बदलाव के लिए पाँच सबक, गैबी हैफनर के साथ

חמישה שיעורים למעבר מתאגיד לסטארט-אפ עם גבי הפנר

ギャビー・ハフナーによる企業からスタートアップへの移行に関する 5 つの教訓

Penkios pamokos, kaip pereiti nuo įmonės prie steigimo su Gaby Haffner

Gaby Haffner ile Kurumsaldan Startup'a Geçiş İçin Beş Ders

П’ять уроків для переходу від корпоративного бізнесу до стартапу з Габі Гаффнер

Gaby Haffner 谈从企业转型为初创企业的五个经验教训

First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change Over the last five years, the dominant narrative surrounding data infrastructure has emphasized the importance of companies owning and leveraging their data as they acquire as much information about their users and customers as possible. Due to mounting privacy regulations, they need to gather the data themselves instead of relying on third-party data operators, such as ad networks or platforms like Google, Meta, and Amazon. Companies have fallen in line with this narrative and made the shift. But, in the battle for the best data, is first-party really better? Not by itself, but it could be with a little help from vectors, frameworks like RAG, and open-source foundation models like Llama 3. The Push for First-Party Data The argument for first-party data generally goes like this: companies need to become better stewards of data acquisition and management amid growing calls for data privacy. Consumers increasingly want to know who is hanging onto their personal information, how they got it, why they have it, and what is being done with it– and they generally don’t like the answers to those questions. Access and Delete requests are rapidly increasing, and the data privacy landscape is changing quickly. Companies are finding it hard enough to figure out their own privacy management processes; they don’t want to have to worry about third parties' as well. But the shift to first-party is not all about privacy. There is also the notion that third-party data will lose value as we move towards the proverbial cookieless future. Companies can’t get the same granular details they once could so why should they devote budget to a service that delivers less than it used to? And then there is the perpetual concern that the big platforms and ad networks will make unexpected changes. For example, they could change their algorithms, restrict access to certain types of data, or alter advertising policies in a manner that could harm a business’ performance with little or no notice. Being dependent on another company’s practices leaves one vulnerable. Companies feel stuck because they’ve already invested so much time, money, and resources into their data strategy. In this light, the push to take back control of data seems essential. But is it practical? The First-Data Problems No One is Talking About Early results for companies taking a gamble on first-party data have not met expectations. We’re seeing case after case of consumer companies that made the shift suffering. Farfetch, Allbirds, and  SmileDirectClub are just a few examples. Would better, more strategic use of first-party data turn the tide on the rising customer acquisition costs? Still, the current reliance on first-party data– and the practices to extract that data– is a commonality across several companies that are experiencing tougher times today. It’s noticeable enough to make analysts, VCs, and marketers themselves ask if they were wrong to prioritize first-party data. The downsides of first-party data, as it is currently acquired and leveraged, are often considered to be underestimating the disappearance of device IDs, changing IP addresses, consumer adoption of fake emails, and ad blockers. While true, much more significant issues are at play. First, there is a massive talent gap. Big Tech and the platform companies snag the best. They have the most to offer, making it difficult for up-and-coming consumer companies to compete for the data scientists and ML talent needed to make sense of the information being collected and analyzed. Without truly exceptional people, companies have struggled. There is also a real tooling problem. The offerings available to companies are nowhere near equivalent to the tools Big Tech companies boast internally (which can be a factor in the talent gap). Tooling makes a difference, and most companies simply can’t compete right now. Finally, there is a volume challenge. Big Tech and the ad networks have mountains of data, as these companies pool and anonymize hundreds of billions of data points to make their models run effectively. By contrast, if a company has only its data to work with, there is just not enough for ML to work as promised. While these problems seem serious, is it time to give up on the potential– and the need for– first-party data? No way! The Power of Vectors The biggest issue hindering first-party data is how companies are trying to access it. Up to this point, businesses have taken an Old World approach. Models must be built from scratch to extract the value from data that businesses require. This takes time, money, and most of all talent; it depends on how good your ML engineers and data scientists are. However, as discussed above, there is not enough available talent to make this approach work better than leveraging third-party data. The lack of talent creates the bottleneck. However, this does not mean we need to give up on first-party data. We just need to change how we approach it. In the New World that is possible today, vectors and vector embeddings are the key. Vectors are generic mathematical objects that can represent data points' features or attributes while embedding models analyze patterns in data to generate these information-packed, meaningful representations learned from data; they capture the semantic relationships. Vector embeddings are the format that can encode everything you know about a user or a customer and make that information accessible to an analytics system, or leverageable to personalize the user experience, or even to catch fraud. There are so many possibilities. Vectors are poised to drive a sea change because they can power analytics in a fundamentally different way. Retrieval-augmented generation (RAG) is generating a lot of buzz right now for all it can enable, but vector embeddings are what make RAG useful. They are a central component of the framework that helps with context, responses, integration of retrieval and fine-tuning of models. Generating high-quality vectors and querying them correctly is a critical task that enables any RAG system to actually work. There are other frameworks, but RAG is particularly well-suited for a first-party data revolution. Sounds great. Let’s all figure out how to use vectors and vector embeddings. This is not the total answer. The issues like limited data sets and tooling remain. All is not yet wrapped up in a pretty bow, but I believe it soon will be. Because open-source, pre-trained foundation models like Meta’s Llama 2, which will give way to the more robust Llama 3 in July, can level the playing field. The issue of not having enough data volume compared to BigTech gets mitigated. By using an open source model pre-trained on large and diverse datasets, that model has a certain level of knowledge and understanding built in. Companies simply need to fine-tune Llama 2 (or Llama 3) on their specific domain or task with their data. This eases the bottleneck because in many cases, you no longer have to train a model from scratch. This may sound like an oversimplification, given that Llama helps companies deal with text, but most of the data companies run on is not text. The structured data companies run on need to be integrated into this process. For example, the user behavioural events which form a large percentage of first-party data typically, are not suitable to be processed by any LLM. This is changing so companies should be ready as new multimodal solutions emerge. Similarly, tooling is still lacking, but there is a lot of attention on the space, so great strides are being taken. It’s coming along! With the biggest issues fundamentally addressed, first-party data hype is back, baby! Companies don’t have to worry about third parties violating privacy, nor do they have to depend on Big Tech to help them know their customers. Expect to see first-party data explode this year as companies finally take full advantage– especially with Llama 3 at the ready. For all of its promise, perhaps the greatest potential for Llama 3 will be to actually solve the first-party data problem once and for all. First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change First-Party Data is Back … With a Little Help From Its Friends. How Vectors, RAG and LLAMA 3 Are Driving a Sea Change Over the last five years, the dominant narrative surrounding data infrastructure has emphasized the importance of companies owning and leveraging their data as they acquire as much information about their users and customers as possible. Due to mounting privacy regulations, they need to gather the data themselves instead of relying on third-party data operators, such as ad networks or platforms like Google, Meta , and Amazon . Companies have fallen in line with this narrative and made the shift. Google, Meta Amazon But, in the battle for the best data, is first-party really better? Not by itself, but it could be with a little help from vectors, frameworks like RAG, and open-source foundation models like Llama 3 . Llama 3 The Push for First-Party Data The argument for first-party data generally goes like this: companies need to become better stewards of data acquisition and management amid growing calls for data privacy . Consumers increasingly want to know who is hanging onto their personal information, how they got it, why they have it, and what is being done with it– and they generally don’t like the answers to those questions. Access and Delete requests are rapidly increasing, and the data privacy landscape is changing quickly. Companies are finding it hard enough to figure out their own privacy management processes; they don’t want to have to worry about third parties' as well. first-party data data privacy Access Access Delete Delete But the shift to first-party is not all about privacy. There is also the notion that third-party data will lose value as we move towards the proverbial cookieless future. Companies can’t get the same granular details they once could so why should they devote budget to a service that delivers less than it used to? And then there is the perpetual concern that the big platforms and ad networks will make unexpected changes. For example, they could change their algorithms, restrict access to certain types of data, or alter advertising policies in a manner that could harm a business’ performance with little or no notice. Being dependent on another company’s practices leaves one vulnerable. Companies feel stuck because they’ve already invested so much time, money, and resources into their data strategy. In this light, the push to take back control of data seems essential. But is it practical? The First-Data Problems No One is Talking About Early results for companies taking a gamble on first-party data have not met expectations. We’re seeing case after case of consumer companies that made the shift suffering. Farfetch , Allbirds , and SmileDirectClub are just a few examples. Would better, more strategic use of first-party data turn the tide on the rising customer acquisition costs? Farfetch Farfetch Allbirds Allbirds SmileDirectClub SmileDirectClub Still, the current reliance on first-party data– and the practices to extract that data– is a commonality across several companies that are experiencing tougher times today. It’s noticeable enough to make analysts, VCs, and marketers themselves ask if they were wrong to prioritize first-party data. The downsides of first-party data, as it is currently acquired and leveraged, are often considered to be underestimating the disappearance of device IDs, changing IP addresses, consumer adoption of fake emails, and ad blockers. While true, much more significant issues are at play. First, there is a massive talent gap. Big Tech and the platform companies snag the best. They have the most to offer, making it difficult for up-and-coming consumer companies to compete for the data scientists and ML talent needed to make sense of the information being collected and analyzed. Without truly exceptional people, companies have struggled. There is also a real tooling problem. The offerings available to companies are nowhere near equivalent to the tools Big Tech companies boast internally (which can be a factor in the talent gap). Tooling makes a difference, and most companies simply can’t compete right now. Finally, there is a volume challenge. Big Tech and the ad networks have mountains of data, as these companies pool and anonymize hundreds of billions of data points to make their models run effectively. By contrast, if a company has only its data to work with, there is just not enough for ML to work as promised. While these problems seem serious, is it time to give up on the potential– and the need for– first-party data? No way! The Power of Vectors The biggest issue hindering first-party data is how companies are trying to access it. Up to this point, businesses have taken an Old World approach. Models must be built from scratch to extract the value from data that businesses require. This takes time, money, and most of all talent; it depends on how good your ML engineers and data scientists are. However, as discussed above, there is not enough available talent to make this approach work better than leveraging third-party data. The lack of talent creates the bottleneck. However, this does not mean we need to give up on first-party data. We just need to change how we approach it. In the New World that is possible today, vectors and vector embeddings are the key. Vectors are generic mathematical objects that can represent data points' features or attributes while embedding models analyze patterns in data to generate these information-packed, meaningful representations learned from data; they capture the semantic relationships. Vector embeddings are the format that can encode everything you know about a user or a customer and make that information accessible to an analytics system, or leverageable to personalize the user experience, or even to catch fraud. There are so many possibilities. Vectors are poised to drive a sea change because they can power analytics in a fundamentally different way. Retrieval-augmented generation (RAG) is generating a lot of buzz right now for all it can enable, but vector embeddings are what make RAG useful. They are a central component of the framework that helps with context, responses, integration of retrieval and fine-tuning of models. Generating high-quality vectors and querying them correctly is a critical task that enables any RAG system to actually work . There are other frameworks, but RAG is particularly well-suited for a first-party data revolution. to actually work Sounds great. Let’s all figure out how to use vectors and vector embeddings. This is not the total answer. The issues like limited data sets and tooling remain. All is not yet wrapped up in a pretty bow, but I believe it soon will be. Because open-source, pre-trained foundation models like Meta’s Llama 2, which will give way to the more robust Llama 3 in July, can level the playing field. The issue of not having enough data volume compared to BigTech gets mitigated. By using an open source model pre-trained on large and diverse datasets, that model has a certain level of knowledge and understanding built in. Companies simply need to fine-tune Llama 2 (or Llama 3) on their specific domain or task with their data. This eases the bottleneck because in many cases, you no longer have to train a model from scratch. This may sound like an oversimplification, given that Llama helps companies deal with text, but most of the data companies run on is not text. The structured data companies run on need to be integrated into this process. For example, the user behavioural events which form a large percentage of first-party data typically, are not suitable to be processed by any LLM. This is changing so companies should be ready as new multimodal solutions emerge. Similarly, tooling is still lacking, but there is a lot of attention on the space, so great strides are being taken. It’s coming along! With the biggest issues fundamentally addressed, first-party data hype is back, baby! Companies don’t have to worry about third parties violating privacy, nor do they have to depend on Big Tech to help them know their customers. Expect to see first-party data explode this year as companies finally take full advantage– especially with Llama 3 at the ready. For all of its promise, perhaps the greatest potential for Llama 3 will be to actually solve the first-party data problem once and for all.

How Vectors, RAG and Llama 3 Are Changing First-Party Data

কিভাবে ভেক্টর, রাগ এবং লামা 3 প্রথম পক্ষের ডেটা পরিবর্তন করছে

Wie Vectors, Rag und Llama 3 First-Party-Daten verändern

Cómo Vectors, Rag y Llama 3 están cambiando los datos propios

Comment Vectors, Rag et Llama 3 modifient les données propriétaires

वेक्टर्स, रैग और लामा 3 किस तरह से फर्स्ट-पार्टी डेटा को बदल रहे हैं

Vectors、Rag、Llama 3 がファーストパーティ データをどのように変えているのか

Vector, Rag 및 Llama 3가 자사 데이터를 어떻게 변화시키는가

Como Vectors, Rag e Llama 3 estão mudando os dados primários

Как Vectors, Rag и Llama 3 меняют собственные данные

Vektörler, Rag ve Llama 3 Birinci Taraf Verilerini Nasıl Değiştiriyor?

Cách Vectors, Rag và Llama 3 thay đổi dữ liệu của bên thứ nhất

Vectors、Rag 和 Llama 3 如何改变第一方数据

The current decade will always remain etched in the annals of tech history for the unprecedented advent of artificial intelligence. It wouldn’t be farfetched to say that AI is making an indelible mark on almost every aspect of human life. Tech pundits and gurus are already predicting its far-reaching transformative effects and the occupations it will render obsolete. A potent question is whether the cybersecurity landscape is slated to incur ramifications from this onslaught. Is AI going to edge out humans, or is it likely to augment human roles? What do the industry trends tell us about it? The Ever-evolving World of Cyber Threats According to Forbes, the share impact of AI is likely to affect 15% of the global workforce. However, the Cybersecurity realm will remain immune to it because of a few key points. First is the ever-evolving nature of cyber threats. What was once considered a bane for cyber security specialists has become a blessing in disguise. AI models are adept at identifying threats and risks, but they depend on pattern feed, allowing them to pinpoint deviations. AI lacks the intuition and critical thinking to cope with anomalies falling outside the jurisdiction of the feed. In this particular scenario, AI would be incapable of formulating timely countermeasures, resulting in the compromise of organizational infrastructure. A human brain, on the other hand, is capable of rational thinking and contextual understanding, making it more adaptable, superior, and capable of interpreting previously unencountered threats and security lapses. It can also devise on-the-spot inventive measures to safeguard digital assets. After such an instance, the AI feed pattern will likely be updated with the latest information to improve and enhance its threat detection by a human engineer! Ethical and Legal Considerations Traversing ethical and legal considerations is still not the forte of artificial intelligence. When it comes to cybersecurity, these ethics and considerations become very convoluted with human behavior added to the mix. For instance, how can privacy violations of an individual’s data be curbed in a way that doesn’t infringe any rights? Regulation of surveillance and monitoring in a non-pervasive manner and, lastly, drawing a boundary between user responsibility and organizational security protocols. In broad-spectrum, AI can be taught to discern these factors but it can't substitute for human judgment and decisiveness to reach decisions that are majorly aligned with implemented standards and practices. Incident Response and Management Incident response and management capability of AI and ML algorithms have been a much-debated topic. Some proponents deem it indispensable, and some opponents are quick to point out various proverbial chinks in the armor. Though the autonomous response of cybersecurity-based AI systems operates on containment, remediation, and recovery, but again they rely on historical security and threat analysis data points. The delegation of tasks according to the criticality of threat level and undertaking adaptive strategies are better deployed when relying on the collective cognitive prowess of a human-led response team. Who is Protecting the Protector? Lastly, the AI system, which an organization might trust to shield itself, is prone to attacks. In the absence of human oversight and supervision, miscreants find inventive ways to manipulate the AI and achieve their nefarious objectives. This includes intentional and subtle data manipulation to hoodwink the AI. The technical moniker of this is “adversarial attacks”. Adversarial attacks dupe the AI into misclassifying or failing to detect a threat. Hackers might also keenly and zealously observe the AI model and record its behavior when given certain inputs. This allows them to deduce the model’s behavior, response time, and preventive measures and then develop ways to bypass it. Conclusion In conclusion, it is safe to assume that in the near future, AI will play a pivotal role in cybersecurity, but it still hasn’t attained the level of maturity, refinement, and dependability to circumvent the human element. Cybersecurity specialists will remain crucial for nurturing, training, and refining these systems for peak performance. The current decade will always remain etched in the annals of tech history for the unprecedented advent of artificial intelligence. It wouldn’t be farfetched to say that AI is making an indelible mark on almost every aspect of human life. Tech pundits and gurus are already predicting its far-reaching transformative effects and the occupations it will render obsolete. A potent question is whether the cybersecurity landscape is slated to incur ramifications from this onslaught. Is AI going to edge out humans, or is it likely to augment human roles? What do the industry trend s tell us about it? industry trend The Ever-evolving World of Cyber Threats According to Forbes, the share impact of AI is likely to affect 15% of the global workforce. However, the Cybersecurity realm will remain immune to it because of a few key points. First is the ever-evolving nature of cyber threats. What was once considered a bane for cyber security specialists has become a blessing in disguise. AI models are adept at identifying threats and risks, but they depend on pattern feed, allowing them to pinpoint deviations. Forbes , Forbes AI lacks the intuition and critical thinking to cope with anomalies falling outside the jurisdiction of the feed. In this particular scenario, AI would be incapable of formulating timely countermeasures, resulting in the compromise of organizational infrastructure. A human brain, on the other hand, is capable of rational thinking and contextual understanding, making it more adaptable, superior, and capable of interpreting previously unencountered threats and security lapses. It can also devise on-the-spot inventive measures to safeguard digital assets. After such an instance, the AI feed pattern will likely be updated with the latest information to improve and enhance its threat detection by a human engineer! Ethical and Legal Considerations Traversing ethical and legal considerations is still not the forte of artificial intelligence. When it comes to cybersecurity, these ethics and considerations become very convoluted with human behavior added to the mix. For instance, how can privacy violations of an individual’s data be curbed in a way that doesn’t infringe any rights? Regulation of surveillance and monitoring in a non-pervasive manner and, lastly, drawing a boundary between user responsibility and organizational security protocols. In broad-spectrum, AI can be taught to discern these factors but it can't substitute for human judgment and decisiveness to reach decisions that are majorly aligned with implemented standards and practices. Incident Response and Management Incident response and management capability of AI and ML algorithms have been a much-debated topic. Some proponents deem it indispensable, and some opponents are quick to point out various proverbial chinks in the armor. Though the autonomous response of cybersecurity-based AI systems operates on containment, remediation, and recovery, but again they rely on historical security and threat analysis data points. The delegation of tasks according to the criticality of threat level and undertaking adaptive strategies are better deployed when relying on the collective cognitive prowess of a human-led response team. cybersecurity-based AI systems cybersecurity-based AI systems Who is Protecting the Protector? Lastly, the AI system , which an organization might trust to shield itself, is prone to attacks. In the absence of human oversight and supervision, miscreants find inventive ways to manipulate the AI and achieve their nefarious objectives. This includes intentional and subtle data manipulation to hoodwink the AI. The technical moniker of this is “adversarial attacks”. the AI system the AI system Adversarial attacks dupe the AI into misclassifying or failing to detect a threat. Hackers might also keenly and zealously observe the AI model and record its behavior when given certain inputs. This allows them to deduce the model’s behavior, response time, and preventive measures and then develop ways to bypass it. Adversarial attacks Adversarial attacks Conclusion In conclusion, it is safe to assume that in the near future, AI will play a pivotal role in cybersecurity, but it still hasn’t attained the level of maturity, refinement, and dependability to circumvent the human element. Cybersecurity specialists will remain crucial for nurturing, training, and refining these systems for peak performance.

The Persistent Human Element in the Future of Cybersecurity

Understanding Core Web Vitals: The Vital Signs of Your Website

মূল ওয়েব ভাইটাল বোঝা: আপনার ওয়েবসাইটের গুরুত্বপূর্ণ লক্ষণ

Grundlegende Web-Vitalwerte verstehen: Die Vitalfunktionen Ihrer Website

Comprensión de los elementos básicos de la web: los signos vitales de su sitio web

Comprendre les éléments essentiels du Web : les signes vitaux de votre site Web

कोर वेब वाइटल्स को समझना: आपकी वेबसाइट के महत्वपूर्ण संकेत

Core Web Vitals を理解する: Web サイトの重要な兆候

핵심 웹 바이탈 이해: 웹사이트의 바이탈 사인

Compreendendo os principais sinais vitais da Web: os sinais vitais do seu site

Понимание основных веб-жизненных показателей: жизненно важные признаки вашего веб-сайта

Temel Web Verilerini Anlamak: Web Sitenizin Yaşamsal Belirtileri

Hiểu các chỉ số quan trọng của trang web: Các dấu hiệu quan trọng của trang web của bạn

了解核心网络生命：您网站的生命体征

Businesses across the genres craving for , has been all about consistent revamping and digital evolution chains. Traversing from mainstream processes to tech-efficient landscapes has been a mammoth move and of course an imperative one. However, now one can say that scratching off the surface of digital transformation for businesses has come full circle with AI in metaverse. Where personalization, massive data optimization and immersive roll outs are easier than ever. But again, this is only the businesses capitalize on futuristic catalysts like ‘Fueling Innovation & Step-Ups’ if and when AI and Metaverse. Yes, there are virtual shopping, interactive displays, and AI-powered virtual assistants for sales. Or even the AI-powered virtual meeting platforms have been super tech-savvy catch-ups. But I can now safely say that it was quite last season because synergizing on the symphony of Metaverse with AI is the next wave. So, for all the modern businesses to hover around the ultra-competitive times and level up comprehensively on all fronts, calling dibs on Metaverse with AI is the only way out. The duo is not just rolling out benefits and perks in abundance for the businesses. They are changing the entire trajectory of digital transformation. So, let’s unravel the prowess of this tech-fusion ahead! The Need of the Hour: AI and Metaverse . But wait, is it the only catch when times have changed dramatically, and businesses are on an outlook for innovative leverages? Certainly, no! Right? AI and Metaverse have moved past the Traditional Bandwidths! Transcending limitations, Automating tasks and zapping the gap between the real and virtual worlds still does shine and serve for the businesses I mean, the swaps from manual to automation and clutching in the meeting rooms to highly hyper-realistic platforms are game changers. But, again would it streamline operations, as we live in the datasphere. Clearly businesses across the globe are equipped with data flowing from all directions and they do rely on it extensively. Well, was that it? Was that the big final? Without a second thought, NO! Okay, automation was magical. But considering the present times and the competitive march towards the future, integrating AI and the metaverse is not just a looming luxury but a necessity. Now, it’s natural to think why and how. For that, let us get you navigating through the same. Considering the cluster of futuristic capabilities that AI hosts, it is understood that the initial drive for the businesses to integrate AI was to add a ‘Dollop of automation, smart and modern dynamics’. However, with the digital horizons taking up on a massive scale, AI delivers more than ever. Why AI? Let’s explore your businesses as a ‘ Robust Machine.’ And for that machine to run efficiently, AI is the ‘Core Mechanism.’ It means if you, as a business loses the grip on mechanics, the machinery stops working. To be precise, it falls out completely if you let go of AI in digital transformation. For Example- Now, at this point it is quite natural of you to raise the most obvious question; Sure, but what does metaverse bring to the table? Why Metaverse? When it comes to businesses and considering it as a robust machine (for example, same as above), artificial intelligence is clustered machinery that is in charge of the process, production, and operational efficiency. Basically, it is a on the other hand, is a ‘Finished Product, which is not your average batch of products but a personalized, futuristic, and immersive one more like, . In other words, AI in metaverse are like two peas in a pod. And you cannot stack and put chips on one and leave the other. They just synergize so well together that no tech giant would want to ignore their massive impact. back-end ecosystem. , Metaverse a front end ecosystem Having said that- it does not matter if you are into retail, corporate, fintech or even narrowed down to marketing, production or designing. This ultimately brings us down to the HOW. How is this the only seed for taking ‘The next digital transformation leap’? And, for the same we need to tap onto the tech-trail. TECH-DUO How Integrating Metaverse and AI in digital transformation is the only move ahead for businesses? To help you understand it better, let us give you a simple formula. Technically Speaking- AI x Metaverse  (Backend + Frontend) Catering Modern-Day Business Requirements = Intelligent Agents of Computer Science fetching, formatting, filtering, and powering up data. AI: An Immersive space powered by AI, panning out real-life-like aspects for the users. Metaverse: In a nutshell, it does not matter what stream or genre your business falls into. The digital horizon of the times, roots on two thriving technologies i.e Metaverse and Artificial Intelligence. Right from operational efficiency, to acing a competitive edge in the market to delivering next-gen experiences to the users. This dynamic duo is certainly a ‘ solution, and integrating it is the only step to soar high for the businesses. one-size-fits-all’ Besides that, I do have some claims to make for AI in Metaverse; AI and Metaverse are . beyond the basics You can count on it for the ‘Innovative Next-Gen Dive.’ Yes, you ‘ Save money while facelifting your operations.’ No, you ‘Do not have to undertake a trail of tasks.’ Don’t worry, with below listed genres I will affirm the above claims. So, tag along! Training Simulations Break-free the mainstream physical conference room training which aren’t just passive but highly time and cost intensive. Metaverse digital transformation is here with immersive safety training with AI expertise to fuel in the adaptive, tailored yet cost effective training! Fusing in realistic scenarios in a spatial space with life-like learning sessions is what one can say that offers. But, I am still adamant about our claims. These training programs are cost-savvy, highly skill-efficient, and industry-agnostic. You can have medical professionals or even construction personnel to train. Metaverse with AI is the step-up you need. And when I say you need it, I mean it! Metaverse x AI Earlier the training sessions involved manual physical explorations, in-person training and direct machine/equipment contact, which was time consuming but at the same time extremely risky and even resource intensive. And, above all that, it’s now SO LAST SEASON! Here, I will tell you what gap it bridges. You are panning out collaborative virtual training with an expert who takes on a spatial space with all the required equipment/machinery available. By hopping onto this futuristic fusion, you are not just offering safety training and shrinking down the incidental risks. But, you are offering a simulated controlled environment, connecting through experts and dodging barriers. On top of that, you are cutting machine maintenance and operational costs. At the same time, it offers personalized, active, and real-time learning. Consider this: Transform Sales and Marketing Strategies If you thought AI powered chatbots, automated marketing campaigns and collaborative virtual meetings are all that AI and metaverse has gotten, then let me tell you. You are not just highly mistaken, but you are undermining its potential to a great extent. I will equip you with how Metaverse x AI can do wonders and revolutionize your sales and marketing. From consumer & company interaction to consumer x product interaction virtually. Brace Yourself: I meant for the fact all I know that the world is shrinking and businesses are trying their level best to modernize and level up digitally. To help you understand, let’s approach your brain wires in a simpler way. Earlier businesses used to analyze consumer preferences and market trends manually and showcased the products/services in mainstream displays. Personalization was a farfetched pick for businesses because coping with inconsistent consumer trends in the masses was not only difficult but impossible. There was a gap between consumers and businesses offerings due to lack of data analyzation, preference consideration and lack of customer engagement. For Sales: Be it campaigns, personalization virtual launches and down to chatbots everything is streamlined and up to the trends. Businesses can take a backseat in this digital age considering their putting the chips on this dynamic duo. However, this might not do justice to the claims stated above, which is why I’ll just quickly run you through the evolved sales process. With AI and Metaverse in Sales: How does it transform your Sales and Marketing processes? Pools in the data comprehensively, yet segregates the chunks into ‘Preferences, Patterns, Purchase history’ by leveraging predictive analytics. Further, the same data is brought to use in personalizing the entire marketing strategy and virtual immersive 3D launches. With that, AI seeps this data into the Virtual assistants in real-time thereby rolling out a personalized output to the users according to their preferences, likings and choices. AI – The entire data processing across the platforms and servers is lastly brought to show on the business front. Now this can be in the form of modern virtual product launches, 3D virtual try-ons, 3D product demonstrations and all of this in a hyper-realistic space i.e Metaverse. Metaverse Digital Transformation: Imagine virtually trying on a dress or getting to access a virtual test drive for that car that’s recently launched. It is now possible from your own living room but with all the immersiveness of actuality. Plus, what adds a cherry to the cake is all of this will be (automatically and in real-time) tailored to the customer’s preferences via virtual reality development. Not just that, you as a business can get vast amounts of data optimized (in no-time), display products in 3D space and let the products interact with your potential segment of customers (in real-time). And, and, and hit that sales target while finessing the modern marketing and sales game. A win-win right there like never before! Work and Play: Perfectly Balanced Gear up to bail out from the monotonous workloads! It’s time to hunch down the hamster wheel! Taking a backseat and letting things run on auto-pilot has always been synonymous with AI. But, now by syncing on the symphony of metaverse with AI it is no less than living in the future without waiting for 2050. The avatar aura is real and how. You are serving your clients round the clock and bringing in business globally. But there is no employee burnout issue nagging you, nor are the productivity dips hitting your sales anymore. Picture this: If you are running an e-commerce site- by counting on this duo. The AI powered chatbots do not just respond to the user-queries in real time. But, your users will be equipped with a 3D product display and that too tailored to their specific needs. All this is via virtual reality development. For Example- Your Perks: Customer attrition rate, offer modern services while saving resource costs. How time consuming and labor intensive is to run through numerous resumes, shortlisting potential candidates and further dealing with the onboarding process. But, it can be swapped to a rapid, streamlined, cost and time-savvy one. Every company understands the basics, if your employees are happy, they tend to perform better, and your company stays in business lane. Hence, what if there were virtual assistants powered by AI guiding your interns to understand the basics and recognize their potential? I mean think how quickly you will be able to streamline your HR processes before and after onboarding of your employees. Now Picture This- Implementing Metaverse with AI is a hardcore necessity. Because AI algorithms will fill in and sift potential candidates matching your recruitment needs, Metaverse will reform your onboarding process by providing a virtually immersive and simulated workspace for the candidates. And even better with gamification! Do you know how? Well, you guessed it right! In short there is no other way around but implement, integrate and inch in the via the futuristic fusion of Metaverse and AI. digital transformation Conclusion So, if you’re still second guessing on integrating Metaverse with AI and taking on that digital facelift, you are running late. Because while this is a catalyst and not merely an upgrade. The integration changes everything and the entirety of the ecosystem surrounding us. It is certainly a star of its discipline and hitting the throttle for businesses across the genres. As Metaverse x AI is not a mere privilege, you can take a raincheck on but an iconic move that your business will be living on in the future. We at initially thought the next innovative leap is just chatbots and frameless screens. Only ‘few’ perceived the information, trusting its ability to lead the current digital transformation phase. Maybe, its not too late to be the ‘few’ and crave an innovative leap for your business too.

The Next Digital Leap: AI and Metaverse Integration for Businesses

306 Stories To Learn About Artificial Intelligence

361 Stories To Learn About Big Data

Not to sound pompous, but this has been an extraordinary year for the field of artificial intelligence (AI), in more ways than what many experts would have imagined. In a short span of time, artificial intelligence and generative AI platforms have become game changers for many businesses, across a broad range of industries. For years, business leaders have been trying to improve their efficiency and boost productivity, while simultaneously improving their bottom line and reducing costs - now artificial intelligence is doing exactly what they have been looking for all this time. A small business recorded that nearly half, or around 48 percent of small businesses and medium-sized enterprises have already started using AI tools in some capacity in the past year. AI adoption survey It’s safe to say that the adoption of more advanced artificial intelligence tools and generative AI applications has been fairly successful, especially for smaller, more agile companies looking to get their foot in the door, while keeping their balance sheets intact. A Few Wrongs Make for A Terrible Mistake While there were some monumental moments this year that most people will remember as the field of artificial technology continues to develop, this wasn’t without a few or several memorable blunders. At the start of the year, several well-known technology and publication companies found themselves in hot water after publishing countless AI-generated articles that contained factual errors and cases of plagiarism. Quietly removing a few articles from a company website or blog is a lot easier than having to make up more than value losses. In late February, Google launched its generative AI platform, Bard, however, not everything went according to plan, and a few mistakes led to billions being wiped off the big tech’s market value within hours as share prices tumbled. $100 billion in market A flood of deep fakes surfacing on the internet, political leaders using leading up to the presidential elections, and a flurry of lawsuits had many companies up in arms this year as they tried to tame a wild beast running rampant in the streets. AI-generated imagery as part of their campaigns Not to mention former OpenAI CEO and ChatGPT developer, Sam Altman being booted by the same company he helped bring onto the map. Altman would later land a job at Microsoft, however, in a sudden change of direction, a new board of directors at OpenAI had managed to , offering Altman the opportunity to reinstate his role as chief executive at the company. reach an agreement of principles While artificial intelligence has been enjoying its moment in the sun, quite literally, the year ahead only brings more opportunities and new challenges. However, for small and medium-sized companies, with less experience, knowledge, and legal resources compared to their more established peers in Silicon Valley, running into a wall of trouble when adopting new artificial tools and systems can be a bigger, and more costly mistake than simply removing unfounded content from the internet or firing an intern. Problems that Organizations Need to Avoid when Building an AI Strategy Bringing AI into your business can be an exciting opportunity to experiment with new possibilities. However, without the necessary guidance, there are bound to be some failures along the way. As a small business owner, or entrepreneur, you need to clearly understand the importance of having clear objectives, but also what the long-term plan outlook holds. Lacking an AI Strategy Across different levels of the business, having an operational strategy enables you to navigate any challenges, and overcome barriers that you encounter along the way. Your strategy is your blueprint, guiding you toward the main objective of your business, product, service, and staff. Knowing the potential of artificial intelligence in the business environment will enable you to solve complex problems, and build more insightful solutions based on fundamental data metrics. Without a clear guideline, or strategy that will help work as the blueprint, you’ll find it more challenging to understand how artificial intelligence can do any good for your business, and how it can be useful to create more meaningful results. Knowing the business value of artificial intelligence is an important part of building an AI strategy, and creating forward-looking objectives for the organization and team. Being Inexperienced By introducing new tools, business owners will often need to have previous experience with the applications or have an existing team of employees who can skillfully manage working with this technology. However, with artificial intelligence, new applications are constantly changing, meaning that the expertise needed to successfully apply these tools in your business will either need to be an agile individual or a team that has the necessary experience to work with such projects. Having people onboard that are already equipped with the knowledge and experience of working with artificial intelligence, enables you to adapt quicker, and tap into various new opportunities simultaneously. Without a skilled team, or individual that can help guide you through the intricacies of these systems, making AI part of your business will only become more complex to resolve customer and business problems. Implementing the Wrong Tools Right from the beginning it’s important to know that not every AI application will serve a purpose for your business. The value is to understand how these tools work, and what their most basic functions are in terms of solving complex issues and boosting overall business efficiency. Ongoing research by found that more than a third of business leaders said that understanding the business value of artificial intelligence is perhaps one of their main challenges. Deloitte Without a clear strategy, or having considered which applications would be best suited for your business, customers, and employees, you will most likely end up wasting valuable resources on systems that are either impractical or don’t serve the long-term progress of your business. Knowing exactly what your business’s pain points are, and how AI can help resolve these problems could help create a more clear pathway to understanding the available various tools, and how each of them can contribute to the overarching development of your business. Failing to Test and Optimize Applications Contrary to your expectations, some applications have limited capabilities. This is often the case for more experimental tools that are either still in their development phase or have lesser-known real-world applications. Choosing the right tools isn’t enough. As a business owner and leader, you will need to constantly test these applications, and further ensure that these tools can be optimized for improved functionality. Consider a medium-sized real estate company that provides renters and prospective buyers with assistance in . Without optimization, these tools will quickly become outdated, perhaps providing clients with insufficient information or data related to their search queries. finding their next property By conducting thorough testing, and further making the necessary adjustments, you will ensure that the tools you have incorporated are working to the advantage of your business, and not in the opposite direction. Having Insufficient or Outdated Data Data has become the name of the game, and without having clear and relevant data, you will find yourself falling behind competitors and losing customers. Nowadays, nearly every company is using data as part of their decision-making process. By utilizing data, companies can make more informed decisions, develop tailored products and services based on their customer’s needs, and further optimize their systems to find applicable solutions. Within the ecosystem of artificial intelligence, data is a valuable asset that enables systems to efficiently resolve complex problems, or deliver more effective solutions. Using data, and most importantly, updating the data throughout the process, will ensure that AI systems can monitor changing circumstances, and overcome more challenging problems. Unlike more antiquated systems, AI systems will need to be constantly updated, monitored, and optimized to ensure more effective delivery of results. Additionally, as a business owner, you will need to consider how data changes over time, and that the outcomes delivered during the implementation process will likely be different several weeks or months down the line. Remember that implementing quality data can be just as important to the overall success and outcomes of AI tools. While having access to vast amounts of information can help a business or team better understand specific pockets of the market, it becomes a lot less valuable if the data is either outdated or no longer serves a direct purpose over the long term. Ignoring the Importance of A Risk Management Strategy Being a business owner is more than being a thought leader, or helping the company scale. A closer look reveals that many business owners often lead their teams through various challenges, and develop strategies to mitigate risks that can affect their near and long-term goals. Incorporating AI systems with a proper risk management strategy or plan can lead to bigger problems, resulting in the delivery of insufficient solutions, and further creating more cracks in the process. Although there are many business leaders and organizations that have been incorporating artificial intelligence within their business model over recent years - the majority of them often don’t have an AI risk management strategy or responsible AI program in place. In fact, one research by MIT Sloan Management Review and Boston Consulting Group found that while 42 percent of organizations had developed an AI strategy, only 19 percent said that they had implemented a risk management or responsible AI program. report Ignoring the importance of AI risk management, or not having a plan in place for when things go south can derail forward-looking plans, and require additional resources to resolve. Having Budget and Resource Constraints Incorporating artificial intelligence into your business model requires a substantial investment. For starters, advanced technology has become more than the traditional plug-in-and-play applications we’ve become accustomed to during the later years of the dot-com era. Having artificial intelligent systems is a long-term, and often continuous investment in the technology, data metrics, and expertise to manage and monitor these applications. Encountering budget and resource constraints could further back-track your progress, and derail the potential long-term objectives you’ve established. Part of the planning and implementation process requires the necessary allocation of resources that will help to support these applications, but more importantly, the various add-ons that help power artificial systems. AI technology is constantly changing and evolving. This would mean that your business needs to have the necessary resources to support this change and adjust direction every so often to ensure that existing applications are relevant to your long-term strategy, but more importantly, can adjust to a changing market dynamic. Ignoring New Opportunities for Growth As a small business, you will most likely pilot one or several AI projects on a small scale to consider the long-term needs of each application. However, those efforts would require you to plan for new growth opportunities, and ensure that the technology you’re using can scale as your business expands. With each new project, you will need to consider scalability over both the near and long term. As you begin to experiment with newer, and more advanced systems, you will notice different opportunities where these tools can be applied. You might find that one pilot project helps to resolve various problems you’ve encountered, however, applying this on another business level could mean that you are building towards a more robust approach to implementing AI applications across various functions of your business. On top of this, you will need to plan for ways in which you can allow these systems to scale as your business experiences different growth periods. As demand on one side of your business begins to pick up, you will need to ensure that AI tools are scaling with this demand and providing more efficient delivery. Not Aligning Your Expectations Having farfetch'd expectations regarding the possibilities of artificial intelligence can lead to increased bottlenecks in your business, and further cause you to incorporate different tools or develop different strategies to overcome these problems. Having a more dynamic, and open-minded approach will enable you to notice new opportunities that might present themselves at any given moment. More than this, it will allow you to become more agile, and realize that AI applications will only solve the problems it has been programmed to resolve. As a way to encourage the potential of any AI project, avoid setting the bar too high, as this would only lead to exaggerated expectations. Ensure that each project is carefully considered beforehand, but more than this, that you encourage a level of expectation that allows you to fail and make the necessary adjustments based on the overall success. Overlooking the Importance of Ethics As much as artificial intelligence is helping improve business efficiency and resolve critical problems, there are multiple concerns regarding the potential privacy violations these tools can have on a business and its clients. Already we’re seeing major players such as Google's parent company, Alphabet, and ChatGPT developers, OpenAI being regarding the breach of personal data and information of users to train AI models. hit with lawsuits While the use of this data is crucial for the forward-looking development of these models, illegally collecting this data, without prior user consent can lead to increased legal complications and damage your company’s reputation. As a business owner, you should be encouraged to address privacy issues with a transparent approach and ensure users or customers that the collection of sensitive data will be used transparently, and concerning existing privacy regulations. Keep in mind that artificial intelligence is an ever-evolving ecosystem that requires lawmakers to establish new ground rules that will help regulate the collection of personal data and information. But more importantly, protects users against unlawful data harvesting to train new AI models. Final Take Artificial intelligence can provide your business with new opportunities to overcome challenges and develop more dynamic solutions within a changing marketplace. As the landscape evolves, business owners will need to be encouraged about the potential these tools can present them with, but more importantly, how these tools can complement their forward-looking objectives.

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错误404！组织在实施人工智能时需要避免的问题

Introduction: In the realm of modern retail, a point-of-sale (POS) system is the beating heart of any business. It manages sales, inventory, customer interactions, and more. Two prominent contenders in the POS arena are the Lightspeed R-Series POS and VendHQ ( ). Lightspeed X Series POS Each offers a unique set of features designed to enhance the retail experience. In this in-depth comparison, we’ll explore the strengths and nuances of both systems to help you make an informed decision for your business. Lightspeed R-Series POS: Elevating Retail Management The Lightspeed R Series POS is a testament to innovation and adaptability in the retail sector. Engineered with cutting-edge technology, this system offers a plethora of tools to streamline operations and optimize customer interactions. Seamless Inventory Management: The R-Series POS excels in inventory management, allowing you to monitor stock levels, set up alerts for low inventory, and even reorder products seamlessly. The system’s intelligent analytics provide insights into sales trends, enabling you to make informed restocking decisions. Customer-Centric Features: With the R-Series, you can build robust customer profiles that include purchase history, preferences, and contact information. This enables personalized interactions and targeted marketing campaigns, fostering customer loyalty and satisfaction. Versatile Payment Options: The R-Series supports multiple payment methods, including credit/debit cards, mobile wallets, and contactless payments. This flexibility enhances the shopping experience and caters to a wide range of customer preferences. VendHQ (Lightspeed X-Series) POS: Simplicity and Scalability VendHQ, part of the Lightspeed X Series, is a cloud-based POS solution that emphasizes user-friendliness and scalability. Designed for businesses of all sizes, it empowers retailers to manage their operations efficiently. Cloud-Based Convenience: VendHQ’s cloud-based architecture ensures that your data is accessible from anywhere with an internet connection. This feature is especially beneficial for multi-location businesses or those with an online presence. Intuitive Interface: The X-Series POS prides itself on its user-friendly interface, making it easy for staff to learn and use the system effectively. Minimal training is required, enabling a smooth onboarding process for new employees. Add-On Integrations: VendHQ offers an array of integrations with third-party applications such as accounting software, e-commerce platforms, and CRM systems. This ecosystem allows you to tailor the POS system to your specific business needs. You can also use third-party applications like SKUPlugs to integrate the POS systems with marketplaces. SKUPlugs provides integration for both platforms with major e-commerce platforms like Shopify, Woocommerce, Bigcommerce, Wix, Magento, Squarespace, Amazon, Walmart, and Farfetch etc. Conclusion: Choosing the Right POS for Your Business Selecting between the and VendHQ (Lightspeed X-Series) POS hinges on your business requirements and priorities. The R-Series excels in robust inventory management and customer engagement, making it ideal for businesses seeking a comprehensive solution. Lightspeed R-Series POS On the other hand, VendHQ’s cloud-based simplicity and scalability cater to businesses aiming for a streamlined yet adaptable POS system. Both systems reflect Lightspeed’s commitment to innovation and customer satisfaction. Ultimately, your choice should align with your business goals, size, and operations. Whichever you choose, embracing the power of a Lightspeed POS system can undoubtedly elevate your retail management game, leading to enhanced customer experiences and operational efficiency. - Original Source Lightspeed R-Series Vs X-Series Comparison

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The Big Four by Agatha Christie, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . DISAPPEARANCE OF A SCIENTIST V. DISAPPEARANCE OF A SCIENTIST Personally, I don't think that, even when a jury had acquitted Robert Grant, alias Biggs, of the murder of Jonathan Whalley, Inspector Meadows was entirely convinced of his innocence. The case which he had built up against Grant—the man's record, the jade which he had stolen, the boots which fitted the footprints so exactly—was to his matter-of-fact mind too complete to be easily upset; but Poirot, compelled much against his inclination to give evidence, convinced the jury. Two witnesses were produced who had seen a butcher's cart drive up to the bungalow on that Monday morning, and the local butcher testified that his cart only called there on Wednesdays and Fridays. A woman was actually found who, when questioned, remembered seeing the butcher's man leaving the bungalow, but she could furnish no useful description of him. The only impression he seemed to have left on her mind was that he was clean-shaven, of medium height, and looked exactly like a butcher's man. At this description Poirot shrugged his shoulders philosophically. "It is as I tell you, Hastings," he said to me, after the trial. "He is an artist, this one. He disguises himself not with the false beard and the blue spectacles. He alters his features, yes; but that is the least part. For the time being he is the man he would be. He lives in his part." Certainly I was compelled to admit that the man who had visited us from Hanwell had fitted in exactly with my idea of what an Asylum attendant should look like. I should never for a moment have dreamt of doubting that he was genuine. It was all a little discouraging, and our experience on Dartmoor did not seem to have helped us at all. I said as much to Poirot, but he would not admit that we had gained nothing. "We progress," he said; "we progress. At every contact with this man we learn a little of his mind and his methods. Of us and our plans he knows nothing." "And there, Poirot," I protested, "he and I seem to be in the same boat. You don't seem to me to have any plans, you seem to sit and wait for him to do something." Poirot smiled. "Mon ami, you do not change. Always the same Hastings, who would be up and at their throats. Perhaps," he added, as a knock sounded on the door, "you have here your chance; it may be our friend who enters." And he laughed at my disappointment when Inspector Japp and another man entered the room. "Good-evening, moosior," said the Inspector. "Allow me to introduce Captain Kent of the United States Secret Service." Captain Kent was a tall, lean American, with a singularly impassive face which looked as though it had been carved out of wood. "Pleased to meet you, gentlemen," he murmured, as he shook hands jerkily. Poirot threw an extra log on the fire, and brought forward more easy-chairs. I brought out glasses and the whisky and soda. The captain took a deep draught, and expressed appreciation. "Legislation in your country is still sound," he observed. "And now to business," said Japp. "Moosior Poirot here made a certain request to me. He was interested in some concern that went by the name of the Big Four, and he asked me to let him know at any time if I came across a mention of it in my official line of business. I didn't take much stock in the matter, but I remembered what he said, and when the captain here came over with rather a curious story, I said at once, 'We'll go round to Moosior Poirot's.'" Poirot looked across at Captain Kent, and the American took up the tale. "You may remember reading, M. Poirot, that a number of torpedo boats and destroyers were sunk by being dashed upon the rocks off the American coast. It was just after the Japanese earthquake, and the explanation given was that the disaster was the result of a tidal wave. Now, a short time ago, a round-up was made of certain crooks and gunmen, and with them were captured some papers which put an entirely new face upon the matter. They appeared to refer to some organisation called the 'Big Four,' and gave an incomplete description of some powerful wireless installation—a concentration of wireless energy far beyond anything so far attempted, and capable of focusing a beam of great intensity upon some given spot. The claims made for this invention seemed manifestly absurd, but I turned them in to headquarters for what they were worth, and one of our highbrow professors got busy on them. Now it appears that one of your British scientists read a paper upon the subject before the British Association. His colleagues didn't think great shakes of it, by all accounts, thought it farfetched and fanciful, but your scientist stuck to his guns, and declared that he himself was on the eve of success in his experiments." "Eh, bien?" demanded Poirot, with interest. "It was suggested that I should come over here and get an interview with this gentleman. Quite a young fellow, he is, Halliday by name. He is the leading authority on the subject, and I was to get from him whether the thing suggested was anyway possible." "And was it?" I asked eagerly. "That's just what I don't know. I haven't seen Mr. Halliday—and I'm not likely to, by all accounts." "The truth of the matter is," said Japp, shortly, "Halliday's disappeared." "When?" "Two months ago." "Was his disappearance reported?" "Of course it was. His wife came to us in a great state. We did what we could, but I knew all along it would be no good." "Why not?" "Never is—when a man disappears that way." Japp winked. "What way?" "Paris." "So Halliday disappeared in Paris?" "Yes. Went over there on scientific work—so he said. Of course, he'd have to say something like that. But you know what it means when a man disappears over there. Either it's Apache work, and that's the end of it—or else it's voluntary disappearance—and that's a great deal the commoner of the two, I can tell you. Gay Paree and all that, you know. Sick of home life. Halliday and his wife had had a tiff before he started, which all helps to make it a pretty clear case." "I wonder," said Poirot thoughtfully. The American was looking at him curiously. "Say, mister," he drawled, "what's this Big Four idea?" "The Big Four," said Poirot, "is an international organisation which has at its head a Chinaman. He is known as Number One. Number Two is an American. Number Three is a Frenchwoman. Number Four, the 'Destroyer,' is an Englishman." "A Frenchwoman, eh?" The American whistled. "And Halliday disappeared in France. Maybe there's something in this. What's her name?" "I don't know. I know nothing about her." "But it's a mighty big proposition, eh?" suggested the other. Poirot nodded, as he arranged the glasses in a neat row on the tray. His love of order was as great as ever. "What was the idea in sinking those boats? Are the Big Four a German stunt?" "The Big Four are for themselves—and for themselves only, M. le Capitaine. Their aim is world domination." The American burst out laughing, but broke off at the sight of Poirot's serious face. "You laugh, monsieur," said Poirot, shaking a finger at him. "You reflect not—you use not the little gray cells of the brain. Who are these men who send a portion of your navy to destruction simply as a trial of their power? For that was all it was, Monsieur, a test of this new force of magnetical attraction which they hold." "Go on with you, moosior," said Japp good-humouredly. "I've read of super criminals many a time, but I've never come across them. Well, you've heard Captain Kent's story. Anything further I can do for you?" "Yes, my good friend. You can give me the address of Mrs. Halliday—and also a few words of introduction to her if you will be so kind." Thus it was that the following day saw us bound for Chetwynd Lodge, near the village of Chobham in Surrey. Mrs. Halliday received us at once, a tall, fair woman, nervous and eager in manner. With her was her little girl, a beautiful child of five. Poirot explained the purpose of our visit. "Oh! Monsieur Poirot, I am so glad, so thankful. I have heard of you, of course. You will not be like these Scotland Yard people, who will not listen or try to understand. And the French Police are just as bad—worse, I think. They are all convinced that my husband has gone off with some other woman. But he wasn't like that! All he thought of in life was his work. Half our quarrels came from that. He cared for it more than he did for me." "Englishmen, they are like that," said Poirot soothingly. "And if it is not work, it is the games, the sport. All those things they take au grand sérieux. Now, madame, recount to me exactly, in detail, and as methodically as you can, the exact circumstances of your husband's disappearance." "My husband went to Paris on Thursday, the 20th of July. He was to meet and visit various people there connected with his work, amongst them Madame Olivier." Poirot nodded at the mention of the famous French woman chemist, who had eclipsed even Madame Curie in the brilliance of her achievements. She had been decorated by the French Government, and was one of the most prominent personalities of the day. "He arrived there in the evening and went at once to the Hotel Castiglione in the Rue de Castiglione. On the following morning, he had an appointment with Professor Bourgoneau, which he kept. His manner was normal and pleasant. The two men had a most interesting conversation, and it was arranged that he should witness some experiments in the professor's laboratory on the following day. He lunched alone at the Café Royal, went for a walk in the Bois, and then visited Madame Olivier at her house at Passy. There, also, his manner was perfectly normal. He left about six. Where he dined is not known, probably alone at some restaurant. He returned to the hotel about eleven o'clock and went straight up to his room, after inquiring if any letters had come for him. On the following morning, he walked out of the hotel, and has not been seen again." "At what time did he leave the hotel? At the hour when he would normally leave it to keep his appointment at Professor Bourgoneau's laboratory?" "We do not know. He was not remarked leaving the hotel. But no petit déjeuner was served to him, which seems to indicate that he went out early." "Or he might, in fact, have gone out again after he came in the night before?" "I do not think so. His bed had been slept in, and the night porter would have remembered any one going out at that hour." "A very just observation, madame. We may take it, then, that he left early on the following morning—and that is reassuring from one point of view. He is not likely to have fallen a victim to any Apache assault at that hour. His baggage, now, was it all left behind?" Mrs. Halliday seemed rather reluctant to answer, but at last she said:—- "No—he must have taken one small suit-case with him." "H'm," said Poirot thoughtfully, "I wonder where he was that evening. If we knew that, we should know a great deal. Whom did he meet?—there lies the mystery. Madame, myself I do not of necessity accept the view of the police; with them is it always 'Cherchez la femme.' Yet it is clear that something occurred that night to alter your husband's plans. You say he asked for letters on returning to the hotel. Did he receive any?" "One only, and that must have been the one I wrote him on the day he left England." Poirot remained sunk in thought for a full minute, then he rose briskly to his feet. "Well, madame, the solution of the mystery lies in Paris, and to find it I myself journey to Paris on the instant." "It is all a long time ago, monsieur." "Yes, yes. Nevertheless, it is there that we must seek." He turned to leave the room, but paused with his hand on the door. "Tell me, madame, do you ever remember your husband mentioning the phrase, 'The Big Four'?" "The Big Four," she repeated thoughtfully. "No, I can't say I do." About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Agatha Christie (2023). The Big Four. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/70114/pg70114-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

DISAPPEARANCE OF A SCIENTIST

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