5. Add half the shredded swiss cheese. Simmer until the cheese has melted and the soup is nice & creamy. 6. Add in the thyme as well as salt and pepper to your liking. 7. Dish into oven same bowls or ramekins. Top with a few croutons or a slice of dry ...

A delicious comfort food recipe for Swiss Onion Soup

I once weighed 485 pounds. I now weigh a bit over half that. My addiction to sugar and refined carbohydrates was killing me. When I understood that, I safely lost 244 pounds over the course of two years by simply abstaining from refined carbs.

Bill Schubart: We poison ourselves with industrial food, sugar and pills

Originally announced in August, the $2.7 billion acquisition is now unlikely to close until next year following a new request from the FTC

Davis Polk, Hogan Lovells advise on delayed Campbell-Sovos deal

Campbell Soup Co.’s $2.7 billion acquisition of Sovos Brands Inc ... and incorporate the company into its meals and beverage division. Under the terms of the all-cash offer, approved Oct. 16 by Sovos’ stockholders, the purchase will be financed ...

Campbell’s $2.7B Rao’s acquisition pushed to mid-2024

The brand accounted for 69% of its adjusted net sales last year. “Sovos Brands is the most compelling growth story food right now,” Mick Beekhuizen, president of meals and beverages at Campbell, told Food Dive in August.

Campbell Soup’s $2.7B purchase of Sovos delayed after FTC seeks more information

Campbell Soup said on Monday its $2.33 billion acquisition of Rao's owner Sovos Brands was delayed until next year after the U.S. Federal Trade Commission (FTC) asked for more details on the deal. Campbell said the request for additional information by the FTC was part of the agency's review of the transaction and was a common feature of a regulatory review.

Campbell's closing of $2.33 billion Sovos deal pushed to 2024

RBC Capital also thinks the food and beverage sector is investable as it called the GLP-1 bear thesis overdone. The firm reminded that the industry has seen other feared disruptors fall flat, with the alternative meat sector and Beyond Meat's ( BYND) share price collapse called a prime example.

Food and beverage stocks that stand out amid interest rate, GLP-1 anxiety

Campbell Soup Company’s CPB Snacks business is thriving, driven by consumers’ rising demand. Though the company continues to face cost inflation, its strategic pricing actions and cost-saving plans have been offering some relief.

Robust Snacks Unit Aids Campbell Soup (CPB), Costs Woes Hurt

A new class of weight-loss drugs has rattled global equity markets, sending shares of everything from food and beverage companies to medical-device makers tumbling. As earnings season kicks into ...

Ozempic Threatens Profits at Food and Beverage Makers Worldwide

Get the most recent info and news about Campbell Soup on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Campbell Soup Company, together with its subsidiaries, manufactures and markets food and beverage products. It operates through Meals & Beverages and Snacks segments. The Meals & Beverages segment engages in the retail and foodservice businesses in the United States and Canada. This segment provides condensed and ready-to-serve soups; Swanson broth and stocks; Pacific Foods broth, soups, non-dairy beverages, and other simple meals; Prego pasta sauces; Pace Mexican sauces; Campbell's gravies, pasta, beans, and dinner sauces; Swanson canned poultry; Plum baby food and snacks; V8 juices and beverages; and Campbell's tomato juice. The segment also offers simple meals and shelf-stable beverages in Latin America. The Snacks segment retails Pepperidge Farm cookies, crackers, fresh bakery, and frozen products in the United States; Milano cookies and Goldfish crackers; and Snyder's of Hanover pretzels, Lance sandwich crackers, Cape Cod and Kettle Brand potato chips, Late July snacks, Snack Factory Pretzel Crisps, Pop Secret popcorn, Emerald nuts, and other snacking products in the United States and Canada. The segment also engages in the chips business in Europe. The company sells its products through retail food chains, mass discounters, mass merchandisers, club stores, convenience stores, drug stores, and dollar stores, as well as e-commerce and other retail, commercial, and non-commercial establishments; and independent contractor distributors. Campbell Soup Company was founded in 1869 and is headquartered in Camden, New Jersey.

Campbell Soup

Consider the last time you heard about a famous painting selling for millions. Chances are it ended up in a private collection, out of the public eye. Discover the story of the startup that is changing that trend and bringing you a private pass to the art world. is redefining what it means to interact with . With the help of cutting-edge blockchain technology, it makes masterpieces by legendary artists like Picasso, Banksy and others accessible to everyone and anywhere in just a few clicks. 10101.art art in the digital age marks a pivotal moment for 10101.art as it closes the pre-sale of , an iconic piece in its collection, in less than two hours. This event is a significant step in the platform's mission to go beyond just viewing art online – it's about enabling anyone to own a part of art history. November 27th Banksy's 'Turf War' Breaking Down Barriers With blockchain technology, 10101.art simplifies art ownership, making it more than just a privilege for the elite. This platform isn’t just showcasing art – it’s changing how art is shared and experienced, making it a part of everyday life for a diverse, global audience. How It Works The innovative model of 10101.art is deeply rooted in blockchain technology. Let’s see how it works: The process at 10101.art begins with a meticulous selection of artworks, involving globally recognized art dealers and experts. Each piece is put through a comprehensive verification process. This involves assessing the seller's reputation in the art world, be it a renowned gallery, auction house, or esteemed private collector. Then each artwork's current condition is scrutinized through up-to-date photos and videos, followed by a physical inspection by an art expert, ensuring its quality and authenticity align with its documentation. Artwork Selection: Before digitization, each artwork undergoes preparation within a robust legal framework. This framework is designed to address varying requirements based on the artist, such as attaching a Pest Control COA for Banksy's works or verifying the presence in official catalogs for Picasso's and Dalí's pieces. The framework includes expert appraisals and provenance checks, establishing a basis for legal ownership and ensuring the digital representation of each artwork is as legitimate as its physical counterpart. Legal Framework: After legal preparation, the artwork is digitized and tokenized. These digital tokens represent a share in the physical art, enabling a new form of ownership that blends the physical and digital worlds. Digitization: Such NFTs can be bought and sold on the 10101.art platform, making each buyer a legal co-owner of the actual artwork. This process is governed by a detailed co-ownership agreement specific to each piece, clarifying the rights and responsibilities of every co-owner. This legal structure democratizes the experience of art ownership, making it possible for art enthusiasts around the world to own and trade a part of art history in a secure and transparent manner. Ownership & Trading: 10101.art is more than a platform – it's also an art & DeFi community. With plans to implement a DAO (Decentralized Autonomous Organization) governance system, the platform will allow its community to have a say in its operations and future. Community & Governance: This model is not just about buying art – it's about making art accessible and interactive using cutting-edge tokenization technology. The platform guarantees the security of each transaction, the genuineness of the legendary artworks, and their physical safety. Home for 10101.art Paintings Besides the innovative DeFi Protocol and unique legal structure, 10101.art's innovation also lies in its tangible connections in the art world. And a cornerstone of this is its exclusive partnership with the Monada Art Gallery in DIFC, Dubai. This collaboration bridges the gap between the physical and digital realms of art. The gallery, more than just a space for showcasing art, symbolizes 10101.art's commitment to authenticity and quality. Here, art enthusiasts can witness the original masterpieces from world-renowned artists like Banksy, Andy Warhol, and Pablo Picasso, carefully kept in the project's collection. This tangible presence, managed by , an expert in navigating the art market, adds a layer of trust and engagement to the 10101.art experience. Moreover, the project plans to expand physical galleries globally to create a worldwide network of masterpieces reachable to everyone. Tehani Zeramdini The Minds Behind 10101.art The success of 10101.art is anchored in its team's expertise, combining deep knowledge in legal aspects, art curation, and, notably, blockchain and DeFi technologies. This blend of skills has been crucial in developing a platform that facilitates art transactions and ensures their security and transparency through blockchain. The result is a revolutionary marketplace where art ownership is redefined for the digital age. Leading this team of experts is , whose diverse background in banking and crypto provides a unique perspective crucial for navigating the complex intersection of art and technology. Alina Krot Her experience has been vital in steering the platform to address the gaps in traditional art markets, particularly in making art more accessible and verifiable. As Alina herself articulates, “We at 10101.art are creating a new marketplace that was previously limitedly available. Now people all over the world will have a unique opportunity to admire works of art that were previously hidden in private collections.” Banksy Sale & Future Milestones Despite its relatively recent inception, 10101.art has already made noteworthy progress. They've successfully developed their platform, tailored a unique legal structure, and established key partnerships, setting the stage for a new era in art and blockchain integration. Along the way, the team has even made their mark at top-tier forums like the FinTech Summit and Blockchain Life 2023, where they original Warhol's 'Campbell's Soup'. showcased November marked a significant milestone for 10101.art with the launch of their platform, beginning with an overwhelmingly successful pre-sale of . All available in the pre-sale were within just two hours, highlighting the intense interest in this unique opportunity to own a part of art history. This early success sets a promising stage for the main sale opening in December, where an additional will be priced at . This upcoming sale extends the opportunity to a broader audience to participate in this unique art experience. 'Turf War' by Banksy 300 pieces sold out 1200 pieces $100 each Looking forward, 10101.art has ambitious plans to expand its platform. Besides implementing the DAO governance system and opening new galleries worldwide, near-term goals include broadening the art experts' team and increasing their tokenized artwork collection. This expansion also involves hosting exclusive events and providing special privileges for NFT owners, enhancing the overall experience of being part of the 10101.art community. Join the Art & DeFi Evolution Ready to begin your art journey powered by blockchain technologies? Explore , starting with the 'Turf War' collection by Banksy, and discover how you can be a part of art history in a new, tech-forward way! 10101.art platform

The Art of Inclusion: 10101.art is Revolutionizing Global Art Ownership with Blockchain Tech

অন্তর্ভুক্তির শিল্প: 10101.art ব্লকচেইন প্রযুক্তির সাথে বৈশ্বিক শিল্প মালিকানার বিপ্লব ঘটাচ্ছে

Die Kunst der Inklusion: 10101.art revolutioniert den globalen Kunstbesitz mit Blockchain-Technologie

El arte de la inclusión: 10101.art está revolucionando la propiedad mundial del arte con tecnología Blockchain

L'art de l'inclusion : 10101.art révolutionne la propriété artistique mondiale avec Blockchain Tech

समावेशन की कला: 10101.art ब्लॉकचेन टेक के साथ वैश्विक कला स्वामित्व में क्रांति ला रहा है

インクルージョンの芸術: 10101.art はブロックチェーン技術で世界的なアートの所有権に革命を起こしています

포용의 예술: 10101.art는 블록체인 기술을 통해 글로벌 예술 소유권에 혁명을 일으키고 있습니다.

A arte da inclusão: 10101.art está revolucionando a propriedade global da arte com a tecnologia Blockchain

Искусство инклюзивности: 10101.art революционизирует глобальное владение произведениями искусства с помощью технологии блокчейн

Katılım Sanatı: 10101.art, Blockchain Tech ile Küresel Sanat Sahipliğinde Devrim Yaratıyor

Nghệ thuật hòa nhập: 10101.art đang cách mạng hóa quyền sở hữu nghệ thuật toàn cầu bằng công nghệ Blockchain

包容的艺术：10101.art 正在利用区块链技术彻底改变全球艺术品所有权

Discover how these top five skills can lead to new career opportunities. In the 1960s, about half of the scientists worked in their field for around 35 years. By the 2010s, that tenure was cut down to five years. The times have changed, and working parameters continue to evolve. Workers have transitioned into a global workforce, and the economy has led to a new wave of transferable skills. The person we are when we enter academia is often not the same person who enters the workforce – and even then, that person will grow and change over time! In addition, sectors such as science and mathematics often create highly specialized career paths, even in our more modern working world. Every job contains a particular set of skills that can be transferable to another industry or sector, no matter how specialized the work is. Identifying these transferable skills and applying them to the broader scope of a career can help open up new opportunities and substantially impact your new job search. Below we will look at an inspiring story of someone who used their transferable skills in the science field to build the career of their dreams – with the bonus of improved work/life balance! As well as the top five transferable skills and how best to apply them to your work history. From Science to Soup In ," Alaina Levine, a scientist turned career consultant discusses her career shift after being unhappy with her post-graduate physics job. In her conversation on the podcast, Alaina brings up one of her favorite stories about a professor of particle physics who found his dream job in the soup industry. CTRL Shift! latest podcast episode, "The scientific exodus to business This professor worked at a prominent US university while his wife was a food scientist at the Campbell Soup Company. Due to their job's differing locations, the couple was constantly apart and couldn't seem to create a work/life balance that suited their needs. The professor discovered over dinner one night that the soup factory was facing a consistency problem. They couldn’t find a solution for each variety of soup that the factory processed—the contents of that soup needed to be the same in conjunction with the particular soup flavor. For example, opening several cans of mushroom soup at once should show the consumer a replica of the same soup in each can, from the color to the flavor, to the consistency. However, any factor of ingredients can change the soup slightly. As with the mushroom soup, certain types of mushrooms could create a slight variation in the color, and so on. These variables not only threatened the consistency of the soup but were also extremely difficult to manage and fix. The question posed was how does the factory facilitate removing undesirable ingredients from the process effectively and efficiently? The professor and his wife had heard about this problem over dinner with executives. While hearing these grievances, the physicist professor was compelled to share an idea concerning an optical device in his lab that he thought might be just what they needed to solve the soup factory's problem. The executives were impressed and very interested and invited the professor into the factory to discuss the solution. In the end, the Campbell’s Soup Company Factory created a job for the professor where he could apply his physics background to more aspects of the factories processes creating innovative and powerful solutions. Furthermore, this new job meant that the professor could now live in the same city as his wife, spend more time with her, embark on an exciting new career, and find perfect harmony in his work/life balance. What makes Alaina’s story about the scientist who pivoted his career into soup so interesting is both the juxtaposition between the two opposite career fields and how the professor's transferable skills were applied seamlessly to something new. He was not just a physicist or a professor, but a businessman, a thought leader, and much more. Top 5 Transferrable Skills Transferable skills are essential in every field, and identifying those specific skills you possess will be the most impactful step to pivoting your career. Just as the physicist professor was able to identify how his unique skills would be an asset to the Campbells Soup Company, anyone who may feel their skills are too specialized should take a look at the broader scope of their work and try to identify their most valuable assets and translate them into transferable skills. Let’s examine the top five most transferable skills and how they can be used to span a variety of career paths. Industry Expertise Relevant expertise is not just for moving up within an organization or specific industry. Having in-depth knowledge of a specific field can demonstrate to potential jobs how dedicated you are to understanding a particular market at an expert level. In addition, expert knowledge of an industry, market, or skill allows for the possibility of innovation, and application, both of which are desirable skills, especially when jobs are seeking highly skilled professionals to help solve a specific problem. While some industry experts may feel they are pigeonholed for work opportunities, learning how to apply that expertise to broader concepts can help to open new areas. Analytical Skills Similarly to communication skills, analytical skills are critical to success in all career fields. Today, data drives modern business and remains at the forefront of growth industries. A lot of highly specialized areas utilize data and analytical skills. The consistent application of analytical skills within traditional and emerging industries makes this an essential transferable skill. The best way to showcase your analytical skills is to detail projects which require you to cultivate these abilities to create successful outcomes. This includes listing any relevant software you may have used, results you discovered, or any other practical applications of these skills within a project's scope. Project Management Overseeing projects is a commonplace deliverable in many job descriptions. Whether the responsibilities include managing a team, developing a product, or supervising processes, the skills you acquire leading a task (or a team) from conception to completion are fundamental transferable skills that can lead to several roles. Many skills fall into the category of project management. Two of the most important to identify are time management and general organization. These two are critical because each skill set demonstrates that you know how to establish priorities, stick to a schedule, and manage a project or team to meet deadlines and complete tasks successfully. The best way to communicate project management skills is to reference previous projects you have managed and either describe the details of seeing the project through to completion or use them to prop up the larger professional working scopes. Even if these projects seem unrelated to the role you are applying to, a successfully managed project is a highly desirable quality. Creativity and Critical Thinking There is high value in creativity and critical thinking skills, and more companies are noticing how necessary these transferable skills are to their business in new ways every day. For those who have developed these skills in creative industries, the continued need for skills in visual arts, design, or marketing, is growing exponentially. Yet, even in sectors that are not traditionally “creative,” creativity and critical thinking skillset can be effectively utilized. Creative and critical thinkers are known for innovative ideas and gathering anecdotal data and applying it in inspiring ways, pushing the boundaries, and approaching problems from new angles. The most crucial factor in creativity is to be able to demonstrate and follow through on any idea. When detailing invention as a transferable skill, describe how you not only come up with ideas but also how you have the tools to focus those ideas into successful action that yields results. Leadership Skills Having team members with practical leadership skills is an incredible asset to any team. For this reason, adequately demonstrating leadership skills through a broad lens by using examples from your learned experience is a highly valuable transferable skill set. If your current or most recent roles involved leadership or management, that experience should be easy to convey to potential recruiters. Even if you do not have direct management experience within your current work history, pinpointing specific instances where you have demonstrated leadership qualities and taken on the responsibilities of a leader is just as important. Speaking on particular cases and applying your leadership skills to those instances will make you a desirable candidate in any field. Bottomline - You Already Have Everything You Need It is no easy decision to make a career shift, but that being said, you don't have to feel stuck in your path if it is no longer serving you. There are a lot of factors that go into the decision to pivot careers: some involve changes in goals, values, worldview, or personal circumstances. Whatever the reasoning, a successful career path is at your fingertips because, in the end, you do not always need another decade of academia or years of clinical research. Often the skills you need are the ones you have been cultivating all along. Discover your valuable transferable skills and see what new opportunities open their doors for you.

From Science to Soup: The Power of Transferable Skills 

A couple of American entertainment heavyweights found themselves in court late last year because of the alleged theft of intellectual property. Los Angeles-based Miramax sued filmmaker Quentin Tarantino for allegedly breaching an agreement where the latter supposedly agreed to surrender all copyrights and trademarks of the 1994 hit "Pulp Fiction." For the first time, an American court will finally have its opinion on within blockchain technology's state of affairs. If that is indeed the case, this ruling could very well become a landmark case with a significant impact on how NFT artists and creators move forward. intellectual property rights Tarantino, known for his violent, neo-noir, and dark humour films, announced a plan to sell NFTs related to "Pulp Fiction," a film that brought him worldwide recognition. The NFTs would include excerpts of the movie script and audio commentaries. Tarantino pitched the "secret" or surprise nature of the NFTs, explaining that the buyer will only know what they bought after the NFT is transferred to them. Hence, there's no way the public would know what was sold. But Miramax isn't having any of it. The company claims that Tarantino committed trademark infringement. The case opens up a lot of questions that the courts will now have to try and untangle. This doesn't bode well for Tarantino, but what does this mean for NFT creators and buyers? ‍ Intellectual Property Concerns What are Intellectual Property rights? states: “Intellectual Property is a broad categorical description for the set of intangible assets owned and legally protected by a company or individual from outside use or implementation without consent. An intangible asset is a non-physical asset that a company or person owns.” Investopedia Creatives are influenced by the world around them. It inspires them and fuels their desire to create, express, make and build. When Andy Warhol set out to create the single greatest piece of pop art, he did not get a licence from Campbell Soup; technically he was breaking the law when he made his most famous artwork. And indeed, the executives at Campbell’s were initially very hesitant but, gradually, as their sales climbed from the free publicity they relented and eventually came around to embrace their connection to one of the most famous artists of the 20th century. In fact, fifty years later, Campbell Soup even collaborated with The Warhol Foundation by offering , commemorating the artwork. limited-edition cans Now the Warhol Foundation has just sold 5 for a total of $3.4 million. One of those NFTs is based on the famous Campbell Soup painting. The money has gone to the Foundation to cover costs for the Andy Warhol Museum in Pittsburgh which saw a dip in visitors after COVID, and to help emerging artists who are also suffering. Warhol NFTs There is no indication on Christie's website, the online auctioneer which sold the NFTs, that Campbell's Soup was compensated as . Once again Campbell’s Soup was left out in the cold. Perhaps they are hoping for another boost in sales. Intellectual Property holders This just illustrates the sometimes murky waters that exist between creators and intellectual property holders and how sometimes this relationship can be mutually beneficial even without a clear agreement. And sometimes, as in the case of Pulp Fiction, the two sides can be fractious and it can lead to lawsuits. Intellectual property is designed to protect artists and creators so their assets aren’t sold or used in ways the creator didn’t intend. This might include a logo, a business name, designs, artwork or even ideas. This is the reason why you cannot draw a and sell it as either a physical asset or an NFT. Disney created those characters and even though you might have been the artists and your artwork might be somewhat original, the likeness and creative idea still belong to Disney. Disney character But as demonstrated, not everything is so black and white. If we go back to Pulp Fiction for a moment, could John Travolta sell an NFT of a photograph of himself acting in Pulp Fiction? It’s his likeness after all, but does Miramax own that as well? Or does it belong to whoever took the photograph? It might depend on what was in John Travolta’s contract, but the validity of either argument hasn’t ever been tested in court. You might think this is hair-splitting, but the popularity of NFTs has made what once was a fun keepsake into something potentially worth thousands or (in the case of NFT) millions of dollars. Quentin Tarantino’s Another factor is that an NFT can also be any type of asset from audio, to a picture, to a text which opens up more possibilities and flexibility causing legal conundrums. Whereas once we were limited by the physical ability to create something, NFTs have almost no limits – and therefore more confusion. For example, if you recorded an audio clip of your opinion on a Disney film could you then sell that as an NFT? Is that your intellectual property or Disney’s? Or how about Jack Dorsey’s Tweet that sold for $2.9 million? Should Dorsey be able to sell that Tweet or should Twitter have taken the money? (Dorsey, while the founder of Twitter only of the common shares) When social media enters the picture, you need to be careful. While the original author owns the intellectual property, you have to to use that content for other purposes, potentially to make an NFT and sell it without you ever seeing a dime. owns 2% licence social media Again, this hasn’t been proven in court so we don’t know for sure. Under the licence agreement that you most certainly didn’t read when you first signed up for the platform, it grants the social media platform great leeway and flexibility in choosing how to use your content and is not legally entitled to inform you. If Facebook wanted to, it could be possible that they could create NFTs of their most popular memes, potentially without any of the money going to the artists. ‍ Intellectual Property Rights in the Context of an Underlying Asset Anyone who plans to buy an NFT must understand what they're getting. Remember that an NFT purchase doesn't automatically give you to the underlying asset or vice versa. To own the underlying asset, you’ll need a separate document that transfers the intellectual property rights to the new owner. This should be clearly stated in the documentation. ownership rights Generally, the burden is on the buyer as he or she has no recourse once the sale is complete. However, describing the assignment (or transfer) of in the sale of NFTs can easily be understood if you break it down to its most basic parts. intellectual property rights The seller (if he or she happens to own the intellectual property rights of the underlying asset) could easily to the buyer. But everything must be done explicitly in writing to ensure the official transfer. transfer those rights There's also the option of selling the NFT along with its underlying asset (if there is one). This way, it gets easier for the buyer to obtain proof of ownership. But like every NFT sale by default, the would-be buyer must first determine who owns the . This is critical because the absence of this information may lead to a dispute later and protects collectors from being the victim of fraud. underlying asset Just like every other sector, NFTs are not immune to scammers and fraudsters, who will use the market’s excitement, the lack of knowledge of this new innovation, and the desire to make quick money to con, defraud, scam and steal. As a collector, you should make sure the purchase should push through only if there are regarding ownership from either party. This prevents any disputes from arising over intellectual property rights. clear terms and conditions When it comes to securing intellectual property rights and ensuring there are no future disputes, sellers and buyers of the use of the rights in the underlying asset for a specific purpose. NFTs can agree to license When the issues of intellectual property rights are settled, digital asset owners who minted their works into NFTs may incorporate royalty payment in the NFTs code. It means that once the buyer sells the asset, a certain percentage of the sale goes to the original owner. This is one of the key benefits of NFTs that creates new income sources for creators. ‍ Conclusion When Bitcoin and cryptocurrencies became part of the public conscience much of the lack of understanding and misconceptions people had of them were the result of the lack of understanding people have of how currency and money works - many still think their currency is backed by gold which has stopped being a thing since Nixon! With NFTs, we see a similar situation, where people’s lack of understanding and misconceptions are driven by their lack of understanding of how - many don’t know that you don’t own the copyrights to a thing just because you paid for it and that the transfer of copyrights is an explicit process that needs to be documented to take place. intellectual property works No matter what becomes of the lawsuit between Miramax and Quentin Tarantino, there will have to be more clarity between what is creatively acceptable and what needs to be licenced. NFTs were not foreseen by intellectual property holders, content creators or lawmakers but they are here to stay. We need to adapt to this multibillion-dollar market that is seeing millions of dollars change hands. These blurred lines and new realities need to be addressed through more information sharing, better education, and new legislative frameworks. ‍ By Hussein Hallak

“An NFT purchase does not automatically give you ownership rights.”

"Una compra de NFT no le otorga automáticamente derechos de propiedad".

"Un achat NFT ne vous donne pas automatiquement des droits de propriété."

"एक एनएफटी खरीद स्वचालित रूप से आपको स्वामित्व अधिकार नहीं देती है।"

「NFT を購入しても、自動的に所有権が付与されるわけではありません。」

“Uma compra NFT não lhe dá automaticamente direitos de propriedade.”

“Giao dịch mua NFT không tự động cung cấp cho bạn quyền sở hữu.”

“购买 NFT 不会自动赋予你所有权。”

Every day another high profile founder like the creator of Ruby on Rails ( ) writes an . DHH anti-disrupt-o-mania article Anti-unicorns. ? Should we feel pleasure when seeing Gilt join the unicorpses Instead, we are advised to create profitable startups that are self-reliant. Forget the VCs and investors and do it on your own terms. Be in control of your own destiny. 100% of $50 million is a lot better than 5% of $1 billion. Not according to , co-founder of , who bemoans their founder-only board in of (~minute 55). It turns out that when things go south, an experienced board that can make the tough decisions is tremendously . Matt Galligan Circa this episode This Week in Startups important Moreover, having a profitable means you have money that you could have spent on growth, customers, or product. But instead you kept it for a rainy day. Do you think your competitor made the same decision? (doubtful) They probably raised another round. startup So which way should we go? If you are growing faster than your peers, raise all the money you can. Give up some control, delay profitability, and go for >10X and take over the market. If you are growing slower than your peers, go back to product. Turn down the VCs, and open that Ramen noodle 12-pack. Stretch that “profitability” for as long as it takes to grow faster than your peers. Why is growth so important? Growth is the currency of startups. Revenue, page views, retention, not even profit is as important as growth. Why? Because we don’t sell Campbell Soup. We don’t sell Viagra. We sell a pseudo-unique software product with no patent protection. Anyone can (and will) replicate it immediately. So the only reason our companies are still alive is that we’ve managed to stay a few steps ahead of the others. (network effects, unique data, timing, etc) Since much of the world around us does not function by the same rules, we tend to get overconfident. We think we’ve achieved immortality in the 10 million users, in $10 million in revenue, in 10% market penetration, in 50% EBITDA. Instead, we should invest everything back in growth. Our time, our profits, our equity, and VCs (at the right time as mentioned in #1 above). Most unicorn investments are justified and I would challenge any of you to pass on the Series A of any of the You show me a SaaS startup with over $1M ARR growing >20% MoM, and I’ll invest in it no matter what their profit is. current unicorns.

How important is profitability in a startup?

This paper is under CC 4.0 license. available on arxiv Authors: (1) Joel Jang, CarperAI,University of Washington & Allen Institute for AI; (2) Seungone Kim, KAIST AI; (3) Yizhong Wang, University of Washington; (4) Jack Hessel, University of Washington; (5) Luke Zettlemoyer, Aleph Alpha; (6) Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; (7) Yejin Choi, UC San Diego. 5 CONCLUSION Previous work has shown that adapting LLMs with RLHF helps them generate outputs that are preferred by humans over the supervised fine-tuned counterpart. However, recent work has also pointed out that simply training LLMs to abide by the preference of the general may result in ignoring individual preferences and values. In this work, we provide the first steps to tackle this issue by proposing Reinforcement Learning from Personalized Human Feedback as a multi-objective problem so that LLMs can be aligned to follow conflicting preferences. We propose a promising method called P-SOUPS that is able to composite models trained on single objectives on the fly during inference. We also highlight the scalability of P-SOUPS by showing that it scales linearly, instead of exponentially like the MORL baseline, with regards to the number of new preferences, which is required to provide true personalization to individual users. ACKNOWLEDGMENTS Thanks to Minyoung Hwang, Sungdong Kim, Tim Dettmers, Yoonjoo Lee, and Margaret Li for helpful feedback and discussion. REFERENCES Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, and Xing Xie. PENS: A dataset and generic framework for personalized news headline generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 82–92, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.acl-long.7. URL https: //aclanthology.org/2021.acl-long.7. Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, T. J. Henighan, Nicholas Joseph, Saurav Kadavath, John Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom B. Brown, Jack Clark, Sam McCandlish, Christopher Olah, Benjamin Mann, and Jared Kaplan. Training a helpful and harmless assistant with reinforcement learning from human feedback. ArXiv, abs/2204.05862, 2022a. URL https://api.semanticscholar.org/ CorpusID:248118878. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022b. Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jer´ emy Scheurer, Javier ´ Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al. Open problems and fundamental limitations of reinforcement learning from human feedback. arXiv preprint arXiv:2307.15217, 2023. Deborah Cohen, Moonkyung Ryu, Yinlam Chow, Orgad Keller, Ido Greenberg, Avinatan Hassidim, Michael Fink, Yossi Matias, Idan Szpektor, Craig Boutilier, et al. Dynamic planning in openended dialogue using reinforcement learning. arXiv preprint arXiv:2208.02294, 2022. Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, and Leshem Choshen. Cold fusion: Collaborative descent for distributed multitask finetuning. arXiv preprint arXiv:2212.01378, 2022. Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. Alpacafarm: A simulation framework for methods that learn from human feedback. arXiv preprint arXiv:2305.14387, 2023. Shangbin Feng, Chan Young Park, Yuhan Liu, and Yulia Tsvetkov. From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair nlp models. arXiv preprint arXiv:2305.08283, 2023. Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. Koala: A dialogue model for academic research. Blog post, April, 1, 2023. Conor F Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan K ˘ allstr ¨ om, Matthew Macfarlane, ¨ Mathieu Reymond, Timothy Verstraeten, Luisa M Zintgraf, Richard Dazeley, Fredrik Heintz, et al. A practical guide to multi-objective reinforcement learning and planning. Autonomous Agents and Multi-Agent Systems, 36(1):26, 2022. Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum? id=nZeVKeeFYf9. Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin. Lorahub: Efficient cross-task generalization via dynamic lora composition. ArXiv, abs/2307.13269, 2023. URL https://api.semanticscholar.org/CorpusID:260155012. Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. ICLR, 2022. Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, and Minjoon Seo. Exploring the benefits of training expert language models over instruction tuning. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.), Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 14702–14729. PMLR, 23–29 Jul 2023. URL https://proceedings.mlr.press/v202/jang23a. html. Sungdong Kim, Sanghwan Bae, Jamin Shin, Soyoung Kang, Donghyun Kwak, Kang Min Yoo, and Minjoon Seo. Aligning large language models through synthetic feedback. arXiv preprint arXiv:2305.13735, 2023. Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, and Scott A Hale. Personalisation within bounds: A ¨ risk taxonomy and policy framework for the alignment of large language models with personalised feedback. arXiv preprint arXiv:2303.05453, 2023. Noriyuki Kojima, Alane Suhr, and Yoav Artzi. Continual learning for grounded instruction generation by observing human following behavior. Transactions of the Association for Computational Linguistics, 9:1303–1319, 2021. Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A Smith, and Luke Zettlemoyer. Branch-train-merge: Embarrassingly parallel training of expert language models. arXiv preprint arXiv:2208.03306, 2022. Pan Li and Alexander Tuzhilin. Towards controllable and personalized review generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3237–3245, Hong Kong, China, November 2019. Association for Computational Linguistics. doi: 10.18653/v1/D19-1319. URL https://aclanthology.org/D19-1319. Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, and Julian McAuley. Generating personalized recipes from historical user preferences. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5976–5982, Hong Kong, China, November 2019. Association for Computational Linguistics. doi: 10.18653/v1/D19-1613. URL https://aclanthology.org/D19-1613. Pierre-Emmanuel Mazare, Samuel Humeau, Martin Raison, and Antoine Bordes. Training mil- ´ lions of personalized dialogue agents. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2775–2779, Brussels, Belgium, OctoberNovember 2018. Association for Computational Linguistics. doi: 10.18653/v1/D18-1298. URL https://aclanthology.org/D18-1298. Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, et al. Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147, 2022. Reiichiro Nakano, Jacob Hilton, S. Arun Balaji, Jeff Wu, Ouyang Long, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. Webgpt: Browser-assisted question-answering with human feedback. ArXiv, abs/2112.09332, 2021a. URL https://api.semanticscholar.org/CorpusID:245329531. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021b. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe. Training language models to follow instructions with human feedback. ArXiv, abs/2203.02155, 2022a. URL https://api.semanticscholar.org/ CorpusID:246426909. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730–27744, 2022b. Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. Instruction tuning with gpt-4. arXiv preprint arXiv:2304.03277, 2023. Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin, Xian Li, James Cross, Sebastian Riedel, and Mikel Artetxe. Lifting the curse of multilinguality by pre-training modular transformers. arXiv preprint arXiv:2205.06266, 2022. Alexandre Rame, Guillaume Couairon, Mustafa Shukor, Corentin Dancette, Jean-Baptiste Gaya, Laure Soulier, and Matthieu Cord. Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards. arXiv preprint arXiv:2306.04488, 2023. Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. Lamp: When large language models meet personalization. arXiv preprint arXiv:2304.11406, 2023. Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. Whose opinions do language models reflect? arXiv preprint arXiv:2303.17548, 2023. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. Umer Siddique, Paul Weng, and Matthieu Zimmer. Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards. In International Conference on Machine Learning, pp. 8905–8915. PMLR, 2020. David Silver, Satinder Singh, Doina Precup, and Richard S Sutton. Reward is enough. Artificial Intelligence, 299:103535, 2021. Prasann Singhal, Tanya Goyal, Jiacheng Xu, and Greg Durrett. A long way to go: Investigating length correlations in rlhf. arXiv preprint arXiv:2310.03716, 2023. Feifan Song, Bowen Yu, Minghao Li, Haiyang Yu, Fei Huang, Yongbin Li, and Houfeng Wang. Preference ranking optimization for human alignment. arXiv preprint arXiv:2306.17492, 2023. Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008–3021, 2020. Alane Suhr and Yoav Artzi. Continual learning for instruction following from realtime feedback. arXiv preprint arXiv:2212.09710, 2022. Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018. Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239, 2022. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee´ Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and ` efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin, and Jane Mummery. Humanaligned artificial intelligence is a multiobjective problem. Ethics and Information Technology, 20:27–40, 2018. Kristof Van Moffaert and Ann Nowe. Multi-objective reinforcement learning using sets of pareto ´ dominating policies. The Journal of Machine Learning Research, 15(1):3483–3512, 2014. Kristof Van Moffaert, Madalina M Drugan, and Ann Nowe. Scalarized multi-objective reinforce- ´ ment learning: Novel design techniques. In 2013 IEEE symposium on adaptive dynamic programming and reinforcement learning (ADPRL), pp. 191–199. IEEE, 2013. Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A Smith, Iz Beltagy, et al. How far can camels go? exploring the state of instruction tuning on open resources. arXiv preprint arXiv:2306.04751, 2023. Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael Rabbat, and Ari S Morcos. lo-fi: distributed fine-tuning without communication. arXiv preprint arXiv:2210.11948, 2022a. Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In International Conference on Machine Learning, pp. 23965–23998. PMLR, 2022b. Jeff Wu, Long Ouyang, Daniel M Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, and Paul Christiano. Recursively summarizing books with human feedback. arXiv preprint arXiv:2109.10862, 2021a. Yuwei Wu, Xuezhe Ma, and Diyi Yang. Personalized response generation via generative split memory network. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1956–1970, 2021b. Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. Fine-grained human feedback gives better rewards for language model training. arXiv preprint arXiv:2306.01693, 2023. Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, and Wojciech Matusik. Prediction-guided multi-objective reinforcement learning for continuous robot control. In International conference on machine learning, pp. 10607–10616. PMLR, 2020. Jing Xu, Arthur Szlam, and Jason Weston. Beyond goldfish memory: Long-term open-domain conversation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5180–5197, Dublin, Ireland, May 2022. Association for Computational Linguistics. doi: 10.18653/v1/2022.acl-long.356. URL https: //aclanthology.org/2022.acl-long.356. Runzhe Yang, Xingyuan Sun, and Karthik Narasimhan. A generalized algorithm for multi-objective reinforcement learning and policy adaptation. Advances in neural information processing systems, 32, 2019. Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. Personalizing dialogue agents: I have a dog, do you have pets too? arXiv preprint arXiv:1801.07243, 2018. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. arXiv preprint arXiv:2306.05685, 2023. Yinhe Zheng, Guanyi Chen, Minlie Huang, Song Liu, and Xuan Zhu. Personalized dialogue generation with diversified traits. arXiv preprint arXiv:1901.09672, 2019. Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019. A DETAILS OF EVALUATION SETUP We use a modified version of the GPT4 annotation prompt used by Dubois et al. (2023). We modify the criteria to perform the pairwise evaluation from general to a single preference dimension. We also provide 3 demonstrations: one scenario where there is a tie because both responses do not contain any notion of the preference (e.g. both responses do not show any signs of friendliness), one scenario where there is a clear winner, and one scenario where they are both good, but one is better than the other. Simulated pairwise evaluation We recruited 24 crowd workers for our human evaluation. Figure 5 shows the interface used for human evaluation. We consider both the ‘Tie’ and ‘Both are bad’ options to be Ties. B CRITERIA-WISE EVALUATION The criteria-wise win rate (%) across all the methods are shown in Figure 6. The criteria-wise win rate was calculated by getting the average win-rate of the preference combinations that contained the specific preference dimension. For example, when calculating the criteria-wise win rate of ‘Elementary’, we got the average win-rate of the preference combinations that contained the ‘Elementary’ preference, which includes AAA, AAB, ABA, and ABB. C THE FULL LIST OF EVALUATION PROMPTS The full list of evaluation prompts used in our experiments are provided in Table 5 D DETAILED RESULTS FOR HUMAN EVALUATION AND GPT-4 EVALUATION We provide detailed results (win / loss / tie for each of the preference combinations for our main experimental results. Table 6 shows the GPT4 evaluation and Table 7 shows the human evaluation results. E EXAMPLES OF P-SOUPS TEXT GENERATIONS Table 8 shows empirical examples of the text generated from each preference combination of the 16 preference combination experiments for the same prompt.

Personalized Soups: LLM Alignment Via Parameter Merging - Conclusion & References

A term everyone has probably heard about is apparently widely misunderstood. Take Uber, for instance. Many believed it to be a perfect example of disruption when, in reality, Uber’s concept doesn’t really align with the core principles of the theory. As confusing as it may sound, not every business that somehow transforms the market can qualify as a disruption. . Disruptive innovation Before we get to the main idea of how disruptive innovation transformed the art market in particular, let’s start with the basics by figuring out the true meaning of the term in question. History of Disruptive Innovation is a term coined by Clayton Christensen, an American academic and business theorist, to describe a process by which a new product or service takes root initially in a low-end or new market and then gradually moves upmarket, eventually displacing established competitors. Disruptive innovation The concept of disruptive innovation has been widely applied to various industries, including technology, art, healthcare, education, manufacturing, etc. It has become an important framework for understanding how new technologies and business models can disrupt established industries and create new market opportunities. Disruptive Innovation in Tech Several businesses have been mislabeled as disruptive innovations, often due to their rapid growth or innovative products. One of such businesses is Uber. The definition we added above contains one of the reasons why Uber is not a disruptive innovation. As we mentioned, in order to be considered disruptive, new products or services should originate from a low-end or new market. Uber doesn’t originate from either. Uber was founded in 2009 in San Francisco. At that time, people were already used to calling cabs. Therefore, the market wasn’t new. On the other hand, it wasn’t a low-end opportunity since the cab service has not yet been as advanced. Another reason is Uber’s targeting a mainstream audience from the very beginning. One of the core principles of the theory is that disruptive innovations initially appeal to niche markets and only gain widespread adoption once they reach mainstream quality standards. So, was Uber a major breakthrough? Definitely. But is it really a disruptive innovation? Not really. Here are the core principles of the disruptive innovation theory: ocus on underserved segments; F implicity and affordability; S ncremental improvement; I oherent value network. C All the information above naturally raises a question: What can actually be considered disruptive innovation, and why does compliance with the theory actually matter? Well, some examples of the theory have affected our lives profoundly, like the Internet. This one must be obvious. Yet this disruption has led to creating even more disruptions. One of the classic examples is Amazon. You may be surprised, but the industry that’s been disrupted by the creation of the platform is bookselling. The reason for that is simple: Amazon doesn’t require physical stores to showcase and sell its products. Born from a humble garage business catering to online book enthusiasts, Amazon has turned into a digital titan, immeasurably impacting the physical retail landscape and securing an unrivaled market share. Its meteoric rise has cast a shadow over traditional bookstores, leading many to shut down. Today, Amazon's online shopping platform is well-known for a wide variety of products, from groceries to gadgets, all effortlessly accessible from one's doorstep. Another example of disruptive innovation is Netflix. This company hasn’t always been the way we know it. Netflix originally started as an online DVD and VHS rental with mail delivery. The company captured an opportunity to utilize the Internet and thus disrupted traditional DVD rentals, such as Blockbuster. As Netflix was expanding its service, having added streaming capabilities in 2007, the company gradually solidified its disruptive position. When the new service improved, and its content library grew, the company attracted more mainstream customers, leaving companies like Blockbuster behind. Disruptive Innovation in Art Perhaps the most revolutionizing disruptive innovations from the art world are and (Real World Assets). Let’s take a closer look at them and find out their working principles. art tokenization RWAs (RWAs) are a broad category of assets that exist outside of the blockchain but are represented by digital tokens on a blockchain. These assets can be anything from physical goods like real estate or commodities to intangible assets like intellectual property or financial instruments. Real-world assets The process of RWA tokenization involves creating a digital representation of the asset on a blockchain and issuing tokens that represent ownership of the asset. The tokens can then be traded on decentralized exchanges (DEXs) or used to access financial services. By the way, every image in this article was generated by AI – another major disruptive innovation that has already greatly affected modern art. Art can also be tokenized, and is actually a top-tier project in this sphere. Just as Netflix disrupted the traditional media landscape by making content accessible for many, 10101.art is democratizing art ownership by giving an opportunity for anyone to own a part of a physical masterpiece. 10101.art is the process of representing artwork as digital tokens on a blockchain. For instance, on 10101.art, we tokenize famous paintings by internationally renowned artists and ensure transparency of ownership and accessibility of masterpieces. The process of art tokenization can be broken down into the following steps: Art tokenization electing an artwork. This could be a physical artwork such as a painting or sculpture or a digital one such as a photograph or piece of graphic design. Tokenization of physical artworks can cause certain problems with authentication and insurance. Authentication requires having experienced dealers and curators on the team. Insurance, in turn, can be problematic because of the numerous anonymous owners. S reating a smart contract. A smart contract is a self-executing contract that is stored on a blockchain. It will define the terms of ownership of the artwork, such as the number of tokens that will be issued and the initial price. C ploading the artwork. The artwork will be uploaded to a decentralized file storage system. This ensures that the artwork is stored securely and can be easily accessed by anyone who owns a token. U inting the tokens. The tokens will be minted on the blockchain using the smart contract. This minting process will create a unique identifier for each token and associate it with the artwork. M isting the tokens for sale. The tokens will be listed for sale on an NFT marketplace – an online platform where people can buy and sell NFTs. L ransferring ownership. When a buyer purchases a token, they will be transferred ownership of the artwork. This ownership is recorded on the blockchain and is tamper-proof. T We have already tokenized and Warhol’s is next in line, and we are also preparing our and artworks for this process. And if you want to see the artwork live, we'll be waiting for you at our partner gallery opening at “The Ritz-Carlton” (DIFC) on . Follow our to learn more about the upcoming event. Banksy’s “Turf War” “Campbell's Soup Cans“ Dali Picasso April 15 social media Ready to begin your art journey powered by blockchain technologies? Explore platform, starting with the 'Turf War' collection by Banksy, and discover how you can be a part of art history in a new, tech-forward way! 10101.art

CAMPBELL SOUP

campbellsoupcompany.com

10K+ employees

Since 1869

COMPANY RANKING

CAMPBELL SOUP

EVERGREEN INDEX #2084

Representing this company? Click here to claim and customize this page!

Campbell Soup WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

CAMPBELL SOUP

campbellsoupcompany.com

10K+ employees

Since 1869

COMPANY RANKING

CAMPBELL SOUP

EVERGREEN INDEX #2084

RELATED COMPANIESRANK

STORIES ABOUT CAMPBELL SOUP

Representing this company? Click here to claim and customize this page!

Campbell Soup WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!