Actimi

As the crypto landscape buzzes with activity, Bitcoin (BTC) surpasses the $70,000 mark, energized by the approaching halving event that historically signifies bullish cycles. Ethereum (ETH) also sees dramatic action, with significant whale transactions hinting at potential strategic moves amid market volatility. Amid these established giants, Furrever Token (FURR) shines in its presale phase, amassing substantial funds and demonstrating robust interest in emerging crypto opportunities. These events showcase the vibrant dynamics of the cryptocurrency market, where major movements by established tokens and promising starts by new entrants captivate investors’ attention and speculation. Bitcoin (BTC) Trading Over the $70,000 Mark as Halving Approaches At the time of writing, Bitcoin is trading at $70,396.30. The dynamic cryptocurrency has seen significant fluctuations over the past year, with its highest point reaching $73,835.57 on March 14, 2024. This peak contrasts starkly with its lowest value in the same period, which was $24,780.17 on June 15, 2023. Over the past year, Bitcoin has shown a remarkable increase of 132.99%, demonstrating its volatile yet upward trend in the market. Since its inception in January 2009, Bitcoin has revolutionized the concept of money and financial transactions, evolving from humble beginnings to a dominant force in global financial markets with a current market capitalization of $1.38 trillion. This shift underscores Bitcoin's growing popularity as an alternative to traditional fiat currencies like the U.S. dollar, which are subject to inflation and can depreciate over time. Bitcoin's valuation is driven purely by market supply and demand, without the influence of tangible assets, earnings, revenue, or cash flow. New bitcoins are introduced into circulation through mining, where miners are rewarded with bitcoins for verifying and adding new transaction blocks to the blockchain. The total supply of Bitcoin is finite, capped at 21 million BTC, which fundamentally influences its price dynamics. The first recorded transaction that set a price for Bitcoin occurred in late 2009 when 5,050 BTC were exchanged for $5.02 via PayPal, valuing each bitcoin at approximately $0.00099. This event marked the beginning of its monetary value, starting at just about one-tenth of a cent. Bitcoin undergoes a process known as "halving," where the reward for mining new blocks is halved approximately every four years, thus reducing the rate at which new bitcoins are generated. This mechanism is designed to control inflation and diminish the supply over time, theoretically supporting the price of Bitcoin. The next halving is anticipated in April 2024, where the mining reward will decrease from 6.25 BTC to 3.125 BTC. Although halvings reduce the supply of new bitcoins, their direct impact on Bitcoin’s price isn’t immediate or guaranteed. Historically, Bitcoin prices tend to hit a cyclical bottom about a year before a halving and rise steadily afterward for more than a year. This pattern suggests that while halvings are not a direct catalyst for price increases, they contribute to bullish trends over time due to decreased supply. As Bitcoin continues to navigate through these cycles of highs and lows, it remains a focal point for investors and analysts who speculate on its future amidst the evolving financial landscape. Ethereum (ETH) Whale Makes Estimated Profit of $68.5 Million In the ever-volatile cryptocurrency landscape, Ethereum (ETH) has once again been at the center of a tumultuous trading day. The leading altcoin experienced a sharp decline, dropping 3.1% to a low of $3,411, only to make a surprising rebound to a peak of $3,615. This rapid fluctuation occurred within a span of 24 hours, with the price eventually settling at $3,512. Despite these dramatic swings, the day closed with Ethereum's value relatively unchanged over the 24-hour period. Amidst this whirlwind of market activity, the actions of significant cryptocurrency investors, commonly known as crypto whales, have drawn considerable attention. According to a report by Spot On Chain, a notable transaction was observed where a whale, identified as "0x347," deposited an impressive 9,000 ETH into Binance. This deposit, valued at $32 million, represents the largest single transfer of Ethereum by this investor to date. Following this transaction, "0x347" now holds a total of 29,738 ETH, estimated to be worth around $106 million, realizing an estimated profit of $68.5 million. Concurrently, data from Lookonchain highlighted another whale engaging in substantial acquisitions, purchasing 10,309 ETH for approximately $35.82 million during the market's low point. This investor also executed a significant buy prior to a notable increase in Bitcoin’s price on April 8. These significant whale activities underscore a divide in market sentiment concerning Ethereum's immediate future. While some market participants interpret the recent price recovery as an indication of a bullish trend continuation, others are cautious, suggesting the rebound could be a temporary technical correction. As Ethereum continues to be a central topic of discussion and analysis within the crypto community, the contrasting strategies of key market players provide a clear picture of the speculative nature inherent in the cryptocurrency markets. Furrever Token (FURR) Secures Over $770,000 at Presale Furrever Token (FURR) is swiftly establishing itself as a prominent player in the cryptocurrency market, captivating a broad audience with its significant growth potential and compelling investor advantages. Its remarkable traction in presale events has been a critical factor in its rising appeal, attracting hefty investments in each phase. Currently, in its sixth stage of presale, Furrever Token (FURR) has successfully amassed over $770,000, underscoring its increasing popularity and solid support within the cryptocurrency community. Offering investors the potential for up to 15X returns on each presale stage, Furrever Token (FURR) emerges as a lucrative option for those seeking profitable opportunities within the crypto space. With a current price of $0.00048, FURR provides an excellent chance for investors to participate early in its anticipated growth trajectory. The strength of Furrever Token’s appeal is further enhanced by the vibrant community behind it. With more than 4,300 active members on its official Telegram channel, Furrever Token (FURR) thrives as a dynamic center for engaging discussions, collaborative efforts, and consistent updates, enriching the investment experience for all participants. Looking ahead, the strategic roadmap and development plans of Furrever Token (FURR) demonstrate its determined commitment to establishing a substantial foothold in the meme coin market. Plans to introduce innovative features, establish strategic partnerships, and launch targeted marketing initiatives are set to broaden its adoption and increase its market value significantly. In summary, Furrever Token (FURR) stands out as an exceptionally attractive investment opportunity, backed by solid fundamentals, a robust and active community, and a promising outlook for extensive growth. As Furrever Token (FURR) continues to advance towards its strategic goals, it is well-positioned to yield significant returns for early investors who capitalize on its potential. **Secure the Most Exclusive Presale Opportunity of 2024 Today!
\ Furrever Token Official Website  |   Visit Furrever Token PresaleJoin Official Telegram Group       |   Follow Official X Account As the crypto landscape buzzes with activity, Bitcoin ( BTC ) surpasses the $70,000 mark, energized by the approaching halving event that historically signifies bullish cycles. Ethereum ( ETH ) also sees dramatic action, with significant whale transactions hinting at potential strategic moves amid market volatility. Amid these established giants, Furrever Token (FURR) shines in its presale phase, amassing substantial funds and demonstrating robust interest in emerging crypto opportunities. These events showcase the vibrant dynamics of the cryptocurrency market, where major movements by established tokens and promising starts by new entrants captivate investors’ attention and speculation. BTC ETH Furrever Token (FURR) Furrever Token (FURR) Bitcoin (BTC) Trading Over the $70,000 Mark as Halving Approaches At the time of writing, Bitcoin is trading at $70,396.30. The dynamic cryptocurrency has seen significant fluctuations over the past year, with its highest point reaching $73,835.57 on March 14, 2024. This peak contrasts starkly with its lowest value in the same period, which was $24,780.17 on June 15, 2023. Over the past year, Bitcoin has shown a remarkable increase of 132.99%, demonstrating its volatile yet upward trend in the market. Since its inception in January 2009, Bitcoin has revolutionized the concept of money and financial transactions, evolving from humble beginnings to a dominant force in global financial markets with a current market capitalization of $1.38 trillion. This shift underscores Bitcoin's growing popularity as an alternative to traditional fiat currencies like the U.S. dollar, which are subject to inflation and can depreciate over time. Bitcoin's valuation is driven purely by market supply and demand, without the influence of tangible assets, earnings, revenue, or cash flow. New bitcoins are introduced into circulation through mining, where miners are rewarded with bitcoins for verifying and adding new transaction blocks to the blockchain. The total supply of Bitcoin is finite, capped at 21 million BTC, which fundamentally influences its price dynamics. The first recorded transaction that set a price for Bitcoin occurred in late 2009 when 5,050 BTC were exchanged for $5.02 via PayPal, valuing each bitcoin at approximately $0.00099. This event marked the beginning of its monetary value, starting at just about one-tenth of a cent. Bitcoin undergoes a process known as "halving," where the reward for mining new blocks is halved approximately every four years, thus reducing the rate at which new bitcoins are generated. This mechanism is designed to control inflation and diminish the supply over time, theoretically supporting the price of Bitcoin. The next halving is anticipated in April 2024, where the mining reward will decrease from 6.25 BTC to 3.125 BTC. Although halvings reduce the supply of new bitcoins, their direct impact on Bitcoin’s price isn’t immediate or guaranteed. Historically, Bitcoin prices tend to hit a cyclical bottom about a year before a halving and rise steadily afterward for more than a year. This pattern suggests that while halvings are not a direct catalyst for price increases, they contribute to bullish trends over time due to decreased supply. As Bitcoin continues to navigate through these cycles of highs and lows, it remains a focal point for investors and analysts who speculate on its future amidst the evolving financial landscape. Ethereum (ETH) Whale Makes Estimated Profit of $68.5 Million In the ever-volatile cryptocurrency landscape, Ethereum (ETH) has once again been at the center of a tumultuous trading day. The leading altcoin experienced a sharp decline, dropping 3.1% to a low of $3,411, only to make a surprising rebound to a peak of $3,615. This rapid fluctuation occurred within a span of 24 hours, with the price eventually settling at $3,512. Despite these dramatic swings, the day closed with Ethereum's value relatively unchanged over the 24-hour period. Amidst this whirlwind of market activity, the actions of significant cryptocurrency investors, commonly known as crypto whales, have drawn considerable attention. According to a report by Spot On Chain, a notable transaction was observed where a whale, identified as "0x347," deposited an impressive 9,000 ETH into Binance. This deposit, valued at $32 million, represents the largest single transfer of Ethereum by this investor to date. Following this transaction, "0x347" now holds a total of 29,738 ETH, estimated to be worth around $106 million, realizing an estimated profit of $68.5 million. Concurrently, data from Lookonchain highlighted another whale engaging in substantial acquisitions, purchasing 10,309 ETH for approximately $35.82 million during the market's low point. This investor also executed a significant buy prior to a notable increase in Bitcoin’s price on April 8. These significant whale activities underscore a divide in market sentiment concerning Ethereum's immediate future. While some market participants interpret the recent price recovery as an indication of a bullish trend continuation, others are cautious, suggesting the rebound could be a temporary technical correction. As Ethereum continues to be a central topic of discussion and analysis within the crypto community, the contrasting strategies of key market players provide a clear picture of the speculative nature inherent in the cryptocurrency markets. Furrever Token (FURR) Secures Over $770,000 at Presale Furrever Token (FURR) is swiftly establishing itself as a prominent player in the cryptocurrency market, captivating a broad audience with its significant growth potential and compelling investor advantages. Its remarkable traction in presale events has been a critical factor in its rising appeal, attracting hefty investments in each phase. Currently, in its sixth stage of presale, Furrever Token (FURR) has successfully amassed over $770,000, underscoring its increasing popularity and solid support within the cryptocurrency community. Furrever Token (FURR) Furrever Token (FURR) Offering investors the potential for up to 15X returns on each presale stage, Furrever Token (FURR) emerges as a lucrative option for those seeking profitable opportunities within the crypto space. With a current price of $0.00048, FURR provides an excellent chance for investors to participate early in its anticipated growth trajectory. The strength of Furrever Token’s appeal is further enhanced by the vibrant community behind it. With more than 4,300 active members on its official Telegram channel, Furrever Token (FURR) thrives as a dynamic center for engaging discussions, collaborative efforts, and consistent updates, enriching the investment experience for all participants. Looking ahead, the strategic roadmap and development plans of Furrever Token (FURR) demonstrate its determined commitment to establishing a substantial foothold in the meme coin market. Plans to introduce innovative features, establish strategic partnerships, and launch targeted marketing initiatives are set to broaden its adoption and increase its market value significantly. In summary, Furrever Token (FURR) stands out as an exceptionally attractive investment opportunity, backed by solid fundamentals, a robust and active community, and a promising outlook for extensive growth. As Furrever Token (FURR) continues to advance towards its strategic goals, it is well-positioned to yield significant returns for early investors who capitalize on its potential. **Secure the Most Exclusive Presale Opportunity of 2024 Today!
\ Furrever Token Official Website | Visit Furrever Token Presale Join Official Telegram Group | Follow Official X Account Furrever Token Official Website Furrever Token Official Website Visit Furrever Token Presale Visit Furrever Token Presale Join Official Telegram Group Join Official Telegram Group Follow Official X Account Follow Official X Account

Crypto Profit Potential: BTC Halving, ETH Whale Activity, and Furrever Token $770K Presale

ক্রিপ্টো লাভের সম্ভাবনা: বিটিসি হালভিং, ইটিএইচ হোয়েল অ্যাক্টিভিটি এবং ফুরেভার টোকেন $770K প্রিসেল

Krypto-Gewinnpotenzial: BTC-Halbierung, ETH-Wal-Aktivität und Furrever-Token-Vorverkauf im Wert von 770.000 USD

Potencial de ganancias criptográficas: reducción a la mitad de BTC, actividad de ballenas de ETH y preventa de $770 mil del token Furrever

Potentiel de profit cryptographique : réduction de moitié du BTC, activité des baleines ETH et prévente de 770 000 $ du jeton Furrever

क्रिप्टो लाभ क्षमता: बीटीसी हाफिंग, ईटीएच व्हेल गतिविधि, और फुर्रेवर टोकन $ 770K प्रीसेल

暗号通貨の利益の可能性: BTC の半減期、ETH クジラの活動、Furrever トークンの 77 万ドルのプレセール

암호화폐 수익 잠재력: BTC 반감기, ETH 고래 활동 및 Furrever 토큰 $770K 사전 판매

Potencial de lucro criptográfico: redução pela metade do BTC, atividade de baleias ETH e pré-venda de token Furrever de US$ 770 mil

Потенциал прибыли от криптовалюты: сокращение вдвое BTC, активность ETH Whale и токен Furrever, предварительная продажа на сумму 770 тысяч долларов

Kripto Kâr Potansiyeli: BTC Yarılanması, ETH Balina Faaliyeti ve Furrever Token 770 Bin Dolarlık Ön Satış

Tiềm năng lợi nhuận từ tiền điện tử: Giảm một nửa BTC, Hoạt động của cá voi ETH và Mã thông báo Furrever $ 770K Bán trước

加密货币盈利潜力：BTC 减半、ETH 鲸鱼活动和 Furrever Token 77 万美元预售

Ever feel like your phone knows you better than your best friend? Does your newsfeed seem less like a window to the world and more like some weird fun-house mirror? Mine too. They may even be a little too good. And they’re only going to get better. But here's the secret: you can opt-out. A growing cadre of tech gurus, renegade artists and unstoppable activists are throwing a wrench in the system. Big Tech's algorithms are good. There was no escape. Breathe, eat, and sleep. That was all there was to life in the machine." - Aldous Huxley, Brave New World is not a word commonly used by the masses. The idea of independence feels like a betrayal. Talking about “freedom” sets off triggers. But autonomy is a dynamic force. Today it’s making a global comeback.  If you look closely you can spot it in  tech, art and activism. Autonomy It adapts and evolves with our ever-changing world. It’s a mindset that says “ ” I’m not going to be controlled by outside forces and external interests. Here’s some non-breaking news. The traditional work compact, in which employees traded hard work and loyalty in exchange for job security is shattered. Thought so. How many times have you gone job-hunting in your life? But consider this. boasts . He remixed the for the most recent Super Bowl in Las Vegas. DJ Steve Aoki more than 3 billion music streams NFL on CBS theme Here’s what he said at Gala Music: “In the 10 years I’ve been making music … six albums, and all those advances … in one drop last year in NFTs, I made more money.” - Steve Aoki Want to opt-out and join the revolution? Connect with techs and developers building a . Why give your personal information to some faceless corporation? web where you are in control Learn from artists minting their creations as . For the first time, it’s possible to cut out the middleman and take ownership of your work. Discover how activists bypass the financial gatekeepers. Now you can use crowdfunding to fuel your movements directly from the people. NFTs Keep reading to see how the coolest tools and platforms are putting you back in control. Let’s get this show on the road. Get ready for an empowered future. The Rise of Autonomy Early in my entrepreneurial journey, a millionaire mentor gave me some advice on autonomy. He said, “ How often are your decisions based on your own values and goals? Norm whoever controls your time controls your income. I suggest it be you who does it.” The concept drove my parents. “Get an education. Get a good job. Retire there.” In their day people often stayed with the same company for their entire career. They were even called “a company man” or “company gal.” Life goals consisted of building deep connections with colleagues and a strong sense of pride in your work. company loyalty https://www.youtube.com/watch?v=a4F9u8Z84YI&embedable=true Back then companies invested in their employees through training programs and full benefits. When I started in sales my big tech employer had a “no-layoff” policy (it ended while I was there). The global creator economy is projected to reach $104.2 billion by 2028, reflecting the increasing number of individuals leveraging online platforms to express themselves creatively and generate income independently. - Creator Economy Over the course of several decades, cracks began to appear in the model. Tech advancements led to increased job insecurity. One of the first job killers I remember was voicemail. Yep. It eliminated the need for receptionists to answer phones, take messages, and write them down on those little pink slips for salespeople returning to the office. Wage freezes and layoffs increased and the "good job" became less common. As the internet took hold in the 90’s globalization became sexy. The social contract between employer and employee collapsed, Companies chased cheap labor and maximum profits for shareholders -  wherever they could find them. But in public, they sought to maintain the illusion of the Golden Age. Their corporate PR departments were quite busy. Today despite the perception of freedom your autonomy is limited. own social media networks. They control the search engines and video production platforms. Their TOS and centralization secure their dominance. Maximize profits for the few. Sound familiar? This traditional model locks out millions of creators worldwide. Big tech giants “The goal of most people for the last half century was to get the good job that paid well with benefits..” - Anthony Spadafore, Director Pathfinders Autonomy Success Story My friend Sarah is a graphic designer with six years of experience. During the COVID-19 pandemic, she lost her job at a big ad agency. After chasing hundreds of job posts she decided to be her own boss. Now she works remotely as a freelance designer. Instead of being assigned clients, she sets her own schedule. Her boring projects got replaced by gigs that aligned with her interests. She earns more on her own than she did going into the office. And now she finds personal meaning in her work. The Back-to-Office Push and the Autonomy Challenge A remote work revolution is sweeping around the world. Set free from the confines of the physical office, employees are discovering a newfound sense of autonomy. Do you thrive in an environment tailored to your work style? Can you manage your schedule and responsibilities better with more flexibility? For most of us, the answer is YES! This newfound control increases productivity and boosts morale. Many companies are actively encouraging, some even demanding, a return to “in-office” work. Their  push-back against remote work poses a challenge to employee autonomy. But big business CEOs, government bureaucrats, and even Elon Musk don’t like it. Here's why: :  Remote work helps employees manage their work schedules around personal needs and responsibilities. If your productivity is solid and profits are up why poke the bear? A forced return to the office disrupts this balance and increases stress. Goodbye Flexibility, Hello Frustration : It’s the time and money stupid. We all have 24 hours in our day. Having to commute to the office, pay to park, and upgrade your wardrobe eats into personal time and monthly budgets. The impact on working parents or people with long commutes is huge. The Commute Crisis : Most employees I speak with enjoy the flexibility and convenience of working from home. Some never had this opportunity before COVID-19. Now they realize they can do their job more conveniently. Forcing them back into the office feels like a slap in the face. Morale Killer I think companies that are inflexible with remote work options will lose valuable talent. The job market is competitive. The creator economy allows people like my friend Sarah to level up their skills and create jobs for themselves. The pull of autonomy is strong. Collaboration: Tech, Art and Activism Disruption accelerates when tech, art and activism come together. From interactive installations to digital art platforms, technology has enabled artists to reach wider audiences. You can also spark important conversations about social issues and reform. Techpreneurs are launching tools that empower artists and activists. Modern artists are wielding technology to create art that raises awareness for social causes. Activist groups are reaching a global audience with every social media post. This is the future we're building. A world where creative expression meets social justice. And the power of technology is at the core. - Norm Bond Tech Creators Build Worlds Beyond Reality Tech developers and creators are building alternative platforms to traditional activities. Can you imagine stepping into a painting instead of just looking at it in a museum? You can with . Van Gogh Exhibit: The Immersive Experience The exhibition has been ! This blend of art and cutting-edge technology ranks among the 12 best immersive experiences in the world by CNN. touring since 2017 with +5,000,000 visitors Take a walk alongside Van Gogh during a visually rich journey. It’s a 20,000 square feet sound and light spectacular. The two-story projections leave visitors in awe. You discover the inspiration behind eight of Van Gogh’s iconic works. is one of my favorites. The Starry Night Artists Go Indie with Tech Blockchain technology provides innovative tools for artists to monetize their work through smart contracts, NFTs and crypto. This strategy allows creators to sell their work directly to collectors . Artists are creating digital art and minting it as NFTs. without the need for traditional galleries or intermediaries London's glitchy artist , averaging over $23,000 per sale and topping the charts. His work captivates collectors and redefines the limits of digital art. His signature style is a mesmerizing blend of flashing visuals and dark themes (his website includes a flashing media warning). XCOPY is minting millions with his dystopian NFTs Here’s the thing. XCOPY’s work isn't just visually arresting. Sure there are lots of flashy NFTs and dystopian styles. But his NFT’s spark conversations. He challenges the status quo in the art world. He’s using his creativity to provide a unique commentary on our digital age. . Is a sign of a future where artists control their destinies, or a blip in a system that still favors the powerful? Don’t just wait to see what happens. Take action on YOUR dream. Telling your own story and controlling distribution is an expression of autonomy XCOPY How Activists Use Tech to Win The murder of George Floyd was recorded and spread virally across social media. It ignited Black Lives Matter like a rocket. This wasn't just a tragic event. It was a spark that . The case study reveals how activists can use social media to amplify their message. Consider this. lit the fuse of a movement already simmering online In the month Floyd’s murder, was tweeted more than 400 thousand times. But in the month , the . before #BlackLivesMatter after hashtag spiked and was shared more than 100 million times Nordstrom posted #BlackLivesMatter to Facebook and received more than 5-million likes. The from the brand. engagement was nearly 90 times the level of typical posts Colleges and universities joined in. More than 107 shared . Healthcare systems, nonprofits, corporations, and sports teams also climbed on the #BlackLivesMatter juggernaut. #BlackLivesMatter The entire internet was ablaze with social posts, videos, CEO statements, and brand platitudes to support this trending social movement. It shook the system. The Future of Autonomy The dream of dumping the 9-to-5 and living life on your terms is more than just a hashtag. It’s a call to action. One fueled by a deep desire for autonomy – the freedom to create, work, and live by your own rules. Don’t let it just be a pipe dream. The total value locked (TVL) in DeFi protocols surpassed $300 billion in 2023, demonstrating the growing interest in alternative financial systems that promote individual control over assets and transactions. - DeFi Pulse Revolutionary art movements, tech-driven innovation, and modern activism are converging. This shift provides a path to an exciting future. Break free from traditional structures. Now is your chance to build something empowering. It's a world where you can set the pace, call the shots, and shape your days around your passions. There are lots of challenges ahead. Working together is often necessary for maximum impact. Finding the right balance between maintaining individual artistic vision and working within a collective - can be difficult. But let's keep it real. Breaking free from traditional structures means leaving the financial security that these systems can provide. Artists, activists and techs still have bills to pay. Do you have alternative means of funding your work in place? With the rise of new technologies, there’s a need to ensure that ethical practices are followed. You need to address concerns regarding data privacy. There are questions regarding intellectual property rights to be sorted. The environmental impact of tech-driven projects like crypto and NFTs are a dealbreaker for some people. These challenges will have to be met to increase adoption and scale. They require innovative solutions, resilience and funding. To build a more equitable and creative future demands continuous learning and adaptation. Conclusion We’ve covered a lot. You know the journey toward autonomy is an exciting yet challenging one. This trend in tech, art and activism is attracting more people looking for a way out. The digital age provides new tools to take control of your life. It has never been easier to express yourself and reach a target audience. Artists can leverage blockchain technology. Tech creators can build alternative platforms. Activists can fuel movements without traditional limits. Working collectively can exponentially grow results for both individuals and organizations. But the road ahead is not all roses and sunshine. Challenges include navigating concerns about the environment. Ethical considerations riddle the increased use of AI and ML. Building tools that engage marginalized communities is critical in a market-based economy. Long-established traditional systems are not going to just roll over and embrace change. So take the long view if you want to embrace this autonomy trend. If you’re an artist, technologist or activist it’s time to redefine the possible. I’m in. Let’s create and cause the change you want to see. right here on HN to connect. Visit my profile

Breaking Rules: Autonomy in Tech, Art and Activism

নিয়ম ভাঙা: প্রযুক্তি, শিল্প এবং সক্রিয়তায় স্বায়ত্তশাসন

Regeln brechen: Autonomie in Technologie, Kunst und Aktivismus

Rompiendo reglas: autonomía en tecnología, arte y activismo

Briser les règles : autonomie dans la technologie, l'art et l'activisme

नियम तोड़ना: तकनीक, कला और सक्रियता में स्वायत्तता

ルールの破り: テクノロジー、アート、アクティビズムにおける自律性

규칙 위반: 기술, 예술 및 행동주의의 자율성

Quebrando regras: autonomia em tecnologia, arte e ativismo

Нарушение правил: автономия в технологиях, искусстве и активизме

Kuralları Çiğnemek: Teknolojide, Sanatta ve Aktivizmde Özerklik

Phá vỡ các quy tắc: Quyền tự chủ trong công nghệ, nghệ thuật và hoạt động

打破规则：科技、艺术和行动主义的自主性

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 7: Robot Learning Algorithm Details Object and skill learning details We utilize pre-trained R3M as the feature extractor. Our training procedure aims to learn a latent representation of an input image for inferring the correct object-skill pair in the given scene. The feature embedding model is a fully-connected neural network that further encodes the outputs of the foundation model. Model parameters and training hyperparameters are summarized in Table 7. Collecting human data using a BRI system is expensive. To enable few-shot learning, the feature embedding model is trained using a triplet loss [80], which operates on three input vectors: anchor, positive (with the same label as the anchor), and negative (with a different label). Triplet loss pulls inputs with the same labels together by penalizing their distance in the latent space, as well as pushes inputs with different labels away. The loss function is defined as: where a is the anchor vector, p the positive vector, n the negative vector, f the model, and α = 1 is our separation margin. We test our algorithm in the following generalization settings: Generalization test set. For position generalization, we randomize the initial positions of all objects in the scene with fixed orientation and collect 20 different trajectories. For the pose generalization, we randomize both the initial positions and orientations of all objects. Position and pose. . The context-level generalization refers to placing the target object in different environments, defined by different backgrounds, object orientations, and the inclusion of different objects in the scene. We collect 20 different trajectories with these variations. Context . The instance generalization aims to assess the model’s capability to generalize across different types of objects present in the scene. For our target task (MakePasta), we collect 20 trajectories with 20 different kinds of pasta with different shapes, sizes, and colors. Instance . To understand the separability of latent representations generated by our object-skill learning model, we visualize the 1024-dimensional final image representations using the t-SNE data visualization technique [81]. Results for the MakePasta pose generalization test set are shown in Figure 7. The model can well separate each of the different stages of the task, allowing us to retrieve the correct object-skill pair for an unseen image. Latent representation visualization One-shot parameter learning details We empirically found that using DINOv2’s ViT-B model, alongside a 75x100 feature map and a 3x3 sliding window, with cosine similarity as the distance metric, resulted in the best performance for our image resolutions. Design choices. We test the generalization ability of our algorithm on 1008 unique training and test pairs, encompassing four types of generalizations including 8 position trials, 8 orientation trials, 32 context trials, and 960 instance trials. Generalization test set The position and orientation generalizations, shown in Fig. 8 and Fig. 9 respectively, are tested in isolation, e.g. when the position is varied, the orientation is kept the same. Position and orientation The context-level generalization, shown in Fig. 10, refers to placing the target object in different environments, e.g. the training image might show the target object in the kitchen while the test image shows the target object in a workspace. Here, we allow for position and orientation to vary as well. Context To test our algorithm’s capability of instance-level generalization, shown in Fig. 11, we collected a set of four different object categories, each containing five unique object instances. Our object categories consist of mug, pen, bottle, and medicine bottle, whereas the bottle and medicine bottle categories consist of images from both the top-down and side views. We test all permutations within each object category including train and test pairs with different camera views. Here, we allow for position and orientation to vary as well. Instance We test our method against baselines on 1080 unique training and test pairs, encompassing four types of generalizations including 8 position trials, 8 orientation trials, 32 context trials, 960 instance trials, 48 trials where we vary all four generalizations simultaneously, and 24 trials from the SetTable task. Test set for comparing our method against baselines Finally, we test our algorithm’s ability to generalize when all four variables differ between the training and test image, shown in Fig. 12. Here, the only object category we use is a mug. Position, orientation, context, and instance simultaneously. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 7

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 6: Decoding Algorithms Details For both SSVEP and MI, we select a subset of channels and discard the signals from the rest, as shown in Figure 6. They correspond to the visual cortex for SSVEP, and the motor and visual areas for MI (with peripheral areas). For muscle tension (jaw clenching), we retain all channels. . To predict the object of interest, we apply Canonical Correlation Analysis (CCA) as shown in [77] to the collected SSVEP data. As each potential object of interest is flashing at a different frequency, we are able to generate reference signals Yf for each frequency fi: SSVEP n where f is the sampling frequency and N is the number of samples. s s By calculating the maximum correlation ρfn for each frequency f used for potential objects of interest, we are then able to predict the output class by finding argmax (ρf ) and matching the result to the object of interest with that frequency. n fn n Furthermore, we are able to return a list of predicted objects of interest in descending order of likelihood by matching each object to a list of descending maximum correlations ρf . n Motor imagery. To perform MI classification, we first band-pass filter the data between 8Hz - 30Hz, as that is the frequency range that includes the µ-band and β-band signals relevant to MI. The data is then transformed using the Common Spatial Pattern (CSP) algorithm. CSP is a linear transformation technique that applies a rotation to the data to orthogonalize the components where the over-timestep variance of the data differs the most across classes. We can then use the logvariance of each time series after rotation as features and perform QDA. Thereafter, we extract features by taking the normalized variance of this transformed data (called “CSP-space data”). We then perform Quadratic Discriminant Analysis (QDA) on this data. To calculate our calibration accuracy, we perform K-fold cross validation with KCV = 4, but we use the entire calibrate dataset to fit the classifier for deployment at task-time. Facial muscle tension results in a very significant high-variance signal across almost all channels that is very detectable using simple variance-based threshold filters without having to perform any frequency filters. Recall that we record three 500ms-long trials for each class (“Rest”, “Clench”). In short, for each of the calibration time-series, we take the variance of the channel with the median variance; call this variance m. Then, we just take the mid-point between the maximum m between the rest samples, and the minimum m between the clench samples, and have this be our threshold variance level. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 6

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 5: Experimental Procedure . In our experiments, we use the 128-channel HydroCel Geodesic SensorNet from Magstim EGI, which has sponge tips in its electrode channels. Prior to experiments, the EEG net is soaked in a solution containing dissolved conductive salt (Potassium Chloride) and baby shampoo for 15 minutes. After the soaking, the net is worn by the experiment subject, and an impedance check is done. This impedance check entails ensuring that the impedance of each channel electrode is ≤ 50.0 kΩ, by using a syringe to add more conductive fluid between the electrodes and the scalp. We then carefully put on a shower cap to minimize the drying of conductive fluid over the course of the experiment. EEG device preparation Before commencing the experiments, subjects are given instructions on how to execute the SSVEP, MI, and muscle tension (jaw-clenching) tasks. For SSVEP, they are instructed to simply focus on the flickering object of interest without getting distracted by the other objects on the screen. For MI, similar to datasets such as BCI Competition 2003 [75], and as per extensive literature review [76], we instruct subjects to either imagine continually bending their hands at the wrist (wrist dorsiflexion) or squeezing a ball for the hand actions (“Left”, “Right”), and to imagine depressing a pedal with both feet (feet dorsiflexion) for the “Legs” action. For the “Rest” class, as is common practice in EEG experiments in general, we instruct users to focus on a fixation cross displayed on the screen. Subjects were told to stick with their actions of choice throughout the experiment, for consistency. For muscle tension, subjects were told to simply clench their jaw without too much or too little effort. Instructions to subjects. . For SSVEP, subjects are told in writing on the screen to focus on the object of interest. Thereafter, a scene image of the objects with flickering masks overlaid on each object is presented, and we immediately begin recording the EEG Data over this period of time. For MI, the cues are different during calibration and task-time. During calibration, subjects are presented with a warning symbol (.) on screen for 1 second, before being presented with the symbol representing the action they are to imagine (<-: “Left”, ->: “Right”, v: “Legs”, +: “Rest”), which lasts on screen for 5500 ms. We record the latter 5000 ms of EEG data. After which, there is a randomized period of rest the lasts between 0.5 and 2 seconds, before the process repeats for another randomly chosen action class. This is done in 4 blocks of 5 trials per action, for a total of 20 trials per action. This procedure is again similar to datasets like BCI Competition 2003 [75], that use non-linguistic cues and randomization of rest / task. At task-time, similar to SSVEP, subjects are told in writing on the screen to perform MI to select a robot skill to execute. Thereafter, a written mapping of a class symbol ({<-, ->, v, +}) to skill ({pick from top, pick from side, ...}) is presented, and we begin recording EEG Data after a 2-second delay. For muscle tension, there is also a calibration phase, similar to MI, which entails collecting three 500ms-long trials for each class (“Rest”, and “Clench”) at the start of each experiment. The cues are written on the screen in words. At task time, when appropriate, written prompts are also presented on the screen (e.g. “clench if incorrect”), followed by a written countdown, after which the user has a 500ms window to clench (or not). Interface This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 5

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 4: Task Definitions For systematic evaluation of task success, we provide formal definitions of our tasks in the format of BEHAVIOR Domain Definition Language (BDDL) language [69, 71]. BDDL is a predicate logic-based language that establishes a symbolic state representation built on predefined, meaningful predicates grounded in physical states [71]. Each task is defined in BDDL as an initial and goal condition parametrizing sets of possible initial states and satisfactory goal states, as shown in the figures at the end of the appendix. Compared to scene- or pose-specific definitions which are too restricted, BDDL is more intuitive to humans while providing concrete evaluation metrics for measuring task success. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 4

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 3: System Setup The robot we use in our tabletop manipulation task is a standard Franka Emika robot arm with three RealSense cameras. For mobile manipulation, we use a Tiago++ model from PAL Robotics, with an omnidirectional base, two 7-degrees-of-freedom arms with parallel-yaw grippers, a 1-degree-of-freedom prismatic torso, two SICK LiDAR sensors (back and front of the base), and an ASUS Xtion RGB-D camera mounted on the robot’s head, which can be controlled in yaw and pitch. All sensors and actuators are connected through the Robot Operating System, ROS [74]. The code runs on a laptop with an Nvidia GTX 1070 that sends the commands to the onboard robot computer to be executed. Robot platform. A list of primitive skills along with their parameters can be found in Table 6, eight for Franka (16 tasks) and five for Tiago (four tasks). Human users can accomplish all 20 tasks, which are long-horizon and challenging, using these skills. Primitive skills list. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 3

Authors: (1) Ruohan Zhang, Department of Computer Science, Stanford University, Institute for Human-Centered AI (HAI), Stanford University & Equally contributed; zharu@stanford.edu; (2) Sharon Lee, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (3) Minjune Hwang, Department of Computer Science, Stanford University & Equally contributed; zharu@stanford.edu; (4) Ayano Hiranaka, Department of Mechanical Engineering, Stanford University & Equally contributed; zharu@stanford.edu; (5) Chen Wang, Department of Computer Science, Stanford University; (6) Wensi Ai, Department of Computer Science, Stanford University; (7) Jin Jie Ryan Tan, Department of Computer Science, Stanford University; (8) Shreya Gupta, Department of Computer Science, Stanford University; (9) Yilun Hao, Department of Computer Science, Stanford University; (10) Ruohan Gao, Department of Computer Science, Stanford University; (11) Anthony Norcia, Department of Psychology, Stanford University (12) Li Fei-Fei, 1Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University; (13) Jiajun Wu, Department of Computer Science, Stanford University & Institute for Human-Centered AI (HAI), Stanford University. Table of Links Abstract & Introduction Brain-Robot Interface (BRI): Background The NOIR System Experiments Results Conclusion, Limitations, and Ethical Concerns Acknowledgments & References Appendix 1: Questions and Answers about NOIR Appendix 2: Comparison between Different Brain Recording Devices Appendix 3: System Setup Appendix 4: Task Definitions Appendix 5: Experimental Procedure Appendix 6: Decoding Algorithms Details Appendix 7: Robot Learning Algorithm Details Appendix 2: Comparison between Different Brain Recording Devices We use the EGI NetStation EEG system which uses rapid application 128-channel saline-based EGI SensorNets. Here we justify our choice of using non-invasive, saline-based EEG as the recording device for brain signals. A comparison of different brain reading devices (gel-based EEG, dry EEG, MEG, fMRI, fNIRS, implant) and their advantages and disadvantages are shown in Table 5, using our device as the baseline. Two noticeable alternatives are functional magnetic resonance imaging (fMRI) and invasive implants. fMRI measures the small changes in blood flow that occur with brain activity, which has a very high spatial resolution hence fine-grained information such as object categories and language [31] can be decoded from it. But fMRI suffers from low temporal resolution, and the recording device is extremely costly and cannot be used in daily scenarios. Brain implants have a very good signal-to-noise ratio and have great potential. However, the main concern is that it requires surgery to be applied, and health-related risks are not negligible. This paper is under CC 4.0 license. available on arxiv

NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities: Appendix 2

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