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Owl Autonomous Imaging

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 3 The NOIR System The challenges we try to tackle are: 1) How do we build a general-purpose BRI system that works for a variety of tasks? 2) How do we decode relevant communication signals from human brains? 3) How do we make robots more intelligent and adaptive for more efficient collaboration? An overview of our system is shown in Fig. 2. Humans act as planning agents to perceive, plan, and communicate behavioral goals to the robot, while robots use pre-defined primitive skills to achieve these goals. The overarching goal of building a general-purpose BRI system is achieved by synergistically integrating two designs together. First, we propose a novel modular brain decoding pipeline for human intentions, in which the human intended goal is decomposed into three components: what, how, and where (Sec. 3.1). Second, we equip the robots with a library of parameterized primitive skills to accomplish human-specified goals (Sec. 3.2). This design enables humans and robots to collaborate to accomplish a variety of challenging, long-horizon everyday tasks. At last, we show a key feature of NOIR to allow robots to act more efficiently and to be capable of adapting to individual users, we adopt few-shot imitation learning from humans (Sec. 3.3). 3.1 The brain: A modular decoding pipeline We hypothesize that the key to building a general-purpose EEG decoding system is modularization. Decoding complete behavioral goals (e.g., in the form of natural language) is only feasible with expensive devices like fMRI, and with many hours of training data for each individual [31]. As shown in Fig. 3, we decompose human intention into three components: (a) What object to manipulate; (b) How to interact with the object; (c) Where to interact. The decoding of specific user intents from EEG signals is challenging but can be done with steady-state visually evoked potential and motor imagery, as introduced in Sec. 2. For brevity, details of decoding algorithms are in Appendix 6. Upon showing the task set-up on a screen, we first infer the user’s intended object. We make objects on the screen flicker with different frequencies (Fig. 3a), which, when focused on by the user, evokes SSVEP [26]. By identifying which frequency is stronger in the EEG data, we may infer the frequency of the flickering visual stimulus, and hence the object that the user focuses on. We apply modern computer vision techniques to circumvent the problem of having to physically attach LED lights [27, 28]. Specifically, we use the foundation model OWL-ViT [32] to detect and track objects, which takes in an image and object descriptions and outputs object segmentation masks. By overlaying each mask of different flickering frequencies (6Hz, 7.5Hz, 8.57Hz, and 10Hz [33, 34]), and having the user focus on the desired object for 10 seconds, we are able to identify the attended object. Selecting objects with steady-state visually evoked potential (SSVEP). We use only the signals from the visual cortex (Appendix 6) and preprocess the data with a notch filter. We then use Canonical Correlation Analysis (CCA) for classification [35]. We create a Canonical Reference Signal (CRS), which is a set of sin and cos waves, for each of our frequencies and their harmonics. We then use CCA to calculate the frequency whose CRS has the highest correlation with the EEG signal, and identify the object that was made to flicker at that frequency. . The user then chooses a skill and its parameters. We frame this as a k-way (k ≤ 4) MI classification problem, where we aim to decode which of the k pre-decided actions the user was imagining. Unlike SSVEP, a small amount of calibration data (10-min) is required due to the distinct nature of each user’s MI signals. The four classes are: Left Hand, Right Hand, Legs, and Rest; the class names describe the body parts that users imagine using to execute some skills (e.g. pushing a pedal with feet). Upon being presented with the list of k skill options, we record a 5-second EEG signal, and use a classifier trained on the calibration data. The user then guides a cursor on the screen to the appropriate location for executing the skill. To move the cursor along the x axis, the user is prompted to imagine moving their Left hand for leftward cursor movement. We record another five seconds of data and utilize a 2-way classifier. This process is repeated for x, y, and z axes. Selecting skill and parameters with motor imagery (MI) For decoding, we use only EEG channels around the brain areas related to motor imagery (Appendix 6). The data is band-pass-filtered between 8Hz and 30Hz to include µ-band and β-band frequency ranges correlated with MI activity [36]. The classification algorithm is based on the common spatial pattern (CSP) [37–40] algorithm and quadratic discriminant analysis (QDA). Due to its simplicity, CSP+QDA is explainable and amenable to small training datasets. Contour maps of electrode contributions to the top few CSP-space principal components are shown in the middle row of Fig. 3. There are distinct concentrations around the right and left motor areas, as well as the visual cortex (which correlates with the Rest class). Safety is critical in BRI due to noisy decoding. We follow a common practice and collect electrical signals generated from facial muscle tension (Electromyography, or EMG). This signal appears when users frown or clench their jaws, indicating a negative response. This signal is strong with near-perfect decoding accuracy, and thus we use it to confirm or reject object, skill, or parameter selections. With a pre-determined threshold value obtained through the calibration stage, we can reliably detect muscle tension from 500-ms windows. Confirming or interrupting with muscle tension. 3.2 The robot: Parameterized primitive skills Our robots must be able to solve a diverse set of manipulation tasks under the guidance of humans, which can be achieved by equipping them with a set of parameterized primitive skills. The benefits of using these skills are that they can be combined and reused across tasks. Moreover, these skills are intuitive to humans. Since skill-augmented robots have shown promising results in solving longhorizon tasks, we follow recent works in robotics with parameterized skills [41–52], and augment the action space of our robots with a set of primitive skills and their parameters. Neither the human nor the agent requires knowledge of the underlying control mechanism for these skills, thus the skills can be implemented in any method as long as they are robust and adaptive to various tasks. We use two robots in our experiment: A Franka Emika Panda arm for tabletop manipulation tasks, and a PAL Tiago robot for mobile manipulation tasks (see Appendix for hardware details). Skills for the Franka robot use the operational space pose controller (OSC) [53] from the Deoxys API [54]. For example, Reaching skill trajectories are generated by numerical 3D trajectory interpolation conditioned on the current robot end-effector 6D pose and target pose. Then OSC controls the robot to reach the waypoints along the trajectory orderly. The Tiago robot’s navigation skill is implemented using the ROS MoveBase package, while all other skills are implemented using MoveIt motion planning framework [55]. A complete list of skills for both robots is in Appendix 3. Later, we will show that humans and robots can work together using these skills to solve all the tasks. 3.3 Leveraging robot learning for efficient BRI The modular decoding pipeline and the primitive skill library lay the foundation for NOIR. However, the efficiency of such a system can be further improved. During the collaboration, the robots should learn the user’s object, skill, and parameter selection preferences, hence in future trials, the robot can predict users’ intended goals and be more autonomous, hence reducing the effort required for decoding. Learning and generalization are required since the location, pose, arrangement, and instance of the objects could differ from trial to trial. Meanwhile, the learning algorithms should be sample-efficient since human data is expensive to collect. In NOIR, human effort can be reduced if the robot intelligently learns to propose appropriate object-skill selections for a given state in the task. Inspired by retrieval-based imitation learning [56–58], our proposed method learns a latent state representation from observed states. Given a new state observation, it finds the most similar state in the latent space and the corresponding action. Our method is shown in Fig. 4. During task execution, we record data points that consist of images and the object-skill pairs selected by the human. The images are first encoded by a pre-trained R3M model [59] to extract useful features for robot manipulation tasks, and are then passed through several trainable, fully-connected layers. These layers are trained using contrastive learning with a triplet loss[60] that encourages the images with the same object-skill label to be embedded closer in the latent space. The learned image embeddings and object-skill labels are stored in the memory. During test time, the model retrieves the nearest data point in the latent space and suggests the object-action pair associated with that data point to the human. Details of the algorithm can be found in Appendix 7.1. Retrieval-based few-shot object and skill selection. Parameter selection requires a lot of human effort as it needs precise cursor manipulation through MI. To reduce human effort, we propose a learning algorithm for predicting parameters given an object-skill pair as an initial point for cursor control. Assuming that the user has once successfully pinpointed the precise key point to pick a mug’s handle, does this parameter need to be specified again in the future? Recent advancement in foundation models such as DINOv2 [61] allows us to find corresponding semantic key points, eliminating the need for parameter re-specification. Compared to previous works, our algorithm is one-shot [62–66] and predicts specific 2D points instead of semantic segments [67, 68]. As shown in Fig. 4, given a training image (360 × 240) and parameter choice (x, y), we predict the semantically corresponding point in the test images, in which positions, orientations, instances of the target object, and contexts may vary. We utilize a pre-trained DINOv2 model to obtain semantic features [61]. We input both train and test images into the model and generate 768 patch tokens, each as a pixel-wise feature map of dimension 75 × 100. We then extract a 3 × 3 patch centered around the provided training parameter and search for a matching feature in the test image, using cosine similarity as the distance metric. Details of this algorithm can be found in Appendix 7.2. One-shot skill parameter learning. This paper is under CC 4.0 license. available on arxiv

The NOIR System: Building a General Purpose BRI System 

Let's learn about via these 40 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Digital Nomads /Learn Repo 1. Web Monetization + Gig Economy = Digital Nomadism? Due to various technological developments, it is now easier than ever to earn money online. Known as the gig economy, there is a massive free-market system for independent workers looking to earn an extra buck. Monetizing one's expertise can remain a challenge, however. 2. US Only vs. Work From Anywhere Remote Jobs Problem Remote work is global or it’s not remote work at all! 3. How to Behave during Video Calls: 9 Rules for Communicating with Colleagues These days most companies have asked their employees to stay at home and change their office work for a remote one. In such conditions, the issues of organizing video calls have become especially relevant. It seems that online conferences for dozens and even hundreds of people are a step towards a digital future without borders when people can work from anywhere in the world and get together for negotiations literally by click. But in reality, video calls are often not as good as you might imagine. Someone cannot install the necessary program, someone is nervous  because of the relatives in the background, and someone appears at an important conference call in his pajamas and using the nickname “DimonKiller666”. We have put together some simple rules for making video calls that will help make them comfortable for yourself and your colleagues. 4. How I Beat My Limited Beliefs, Quit My Job, and Became a Island-based Digital Nomad How to become a digital nomad with no experience. Here is how I stopped working 9 to 5 and became a digital nomad. 5. Why I Decided To Build a Community for Remote Work I am the co-founder of Flexiple and Remote Tools. In this post, I describe why we are building an exclusive remote-focused community - The Remote Clan. 6. How to Manage a Remote Team if You’ve Never Done It Before Vita Baryshnikova is a Head of HR at Skyeng where 90% of employees and contractors work remotely. Vita shares her experience of creating effective processes for remote teams. These simple tips can help you prevent burnout and add transparency to your work. 7. Temporary Autonomous Security (T.A.S.) Introduction ...the digital nomad
experience is a pathway to both temporary freedom (escapism) and to sustained
liberation (evolution)...the nomad is a
foundation, a moving point mechanism... -Mariette Papic, Digital Nomad Manifesto 8. A Guide to Remote Working as a Beginner are you a freelancer or a remote employee or contractor working on a remote basis? if you’re any of this then this guide is for you. 9. Seven Expert Tips For Managers To Support Their Remote Teams “The best executive is the one who has sense enough to pick good men to do what he wants done, and self-restraint to keep from meddling with them while they do it.” – Theodore Roosevelt 10. How Technology is Changing Retirement Planning Let's look at some emerging new options that can help you create passive income for retirement. 11. How To Set Up A Home Office The rise of flexible job roles, digital tech careers and more freelance opportunities around the world has led more people into working from the comfort of their home, especially during this Covid19 pandemic. However, in order to get the best and ensure quality service deliveries, remote workers need the right tools to assist their craft. 12. What COVID-19 Means for the Future of Work From Home The worldwide lockdown following the coronavirus pandemic was truly a time for remote work to shine if there ever was one. With most of the globe’s workforce being stranded in their homes and away from the office, the only solution for most was to move their labor to the remote setting. 13. Colombia's Digital Nomad Visa Raises Internet Connectivity Concerns Let’s explore why digital nomads are drawn to Colombia, what the digital nomad visa offers and which locations offer the most reliable internet connections. 14. Best Applications That Will Make Remote Work During Coronavirus More Comfortable Things have changed very rapidly for a lot of people. Just a few months ago nobody would’ve imagined they would have to stay at home for a prolonged period of time, and even though warned, preparation for a global scale epidemic was pretty poor. Right now, when the spread of Covid-19 is at its peak, a lot of people have to get used to self-isolation, which means they can’t go to work to offices anymore but have to work remotely. 15. Tips For Conducting A Remote Presentation Like many others, my workplace has been conducting business remotely most of the year. 16. Impact of Automation on the Future of Work Automation and artificial intelligence (AI) are changing the nature of jobs and many believe that robots will completely put humans out of work in the coming years. 17. How to Become a Successful Digital Nomad Entrepreneur https://www.pexels.com/ 18. 6 Tips for Working Remotely Due to the COVID-19 outbreak, millions of people have suddenly had to relocate their office to their own homes. In normal times, around 8 million Americans work from home, but in just a few days, that number has multiplied, and then multiplied again. 19. Heroku Remote Culture: AMA with Francis Lacoste Francis Lacoste is a Senior Director, Software Engineering at Salesforce Heroku. During Live Ask Me Anything session with Hacker Noon community he shared his experience working remote for over 20 years and main lessons he learned from it. In Francis’s own words: 20. Banking, Finance and More with Noonies Nominee Radley 2021 Noonies Nominee General Interview with Rad Sidwell-Lewis. Read for more on Investments, Finance, Tech, Entrepreneurship, Travel and Startups. 21. Is Upwork The Sick Man Upon the Silicon Bosphorus? [Looking At Numbers] Financial reports are lengthy, confusing, and boring. I promise that I will keep it simple, brief, and straight to the point. I will let Upwork’s numbers do all the talking. 22. 8 Basic Steps to Keep Your Remote Development Teams Secure There is no doubt that the world's workforce is becoming more remote, particularly in tech as developers can now work from any location in the world. But there are a large number of new obstacles that come with this. The most pressing is security. 23. Working from Home During the COVID-19 Outbreak: Challenges and Tips To Help Remote work and the associated social distancing is one of the ways to halt the outbreak of coronavirus - an enormous challenge facing the entire world in 2020. While more and more companies urge their employees to shift to working from home, and people eagerly accept the change to avoid public spaces, there are a wealth of concerns regarding what it will all be like: 24. Two Factor Authentication - The Deep State Tool That Supports Mass Technology Dysfunction 2FA is the initial, mandatory stage of ongoing privacy intrusions that are unwarranted and Orwellian. Alternatives now exist. 25. Coworking Bansko is a Haven for Digital Nomads In this slogging thread, our community discusses Zeitler's initiative, Coworking Bansko, and nomadic working. 26. Build to Rent DAO: Pioneering the Concept of 'Rent-to-Earn' Real estate tokenization system allows users to rent objects from a decentralized autonomous organization or DAO instead of buying it. 27. The Free-Range Lifestyle: How to Not Live and Die in a Cage We Evolved for Free-Range Living 28. Types And Differences of Remote Workers It’s been more than two decades since the term ‘digital nomad’ came to be. In the time since, people have shown they have a lot of imagination when it comes to their remote work-enabled lifestyles. Some stay in one place and lead an otherwise normal life while others use the flexibility of remote work to travel continuously, or at least regularly. As a result, the world of remote work has become a diverse ecosystem where people have varying degrees of commitment and availability, as well as a broad array of purposes for working remotely. 29. Some Tips On Holding a Productive Virtual Offsite Offsites are a big part of remote teams. They allow everyone to socialize, connect more deeply with coworkers, and help build shared experiences and empathy. Even if video calls are a great tool to share information, they can be tiring. It's too easy to miss non-verbal cues. Chance encounters over coffee never happen, and we don’t always experience the same personal connections that come from small-talk. That’s the downside of remote work, and that's why meeting a few times a year is a great way to fill the gap! 30. A Couple of Nomad Makers: finding friends and side project profits This article is the second in a three part series. If you want to read the first one you can do here. 31. Tips For Winning a Proposal For Software Engineers Chances are if you're doing freelancing sooner or later you'll come across a need to write some kind of proposal to your potential customer. 32. Burnout, Budget Smudget, and You Can’t Phone Home This article is the last in a three-part series. Read the previous one here (and the first one here). 33. How To Transition to Remote Work Companies transitioning to the work-from-home model became the recurring theme of the last month. I've spent more than half of the previous decade working in remote teams and have seen quite a few transitioning from the office to full-remote processes. Actively advocating remote work to everyone I know, I feel having to put a few cents into the tip jar. 34. How To Ensure That Your Team Is Happy With Remote Work? A month ago saying that running a company through the crisis caused by this pandemic has been hard would be an understatement. I’m sure this has been unchartered territory for most of us. I mean, most workplaces don’t even have work from home policies that they had to design from scratch. 35. Creative Ways Companies are Keeping Teams Sane And Connected Through WFH Mandate Even prior to the COVID-19 pandemic, remote employees and Work from Home arrangements were becoming increasingly common. An Owl Labs 2019 State of Remote Work Report found that 54% of U.S. workers work remotely at least once per month, 48% work remotely at least once per week, and 30% work remotely full-time. Global Workplace Analytics is forecasting a 25-30% increase in working remotely with adoption increasing proportionately with the amount of time we spend in shelter at home and work from home conditions due to COVID-19. 36. 7 Ways Telephone Systems Support Remote and Home Workers Remote working isn’t becoming popular. It already is. The number of people who work remotely in the US has increased by 140% since 2005, with 34% of US workers saying they would take a pay cut of up to 5% in order to work from home. 37. 5 Tips to Successfully Transit to Remote Working during Coronavirus-and Beyond It has been four months since Coronavirus originated in China's
Wuhan and the outbreak continues to be on the headlines. On one hand, many employers are getting ready for the possible disruptions that Coronavirus would bring to their business in the long run, such as transportation issues, labor shortages, reduced working hours and low consumer traffic. 38. An Anthology of Places and Tools That Can Help You To Find Remote Software Engineering Jobs Working remotely is no longer just a dream. If you can afford a decent laptop and access to the internet, combine it with solid work skills and that gives you a pretty good chance to find a remote job. Professionals in many industries including entrepreneurs, marketers, writers and of course software engineers have already chosen this route. 39. How to Save Toilet Paper for Your Employer As a person that used to track finances and company expenses in their previous life, I know that toilet paper is a huge expense. Everybody does it. Everybody needs it. 40. Our Nomad Makers Origin Story Our journey so far has comprised of one year in South East Asia, five months in Australia and New Zealand, two months in Berlin, two months in the UK, and one month in Eastern Europe. Now we’re working our way through South America, having explored Argentina, Chile, and Bolivia. At the time of writing we’re actually on a bus to Peru, trying to type while bouncing around on the terrible roads out here! Thank you for checking out the 40 most read stories about Digital Nomads on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

40 Stories To Learn About Digital Nomads

The web is more a social creation than a technical one. I designed it for a social effect—to help people work together―and not as a technical toy. Tim Berners-Lee, Weaving the Web, 1999 When you make a phone call, you don't have to be concerned about the brand of the phone or the telecom provider of the person you are calling. In the same way, sending an email does not require us to worry about the recipient's email address or email application. All these systems are interoperable, i.e., they are able to communicate with each other. We consider the interoperability of these communication systems as a given, yet we do not expect interoperability when it comes to Web or mobile applications. Nobody seems surprised to be unable to send messages between WhatsApp and Telegram. Today, our applications don't talk to each other. The lack of interoperability has impacts on the Web and the digital economy in general, but also on organizations and their ability to coordinate and cooperate. This is why Tim Berners-Lee launched the (SOcial LInked Data) project in 2015. Concerned about the growing influence of a few global companies on access to information and personal data, Tim aims to rebuild the technical architecture of the Web based on the principles of decentralization and interoperability. Solid The Web of Data To understand this revolution, we need to talk about the , a W3C initiative meant to foster the publication of structured data on the Web and to avoid producing data silos that are isolated from each other. The goal of the Web of Data is to connect data, regardless of where it is stored, to form a universal information network. Web of Data It is the that enables this linking by providing data with definitions that allow machines to perform contextualized searches. The Semantic Web is based on a language called (Resource Description Framework), which allows the relationships between data to be described in the form of triplets: subject - predicate - object. RDF can use several syntaxes such as XML or JsonLD to represent these relationships. Semantic Web RDF For example, Bob (subject) likes (predicate) cats (object). The data “Bob” and “cats” are linked together by a qualified relationship called a predicate. These data are themselves described by a that represents the concepts of "person" and "cat". Web ontologies contextualize the data using a formal knowledge representation language called , itself based on RDF. Ontologies can describe the set of concepts and relationships between concepts of an entire knowledge domain, for example, the domain of . It is also possible to create much more specific ontologies relating to a profession, an activity, or an organization. web ontology OWL life sciences Once this semantic contextualization of data is established, it is possible to delegate "intelligent" processing to machines, which can then perform logical operations such as inferences. But data semantization is not everything. There are several implementations of RDF and many different standards, which hinders attempts to reach an international consensus on sensitive issues such as permissions management and authentication. This is where the Solid project comes in. The Solid Project Solid was created by Tim Berners-Lee in 2015 at MIT, and it is now supported by a . Solid is an that facilitates interactions between applications based on semantic data. With Solid, it is no longer necessary to develop a specific API for each application in order to access its data, traditionally served by the application's backend and kept under the control of the company operating it. Instead, users can store their data on “ ”, which are storage points that they control. They can decide where pods are located, move them where they want, and authorize applications to access them. Pods are data sources that are independent of application providers, who no longer need to own the data in order to provide their service. W3C working group API standard pods --  By Ruben Verbogh WebIDs and Pods WebID is a unique identifier associated with a person or organization. Anyone can host this identifier on their own identity server, or delegate this function to a third-party provider ("WebID provider"). Identity management is based on open standards (FOAF for people and social graphs, OpenID for authentication, Web Access Control for access rights) to maintain interoperability between users, data, and applications, and user independence from the identity provider. The "pod" refers to the space where a user's data associated with a WebID is stored, i.e., a person or an organization. Each person can select their pod and WebID providers. The pod provider can be the same as the WebID provider, or they can be distinct. Taking Back Control of Your Data The current model of the Web leads to the concentration of power in the hands of centralized actors who provide free services in return for access to our personal data, which they monetize. Google or Facebook, for example, now play the role of identity provider, not only for their own services but also for hundreds of third-party applications. These dominant players collect information about our social activity and our consumption of services and information, far beyond the services they offer themselves. The intimate knowledge of each person that they gain is a form of extreme surveillance, which can and has been exploited for and purposes. commercial political Another issue resulting from the loss of control of our own data is that of fragmentation, which we mentioned at the beginning of this article. Since our identity and our data are controlled by companies providing services, it is illusory to expect them to facilitate portability or interoperability with their competitors. For example, our Deezer playlist will be unusable on Spotify, our reputation as a driver will only be promoted at BlaBlaCar, and the social graph of our friends and acquaintances will remain the property of Facebook. Finally, the hyper-concentration of web applications leads to a uniform user experience. Some applications are used by hundreds of millions, even billions of people, due to network effects favoring global monopolies. Freeing up access to data (while respecting people's rights) would enable a burgeoning explosion of social and collaborative applications, with each community - and even each individual - able to have an interface adapted to their needs. Solid is radically changing the architecture of Web applications in favor of the autonomy of individuals, who regain sole control of their identity and data. Far from being just a technical change, Solid could profoundly transform the digital economy to the detriment of models based on the massive exploitation of user data. With Solid, the success of a project can no longer be based on capturing and retaining data in order to extract economic value. The challenge becomes the intelligent utilization of this data in order to provide a relevant service to a now emancipated user. Solid and Organizations It’s easy to imagine Solid's emancipatory potential for individual users, consumers, and citizens, but what about organizations? Does the distributed web model proposed by Tim Berners-Lee also have virtues for companies? Can it help to enhance cooperation between them? Let’s take the case of a large organization with branches in different regions or countries. In general, software solutions supporting collaboration between the different levels of the organization and with external parties are selected, developed, configured, and deployed centrally by the IT department. IT people must take into account a very large array of factors and engage in complex, lengthy, and often uncertain projects in order to satisfy the needs of their users. Meanwhile, people at the local level, on the edge of the organization, are hampered by unsuitable tools, or mobilize "shadow IT", i.e., solutions that respond to local needs but increase silos and security risks. Today, it is widely accepted that the performance and resilience of organizations depend on the autonomy granted to operational teams, capable of reacting rapidly to local demands and constraints. The , for example, which gives teams, departments, or BUs a great deal of latitude in terms of action and decision-making, requires the decentralization of information and cooperation processes. Agility requires empowering every component of complex organizational systems. But then, how can the overall coherence of the organization be maintained? modular enterprise model Another profound transformation affecting organizations of all sizes relates to open innovation and cooperative and collaborative processes within ecosystems. The pooling of R&D between public and private sectors, synergies between corporates and start-ups, and cooperation within industrial sectors or supply chains are all areas where the centralization of information systems is counterproductive. Finally, the so-called "sharing economy” is another area that should gain from decentralization and interoperability. The term covers very diverse and not always positive realities, from digital labor to work done by swarms of pseudo-self-employed workers. But it also names a deep transformation of the nature of work, and the as described by Laetitia Vitaud in her latest book. The rapid growth of independent work goes hand in hand with new solidarities within workers’ collectives and networks, which can pool resources without sacrificing their members’ autonomy. Here too, traditional monolithic tools are unsuitable, and the need for interoperability between autonomous parties is obvious. transition from labor to work Solid sets the conditions for better coordination between peers, whether they are freelancers, collectives, agencies, or large companies. Data exchange is based on the flexible interoperability framework of the Web of Data, rather than on a rigid data schema and the standardization of applications. Two French projects have taken up the challenge of bringing this vision into reality. The is a non-profit ecosystem of actors who develop digital commons (open source tools, methods, and projects) based on the Semantic Web and Solid, available to everyone. Virtual Assembly The cooperative offers an open-source solution to create Web applications by assembling , as simply as one can create a website with WordPress. Startin'blox Solid components The first application based on the Startin'Blox framework, , showcases how it can improve interoperability between organizations and workers’ collectives. Hubl offers modules such as an instant messaging system, a job board, a profile directory, and so on. Each collective of freelancers can decide which modules they implement and which data they wish to share. It is now possible to interconnect collectives and freelancers within an ecosystem, without a platform dictating its rules to the participants or using its position to extract economic rent! Hubl -- Collectives sharing data through Startin’Blox / Solid Like Web3 applications based on blockchain technology, Solid's Distributed Web promotes the autonomy of individuals while heralding considerable potential for transformation within organizations and ecosystems. Despite its current lack of visibility, its adoption could prove rapid thanks to a reliable technological base... and the response it provides to the obvious needs of many social and economic players! This article was originally written by Alice Poggioli and Philippe Honigman . Alice is a community facilitator at Startin'Blox, Happy Dev, and Virtual Assembly. Phil contributes to various Web3 projects and decentralized organizations. Thanks to Rémi Arnaud, Sylvain Le Bon, Guillaume Rouyer, and the Startin'Blox community for their contributions. Thanks to Kirstin Maulding for her precious help with the translation.

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