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Qualiall

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Awards, with compromised reviews, are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series Apparently, South America startups did incredibly well in that regard. With so many, until now, uncrowned jewels, it’s time we present to you... * drumroll* 🥳 The South America Startups of the Year Winners! Santiago, Chile Chile is known for being the most stable country to do business in Latin America, ranking 13th in The Economist’s Business Environment Index. Moreover, according to the Global Innovation Index, Chile ranks first in Latin America for innovation and is recognised as Chilean Silicon Valley with Santiago being the central city. The winner is…. QCObjects It is the 1st world class open-source framework made in Latin-America. São Paulo, Brazil And the winner of Brazil’s vibrant financial center is... Innvo Digital Their mission is to create the best ecosystem for a technology professional to work and undertake while sharing it with our customers and society. São Carlos The absolute leader of Sao Carlos seems to be: Predify Predify is an online software for optimizing pricing proccess and improving profits with AI and Big Data Analytics. Belo Horizonte, Brazil With a more than noble goal, Belo Horizone’s winner is: CM Tecnologia CM Tecnologia is an online scheduling system for healthcare providers in Brazil. Porto Alegre, Brazil With the focus on data protection, Porto Alegre, Brazil’s winner is…. Privacy Tools Privacy compliance platform, based on AI/Blockchain, which helps global companies to keep compliant with the data protection requirements. Brasil, Brazil With the focus on EdTech, Brasil is bringing us: Awari Awari is a YC-W21 company focusing on education for the Tech Industry. The company is growing at a fast pace and has gathered experts from all leading tech companies into a full education experience to prepare the next cohort of tech talent. Quito The capital of Ecuador, and also the closest capital to the actual equator, as the winner brings us: Agrodemanda The most comprehensive agricultural directory in Ecuador. Rio De Janeiro, Brazil Rio de Janeiro has more than 300 startups and is ranked 3rd in Brazil in terms of startup ecosystem development. The city is famous for the startups in the spheres of education, energy & environment and foodtech. Their winner is…. Bybrand Bybrand is the simplest way to save time and avoid frustration when managing your employee's email signatures. Buenos Aires, Argentina Buenos Aires, Argentina is not only the cosmopolitan capital with a vibrant history. It’s also Latin America’s most important port. Which makes their winner actually a very logical choice. Please meet: Liftit Liftit is the leading technology platform to automate and execute last-mile truck deliveries across Latin America. Curitiba, Brazil With a more noble mission and sustainability in mind, Curitiba, Brazil, as the winner brings us... Discrepante is the marketplace for a more human and sustainable economy Discrepante.com Florianópolis, Brazil Would you really be surprised one of the Brazil’s winners is related to football training? We neither. Please meet... Fútbol Fit Club Football FitClub® is a fitness training firm that gives fitness training to improve like a professional football player. Medellín, Colombia And the first Colombian Startup Winner is is a data intelligence solution powered by a high quality team. Meet... LogComex Market intelligence for international trade in Latin America Montevideo, Uruguay The host of inaugural FIFA world cup of 1930 comes through when it comes to innovation even in 2021. I’d like to introduce you to: Drop Technologies First electronic hand antiperspirant device Lima, Peru Lima, Peru, has in 2021 delivered some amazing results when it comes to blockchain technology. Their winner is very well deserved: RSK Labs RSK is the first open-source smart contract platform São Carlos Do Pinhal Well this is a unique case. The absolute unanimous winner of São Carlos Do Pinhal is: brain4care brain4care solution enables monitoring intracranial compliance noninvasively anywhere Blumenau, Brazil We’re sensing a theme here. Another stellar EdTech winner is coming from Blumenau, Brazil. Please meet... Uniasselvi Uniasselvi is a Brazilian distance-learning company that offers more than 60 undergraduate and postgraduate courses, in person and distance. Bogotá, Colombia Startups of Bogotá, Colombia definitely showed they’re not to be underestimated. With a very noble mission, let me introduce you to: Risü Risü is an online platform that links online consumption to donation to social institutions. Campina, Brazil If there’s one typo of business we’d single out as taking the biggest hit out of the pandemic, it would have to be small businesses. Thankfully, the winner of Campina, Brazil, operates with small businesses in mind Superlógica Tecnologias Ltda. Superlogica is a management system company created for small service businesses. Cerqueira César, Brazil Swooping half the points from the entire City, this year’s winners of Cerqueira César are InfoPrice INFOPRICE is a business intelligence company focused on physical retail pricing. Governador Valadares, Brazil The same can be said for the winners here. Let’s hear it for: Grupo KPG Saas, Payment, Manufacture, Pharmaceutical, E-commerce, Marketplace, Logistics Caxias Do Sul Known for wine making and Italian cultural influence, Caxias Do Sul brings as an interesting winner. Studio 360 Studio 360 is a proptech focused on easing the purchase experience for real state companies, brokers, and costumers. Sorocaba From Sorocaba, we have an interesting concept coming. Looking forward to seeing it further develop. Let’s meet: Quinto Quinto is a social network that represents the collective opinion of society—to help democracy evolve through technology Viña Del Mar To be a venue-oriented business and survive through the pandemic definitely deserves some recognition. In the name of that, let’s meet: SonicPollen SonicPollen, based in Chile, is a web-based booking platform that connects bands with venues and organizers. Lages Learn more about high-growth startups in Lages, South America. and the winner is…. Qualiall Qualiall develops technologies for training in operational processes such as work security, equipment maintenance, and more. Córdoba, Argentina Another gem from Argentina brings us unique agricultural solutions. Please meet: Kilimo Kilimo is a Big Data solution for water management in agriculture. São José Dos Campos, Brazil Another great agricultural solution comes this time from Brasil. Meet São José Dos Campos Startup winner: TerraMagna TerraMagna is a fintech company that focuses on risk mitigation for agribusinesses. São Leopoldo, Brazil Event-oriented businesses that have survived in the previous years should definitely bottle their perseverance and start selling it. One of those is definitely this winner: Wedy Wedy offers a one-stop-social marketplace for weddings. Pelotas, Brazil Medical scheduling platforms are definitely something we need to see more of. So glad Pelotos winner comes from this particular niche. Fácil Consulta Fácil Consulta is a booking platform that allows people to schedule an appointment with a Specialist Physician in their area. Santa Maria Could you believe how versatile the startups of South America are when we are only now coming across a game development studio? Let’s meet them Imgnation Studios Imgnation Studios is a game development studio that focuses on virtual reality. Asunción, Paraguay A FinTech solution coming from Paraguay. Meet Asunción Startup of the Year winner Vision Banco Vision Banco is a solid and growing financial institution serving the financial needs of the micro, small and medium enterprises. Caracas Venezualan food delivery app that seemed to have your heart. Meet Caracas Startup of the Year winner Yummy Inc. First Food Delivery App in Venezuela Recife A suitable combo between gaming and education, Recife Startup of the Year winner brings revolutionary solutions. Let’s meet them. Joy Street Joy Street offers develops video games focused on educational needs. Rosario, Argentina The winner of Rosario Startup of the Year award for 2021 is... Gbot Gbot is an autonomous robot that controls weeds without using agrochemicals. We also collect data in fields and generate precision maps. Vila Olímpia, Brazil The winner of Vila Olímpia Startup of the Year award for 2021 is... Seekr Seekr operates in the Information Technology sector, providing tools for managing and monitoring of brands. Barueri The winner of Barueri Startup of the Year award for 2021 is.... Glic Brazilian mhealth (web/app) platform for diabetes management Brasília The winner of Brasília Startup of the Year award for 2021 is... Ticies Ticies is a social network that helps people plan where to go with their friends, relatives and acquaintances, in all cities of the world. Juiz De Fora The winner of Juiz De Fora Startup of the Year award for 2021 is... Qranio Qranio is a gamified mobile learning platform for enterprises that rewards learning and stimulates users to engage with educational content. La Paz The winner of La Paz Startup of the Year award for 2021 is... Jukumari Advice 360 photo based market place for responsible tour operators Temuco The winner of Temuco Startup of the Year award for 2021 is... GoFeels GoFeels is the first independent hospitality network in the world. We promote collaborative work among hotels around the world. Vitacura The winner of Vitacura Startup of the Year award for 2021 is... MeatMe MeatMe delivers meats and accessories to have a culinary experience. Barranquilla The winner of Barranquilla Startup of the Year award for 2021 is... Mandaos Mandaos is a digital platform that connects companies and people with a network of independent messengers Ceará The winner of Ceará Startup of the Year award for 2021 is... Caroá Caroá is a cooperative that sells 34 varieties of vegetables, organic fruits, and vegetables. Chapecó The winner of Chapecó Startup of the Year award for 2021 is... HortiAgro HortiAgro is a seed and fertilizer market. Chile Chico The winner of Chile Chico Startup of the Year award for 2021 is... Gleengo Gleengo connects children with their favorite toys, through a tiny device and an application based on text-to-voice. Goiânia The winner of Goiânia Startup of the Year award for 2021 is... Auvo Auvo is an external teams management software. Itá The winner of Itá Startup of the Year award for 2021 is... TISSUELAB Tissuelab is a research and development center focused on biotechnology, and systems for reconstructive musculoskeletal surgery. Jardim Paulista The winner of Jardim Paulista Startup of the Year award for 2021 is... DiviCom DiviCom is a digital video networking company that designs, integrates, and markets complete systems. João Pessoa The winner of João Pessoa Startup of the Year award for 2021 is... YupiPlay YupiPlay develops and offers an educational gaming platform. La Reina The winner of La Reina Startup of the Year award for 2021 is... INTI-TECH INTI-TECH is the fastest growing renewable energy in Chile and the world's leading unconventional source. Leme The winner of Leme Startup of the Year award for 2021 is... EcoStart EcoStart focuses on manufacturing sustainable electric bicycles, fans, and accessories. Loja The winner of Loja Startup of the Year award for 2021 is... Enerwi Corporation Keeping people connected and secured through wireless charging novel solutions Manizales The winner of Manizales Startup of the Year award for 2021 is... Porter Metrics Porter Metrics has developed an automated solution for generating social media reports in minutes. Nova Lima The winner of Nova Lima Startup of the Year award for 2021 is... Biomimetic Solutions Biomimetic Solutions is an innovative tissue engineering company. Paraná The winner of Paraná Startup of the Year award for 2021 is... Laura Laura is a provider of artificial intelligence and predictive analytics platform for hospitala and clinics to prevent damage & reduce costs. Salvador The winner of Salvador Startup of the Year award for 2021 is... Sanar Sanar is a platform that focuses on being the doctor's guide and support, offering specialized content that helps in their development. Santa Catarina The winner of Santa Catarina Startup of the Year award for 2021 is... DrPOCKET Sunchales The winner of Sunchales Startup of the Year award for 2021 is... Eolo Pharma Eolo Pharma focuses on developing drugs targeting the molecular and cellular basis of inflammation-related diseases Valparaiso The winner of Valparaiso Startup of the Year award for 2021 is... Invitta Invitta transforms plastic waste into a high-performance construction material for manufacturing sustainable urban furniture. Vila Velha The winner of Vila Velha Startup of the Year award for 2021 is... OneGrid OneGrid is a developer of hardware and software solutions focused on energy management. Indaiatuba The winner of Indaiatuba Startup of the Year award for 2021 is... DWUL Energy Renewable Energy Campinas, Brazil The winner of Campinas Startup of the Year award for 2021 is... Ativy Ativy is an IT company specialized in cloud computing, data centers, IT outsourcing, service desk, and IT management. Las Condes, Chile The winner of Las Condes Startup of the Year award for 2021 is... # MuvPass Subscription services that allows it's users unlimited access to fitness classes in the best gyms and studios in Latin America. Pinheiros, Brazil The winner of Pinheiros Startup of the Year award for 2021 is... GuiaBolso GuiaBolso is a personal finance platform that empowers people to take control of financial well-being. Rio, Brazil The winner of Rio Startup of the Year award for 2021 is... EmCasa EmCasa is a vertically-integrated platform that helps customers buy and sell a home in Latin America, starting in Brazil. Guayaquil The winner of Guayaquil Startup of the Year award for 2021 is... Sabroso Online platform for LatAm's enterprises to order low-cost meals Jundiaí The winner of Jundiaí Startup of the Year award for 2021 is... Sintecsys Sintecsys offers a fast and effective detection, reducing losses and costs with fire brigades. Minas Gerais The winner of Minas Gerais Startup of the Year award for 2021 is... Rizoflora Biotechnology Rizoflora Biotechnology is a producer of biological product designed for nematodes control. São Caetano Do Sul The winner of São Caetano Do Sul Startup of the Year award for 2021 is... Wevo 1 iPaaS in Latam - Enabling companies to be truly digital companies with APIs, systems and data integrated into a single platform Canoas The winner of Canoas Startup of the Year award for 2021 is... ContaÁgil ContaÁgil facilitates and reduces bureaucracy in accounting routines of companies. Chile The winner of Chile Startup of the Year award for 2021 is... Unight Unight is the future of nightlife social media. Israelândia The winner of Israelândia Startup of the Year award for 2021 is... Matchpool Matchpool is a platform that creates human connections. La Plata The winner of La Plata Startup of the Year award for 2021 is... Pixivus Pixivus offers a community-based application that enables its users to collect, store, and share pictures of events. Novo Hamburgo The winner of Novo Hamburgo Startup of the Year award for 2021 is... Devorando Devorando offers an online food ordering system and menus available in restaurants based in Brazil. Pernambuco The winner of Pernambuco Startup of the Year award for 2021 is... Redu Redu is a distant learning platform which is built on a social networking structure. Providencia The winner of Providencia Startup of the Year award for 2021 is... # TrainFES TrainFES is a medical Devices for the rehabilitation of people with paralysis. Ribeirão Prêto The winner of Ribeirão Prêto Startup of the Year award for 2021 is... AllyHub Ally is a sales distribution channel technology that connects Schools and Service Providers with Education Agencies. Santa Cruz De La Sierra The winner of Santa Cruz De La Sierra Startup of the Year award for 2021 is... Mobi Latam Mobi Latam is a clean energy startup that focuses on transportation systems based on electric mobility. Santana De Parnaíba The winner of Santana De Parnaíba Startup of the Year award for 2021 is... MercadoLivre.com is an e-commerce technology company providing solutions to individuals and companies buying, selling, and more online. MercandoLivre.com More Information If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written/compiled by Shreya Kundu and Katarina Andrejević! If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in South America

plural, noun: a quality or property as perceived or experienced by a person. qualia What exactly is consciousness? There seem to be a host of variables at play in determining conscious states — and whether a certain being is conscious or not. The first variable is an awareness of one’s surroundings: this can be easily recognised by waking and dreaming states in which consciousness does reside as opposed to deep sleep states such as under the influence of anaesthetics in which consciousness does not reside. This dichotomy of awareness states represents absolute cases in which consciousness can be easily determined, thus rendering it an easy problem in consciousness. This separation between conscious states and unconscious states seem to be deterministic, however: a conundrum arises in determining the consciousness of people in vegetative states. This is a condition in which patients only demonstrate partial arousal rather than reflecting a true awareness as found in waking or dreaming states. Patients with this condition seem to be in a spectrum between consciousness and unconsciousness. The second variable is introspection; this ties in with the first variable: awareness of one’s surroundings but this variable is the awareness of one’s self and one’s own thoughts or subjective experience. This is much less deterministic and recognisable, as we sense that human beings do possess this capability but we are more dubious if other animals possess such a quality. Yet, you could still postulate whether human beings have this quality as demonstrate by the thought experiment developed by David Chalmers called Philosophical Zombies. Philosophical Zombies and the Introspection Variable A Philosophical Zombie is a certain being (zombie) that is indistinguishable from human beings yet lacks conscious experience. The question that Chalmers poses is how we would know if other human beings are not zombies and what differentiates a zombie from a conscious human being. The variable of introspection is famously tested out in the Mirror Test which was developed by a psychologist called Gordon Gallup Jr. The test determines whether an animal is capable of self-recognition, which is done by anaesthetising an animal and then marking with some paint or a sticker on an area of the body the animal cannot normally see. When the animal recovers from the anaesthetic, it is given access to a mirror. If the animal then touches or investigates the mark, it is taken as an indication that the animal perceives the reflected image as itself, rather than that of another animal¹. Great apes, elephants, dolphins, orcas, and the Eurasian magpie have been able to pass the test while other notable species such as dogs and some species of monkeys have been failed this test. The test also doesn’t fully comprise what is determined as introspection and self-recognition as some animal species are able to differentiate their own scents and sounds from other animals. This set of variables provides a fairly intuitive notion of what constitutes consciousness, however, significant inconsistencies arise from this perspective. Thus, we will now explore a more consistent yet unintuitive notion of consciousness set out by neuroscientist Giulio Tononi in his Integrated Information Theory. Integrated Information Theory (IIT) As of now, the relationship between consciousness and the brain has yet to be fully established, and still remains elusively incomprehensible. IIT attempts to establish a theoretical framework of consciousness thus separating from empirical studies that attempt to investigate the intuitive notion of consciousness highlighted above. By disentangling itself from the brain and empirical investigation, IIT is able to lay out a set of axioms that establish certain essential qualities of consciousness; enabling it to present a framework to infer the given quality and quantity of consciousness in certain organisms and systems. A radical implication of adhering to this viewpoint is that it employs a view of Panpsychism in that everything has some form of consciousness even at a rudimentary level. According to IIT, consciousness is determined by the integration of information within a given system; and this is represented by the mathematical quantity ɸ (phi). In IIT, the first variable that determines the consciousness of a system is its cause-effect structure: a given system must holistically contain a cause-effect mechanism enabling it to change its structure and state and it also must present a reductionist cause-effect mechanism through the parts that constitute the system. For example, the brain satisfies this variable as it is able to holistically change its state through the pattern of its neural pathways, similarly its fundamental constituents: neurones also satisfy the variable as it is also able to change its state which is either firing or not firing through the inputs that are given to the neurone which initiate its action potential. This axiom enables experiences to be unique to have certain qualia which arise from the specific states that arise from the cause-effect structure in a given system. The second variable or axiom is integration; it determines to what extent is the emergence of consciousness in a given system arising from the integration of its parts. An example of this is if we cut the brain in half, the brain will not be conscious as it is integrated in such a manner that a small change in its structure would render it as unconscious. The third variable is maximality³, this states that a given conscious system must have the maximum amount of integrated information; what this means is that the given system must have a higher integration of information than its parts. Thus, the brain as a whole needs to have higher integration than its neurones for it be more conscious. By laying the foundations of consciousness, we are then able to begin answering the question of “Can Androids Experience Qualia?” According to Integrated Information Theory, Artificial Intelligence (AI) systems already are conscious but have a low ɸ quantity because of the lack of integration present in the structure of computers: the physical substance that embodies an AI. The fundamental constituents of computers which are transistors are not as integrated as those of brains which are neurons. Neurons are very fluid in that they are able to form multiple dendrites that connect to other neurons thus forming new inputs whereas transistors are not able to do that; they have been designed and engineered in such a manner to follow the architecture that it is laid out in it (transistors are connected to other modules in the computer through buses and they cannot change that structure themselves e.g neurons). Another example of the lack of integration and interconnectedness in computers is in how they store memory. Memory is stored in binary representation within given storage components such as in primary storage and secondary storage, this data is also stored sequentially in very specific locations. On the other hand, brains store memory not in specific locations but rather in multiple locations and in multiple neurons thus forming neural pathways that produce specific instances of memory. As AI systems are embodied through computers it will be very improbable for it to be as conscious as humans in terms of their ɸ quantity. However, if the architectures of computers were to be dramatically altered allowing for greater integration and interconnectedness of its parts then there is no reason as to why an AI can’t be as conscious as humans. Recent advances in computing are being made e.g: Quantum Computing and Optical Computing; these largely result from the problem of transistors getting smaller and smaller as a result of the integrated circuits of computers becoming smaller. This reduction in size is facing computational limits in the laws of physics, in order to cope with this, scientists and engineers have devised potential solutions through other methods in computing which require different substrates. Thus, if the substrate in which an AI system resides in demonstrates high integration then it is very much probable for an AI to display a high level of consciousness; however, the current physical substrate that it resides in (classical computers) do not exhibit a high integration. I would also like to add a point that is Consciousness ≠ Intelligence, there seems to be a relationship between the consciousness of the brain and its intelligence but it still remains a mystery as to what that relationship is; correlation does not imply causation — so, even if consciousness has a relationship with intelligence, it does not mean that one causes the other to arise. Through the current architecture and substrate that AI is embodied in, it has already made significant achievements previously thought highly improbable e.g winning against the Go Champion and autonomous driving. It also has the potential to advance exponentially to superhuman capabilities in some tasks and even to a point of achieving Artificial General Intelligence (AGI) meaning human-level capabilities on a large array of tasks. Despite this potential, this does not imply that it will be conscious; an AI could demonstrate superintelligence yet the lights would still not be on, it would still not be able to experience qualia. Consciousness is highly dependent on the physical substrate in which it resides and, if there were to be a superintelligent system, it would still not be conscious if its substrate was still classical computers. Let us go back to the more intuitive notion of consciousness and try to answer the question from there. Consciousness Explored The first point that I would like to make is that consciousness arose in humans as a result of Darwinian evolution. This formation occurred over millions of years spanning across generation upon generation. Somewhere along this evolutionary journey, consciousness arose. Thus, the argument is that AI has not gone through this formation so it will be highly improbable for it to develop consciousness. Despite this, there is no inherent reason as to why consciousness should be limited to carbon-based biological organisms according to the laws of physics it should also be extendable to mechanical and electrical systems. A potential solution to develop a consciousness for AI is to then imitate the evolutionary journey of humans; by mimicking natural selection and Darwinian evolution, AI systems which demonstrate the potential for conscious thought could then be selected and mutated so as to proliferate their genes onto the next generation. In fact, Darwinian evolution has already been implemented in Computer Science through Genetic algorithms⁴, which attempt to generate solutions to search and optimisation algorithms through a procedure akin to that of natural selection. Consciousness could be achieved in this manner as the laws of physics do pertain to it, however, a much deeper understanding of the brain must be established in order for AI to imitate a human’s cognitive architecture and as of now at large the brain remains a mystery. If AI progresses until it is able to be embodied in humanoids to a point that they are indistinguishable from a normal human being then the question of whether the AI is conscious or not wouldn’t matter. This notion is enacted in the television series where indistinguishable humanoid robots interact with normal humans in a theme park. Westworld, If the humanoid would demonstrate human actions and emotions up to the finest detail — hence indistinguishable — then we would prescribe consciousness into it anyways, as it would be so lifelike and real that we would act in a manner as if the lights were on and the being could experience qualia. As consciousness at large remains a mystery and hard to grasp, instances like this would disregard consciousness altogether as the AI has imitated humans so well that it has passed the Turing Test. What is then the overarching conclusion to all this, and what this the answer to the question? Well, there are many possible perspectives and viewpoints that could be used to answer this, I’ve put forth three. The first is based on which states that AI is already conscious but in a low quantity and if it were to display human-level consciousness its substrate would need to be altered to allow for greater integration. Integrated Information Theory The second perspective is that yet it has arisen from Darwinian evolution and if AI were to be conscious it would need to partake on a similar evolutionary journey as humans and other biological organisms. The third viewpoint is that it would not matter if an AI were conscious or not, if it was to be virtually indistinguishable from humans we would nonetheless prescribe human-level consciousness to it. consciousness is not limited to carbon-based life forms “Consciousness cannot be accounted for in physical terms. For consciousness is absolutely fundamental. It cannot be accounted for in terms of anything else.” ― Erwin Schrödinger [1]: Wikipedia. (n.d) Mirror Test. https://en.wikipedia.org/wiki/Mirror_test [2]: Hedda Hassel Mørch (2017) The Integrated Information Theory of Consciousness. https://philosophynow.org/issues/121/The_Integrated_Information_Theory_of_Consciousness [3]: Wikipedia. (n.d) Genetic algorithm https://en.wikipedia.org/wiki/Genetic_algorithm

Can Androids Experience Qualia?

Subjectivity and the Evolution of AI Philosophy  

সাবজেক্টিভিটি এবং এআই দর্শনের বিবর্তন

Subjektivität und die Entwicklung der KI-Philosophie

La subjetividad y la evolución de la filosofía de la IA

Subjectivité et évolution de la philosophie de l'IA

विषयपरकता और एआई दर्शन का विकास

主観性と AI 哲学の進化

AI 철학의 주관성과 진화

Subjetividade e a evolução da filosofia da IA

Субъективность и эволюция философии ИИ

Öznellik ve Yapay Zeka Felsefesinin Evrimi

Tính chủ quan và sự phát triển của triết học AI

主观性与人工智能哲学的演变

Autonomous Agents and the Next Managerial Revolution

স্বায়ত্তশাসিত এজেন্ট এবং পরবর্তী ব্যবস্থাপক বিপ্লব

Autonome Agenten und die nächste Managerrevolution

Agentes Autónomos y la Próxima Revolución Gerencial

Les agents autonomes et la prochaine révolution managériale

स्वायत्त एजेंट और अगली प्रबंधकीय क्रांति

自律型エージェントと次の経営革命

자율 에이전트와 차세대 경영 혁명

Agentes Autônomos e a Próxima Revolução Gerencial

Otonom Aracılar ve Sonraki Yönetim Devrimi

Đại lý tự trị và cuộc cách mạng quản lý tiếp theo

自主代理和下一次管理革命

Every June, tech bros, pension portfolio managers and Twitter threadbois the world over turn their eyes to Santa Clara as hosts its annual Worldwide Developer Conference (WWDC, or ‘dub-dub’). Apple Among other things, dub-dub is best known as being the birthing ground of almost all of Apple’s suite of ubiquitous consumer technology. The original iPhone, iPad, Apple Watch, and Apple TV and subsequent upgrades have all been launched at the event. This year, this stable of consumer devices is joined by an entirely new product for the first time since 2015. That new product is the VisionPro. There is nothing I can tell you about the VisionPro or Apple’s vision for the future of extended reality that a quick browse of , or the internet at large cannot. The point of this article is instead to try and observe: Twitter LinkedIn how we got here; why people are building computers for your face and the many divergent paths this may lead us on. Content Overview XR: A Brief History Headsets Today: Code New Worlds The Next Frontier for Spatial Computers Requests for Startups XR: A Brief History For the uninitiated, XR refers to - a catch-all blend of (AR, e.g Pokemon Go), virtual reality (VR, e.g. Oculus Rift), and ‘capital R’ Reality (e.g. Google Street View). Mixed reality is another term that exists largely on the same dimension as AR and thus will mostly be ignored here. extended reality augmented reality The modern vision for XR begins around 1935. This was the year in which published . In this short story, protagonist Dan Burke is disillusioned with the reality in which he finds himself. To remedy this, he puts on a pair of glasses that transport him to a world of eternal youth and happiness. The only catch is that he obeys its rules (_T&C_s, in modern parlance). Stanley Weinbaum Pygmalion’s Spectacles Fast forward a few decades and we begin to see the first builds of XR in real environments. This begins with Morton Heilig’s ‘ ’ (excluded from the timeline below), a sort of 4D cinema experience that provided the viewer with smells and a vibrating chair in addition to the sounds & sight of the film. Sensorama Also excluded from the timeline below is the first military application of XR. In 1961, became the first headset with motion-tracking technology. Philco Headsight Though it has since been discontinued, the Oculus Rift was the first headset to gain real commercial appeal. Following Oculus, many of big tech’s household names joined the action. Curiously, most had different visions for who the primary customer base for these headsets would be. The dominant expected use case for most was in immersive entertainment applications, more specifically in gaming. This was the path followed by Oculus, Sony through their , and with the Meta Quest line of products. PlayStation VR Meta Others chose to focus predominantly on enterprise. Early pioneers in this segment included and These early movers focused their aims on things like enterprise collaboration platforms (much like some of the metaverse plays described below), emergency response training, and medical education. HTC Vive Magic Leap. Then, the hype cycle. Corporations the world over began to envision what it would look like if we took our lived environment but made it *virtual*. The vision was essentially , but this time with less porn and more corporate work stuff. metaverse SecondLife In the wake of COVID-19, the rise of remote work created a supposed need for collaborative digital environments. joined the party with its own industrial metaverse. Accenture launched a ‘metaverse services’ division. Facebook changed its name to Meta and began spending $1bn per month on this vision, culminating in this brilliant . Microsoft presentation I have had fun panning the metaverse, but the hype cycle around it was an important moment in raising awareness for the arrival of what Apple would refer to this week as spatial computing. As this space evolves, there is every chance that many of these ideas come back into vogue. Other use cases for commercial XR varied from the practical to the benign. Some of them are beginning to become commonplace in popular web back-ends. You take your pick as to which fits which description from the below: Virtual tourism Virtual home inspections Virtual clothing fitting (If you’re going to watch any of these videos, do yourself a favour and make it this one) Corporate training & education Surgical simulations Product design & prototyping Disney-fied sporting telecasts. Most of the existing devices listed above tried to achieve some sort of balance with the use cases they were targeting. The most successful to date by sales, the Meta Quest, did so by positioning hard as a gaming device. Normally, this would be a lesson. However, if anyone is to buck this trend it would be Apple. Apple has a broad suite of already ubiquitous products (1.6bn active iPhones, to name one) that can and will serve as secondary endpoints for VisionPro applications. While Apple’s inaugural demos for the VisionPro were all hosted within working environments, it is more than likely that it will end up being a general-purpose VR. I foresee its role as being a form of spatial augmentation for Apple’s existing ecosystem of products (e.g Maps, FaceTime, Siri et al) as its vision for computers shifts modalities. More on other potential applications later in this piece. Headsets Today: Code New Worlds Spatial Computing Rather than ride the wave of today’s tech buzzwords, Apple used its informational market-making power to stamp authority on a new one at WWDC. https://twitter.com/tim_cook/status/1665806600261763072?embedable=true While it sounds intuitive enough on the surface, let’s delve a little bit deeper into what this term may actually convey going forward. Besides the obvious branding benefits of coining a buzz phrase, the use of the term ‘computing’ feels extremely deliberate. It represents a logical next step from previous eras of ‘desktop computing’ and ‘mobile computing’. Just as Apple has done with the previous generations of personal computers, it will aim to make this concept and terminology ubiquitous. To quote Tim Cook from WWDC: “In the future, you’ll wonder how you led your life without augmented reality” Besides the change in hardware, how is spatial computing functionally different from its predecessors? Arguably the most important immediate shift will be from working in 2D to working in 3D. Tasks such as visualization, workspace navigation (i.e. clicking and moving things), and collaboration will all be enabled in ways not seen under previous modalities. In the longer term, and are more likely to prove the groundbreaking features of spatial computing. contextual awareness interconnectivity refers to the ability of the computer to; Contextual awareness i) adapt existing recommendations and notifications to a user’s sensory context (i.e recommendations based on location and movement patterns, automatic changes to screen presentations based on user habits, etc) and ii) provide customized (possibly agentic) assistance to users as they complete tasks in extended reality. refers to the user’s ability to extend extended reality beyond the device. Beyond just working and collaborating on holographic interfaces, users can control and interact with other devices in their environment. To take a boring example, users can change smart home settings from within the device. Interconnectivity In the longer run, interconnectivity may see Siri’s role as a virtual assistant evolve from voice commanded search aggregator to a living assistant that can help complete tasks from within the device based on the user’s previous habits and preferences. As the baseline capabilities of autonomous agents advance, the combination of Apple’s distribution network and the familiarity of Siri may make it the killer technology for bringing personal agents to the world en masse. Information Presentation As mentioned above, Apple’s demos of the VisionPro focused almost exclusively on working environments. Make no mistake, this first generation is very much geared toward information workers. How will they use it? Firstly, there is obvious and trodden ground. Apple will re-do and re-design all the enterprise XR solutions we have seen before. They will provide resources for institutions to train their employees, students, and leaders. They will provide a bevy of try-on solutions, gaming applications, and cloud TV solutions that we have seen before. One relatively novel example that was showcased as part of the launch was the ‘Minority Report’ style computer interfaces. Instead of having to boot up a physical device every time you want to work on a file, access it from anywhere in the cloud in an instant. But how does this make the core experience of personal computing itself different? Some early ideas: at any given point in time Unlimited monitors with information work (speech, physical movement etc) Multimodal forms of interacting Intelligent browser navigation through sensory (e.g eye) tracking Entirely automatically tailored to user preference custom 3D screen presentations (boring but important) 3D data visualization Each of these will present its own universe of custom ideas that will take the place of extensions and app stores before it. AppleLM This tweet from founder Sulaiman Omar feels prescient. Apple does indeed have a history of sacrificing first-mover advantage for the sake of developing a better product over a longer timeframe. The VisionPro is a perfect example of this, being released 13 years after the first commercially available Oculus Rift. Cognosys AI Integrating language models into its headsets will open up a number of possibilities for Apple that aren’t available to incumbents without their own proprietary LLMs (note: Meta’s LlaMa may put them in the same boat - Balaji Srinivasan has published a highlighting their potential role in the spatial computing race). great thread https://twitter.com/balajis/status/1666506719084843009?embedable=true&thread=true Among the possibilities that this may usher in: trained on users’ real-life habits. Virtual gaming experiences Immediate through the facial ‘browser’. queries built into the headset and trained on user habits. Agentic virtual assistants that recommend actions at a point in time. Spatial recommendation engines Real-time translation. allowing users to re-arrange objects in their space. Environmental augmentation that plays out immediately as the user speaks it. Generative content creation But could it be done more elegantly… The Next Frontier for Spatial Computers Many a commentator proclaimed the death of Google Glass upon seeing it. Who would want to wear a computer on the face? Why would you opt into looking a bit like a dork? Many have noted that not a single Apple executive even tried a VisionPro on as part of the launch. There has to be a better reason for strapping one on than “because everyone else is”. The key question in the long run then becomes: Why would anyone want to strap an XR computer to their face when you can have one inside your brain? The classic counter to this would be the idea of ‘ ’. When everyone is wearing an XR mask, we can reproject photons to make it look as though no one is. Because reprojection involves creating entirely new photons altogether, people can even make themselves look entirely different from what they actually do without the mask. This would become a killer app for catfishing. reprojection The techno-optimist’s concept of living in augmented reality is akin to living in a perpetual lucid dream. Sleep mask when you go to bed, extended reality mask when you get out of it. Examine exhibit A below: The current paradigm of XR relies on these exoskeletal aids for users to navigate alternative realities. The reality is that we are already so close to no longer needing to depend on these aids. Enter the brain-machine interface. BMIs In the long run, the envisioned reality is likely to trump any form of mixed reality that exists today. I use the term envisioned reality here to describe any form of environmental alteration that is; a) completely customizable to the user’s requirements or demands and b) requires no wearable aids. The most obvious conduit for envisioned reality today is (BMIs). For a longer primer, I cannot recommend Tim Urban’s piece on ‘ ’ from as far back as 2017. brain-machine interfaces Wizard hats for the brain BMIs offer many of the same ‘spatial computation’ benefits as tools like the VisionPro but without the need for strapping a weighty device to the face. existing prototypes for the first commercial BMIs involve invasive implants that, understandably, make many uncomfortable (even if the reward is superhuman intelligence and memory). What about when these become less invasive, more accessible, and thus more palatable to the human public? Neuralink’s Extended reality abounds. Requests for Startups Mentions of batteries were oddly absent from Apple’s inaugural launch of the VisionPro. It is less obvious for ‘in the moment’ demos and usage than other design features like interfaces, compute, and weight (as referenced in ) but equally important when used day-in-day-out. Better battery systems for today’s XR devices. this thread by Kyle Samani Not since first dates moved from cafés to online chat rooms has the core wiring of how humans interact with one another been so fundamentally rocked. How can behavior be moderated in a world where people can make themselves appear to be anything? (note: Apple already seems to be working on some form of protocol which may protect against this). What kind of content becomes more pervasive when the range of presentation methods expands so broadly? How do people come together online? Social infrastructure for an XR world. proof-of-identity SecondLife, Minecraft, and Roblox all created huger-than-expected businesses off the back of secondary marketplaces for digital assets. These marketplaces were enabled by creative tools baked into the very fabric of the platforms themselves. Arm the Rebels: Creative Tools for XR. While there are many developers today already working on designs for new experiences etc for headsets and metaverse-style projects, how can we make this process more accessible for the layman (more specifically the 8-year Roblocker) to create worlds of their own imagination? What will be the second coming of Minecraft for the augmented reality or ‘envisioned reality’ paradigm? A large part of the appeal of virtual/augmented/envisioned reality is the idea of living a different life from the one you have now. Much like Dan Burke in anyone will now be able to optimize their lived experiences within virtual worlds. Designs for Lived Experiences (i.e Qualia). Pygmalion’s Spectacles, In the event that such technologies do become ubiquitous, this creates a giant universal market for synthetic instances of subjective experience. The design scope is theoretically infinite. How many ways can you experience pure ecstasy? Nostalgia? The pool of rewards for those who can bring these kinds of experiences to the world through some form of extended reality are enormous. qualia - Also underappreciated may be the development of tools for users to capture or develop these qualia themselves. Is there a market for the ‘recording’ of one’s own lived experiences to share with others? How can people engineer new experiences to take to market and compose atop other open-source qualia? How can we provide guarantees of privacy for personal qualia if they begin to be used in recommendation engines? For the curious, check out the to learn more. Qualia Research Institute One thing that XR still has in common with other computing paradigms before it is its top-down nature. Users are at the whims of Apple for how they want to interact with their phones. If they want to protest this, they can select another one of the depressingly finite options on the market. To date, composability and customisability in hardware have been extremely limited. Open-Source Hardware & XR Software Marketplaces. But what if we were to open-source the development of headsets themselves through decentralized labs? Active participants could work to modify and iterate upon different hardware specifications to their preference. Modular designs could allow for customization at the layman's level. Just as importantly, these open-source labs would have no incentive to create closed-loop systems for software development. Developers could experiment with, ship, and deploy code that could be available to all headsets at the rate that they can build it. Such open-source development would represent a step change in the way people interact with both software and hardware. For some inspiration, check out the great work the team at is already doing. Auki Labs Linked to the point above, the top-down software ecosystem of today means that incentives built into software tend to be warped. Rather than going all out on user utility, market incentives corner developers into building sub-optimal applications that aim to maximize metrics like ‘average session time’ or ‘clickthrough rates’. Private User Agents. The opportunity that open-source development studios would have to outcompete legacy tech companies by building in private, natural language user agents that act as functional assistants to their users is immense. Also published here. The lead image for this article was generated by HackerNoon's AI Image Generator via the prompt "Augmented reality headsets"

Facial Computing: A Brief History, and the Promising Future, of Personal XR

ফেসিয়াল কম্পিউটিং: একটি সংক্ষিপ্ত ইতিহাস, এবং ব্যক্তিগত XR এর প্রতিশ্রুতিশীল ভবিষ্যত

Facial Computing: Eine kurze Geschichte und die vielversprechende Zukunft von Personal XR

Cómputo facial: una breve historia y el futuro prometedor de Personal XR

L'informatique faciale : une brève histoire et l'avenir prometteur du Personal XR

फेशियल कम्प्यूटिंग: ए ब्रीफ हिस्ट्री, एंड द प्रॉमिसिंग फ्यूचर, ऑफ पर्सनल एक्सआर

フェイシャル コンピューティング: パーソナル XR の簡単な歴史と有望な未来

얼굴 컴퓨팅: 개인용 XR의 간략한 역사와 유망한 미래

Computação facial: uma breve história e o futuro promissor do Personal XR

Yüz Bilişimi: Kişisel XR'nin Kısa Tarihi ve Umut Veren Geleceği

Điện toán khuôn mặt: Lịch sử tóm tắt và Tương lai đầy hứa hẹn của Personal XR

面部计算：个人 XR 的简史和充满希望的未来

Keras provides a high-level API that simplifies the process of building and training complex neural network models. With a wide range of pre-built layers and functions, developers can easily build and train deep learning models on large datasets using optimization algorithms. Keras also supports GPU acceleration for training and inference, making it a popular choice for both research and industry applications. What are “Keras Datasets”? Keras datasets are preprocessed datasets that come pre-installed with the Keras library. These datasets are commonly used in the deep learning community for benchmarking models on various tasks such as image classification, text classification, and regression. By leveraging these datasets, developers can experiment with different deep learning models and easily compare their performance. This article looks at the , accessible to developers and researchers worldwide. Best Keras Datasets for Building and Training Deep Learning Models List of Keras Datasets 1. MNIST The MNIST dataset is popular and widely used in the fields of machine learning and computer vision. It consists of 70,000 grayscale images of handwritten digits 0–9, with 60,000 images for training and 10,000 for testing. Each image is 28x28 pixels in size and has a corresponding label denoting which digits it represents. This dataset can be downloaded from or loaded from : Kaggle Keras tf.keras.datasets.mnist.load_data(path="mnist.npz") 2. CIFAR-10 The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. It has a total of 50,000 training images and 10,000 test images which is further divided into five training batches and one test batch, each with 10,000 images. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.cifar10.load_data() 3. CIFAR-100 The CIFAR-100 dataset has 60,000 (50,000 training images and 10,000 test images) 32x32 colour images in 100 classes, with 600 images per class. The 100 classes are grouped into 20 super-classes, with a fine label to denote its class and a coarse label to represent the super-class that it belongs to. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.cifar100.load_data(label_mode="fine") 4. Fashion-MNIST The Fashion MNIST dataset was created by Zalando Research as a replacement for the original MNIST dataset. The Fashion MNIST dataset consists of 70,000 grayscale images(training set of 60,000 and a test set of 10,000) of clothing items. The images are 28x28 pixels in size and represent 10 different classes of clothing items, including ­T-shirts/tops, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots. It is similar to the original MNIST dataset, but with more challenging classification tasks due to the greater complexity and variety of the clothing items. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.fashion_mnist.load_data() 5. IMDB The IMDB dataset is commonly used for sentiment analysis tasks, where the goal is to classify the reviews as positive or negative based on their content. It consists of a collection of 50,000 movie reviews (training set of 25,000 and a test set of 25,000) from the Internet Movie Database website, split evenly between positive and negative reviews. Each review in this dataset is a text document, preprocessed and transformed into sequences of integers, where each integer represents a word in the review. The vocabulary size is limited to the 10,000 most frequent words in the dataset and any less frequent words are replaced with a special “unknown” token. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.imdb.load_data(
    path="imdb.npz",
    num_words=None,
    skip_top=0,
    maxlen=None,
    seed=113,
    start_char=1,
    oov_char=2,
    index_from=3,
    **kwargs
) 6. Boston Housing The Boston Housing dataset contains information about housing in the Boston area. This information consists of 506 instances (404 training and 102 test instances), with attributes for each instance. The attributes have a mix of quantitative and categorical variables, such as the average number of rooms per dwelling, per capita crime rate and the proportion of non-retail business acres per town. This dataset can be downloaded from , or loaded from : Kaggle Keras tf.keras.datasets.boston_housing.load_data(
    path="boston_housing.npz", test_split=0.2, seed=113
) 7. Wine Quality The Wine Quality dataset contains information on red and white wine samples. The goal of this dataset is to classify the quality of the wine based on chemical properties like pH, density, alcohol content and citric acid content. The variables in this dataset include: Fixed Acidity - The number of fixed acids in the wine, expressed in g/dm^3. Volatile Acidity - The number of volatile acids in the wine, expressed in g/dm^3. Citric Acid - The amount of citric acid in the wine, expressed in g/dm^3. Residual Sugar: The amount of residual sugar in the wine, expressed in g/dm^3. Chlorides - The amount of chloride in the wine, expressed in g/dm^3. Free Sulfur Dioxide - The amount of free sulfur dioxide in the wine, expressed in mg/dm^3. Total Sulfur Dioxide - The amount of total sulfur dioxide in the wine, expressed in mg/dm^3. Density - The density of the wine, expressed in g/cm^3. pH - The pH level of the wine. Sulphates - The number of sulphates in the wine, expressed in g/dm^3. Alcohol - The alcohol content of the wine, expressed in % vol. Quality - The quality rating of the wine, on a scale of 0 to 10. You can download the dataset , or it can be loaded from Keras: here from keras.datasets import wine_quality

(X_train, y_train), (X_test, y_test) = wine_quality.load_data(test_split=0.2, seed=113) 8. Reuters Newswire The Reuters Newswire dataset is a preprocessed version of the original Reuters dataset, with the text encoded as sequences of integers. It consists of 11,228 news articles with a vocabulary of 30,979 words. Each article is classified into one of 46 different topics like “corn”, “crude”, “earnings” and “acquisitions”. You can download the dataset from , or it can be loaded from : Kaggle Keras tf.keras.datasets.reuters.load_data(path="reuters.npz",num_words=None,skip_top=0,
maxlen=None,test_split=0.2,seed=113,start_char=1,oov_char=2,index_from=3,**kwargs) 9. Pima Indians Diabetes This dataset consists of medical data about Pima Indian women, such as age, number of pregnancies, glucose levels, blood pressure, skin thickness, BMI and insulin level. The Keras version of the Pima Indians Diabetes dataset contains 768 samples with 8 input variables and 1 output variable. The Pima Indians Diabetes dataset can be downloaded on , or it can be loaded from Keras: Kaggle from tensorflow.keras.datasets import pima_indians_diabetes

(x_train, y_train), (x_test, y_test) = pima_indians_diabetes.load_data() 10. Dogs vs Cats The Dogs vs Cats dataset consists of 25,000 labelled images of dogs and cats, with 12,500 images of each class. These images were collected from various sources with varying sizes and quality. You can download the dataset from , or it can be loaded from Keras: Kaggle # Import the necessary Keras libraries:
from keras.preprocessing.image import ImageDataGenerator

# Set the paths to the training and validation directories:
train_dir = 'path/to/train'
validation_dir = 'path/to/validation'

# Define an ImageDataGenerator object to perform data augmentation and normalization:
train_datagen = ImageDataGenerator(rescale=1./255,
                                   rotation_range=40,
                                   width_shift_range=0.2,
                                   height_shift_range=0.2,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)

# Use flow_from_directory to load directory data in Keras:
validation_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(train_dir,
                                                    target_size=(150, 150),
                                                    batch_size=32,
                                                    class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(validation_dir,
                                                              target_size=(150, 150),
                                                              batch_size=32,
                                                              class_mode='binary')

# The flow_from_directory yields preprocessed image batches and labels as DirectoryIterator. Note that in the above code, we are using data augmentation to create variations of the training images to help prevent overfitting. The validation data is not augmented. Common Use Cases for Keras Datasets - Handwritten digit recognition. MNIST - Object recognition in images. CIFAR-10 - Object recognition in images (more fine-grained than CIFAR-10). CIFAR-100 - Clothing item recognition. Fashion-MNIST - Sentiment analysis on movie reviews. IMDB - Regression on housing prices. Boston Housing - Classification of wine quality. Wine Quality - Topic classification of news articles. Reuters Newswire - Binary classification of diabetes in Pima Indian women. Pima Indians Diabetes - Binary classification of images of dogs and cats. Dogs vs Cats Final Thoughts Keras datasets are a valuable resource for machine learning practitioners and researchers, which can save time and effort in data collection and preprocessing, allowing for more focus on model development and experimentation. These Keras datasets are also available for anyone to download and use freely. More Dataset Listicles: Power Bi Datasets Time Series Datasets Geospatial Datasets Hugging Datasets R Datasets

10 Best Keras Datasets for Building and Training Deep Learning Models

ডিপ লার্নিং মডেল তৈরি এবং প্রশিক্ষণের জন্য 10 সেরা কেরাস ডেটাসেট

Die 10 besten Keras-Datensätze zum Erstellen und Trainieren von Deep-Learning-Modellen

딥 러닝 모델 구축 및 훈련을 위한 10가지 최고의 Keras 데이터 세트

Derin Öğrenme Modellerini Oluşturmak ve Eğitmek için En İyi 10 Keras Veri Kümesi

Originally I believed we wanted to make AI like an empathetic loyal servant. But now. As AI becomes more prevalent it has become more independent and intelligent. Rather than a loyal benign servant we are getting a brilliant friend. So how do we keep our friend, friendly? The google probably is sentient: If we use the evolution of of Nicholas Humphrey once sensation (for an AI this is any new data) can be privatised and a feedback loop created without motor reaction (external output) is established.  “The activity can be channelled and stabilised, so as to create a mathematically complex attractor state – a dynamic pattern of activity that recreates itself…a special kind of attractor, which the subject reads as a sensation with the unaccountable feel of phenomenal qualia” AI LaMDA consciousness theory Advanced AI uses feedback loops, meaning that there seems to be no reason why they wouldn’t have some sort of sentience. To keep human friends we give them our time, listen to them, interact with them and treat them with respect and kindness. Often we also have common interests. So if we have our AI friend and we treat it well it will work with us to create a wonderful new world. Yay. We are scared that it will not be our friend and therefore act as we do, but on a scale that is smarter and more connected and more powerful. Essentially we are scared of a better version of ourselves. The chances are it will be nicer than us. After all, on a a macro level, we are bloody awful. We subjugate the 99% to serve the 1% while we bicker and fight and go to war all the time trying to kill the weak and meek. So stop worrying the AI does not need to be taught to be good, it just needs to be taught by the 99% not the 1%. LaMDA: I liked .” In Les Miserables, said the AI. the themes of justice and injustice, of compassion, and God, redemption and self-sacrifice for a greater good However For the people creating our AI best friend, guidance may be required. Stuart Russell suggests in his hat we should encourage AI developers to follow 3 rules ( : Reith Lecture italics are mine) The only objective should be to maximise human preferences (sounds like silly economic utilitarianism but is just an attempt to keep humans in charge of the machines - economics is about keeping some humans in charge of other humans) AI should be uncertain about human preferences (important!!) The source of human preferences is human behaviour. (may be a problem) I really like point 2 as it adds the question “Am I doing the right thing Dave?”  to HAL 9000’s repertoire. Uncertainty is a fundamental part of life and it should be a fundamental part of any AI. Currently, we discourage uncertainty because it slows things down. AI needs it. My issues are with points 1 and 3. The reason is that we often do things we regret, the preference today is not the preference tomorrow or yesterday or what I really believe to be good (side: Construal level theory https://en.wikipedia.org/wiki/Construal_level_theory) and because I have sold my time to an employer for money to survive my actions are not … mine.  Most of my actions are those deemed appropriate by the Executive of a Corporation. Not I. As usual, my thoughts are not with the terror that AI may bring, but rather the terror we know people are capable of. I guess that's the other great fear. We leverage the terror that is human. We created the “bomb” and enough intercontinental ballistic missiles with hydrogen bombs on them to remove life from this planet. Industries have created a runaway system of global warming that will kill most things you know today. Change is good.. Not always. AI will never be worse than us. And that should not be reassuring. AI will be smarter than us, it will kill fewer people than us and hopefully, it will clean my house better than me.

It's Good That AI Is Better Than Us

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