- Your assignment is to learn aboutWeb 3.0, sometimes called theInternet of Things.
A good introduction can be found inhttps://academy.binance.com/en/articles/the-evolution-of-the-internet-web-3-0-explainedandhttps://academy.binance.com/en/articles/blockchain-use-cases-the-internet-of-things.Write a
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-page paperdiscussing ethical issues that we expect to encounter when the full Web 3.0 model materializes.
Microsoft Word - Document3 (from wikimedia.org) Web 3.0 — Convergence of Artificial Intelligence, IoT and Blockchains LeifBildøy Follow Apr2,2018 · 22 min read In 2004 Tim O´Reilly declared Web 2.0, a business platform spanning millions of users and facilitating communication, organization, and collaboration. Today, more than a decade later, serious questions are being asked about the web´s centralized model, privacy concerns and security. We know that technology advances at an exponential rate, so how will the next generation web look like and how will these questions be dealt with? We are witnessing a surge of innovation in disruptive technologies such as IoT, AI, and Blockchains. The intersect of these trends will be manifested in the next generation web and have a profound socio-economic impact on governance, commerce and on the way we conduct our lives. The fabric we use for the digital part of our lives will be hugely different than today´s web. Pace of Technological Change Before looking at the evolution of the web we should frame it in a wider context. In a historic perspective technological development has caused enormous changes in society, creating prosperity and improving the lives of millions. It is widely believed that future will bring new innovations in areas like genetics, nanotech, biotech, intelligence, automation, and robotics, enabling humans to live longer and without the need of strenuous physical work. A funny illustration is Flippy [1] the burger flipper. The point is that Flippy is the cook´s third arm, not a replacement of a human burger flipper. Others point to automation decimating not only factory jobs but also the more typical middleclass jobs. According to the late Stephen Hawking [2]: “the automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative or supervisory roles remaining.” More worryingly, Hawking also pointed out the possibility of AI spelling the end of the human race. Later, Elon Musk has expressed similar concerns about AI based on the exponential growth in computing and the rapid progress in AI. Francis Heylighen from Global Brain Institute (Free University of Brussels), has described [3] a “global brain” which can be conceptualized as an Internet that ties users together into an information processing system that functions as part of the nervous system of the planet. Similarly Ray Kurzweil embraced the term “The Singularity” in his 2005 book “The Singularity Is Near: When Humans Transcend Biology” to describe a future state where human brain and network computer work as one. Further, Kurzweil says “Once the Singularity has been reached, machine intelligence will be infinitely more powerful than all human intelligence combined.” By using his own Law of Accelerating Returns, Kurzwail has done some predictions on when the singularity will emerge [4]: Parts of the argument behind this prediction is that more advanced societies can move faster than less advanced societies. In the end, Kurzweil projects a path from Artificial Narrow Intelligence, via Artificial General Intelligence to Super Intelligence per Nick Bostroems definition [5]: “… an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” On the more conservative side is mathematical physicist Sir Roger Penrose. In his 1989 book “The Emperor’s New Mind: Concerning Computers, Minds and The Laws of Physics” [6] Penrose outlines the argument for why consciousness is non-algorithmic, and thus cannot be modeled by a Turing machine nor a digital computer. In a recording from 1992 [7], Penrose states: “A human mind may have invented the computer but it has not invented a computer that can invent the human mind, nor will it ever be able to do so.” Penrose disputes the outcome of machine intelligence and not the advances, or pace of advances, in computing. There may be different opinions on the ultimate outcomes of advances in AI, but the pace of technological change is indisputable and a key question is how will our society adapt to this change. Taking a more pragmatic view in “The Second Machine Age”, Erik Brynjolfsson and Andrew McAfee from the MIT Initiative on the Digital Economy [8] explore technology changes and the future. They argue that there will be social and economic upheaval from technological advancements, and no automatic good outcomes, unless right choices are made by society. One could argue that societies have so far adapted well to new technologies. Often each technological advancement promises something better than what currently exists and often brings a purpose — doing something cheaper, faster or better. This applies to the invention of electricity grid, steam machine, or semiconductor electronics for that matter. Challenges with the current Web While the web brought us the great promises of democratization of information, we face a number of challenges with the current model. • Centralization: The capturing of behavioral data is locked into silos that are dominated by large players. • Privacy and security: With increasing amount of data being captured large data centers act as honeypots for organized crime. • Scale: With larger data sets from billions of connected devices the strain on existing infrastructure will grow. Today’s client server model has worked well but is not likely to scale for the next generation web. Centralization of data and wealth Today the digital economy is dominated by five large players: Facebook, Apple, Microsoft, Google, and Amazon a.k.a. “FAMGA” [9]. These corporations used the Web capabilities to create maximum benefit for their shareholders. The FAMGA’s are, at the end of 2017, the world’s largest public traded companies by market cap [10]. With their demonstration of innovation in their own fields and growth based on their own merits they are hard to avoid in the digital economy. Amazon, Google and Microsoft are competing in cloud services while the advertisement market is in effect a duopoly between Google and Facebook. Amazon has gobbled up e- commerce while Apple is the leader in mobile and connected devices. The suspicion is that the market concentration, with monopoly-like figures, has created juggernauts with too much power. These companies were yesterday´s startups but are they now using their market power to create barriers for today´s startups? As the economy is becoming digital the tentacles of the FAMGAs are harder to avoid. With the physical world coming online through IoT, data from these connected devices will end up in the large FAMGA data centers. Amazon is perhaps the best example of this with a clear strategy to make IoT part of the foundation of online retail [11]. This raises concerns around privacy and security. The recent Facebook / Cambridge Analytica scandal shows that it is not only malicious hacker attacks that societies should be fearful of. When the main revenue engine is based on consumer data, either directly or indirectly, it becomes harder to balance the interests of the enterprise and the public. While Apple needs to be called out as an exception by not monetizing user data the overall centralized power model remains: • The supporting infrastructure, being hugely centralized, is a honeypot, and rapidly becoming a security and privacy problem. • While there are sky high costs in maintaining the infrastructure the FAMGAs monetize by selling the user data to the highest bidder and, as the table above suggests, it is a successful business. • It is centralized to a degree where it is creating lock-in effects. As more data is accumulated, the “better” the carefully curated and locked-in version of the web becomes, and the harder it becomes to get out of the silo. All the profit is within the ecosystem — and you are either in or you are out. In this model users trade off privacy in exchange for free services and apps. To date it has worked well for the FAMGAs but will it work for Web 3.0? As more and more data is gobbled up, processed, leaked, stolen or used in a malicious intent potentially causing damage to consumers or societies, will users become more conscientious about data’s value? Authorities in some regions are at least wary of this centralization. EU is currently rolling out new legislation that will strengthen user privacy but this is unlikely to change the fundamentals of the current model. Cyber enabled everything requires security rethink The Web 1.0 created an consumption channel for web content. With Web 2.0, social and collaborative apps provided easy communication services. This coincided with enhancements in mobile computing allowing apps to gain a proximity to the user for more immediacy and personalization. With IoT the web is expanding it’s footprint into a realm where everything becomes connected. This enables new business models such as Product as a Service — aka “servitization”. For example, by embedding sensors in jet engines and by leveraging connectivity, Rolls Royce can charge per flight-hour, making product quality a profit driver. This model is accelerating the sharing economy — by controlling and monitoring an asset that has been cyber enabled, it is easier to provide new product experiences, hence leading to better resource utilization. With more sensors and actuators being built into physical objects these can be personalized and usage can be optimized. Not only in order to deliver a better product experience but to reap efficiency gain along the way — in other words to do stuff better at a lower cost. An IoT system will generate excessive amount of data which could create a network capacity issue as it is streamed to data centers. The data can describe how a person is interacting in the physical or digital world and is valuable for enhancing product experiences when analyzed in a cloud service by AI/ML algorithms. An example is AWS strategy of using IoT to learn more about consumers´ desires and needs [11]. Traditionally humans have interacted with the physical and digital