For the most part, Web2 is what we still have today but, from the beginning, Berners-Lee, now an MIT professor, envisaged a much more sophisticated version of the Web. Known as the Semantic Web, it would not only store data, but actually know what it means. The goal is to make all information on the Internet “machine-readable,” so it can be easily processed by computers, like an Excel sheet full of numbers as opposed to human language. We are now in the early stages of the Semantic Web, which incorporates his vision. For example, there is already a cloud of datasets that links thousands of servers without any form of centralized control. However, due to the costs and technological hurdles related to converting human language into something that computers can understand, the Semantic Web remains an ongoing project.
Currently, AI is only able to perform certain tasks, but it can already make healthcare business practices more efficient by leveraging deep learning to analyze data in supply chains. DeepMind, the company that developed AI for defeating chess masters, has also made huge advances in figuring out protein folding and misfolding, which is responsible for some diseases. Currently, AI is not that useful for diagnosing and treating many complex diseases. This is because deep learning is probabilistic, not causal. So, it is able to understand correlation, but not cause and effect.
Like the Web, though, AI is evolving, and the limitations of deep learning could be overcome in the foreseeable future. A number of government programs and private initiatives are dedicated to better understanding human brain complexity and equipping machines with reasoning, common sense, and the ability to understand cause and effect. The Semantic Web could expand the impact of these new cognitive skills by feeding data to AI in more readily accessible formats. This will make machines better at solving hard problems such as diagnosing and treating complex diseases, which involve genetic, lifestyle, and environment factors. These powerful AIs in the realm of healthcare could become an enduring and important feature of Web3.
Blockchain, Smart Contracts and Income Inequality
The Web2 version of the digital age was certainly impactful in altering our lifestyle both positively and negatively. This is predominately because of the business model used by companies such as Meta (formerly Facebook) and Google. By providing useful products like search engines, these companies have lured consumers into giving away their personal data for free, and the companies use this information to detect buying patterns in order to sell advertising. The digital economy made high tech companies billions of dollars while many users became underemployed or jobless.
In recent years, a similar model has been emerging in the realm of genetics. Personalized genomic companies charge a relatively small fee to analyze a fraction of our genes and provide probabilities of having specific medical conditions. While individual data is not valuable, cumulative data is helpful for deep learning. So, these companies can sell the anonymous DNA data to pharmaceutical companies for millions of dollars.
As these companies improve their ability to collect even more data about our genetic vulnerabilities, the technologies of Web3 could protect consumers from giving it away for free. An emerging technology called blockchain is able to provide a Web-based ledger of financial transactions with checks and balances to ensure that its records cannot be faked or altered. It has yet to reach mass adoption by the public, but the computer scientist Jaron Lanier has proposed storing our genomes and electronic health records in blockchain, utilizing electronic smart contracts between individuals and pharma healthcare industry. Micropayments could then be made to individuals for their data, using cryptocurrency.
These individual payments could become more lucrative in the coming years especially as researchers learn how to fully interpret and apply a person’s genetic data. In this way, blockchain could lead to improvements in income inequality, which currently drives health problems and other challenges for many. A number of start-ups are using this business model which has secure data and eliminates middlemen who don’t create any value, while compensating and protecting the privacy of individuals who contribute their health data.
Autonomous Vehicles, Fog Computing and Pollution
A number of trends indicate that modernizing the transportation industry would address a myriad of problems with public health, productivity and the environment. Autonomous vehicles (AVs) could help usher in this new era of transportation, and these AVs would need to be supported by Web3 technologies.
Automobile accidents are the second leading cause of death worldwide, with roughly 1.3 million fatalities annually, according to the World Health Organization. Some estimates suggest that replacing human drivers with AVs could eliminate as many as a million global fatalities annually. Shared AVs would help to reduce traffic congestion that wastes time and fuel, and electric vehicles would help minimize greenhouse gases.
To reap the benefits from replacing gas vehicles with electric, societies will need an infrastructure that enables self-driving cars to communicate with each other. Most data processing in computers is performed using von Neumann architecture, where the data memory and the processor are in two different places. Today, that typically means cloud computing. With self-driving cars, when cameras and sensors generate data to detect objects on the roads, processors will need to rapidly analyze the data and make real-time decisions regarding acceleration, braking, and steering. However, cloud computing is susceptible to latency issues.
One solution to latency is moving processing and data storage closer to where it is needed to improve response times. Edge computing, for example, places the processor at the site where the data is generated. Most new human-driven vehicles contain anywhere from 30 to 100 electronic control units (ECUs) that process data and control electrical systems in vehicles. These embedded systems, typically in the dashboard, control different applications such as airbags, steering, brakes, etc. ECUs process data generated by cameras and sensors in AVs and make crucial decisions on how they operate.
Self-driving cars can benefit by communicating with each other for navigation in the same way that bacteria and animals use swarm intelligence for tasks involving groups. Researchers are currently investigating fog computing which utilizes servers along highways for faster and more reliable navigation and for communicating data analytics among driverless cars.
The Future Landscape of Web3 is Uncertain
The future of Web3 has many possibilities. However, there is no guarantee that blockchain, smart contracts, and fog computing will achieve public acceptance and market saturation or prevail over other technologies or the status quo of Web2. It is also uncertain if or when the breakthroughs in AI will occur that could eradicate complex diseases through Web3.
An example of this uncertainty is the metaverse, which combines blockchain with virtual reality. Currently, the metaverse is primarily used for gaming and recreational use until its infrastructure is further developed. Researchers are interested in the long-term mental health effects of virtual reality, both positive and negative. Using avatars, or virtual representations of humans, in the metaverse, users have greater control of their environment and chosen identities. But, it is unclear what negative mental health effects will occur. As far as regulations, the metaverse is still in the Wild West stage, and bullying or even murder will likely take place. Also, there will be a point where virtual worlds like the metaverse will become so immersive that we won't want to leave them, according to Meta’s Zuckerberg.
The metaverse would rely on virtual reality technology that was developed many years ago, and adoption has been slower than some experts predicted. But most emerging technologies, including other examples related to Web3, follow a similar, nonlinear pattern of development that Gartner has represented in graphical form using the S-curve. To develop a technology forecast for Web3, you can follow the progress along the curve from proof of concept to a particular goal. After a series of successes and failures, entrepreneurs will continue to improve their products until each emerging technology fails or achieves mainstream adoption by the public.
What mix of emerging technologies ultimately defines Web3 will likely be determined by the benefits they provide to society—including whether and how they improve health—how they stimulate the digital economy, and how they address the significant shortcomings of Web2.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu