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This year marks 20 years since we began compiling our annual compilations of flagship technology. Some of them, such as mRNA vaccines, are already changing our lives, while others will have to wait a few more years. Below you will find a brief description of each technology. We hope you appreciate this opportunity to look into the future.
Messenger RNA vaccines
We are incredibly fortunate. The two most effective coronavirus vaccines are based on messenger RNA, a technology that has been in development for 20 years. When the Covid-19 pandemic hit last January, scientists at several biotech companies rushed to work on mRNA-based vaccines. By the end of December 2020, with 1.5 million coronavirus deaths worldwide, vaccines were approved in the United States and the pandemic began to decline.
The new coronavirus vaccines are based on innovative technology and could change medicine, paving the way for a new generation of drugs for a variety of infectious diseases, including malaria. And if the coronavirus continues to mutate, mRNA vaccines can be easily and readily modified. In addition, messenger RNA holds great promise as a basis for inexpensive drugs for sickle cell disease and HIV. Finally, a cure for cancer, also based on mRNA, is being developed.
Large natural language computer models that learn to write and speak on their own are an important step toward artificial intelligence that can better understand and interact with the world. GPT-3 is by far the largest and most literate. With thousands of books and a large part of the Internet, GPT-3 can mimic the human way of composing text with incredible, sometimes even uncanny realism. This makes it the most impressive machine learning language model available today.
But GPT-3 does not understand what it is typing, so sometimes confusion or even nonsense arises. Training requires an enormous amount of computing power, data and money. This multiplies the carbon footprint and inhibits the development of similar models, requiring exceptional resources from the labs. And because it learned from Internet texts, and is replete with misinformation and bias, the model produces similarly biased opusions.
TikTok’s recommendation algorithm.
Since its launch in 2016, TikTok has become one of the fastest growing social networking platforms in the world. The app has collected billions of downloads and united hundreds of millions of users.Why? Because TikTok’s personalized feed algorithms have revolutionized the world and the way people achieve fame online.
If other platforms are more likely to push targeted content to mass audiences, then TikTok’s algorithms are likely to pluck a new creator from obscurity and ignite a new star. And they are particularly adept at delivering relevant content to niche communities with specific interests or distinctive tastes and preferences.
The ability of new contributors to quickly collect a large number of views and the ease with which users discover new content has provided the app with staggering growth. Other social networking companies are struggling to replicate these features in their own apps.
Lithium metal batteries
Selling electric vehicles is no easy task: not only are they more expensive, but they are only sufficient for a few hundred kilometers without recharging; moreover, it takes much longer than refueling. All these disadvantages stem from lithium-ion batteries and their limitations. A well-funded Silicon Valley startup says it has developed a battery that will make electric vehicles much more attractive to the mainstream.
This type of battery is called lithium metal and is being developed by QuantumScape. Preliminary tests have shown that a battery can increase the mileage of an electric vehicle by 80% while charging quickly. The startup has already reached an agreement with Volkswagen, which intends to sell electric vehicles with a new type of battery by 2025.
So far, this is just a prototype, far less than what is needed for a real car. But if QuantumScape and others in the same direction succeed, electric vehicles may finally reach millions of drivers.
Trust in data
It turns out that technology companies don’t store our personal data well. We’ve already lost count of how many times it’s been leaked, hacked and resold. Perhaps the problem is not with us, but with the privacy model we’re used to, where everyone is personally responsible for maintaining privacy.
A data trust offers an alternative approach that some governments have already begun to consider. A data trust is a legal entity that collects and manages individuals’ personal data on their behalf. While the structure and function of these trusts is still being determined and many questions remain, data trust stands out as offering a potential solution to long-standing privacy and security issues.
Hydrogen has always been a curious substitute for fossil fuels. It burns clean, emits no carbon dioxide and also consumes a lot of energy, making it a good way to store energy from non-permanent renewable sources. In addition, liquid synthetic fuels can be produced, which will become an irreplaceable replacement for gasoline or diesel fuel. So far, most hydrogen is produced from natural gas, but the process is dirty and energy-intensive.
However, the price of solar and wind power is dropping rapidly, which means that green hydrogen is now also available to be cost-effective. Simply connect water to electricity and presto, here’s hydrogen for you. Europe is leading the way and is already starting to build the necessary infrastructure. These projects are just the first step towards a future global network of electrolysis plants that will produce pure hydrogen from solar and wind power.
Digital contact tracing
When the coronavirus began to spread around the world, at first it seemed that digital contact tracing would save us. Smartphone apps create meeting records based on GPS or Bluetooth. If someone receives a positive coronavirus test, they can enter this information into the app and it will warn others who might be infected.
Unfortunately, digital contact tracking has not been able to contain the spread of the virus. Apple and Google quickly implemented the notification feature on many smartphones, but health officials failed to convince citizens to use them. The lessons from this pandemic will not only help us prepare for the next one, but will also affect other areas of healthcare.
We all use GPS every day; it has changed our lives and our work. But if modern GPS has an accuracy of 5 to 10 meters, then new technologies for ultra-precise positioning, within a few centimeters or even millimeters. This opens up new possibilities, from landslide warnings to delivery robots and driverless cars that can safely navigate the streets.
China’s BeiDou global navigation system (Ursa Major) was completed in June 2020 and opens up new opportunities in this direction. It provides positioning accuracy of 1.5 to two meters to any user. And thanks to the ground-based differential correction system, it can improve the accuracy to the millimeter. Meanwhile, the GPS system, which has been around since the early 1990s, is also being upgraded: four new satellites for GPS III will be launched in November and more will be in orbit by 2023.
Due to the coronavirus pandemic, the world has switched to remote mode. It is especially important to make no mistake with this transition in healthcare and education. In some countries, efforts have been made to maximize the benefits of these two areas.
To date, the distance learning company Snapask has 3.5 million users in nine Asian countries, and the Indian learning app Byju’s has grown to 70 million. Unfortunately, students in many other countries will not switch to remote classes.
Meanwhile, thanks to advances in telemedicine in Uganda and several other African countries, millions of patients received needed medical care during the pandemic. In a chronically underserved part of the world, telemedicine services are saving lives.
Despite tremendous advances in artificial intelligence in recent years, artificial intelligence and robots in general are still dumb, especially when it comes to solving new problems and navigating unfamiliar environments. They lack the human skills that even young children have: they don’t know how the world works and don’t know how to apply this general knowledge in a new situation.
One promising approach to improving AI is to broaden its sentience. Today, AI with computer vision or sound recognition can sense things, but it can’t “talk” about what it sees and hears using natural language algorithms. But what if you combine these abilities into a single AI system, then will they acquire human-like intelligence? By learning to see, sense, hear and communicate, will a robot become a more skilled helper through more flexible intelligence?