There has and will be substantial development of major super technologies this decade.
There have been several major things with AI. We are in the age of narrow superintelligence.
Here are some definitions for different categorizations of AI.
Artificial narrow intelligence (ANI) has a narrow range of abilities.
Artificial narrow superintelligence (ANSI) has a narrow range of abilities but those abilities are well beyond human level.
Artificial general intelligence (AGI) is a broad range of abilities that are comparable to the range of intelligence for a human.
Artificial general superintelligence (AGSI)is far more capable than a human across all domains of human intelligence.
Google Assistant, Google Translate, Siri and other natural language processing tools are Narrow AI. They are classified as “Weak” AI because they are nowhere close to having human-like intelligence.
Ray Kurzweil calls “fast thinking” AI intelligence – “weak superhumanity”. Google Search is weakly superhuman. It is far faster than human for the task of search.
There have been one million times more computing power devoted to the protein folding problem and drug discovery. Deep Mind has made significant progress in solving protein folding. Alphafold 2 will certainly help to advance biology. It can generate folded structure predictions that can then be used to solve experimental structures by crystallography (and probably other techniques). So this will help the science of structure determination go a bit faster in some cases. This is still short of the 0.3 Angstrom precision needed to be highly useful for drug discovery. However, the work and progress is very promising and it now appears to be a matter of when and not if the problem will be solved in major ways.
The Google Research next-generation AI architecture is called Pathways. It will enable a single model to learn millions of things instead of just one thing. This will be a massive phase change and up-leveling of neural network and AI capabilities.
Computer vision, computer speech, search, language processing, language understanding, translation, protein folding prediction and analysis, self-driving, Teslabot like systems and much more are happening. We will get deeper into the multi-trillion and quadrillion dollar age of super narrow AI dominance. The super-narrow AI will interact. We will be able to order chatbots for delivery or rides or supply chain via self-driving and robotic loading and unloading.
The data advantages of each dominant super-narrow AI will be difficult to displace from a dominated market niche. Imagine the difficulty of a superior search engine trying to displace Google in search. Google has not just search but the advertising ecosystem.
Any new entrant Super AGI or broader super AI will need to displace any market entrenched Narrow Super AI. The owner of a Narrow Super AI can broaden or improve its Narrow Super AI. Big Tech companies can also try to buyout or use mergers or partnerships with entrenched Narrow AI. Narrow AIs that can be broadened or expanded by strong incumbents (like Tesla or Google) will have better chances of increasing reach and displacing weaker competition.
Tesla and other companies are spending billions every year developing self-driving cars. Tesla has 2.5 million cars on the road and each has $3000 in cameras and other hardware to enable full self-driving and will get over the air software updates as the software improves. By 2025, there could be 20-40 million Tesla full self-driving cars on the road. Full self-driving would be considered a narrow superintelligence. I would view FSD performance and capabilities based upon the number of standard deviations better than the average human driver on safety statistics.
Tesla FSD Beta has been made available to over 60,000 drivers. FSD will likely be fully available to over 240,000 prepaid US customers by mid-2022. FSD should reach robotaxi level by 2025.
Will the AI software for protein folding create more value and a dominant position or will the value be from molecular electronics that enables thousands of times faster DNA, RNA and protein reading and synthesis? Roswell Biotechnologies has released a molecular electronics chip that integrates molecules with CMOS. I believe the power will reside with the vastly superior molecular nanotechnology providers and users instead of Deep mind Alphafold software.
Billions and trillions of molecule systems on integrated CMOS means molecular reading and synthesis at trillions of times the speed and scale of today. Rapid, low cost, mobile detection systems for diverse biomarkers. Enabling powerful, in-the-field pathogen detection, infectious disease monitoring, environmental monitoring, and identification of bio-specimens, species or individuals.
By 2030, Roswell Biotech should scale DNA, RNA and protein reading to be billions of times faster instead of thousands of times faster than today.
Atom Technologies – Better and smaller Atomics clocks, QuantumRF and More
ColdQuanta is unlocking atom technologies with laser-trapped atoms for quantum computers with millions of error-corrected qubits. There will be millions of ultra-precise atomic clocks which will transform GPS locations’ precision and master clocks for data centers. This will mean that Google’s Spanner database would be enabled to have hundreds to thousands of times greater speed or scale. They will have QuantumRF which will transform the energy efficiency and speed of communication.
In the most recent SpaceX Starship presentation, Elon Musk stated that the SpaceX Starship can become lower cost than airplanes for long haul cargo delivery.
I have some detailed calculations. Starship will double the payload capacity. Full fueling costs are about the same for the 1200 tons of fuel for Starship versus 50,000 gallons of jet fuel for a long-range cargo plane for each flight. However, twice payload means half the cost. Non-fuel costs for the vehicles and the frequency of flights are hugely in Starship favor. Starship should have twice the payload.
Starship starts off at half of the loaded cost per flight and four times cheaper per ton of payload and then will proceed to be ten to fifteen times cheaper. Plus they will have some initial new no-competition markets for ultra-fast delivery.
There are over 170 companies working aging damage reversal. First and second-generation versions of these treatments against each of the seven kinds of aging damage will be widely available and in use by 2030.
Aging damage repair treatment will be mostly periodic injections. If there are many companies each with their own injectable aging damage repair treatment then it will be like COVID vaccinations where there are many treatment options.
Currently, there are about 130 million births per year and about 60 million deaths per year. About 60% of the deaths are from old age-related conditions. About 18 million deaths (30%) per year are from diseases and conditions related to global poverty. About 2-3 million deaths are related to various kinds of accidents.
It would take time for successful antiaging treatments to get broadly deployed and to alter the amount of deaths per years by the millions. If there was a 5% drop in age-related deaths every year from 2025 onwards. This would mean 1.5 million fewer deaths in 2025, 3 million in 2026 and 9 million fewer deaths in 2030. This scenario would require major progress and success and against all seven types of aging damage.
Limited Aging damage reversal success (that would still be substantial) would mean the curing or substantial reduction in arthritis or aging eye diseases and other isolated aging conditions.
SOURCES – Roswell Biotech, ColdQuanta, SpaceX, Tesla
Written By Brian Wang, Nextbigfuture.com
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.