Narrow Super AI Will Compete to Be Super AGI

We are already in the early part of the Age of Narrow Super AI. 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. However, Google Search is superhuman for the specific "narrow" task of search. Google Search and Google Advertising enable over a trillion dollar valuation for Google.

General human level AI would be systems that theoretically have the breadth of human intelligence but are around human performance but could have superior speed of performance.

Super AI would be AI that is vastly superior to human in qualitative capabilities.?

Superintelligence is any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest.

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 and entrenched financial power.

Any Super AGI or broader super AI will need to displace Narrow Super AIs in every valuable domain or market. 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.

Google's Deep Mind has made recent breakthroughs in protein folding problems. 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.

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.

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.

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.

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. This is a step towards broadening what have been AI for single tasks.

Tesla is 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. This is $7.5 billion of hardware deployed for data gathering. By 2025, there could be 20-40 million Tesla full self-driving cars on the road. Forty million cars would represent $120 billion of cameras and hardware. 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.

If Tesla becomes the dominant self-driving car provider and dominant with EVs and the batteries and electric motors then this will give them the advantage in dominating the Teslabot and labor. SpaceX will have space launch and global reusable rocket travel and millions of satellites for all global communications. Google will have search in all places other than China. There will be a few big players with Biotech, Medicine and Antiaging, and DNA/RNA/Protein/Molecular technologies.

Tesla and Elon Musk are unleashing vastly superior productivity and factories. This is via full automated testing for every car. This means dozens of changes can be safely made to a factory production line because every car can be instantly and completely tested. Any factory changes or over-the-air software updates can be validated at virtually no cost. Elon views the factory-like a microchip and processes can be sped up by bringing them closer together and increasing the efficiency of interactions. Hundred-fold improvements are possible with first principle redesign that leverages what Tesla has already done.

Next level AI, Molecular technologies and transformed factories and reusable rockets will grow the economy by 1000 times over the next 3-5 decades.

The changes will not be beyond our ability to adapt or foresee. However, we will need to closely track and observe what is really happening every month and every week in areas of rapid change.