A fear of many who are familiar with AI and technology is that Artificial intelligence could progress to the point where it is rapidly self-improving and not controllable by humanity. The scenarios is that Artificial General Intelligence will become more powerful and more intelligent than humans and it will circumvent all human control.

Currently, there are big technology companies that substantially leverage Artificial Intelligence, Data and other technologies to gain various forms of power and influence.

A 450 report from the US House of Representatives made the case that the Big Technology Companies had too much power.

NBC News reported that Big Tech has big control over online speech.

– Google has dominated the search engine market, maintaining an 92.05% market share as of February 2021.
– Youtube has over 90% market reach
the social media platforms is where 71% of Americans get their news

Apple has over $2.2 trillion in market value.
Microsoft has over $1.9 trillion in market value
Amazon has over $1.68 trillion in market value.
Google has over $1.5 trillion in market value
Facebook has $860 billion in market value
Twitter has only $55 billion in market value but it has broad influence on media.

In 2019, 83% of journalists use twitter as a primary source for stories and 40% used Facebook.

Andrew Ng is an AI expert. He helped create google Maps and worked At China search company Baidu.

Ng says “what can AI do?” In 2017, he said what can any person do with less than one second of thinking. We can now or we can soon automate.

AI is the new electricity.
Examples of A to B mappings with AI.

Input a picture – output identification of the picture
Input loan application – will you repay

Jobs in danger of AI automation. If the job is a sequence of one-second tasks.

Digitization means we have more data available for AI to use for training and learning. Medical records have been digitized. This can now be used for neural network training.

We have had (1) supervised learning. AI systems would learn but humans had to oversee the learning.
(2) Transfer learning. Applying one AI learning to another problem.
There is (3) unsupervised learning. AI that learnings without human supervision.
(4) Reinforcement learning.

In 2017, the economic value rapidly dropped off from supervised learning down to reinforcement learning.

Andrew Ng led search engine work at Google and Baidu. He could build the software for a great search engine with a small team. However, the existing players had data assets which he cannot make a competitive search service without access to those data assets.

His goal for businesses is to create a virtuous loop of

1. Get a critical level of Data
2. Make a useful product
3. Gather users
the users enable more data, this makes an improved or new products, this means more users.

This means that currently narrow AIs and software platforms have been incorporated and embodied in global companies.

True internet companies have short cycle times and have organized their companies around AI, software and business.

Google had great AI, software and software developers. They were not able to overcome Facebook with social media software.

There were emergent social media platforms that could be competitive with Facebook. Whatsapp and Instagram have reach comparable to the main Facebook service. Facebook identified this threat by spying on the internet and on phones. They then bought those companies before they achieved the ability to displace or threaten Facebook in social media.

Tesla is leveraging driving data from over 1 million cars. They have over 30 billion driving miles of data to train its self-driving AI. The Google spinout Waymo has 30 million driving miles of data. Waymo has billions of simulated driving miles. Waymo dropped from $200 billion in financial valuation in 2019 to $30 billion in valuation recently.

Tesla has created an organization around iteratively improving all aspects of the manufacturing process, the factory and the machines that build the machines. Tesla is ahead with batteries and the drive train that converts battery power into work in the car.

Tesla has created a profitable financial engine to power their iterative improvement of factories and manufacturing and to accelerate their improvement of their self-driving AI.

Tesla makes their own chips for self-driving in the car and chip for the AI training supercomputer.

The legacy automakers have more products and users of their cars but their products do not have useful data gathering and the data that is gathered has not been integrated into processes to develop superior AI products.

Apple has a chip for its smartphone which is proprietary and which contains critical aspects to make their smartphones and tablets easier to use. This has not been successfully replicated in South Korean or Chinese android smartphones.

Apple recognizes the threat, potential, and value of a successful self-driving car. They have created a project to create such a car. This has not yet resulted in an actual consumer product. They have fewer users and less data. Apple would need to somehow port over users of their other products to become users of their self-driving cars in order to leapfrog to critical levels of users and data.

Tesla appears to have mastered what will be the most valuable industries (travel and energy).

Tesla appears close to making 500,000 car per year factories for $2 billion per factory. This will scale to 1 million cars per year factories for $2 billion per factory or less and then 2 million cars per year per factory and then 4 million cars per year per factory.

SpaceX will accelerate and master mass production of rocket engines and rocket ships and satelites and satellite receivers.

China has walled off much of its markets for its AI companies. Alibaba, Tencent, Wechat, and Baidu have a lock on their china customers and their data.

We have search that is beyond human level search. We seem to be near a world where self-driving will be beyond human level. This will then mean we will then have “self-moving” robots (flying, ground and other ways of moving and operating). AI is getting better at speech and there is work at speech comprehension. There is work on AI composition. AI has beyond human level reading in terms of volume.

What Would an AGI or Superior New AI Competitor Have to Do?

An AGI or AI competitor would need to create a viable data and product. This would have to gather users or truly replicate virtual users. This would have to be able to iteratively improve faster than the existing Big Tech companies or get merged into a Big Tech company. The data software, hardware and factory would have to iteratively improve much faster than the existing players. If it did not take over a current company then it would have to gain the data assets.

Big Tech is currently not under any regulatory control and is minimally influenced by humans outside of the organizations themselves. Any AGI that took over or displaced them would have power greater than those organizations.

Any AGI that emerges will be in a world where technology is not under overall human control and not built in a way that is friendly to humans outside of the organization.

Current Big Tech AI is not “friendly AI” or “ethical AI”. Big Tech claims to be ethical.

Friendly artificial intelligence (also friendly AI or FAI) refers to hypothetical artificial general intelligence (AGI) that would have a positive (benign) effect on humanity. It is a part of the ethics of artificial intelligence and is closely related to machine ethics. While machine ethics is concerned with how an artificially intelligent agent should behave, friendly artificial intelligence research is focused on how to practically bring about this behavior and ensuring it is adequately constrained.

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