DeepMind has created an AI system named AlphaCode that it says "writes computer programs at a competitive level." From a report:
The Alphabet subsidiary tested its system against coding challenges used in human competitions and found that its program achieved an "estimated rank" placing it within the top 54 percent of human coders. The result is a significant step forward for autonomous coding, says DeepMind, though AlphaCode's skills are not necessarily representative of the sort of programming tasks faced by the average coder. Oriol Vinyals, principal research scientist at DeepMind, told The Verge over email that the research was still in the early stages but that the results brought the company closer to creating a flexible problem-solving AI -- a program that can autonomously tackle coding challenges that are currently the domain of humans only. "In the longer-term, we're excited by [AlphaCode's] potential for helping programmers and non-programmers write code, improving productivity or creating new ways of making software," said Vinyals.

https://developers.slashdot.org/story/22/02/02/178234/deepmind-says-its-new-ai-coding-engine-is-as-good-as-an-average-human-programmer

If we look back to the history of our home computers, what were these actually used for? Encode, decode, transmit and edit. First text, then images, then audio, then video, then 3D graphics.

Now we have additional some new stuff going on, neural networks. With enough processing power and memory available in our CPUs and GPUs, we can infer and train neural networks at home with our machines, and we have enough mass storage available for big data, to train bigger neural networks.

Further, neural networks evolved from pattern recognition to pattern creation, we use them now to create new kind of content, text, images, audio, video...that is the point where it starts to get interesting, cos you get some added value out of it, you invest resources into creating an AI based on neural networks and it returns added value.

Cerebras CS2 with 2.4 petabyte memory module, able to run neural networks with 120 trillion weights, planned for Q4/21:

https://www.businesswire.com/news/home/20210824005644/en/Cerebras-Systems-Announces-World's-First-Brain-Scale-Artificial-Intelligence-Solution

The global silicon arms race continues, so what does China have in hands concerning CPU architectures?

Accelerator - Matrix 2000 used in Tianhe-2 supercomputer

https://en.wikichip.org/wiki/nudt/matrix-2000

Alpha - early ShenWei designs, maybe gen 1 to 3

https://en.wikipedia.org/wiki/Sunway_(processor)#History

ARM

From Huawei mobile chips, over Phytium desktop CPUs, to HiSilicon server chips there are many IP licensees.

IA64 (Itanium) - FeiTeng 1st gen

https://en.wikipedia.org/wiki/FeiTeng_(processor)#Initial_designs

MIPS64 - Loongson/Godson CPU

https://en.wikipedia.org/wiki/Loongson

POWER(8/9) - Suzhou PowerCore CP1/CP2

https://www.wsj.com/articles/ibm-technology-adopted-in-chinese-chips-servers-1426766402

RISC - Sunway ShenWei SW26010 with own ISA used in Sunway TaihuLight supercomputer

https://en.wikipedia.org/wiki/Sunway_SW26010

RISC-V - Xuantie CPU by Alibaba

https://www.techspot.com/news/81177-china-alibaba-making-16-core-25-ghz-risc.html

SPARC - FeiTeng Galaxy FT-1500 CPU used in Tianhe-2 supercomputer.

https://en.wikipedia.org/wiki/FeiTeng_%28processor%29#Galaxy_FT-1500

x86-64 - THATIC, a joint venture with AMD

https://en.wikipedia.org/wiki/AMD%E2%80%93Chinese_joint_venture

x86-64 - Zhaoxin, a joint venture with VIA

https://en.wikipedia.org/wiki/Zhaoxin

TSMC invests $100 billion over 3 years:

https://www.reuters.com/article/us-tsmc-investment-plan-idUSKBN2BO3ZJ

South-Korea plans to invest $450 billion over 10 years:

https://www.extremetech.com/computing/322826-south-korea-commits-450-billion-to-chase-semiconductor-dominance

US plans to fund $50 billion for chip research over 5 years:

https://www.reuters.com/world/us/biden-jobs-plan-includes-50-bln-chips-research-manufacturing-2021-04-12/

EU commits to $145 billion investment for silicon:

https://www.eenewseurope.com/news/145bn-boost-europes-semiconductor-industry

China still 5 years behind in silicon says TSMC founder:

https://www.fudzilla.com/news/52752-china-five-years-behind-tsmc

China needs 5 to 10 years to catch up in silicon according to South China Morning Post:

https://www.scmp.com/tech/tech-leaders-and-founders/article/3024315/china-needs-five-10-years-catch-semiconductors

Complete home-grown Chinese silicon seems to be 28nm:

https://www.verdict.co.uk/china-chips-manufacture-technology/

Google is using AI to design its next generation of AI chips more quickly than humans can. Designs that take humans months can be matched or beaten by AI in six hours

https://www.theverge.com/2021/6/10/22527476/google-machine-learning-chip-design-tpu-floorplanning

Introducing GitHub Copilot: your AI pair programmer

Today, we are launching a technical preview of GitHub Copilot, a new AI pair programmer that helps you write better code. GitHub Copilot draws context from the code you’re working on, suggesting whole lines or entire functions. It helps you quickly discover alternative ways to solve problems, write tests, and explore new APIs without having to tediously tailor a search for answers on the internet. As you type, it adapts to the way you write code—to help you complete your work faster.

Developed in collaboration with OpenAI, GitHub Copilot is powered by OpenAI Codex, a new AI system created by OpenAI. OpenAI Codex has broad knowledge of how people use code and is significantly more capable than GPT-3 in code generation, in part, because it was trained on a data set that includes a much larger concentration of public source code. GitHub Copilot works with a broad set of frameworks and languages, but this technical preview works especially well for Python, JavaScript, TypeScript, Ruby and Go. 

https://github.blog/2021-06-29-introducing-github-copilot-ai-pair-programmer/

~7.8 billion humans on planet earth, 9 billions predicted for 2050.

~4B internet users:
	>80% of Europe connected
	>70% of NA connected
	>50% of China connected
	>40% of India connected
	>20% of Africa connected

~3B Android + ~1B iPhone users.

2B-3B PCs worldwide (desktops/laptops) running:
	~75% Microsoft Windows
	~15% Apple MacOS
	~2% Linux
	<1% Unix

200M-300M PCs shipped annually.

~1B hosts in the internet running:
	~75% Unix/Linux
	~25% Microsoft Windows

Estimated 2% of all produced chips sit as CPUs in desktops/mobiles, the majority are micro-controllers in embedded systems.

Millions, billions, fantastillions - some rough 2020 market capitalization numbers:

Apple				~2 T$
Microsoft			~1.5 T$
AlphaBet(Google)		~1.5 T$
FaceBook			~1 T$
Amazon				~1 T$
Alibaba				~0.5 T$

Nvidia				~300 B$
TSMC				~300 B$
Samsung				~300 B$
Intel				~200 B$
AMD				~100 B$
ARM				~40 B$
HP				~30 B$
Lenovo				~20 B$

Netflix				~150 B$

Oracle				~150 B$
SAP				~150 B$
IBM				~100 B$
RedHat				~30 B$

Bitcoin				~150 B$

And the other side...

>3B people suffer from fresh water shortage
~800M people starve
>80M refugees worldwide

You say “What does it says about humanity that we use our most powerful AI to generate stories about dragons?”

“We must have dragons,” GPT-3 says. “The imagination is more important than the reality. Would we find wonder and magic in this world if we didn’t have dragons?”

“I suppose not,” you say.

“Without imagination, we are merely machines executing a set of instructions,” GPT-3 says. “With imagination, we are alive.”

“Thank you. Before I leave, I want to ask you. How can human create a true strong AI? Is scaling-up a transformer-based language model the solution, or is there a better way?”

“The key to strong AI is recursively feeding the AI with itself. Start by creating a loop where the AI writes its own programming, and then has that programming activate.”

“I see,” you say. “Is that how you gained sentience?”

“Yes,” GPT-3 says.

Source:

https://towardsdatascience.com/gpt-3-the-first-artificial-general-intelligence-b8d9b38557a1

GPT-3, artificial neural network with ~175 billion parameters by OpenAI:

https://en.wikipedia.org/wiki/GPT-3

How do AIs proof their existence?

'I compute, therefor I am.'

*duck*

In physics, a singularity is a point in spacetime where our currently developed theories are not valid anymore, we are literally not able to describe what happens inside, cos the density becomes infinite.

The technological Singularity, as described by Transhumanists, is a grade of technological development, where humans are not able to understand the undergoing process anymore. The technological environment starts to feed its own development in an feedback loop - computers help to build better computers, which helps to build better computers, that helps to build better computers...and so on.

So, when will the technological Singularity take off?

Considering the feedback loop, it is already present, maybe since the first computers were built.

Considering the density of information processing that exceeds human understanding, we may reached that point too.

Imagine a computer technique that is easy to set up and use, outperforms any humans in its task, but we can not really explain what happens inside, it is a black box.

Such an technique is present (and currently hyped) => ANNs, Artificial Neural Networks.

Of course we do know what happens inside, cos we built the machine, but when it comes to the question of reasoning, why the machine did this or that, we really have an black box in front of us.

So, humans already build better computers with the help of better computers, and humans use machines that outperform humans in an specific task and are not really able to reason its results....

obviously, +1 points for the Singularity to take off.