Category Archives: Computer Science

Corbett Report: Meet Bill Gates (34 min)

TRANSCRIPT AND SOURCES: http://www.corbettreport.com/gates
There can be no doubt that Bill Gates has worn many hats on his remarkable journey from his early life as the privileged son of a Seattle-area power couple to his current status as one of the richest and most influential people on the planet. But, as we have seen in our exploration of Gates’ rise as unelected global health czar and population control advocate, the question of who Bill Gates really is no mere philosophical pursuit. Today we will attempt to answer that question as we examine the motives, the ideology, and the connections of this man who has been so instrumental in shaping the post-coronavirus world.

 

“Nothing Like This Has Happened Before”: China To Invest $1 Trillion In New Plan To Overtake US In Tech

As we have been writing since late 2018, when it comes to the technological arms race between the US and China, one place where China has been badly lagging the US, is in the production of semiconductors, which is also China’s biggest weakness in its ongoing scramble to catch up with the US technologically.

China’s media agrees: over the weekend, we quoted from a Global Times op-ed according to which “although the US had experienced a large-scale deindustrialization in the second half of the 20th century, it still maintains advantages in the semiconductor sector with companies such as Intel, which could complete the whole process of the chip design to producing. The country has held on to cutting-edge semiconductor manufacturing techniques over the past decade.”

And now that the cold war between the US and China is about as formal as it can get, China has decided it can no longer rely on the US for being its primary source of high-end technology, and according to Bloomberg, Beijing is accelerating its bid for global leadership in key technologies, and will pump more than a trillion dollars into the economy “through the rollout of everything from wireless networks to artificial intelligence.”

Purposefully invoking the spirit of “Made in China 2025”, a plan that has in the past infuriated the White House, China’s strategic “masterplan” is backed by President Xi Jinping himself, and will see China invest an estimated $1.4 trillion over six years to 2025, “calling on urban governments and private tech giants like Huawei Technologies to lay fifth generation wireless networks, install cameras and sensors, and develop AI software that will underpin autonomous driving to automated factories and mass surveillance.”

This also means that while pursuing China’s plans to reinvent its technological base and to restructure its entire semiconductor supply chain, Beijing will also create the supreme police state dystopia, one which is even more powerful than the current iteration.

Predictably, the new infrastructure initiative is expected to rely on local giants from Alibaba and Huawei to SenseTime Group while shunning U.S. companies. And as Bloomberg adds, “as tech nationalism mounts, the investment drive will reduce China’s dependence on foreign technology, echoing objectives set forth previously in the Made in China 2025 program. Such initiatives have already drawn fierce criticism from the Trump administration, resulting in moves to block the rise of Chinese tech companies such as Huawei.”

“Nothing like this has happened before, this is China’s gambit to win the global tech race,” said Digital China Holdings Chief Operating Officer Maria Kwok, as she sat in a Hong Kong office surrounded by facial recognition cameras and sensors.

“Starting this year, we are really beginning to see the money flow through.”

Maria Kwok’s company is a government-backed systems integration provider, among many that are jumping at the chance. In the southern city of Guangzhou, Digital China is bringing half a million units of project housing online, including a complex three quarters the size of Central Park. To find a home, a user just has to log on to an app, scan their face and verify their identity. Leases can be signed digitally via smartphone and the renting authority is automatically flagged if a tenant’s payment is late.

The tech investment push is part of a broader fiscal package waiting to be signed off by China’s legislature, which convenes this week. The government is expected to announce infrastructure funding of as much as $563 billion this year, against the backdrop of the country’s worst economic performance since the Mao era. It will also include an expansion in the PLA’s budget to contain the “growing threat of US conflict“, as we discussed last night.

As Vital Knowledge points out in a note on Wednesday afternoon, “depending on how Beijing frames its tech ambitions around the NPC, this $1T+ blueprint could draw the ire of the White House and spur further measures aimed at inhibiting Chinese IT firms (recall the White House pushed hard for China to drop its prior “Made in China 2025” tech plan).”

Google Scientists Are Creating an Artificial Intelligence That Evolves on Its Own

(TMU) — One of the biggest global players in artificial intelligence (AI) is Google and their high-tech Brain division has been pushing the envelope for years.

Now scientists working for their AutoML project have a new paper in which they claim to be developing algorithms that can evolve on their own without human input. Even more stunning is their claim that they can induce “mutations” into new generations of algorithms, which mimics principles of Darwinian evolution, namely “survival of the fittest.”

The team started with one of the most basic ideas in modern AI: machine learning. Machine learning tools allow us to use algorithms to search through massive troves of data and quickly identify patterns. But the traditional problem with this method is the element of human bias.

As the team paper states: “Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML. Innovation is also limited by having fewer options: you cannot discover what you cannot search for.”

To bypass this problem, the team wanted to develop a system by which AI can grow on its own.

The team used simple math equations to develop machine learning algorithms that author 100 “candidate algorithms.” These candidates compete using basic machine learning tools like neural network image differentiation tests and the best-performing algorithms then mutated, or evolved, via random code alteration.

The system can cull through tens of thousands of algorithms each second in search of a solution while dismissing “evolutionary dead-ends” and duplicates. Over multiple generations, the process grows a library of high-performance algorithms. According to the Google team, these new algorithms have already reproduced decades worth of human-led AI discoveries in only days.

Perhaps most astonishingly, the new AI algorithmic evolution is able to eliminate the problem of human bias that is often introduced during data input. The AutoML-Zero can essentially “automatically discover” unknown algorithms and develop new previously undiscovered AI programs without any human intervention, using only basic mathematical concepts.

Haran Jackson, the chief technology officer (CTO) at Techspert, explains why the new paper is so interesting:

“As exciting as AutoML is, it is restricted to finding top-performing algorithms out of the, admittedly large, assortment of algorithms that we already know of. There is a sense amongst many members of the community that the most impressive feats of artificial intelligence will only be achieved with the invention of new algorithms that are fundamentally different to those that we as a species have so far devised.

“This is what makes the aforementioned paper so interesting. It presents a method by which we can automatically construct and test completely novel machine learning algorithms.”

As noted before, the scientists say the AI programs can improve upon each previous generation, producing a kind of “survival of the fittest” that resembles Darwin’s view of evolution in natural biological systems.

While much testing and review still await the Google team, their new paper, titled “Evolving Machine Learning Algorithms From Scratch,” suggests the tantalizing, albeit unnerving likelihood that AI of the future will be designed by other AI machines.

One can only impishly guess at what self-replicating AI algorithms evolving on their own will mean for the future of life on Earth.