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The Big Nine

AI isn’t a tech trend, a buzzword, or a temporary distraction – it is the third era of computing.

In the US, relentless market demands and unrealistic expectations for new products and services have made long-term planning impossible. We expect Google, Amazon, Apple, Facebook, Microsoft, and IBM to make bold new AI product announcements at their annual conferences, as though R&D breakthroughs can be scheduled.

We effectively outsourced the future development of AI to six publicly traded companies. In China, AI’s development track is tethered to the grand ambitions of government.

Voluminous amounts of data are required to refine pattern recognition algorithms which is why Chinese face recognition systems like Megvii and SenseTime are so attractive to investor.

One big advantage for China: it doesn’t have the privacy and security restrictions that might hinder progress in the US.

Some of the activities that China is pushing are activities like China requiring all foreign companies to store Chinese citizens’ data on servers within Chinese borders. An AI-powered Social Credit System, was developed to engineer a problem-free society. The Global Energy Interconnection is another strategy used by China. It has already figured out how to scale a new kind of ultra-high-voltage cable technology that can deliver power from the far western regions to Shanghai. China’s AI push is part of a coordinated attempt to create a new world order led by President Xi.

What happens to society when we transfer power to a system built by a small group of people that is designed to make decisions for everyone?

Mind and machine: A very brief history of AI

Dr. Benjamin Bloom published in 1956 Bloom Taxonomy, which outlined learning objectives and levels of achievement observed in education. The foundational layer is remembering facts and basic concepts, followed in order by understanding ideas; applying knowledge in new situations; analyzing information by experimenting and making connections; evaluating, defending, and judging information; and finally, creating original work.

The wiring of modern AI is based on a long evolutionary trail extending back to ancient mathematicians, philosophers, and scientists. Aristotle invented syllogistic logic and our first formal system of deductive reasoning. Euclid created the first algorithm. In 1560, a Spanish clockmaker named Juanelo Turriano created a tiny mechanical monk as an offering to the church. It was first automation, a mechanical representation of a living thing. Leibniz examined the idea that the human soul was itself programmed, arguing that the mind itself was a container. Leibniz’s theoretical step reckoner laid the groundwork for more theories, which included the notion that if logical thought could be reduced to symbols and as a result could be analyzed as a computational system, than everything could be reduced to bits. English mathematician Ada Lovelace and scientist Charles Babbage invented a machine called the “Difference Engine”. George Boole invented Boolean algebra as a way of simplifying logical expressions by using symbols and numbers. Claude Shannon had figured out that computers had two layers: physical (the container) and logical (the code).

We too are containers (our body), programs (autonomous cellular functions), and data (our DNA combined with indirect and direct sensory information).

The link between intelligent computer systems and autonomous decision-making became clearer once John von Neumann published massive treatise of applied math. Together with Oskar Morgenstern in 1944 they explained game theory and revealed the foundation of all economic decisions.

In 1955 Marvin Minsky, John McCarthy, Claude Shannon and Nathaniel Rochester proposed a workshop in Dartmouth to find out if AI can be developed. But there wasn’t enough time and money to evolve AI from theory to practice.

Frank Rosenblatt created a system called Perceptron. It was the first artificial neural network (ANN).

AI winter is a known period that followed the hype after Dartmouth and it lasted three decades.

What we know by 2011 was that AI now outperformed humans during certain thinking tasks because it could access and process massive amounts of information without succumbing to stress.

Cognitive scientist Geoff Hinton and computer scientist Yann Lecun and Yoshua Bengio each believed that neural net-based systems would become the basis for what artificial intelligence would become.

All of the advancements being made in America weren’t going unnoticed in Beijing. The number of scientific papers on AI published by Chinese researchers more than doubled between 2010 and 2017.

There are two parallel tracks AI is now moving along: China, alarmed, throws money and people at making domestic products more competitive, while in the US, their expectations are that fantastical AI products will soon hit the marketplace.

Google Brain revealed that they built an AI that’s capable of generating its own AI’s – AutoML.

Baidu figured out how to transfer skills from one domain to another. It’s an easy task for humans, but a tricky one for AI.

The insular world of AI’s tribes

The future of AI is being built by a relatively few like-minded people within small, insulated groups. Those working within AI belong to a tribe of sorts. The problem with tribes is what makes them so powerful. The more connected and established a tribe becomes, the more normal its groupthink and behavior seems. AI has the mind of its tribe, prioritizing its creators’ values, ideals, and worldviews. But it is also starting to develop a mind of its own.

AI’s tribe has a familiar, catchy rallying cry: fail fast and fail often.

That the senior leadership of Google, Apple, Amazon, Facebook, Microsoft, and IBM don’t accurately represent all Americans could be said of the companies in any industry. The difference is that these particular companies are developing autonomous decision-making systems intended to represent all of our interests. Talking about diversity – asking for forgiveness and promising to do better – is not the same thing as addressing diversity within the data-bases, algorithms, and frameworks that make up the AI ecosystems.

The truly diverse team would have only one primary characteristic in common: talent.

In North America AI tribes focus is on hard skills, like master of the R and Python programing and other skills. The Big Nine uses AI-powered software to sift through resumes, and it’s trained to look for specific keywords describing hard skills. As the tribe scales, it’s expanding withing a bubble and bringing out some terrible behaviors.

China’s AI tribes begin at universities too, where there is even more focus on skills and commercial applications. China’s AI development is their space race.

Baidu got started at a 1998 summer picnic in Silicon Valley. John Wu, Robin Li and Eric Xu formed it. In 2012, Baidu approached Andrew Ng, a prominent researcher at Google’s Brain division. Baidu’s conversational AI platform is called DuerOS. They have autonomous driving platform called Apollo and source code is publicly available.

Alibaba Hema is automated, cashless multifunctional retail operation combining groceries: a fast, casual food market; and delivery service.

Tencent was founded in 1998 by Ma Huateng and Zhang Zhidong. In 2011 they launched WeChat. They created digital assistant Hiaowei, a mobile payment system Tenpay, a cloud service Weiyun and a movie studio Tencent Pictures. They also invest in UK based health startups. In 2018 they were the first Asian company to surpass a market value of 500 billion USD.

China stopped hiding its capabilities and started its global expansion. They are spending 150 billion USD per year on infrastructure projects in 68 countries around the world.

Not only is the government investing in the BAT, it’s protecting them from the world’s most formidable competition. BAT companies are at the heart of the government’s 2030 plan, which rely heavily on their technologies: Baidu’s autonomous driving systems, Alibaba’s IoT and connected retail systems, and Tencent work in conversational interfaces and health care.

China’s will leverage its advancements in AI and economic stimulus to modernize its military, giving it and advantage over Western nations. In the future, wars will be fought by code.

China’s is using AI in an effort to create and obedient populace. What if China starts influencing its Belt and Road Initiative partners such that one its primary exports is its national social credit score systems.

China’s talent pipeline is draining researchers back into the mainland as part of its Thousand Talents Plan.

If Ai is China’s space race, it’s currently positioned to win, and to win big.

In US, the six companies part of Big nine, do function as mafia in the purest (but not pejorative) sense: it’s a closed supernet work of people with similar interests and backgrounds working within one field who have a controlling influence over our futures. Author calls them G-MAFIA.

General AI will need different type of computing. One solution is edge computing. The others are connected with more power for better training models. Google created its own custom silicon, called Tensor Processing Units (TPUs). IBM also developed its own chip to push neural nets further. The new kind of chip uses two kinds of synapses, one for long-term memory and the other for short-term computation.

As you buy more stuff – like mobile phones, connected refrigerators, or smart earbuds – you’ll find that the G-MAFIA has become an operating system for your everyday life. Humanity is being made an offer that we just can’t refuse.

AI serves three masters: Capitol Hill, Wall Street and Silicon Valley.

The G-MAFIA business model is predicated on surveillance capitalism. We agree to constant surveillance in exchange for services.

A thousand paper cuts: AI’s unintended consequences

Because AI stands to make a great impact on all of humanity, the Big Nine’s values should be detailed explicitly.

What’s missing is a strongly worded declaration that humanity should be at the center of AI’s development and that all future efforts should focus on bettering the human conditions.

Computing, like all fields in technology or elsewhere, reflects the worldview and experiences of the team working on innovation.

In the absence of codified humanistic values within the Big Nine, personal experiences and ideals are driving decision-making.

AI might be inspired by our human brains, but humans and AI make decisions and choices differently. Inability to observe how AI is optimizing and making its decisions is what’s known as the ‘black box problem’. Commercial AI applications are designed for optimization – not interrogation or transparency. The optimization effect sometimes causes brilliant AI tribes to make dumb decisions.

Our expectations are constant, big wins are a huge distraction for those people charged with completing their research and testing it in a reasonable amount of time. We’re rushing a process that can’t keep pace with all the exuberant promises being made well outside of AI’s trenches where the actual work is being done.

AI’s tribes, optimizing machines for short-term goals, can make life uncomfortable for a lot of humans.

In behavioral science and game theory, a concept known as “nudging” provides a way to indirectly achieve a certain desired behavior and decision. Nudging is widely used throughout all of our digital experiences.

Building AI means predicting the values of the future. Our values aren’t static. How do we teach machines to reflect our values without influencing them. The G-MAFIA has started to address the problem of guiding principles through various research and study groups. Within MS a team called FATE – for Fairness, Accountability, Transparency and Ethics in AI.

What is not on the table, at the G-MAFIA or BAT, is optimizing for empathy. Take empathy out of the decision-making process, and you take away our humanity.

From here to artificial superintelligence; The warning signs

The evolution of artificial intelligence, from robust systems capable of completing narrow tasks to general thinking machines is now underway.

We mistakenly treat artificial intelligence like a digital platform, similar to the internet, with no guiding principles or long-term plans for its growth. We have failed to recognize that AI has become a public good.

AI is rapidly concentrating power among the few, even as we view AI as an open ecosystem with few barriers. The future of AI is being built by two countries, America and China. There is a reason for this concentration of power: it’s taken several decades of R&D and investment to get AI where it is today.

Not even AI could tell us exactly what will AI look like in the future. Author developed a methodology to model deep uncertainty. It’s a six step process that surfaces emerging trends, identifies commonalities and connections between them, maps their trajectories over time, achieve a desired future. The first half of the methodology explains the what, while the second half describes the what-if. The second half, more formally, is called ‘scenario planning’.

Scenario planning originated in the 1958 by Herman Kahn, futurist at the RAND Corporation. Scenarios are a tool to help us cope with a cognitive bias behavioral economics and legal scholar Cass Sunstein calls “probability neglect”.

We need a set of public-facing scenarios that describe all the ways in which AI and the Big Nine could affect us collectively as AI progresses from narrow applications to generally intelligent systems and beyond. Three scenarios are optimistic, pragmatic and catastrophic.

Getting from where we are today to widespread AGI means making use of “evolutionary algorithms”. Evolutionary algorithms with the power to mutate will help advance AI on its own, and that’s tempting possibility, but one with a cost.

We never consider that humans might someday find ourselves dumber than technology. Average human’s IQ scores have been rising at a rate of three points per decade. IQ method was developed in 1912 by German psychologist William Stern. As our intellectual ability improves, so will AI’s.

AI is extensible in ways that humans aren’t without changing the core architecture of our brains. Super intelligent AI would likely make decisions in a nonconscious way using logic that’s alien to us.

The coming “intelligence explosion” describes not just the speed of supercomputers or power algorithms, but the vast proliferation of smart thinking machines bent on recursive self-improvement. Former DARPA manager Gill Pratt argues that we’re in the midst of a Cambrian explosion right now – a period in which AI learns from the experience of all AIs, after which our life on Earth could look dramatically different than it does today.

  • An optimistic scenario is asking what happens if the Big Nine decide to champion sweeping changes to ensure AI benefits to ensure AI benefits all of us.
  • Pragmatic scenario is describing how the future would look if the Big Nine only make minor improvements in the short term.
  • The catastrophic scenario explains what happens if all of the signals are missed, the warning signs are ignored, we fail to actively plan for the future, and the Big Nine continue to compete against themselves.

One probable near-term outcome of AI and a through-line in all three of the scenarios is the emergence of what I’ll call a ‘personal data record’ or PDR. This is a single unifying ledger that includes all of the data we create as a result of our digital usage. But it would also include other sources of information. You’re using a proto-PDR now. It’s your email address.

Thriving in the third age of computing: the optimistic scenario

We recognize why China has invested strategically in AI. China isn’t trying to tweak the trade balance: it is seeking to gain an absolute advantage over the US in economic power, workforce development, geopolitical influence, military might, social clout, and environmental stewardship.

In US government increases spending on AI. G-MAFIA creates a coalition that cooperate for benefits of all. All other Western countries are following the same scenario. Taking inspiration from Greek mythology and the ancestral mother of Earth, they form GAIA: the Global Alliance on Intelligence Augmentation. Locked out oof GAIA, China finds its global influence waning.

Individuals own their own PDRs, which are as private or as public as we want them to be and are fully interoperable. The G-MAFIA are the custodians of AI and of our data, but they own neither.

Middle-class homes rely on AI to make life a little easier. Devices, platforms, and other services are interoperable even between countries.

The G-Mafia coalescing around a single standard for personal data records ushered in a set of standardized electronic medical record formats, protocols, frameworks, and user interfaces. As a result, the health care system is far more efficient.

Health care can be offered anywhere not only in hospitals.

Evolutionary algorithms are smarter solution for online daters, so dating and sex get better.

AI is complementing human creativity.

AI is helping organizations of all stripes be more creative in their approach to management.

IBM has brough Socrates back to life as an AI agent. The Socratic AI system, which evolved out of Watson. IBM’s Socratic AI is useful ally with newsrooms, helping journalists further investigate their reporting as they discuss a story’s possible angles.

As it evolves, AI is helping us mature into better humans. With the G-MAFIA, federal government, and GAIA taking active roles in the transition from artificial narrow intelligence to artificial general intelligence, we feel comfortably nudged.

Rather than judging an AGI on whether or not it could “think” exactly like we do, the AI community finally adopted a new test to measure the meaningful contributions of an AGI, which would judge the value of cognitive and behavioral tasks we could not perform on our own.

Making a valuable contribution involves many different skills:

  • Making educated guesses.
  • Correctly extracting meaning from words, pauses, and ambient noise.
  • Using experience, knowledge, and historical context for understanding.
  • Reading the room.

In this scenario our genomes are sequenced at birth and information can be used for health treatments or matchmaking, if we choose to share it.

While newspapers in print are gone, the news media has adopted AGI as a means for distribution. AGI hackers – which most often are other AGIs, are an ongoing irritant.

Brain-to-machine interfaces are viable option.

Learning to live with millions of paper cuts: the pragmatic scenario

We’ve acknowledge AI’s problems but along the way decided to make only minor tweaks in the developmental track of AI.

The absence of a coalition and coherent national AI strategy foment paper cuts – millions and millions of them – which over time start to bleed.

We don’t give the Big Nine time for errors. Data sets are not mature yet.

Another paper cut is that some Ais have figured out how to hack and game their own systems. So we have some “reward hacking” where a system will exploit evolutionary and machine learning algorithms to win using trickery and deception.  

Another paper cut: malicious actors can inject poisonous data into AI’s training programs.

Another paper cut: when complex algorithms work together, sometimes they compete against each other to accomplish a goal, and that can poison an entire system.

Amazon now owns e-commerce and our homes, while Google owns search, location, personal communications, and the workplace. Microsoft owns enterprise cloud computing, while IBM owns enterprise level AI applications and applied health systems. Facebook owns social media, and Apple makes hardware.

Google builds their ultimate OS for everything. Apple and Amazon partner exclusively to build out a comprehensive OS that will power hardware made for both companies. This led to two poles consolidation of AI field.

Smartphones are replaced with wearable devices. Applezone and Google have incentivized you to lease, rather than to own, all of the equipment and that subscription includes access to your PDR.

Baidu was first with its “neuroenhancing headband”.

Nagging is the new nudging as Google and Applezon unintentionally harass you into better health.

Google launces Calico in cooperation with IBM Watson. Apple launches partnership with Amazon e-pharmacies. Health field is also duo-polized.

In most large companies, the previous hierarchy has collapsed into two tiers of workers: skilled and senior management.

Government is not ready for loss of jobs for knowledge workers and creativity industry.

AI crime wave is another problem.

China is practicing a new colonialism. The six of Big Nine from US are now five, IBM and Google partnership and Amazon and Apple partnership are the strongest, Microsoft is supporting legacy systems and Facebook is out.

ASI is developed by China and is now controlling everything from transportation, bank, health care system, light switches and refrigerators.

The Rengong Zhineng dynasty: The Catastrophic scenario

We have closed our eyes to AI developmental track. We missed all the signals, we ignored the warning signs, and we failed to actively plan for the future.

The G-MAFIA are the sole owners of your personal data record, which grows to encompass every aspect of your human existence. Algorithms make decisions for your using all the data.

China is in expansionist mode. Chinese citizens are learning to live with automated monitoring and consequences of stepping out of line.

Washington views its relationship with the G-MAFIA as transactional.

Microsoft and IBM are still around, but they are minor players in the AI space. Facebook is quietly going the way of MySpace.

We are Google families, Apple families or Amazon families.

By choosing Google, Apple or Amazon, you are forced to align your family values with the values of the corporation. Apple families are rich, maybe a little less AI-savvy, and live in fancy houses. Google families might be rich and techy, or middle class and fine with marketing, or complacent enough that having a lot of choices in life doesn’t matter all that much. There is no way to sugarcoat Amazon families; they’re poor, even if they have free access to cool gadgets.

In America, society is beginning to feel uncomfortably Huxleian, as we acquiesce, get married, and have babies with our fellow Apples, or Google Blues, or Amazons.

AI bestows immense economic power on Google, Apple, and Amazon and unimaginable geopolitical and military power on China.

A concentration of wealth has allowed the GAA (Google, Apple and Amazon) to achieve amazing breakthroughs in health. Nanorobots now come in many different forms.

Large corporations are led now by CAIOs, Chief AI Officers, who calculate strategic risk and opportunity.

Pebbles and boulders: How to fix AI’s future

GAIA nations should collaborate on frameworks, standards, and best practices for AI. While it is unlikely that China would join, an invitation should be extended for CCAP leaders and for the BAT to join.

GAIA should create a new social contract between citizens and the Big Nine.

GAIA should consider a framework of rights and balances individual liberties with the greater global good. Any framework should include the following principles:

  • Humanity should always be at the center of AI’s development.
  • AI systems should be safe and secure.
  • The Big Nine must prioritize safety above speed.
  • If an AI system causes harm, it should be able to report out what went wrong.
  • AI should be explainable.
  • Everyone in the AI ecosystem must recognize that they are making ethical decisions all the time.
  • The Human Values Atlas should be adhered to for all AI projects.
  • There should be published, easy-to-find code of conduct governing all people who works on AI and its design, build, and deployment.
  • All people should have the right to interrogate AI systems.
  • The terms of service for and AI application, should be written in language plain enough that a third grader can comprehend it.
  • PDRs should be opt-in and developed using a standardized format.
  • PDRs should be decentralized as much as possible, ensuring that no one party has complete control.
  • To the extent possible, PDRs should be protected against enabling authoritarian regimes.
  • There must be a system of public accountability.
  • All data should be treated fairly and equally.

In 2017 alone G-MAFIA spent a combined 63 million USD on R&D, nearly five times the US government’s total science and tech research budget.

AI progress is happening weekly, which means that any meaningful regulations would be too restrictive and exacting to allow for innovation and progress.

The Big Nine need our data. However, they should earn, rather than assume, our trust.

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