Augmented Mind: AI, Superhumans and the Next Economic Revolution



Alex Bates—CEO, founder, and investor of multiple AI startups—examines how artificial intelligence and intelligence augmentation will create superhumans in his new book, Augmented Mind: AI, Superhumans and the Next Economic Revolution.

Knowledgeable but accessible, Augmented Mind explains how the combination of AI with human intelligence—Intelligence Augmentation—has revolutionary potential. After a decade on the front lines of AI research and implementation facilitating the collaboration of humans and AI, Alex saw firsthand how highly complementary they were. He then created a global mastermind network of applied AI technologists and researchers to get more perspectives on these issues, with membership from Caltech, MIT, Harvard, Oxford, UCSD, USC, and other leading institutions and companies. Augmented Mind represents the culmination of this research and outlines a framework for creating hybrid solutions by combining AI, machine learning, and human intuition that can predict the future, improve our social lives, eliminate scarcity, and provide a clear road map to abundance and prosperity.



I was thirteen, sitting in front of my family computer with minutes

left in my turn online, when I stumbled across an article

titled “Could a Machine Think?” It was the summer of 1990. A

few weeks later, the president of the United States would declare

the ’90s the Decade of the Brain, a decade that would see the

emergence of the internet and the world wide web as well as a

new generation of neuroscientists and computer scientists who

were going to change the world. Th e article mentioned a growing

research area: artifi cial neural networks, or computer programs

that imitated how our brain learns. Th e networks could automatically

learn from examples by modifying their synaptic weights.

I was mystified. How could they learn automatically? Self-learning?

Was this fi nal proof of Cartesian dualism, where the mind

arose from a magical realm outside our physical universe? It

seemed like sorcery.

My mom called from downstairs. “Alex, it’s your brother’s

turn!” I was allowed only two hours each day, to limit screen time

and get us outdoors. It was never enough. A revolution was in

progress. Telephone networks had given rise to something called

the internet. Suddenly, anyone with moderate technical prowess



could travel the digital world and access anything. Some things

that were meant to be accessed. Some that were not.

Th e walls had come down. It was the cyber version of the Wild

West. Th e new frontier was open. My parents couldn’t aff ord subscriptions

to paid content, but it didn’t matter. My brother, sister,

and I were able to access anything we wanted. Books, articles, schematics.

Searching, exploring, discovering, liberating content. Suddenly,

teenagers had power. We were able to do things our parents

couldn’t understand. Download technical manuals and build things

like blue boxes that could make free phone calls (illegally, of course).

Communicate online with other hackers, sharing tips, tricks, and

failures. A global community was born. For a socially awkward

teenager, it was paradise.

Th e computer fan started to vibrate, like it was about to overheat

and shut down. It was a sweltering summer day in my parents’

basement in Portland, Oregon. Without air-conditioning, the humidity

had fused my shirt to my skin. I barely noticed. I was plugged

in to the family computer in a race against time.

But my time was up. My younger brother approached with a

cruel grin, recognizing my anguish and relishing the moment as

he took control of the family computer. I knew the drill: if I resisted,

he would pull the power cord, causing me to lose all my work.

I was banished, cut off from the cyber world, until tomorrow. An


As I left the room, my mind returned to the article about thinking

machines. A seed had been planted. I headed for Portland’s

iconic bookstore, Powell’s, and picked up an introductory textbook

on neural networks. Th ey seemed magical, and I needed to intuitively

understand how they worked. I sat down and manually calculated

the fl ow of data through a neural network, how it would



learn from a mistake, calculating an error signal and updating the

artifi cial synaptic weights all the way back to the input layer of the

neural network (a process called back propagation). Th e numbers

all checked out. Magic.

I had been intrigued by the mind and brain for years, liberating

philosophy books from my grandparents’ bookshelves when I visited

them in New York. William James and the stream of consciousness.

David Hume and the rational basis of behavior. Oliver Sacks

and localized neurological disorders. Early attempts to understand

understanding. Understanding ourselves as a way to understand

the universe and its purpose, if there was one.

I embarked on an obsessive journey into the world of neural

networks. Or artifi cial neural networks, as they were called back

then. When I enrolled in the University of Oregon’s Clark Honors

College, I found every course a lens into the mind and brain. I double-

majored in computer science and mathematics, with an unrecognized

third major in neuroscience. Several professors observed

my obsession and invited me to do research at their laboratories. In

a biophysics lab, we conducted patch clamp recording to explore

how the fruit fl y (drosophila) nervous system worked. In a neurobiology

lab, we explored how tiny neural networks (with a few hundred

neurons) gave rise to complex behaviors. In a psychology lab,

we explored inhibitory mechanisms involved in memory and cognition.

In the summers, I returned to Portland to do research at a

private institute focused on biomedical diagnostics.

At graduation, my advisors were surprised to hear that I was

taking a leave of absence from academics to enter private industry.

I had huge respect for the academic world and the rigor of peer-reviewed

publishing and scientifi c debate. I had published fi ve

peer-reviewed publications in computational diagnostics, a fi eld

that used computing techniques to detect the early onset of biomedical

disorders, such as glaucoma so that doctors could inter4


vene before blindness set in. But I felt I needed to leave to give myself

time to build, to manifest, to work on solutions out in the messy

real world, tackling all the challenges that came with it.

I fi gured I would return to academics at some point. I decided

to take a day job, so I could pay off college debt and work on secret

technology projects at night. I moved to San Diego and became a

soft ware engineer doing fi nancial analytics at Teradata, a pioneer in

massively parallel processing (MPP) technology, working on the

largest databases in the world, from those in three-letter agencies to

those in large banks. Data is fuel that helped spark the rise of AI.

Th e world of tech entrepreneurship was intoxicating. I had

been inspired by the stories of mainstream leaders like Steve Jobs

and Bill Gates as well as lesser known ones like the Monk and the

Riddle. And combining tech entrepreneurship and neural networks

was the most addictive drug I had ever encountered. You could create

technology and put it out in the real world, with no layers in


At this point in the early 2000s, we were in the middle of a major

recession and an “AI winter.” An AI winter is a period when the

funding in artifi cial intelligence dries up, with university researchers

losing grants, companies going out of business, and the general

public losing interest. It’s like a recession that impacts one specifi c

area. I didn’t let any of that distract me and never lost faith in the

immense power of neural networks and AI.

Eve rything Is Connected

I would occasionally escape the world of technology to explore

nature on camping trips or during underwater diving adventures. It

was on one of these trips that I met Paul Rahilly. His background

was mechanical engineering and equipment reliability, and we talked

about all the problems big energy companies were having with



equipment breakdowns. I had never planned to work in the industrial

realm, but I was drawn by the gravity of the problems he described.

People were getting killed on a regular basis, along with the

environmental catastrophes we saw in the news.

He and I founded a company called Mtell, which applied neural

networks to industrial diagnostics, monitoring heavy machinery

in manufacturing and process industries. Our technology was

similar to the precogs in the movie Minority Report—we predicted

events (equipment failure) and then prevented them from happening

with intelligent interventions (the maintenance equivalent of

Tom Cruise, Chief of PreCrime).

We launched the company in the middle of an AI winter, and

investors thought we were crazy. Industrial soft ware wasn’t sexy. AI

and neural networks were bad words. Apparently, combining two

unpopular categories created something that was even more unsexy

for investors.

But we were convinced we were doing something that would

last—and, more than that, impact the world. Our mission statement

was “creating a world that doesn’t break down”—a mission

that most industrial companies laughed at, until the Gulf oil spill of

2010 involving the Deepwater Horizon off shore rig. Eleven lives

lost, many of the bodies never found, bereaved families left to

search for closure. On the fi nancial front, there was $45 billion in

fi nancial losses for BP. We might never know the extent of the damage

to the Gulf underwater ecosystem. It was a wakeup call.

At Mtell headquarters, we had a somber gathering that day,

asking if we could have prevented the catastrophe. It was a watershed

moment for us. We decided to focus on upstream drilling

and see if we could make a difference. We knew from the

inside that these companies were using old-school maintenance

techniques and ignoring the deluge of sensor data that was now



available for their equipment. It was too much data for their operators

and maintenance techs (who were already overworked

and overwhelmed) to analyze. We figured our neural network

technology could help.

One of the biggest challenges in these environments is that everything

is connected. If one part fails, it can bring down a system

that could cost millions of dollars per day in lost work, fi nes, and

cleanup, not to mention loss of life and environmental damage.

What Mtell developed was a system that could predict failures in

these complex facilities up to six months before they would fall

apart and isolate exactly what would go wrong and where. So, you

could replace that part that would only cost fi ve to six fi gures, and

the business would be back to producing product in a matter of

days. Without this solution, a part could fail, no one would know

about it, and there’d be leaking oil or fl ammable gas, leading to all

kinds of environmental damage and loss of human lives.

Succes s Is About People

Like all start-ups experience, life became an unpredictable roller

coaster. On a typical week, I would ricochet from worrying about

mounting debt, to celebrating good news from a major customer, to

panicking about a press release by a gargantuan competitor. Th ere

were many days when our eff orts seemed futile, but we took a deep

breath and kept going.

Our journey culminated in Mtell being acquired by AspenTech

(NASDAQ: AZPN), a publicly traded company that spun out of

MIT in the 1980s and had grown into the world leader in process

optimization. With AspenTech’s depth in process and our depth in

machines used in the process, combining the two approaches was

an exciting prospect. We sold for approximately $40 million—two

years aft er raising $1 million in venture capital.



Th e decision to sell is never an easy one. We were starting to

gain traction with our soft ware and could have raised another

round of venture capital, taking more time to grow the company

independently. But having spent a decade in the industrial sector, I

was ready to embark on the next chapter. I stayed at AspenTech for

two years aft er the acquisition to integrate the companies, watching

their stock price double to an $8 billion market cap. It was amazing

to hear Wall Street analysts give a stamp of approval to AspenTech’s

AI strategy, with Barron’s highlighting AspenTech as one of the top

three AI investments outside of FAANG (Facebook, Apple, Amazon,

Netfl ix, Google) in an article published in June 2018.

Our success was ultimately about people. Everyone on our

team understood that our clients worked in extreme environments,

going to work every day not knowing if they were going to

make it home. We made an eff ort to fl y every new hire out to a

drilling rig to see, hear, feel what those extreme environments

were like. To feel a top drive on a rig start to vibrate out of control

and get a pit of fear in your stomach like during the worst turbulence

you’ve ever experienced on a plane. And to observe the intelligence

and creativity of the operators, some of whom had only

a high school education but knew how their rig worked better

than PhD chemical engineers.

Aft er a decade of my working with hundreds of customers on

AI adoption, a key insight emerged: that humans’ role was essential.

Humans are the unsung heroes of the AI revolution, unrecognized,

both in terms of media coverage and in terms of receiving research

and development . I saw fi rsthand the breakthroughs, the spark of

something great, when people started working with AI as a team

and when AI was able to incorporate the unique intuition and insights

of the human technicians who brought decades of experience

with a facility and its equipment and had developed instinctive

situational awareness.



When people saw the AI as an ally and not as a foe, they began

to use it to have their own hero moments, leading to promotions

and discussing with pride the AI agents that they had created using

our soft ware. Th ere was a metals and mining site that avoided environmental

damage and associated $10 million environmental fi nes.

A rail company that avoided engine failures and track interruptions,

worth hundreds of millions of dollars of savings. Our clients

were saving lives and creating heroes at their facilities.

Forward Motion

AI was back in a big way. Th e world had gone from an AI winter

to a global AI arms race. Th ere were some amazing breakthroughs

in AI in the last decade, including the deep-learning revolution.

Th ere were also tech luminaries talking the threat of

artifi cial superintelligence and the need for regulation. But with all

the recent advances and fears, the human element was lost in the

shuffl e. Th e unique and essential role that humans played in the actual

AI projects being used around the world. Real-world, operationalized,

applied AI deployments are messy. Unlike idealized

gaming environments, we live in world of complexity, of nonlinear

interactions that make things like prediction and causal analysis

diffi cult. Yet this was just laying the groundwork for the next phase:


About the author

ALEX BATES spent the last decade bringing A.I. and machine learning to the forefront of the industrial market. He led DARPA-funded research in neural networks; applied analytics for the world’s largest data warehouses; become an AI angel investor and member of Peter Diamandis’ Abundance 360 Network. view profile

Published on May 15, 2019

Published by Neocortex Ventures

50000 words

Genre: Technology

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