An Elementary Introduction to the Wolfram Language - Stephen Wolfram
The Physicist Turned a Successful Entrepreneur€¦ · Wolfram Alpha: The Physicist Turned a...
41
The Physicist Turned a Successful Entrepreneur THE STORY ABOUT STEPHEN WOLFRAM by Gennaro Cuofano FourWeekMBA.com
Transcript of The Physicist Turned a Successful Entrepreneur€¦ · Wolfram Alpha: The Physicist Turned a...
The Physicist Turned a Successful
EntrepreneurTHE STORY ABOUT STEPHEN WOLFRAM
by Gennaro Cuofano
FourWeekMBA.com
Wolfram Alpha: The
Physicist Turned a
Successful Entrepreneur -
The Four-Week MBA
Wolfram Alpha: The Physicist Turned a
Successful Entrepreneur
Why Wolfram Alpha?
Among my passions, I love to check financial data about companies I like.
Recently I was looking for a quick way to get reliable financial information for
comparative analyses. At the same time, I was looking for shortcuts to perform
that analysis.
I was researching for myself. I thought why waste so much time on scraping
financial reports? While surfing the web, I was looking for a solution and a term
popped to my eyes “computational engine.”
Wolfram Alpha! That search opened me a universe I wasn’t aware of. Yet that
universe wasn’t only about a fantastic tool I learned to use in several ways. I
found out the most amazing entrepreneurial story. That is how I jumped in and
researched as much as I could about this topic! Why is this story so remarkable?
Imagine a kid that as many others, is struggling at arithmetic. Imagine that same
kid at 12 years old building a physics dictionary and by the age of 14
drafting three books about particle physics. A few years later at 23, that kid, now
a man gets awarded as a prodigious physicist. We could stop this story here,
and it would be already one of the most incredible stories you’ll ever hear.
Yet this is only the beginning. In fact, what if I told you, that same person turned
into a successful entrepreneur, which built several companies and a whole new
science from scratch! (Let’s save some details for later)
That isn’t only the story of Wolfram Alpha, a tool that I learned to use and
cherish. That is the story of one of the smartest people of our century, a shrewd
entrepreneur, and polymath, which turned to influence and being influenced by
people like Steve Jobs and Benoit Mandelbrot.
This is the story of Stephen Wolfram.
Writing this post for me has been a pleasure and torture. On the one hand, I
jumped into Stephen Wolfram‘s videos, books, and articles. The more I found
out, the more I wanted to know. It was an endless loop.
However, the unbounded intelligence of Stephen Wolfram is such that trying to
circumscribe it in one post, it is like trying to close the universe in a box. Yet
more than a post this is an e-book, and I hope you’ll enjoy reading it as much as I
enjoyed writing it!
Before we get to the practical matters related to Wolfram Alpha, I deconstructed
his life and ideas.
The Quest to Unfold Complexity: who is
Stephen Wolfram?
Because, after all that’s what technology is all about: setting upsystems to achieve human purposes
Stephen Wolfram in Computation and the Future of the HumanCondition
Stephen Wolfram is the founder of Wolfram Alpha, a powerful computational
engine (more about what a computational engine is later on). Yet the path that
brought Stephen Wolfram to the launch of its latest creature looked more like a
life-long quest.
Born in London in 1959. Stephen Wolfram showed incredible qualities since a
young age. In fact, by age 15 he had drafted three physics books and his first
scientific paper. By the age of 21, he had received an important fellowship, which
launched him on a life-long quest: understanding complex systems by stripping
out their complexity.
Yet Stephen Wolfram approach was unique for a couple of reasons, I believe.
First, he understood that computation was the most compelling discovery of the
past century. Therefore, he focused since the beginning of using machines to
enhance human abilities.
Second, he believed that to understand complexity he had to look at natural
processes to find the most essential programs that mother nature used by time
to time to run the show of life.
Yet Stephen Wolfram didn’t spend his life as a hermit (except when, for practical
purposes, he had to put together a book, which would become a New Kind of
Science) or isolated from the world. Instead, he understood the importance of
more practical matters, such as managing people.
The quest to complexity started from the study of cellular automata, which
launched him to formulating a computing theory of everything. So, what are
cellular automata?
Computing a theory of everything
So I want to talk today about an idea. It’s a big idea. Actually, I thinkit’ll eventually be seen as probably the single biggest idea that’semerged in the past century. It’s the idea of computation. Now, ofcourse, that idea has brought us all of the computer technology wehave today and so on. But there’s actually a lot more to computationthan that. It’s really a very deep, very powerful, very fundamentalidea, whose effects we’ve only just begun to see.
Stephen Wolfram TED Talk
Cellular automata are programs that follow simple deterministic rules but show
complex behaviors, the more steps they take along their evolution. What does
that mean and what makes them so valuable from a scientific standpoint?
Imagine starting playing a game with fundamental and straightforward rules.
Chances are you’ll start projecting yourself at the end of the game, foreseeing a
particular scenario. However, as much as you would love to imagine, even if you
had Albert Einstein‘s or Salvador Dali’s ability to day-dream you will never
manage to foresee the complex behaviors that will arise along the way from
those trivial programs.
How is that possible that from such simple programs spring up so much
complexity of behavior? The answer lies in rule number 30! Let’s dive a bit into it
to see how it works.
Rule Number 30: simplicity as the mother of
all creations
The weather has a mind of its own” isn’t such a primitive thing to say:the fluid dynamics of the weather is just as sophisticated as somethinglike a brain
Stephen Wolfram, on blogs.scientificamerican.com
It probably was the summer of 1985 – as recalled in Idea Makers – when Steve
Wolfram stumbled upon something that would leave a mark on his life and guide
him toward a life-long quest. What was that?
It all started from rule number 30. As someone that found computation as the
most important discovery of the past century Stephen Wolfram didn’t waste time
doing calculations. Rather he let computers run all the possible programs that
could be found in nature, as simple cellular automata and look at what behaviors
they would show.
That is what happened that summer in 1985. Cellular automata are self-
replicating systems showed as a grid of changing cells. Each cell in the grid
reacts based on the neighboring cells. In other words, you start from a grid like
the one below
A simple rule determines whether a cell will be on or off in the next generation
based on the configuration of its neighborhood. For instance, if a cell is white,
and the one on its left and right are white, then the cell stays white. Instead, if a
white cell falls in-between two black cells, then it turns black. And so on for all
the possible arrangements.
The possible configurations on a grid comprised of three cells as you can see
from the red rectangle above are eight. But the possible combinations, given
the fact that each cell can be either black or white (in a binary state) can be 256
– 2 ^ 8 (therefore the two possible states, black or white, at the power of the
eight possible combinations).
We start by letting the cellular automaton take 20 steps,
We can see already a more complex behavior so far. Yet nothing exciting.
When we start taking additional steps, the more steps we take, the more
complexity arises.
That is what we get after 100 steps. As you can see the patterns created by a
simple cellular automaton starts to become kind of interesting.
Source: blog.stephenwolfram.com
When in the 1980s Stephen Wolfram observed this kind of behavior he was
shocked. That kind of shock that changes your life, the aha moment! In fact, the
more steps he let rule 30 take, the more complexity arose out of simple
deterministic rules!
The fact that simple rules could replicate nature is pretty counter-intuitive, yet
quite effective.
Cone Snail, Photographer: Richard Ling Cellular Automata Rule 30
Source: artfail.com
The most powerful part is that to build such complexity you don’t need a super
powerful computer, but only a three-digit number grid that follows super simple
rules. Rule number 30 above all, was the beginning of a quest that would lead
Stephen Wolfram to formulate a New Kind of Science.
It also opens up a new way of thinking, where intelligence isn’t solely a human
thing, but it can be found anywhere in nature. Therefore, the complexity arising
from our brain isn’t different from what happens in nature. Both are described
well by computations. Before we dive more into what would become the
principles of Stephen Wolfram‘s book, A New Kind of Science, let’s dive more
into his life.
Before Wolfram Alpha
If you’ve been using the iPhone, chances are you’ve also been using Wolfram
Alpha all along. In fact, you may not know it, but your built-in intelligent
assistant, Siri, uses Wolfram Alpha‘s API to provide answers to any question.
Yet before we dive into the technical stuff, how did it all start? From studying
Stephen Wolfram‘s life, I understood one fundamental concept, which before
was a bit elusive for me. In short, technologies, ideas, and enterprises don’t
come in isolation. More often than not they spring up from people’s lives
context.
In other words, if you want to understand an idea or a technology better
probably the best place to start isn’t how it works but how it originated in that
person’s mind. Also before that, you may want to investigate what, who, where
and when inspired those ideas, if you want to understand the why.
As trivial as it may sound, I ended up discovering quite a few interesting facts by
using Stephen Wolfram‘s approach. The paradox is that his approach helped me
to better understand him as a person, therefore as an entrepreneur and scientist.
It all started from a life-long friendship with an incredible man, which for better
or for worse (according to your perspective) revolutionized our times, Steve
Jobs.
NeXT, Mathematica and the batch of
computers that built the Web
Source: Commons.Wikimedia.org
According to Stephen Wolfram, in his book Idea Makers, he first met Steve Jobs
back in 1987. At that time Steve Jobs was focused on building what would
become the first NeXT computer.
In fact, business magnate Ross Perot, founder of Electronic Data Systems, which
was sold to General Electric in 1984 for $2.4 billion had enough liquidity to make
risky investments. Therefore, in 1987 Perot invested $20 million, in Steve Jobs‘
startup, NeXT, which although valued about $125 million hadn’t yet released any
product.
In 1996 Apple bought NeXT for $429 million. After that, when Steve Jobs, in July
1997 took back the reins of Apple, NeXT experience played a vital role. In fact,
Steve Jobs installed his NeXT executive team at Apple.
Goin back to our story, at the time (1987) Stephen Wolfram was as busy in
figuring out the details of what would later become his first company,
Mathematica. It turns out that in a way or another Steve Jobs played a key role in
Stephen Wolfram’s company success and vice-versa.
Why is it called Mathematica?
Stephen Wolfram was undecided about how to call the company he was about to
launch. Among the names he had in mind – as he reported in Idea Makers –
there was either Omega or PolyMath. Yet one day by talking to Steve Jobs, he
told him “you should call it Mathematica.”
According to Stephen Wolfram, Jobs had a theory about company’s names. In
short, you start with a very generic concept, and then you romanticize it. That is
where Mathematica came from.
What is Mathematica?
Mathematica is a software that does scientific computations of any kind. From
data analysis to visualizations and much more, Mathematica laid the foundations
for what would later become Wolfram Alpha.
One interesting fact is that Mathematica walked hand in hand with NeXT. In fact,
Steve Jobs intuition made him realize that each NeXT should have been bundled
with Mathematica and that is what happened.
The most interesting part of the story – as explained by Stephen Wolfram in Idea
Makers – is that a batch of NeXT computers would later be given to the CERN of
Geneva. In other words, NeXT computers bundled with Mathematica happened
to be the computers used to develop the web!
Yet between Mathematica and the launch of Wolfram Alpha, Stephen Wolfram
put together the ideas he had observed throughout his scientific discoveries, in a
controversial book, A New Kind of Science.
A New Kind of Science
Stephen Wolfram‘s A New Kind of Science is a remarkable book inmany ways. It is certainly the most arrogant piece of science writing Ihave ever read. It also displays a jaw-dropping ignorance of some keyissues, of which more later. Yet, despite its shortcomings, it may be
the most important contribution to science this decade.
by Chris Lavers on theguardian.com
In 2002, Stephen Wolfram‘s book A New Kind of Science was out, and it was
quite controversial. That is also clear if you look at its Amazon reviews,
One thing I understood by looking at Stephen Wolfram‘s personality is either you
love him, or you hate him. More precisely either you think he’s a genius or you
think he’s mad. In fact, it is also funny to see how polarised are the feedbacks
about his book,
The book has as much 1 star reviews, then 5-star ones. You may think that those
one-star reviews are from people that were not erudite enough to comprehend
it, and vice-versa the five-star came from the scientific community. That is not the
case,
Throughout my research, I’ve come to “know” Stephen Wolfram at a personal
level (take it with a pinch of salt). Since the beginning, I tried to keep a neutral
view about him. Yet I found his ideas original and refreshing in many ways.
Of course, it can be argued that his ego is massive. However, if you look at his
life, you realize that he’s one of the greatest minds of our times. It is very hard to
understand how a person is contributing to our history until that person is no
longer alive. We love to look at things with a nostalgic eye. That is why things
from the past always have this halo that makes them look almost mystic and
mysterious.
That is why when you see Stephen Wolfram comparing himself to Isaac Newtown
you get the goosebumps. Only time will tell us what impact Stephen Wolfram‘s
ideas will have.
Going back to A New Kind of Science, one of the people who contributed to
Stephen Wolfram‘s growth was physicist Richard Feynman. Although they had
two very different mindsets, they shared a common passion, physics.
They met when Stephen Wolfram was 18, while Richard Feynman was 60.
Stephen Wolfram first met Richard Feynman at Caltech. After that they also
worked together for a company called Thinking Machines Corporation (once a
successful tech company then bankrupted in 1994).
Richard Feynman loved to do calculations for the sake of feeling the pleasure of
discovery. Stephen Wolfram instead, loved to automate processes for the sake of
understanding the principles underlying, so that unknown would eventually
become more relatable.
I leave the judgment on whether a New Kind of Science’s ideas are worth or not
your attention to Richard Feynman. In fact, when Stephen Wolfram stumbled
upon rule 30 Richard Feynman told him,
OK, Wolfram, I can’t crack it. I think you’re on to something. (source:Idea Makers)
Let’s dive into a couple of core principles from Stephen Wolfram‘s book.
The principle of computational equivalence
As Stephen Wolfram reminds us nature has no constraints when it comes to
computation. In other words, mother nature can pick up whatever “little
computational program” exist out there to start building complex stuff.
However, that complexity always springs from a low level of sophistication, which
is equal for all the “programs” that you can ever find in nature. In other words, it
doesn’t matter how complicated the outcome or a process it seems. What’s
behind it are always relatively simple deterministic rules!
Why is this principle staggering in a way? If you didn’t realize, this principle
contradicts something which humans always believed to be true. The fact that
something complex is at its root complex. Also that in some way our intelligence
us unique or in some way distinct from any other natural process.
Yet what Stephen Wolfram shows us is the opposite. Nature works through
computational equivalence. Instead, human processes evolve through the
purposes that by time to time become relevant. At times what becomes relevant
by human standard may well be “an accident.”
For instance, as Stephen Wolfram recollects, if you take mathematics, that is
based on a few axioms. But are those the only possible axioms? Of course, they
are not. There are infinitely many other ones. Instead, the mathematics we have
today is only “an historical accident.”
Therefore, many technological inventions often carry the heavy baggage of
history.
That also brings us toward another pillar of the New Kind of Science:
computational irreducibility.
Computational Irreducibility: how does our
universe work according to Stephen Wolfram?
If complexity in nature arises from simple deterministic rules, does that mean that
if we figure out those simple rules, we will also be able to predict the outcome of
the system?
The answer to that is controversial. According to Stephen Wolfram‘s
computational irreducibility, we won’t be able to out-compute a system unless
we take each step of it. In short, it doesn’t matter if you’re able to understand
the underlying deterministic rules that are behind a complex system.
If you don’t follow how the system evolves at each stage, you won’t be able to
know what the output of that system will be. Therefore, you will not be able to
predict its behavior. In part, this is reassuring as it gives us back our free will.
Why? For most cases, we don’t have a choice but wait and see how the behavior
unfolds.
However, this isn’t always true. As Stephen Wolfram suggests for certain
processes, there are always endless of what he calls “pockets of reducibility”
which allow at least some kind of predictions.
Now that we covered most of Stephen Wolfram‘s life, and how his mains ideas
formed, we can look more at the entrepreneurial side of the story.
The launch of Wolfram Alpha
On May 18, 2009, finally, Wolfram Alpha launch was announced. At the eye of
the average onlooker that seemed the start of something entirely new. Yet
instead, it was only the next stage in the evolution of Stephen Wolfram‘s
research he had put together throughout his whole life.
In fact, Wolfram Alpha was launched as a spin-off of Wolfram Research, Inc., the
company that developed Mathematica back in 1986.
As an LLC Wolfram Alpha was launched without outside investments.
How does Wolfram Alpha work?
When you surf the web, you use search engines. For instance, each time you ask
something to Google, it is providing you answers based on what it finds by
crawling, indexing and ranking the web pages available.
Eventually, you will get links to pages that exist on the web. Those pages though
are manufactured by other humans. Wolfram Alpha instead, computes answers
to specific questions using its knowledge base and algorithms.
You may think that Wolfram Alpha is more like a fact-based engine. And in fact,
compared to Wikipedia, it gives facts not narratives.
In short, Wolfram Alpha goes into its internal knowledge base, made of several
official sources and curated data and computes the answers, based on which
ones seemed to be the most appropriate for the query.
The answer to a query comes from an algorithmic computation that looks into
the internal knowledge base, with its extensive internal semantics and ontology.
To read the users’ queries, Wolfram Alpha does not use traditional NLP because
it has to deal with linguistic fragments rather than full grammatical sentences.
The technologies behind Wolfram Alpha can be divided into four key general
areas:
Currently Wolfram Alpha comprises more than 10 million lines of
symbolic Mathematica code, together with many terabytes of data. When you
type your queries into Wolfram Alpha, you’re making it better and better. In fact,
it looks at the user behavior to understand more about linguistic and compute
better answers.
Source: WolframAlpha.com
How does Wolfram Alpha make money?
Wolfram Alpha makes money in a few ways:
Each App ranges from $0.99 up to $2.99 (even though I can see that on the
European App Sore some apps are priced around €3.49, about $4.16).
How much money are they making with that? It is very hard to give this answer
considering that I couldn’t find any announcement from the company stating
how much money they are making.
I used Similar Web to understand how many installs the main app had.
Assuming that each install is a paying customer they made on that single app
anywhere from $1.5 up to $3 million for a single app. My assumption (take this
premise like a pinch of salt) is that overall the other apps may have paid off
about the same. Therefore, if I have to give a rough estimate, I would say they
made anywhere from $1 million up to $5 million on all the apps they sold so far.
API: those are a set of prepackaged instructions to integrate an application
within another platform. For instance, as we saw throughout this research, the
iPhone and other Apple devices use Wolfram Alpha API to make their built-in
intelligent assistant, Siri, provide the answers to its users.
API usually have a cost based on the volume of calls you make through queries.
Therefore, the more queries go through Wolfram Alpha, the more it will cost
regarding API. Based on what they have on the website this is the price of
Wolfram Alpha‘s API for a thousand queries,
I believe this is where most of the company’s revenues come from. In fact, if I had
a business that has to offer a certain amount of computational power to my users
I would use Wolfram Alpha rather than going through the process of
developing a new stack of technologies.
Now, the hardest question. How much money does Wolfram Alpha make with its
API?
I assume that Apple is Wolfram Alpha largest client. If back in 2012, Siri’s queries
drove 25% of Wolfram Alpha, if this still holds true we can run some rough
estimates.
If there are 3.3 million hits per day, if we assume a hit is a query, then there are
about 825,000 queries coming from Siri, which translates into about 825 API calls
by a thousand queries per day. If we assume that Siri calls are “Full Results API”
this means that they are priced $50 per thousand calls, which would cost over
$15 million annually.
I don’t think Apple is paying Wolfram Alpha that much for several reasons. First,
being featured in Siri is already free marketing for Wolfram Alpha. Second, the
more API calls you buy, the better deal you will get. Therefore, I believe if ever
Apple does pay Wolfram Alpha, the price it pays is well below the $50 mark per
a thousand.
However, I do believe that the API is the most important part of Wolfram Alpha
business.
Also, apparently Siri isn’t the only intelligent assistant powered by Wolfram
Alpha. In fact, Cortana may be using it too.
It’s impossible to determine the exact number of PRO users since they don’t
share that info. However, since Wolfram Alpha is a Freemium, we can assume
that their rate of conversion may be anywhere from 0.5% up to 27%.
Source: process.st
Let’s assume the lowest conversion rate, 0.5%, which means that Wolfram Alpha
(I don’t see any strategy which is focused on improving its conversion rates)
might have about ten thousand paying customers each year (0,5% times 1.9
million daily visitors, assuming they are returning). It means about $77,400 in
revenue each year (I took the average of the two packages, student for $5.49
and educator for $9.99, and multiplied it by 10,000, the number of paying
customers each year.
Now the hardest question,
Is Wolfram Alpha profitable?
I’m not going to do crazy calculations to figure this out. Since for a software
company the greatest cost is related to its personnel, according to CrunchBase,
Wolfram Alpha has about 19 employees, including Stephen Wolfram as a
founder.
I also looked at the whole Wolfram Research profile on Glassdoor,
I wanted to have a rough idea about the average salary paid by the company,
I will never stop saying, my analysis is full of assumptions and estimates based on
those. Therefore, take it as an attempt to put a $ on a company that I didn’t
know anything about before. Nothing more!
Since in Wolfram Alpha, most profiles are senior, thus above the average I will
estimate the average salary to be 30% above of a Software Engineer. In short, it
will be around $80k per year. Taking this into account the total annual estimated
costs are $1.6 million (I assume that Stephen Wolfram gets paid like any other
employee at least at Wolfram Alpha).
If we take the total estimated revenues by accounting only for the API paid by
Apple, $15 million per year and the total estimated costs of its employees, $1.6
million, then the company is profitable!
Wolfram Alpha vs. Google
It has often been said that Wolfram Alpha is challenging Google. For how I love
this perspective, those companies have two opposite approach to business. As
we saw, Wolfram Alpha makes money by selling its premium version, its API, and
its apps.
Google makes most of its revenues through advertising. For instance, in 2016
Google made more than 90 billion in revenue and almost 88% ($79 billion) came
from advertising. In other words, comparing Wolfram Alpha to Google is like
comparing apples to oranges. However, if we look at raw data, Google wins
many times over,
Also if we look at the rankings, Wolfram Alpha is tiny,
In short, Wolfram Alpha is a niche engine compared to Google. Will it go
mainstream? I believe it will, and it already did. But most people don’t and won’t
know that. As we saw Siri uses Wolfram Alpha API, yet only a small percentage of
iPhone users know that.
What can you do with Wolfram Alpha?
There is a multitude of ways you can use Wolfram Alpha, once you understand its
basics.
Math, physics, and statistics are useful for researchers. Yet there are quite a few
practical things you can do either if you work in the financial field but also as a
digital marketer.
Get Financial Data and Perform Financial
Analyses Quickly
For instance, by inputting a simple query, you can get financial data and a
complete financial comparison,
Check a website statistics
By inserting a website URL into Wolfram Alpha, you can get its main metrics,
With the same logic, you can compare sites’ metrics,
There are also a bunch of other things you can do. I dive into them more in detail
below,
How to Use Wolfram Alpha for Finance Professionals to Boost YourCareer
How to Use Wolfram Alpha as Personal Fitness Assistant to ImproveYour Health
Add Wolfram Alpha Widget to your website
If you want to make your website more appealing to your visitors, you may want
to embed a Wolfram Alpha widget to your site,
You can customise it a bit. That is the way it will look like,
Now that we saw quite a few things you can do with Wolfram Alpha let’s
investigate a bit more.
Can you optimise for Wolfram Alpha?
You can optimise your queries to get the best possible results. For instance, if
you go into the Examples by Topics section, you will find some use cases on how
to submit queries to Wolfram Alpha,
I checked the statistics and data analysis section, and that is what I got,
What about your content? Can the content on your website be optimised for
Wolfram Alpha? The answer I believe is not! Wolfram Alpha is a computation
engine. That means that also when a source, like Wikipedia, is taken into
account, it is stripped of its narrative to show what can be considered as facts.
In other words, as we saw Wolfram Alpha doesn’t crawl the web as Google does.
But it has an internal knowledge base, which is updated and curated by time to
time. Also, let’s say that Wolfram Alpha can compute an answer from a piece of
extract coming from a web page of your site. Even though it does so, this will not
translate in traffic or domain authority back to your site. Why? Because
Wolfram Alpha does not provide links or backlinks, the foundation for other
search engines.
Summary and Conclusions
Throughout this story, we saw the life of one of the most interesting people of
our times. Since a young age, he showed keen interest and talent for physics. Yet
instead of pursuing only its interests in physics and philosophy Stephen Wolfram
became a successful entrepreneur.
He managed to build from scratch Mathematica, a company bundled with NeXT.
Also, he managed to take time to reflect and understand the consequences of
his earlier studies on cellular automata to create a framework, which would
become a New Kind of Science. If that was not enough, Stephen Wolfram
realized it was time to democratize math and bring it to the masses.
That is what he did when he launched Wolfram Alpha, which as we saw is an
incredible tool that can be used in a variety of ways. It is almost like having
Einstein in your pocket. With the main difference that Wolfram Alpha never
sleeps.
Not only Wolfram Alpha is a great tool but also a profitable company (if my
assumptions are correct).
You may like or dislike Stephen Wolfram‘s personality. One thing is sure; he’s
making a difference in many fields. At the end of it all if he’s right on
computational irreducibility there’s only one way to know how much Stephen
Wolfram has contributed to humankind, time!
Suggested Readings
Quotes I love from Stephen Wolfram
brains are no more computationally sophisticated than lots of systemsin nature, and even than systems with very simple rules
What is inevitable about future machines is that they’ll operate in wayswe can’t immediately foresee. In fact, that happens all the timealready; it’s what bugs in programs are all about.
The main thing we humans do that can’t meaningfully be automated isto decide what we ultimately want to do.
Human goals will certainly evolve, and the things people will think arethe best possible things to do in the future may well be things wedon’t even have words for yet.
Source: blogs.scientificamerican.com
