I’ve used dreams in my fiction in various ways. In order to keep from writing a long essay on this subject, I’ll provide only a few examples. In some of my stories the protagonist will have a dream that advances the plot, which is the simplest and most often used function of dreams in fiction. In “The Sect of the Idiot,” for instance, a dream turns out to be a vision of something fantastic that turns out to exist in reality. In this case, the dream prepared the revelation of a fantastic metaphysics. The inversion of this method is familiar to readers of ghost stories in which a character meets someone who is later revealed to be dead and therefore could only have been a ghost. In other stories of mine, a dream might explicate a concept, theme, or something that might otherwise come across as too cerebral in the waking section of the narrative. The dream in “The Sect of the Idiot” also serves this function. In “The Bungalow House,” I described a series of what I designated as “dream monologues” that were recorded on tape and intended to be works of art. The first dream monologue was a transcription of an actual dream I had and wrote down soon after I awoke, so it was also initiated my writing of the entire story. A second dream monologue in “The Bungalow House” was only summarized, while a third was simply given a title, because at that point I had established the nature of the dream monologues in their incidents and meaning. For my purposes, to describe each dream monologue in its entirety would have slowed the pace of the story. All of the dream monologues were used to characterize the peculiar nature of the main character’s psychology. Sometimes I’ll characterize the events of a narrative as being dreamlike in some specific way, because over the years I’ve noted qualities that characterize dreams, such as that they have no beginning, an idea that was recently used in the movie Inception to prove to a character that she was functioning in a dream and not in conventional reality. A very short story I wrote called “One May Be Dreaming” is pretty obviously a dream from beginning to end. The whole point of the story was that the protagonist was having a dream at the same time he was dying in real life. Usually, it’s not exposed until the end of the story that the whole thing was a dream. For his story “Where He Was Going,” William Burroughs employs this method, his use of which he credits to Ernest Hemingway’s “Snows of Kilimanjaro.” “Man from the South” was Jorge Luis Borges’s rendition of this narrative structure. Perhaps I should say that I don’t think that dreams are anything more than rearranged experiences, sensations, and emotions. While they may easily be interpreted as symbolic or premonitory or whatever, I don’t believe that they are anything but intrusions upon what might otherwise be wholly unconscious hours of sleep.

Thomas Ligotti
Interview with Wonderbook

a writer’s toolbox

November 29, 2017

a hand with this

Ego is a critical part of a writer’s toolbox. Without ego, you’d succumb to the fear that the eighty-to-one-hundred thousand words you’re preparing to dump on the world may not measurably improve it. This will only seem ridiculous if you have a tremendous ego, the kind that can look upon a work-in-progress crammed with of plot holes, opaque character motivations, and spelling errors that have dogged you since third grade, and think: Mmm…not bad.

What works for me is telling myself that all writers’ first drafts are bad. I can’t tell for sure because other writers don’t show me their first drafts—probably because they’re so bad. You see? So what I hawk out onto the screen in draft one is pretty good, relatively speaking.

Remember the last time you read a novel that was so awful, just getting to the end was tortuous? Someone managed to write that. They typed out every single word. And they did it as a first draft, when it would have been worse. Thanks to ego.

Max Barry
How to write a great science fiction novel


Today, for the mass of humanity, science and technology embody ‘miracle, mystery, and authority’. Science promises that the most ancient human fantasies will at last be realized. Sickness and ageing will be abolished; scarcity and poverty will be no more; the species will become immortal. Like Christianity in the past, the modern cult of science lives on the hope of miracles. But to think that science can transform the human lot is to believe in magic. Time retorts to the illusions of humanism with the reality: frail, deranged, undelivered humanity. Even as it enables poverty to be diminished and sickness to be alleviated, science will be used to refine tyranny and perfect the art of war.

John N. Gray
Straw Dogs: Thoughts on Humans and Other Animals

They are scarcely adult

November 28, 2017

He feared me as many men fear women: because their mistresses (or their wives) understand them. They are scarcely adult, some men: they wish women to understand them, and to that end they tell them all their secrets; and then, when they are properly understood, they hate their women for understanding them.

Julian Barnes
Flaubert’s Parrot

…though we know the sea to be an everlasting terra incognita, so that Columbus sailed over numberless unknown worlds to discover his one superficial western one; though, by vast odds, the most terrific of mortal disasters have immemorially and indiscriminately befallen tens and hundreds of thousands of those who have gone upon the waters; though but a moment’s consideration will teach that, however baby man may brag of his science and skill, and however much, in a flattering future, that science and skill may augment; yet for ever and and for ever, to the crack of doom, the sea will insult and murder him, and pulverize the stateliest, stiffest frigate he can make; nevertheless, by the continual repetition of these very impressions, man has lost that sense of the full awfulness of the sea which aboriginally belongs to it.

Herman Melville

Robots will take your job

November 28, 2017

Lucinda Cowell - Panic o'clock

On Dec. 2, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words.

Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, you may have heard it but couldn’t fully understand.

The language is something called deep learning. And the whispered word was a computer’s use of it to defeat one of the world’s top players in a game called Go. Go is a board game so complex that it can be likened to playing 10 chess matches simultaneously on the same table.

a robot reception

This may sound like a small accomplishment, another feather in the cap of machines as they continue to prove themselves superior in parlour games that humans invented to fill their idle hours. But this feat is about far more than bragging rights. This was considered a “holy grail” level of achievement, and it’s a clear signal that advances in technology are now so exponential that milestones we once thought far away will start arriving rapidly.

What’s more, humans are entirely unprepared. These exponential advances, most notably in forms of artificial intelligence, will prove daunting for as long as we continue to insist upon employment as our primary source of income. The White House, in a stunning report to Congress this week, put the probability at 83 percent that a worker making less than $20 an hour in 2010 will eventually lose his job to a machine. Even workers making as much as $40 an hour face odds of 31 percent.

Life like robots for sale via Japan’s Sogo & Seibu department stores

We’re building a world where a universal basic income may be the only rational, fair way for society to function — and that’s not a future we should fear.

First, a word on how we got here. All work can be divided into four types: routine and nonroutine, cognitive and manual. Routine work is the same stuff day in and day out, while nonroutine work varies. Within these two varieties, is the work that requires mostly our brains (cognitive) and the work that requires mostly our bodies (manual). Routine work started to stagnate in 1990, because some of that work can be best handled by machines.

Of course, routine work once formed the basis of the American middle class. It’s routine, manual work that Henry Ford paid people middle-class wages to perform, and it’s routine cognitive work that once filled American office buildings. That world is dwindling, leaving only two kinds of jobs with rosy outlooks: jobs that require so little thought that they pay next to nothing, and jobs that require so much thought that the salaries are exorbitant.

A four-engine plane can stay aloft with only two engines working. But what happens when the last two begin to sputter? That’s what the advancing fields of robotics and AI represent to those final two engines of nonroutine work because, for the first time, we are successfully teaching machines to learn.

Machines are getting smarter because we’re getting better at building them. And we’re getting better at it, in part, because we are smarter about the ways in which our own brains function.

What’s in our skulls is essentially a mass of interconnected cells. Some of these connections are short, and some are long; some cells are only connected to one other, and some are connected to many. Electrical signals then pass through these connections, at various rates, and subsequent neural firings happen in turn. It’s all kind of like falling dominoes, but far faster, larger, and more complex.

Deep neural networks are kind of like pared-down virtual brains. They provide an avenue to machine learning that’s made incredible leaps previously thought to be much further down the road. How? It’s not just the obvious growing capability of our computers and our expanding knowledge in the neurosciences, but the vastly growing expanse of our collective data.

Big data isn’t just some buzzword. We’re creating and standardizing so much data that a 2013 report by SINTEF estimated that 90 percent of all data in the world had been created in just the prior two years. This incredible rate of data creation is doubling every 18 months thanks to the Internet, where we uploaded 300 hours of video to YouTube and sent 350,000 tweets each minute last year.

Everything we do is generating data, and lots of data is exactly what machines need in order to learn to learn. Imagine programming a computer to recognize a chair. Early incarnations of the program would be far better at determining what isn’t a chair than what is.

Humans learn the difference as children, when chairs are identified for us by name. If children point at a table and say “chair,” they’re corrected with “table.” This is called reinforcement learning. The label “chair” gets connected to every chair, such that certain neural pathways are weighted and others aren’t. For “chair” to fire in our brains, what we perceive has to be close enough to our previous chair encounters. Essentially, our lives are big data filtered through our brains.

The unprecedented power of deep learning is that it’s a way of using massive amounts of data to get machines to operate more like we do without giving them explicit instructions. Instead of describing “chairness” to a computer, we can just plug it into the Internet and feed it millions of pictures of chairs for a general idea. Next, we test it with even more images. When the machine is wrong, it’s corrected, further improving its “chairness” detection.

Repetition of this process results in a computer that knows what a chair is when it sees it, often as well as a human can. Unlike us, however, it can then sort through millions of images within a matter of seconds. And when one machine learns something, it can pass on that knowledge to an entire network of connected machines — instantly.

One powerful example of this learning process comes from the electric car maker Tesla. Google spent six years accumulating 1.7 million miles of driving data with its prototype self-driving cars. Tesla, on the other hand, simply sent out a software update, instantly teaching its fleet how to drive themselves with a new “autopilot” ability. The network started racking up Google’s total mileage every week. Every single Tesla is now effectively teaching all other Teslas the “chairness” of driving.

Extend the Tesla example to the Internet of Things, where any interaction with a connected object has the potential of teaching something new to every connected object, and the immense scaling of networked machine learning becomes almost unimaginable.

In a frequently cited paper, an Oxford University study estimated the potential automation of about half of all existing jobs by 2033. Meanwhile self-driving vehicles, again thanks to machine learning, have the capability of drastically affecting all economies by eliminating millions of jobs within a short span of time. New jobs are no longer created faster than technology destroys them. A report by the World Economic Forum has estimated that despite the creation of millions of new jobs over the next four years, there will likely be a net loss of 5 million.

a robot sale - walk in walk out

Walk in, walk out robot sale

All of this is why it’s those most knowledgeable in the AI field who are now actively sounding the horn for basic income. During a panel discussion at the end of 2015 at Singularity University, prominent data scientist Jeremy Howard asked, “Do you want half of people to starve because they literally can’t add economic value, or not?” before going on to suggest, “If the answer is not, then the smartest way to distribute the wealth is by implementing a universal basic income.”

The combination of deep learning and Big Data has resulted in astounding accomplishments just in the past year. Google’s DeepMind AI learned how to read and comprehend what it read through hundreds of thousands of annotated news articles. DeepMind also taught itself to play dozens of Atari 2600 video games better than humans, just by looking at the screen and its score, and playing games repeatedly. An AI named Giraffe taught itself how to play chess in a similar manner using a dataset of 175 million chess positions, attaining International Master level status in just 72 hours by repeatedly playing itself.

In 2015, an AI even passed a visual Turing test by learning to learn in a way that enabled it to be shown an unknown character in a fictional alphabet, then instantly reproduce that letter in a way that was entirely indistinguishable from a human given the same task. These are all major milestones in AI.

Nonetheless, when asked to estimate how long it would take a computer to defeat a prominent player in the game of Go, the answer — just months prior to the announcement by Google of AlphaGo’s victory — was about a decade. That was considered a fair guess because Go is a game with more possibilities than atoms in the known universe. That made impossible any brute-force approach to scan every possible move to determine the next best move. But deep neural networks got around that barrier in the same way our own minds do, by learning to estimate what feels like the best move. We do this through observation and practice, and so did AlphaGo. It analyzed millions of professional games and played itself millions of times. For the game of Go, the enemy wasn’t a month’s march from the castle — it was already inside the keep, feet up on the table, eating the king’s lunch.

The Go lesson shows us that nothing humans do as a job is safe anymore. From making hamburgers to anesthesiology, machines will be able to successfully perform such tasks and at lower costs than humans.

Amelia is many things. But she’ll never take a sick day, join a union, or waste time on Facebook on the job. Created by IPsoft over the past 16 years, the AI system learned how to perform the work of call center employees. She can learn in seconds what takes humans months to master, and she can do it in 20 languages. Because she’s able to learn, she’s able to do more over time. In one company trial, she successfully handled one of every 10 calls in the first week, and by the end of the second month, she could resolve six in 10. Deploy her worldwide, and 250 million people can start looking for a new job.

Viv is an AI coming soon from the creators of Siri who’ll be our own personal assistant. She’ll perform tasks online for us and even function as a Facebook News Feed on steroids by suggesting we consume the media she’ll know we’ll like best. With Viv doing all this for us, we’ll see far fewer ads, and that means the entire advertising industry — that industry the entire Internet is built upon — stands to be hugely disrupted.

A world with Amelia and Viv — and the countless other AI counterparts coming online soon — is going to force serious societal reconsiderations. Is it fair to ask any human to compete against a potentially flawless machine in the next cubicle? If machines are performing most of our jobs and not getting paid, where does that money go instead? And what does that unpaid money no longer buy? Is it even possible that many of the jobs we’re creating don’t need to exist at all, and only do because of the incomes they provide?

We must seriously start talking about decoupling income from work. Adopting a universal basic income, aside from immunizing against the negative effects of automation, also decreases the risks inherent in entrepreneurship, and the sizes of bureaucracies otherwise necessary to boost incomes. It’s for these reasons, it has cross-partisan support, and is even now in the beginning stages of implementation in countries like Switzerland, Finland, and the Netherlands.

Artificial intelligence pioneer Chris Eliasmith, director of the Centre for Theoretical Neuroscience, also warned about the immediate impacts of AI on society in a recent interview with Futurism, “AI is already having a big impact on our economies. . . . My suspicion is that more countries will have to follow Finland’s lead in exploring basic income guarantees for people.”

Even Baidu’s chief scientist and founder of Google’s “Google Brain” deep learning project, Andrew Ng, during an onstage interview at this year’s Deep Learning Summit, expressed the shared notion that basic income must be “seriously considered” by governments, citing “a high chance that AI will create massive labour displacement.”

When those building the tools begin warning about the implications of their use, shouldn’t those wishing to use those tools listen with the utmost of attention, especially when it’s the very livelihoods of millions at stake?

No nation is yet ready for the changes ahead. High rates of labour force nonparticipation leads to social instability, as does a lack of consumers within consumer economies. It turns out, humans are good at designing things, but not so great at picturing a world that their technology will create. What’s the big lesson to learn, in a century when machines can learn? Maybe it is that jobs are for machines, and life is for people.

Scott Santens
Robots will take your job
The Boston Globe, 25th February 2016

Can I Fly Too?

November 26, 2017

You are a witch.
You taught me
To hear in the slurping of mud
The cry of the Ban Shee
To see in the life cycle of the caterpillar
The struggle of the soul
Towards immortality.
Take me.
You alone could turn the weight of years
Into release, ecstasy.

Philip Hobsbaum

magic perfumes

November 26, 2017

There are strange and wondrous things in these lands of darkness … I must intoxicate myself on magic perfumes in order to fathom the secrets that lie hidden in the abysses of the Unconscious.

Carl Jung
Letter to Sigmund Freud, 1911 from The Dionysian Self: C.G. Jung’s Reception of Friedrich Nietzsche

We only have this one, short life. It’s not a damn rehearsal, so make the most of what’s on offer…

To those who doubt

November 26, 2017

You think witchcraft doesn’t work? Well, fine. Good for you. Let me practice it in peace, is all I ask. It makes me feel good to cast a spell, to focus my energy, and produce a definite action. A spell has an end and I can tell myself: ‘There, it’s done!’

While you hide yourself away behind your wall of “received wisdom”, what you should remember is we are all the Universe – but trying to be individuals!

In January of this year, scientists created metallic hydrogen for the first time in the world. This was previously believed to be impossible. For the first time hydrogen exists in a metallic state on our Earth. In this metallic state it can act as a genuine superconductor and could revolutionize everything from energy storage to rocketry…The “NOT POSSIBLE” of ‘received wisdom’ was wrong. It can be done. It is possible.

So go in peace with your doubts and leave me to get on with my craft.