Modern mythology is created in film studios. There, the stories the global village has come to depend on for its entertainment and inspiration are turned into drama, comedy, action, science fiction, fantasy, thriller, horror, crime, noir, epic, western, war, romance, musicals, blockbusters…
As in the days of Homer, the secret of success is to tell the tale with emotion and imagery that the audience cannot forget. Easier said than done, of course, but if you’ve got $43,000 the American Film Institute Conservatory in Hollywood Hills will take it and, in return, you’ll get some of that old storytelling magic. Blurb: “Tomorrow’s storytellers are placed in a hands-on, production-based environment and are trained by a group of dedicated working professionals from the film and television communities.” In the end, however, it all comes back to Homer: “The AFI Conservatory doesn’t believe there is a formula for making films. Fellows are instead encouraged to find and develop a unique voice as they are trained in the art of storytelling.”
This is all very touching but it doesn’t mention the Epagogix algorithm. Epagogix is a privately-held UK company that “brings together expertise in risk, finance, artificial intelligence and film analysis to create innovative tools and solutions for the hard decisions that senior company directors need to make.” Yes, that’s right, “film analysis.” So, how does it work? Here goes:
Epagogix works confidentially with the senior management of major film studios, large independents and other media companies, assisting with the selection and development of scripts by identifying likely successes and probable ‘Turkeys’; helping to quantify a script/project’s commercial success; and advising on enhancements to the Box office/audience share potential.
Epagogix’s approach helps management of this most critical financial risk by delivering accurate predictive analysis of the Box Office value of individual film scripts, and by identifying and quantifying how and where to improve their commercial value. If requested, Epagogix sensitively bridges the gap between the financial and creative aspects of film production by providing quantified insights and advice to those responsible for script development.
Note the advice “to those responsible for script development.” That’s you, Homer.Tweet
Decimus Magnus Ausonius was the most famous poet of his time and Emperor Valentinian I summoned him to Rome to teach his son Gratian. He spent the years between 365 and 388 there and then returned to his native Bordeaux. In this epigram for his wife, Attusia, he pictures them still youthful in their old age. It is a haunting vision of a couple fully in love ageing together… and it’s entirely imaginary because Attusia died when she was 28. Forty years later, Ausonius wrote Ad Uxorem (To His Wife).
To His Wife
Love, let us live as we have lived, nor lose
The little names that were the first night’s grace,
And never come the day that sees us old,
I still your lad, and you my little lass.
Let me be older than old Nestor’s years,
And you the Sibyl, if we heed it not.
What should we know, we two, of ripe old age?
We’ll have its richness, and the years forgot.
Translation from the Latin by Helen Waddell (1889 – 1965)
Uxor, vivamus quod viximus et teneamus
nomina quae primo sumpsimus in thalamo;
nec ferat ulla dies, ut commutemur in aevo,
quin tibi sim iuvenis tuque puella mihi.
Nestore sim quamvis provectior aemulaque annis
vincas Cumanam tu quoque Deiphoben,
nos ignoremus quid sit matura senectus,
scire aevi meritum, non numerare decet.
Ausonius (310 – 395)
Hardly a week goes by without some “expert” or other predicting that by, say, 2020, millions and millions of jobs will be lost in developed economies due to robotics, AI, cloud computing, 3D printing, machine learning and related technologies. Hardest hit will be people doing office and factory work, but other sectors, from trucking to healthcare, will be affected “going forward,” as lovers of business cliché love to say.
The Twitter feed @HumanVSMachine features images showing the increasing automation of work. The footage of people doing a job side-by-side with videos of robots doing the same thing suggests a sombre future of post-human work.
Philippe Chabot from Montreal is the human behind @HumanVSMachine. He was a graphic artist in the video industry and he had plenty of work, once upon a time. But companies began outsourcing their artwork and Chabot found himself competing a globalized market where rivals can create a logo for $5 and software automatically designs avatars. Today, Philippe Chabot works in a restaurant kitchen and he feeds @HumanVSMachine in his free time.
Note: This image of “Robot Baby Feeder; Robot, baby bottle, crib, toy” by Philipp Schmitt is included in the “Hello, Robot. Design between Human and Machine” exhibition at the Vitra Design Museum in Weil am Rhein in Germany.Tweet
“Limerick’s Lorca” is how Seamus Heaney, the Nobel Prize winner for Literature in 1995, described the poet Michael Hartnett. “I am the immense shadow of my tears,” said Federico García Lorca and Death of an Irishwoman echoes Lorca’s flamenco-inspired cante jondos (deep songs) that explore love and tragedy.
Death of an Irishwoman
Ignorant, in the sense
she ate monotonous food
and thought the world was flat,
and pagan, in the sense
she knew the things that moved
at night were neither dogs nor cats
but púcas and darkfaced men,
she nevertheless had fierce pride.
But sentenced in the end
to eat thin diminishing porridge
in a stone-cold kitchen
she clenched her brittle hands
around a world
she could not understand.
I loved her from the day she died.
She was a summer dance at the crossroads.
Michael Hartnett (1941 – 1999)
The “the fire hose” lava flow continues to gush from Hawaii’s Kilauea volcano and pour into the ocean Kamokuna. As the “Lava viewing guide for the Big Island” puts it, “Hawaii wouldn’t exist if it were not for the continuous volcanic activity that created all the islands in the state.” Going with this flow, Givot Media, a creative agency based in Los Angeles, made the spellbinding “Hawaii — The Pace of Transformation.”Tweet
When Hewlett-Packard was split in two in 2015, HP Inc focused on consumer products like PCs and printers, while Hewlett Packard Enterprise concentrated on business services such as cloud computing and data analytics. On Wednesday, the facility making ink cartridges in Kildare in Ireland told staff that up to 500 jobs will be lost at the plant. It was a nasty reminder that disruption drives the Fourth Industrial Revolution forward, fast and furiously.
Tech jobs come and tech jobs go and most will be redefined in the coming year(s) anyway as a raft of new concepts, such as the machine learning that’s been our theme there this week, make their presence felt. Clearly, the market for PCs and printers is shrinking, but those at currently at the top of the tech tree, programmers, should not rest on those laurels because as Clive Thompson has just warned readers of Wired, “The Next Big Blue-Collar Job is Coding.” But that may not be a bad thing, Thompson says:
“Across the country, people are seizing this opportunity, particularly in states hit hardest by deindustrialization. In Kentucky, mining veteran Rusty Justice decided that code could replace coal. He cofounded Bit Source, a code shop that builds its workforce by retraining coal miners as programmers. Enthusiasm is sky high: Justice got 950 applications for his first 11 positions. Miners, it turns out, are accustomed to deep focus, team play, and working with complex engineering tech. ‘Coal miners are really technology workers who get dirty,’ Justice says.”
With a story about coal miners learning to program thanks to Bit Source, we end our week of machine learning on an optimistic note.Tweet
All our posts about machine learning this week have been prompted by a dramatic shift going on right now called “Industry 4.0.” In essence, this is the end-to-end digitization of all physical assets and their integration into digital ecosystems. Along with machine learning, Industry 4.0 buzzwords include connectivity, supercomputing, artificial intelligence, robots, self-driving cars gene editing and globalization.
The preceding industrial revolutions freed us from animal power, made mass production possible and opened digital doors for billions of people. This Fourth Industrial Revolution, Industry 4.0, is profoundly different in that its technologies are melding the physical, digital and biological worlds and forcing us to confront uncomfortable questions about work, identity and life itself.
The World Economic Forum, which is “committed to improving the state of the world,” produced this clip about Industry 4.0. It’s a positive view, overall, but it does not address the issue that’s roiling politics today: the conflict between the elites, who stand to gain from early access to the upsides of this transformation, and the precariat, which stands to lose the jobs that glue their communities together. More on this here tomorrow.Tweet
That’s the title of an wry and informative take by Grzegorz Ziemoński in DZone. His definition of Machine Learning is worth memorizing: “Computer doing statistics on Big Data.” If you want to learn some Machine Learning, but don’t know where to start, his “text is for dummies just like us” is recommended.
Along with offering a concise definition of Machine Learning, Ziemoński takes readers through the difference between supervised and unsupervised Machine Learning and he shows us how to use Amazon Machine Learning to make a simple prediction. The key questions for those wishing to do more are: What do you want to predict? What data do you have? What can you do to make it work?
For those still foggy about the relevance of this stuff, substitute Machine Learning for Big Data in this quote by Dan Ariely, founder of The Center for Advanced Hindsight: “Big Data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”
Thanks to Grzegorz Ziemoński, we can now get to first base, as it were.Tweet
The language used by the acolytes of the high priests of the Information Age is richly encoded. Example: “TensorFlow is now available in a Docker image that’s compatible with Python 3, and for all Python users, TensorFlow can now be installed by pip, Python’s native package manager.”
That’s from an InfoWorld story by Serdar Yegulalp in which he says machine learning will one day run on a smartphone, without cloud support. At the heart of this development is TensorFlow the open-source, deep-learning framework developed by Google. Here’s how the engineers, using human language, decode it:Tweet
Rapid and Accurate Image Super-Resolution is a bit of a mouthful so we should welcome the acronym: RAISR. What it means is that machine learning is used to sharpen low-resolution images. Google, which provided the headline for this post, claims that RAISR is so fast that the process can run in real-time on a mobile device. Nerds love this kind of thing, but photographers should be pleased as well because RAISR can avoid aliasing artifacts in the final image, even when artifacts exist in the low-resolution original.
Note: Google says it will expand RAISR beyond Android over the coming months and in his recent post on the future of phones, Mobile 2.0, Benedict Evans pointed out the role machine learning will play in the coming changes…
“Web 2.0 was followed not by anything one could call 3.0 but rather a basic platform shift, as the iPhone triggered the move from desktop to mobile as the centre of tech. AirPods, Spectacles, watches and Alexa also reflect or perhaps prefigure platform shifts. In some of them, on one hand, one can see the rise of machine learning as a fundamental new enabling technology, and in some, on the other hand, more and more miniaturisation and optimisation of computing. I think one can see quite a lot of hardware building blocks for augmented reality glasses in some of Apple’s latest little devices, and AR does seem like it could be the next multi-touch, while of course machine learning is also part of that, as computer vision and voice recognition.”
True story: A player named Libratus sat down at a poker table in a high-stakes game of no-limit Texas Hold’em. The gruelling 20-day tournament ended a week ago in a dramatic victory for Libratus over four of the world’s top players. Libratus is no cigar-smoking dandy cowboy, however. It’s an artificial intelligence (AI).
Machines are getting smarter, and AI is entering society in all kinds of intriguing and disturbing way. But who creates these machine-learning programs and who writes the algorithms that produce everything from stock market predictions to data journalism to poker-winning strategies? It’s time we found out and it’s time to learn how to do it ourselves. But how and where and when?
The ScienceAlert Academy is offering a 73.5-hour course titled “The Complete Machine Learning Bundle” for $39. This is the kind of immersion in the stuff you’ll need to plan a career or take your hobby to the next level. The package contains 10 different courses, including “Hadoop & MapReduce for Big Data Problems” and “From 0 to 1: Learn Python Programming – Easy as Pie”.Tweet