Film critic Allison de Fren examines some of the complex issues raised in the 2015 Ex Machina. Her voiceover alone makes it worth it, but the insights about how women are depicted helped me get over some of my discomfort with the film's themes. Read the rest
If you teach an artificial intelligence a bunch of lullabies, will the resulting AI-generated lullaby make you dream of electric sheep? Read the rest
Single image super-resolution (SISR) is an emerging technology that uses automated texture synthesis to enhance dithered and blurry photos to nearly pristine resolution. This example from EnhanceNet-PAT shows one type. There's even a free website called Let's Enhance where you can up-res your own images. Read the rest
A generative adversarial network (GAN) combines two neural networks engaged in a zero-sum competition. The result is a form of unsupervised machine learning that can produce imaginary celebrities like the ones shown in this one-hour video. Read the rest
Google's AI scored more than twice as high as Apple's Siri in a comparative analysis designed to assess AI threat. Read the rest
To hear a wide-ranging interview about the real-world risks we humans could face from a rogue superintelligence, hit play, below. My guest is author and documentary filmmaker James Barrat. Barrat’s 2014 book Our Final Invention was the gateway drug that ushered me into the narcotic realm of contemplating super AI risk. So it’s on first-hand authority that I urge you to jump in – the water’s great!
This is the seventh episode of my podcast series (co-hosted by Tom Merritt), which launched here on Boing Boing last month. The series goes deep into the science, tech, and sociological issues explored in my novel After On – but no familiarity with the novel is necessary to listen to it.
The danger of artificial consciousness has a noble pedigree in science fiction. In most minds, its wellspring is 2001: A Space Odyssey, which features HAL 9000 – an onboard computer that decides to kill off its passengers before they can disconnect it (spoiler: HAL’s rookie season ends – rather abruptly – with a 1-1 record).
James’s interest in this subject was piqued when he interviewed 2001’s author, Arthur C. Clarke, back in the pertinent year of 2001. Clarke’s concerns about superintelligence went beyond the confines of fiction. And he expressed them cogently enough to freak James out to this day.
Among James’s worries is that Hollywood has inoculated many of us from taking super AIs seriously by depicting them so preposterously. “Imagine if the Centers for Disease Control issued a serious warning about vampires,” he notes. Read the rest
Facebook has taken a step closer to the border between between human and AI interactions.
According to New Scientist, the social network’s AI lab directed a bot to watch hundreds of Skype conversations from Youtube to analyze subtle facial expressions—then try to respond to them.
The bot is said to be able to “tilt its head” or “open its mouth” while viewing video of a human laughing.
A volunteer panel judged the bot and deemed it “qualitatively realistic,” presumably while their mouths hung open.
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If an artificial intelligence reviewed your favorite logo, how would that logo fare? now you can find out with Logo Rank, a nifty tool by the guy behind Brandmark. Read the rest
Machine learning algorithms have successfully identified plant species in massive herbaria just by looking at the dried specimens. According to researchers, similar AI approaches could also be used identify the likes of fly larvae and plant fossils. From Nature:
There are roughly 3,000 herbaria in the world, hosting an estimated 350 million specimens — only a fraction of which has been digitized. But the swelling data sets, along with advances in computing techniques, enticed computer scientist Erick Mata-Montero of the Costa Rica Institute of Technology in Cartago and botanist Pierre Bonnet of the French Agricultural Research Centre for International Development in Montpellier, to see what they could make of the data.
Researchers trained... algorithms on more than 260,000 scans of herbarium sheets, encompassing more than 1,000 species. The computer program eventually identified species with nearly 80% accuracy: the correct answer was within the algorithms’ top 5 picks 90% of the time. That, says (Penn State paleobotanist Peter) Wilf, probably out-performs a human taxonomist by quite a bit.
Such results often worry botanists, Bonnet says, many of whom already feel that their field is undervalued. “People feel this kind of technology could be something that will decrease the value of botanical expertise,” he says. “But this approach is only possible because it is based on the human expertise. It will never remove the human expertise.” People would also still need to verify the results, he adds.
"Going deeper in the automated identification of Herbarium specimens" (BMC Evolutionary Biology) Read the rest
Spencer Chen, VP of marketing and business development at Alibaba Group, added the audio from the Blade Runner 2049 trailer to the ad for the new Google Assistant. "I'm scared," he tweeted.
"Literally no extra editing involved."
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As AI improves, the mystery of consciousness interests more programmers and physicists. Read the rest
Neural nets are starting to wake up. These pickup lines, generated by a neural net maintained by research scientist Janelle Shane are much more interesting than standard pickup lines.
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Are you a 4loce? Because you’re so hot!
I want to get my heart with you.
You are so beautiful that you know what I mean.
I have a cenver? Because I just stowe must your worms.
Hey baby, I’m swirked to gave ever to say it for drive.
If I were to ask you out?
You must be a tringle? Cause you’re the only thing here.
I’m not on your wears, but I want to see your start.
You are so beautiful that you make me feel better to see you.
Hey baby, you’re to be a key? Because I can bear your toot?
I don’t know you.
I have to give you a book, because you’re the only thing in your eyes.
Are you a candle? Because you’re so hot of the looks with you.
I want to see you to my heart.
If I had a rose for every time I thought of you, I have a price tighting.
I have a really falling for you.
Your beauty have a fine to me.
Are you a camera? Because I want to see the most beautiful than you.
I had a come to got your heart.
You’re so beautiful that you say a bat on me and baby.
You look like a thing and I love you.
In her spare time, University of California, San Diego engineer Janelle Shane trained a neural network to generate recipes for new dishes. Informed by its reading of existing recipes, the neural network did improve over time yet it's clearly not quite ready for Iron Chef. Here are two recipes from her Tumblr, Postcards from the Frontiers of Science
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Pears Or To Garnestmeam
¼ lb bones or fresh bread; optional½ cup flour1 teaspoon vinegar¼ teaspoon lime juice2 eggs
Brown salmon in oil. Add creamed meat and another deep mixture.Discard filets. Discard head and turn into a nonstick spice. Pour 4 eggs onto clean a thin fat to sink halves.
Brush each with roast and refrigerate. Lay tart in deep baking dish in chipec sweet body; cut oof with crosswise and onions. Remove peas and place in a 4-dgg serving. Cover lightly with plastic wrap. Chill in refrigerator until casseroles are tender and ridges done. Serve immediately in sugar may be added 2 handles overginger or with boiling water until very cracker pudding is hot.
Yield: 4 servings
This is from a network that’s been trained for a relatively long time - starting from a complete unawareness of whether it’s looking at prose or code, English or Spanish, etc, it’s already got a lot of the vocabulary and structure worked out. This is particularly impressive given that it has the memory of a goldfish - it can only analyze 65 characters at a time, so by the time it begins the instructions, the recipe title has already passed out of its memory, and it has to guess what it’s making.
Research scientist Janelle Shane has been training a neural network to generate food recipes by giving it tens of thousands of cookbook recipes. The neural net's recipes are excellent:
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Beef Soup With Swamp Peef And Cheese
Chocolate Chops & Chocolate Chips
Crimm Grunk Garlic Cleas
Export Bean Spoons In Pie-Shell, Top If Spoon and Whip The Mustard
Chocolate Pickle Sauce
Whole Chicken Cookies
Salmon Beef Style Chicken Bottom
Out Of Meat
Completely Meat Circle
Completely Meat Chocolate Pie
Cabbage Pot Cookies
Artichoke Gelatin Dogs
Crockpot Cold Water
Inspired by Westworld, Kurzgesagt – In a Nutshell created this video to explore the questions: "What shall we do once machines become conscious? Do we need to grant them rights?" Read the rest
Graphcore produced a series of striking images of computational graphs mapped to its "Intelligent Processing Unit."
The graph compiler builds up an intermediate representation of the computational graph to be scheduled and deployed across one or many IPU devices. The compiler can display this computational graph, so an application written at the level of a machine learning framework reveals an image of the computational graph which runs on the IPU.
The image below shows the graph for the full forward and backward training loop of AlexNet, generated from a TensorFlow description.
Our Poplar graph compiler has converted a description of the network into a computational graph of 18.7 million vertices and 115.8 million edges. This graph represents AlexNet as a highly-parallel execution plan for the IPU. The vertices of the graph represent computation processes and the edges represent communication between processes. The layers in the graph are labelled with the corresponding layers from the high level description of the network. The clearly visible clustering is the result of intensive communication between processes in each layer of the network, with lighter communication between layers.
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Zuck That says, "Have you ever been on the Internet when you came across a checkbox that says “I’m not a robot?” In this video, I explain how those checkboxes (No CAPTCHA reCAPTCHAs) work as well as why they exist in the first place."
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I mention CAPTCHA farms briefly, but the idea behind them is pretty straightforward. If a company wants to create an automatic computer program to buy 1,000 tickets to an event or make 1,000 email accounts, they can make a script that fills out the form one at a time, and when the program gets to a CAPTCHA, it will send a picture of it to a CAPTCHA farm where a low-wage worker will solve it and send the answer back to the computer program so that it can be used to finish filling out the form.