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."
Originals:
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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.
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.
Hello.
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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:
Pears Or To Garnestmeam
meats
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.
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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:
Beef Soup With Swamp Peef And Cheese
Chocolate Chops & Chocolate Chips
Crimm Grunk Garlic Cleas
Beasy Mist
Export Bean Spoons In Pie-Shell, Top If Spoon and Whip The Mustard
Chocolate Pickle Sauce
Whole Chicken Cookies
Salmon Beef Style Chicken Bottom
Star *
Cover Meats
Out Of Meat
Completely Meat Circle
Completely Meat Chocolate Pie
Cabbage Pot Cookies
Artichoke Gelatin Dogs
Crockpot Cold Water
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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."
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.
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Gaetan Hadjeres and Francois Pachet at the Sony Computer Science Laboratories in Paris created DeepBach, then entered Bach's 352 chorales. The resulting composition is certainly in the style. So why does this work better than some other attempts? Read the rest
My friend and Cool Tools business partner Kevin Kelly spoke at TEDSummit about the rapid rise of artificial intelligence. The talk is based on his excellent bestselling book, The Inevitable.
"The actual path of a raindrop as it goes down the valley is unpredictable, but the general direction is inevitable," says digital visionary Kevin Kelly — and technology is much the same, driven by patterns that are surprising but inevitable. Over the next 20 years, he says, our penchant for making things smarter and smarter will have a profound impact on nearly everything we do. Kelly explores three trends in AI we need to understand in order to embrace it and steer its development. "The most popular AI product 20 years from now that everyone uses has not been invented yet," Kelly says. "That means that you're not late."
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Lip-reading algorithms have all sorts of real-world applications, and LipNet shows great promise in machine-learning lipreading of constructed sentences from the GRID sentence corpus. Read the rest
Google's neural net is amazingly good at figuring out what you draw. In this game, it correctly guessed five out of six doodles I drew: cookie, saw, scissors, beach, grass. It missed watermelon. Read the rest
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In his 1854 book, Walden, Henry David Thoreau wrote, “Men have become the tools of their tools.” Thoreau’s assertion is as valid today as it was when he made it over one hundred and sixty years ago. Whenever we shape technology, it shapes us, both as individuals and as a society. We created cars, and cars turned us into motorists, auto mechanics, and commuters.
Over the centuries we’ve populated our world with machines that help us do things we can’t or don’t want to do ourselves. Our world has become so saturated with machines that they’ve faded into the background. We hardly notice them. We are reaching a new threshold. Our machines are getting networked, and enabling new forms of human machine symbiosis. We’re entering a new era where fifty billion machines are in constant communication, automating and orchestrating the movement and interactions among individuals, organizations, and cities.
Institute for the Future (IFTF) is a non-profit think tank in Silicon Valley, that helps organizations and the public think about long term future plans to make better decisions in the present. Mark Frauenfelder, a research director at IFTF interviewed Rod Falcon, IFTF’s Director of the Technology Horizons Program, which combines a deep understanding of technology and societal forces, to identify and evaluate these discontinuities and innovations in the near future. Rod discussed Tech Horizon’s recent research into how machine automation is becoming an integrated, embedded, and ultimately invisible part of virtually every aspect of our lives. Read the rest
The Allen Institute for Artificial Intelligence (AI2), funded by billionaire Paul Allen's, is developing projects like an AI-based search engine for scientific papers and a system to extract "visual knowledge" from images and videos. According to Scientific American, another goal of AI2 is "to counter messages perpetuated by Hollywood and even other researchers that AI could menace the human race." SciAm's Larry Greenemeier interviewed AI2 CEO and computer scientist Oren Etzioni:
Why do so many well-respected scientists and engineers warn that AI is out to get us?
It’s hard for me to speculate about what motivates somebody like Stephen Hawking or Elon Musk to talk so extensively about AI. I’d have to guess that talking about black holes gets boring after awhile—it’s a slowly developing topic. The one thing that I would say is that when they and Bill Gates—someone I respect enormously—talk about AI turning evil or potential cataclysmic consequences, they always insert a qualifier that says “eventually” or this “could” happen. And I agree with that. If we talk about a thousand-year horizon or the indefinite future, is it possible that AI could spell out doom for the human race? Absolutely it’s possible, but I don’t think this long-term discussion should distract us from the real issues like AI and jobs and AI and weapons systems. And that qualifier about “eventually” or “conceptually” is what gets lost in translation...
How do you ensure that an AI program will behave legally and ethically?
If you’re a bank and you have a software program that’s processing loans, for example, you can’t hide behind it.
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At this week's London Design Festival, design firm Uniform displayed Solo Radio. Stand in front of the device and it scans your face for input into software that assesses your emotions. Then it plays a song via Spotify algorithms with the appropriate mood. Read the rest
A group of some of the most powerful technology companies on the planet have formed a partnership on artificial intelligence.
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