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Alexander Wittig

The Whorfian Time Warp: Representing Duration Through the Language Hourglass. - 0 views

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    How do humans construct their mental representations of the passage of time? The universalist account claims that abstract concepts like time are universal across humans. In contrast, the linguistic relativity hypothesis holds that speakers of different languages represent duration differently. The precise impact of language on duration representation is, however, unknown. Here, we show that language can have a powerful role in transforming humans' psychophysical experience of time. Contrary to the universalist account, we found language-specific interference in a duration reproduction task, where stimulus duration conflicted with its physical growth. When reproducing duration, Swedish speakers were misled by stimulus length, and Spanish speakers were misled by stimulus size/quantity. These patterns conform to preferred expressions of duration magnitude in these languages (Swedish: long/short time; Spanish: much/small time). Critically, Spanish-Swedish bilinguals performing the task in both languages showed different interference depending on language context. Such shifting behavior within the same individual reveals hitherto undocumented levels of flexibility in time representation. Finally, contrary to the linguistic relativity hypothesis, language interference was confined to difficult discriminations (i.e., when stimuli varied only subtly in duration and growth), and was eliminated when linguistic cues were removed from the task. These results reveal the malleable nature of human time representation as part of a highly adaptive information processing system.
jcunha

Space data representation - 1 views

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    A common data hub that allows the representation and comparison of data from numerous space missions. "The IMPEx portal offers tools for the visualization and analysis of datasets from different space missions. Furthermore, several computational model databases are feeding into the environment." As they say, with its massive 3D-visualization capabilities it offers the possibility of displaying spacecraft trajectories, planetary ephemerides as well as scientific representations of observational and simulation datasets.
Guido de Croon

Will robots be smarter than humans by 2029? - 2 views

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    Nice discussion about the singularity. Made me think of drinking coffee with Luis... It raises some issues such as the necessity of embodiment, etc.
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    "Kurzweilians"... LOL. Still not sold on embodiment, btw.
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    The biggest problem with embodiment is that, since the passive walkers (with which it all started), it hasn't delivered anything really interesting...
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    The problem with embodiment is that it's done wrong. Embodiment needs to be treated like big data. More sensors, more data, more processing. Just putting a computer in a robot with a camera and microphone is not embodiment.
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    I like how he attacks Moore's Law. It always looks a bit naive to me if people start to (ab)use it to make their point. No strong opinion about embodiment.
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    @Paul: How would embodiment be done RIGHT?
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    Embodiment has some obvious advantages. For example, in the vision domain many hard problems become easy when you have a body with which you can take actions (like looking at an object you don't immediately recognize from a different angle) - a point already made by researchers such as Aloimonos.and Ballard in the end 80s / beginning 90s. However, embodiment goes further than gathering information and "mental" recognition. In this respect, the evolutionary robotics work by for example Beer is interesting, where an agent discriminates between diamonds and circles by avoiding one and catching the other, without there being a clear "moment" in which the recognition takes place. "Recognition" is a behavioral property there, for which embodiment is obviously important. With embodiment the effort for recognizing an object behaviorally can be divided between the brain and the body, resulting in less computation for the brain. Also the article "Behavioural Categorisation: Behaviour makes up for bad vision" is interesting in this respect. In the field of embodied cognitive science, some say that recognition is constituted by the activation of sensorimotor correlations. I wonder to which extent this is true, and if it is valid for extremely simple creatures to more advanced ones, but it is an interesting idea nonetheless. This being said, if "embodiment" implies having a physical body, then I would argue that it is not a necessary requirement for intelligence. "Situatedness", being able to take (virtual or real) "actions" that influence the "inputs", may be.
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    @Paul While I completely agree about the "embodiment done wrong" (or at least "not exactly correct") part, what you say goes exactly against one of the major claims which are connected with the notion of embodiment (google for "representational bottleneck"). The fact is your brain does *not* have resources to deal with big data. The idea therefore is that it is the body what helps to deal with what to a computer scientist appears like "big data". Understanding how this happens is key. Whether it is the problem of scale or of actually understanding what happens should be quite conclusively shown by the outcomes of the Blue Brain project.
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    Wouldn't one expect that to produce consciousness (even in a lower form) an approach resembling that of nature would be essential? All animals grow from a very simple initial state (just a few cells) and have only a very limited number of sensors AND processing units. This would allow for a fairly simple way to create simple neural networks and to start up stable neural excitation patterns. Over time as complexity of the body (sensors, processors, actuators) increases the system should be able to adapt in a continuous manner and increase its degree of self-awareness and consciousness. On the other hand, building a simulated brain that resembles (parts of) the human one in its final state seems to me like taking a person who is just dead and trying to restart the brain by means of electric shocks.
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    Actually on a neuronal level all information gets processed. Not all of it makes it into "conscious" processing or attention. Whatever makes it into conscious processing is a highly reduced representation of the data you get. However that doesn't get lost. Basic, low processed data forms the basis of proprioception and reflexes. Every step you take is a macro command your brain issues to the intricate sensory-motor system that puts your legs in motion by actuating every muscle and correcting every step deviation from its desired trajectory using the complicated system of nerve endings and motor commands. Reflexes which were build over the years, as those massive amounts of data slowly get integrated into the nervous system and the the incipient parts of the brain. But without all those sensors scattered throughout the body, all the little inputs in massive amounts that slowly get filtered through, you would not be able to experience your body, and experience the world. Every concept that you conjure up from your mind is a sort of loose association of your sensorimotor input. How can a robot understand the concept of a strawberry if all it can perceive of it is its shape and color and maybe the sound that it makes as it gets squished? How can you understand the "abstract" notion of strawberry without the incredibly sensible tactile feel, without the act of ripping off the stem, without the motor action of taking it to our mouths, without its texture and taste? When we as humans summon the strawberry thought, all of these concepts and ideas converge (distributed throughout the neurons in our minds) to form this abstract concept formed out of all of these many many correlations. A robot with no touch, no taste, no delicate articulate motions, no "serious" way to interact with and perceive its environment, no massive flow of information from which to chose and and reduce, will never attain human level intelligence. That's point 1. Point 2 is that mere pattern recogn
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    All information *that gets processed* gets processed but now we arrived at a tautology. The whole problem is ultimately nobody knows what gets processed (not to mention how). In fact an absolute statement "all information" gets processed is very easy to dismiss because the characteristics of our sensors are such that a lot of information is filtered out already at the input level (e.g. eyes). I'm not saying it's not a valid and even interesting assumption, but it's still just an assumption and the next step is to explore scientifically where it leads you. And until you show its superiority experimentally it's as good as all other alternative assumptions you can make. I only wanted to point out is that "more processing" is not exactly compatible with some of the fundamental assumptions of the embodiment. I recommend Wilson, 2002 as a crash course.
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    These deal with different things in human intelligence. One is the depth of the intelligence (how much of the bigger picture can you see, how abstract can you form concept and ideas), another is the breadth of the intelligence (how well can you actually generalize, how encompassing those concepts are and what is the level of detail in which you perceive all the information you have) and another is the relevance of the information (this is where the embodiment comes in. What you do is to a purpose, tied into the environment and ultimately linked to survival). As far as I see it, these form the pillars of human intelligence, and of the intelligence of biological beings. They are quite contradictory to each other mainly due to physical constraints (such as for example energy usage, and training time). "More processing" is not exactly compatible with some aspects of embodiment, but it is important for human level intelligence. Embodiment is necessary for establishing an environmental context of actions, a constraint space if you will, failure of human minds (i.e. schizophrenia) is ultimately a failure of perceived embodiment. What we do know is that we perform a lot of compression and a lot of integration on a lot of data in an environmental coupling. Imo, take any of these parts out, and you cannot attain human+ intelligence. Vary the quantities and you'll obtain different manifestations of intelligence, from cockroach to cat to google to random quake bot. Increase them all beyond human levels and you're on your way towards the singularity.
santecarloni

innerSuper - 5 views

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    never seen a representation so accurate...
LeopoldS

Map of the Day - National Geographic Magazine - 2 views

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    nice representation of where "we went"
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    Can't find the Lagrange points missions...
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    you are right - they are not there ...
LeopoldS

physicists explain what AI researchers are actually doing - 5 views

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    love this one ... it seems to take physicist to explain to the AI crowd what they are actually doing ... Deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data. Recently, such techniques have yielded record-breaking results on a diverse set of difficult machine learning tasks in computer vision, speech recognition, and natural language processing. Despite the enormous success of deep learning, relatively little is understood theoretically about why these techniques are so successful at feature learning and compression. Here, we show that deep learning is intimately related to one of the most important and successful techniques in theoretical physics, the renormalization group (RG). RG is an iterative coarse-graining scheme that allows for the extraction of relevant features (i.e. operators) as a physical system is examined at different length scales. We construct an exact mapping from the variational renormalization group, first introduced by Kadanoff, and deep learning architectures based on Restricted Boltzmann Machines (RBMs). We illustrate these ideas using the nearest-neighbor Ising Model in one and two-dimensions. Our results suggests that deep learning algorithms may be employing a generalized RG-like scheme to learn relevant features from data.
alekenolte

Research Blog: Inceptionism: Going Deeper into Neural Networks - 0 views

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    Deep neural networks "dreaming" psychedelic images
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    Although that's not technically correct. The networks don't actually generate the images, rather the features that get triggered in the network already get amplified through some heuristic. Still fun tho`
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    Now in real time: http://www.twitch.tv/317070
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    Yes, true for the later images, but for the first images they start with random noise and a 'natural image' prior, no? But I guess calling it "hallucinating" might have been more accurate ;)
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    Funny how representation errors in NNs suddenly become art. God.... neo-post-modernism.
Juxi Leitner

Data Nerds Hack NASA - in a Good Way | Wired Science | Wired.com - 0 views

  • The event is just one of dozens this weekend being promoted by the Sunlight Foundation as part of its Great American Hackathon. Each one is being organized by volunteers who want to make government data easier to access and more useful to the public. In Pittsburgh, the hackers will be working on making stimulus spending easier to understand. In Boston, the Massachusetts Department of Transportation data will be the focus.
Francesco Biscani

STLport: An Interview with A. Stepanov - 2 views

  • Generic programming is a programming method that is based in finding the most abstract representations of efficient algorithms.
  • I spent several months programming in Java.
  • for the first time in my life programming in a new language did not bring me new insights
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  • it has no intellectual value whatsoever
  • Java is clearly an example of a money oriented programming (MOP).
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    One of the authors of the STL (C++'s Standard Template Library) explains generic programming and slams Java.
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    "Java is clearly an example of a money oriented programming (MOP)." Exactly. And for the industry it's the money that matters. Whatever mathematicians think about it.
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    It is actually a good thing that it is "MOP" (even though I do not agree with this term): that is what makes it inter-operable, light and easy to learn. There is no point in writing fancy codes, if it does not bring anything to the end-user, but only for geeks to discuss incomprehensible things in forums. Anyway, I am pretty sure we can find a Java guy slamming C++ ;)
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    Personally, I never understood what the point of Java is, given that: 1) I do not know of any developer (maybe Marek?) that uses it for intellectual pleasure/curiosity/fun whatever, given the possibility of choice - this to me speaks loudly on the objective qualities of the language more than any industrial-corporate marketing bullshit (for the record, I argue that Python is more interoperable, lighter and easier to learn than Java - which is why, e.g., Google is using it heavily); 2) I have used a software developed in Java maybe a total of 5 times on any computer/laptop I owned over 15 years. I cannot name of one single Java project that I find necessary or even useful; for my usage of computers, Java could disappear overnight without even noticing. Then of course one can argue as much as one wants about the "industry choosing Java", to which I would counterargue with examples of industry doing stupid things and making absurd choices. But I suppose it would be a kind of pointless discussion, so I'll just stop here :)
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    "At Google, python is one of the 3 "official languages" alongside with C++ and Java". Java runs everywhere (the byte code itself) that is I think the only reason it became famous. Python, I guess, is more heavy if it were to run on your web browser! I think every language has its pros and cons, but I agree Java is not the answer to everything... Java is used in MATLAB, some web applications, mobile phones apps, ... I would be a bit in trouble if it were to disappear today :(
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    I personally do not believe in interoperability :)
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    Well, I bet you'd notice an overnight disappearance of java, because half of the internet would vanish... J2EE technologies are just omnipresent there... I'd rather not even *think* about developing a web application/webservice/web-whatever in standard C++... is it actually possible?? Perhaps with some weird Microsoft solutions... I bet your bank online services are written in Java. Certainly not in PHP+MySQL :) Industry has chosen Java not because of industrial-corporate marketing bullshit, but because of economics... it enables you develop robustly, reliably, error-prone, modular, well integrated etc... software. And the costs? Well, using java technologies you can set-up enterprise-quality web application servers, get a fully featured development environment (which is better than ANY C/C++/whatever development environment I've EVER seen) at the cost of exactly 0 (zero!) USD/GBP/EUR... Since many years now, the central issue in software development is not implementing algorithms, it's building applications. And that's where Java outperforms many other technologies. The final remark, because I may be mistakenly taken for an apostle of Java or something... I love the idea of generic programming, C++ is my favourite programming language (and I used to read Stroustroup before sleep), at leisure time I write programs in Python... But if I were to start a software development company, then, apart from some very niche applications like computer games, it most probably would use Java as main technology.
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    "I'd rather not even *think* about developing a web application/webservice/web-whatever in standard C++... is it actually possible?? Perhaps with some weird Microsoft solutions... I bet your bank online services are written in Java. Certainly not in PHP+MySQL :)" Doing in C++ would be awesomely crazy, I agree :) But as I see it there are lots of huge websites that operate on PHP, see for instance Facebook. For the banks and the enterprise market, as a general rule I tend to take with a grain of salt whatever spin comes out from them; in the end behind every corporate IT decision there is a little smurf just trying to survive and have the back covered :) As they used to say in the old times, "No one ever got fired for buying IBM". "Industry has chosen Java not because of industrial-corporate marketing bullshit, but because of economics... it enables you develop robustly, reliably, error-prone, modular, well integrated etc... software. And the costs? Well, using java technologies you can set-up enterprise-quality web application servers, get a fully featured development environment (which is better than ANY C/C++/whatever development environment I've EVER seen) at the cost of exactly 0 (zero!) USD/GBP/EUR... Since many years now, the central issue in software development is not implementing algorithms, it's building applications. And that's where Java outperforms many other technologies." Apart from the IDE considerations (on which I cannot comment, since I'm not a IDE user myself), I do not see how Java beats the competition in this regard (again, Python and the huge software ecosystem surrounding it). My impression is that Java's success is mostly due to Sun pushing it like there is no tomorrow and bundling it with their hardware business.
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    OK, I think there is a bit of everything, wrong and right, but you have to acknowledge that Python is not always the simplest. For info, Facebook uses Java (if you upload picture for instance), and PHP is very limited. So definitely, in company, engineers like you and me select the language, it is not a marketing or political thing. And in the case of fb, they come up with the conclusion that PHP, and Java don't do everything but complement each other. As you say Python as many things around, but it might be too much for simple applications. Otherwise, I would seriously be interested by a study of how to implement a Python-like system on-board spacecrafts and what are the advantages over mixing C, Ada and Java.
marenr

NeuroNex - Odor2Action - 0 views

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    Let's keep a eye on this... Animals use odor cues to navigate through their environments, helping them locate targets and assess danger. Much of how animal brains organize, read out, and respond to odor stimuli across spatial and temporal scales is not well understood. To tackle these questions, Odor2Action uses a highly interdisciplinary team science approach. Our work uses fruit fly, honeybee, and mouse models to determine how neural representations of odor are generated, reformatted, and translated to generate useful behaviors that guide how animals interact with their environment.
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    reminds me of the methan smelling source finding study we did ...
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