The paper "Wasserstein Learning of Deep Generative Point Process Models" published at the NIPS 2017 conference has some interesting ideas in it, connecting generative deep learning - which is mostly used for dense data such as pixels - together with point processes, which are useful for "spiky" timestamp events.
They use the Wasserstein distance (aka the "earth-mover's distance") to compare sequences of spikes, and they do acknowledge that this has advantages and disadvantages. It's all about pushing things around until they match up - e.g. move a spike a few seconds earlier in one sequence, so that it lines up with a spike in the other sequence. It doesn't nicely account for insertions or deletions, which is tricky because it's quite common to have "missing" spikes for added "clutter" in data coming from detectors, for example. It'd be better if this method could incorporate more general "edit distances", though that's non-trivial.
So I was thinking about distances between point processes. More reading to be done. But a classic idea, and a good way to think about insertions/deletions, is called "thinning". It's where you take some data from a point process and randomly delete some of the events, to create a new event sequence. If you're using Poisson processes then thinning can be used for example to sample from a non-stationary Poisson process, essentially by "rejection sampling" from a stationary one.
Thinning is a probabilistic procedure: in the simplest case, take each event, flip a coin, and keep the event only if the coin says heads. So if we are given one event sequence, and a specification of the thinning procedure, we can define the likelihood that this would have produced any given "thinned" subset of events. Thus, if we take two arbitrary event sequences, we can imagine their union was the "parent" from which they were both derived, and calculate a likelihood that the two were generated from it. (Does it matter if the parent process actually generated this union list, or if there were unseen "extra" parent events that were actually deleted from both? In simple models where the thinning is independent for each event, no: the deletion process can happen in any order, and so we can assume those common deletions happened first to take us to some "common ancestor". However, this does make it tricky to compare distances across different datasets, because the unseen deletions are constant multiplicative factors on the true likelihood.)
We can thus define a "thinning distance" between two point process realisations as the negative log-likelihood under this thinning model. Clearly, the distance depends entirely on the number of events the two sequences have in common, and the numbers of events that are unique to them - the actual time positions of the events has no effect, in this simple model, it's just whether they line up or not. It's one of the simplest comparisons we can make. It's complementary to the Wasserstein distance which is all about time-position and not about insertions/deletions.
This distance boils down to:
NLL = -( n1 * log(n1/nu) + n2 * log(n2/nu) + (nu-n1) * log(1 - n1/nu) + (nu-n2) * log(1 - n2/nu) )
where "n1" is the number of events in seq 1, "n2" in seq 2, and "nu" in their union.
Does this distance measure work? Yes, at least in limited toy cases. I generated two "parent" sequences (using the same rate for each) and separately thinned each one ten times. I then measured the thinning distance between all pairs of the child sequences, and there's a clear separation between related and unrelated sequences:
Distances between distinct children of same process: Min 75.2, Mean 93.3, Median 93.2, Max 106.4 Distances between children of different processes: Min 117.3, Mean 137.7, Median 138.0, Max 167.3
This is nice because easy to calculate, etc. To be able to do work like in the paper I cited above, we'd need to be able to optimise against something like this, and even better, to be able to combine it into a full edit distance, one which we can parameterise according to situation (e.g. to balance the relative cost of moves vs. deletions).
This idea of distance based on how often the spikes coincide relates to "co-occurrence metrics" previously described in the literature. So far, I haven't found a co-occurrence metric that takes this form. To relax the strict requirement of events hitting at the exact same time, there's often some sort of quantisation or binning involved in practice, and I'm sure that'd help for direct application to data. Ideally we'd generalise over the possible quantisations, or use a jitter model to allow for the fact that spikes might move.
I'm lucky to be working with a great set of PhD students on a whole range of exciting topics about sound and computation. (We're based in C4DM and the Machine Listening Lab.) Let me give you a quick snapshot of what my students are up to!
I'm primary supervisor for Veronica and Pablo:
Veronica is working on deep learning techniques for jointly identifying the species and the time-location of bird sounds in audio recordings. A particular challenge is the relatively small amount of labelled data available for each species, which forces us to pay attention to how the network architecture can make best use of the data and the problem structure.
- A paper by Veronica (not deep learning, that paper; it's on its way)
Pablo is using a mathematical framework called Gaussian processes as a new paradigm for automatic music transcription - the idea is that it can perform high-resolution transcription and source separation at the same time, while also making use of some sophisticated "priors" i.e. information about the structure of the problem domain. A big challenge here is how to scale it up to run over large datasets.
I'm joint-primary supervisor for Will and Delia:
Will is developing a general framework for analysing sounds and generating new sounds, combining subband/sinusoidal analysis with probabilistic generative modelling. The aim is that the same model can be used for sound types as diverse as footsteps, cymbals, dog barks...
Delia is working on source separation and audio enhancement, using a lightweight framework based on nonlocal median filtering, which works without the need for large training datasets or long computation times. The challenge is to adapt and configure this so it makes best use of the structure of the data that's implicitly there within an audio recording.
I'm secondary supervisor for Jiajie and Sophie:
Jiajie is studying how singers' pitch tuning is affected when they sing together. She has designed and conducted experiments with two or four singers, in which sometimes they can all hear each other, sometimes only one can hear the other, etc. Many singers or choir conductors have their own folk-theories about what affects people's tuning, but Jiajie's experiments are making scientific measurements of it.
Sophie is exploring how to enhance a sense of community (e.g. for a group of people living together in a housing estate) through technological interventions that provide a kind of mediated community awareness. Should inhabitants gather around the village square or around a Facebook group? Those aren't the only two ways!
- (A paper coming later!)
I'm just flying from the International Bioacoustics Congress 2017, held in Haridwar in the north of India. It was a really interesting time. I'm glad that IBAC was successfully brought to India, i.e. to a developing country with a more fragmented bioacoustics community (I think!) than in the west. For me, getting to know some of the Indian countryside, the people, and the food was ace. Let me make a few notes about research themes that were salient to me:
- "The music is not in the notes, but in the silence between" - this Mozart quote which Anshul Thakur used is a lovely byline for his studies - as well as some studies by others - on using the durations of the gaps between units in a birdsong, for the purposes of classification/analysis. Here's who investigated gaps:
- Anshul Thakur howed how he used the gap duration as an auxiliary feature, alongside the more standard acoustic classification, to improve quality.
- Florencia Noriega discussed her own use of gap durations, in which she fitted a Gaussian mixture model to the histogram of log-gap-durations between parrot vocalisation units, and then used this to look for outliers. One use that she suggested was that this could be a good way to look for unusual vocalisation sequences that could be checked out in more detail by further fieldwork.
- (I have to note here, although I didn't present it at IBAC, that I've also been advocating the use of gap durations. The clearest example is in our 2013 paper in JMLR in which we used them to disentangle sequences of bird sounds.)
- Tomoko Mizuhara presented evidence from a perceptual study in zebrafinches, that the duration of the gap preceding a syllable exhibits some role in the perception of syllable identity. The gap before? Why? - Well one connection is that the gap before an event might relate if it's the time the bird takes to breathe in, and thus there's an empirical correlation, whether the bird is using that purely empirically or for some more innate reason.
Machine learning methods in bioacoustics - this is the session that I organised, and I think it went well - I hope people found it useful. I won't go into loads of detail here since I'm mostly making notes about things that are new to me. One notable thing though was Vincent Lostanlen announcing a dataset "BirdVox-70k" (flight calls of birds recorded in the USA, annotated with the time and frequency of occurrence) - I always like it when a dataset that might be useful for bird sound analysis is published under an open licence! No link yet - I think that's to come soon. (They've also done other things such as this neat in-browser audio annotation tool.)
Software platforms for bioacoustics. When I do my research I'm often coding my own Python scripts or suchlike, but that's not a vernacular that most bioacousticians speak. It's tricky to think what the ideal platform for bioacoustics would be, since there are quite some demands to meet: for example ideally it could handle ten seconds of audio as well as one year of audio, yet also provide an interface suitable for non-programmers. A few items on that theme:
- Phil Eichinski (standing in for Paul Roe) presented QUT's Eco-Sounds platform. They've put effort into making it work for big audio data, managing terabytes of audio and optimising whether to analyse the sound proactively or on-demand. The false-colour "long duration spectrograms" developed by Michael Towsey et al are used to visualise long audio recordings. (I'll say a bit more about that below.)
- Yves Bas presented his Tadarida toolbox for detection and classification.
- Ed Baker presented his BioAcoustica platform for archiving and analysing sounds, with a focus on connecting deposits to museum specimens and doing audio query-by-example.
- Anton Gradisek, in our machine-learning session, presented "JSI Sound: a machine learning tool in Orange for classification of diverse biosounds" - this is a kind of "machine-learning as a service" idea.
- Then a few others that might or might not be described as full-blown "platforms":
- Tomas Honaiser wasn't describing a new platform, but his monitoring work - I noted that he was using the existing AMIBIO project to host and analyse his recordings.
- Sandor Zsebok presented his Ficedula matlab toolbox which he's used for segmenting and clustering etc to look at cultural evolution in the Collared flycatcher.
- Julie Elie mentioned her lab's SoundSig Python tools for audio analysis.
- Oh by the way, what infrastructure should these projects be built upon? The QUT platform is built using Ruby, which is great for web developers but strikes me as an odd choice because very few people in bioacoustics or signal processing have even heard of it - so how is the team / the community going to find the people to maintain it in future? (EDIT: here's a blog article with background information that the QUT team wrote in response to this question.) Yves Bas' is C++ and R which makes sense for R users (fairly common in this field). BioAcoustica - not sure if it's open-source but there's an R package that connects to it. --- I'm not an R user, I much prefer Python, because of its good language design, its really wide user base, and its big range of uses, though I recognise that it doesn't have the solid stats base that R does. People will debate the merits of these tools for ever onwards - we're not going to all come down on one decision - but it's a question that I often come back to, how best to build software tools to ensure they're useable and maintainable and solid.
So about those false-colour "long duration spectrograms". I've been advocating this visualisation method ever since I saw Michael Towsey present it (I think at the Ecoacoustics meeting in Paris). Just a couple of months ago I was at a workshop at the University of Sussex and Alice Eldridge and colleagues had been playing around with it too. At IBAC this week, ecologist Liz Znidersic talked really interestingly about how she had used them to detect a cryptic (i.e. hard-to-find) bird species. It shows that the tool helps with "needle in a haystack" problems, including those where you might not have a good idea of what needle you're looking for.
In Liz's case she looked at the long-duration spectrograms manually, to spot calling activity patterns. We could imagine automating this, i.e. using the long-dur spectrogam as a "feature set" to make inferences about diurnal activity. But even without automation it's still really neat.
Anyway back to the thematic listings...
- Trills in bird sounds are fascinating. These rapidly-frequency-modulated sounds are often difficult and energetic to do, and this seems to lead to them being used for specific functions.
- Tereza Petruskova presented a poster of her work on tree pipits, arguing for different roles for the "loud trill" and the "soft trill" in their song.
- Christina Masco spoke about trills in splendid fairywrens (cute-looking birds those!). They can be used as a call but can also be included at the end of a song, which raises the question of why are they getting "co-opted" in this way. Christina argued that the good propagation properties of the trill could be a reason - there was some discussion about differential propagation and the "ranging hypothesis" etc.
- Ole Larsen gave a nice opening talk about signal design for private vs public messages. It was all well-founded, though I quibbled his comment that strongly frequency-modulated sounds would be for "private" communication because if they cross multiple critical bands they might not accumulate enough energy in a "temporal integration window" to trigger detection. This seems intuitively wrong to me (e.g.: sirens!) but I need to find some good literature to work this one through.
- Hybridisation zones are interesting for studying birdsong, since they're zones where two species coexist and individuals of that species might or might not breed with individuals of the other species. For birds, song recognition might play a part in whether this happens. It's quite a "strong" concept of perceptual similarity, to ask the question "Is that song similar enough to breed with?"!
- Alex Kirschel showed evidence from a suboscine (and so not a vocal learner) which in some parts of Africa seems to hybridise and in some parts seems not to - and there could be some interplay with the similarity of the two species' song in that region.
- Irina Marova also talked about hybridisation, in songbirds, but I failed to make a note of what she said!
- Duetting in birdsong was discussed by a few people, including Pedro Diniz and Tomasz Osiejuk. Michal Budka argued that his playback studies with Chubb's cisticola showed they use duet for territory defence and signalling commitment but not for "mate-guarding".
- Oh and before the conference, I was really taken by the duetting song of the grey treepie, a bird we heard up in the Himalayan hills. Check it out if you can!
As usual, my apologies to anyone I've misrepresented. IBAC has long days and lots of short talks (often 15 minutes), so it can all be a bit of a whirlwind! Also of course this is just a terribly partial list.
(PS: from the archives, here's my previous blog about IBAC 2015 in Murnau, Germany.)
In the early twentieth century when the equations of quantum physics were born, physicists found themselves in a difficult position. They needed to interpret what the quantum equations meant in terms of their real-world consequences, and yet they were faced with paradoxes such as wave-particle duality and "spooky action at a distance". They turned to philosophy and developed new metaphysics of their own. Thought-experiments such as Schrodinger's cat, originally intended to highlight the absurdity of the standard "Copenhagen interpretation", became standard teaching examples.
In the twenty-first century, researchers in artificial intelligence (AI) and machine learning (ML) find themselves in a roughly analogous position. There has been a sudden step-change in the abilities of machine learning systems, and the dream of AI (which had been put on ice after the initial enthusiasm of the 1960s turned out to be premature) has been reinvigorated - while at the same time, the deep and widespread industrial application of ML means that whatever advances are made, their effects will be felt. There's a new urgency to long-standing philosophical questions about minds, machines and society.
So I was glad to see that Neil Lawrence, an accomplished research leader in ML, published an article on these social implications. The article is "Living Together: Mind and Machine Intelligence". Lawrence makes a noble attempt to provide an objective basis for considering the differences between human and machine intelligences, and what those differences imply for the future place of machine intelligence in society.
In case you're not familiar with the arXiv website I should point out that articles there are un-refereed, they haven't been through the peer-review process that guards the gate of standard scientific journals. And let me cut to the chase - in this paper, I'm not sure which journal he was targeting, but if I was a reviewer I wouldn't have recommended acceptance. Lawrence's computer science is excellent, but here I find his philosophical arguments are disappointing. Here's my review:
Embodiment? No: containment
A key difference between humans and machines, notes Lawrence, is that we humans - considered for the moment as abstract computational agents - have high computational capacity but a very limited bandwidth to communicate. We speak (or type) our thoughts, but really we're sharing the tiniest shard of the information we have computed, whereas modern computers can calculate quite a lot (not as much as does a brain) but they can communicate with such high bandwidth that the results are essentially not "trapped" in the computer. For Lawrence this is a key difference, making the boundaries between machine intelligences much less pertinent than the boundaries between natural intelligences, and suggesting that future AI might not act as a lot of "agents" but as a unified subconscious.
Lawrence quantifies this difference as the numerical ratio between computational capacity and communicative bandwidth. Embarrassingly, he then names this ratio the "embodiment factor". The embodiment of cognition is an important idea in much modern thought-about-thought: essentially, "embodiment" is the rejection of the idea that my cognition can really be considered as an abstract computational process separate from my body. There are many ways we can see this: my cognition is non-trivially affected by whether or not I have hay-fever symptoms today; it's affected by the limited amount of energy I have, and the fact I must find food and shelter to keep that energy topped up; it's affected by whether I've written the letter "g" on my hand (or is it a "9"? oh well); it's affected by whether I have an abacus to hand; it's affected by whether or not I can fly, and thus whether in my experience it's useful to think about geography as two-dimensional or three-dimensional. (For a recent perspective on extended cognition in animals see the thoughts of a spiderweb.) I don't claim to be an expert on embodied cognition. But given the rich cognitive affordances that embodiment clearly offers, it's terribly embarrassing and a little revealing that Lawrence chooses to reduce it to the mere notion of being "locked in" (his phrase) with constraints on our ability to communicate.
Lawrence's ratio could perhaps be useful, so to defuse the unfortunate trivial reduction of embodiment, I would like to rename it "containment factor". He uses it to argue that while humans can be considered as individual intelligent agents, for computer intelligences the boundaries dissolve and they can be considered more as a single mass. But it's clear that containment is far from sufficient in itself: natural intelligences are not the only things whose computation is not matched by their communication. Otherwise we would have to consider an air-gapped laptop as an intelligent agent, but not an ordinary laptop.
Agents have their own goals and ambitions
The argument that the boundaries between AI agents dissolve also rests on another problem. In discussing communication Lawrence focusses too heavily on 'altruistic' or 'honest' communication: transparent communication between agents that are collaborating to mutually improve their picture of the world. This focus leads him to neglect the fact that communicating entities often have differing goals, and often have reason to be biased or even deceitful in the information shared.
The tension between communication and individual aims has been analysed in a long line of thought in evolutionary biology under the name of signalling theory. For example the conditions under which "honest signalling" is beneficial to the signaller. It's important to remember that the different agents each have their own contexts, their own internal states/traits (maybe one is low on energy reserves, and another is not) which affect communicative goals even if the overall avowed aim is common.
In Lawrence's description the focus on honest communication leads him to claim that "if an entity's ability to communicate is high [...] then that entity is arguably no longer distinct from those which it is sharing with" (p3). This is a direct consequence of Lawrence's elision: it can only be "no longer distinct" if it has no distinct internal traits, states, or goals. The elision of this aspect recurs throughout, e.g. "communication reduces to a reconciliation of plot lines among us" (p5).
Unfortunately the implausible unification of AI into a single morass is a key plank of the ontology that Lawrence wants to develop, and also key to the societal consequences he draws.
There is no System Zero
Lawrence considers some notions of human cognition including the idea of "system 1 and system 2" thinking, and proposes that the mass of machine intelligence potentially forms a new "System Zero" whose essentially unconscious reasoning forms a new stratum of our cognition. The argument goes that this stratum has a strong influence on our thought and behaviour, and that the implications of this on society could be dramatic. This concept has an appeal of neatness but it falls down too easily. There is no System Zero, and Lawrence's conceptual starting-point in communication bandwidth shows us why:
- Firstly, the morass of machine intelligence has no high-bandwidth connection to System 1 or to System 2. The reason we talk of "System 1 and System 2" coexisting in the same agent is that they're deeply and richly connected in our cognition. (BTW I don't attribute any special status to "System 1 and System 2", they're just heuristics for thinking about thinking - that doesn't really matter here.) Lawrence's own argument about the poverty of communication channels such as speech also goes for our reception of information. However intelligent, unified or indeed devious AI becomes, it communicates with humans through narrow channels such as adverts, notifications on your smartphone, or selecting items to show to you. The "wall" between ourselves as agents and AI will be significant for a long time.
- Direct brain-computer interfacing is a potential counterargument here, and if that technology were to develop significantly then it is true that our cognition could gain a high-bandwidth interface. I remain sceptical that such potential will be non-trivially realised in my lifetime. And if they do come to pass, they would dissolve human-human bottlenecks as much as human-computer bottlenecks, so in either case Lawrence's ontology does not stand.
- Secondly AI/ML technologies are not unified. There's no one entity connecting them all together, endowing them with the same objective. Do you really think that Google and Facebook, Europe and China, will pool their machine intelligences together, allowing unbounded and unguarded communication? No. And so irrespective of how high the bandwidth is within and between these silos, they each act as corporate agents, with some degrees of collusion and mutual inference, sure, but they do not unify into an underlying substrate of our intelligence. This disunification highlights the true ontology: these agents sit relative to us as agents - powerful, information-rich and potentially dangerous agents, but then so are some humans.
Disturbingly Lawrence claims "Sytem Zero is already aligned with our goals". This starts from a useful observation - that many commercial processes such as personalised advertising work because they attempt to align with our subconscious desires and biases. But again it elides too much. In reality, such processes are aligned not with our goals but with the goals of powerful social elites, large companies etc, and if they are aligned with our "system 1" goals then that is a contingent matter.
Importantly, the control of these processes is largely not democratic but controlled commercially or via might-makes-right. Therefore even if AI/ML does align with some people's desires, it will preferentially align with the desires of those with cash to spend.
We need models; machines might not
On a positive note: Lawrence argues that our limited communication bandwidth shapes our intelligence in a particular way: it makes it crucial for us to maintain "models" of others, so that we can infer their internal state (as well as our own) from their behaviour and their signalling. He argues that conversely, many ML systems do not need such structured models - they simply crunch on enough data and they are able to predict our behaviour pretty well. This distinction seems to me to mark a genuine difference between natural intelligence and AI, at least according to the current state of the art in ML.
He does go a little too far in this as well, though. He argues that our reliance on a "model" of our own behaviour implies that we need to believe that our modelled self is in control - in Freudian terms, we could say he is arguing that the existence of the ego necessitates its own illusion that it controls the id. The argument goes that if the self-model knew it was not in control,
"when asked to suggest how events might pan out, the self model would always answer with "I don't know, it would depend on the precise circumstances"."
This argument is shockingly shallow coming from a scientist with a rich history of probabilistic machine learning, who knows perfectly well how machines and natural agents can make informed predictions in uncertain circumstances!
I also find unsatisfactory the eagerness with which various dualisms are mapped onto one another. The most awkward is the mapping of "self-model vs self" onto Cartesian dualism (mind vs body); this mapping is a strong claim and needs to be argued for rather than asserted. It would also need to account for why such mind-body dualism is not a universal, across history nor across cultures.
However, Lawrence is correct to argue that "sentience" of AI/ML is not the overriding concern in its role in our society; rather, its alignment or otherwise with our personal and collective goals, and its potential to undermine human democratic agency, is the prime issue of concern. This is a philosophical and a political issue, and one on which our discussion should continue.
This season, I'm lead organiser for two special conference sessions on machine listening for bird/animal sound: EUSIPCO 2017 in Kos, Greece, and IBAC 2017 in Haridwar, India. I'm very happy to see the diverse selection of work that has been accepted for presentation - the diversity of the research itself, yes, but also the diversity of research groups and countries from which the work comes.
The official programmes haven't been announced yet, but as a sneak preview here are the titles of the accepted submissions, so you can see just how lively this research area has become!
Accepted talks for IBAC 2017 session on "Machine Learning Methods in Bioacoustics":
A two-step bird species classification approach using silence durations in song bouts
Automated Assessment of Bird Vocalisation Activity
Deep convolutional networks for avian flight call detection
Estimating animal acoustic diversity in tropical environments using unsupervised multiresolution analysis
JSI sound: a machine-learning tool in Orange for classification of diverse biosounds
Prospecting individual discrimination of maned wolves’ barks using wavelets
Accepted papers for EUSIPCO 2017 session on "Bird Audio Signal Processing":
(This session is co-organised with Yiannis Stylianou and Herve Glotin)
Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection preprint
Densely Connected CNNs for Bird Audio Detection preprint
Classification of Bird Song Syllables Using Wigner-Ville Ambiguity Function Cross-Terms
Convolutional Recurrent Neural Networks for Bird Audio Detection preprint
Joint Detection and Classification Convolutional Neural Network (JDC-CNN) on Weakly Labelled Bird Audio Data (BAD)
Rapid Bird Activity Detection Using Probabilistic Sequence Kernels
Automatic Frequency Feature Extraction for Bird Species Delimitation
Two Convolutional Neural Networks for Bird Detection in Audio Signals
Masked Non-negative Matrix Factorization for Bird Detection Using Weakly Labelled Data
Archetypal Analysis Based Sparse Convex Sequence Kernel for Bird Activity Detection
Automatic Detection of Bird Species from Audio Field Recordings Using HMM-based Modelling of Frequency Tracks
Please note: this is a PREVIEW - sometimes papers get withdrawn or plans change, so these lists should be considered provisional for now.
People love to take the vegans down a peg or two. I guess they must unconsciously agree that the vegans are basically correct and doing the right thing, hence the defensive mud-slinging.
There's a bullshit article "Being vegan isn’t as good for humanity as you think". Like many bullshit articles, it's based on manipulating some claims from a research paper.
The point that the article is making is summarised by this quote:
"When applied to an entire global population, the vegan diet wastes available land that could otherwise feed more people. That’s because we use different kinds of land to produce different types of food, and not all diets exploit these land types equally."
This is factually correct, according to the original research paper which itself seems a decent attempt to estimate the different land requirements of different diets. The clickbaity inference, especially as stated in the headline, is that vegans are wrong. But that's where the bullshit lies.
Why? Look again at the quote. "When applied to an entire global population." Is that actually a scenario anyone expects? The whole world going vegan? In the next ten years, fifty years, a hundred? No. It's fine for the research paper to look at full-veganism as a comparison against the 9 other scenarios they consider (e.g. 20% veggy, 100% veggy), but the researchers are quite clear that their model is about what a whole population eats. You can think of it as what "an average person" eats, but no it's not what "each person should" eat.
The research concludes that a vegetarian diet is "best", judged on this specific criterion of how big a population can the USA's farmland support. And since that's for the population as a whole, and there's no chance that meat-eating will entirely leave the Western diet, a more sensible journalistic conclusion is that we should all be encouraged to be a bit more vegetarian, and the vegans should be celebrated for helping balance out those meat-eaters.
Plus, of course, the usual conclusion: more research is needed. This research was just about land use, it didn't include considerations of CO2 emissions, welfare, social attitudes, geopolitics...
The research illustrates that the USA has more than enough land to feed its population and that this could be really boosted if we all transition to eat a bit less meat. Towards the end of the paper, the researchers note that if the USA moved to a vegetarian diet, "the dietary changes could free up capacity to feed hundreds of millions of people around the globe."
People who do technical work with sound use spectrograms a heck of a lot. This standard way of visualising sound becomes second nature to us.
As you can see from these photos, I like to point at spectrograms all the time:
(Our research group even makes some really nice software …
Last year I took part in the Dagstuhl seminar on Vocal Interactivity in-and-between Humans, Animals and Robots (VIHAR). Many fascinating discussions with phoneticians, roboticists, and animal behaviourists (ethologists).
One surprisingly difficult topic was to come up with a basic data model for describing multi-party interactions. It was so easy to …
A colleague pointed out this new review paper in the journal "Animal Behaviour": Applications of machine learning in animal behaviour studies.
It's a useful introduction to machine learning for animal behaviour people. In particular, the distinction between machine learning (ML) and classical statistical modelling is nicely described (sometimes tricky to …
InterSpeech 2016 was a very interesting conference. I have been to InterSpeech before, yes - but I'm not a speech-recognition person so it's not my "home" conference. I was there specifically for the birds/animals special session (organised by Naomi Harte and Peter Jancovic), but it was also a great opportunity …