Negative Feedback

Category: Uncategorized

  • Novelty is easy, quality is hard

    I am often surprised when people claim that “AI can’t make anything truly new”. I think making new things is very easy

    Let’s try generating some novel text:

    fHabBiCcABnqjSAIysqcyJAnCTWCIk

    That’s very novel. I used Python to produce random letters:

    import random
import string

print(''.join(random.choice(string.ascii_letters) for _ in range(20)))

    Oh, but those aren’t even words, you say? OK, let’s try again:

    anthocyan Delhi iliococcygian methodologically opah reapparel pretimely humoristic Jingbai specks

    This was made by randomly selecting words from my system dictionary.

    import random
import pathlib

words = pathlib.Path('/usr/share/dict/words').read_text().splitlines()
print(' '.join(random.choice(words) for _ in range(10)))

    Very novel. I now hear someone say that this is not a well-formed English sentence.

    We now stand at a crossroads. We can enforce the structure of a sentence (something like “the <noun> <verb>ed the <noun>”) or we could work statistically off some corpus with simple Markov chains. These simple Markov processes can generate some surprisingly readable text (without using any rules about the english language). Here’s a snippet that was on top of the kingjamesprogramming tumblr site when I went there. This was generated by a Markov process trained on the King James bible and “The structure and interpretation of computer programs“.

    then shall they call upon me, but I will not cause any information to be accumulated on the stack.

    I’m pretty sure that’s a novel thought. But we’re starting to get to what people would perhaps agree is a sentence but start objecting to more on semantic grounds. Does this sentence mean anything? Is it true?

    If we go down the “The noun verbed the noun” route, we end up with a slightly different feeling. A feeling of “sameness”. I think often this is what people react to when they spend a bit of time conversing with ChatGPT. It’s not the novelty of the words or even ideas they’re responding to, it’s more the sameness of the vibe. Something that in people could be called their writing voice.

    This is a very deep concept, but can also be circumvented by leaning harder into randomness. In the GPT API, you can increase the parameter called “temperature” to get some more “out there” combinations.

    In the end we’re back to the same thing: It’s not the novelty that’s the problem. It’s the quality.

  • Don’t judge your open-loop predictions by closed-loop outcomes

    Many of us have driven vehicles with backup cameras that allow us to see where we would go if we kept the steering wheel at a particular angle. When the screen shows the lines colliding with an obstacle, we know we should behave differently: take this prediction into account and turn the wheel a different way.

    Backup camera view showing vehicle trajectory.

    These lines are an example of “open-loop” predictions. In other words, they show what will happen if you didn’t use the output of the model (no feedback). In contrast, what actually happens is a “closed-loop” outcome – you are using all your information to avoid the collision.

    I often hear people say that predictions were “wrong” because the bad outcome predicted didn’t happen. This is only valid if no corrective action was taken due to the prediction.

    We should rather evaluate predictions based on experiments where we fix the inputs completely. If a predictive model is doing its job right, and you take corrective action, the worst predictions will never occur, because you used the model to predict and sidestep them.

  • Generative AI will help us mine the creative space faster

    With the important leaps being made in the world of AI generative art I’ve been returning in my mind to a short story by Spider Robinson called Melancholy Elephants. It had a significant impact on how I think about creativity. Most of what I say will be about music, two-dimensional visual art and the written word since these are the fields most affected by progress made in the last year or so. Not coincidentally, these are forms of art that have been most successfully digitised.

    Large is not infinite

    In the story, they are considering removing the limitations on the length of copyright. But automated copyright checking has become a reality and there are more and more people who want to produce art. Robinson did not anticipate the development of AI generated art, but the main point is still sound. The story turns on the tendency of humans to round large numbers up to infinity.

    How long can you send your sewage into an infinite ocean? Forever. How long can you send your smoke into an infinite atmosphere? Forever. We keep getting surprised when very large things turn out to be finite. 

    Robinson observes that our creative endeavour is limited by our perceptual machinery. In the story this is why infinite copyright combined with automated enforcement is a bad idea, because artists need to be allowed to rediscover forgotten art.

    How can art be finite? 

    Let’s start with the basics. The three types of art I mentioned before (words, sound and images) can all be digitised. Literally, that means they are reduced to numbers. Every file on your computer is a number – a sequence of bits. For finite-length files, there is a finite number of combinations of bits. But this space – the space of all possible files smaller than 5 MB, say, is really quite large. Like atoms in the universe large. Unfortunately that is not the size that is important.

    Consider that a picture like the Mona Lisa can be represented by pixels on a screen. At what resolution can we still recognise the Mona Lisa? What amount of noise can be added, what colour changes, before you declare the new image to be novel instead of a derivative work? Similarly, you wouldn’t have made a new song if you simply took the first minute of Stairway to Heaven, or appended two copies of it back-to-back. These are “new numbers” but they are also “the same” as other existing numbers.

    There is a great deal of confusion around attribution, recognition, novelty, and the protections provided by the law. Copyright, considered by itself, is specifically a restriction on copying. However, what constitutes a copy of a story, image or piece of music is becoming more broadly defined all the time.

    These questions about what kinds of mutations constitute copying are very interesting. In the literary space, the issue of retelling a story is well understood. However, in the visual arts, the issue is different. People still value the uniqueness of an original painting. The provenance is important. You couldn’t demand the same price for a replica, no matter how good, if you clearly stated that it was a replica. However, in terms of appreciation of art or images, there is a much bigger market for electronically reproduced images than there is for paintings.

    In the musical space, the process of musical notation or the representation of a musical piece in a computer file allows for the reproduction of a performance to a high degree of accuracy. Many of us consume music mostly through computer means or recordings, and the connection to live performance is less emphasized.

    This means that the copyright granted on these forms of art is not only specific to one particular representation, but also a large cloud of numbers that are related to that representation through electronic operations.

    So the space is made smaller by the fact that theoretically different points are practically considered similar.

    Additionally, most of the space represent combinations of words, sounds or images which are not pleasing to humans. This is much like the way that most of the earth is filled with “useless” rock, while the valuable gold or diamonds occur in reasonable concentration only in a small number of places and need to be mined. A good way to get a feeling for how sparsely the good bits are populated is to imagine randomly generating a page of text. If you just chose letters at random, you would be exceedingly unlikely to create even one sentence that made sense, let alone have some literary value.

    Technology improves our exploratory speed

    My view is that as new technologies for producing images, music, or words emerge, they allow us to more rapidly explore the creative space, focusing on the good bits. There is nothing like creativity in the sense that that word implies, because we do not create these pieces of art, we just discover these pleasing locations in the space. Our intuition about these creative spaces is often informed by a first arrival principle. We stake our claim to a particular point or a particular number and that gives us ownership of the number and its immediate neighbourhood. It used to be that to discover a place, it had be playable on an instrument made for humans. With sequencers, synthesisers and other modern audio technology this is no longer a real constraint. 

    For example, when electronic sound synthesis was developed, we had an explosion of new music. Changes in methods for reproduction also have an impact on music. Music used to be built to be radio-friendly, resistant to being filtered by radio, and still sound good over poor quality speakers. As the reproduction methods improve, that allows some previously unexplored space to be mined. 

    Modern generative AI techniques have exploded many of the constraints that used to hold on these forms of art. Anybody can supply a prompt to ChatGPT and get a decent starting point for an essay. Midjourney creates epic artistic pictures of characters or landscapes, and there are several recent advances in generating music. They already allow us to generate convincing lounge music, that can be indistinguishable from live performance, and it challenges our understanding of what constitutes originality, creativity, and ownership in art.

    The development of generative AI techniques in the field of art is rapidly advancing, and this progress is causing a shift in the way we think about art and the skills required to create it. 

    An analogy can be drawn to the impact that digital computers have had on the field of arithmetic, where human computation has been replaced by machines. Similarly, as generative AI techniques become more advanced, certain skills that were previously required for artists may no longer be in demand. This trend may lead to a decrease in the number of jobs in the field of music production, and a shift towards filtering and trade, where pre-existing art is consumed, rather than investment in “new” music. 

    However, there may also be an initial spike in novelty produced by these algorithms, which can be exploited by artists. Furthermore, the proliferation of AI generated art may change the way we think about art and its creation. As the technology continues to advance, we may begin to see vast sections of the creative space being mined by AI. Additionally, it is important to note that most of our experiences with art are now transmitted through digital mediums, and this shift in technology will likely continue to shape the way we consume and interact with art in the future.

    What about the emotional component?

    A common counter-argument I hear when I talk to people about this topic is that they appreciate the emotional connection to an artist. The suffering of van Gogh or Kobain speaks through their works. I think the romantic notion that you can feel the pain of the artist through their work is mumbo-jumbo.

    There’s nothing in the art but the art. To the extent that you are particularly impressed by an artist’s pain, you would have had to have learned that through another channel. I think we are very close to AI producing convincing pieces of art that allow people to have very satisfying emotional interactions with them. 

    A moment’s introspection will also allow you to understand that the transmission of emotion model must be  wrong. Just consider how many touching or moving moments you have experience from advertising I’m sure most people would agree that the people on the other side are not necessarily having a bona fide emotional connection with the audience any more than a good well-trained computer model without doing instrumental art.

    In other words, art that is trying to make you feel something or art that is trying to make you buy something is not that different.

    Art is a controversial term in this discussion because people bring in a whole lot of extra baggage that they connect to that word. I think they over-emphasise the importance of the artist’s intent.

    But, in fact, I think we should focus on the artefact, in other words, focus on the experience of being subjected to this particular set of numbers and ask yourself how much it means for an artist to be a real human.

    Can you tell? And if you can’t, does it matter?

  • 2020 wrap-up of South African COVID-19 deaths

     4 short months ago I collected some data on the causes of death in South Africa, placing the deaths attributed to COVID-19 in context, mostly for my own edification. Even then, when they numbered less than half of what they are now, it was clear that this was a serious concern. At the time, I neglected some analysis due to a lack of time. Notably I didn’t normalise by population, even though the figures I quoted were from different years.

    I’ve taken advantage of a more relaxed schedule to extend my analysis to the table below:

    I will repeat my sources below to save you from paging through to the previous post. As before I must mention that the “influenza and pneumonia” figures for 2017 were without significant countermeasures like lockdowns or mask wearing.

    Note that the population normalisation doesn’t affect the ranking. 

    In order to keep things simple, I’ve kept the original 2017 death figures up, even though some of the numbers could be updated, notably the number for TB, which appears to have grown to around 64 000 in 2019 [5] (although I believe these figures are not attributing utlimate causes quite the same way).

    With the second surge of deaths, the disease has moved up to be comfortably above the HIV deaths in 2017, roughly equal to all other viral diseases.

    Comparing daily rates is also revealing. In 2017, a total of 465286 deaths were recorded, an average daily death rate of around 1274. At the time of my writing this, the average death rate per day from COVID-19 is around 280 and climbing, which would have been a full 20 % of these deaths.

    Sources

    † Underlying cause of death (previously known as primary cause) is the disease or injury that initiated the sequence of events leading directly to death; or the circumstances of the accident or violence which produced the fatal injury. [1]

  • South African COVID-19 deaths in context

     I was curious this morning, so I spent some time digging for context to the numbers we’re constantly seeing ticking up on our reports these days.

    The following chart shows this context.

    As always, it’s hard to get recent statistics for South Africa. I started with the top 10 natural causes of death in South Africa from StatsSA [1]. I’ve used their attribution of the underlying causes†, Table 4.16 on p58 and also show the lumped “All non-natural causes” from the same report. Although this document was published in 2020, it only goes up to 2017. Viewing COVID-19 deaths [2] in this light shows it is considerable, even though it is at the bottom of the list. Note that the COVID-19 figures don’t distinguish between underlying or immediate cause and include significant interventions to mitigate their effects when compared to for example the Influenza and pneumonia figure in the chart.

    Road fatalities are always quite worrying to me – I don’t think the average South African is worried enough about them. I used the official Dept of Transport report for 2018 [3]. I find it interesting that these numbers are comparable.

    I know that people in South Africa are particularly worried about the murder rate, so I added the Africa Check reported numbers for 2019 [4]. Murder occupies a sobering position in the chart.

    Some additional notes are in order to establish more context.

    • I haven’t normalised by population, mainly because these numbers involve yet another dive into the data sets for South Africa.
    • The COVID-19 numbers are for a partial year, not for a whole year
    • The influenza and pneumonia numbers do not include any active measures like lockdown or mask wearing.
    • Many of these numbers are not directly comparable in terms of mechanism, and the number for murder for instance would be included in the “All non-natural causes” number if I were designing this correctly.
    My personal conclusion from this is that COVID-19 deaths are significant when compared to these other causes of death.

    Sources

    † Underlying cause of death (previously known as primary cause) is the disease or injury that initiated the sequence of events leading directly to death; or the circumstances of the accident or violence which produced the fatal injury. [1]