I heard about Reddit for the first time around August 2010, when Digg
redesigned its website
for various reasons (none of them related to improving the user experience) and
lost most of its users (many of whom were already annoyed).
The "Digg Exodus" is how I moved to Reddit, and I've used since then as my main
source of news.
And yet, the shadow of a bad redesign hangs over Reddit. Even though they
have been redesigning the website since 2018, their results have been uniformly
awful. A quick test I made about a year ago showed that, while
the "old" interface downloaded 410Kb of data and rendered in 81ms,
the "new" interface would download 10Mb rendered in about 3.2s. Last week
Reddit announced proudly that their team had reduced loading times by 2
seconds,
which is still 15 times slower than the performance they had five years ago.
The pop-ups, modals, hidden comments and low information density are not
helping either.
I rarely saw any of this for one simple reason: i.reddit.com. This interface
was a leftover from the earlier times of the mobile web and therefore
it was fast, simple, and straight to the point.
And precisely because it was so convenient at delivering the content
that matters the most to me it had to die, killed by Reddit administrators
yesterday and buried deep into this announcement post.
There's a widespread belief that the Reddit website is made awful on purpose
with the end goal of exhausting you into using their mobile app where they can
get better data about you and where you can no longer block their ads and
sponsored posts. I do not know whether this theory is correct, but I can agree
with half of it: now that i.reddit.com has shuffled off his mortal coil I
am indeed exhausted, and today I am leaving the site for greener pastures.
I have already made a GDPR request for all of my data in case I ever need to
refer to some old post of mine, and once that's done I'll be gone for good.
Deciding where to go is a bit trickier.
For tech news I already have
Hacker News which is fine (a bit too obsessed
with GPT at the moment, but we've been there before
and hopefully this too shall pass). Local news will require some more research.
I assume some part of Discord might do the trick, but I'd honestly prefer
something more open.
So long, Reddit. It was fun while it lasted.
I have found myself often enough trying to classify a text that I decided to
be lazy and write a solution
that I could always rely on.
The following script is an example on how to pre-train a HuggingFace transformer
for text classification. This example uses only two classes, identifying whether
the sentiment of a text is positive or negative, but nothing stops you from
adding more classes as long as you predict just one.
There are no difficult imports that I can tell - I think you can install all
the required libraries with the command
pip install transformers datasets evaluate nltk
but I'll double check just in case.
Update: I have changed the script to extract the function get_dataset_from_json_files().
You can use this function as is to replace the calls to get_data(). I also updated the prediction code to give an example of
how to make predictions for a lot of texts at once.
import json
import numpy as np
import os
import random
import tempfile
# These are all HuggingFace libraries
from datasets import load_dataset
import evaluate
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification,\
TrainingArguments, Trainer, TextClassificationPipeline
# KeyDataset is a util that will just output the item we're interested in.
from transformers.pipelines.pt_utils import KeyDataset
# Library for importing a sentiment classification dataset.
# Only here for demo purposes, as you would use your own dataset.
from nltk.corpus import movie_reviews
# The BERT tokenizer is always the same so we declare it here globally.
tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
def get_data():
""" Returns a dataset built from a toy corpora.
Returns
-------
Dataset
An object containing all of our training, validation, and test data.
This object stores all the information about a text, namely, the raw
text (which is stored both as text and as numeric tokens) and the
proper class for every text.
Notes
-----
If you have texts in this format already then you can skip most of this
function and simply jump to the `get_dataset_from_files` function.
References
----------
Most of this function is taken from the code in
https://huggingface.co/course/chapter5/5?fw=pt.
Note that that link is very confusing at times, so if you read that code
remember to be patient.
"""
# We read all of our (toy) data from NLTK. The exception catches the case
# where we haven't downloaded it yet and downloads it.
try:
all_ids = movie_reviews.fileids()
except LookupError:
import nltk
nltk.download('movie_reviews')
all_ids = movie_reviews.fileids()
# We identify texts by their ID. Given that we have those IDs now, we
# proceed to shuffle them and assign each one to a specific split.
# Note that we fix the random seed to ensure that the datasets are always
# the same across runs. This is not strictly necessary after creating a
# dataset object (because we want to reuse it), but it is useful if you
# delete that dataset and want to recreate it from scratch.
random.seed(42)
random.shuffle(all_ids)
num_records = len(all_ids)
# This dictionary holds the name of the temporay files we will create.
tmp_files = dict()
# We assigne the IDs we read before to their data splits.
# Note that we use a fixed 80/10/10 fixed split.
for split in ['train', 'val', 'test']:
if split == 'train':
split_ids = all_ids[:int(0.8 * num_records)]
elif split == 'val':
split_ids = all_ids[int(0.8 * num_records):int(0.9 * num_records)]
else:
split_ids = all_ids[int(0.9 * num_records):]
all_data = []
# We read the data one record at the time and store it as dictionaries.
for text_id in split_ids:
# We read the text.
text = movie_reviews.raw(text_id)
# We read the class and store it as an integer.
if movie_reviews.categories(text_id)[0] == 'neg':
label = 0
else:
label = 1
all_data.append({'id': text_id, 'text': text, 'label': label})
# Step two is to save this data as a list of JSON records in a temporary
# file. If you use your own data you can simply generate these files as
# JSON from scratch and save yourself this step.
# Note that we also save the name of the temporary file in a dictionary
# so we can re-read it later.
tmp_file = tempfile.NamedTemporaryFile(delete=False)
tmp_files[split] = tmp_file.name
tmp_file.write(json.dumps(all_data).encode())
tmp_file.close()
# Step three is to create a data loader that will read the data we just
# saved to disk.
dataset = get_dataset_from_json_files(tmp_files['train'], tmp_files['val'], tmp_files['test'])
# Remove the temporary files and return the tokenized dataset.
for _, filename in tmp_files.items():
os.unlink(filename)
return dataset
def get_dataset_from_json_files(train_file, val_file=None, test_file=None):
""" Given a set of properly-formatted files, it reads them and returns
a Dataset object containing all of them.
Parameters
----------
train_file : str
Path to a file containing training data.
val_file : str
Path to a file containing validation data.
test_file : str
Path to a file containing test data.
Returns
-------
Dataset
An object containing all of our training, validation, and test data.
This object stores all the information about a text, namely, the raw
text (which is stored both as text and as numeric tokens) and the
proper class for every text.
Notes
-----
IMPORTANT: the first time you run this code, the resulting dataset is saved
to a temporary file. The console will tell you where it is (in Linux it is
/home/<user>/.cache/huggingface/datasets/...). This temporary location is
used in all subsequent calls, so if you change your dataset remember to
remove this cached file first!
This function is a thin wrapper around the `load_dataset` function where
we hard-coded the file format to use JSON.
If you don't want to read that function's documentation, it is enough to
provide files whose content is simply a JSON list of dictionaries, like so:
[{'id': '1234', 'text': 'My first text', 'label': 0}, {'id': '5678', 'text': 'My second text', 'label': 1}]
"""
files_dict = {'train': train_file}
if val_file is not None:
files_dict['val'] = val_file
if test_file is not None:
files_dict['test'] = test_file
dataset = load_dataset('json', data_files=files_dict)
# Tokenize the data
def tokenize_function(examples):
return tokenizer(examples['text'], padding='max_length', truncation=True)
tokenized_dataset = dataset.map(tokenize_function, batched=True)
# Return the tokenized dataset.
return tokenized_dataset
if __name__ == '__main__':
# In which mode we want to use this script
# 'train': start from a pre-trained model, fine-tune it to a dataset, and then
# save the resulting model
# 'test': evaluate the performance of a fine-tuned model over the test data.
# 'predict': make predictions for unseen texts.
mode = 'predict'
assert mode in ['train', 'test', 'predict'], f'Invalid mode {mode}'
# We define here the batch size and training epochs we want to use.
batch_size = 16
epochs = 10
# Where to save the trained model.
trained_model_dir = 'training_output_dir'
if mode == 'train':
# Train mode. Code adapted mostly from:
# https://huggingface.co/docs/transformers/training#train-with-pytorch-trainer.
# Collect the training data
inputs = get_data()
# Since we are starting from scratch we download a pre-trained bert model
# from HuggingFace. Note that we are hard-coding the number of classes here!
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
# Define training arguments
# You can define a loooot more hyperparameters - see them all in
# https://huggingface.co/docs/transformers/v4.26.1/en/main_classes/trainer#transformers.TrainingArguments
training_args = TrainingArguments(output_dir=trained_model_dir,
evaluation_strategy='epoch',
per_device_train_batch_size=batch_size,
num_train_epochs=epochs,
learning_rate=1e-5)
# Define training evaluation metrics
metric = evaluate.load('accuracy')
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
# Define the trainer object and start training.
# The model will be saved automatically every 500 epochs.
trainer = Trainer(model=model,
args=training_args,
train_dataset=inputs['train'],
eval_dataset=inputs['val'],
compute_metrics=compute_metrics)
trainer.train()
elif mode == 'test':
# Perform a whole run over the validation set.
# This code is adapted mostly from:
# https://huggingface.co/docs/evaluate/base_evaluator
# Some useful information also here:
# https://huggingface.co/docs/datasets/metrics
# Collect the validation data
inputs = get_data()
# Create our evaluator
task_evaluator = evaluate.evaluator("text-classification")
# Use the model we trained before to predict over the validation data.
# Note that we are providing the name of the classes by hand. We are not
# using these labels here, but it is useful for the prediction branch.
model = DistilBertForSequenceClassification.from_pretrained(f'./{trained_model_dir}/checkpoint-1000/',
num_labels=2,
id2label={0: 'negative', 1: 'positive'})
# Define the evaluation parameters. We use the common text evaluation
# measures, but there are plenty more.
eval_results = task_evaluator.compute(
model_or_pipeline=model,
tokenizer=tokenizer,
data=inputs['test'],
metric=evaluate.combine(['accuracy', 'precision', 'recall', 'f1']),
label_mapping={"negative": 0, "positive": 1}
)
# `eval_results` is a dictionary with the same keys we defined in
# the `metric` parameters, plus some time measures.
print(eval_results)
elif mode == 'predict':
# Make predictions for individual texts.
# Same as above, we use the model we trained before to predict a single text.
model = DistilBertForSequenceClassification.from_pretrained(f'./{trained_model_dir}/checkpoint-1000/',
num_labels=2,
id2label={0: 'negative', 1: 'positive'})
# We build a text classification pipeline.
# Note that `top_k=None` gives us probabilities for every class while
# `top_k=1` returns values for the best class only.
# Inspired on https://discuss.huggingface.co/t/i-have-trained-my-classifier-now-how-do-i-do-predictions/3625
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer,
batch_size=batch_size, top_k=None)
sequences = ['Hello, my dog is cute', 'Hello, my dog is not cute']
predictions = pipe(sequences)
print(predictions)
# The above print shows something like:
# [[{'label': 'positive', 'score': 0.574},
# {'label': 'negative', 'score': 0.426}],
# [{'label': 'negative', 'score': 0.868},
# {'label': 'positive', 'score': 0.132}]]
# For a more heavy-duty approach we now classify an entire dataset.
inputs = get_data()
test_inputs = inputs['test']
outputs = []
# Note that 'text' is the key we defined in the `get_data` function.
for out in pipe(KeyDataset(test_inputs, 'text'), batch_size=batch_size, truncation=True, max_length=512):
outputs.append(out)
# Now that we collected all outputs we massage them a little bit for
# a more friendly format. For every prediction we will print a line like:
# 'pos/cv093_13951.txt: positive (0.996)'
for i in range(len(test_inputs)):
id = test_inputs[i]['id']
pred_label = outputs[i][0]['label']
pred_prob = outputs[i][0]['score']
print(f'{id}: {pred_label} ({pred_prob})')
Now that everyone is talking about Large Language Models (LLMs) in general and
ChatGPT in particular I thought I would share a couple thoughts I've been
having about this technology that I haven't seen anywhere else.
But first, let's talk about chess.
Chess is a discipline notorious for coming out on top when the robots came to
take its lunch. Once it became clear that even the humblest of PCs can play
better than a World Chess Champion, the chess community started using these
chess engines to improve their games and learn new tactics.
Having adopted these engines as a fact of life, chess is as popular as ever
(if not more).
Sure, a computer can do a "better" job on a fraction of the time, but who cares?
It's not like there's an unmet economic need for industrial-strength chess players.
In contrast, one field that isn't doing as well is digital art.
With the advent of diffusion models like Midjourney and Stable Diffusion
many artists are worried that their livelihoods are now at stake and are letting
the world know, ranging from comics (I like this one
more but this one
hits closer to home) all the way to class action lawsuits.
My quick take is that these models are here to stay and that they won't
necessarily destroy art but they will probably cripple the art business
the same way cheap restaurants download food pictures from the
internet instead of paying a professional photographer.
LLMs are unusual in that sense because they aren't disrupting a field as much
as a means of communication. "Chess player" is something one does, and so is
"artist"1. But "individual who uses language" is on a different class
altogether, and while some of the effects of these technologies are easy to
guess, others not so much.
Random predictions
What are we likely to see in the near future?
The easiest prediction is a rise in plagiarism.
Students are already using ChatGPT to generate essays regardless
of whether the output makes sense or not. And spam will follow closely behind:
we are already awash in repost spam and ramblings disguised as recipes, but once people start
submitting auto-generated sci-fi stories to magazines we will see garbage in any medium that
offers even the slightest of rewards, financial or not.
Do you want a high-karma account on Reddit to establish yourself as
not-a-spammer and use it to push products?
Just put your payment information here and the robots will comment for you.
No human interaction needed2.
What I find more interesting and likely more disruptive is the replacement of
search engines with LLMs. If I ask ChatGPT right now about my PhD advisor I
get an answer - this particular answer happens to be all wrong3,
but let's pretend that it's not 4.
This information came from some website, but the system is not telling me which one.
And here there's both an opportunity and a risk. The opportunity is the chance
of cutting through the spam: if I ask for a recipe for poached eggs
and I get a recipe for poached eggs then I no longer have to waddle
through long-winded essays on how poached eggs remind someone of evening at
their grandma's house.
On the other hand, this also means that all the information we collectively
placed on the internet would be used for the profit of some company without
even the meagre attributions we currently get.
On the long tail, and entering into guessing territory, it would be tragic if
people started writing like ChatGPT. These systems have a particular writing
style composed of multiple short sentences and it's not hard to imagine that
young, impressionable people may start copying this output once it is
widespread enough. This has happened before with SMS, so I don't see why it
couldn't happen again.
Pointless letters and moving forward
One positive way to move forward would be to accept that a lot of our daily
communication is so devoid of content that even
a computer with no understanding of the real world can do it and work on that.
When I left my last job I auto-generated a goodbye e-mail with GPT-3,
and the result was so incredibly generic that no one would have been able
to learn anything from it. On the other hand, I doubt anyone would have noticed:
once you've read a hundred references to "the good memories" you no longer
stop to wonder whether there were any good memories to begin with.
I didn't send that auto-generated e-mail. In fact, I didn't send anything:
I had already said goodbye in person to the people that knew me and there was
no reason to say anything else. The amount of information that was conveyed
was exactly the same, but my solution wasted less of other people's time.
Maybe this is our opportunity to freshen up our writing and start writing
interestingly, both in form (long sentences for the win!) and in content.
The most straightforward solution would be cursing: these models have
to be attractive to would-be investors they are strictly programmed not to use
curse words and NSFW content (I just tried).
So there's a style that no AI will be copying in the near future.
Footnotes
- Note that this is a simplification for the sake of the argument. As someone
who often said "being a programmer is both what I do and what I am" I am
aware that "artist" (like so many other professions) isn't just a job but also
a way of looking at the world.
- I have noticed that people on Reddit will upvote anything without reading
it first, so this is not a high bar to clear.
- The answer mashed together several researchers into one. One could argue that
I got more researchers per researcher, which is definitely a take.
- The answer doesn't have to be correct - all it takes is for the person using
the system to believe that the answer is correct, something we are already
seeing despite the overwhelming evidence to the contrary.
In my previous entry about home automation
I mentioned that I was ready to start plugging things into other things and
connecting everything to a central computer running OpenHab.
This is the story about how everything went wrong.
Networking
Things starting going badly from the get go when I discovered that whatever
"vectoring" is it has rendered my DSL router obsolete.
Only two (available) modems had the right technology: a TP-Link TD-W9960v
and a Fritz!Box 7530 AX.
I should have gone with TP-Link, but alas, I did not: I decided against the
TP-Link because it was not supported by OpenWRT, but failed to notice that
the other one was not supported either.
The reason I regret my choice is because the Fritz!Box router provides DSL,
Telephone, Wifi, WLAN Mesh, Media server, NAS, some type of IoT compatibility,
it updates automatically, and who knows what else out of the box.
I have never owned any other device with such a large attack surface,
and yet I am expected to put all of my data in it and then plug it to the
internet? Yeah, no. I disabled everything instead, plugging all of my devices
to my old router (which uses dd-wrt after OpenWrt
dropped its support for it), relegated the Fritz!Box to a mere modem, and added
every network access restriction I know to keep the later from entering my
internal network.
Speaking of my old router, setting up sub-networks in dd-wrt was like pulling
teeth thanks to my favorite internet problem: documentation that still refers
to older, deprecated versions of software that nonetheless rank stupidly high
in search engines because they have been around forever. In my case, all links
pointed to this complicated method that
has been improved by this much simpler method.
Do you know how frustrating it is to debug network errors when bad
configuration means you can no longer access the thing you are debugging?
Because I do.
Smart lights
This is the only part that worked as intended, only to realize that what I
thought I wanted is not what I actually needed.
I configured the lights to use their own Guest WiFi network that, once I'm done,
will be disconnected from the internet entirely. That part worked well, and in
fact I am typing way after midnight with the "candlelight" setting just because
I can. The tricky part was the dilemma that came afterwards.
In order to work properly, the lights remain "on" all the time (to receive
commands) and you turn them on/off via software. So what happens if I
enter a room without my phone? At this moment I have three choices:
* I can go back, pick my phone, unlock it, turn on the light, and then turn
it off once I leave
* I can turn the light off and then on again, turning the lights on. I then
need to leave them on and turn them off via software once I have my phone
back.
* I can stay in the dark and convince myself that this is fine.
The solution to this problem is a small remote control I can keep on me all
the time and turn lights on and off with as many programmable buttons as smart
lights. I know this is a good solution because every single compatible remote is
currently sold out.
Heating
Even before lights became an issue, my dream was to control my heating to turn
it on when I get up and turn it off shortly after.
My first step was to buy a cheap Bosch thermostat. This was a bad
choice, as Bosch equipment require a central hub with constant internet
connection. If you want to turn off your heating you have to send your request
to Bosch headquarters, who in turn will tell your hub to tell your heating that
it should turn off. Good thing I only bought one.
My second attempt was an Eurotronic Spirit Z-Wave plus. These devices connect
via Z-Wave, a protocol that requires a dongle instead of internet connection.
My research suggested an Aeotec Z-Stick, which would have been a fine choice if
I had stuck to v5.
Instead, I bought the newest v7 (newer is always better, right?) which has known
compatibility issues and whose firmware can only be upgraded using Windows
(a luxury I no longer have). So I bought a v5 and all those problems went away.
Both the v5 stick and the Bosch thermostat will be landing on eBay eventually.
OpenHab
All of my equipment was supposed to be controlled with OpenHab, an experience
that so far has been mixed: when it works it works great, but when it doesn't
it is quite difficult to find out why. That compatibility problem I mentioned
above regarding Z-Wave? All I found in the forums (before giving another stick
a try) was the suggestion that my thermostat was asleep and that all I had to do
was sit next to it and keep it awake for an indeterminate time until it worked.
Once I changed the stick, however, everything worked just fine.
I installed OpenHab in my old laptop, where things worked mostly fine. But once
I decided to move it to its own Raspberry Pi (using the
OpenHabian distro) it
all fell into pieces: my Pi is too underpowered, the memory card too slow, and
the default Java installer doesn't support my architecture.
So not only do I need a new dongle, but also a new Raspberry Pi.
Progress and next steps
The next steps are getting a proper Raspberry Pi, a remote control,
and make everything talk to everything else.
I still have some hopes for OpenHab - the thermostat have eventually been
detected, the lights worked just fine, and the new old Z-Wave dongle does its
job. I could look deeper into its voice recognition functions, but since my
plan was installing Mycroft I might as well stick to that.
With the working up I have finally been able to re-plug my TV.
The TV is old-ish and therefore controlled with a Raspberry Pi 3
running OSMC, with all of its content coming either from
YouTube or the NAS (which I have once again turned on).
This is not technically part of the smart home, but it is worth mentioning
anyway. And no, I am not re-purposing this Raspberry Pi for OpenHab.
And last but not least, a word of caution: living in a non-functioning house
(well, apartment) is exhausting, and I do not recommend following my steps
until you know things will work fine.
I'll keep you updated.
