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A Information To Synthetic Intelligence in Simple English

An advanced ML algorithm attempts to write this blogpost for me, writing: My mum and I had a conversation about a year ago about DALLE. I was trying to explain to her what it was, and she was like, "Oh, that's just AI." I was like, "No, it's not. It's a very specific kind of AI." She was like, "Oh, it's just AI." and that continues on and on.

I had a dialog with my mum some time in the past in regards to the new DALL-E ‘factor’ that got here out. My mum’s an artist and a author– not a technophobe or a luddite by any means, however not a developer both. I’m undecided if I imagined the priority in her voice or not, however the tone of the dialog was bleak. The pictures generated by DALL-E have been vivid, and delightful, and appeared like artwork.

Nicely, perhaps that’s not honest. Possibly not ‘appeared.’

This weblog publish is an summary and abstract of a number of completely different AI subjects. I’m writing particularly for audiences unfamiliar with or snug with AI, coding, or math. A few of these subjects I’m planning on giving deeper dives on their very own, however I feel we’re sort of at a tipping level right here. There’s a brand new sort of automation on the horizon, and I feel it’s essential that details about that is accessible and comprehensible to everybody.

What’s AI?

One factor that my mom instructed me on our name, was: “In the event you write an explainer blogpost, use ‘AI’ within the title, not ‘ML.’ No one is aware of what ML means.” She might need been exaggerating, however she’s not fallacious. Most individuals consult with AI, even when it’s simply speaking about Skynet, the Matrix, and so forth. ML is a little more of a thriller to folks.

So– what’s AI? Nicely, for one factor, it’s a misnomer.

To grasp the phrase ‘Synthetic Intelligence,’ you should first have the ability to outline intelligence. This can be a bit like saying ‘to grasp archery you should first have the ability to shoot the solar.’ Certain, you’ll most likely purpose it the precise approach, however you’re not going to simply hit your goal.

What’s intelligence? As a stereotype, your mind would possibly first soar to “a man who can add up numbers in a short time.” Possibly you may have some folks in thoughts– Einstein, Newton, Feynman. Possibly you’re conscious that your first ideas are white and western and male, so that you additionally take into consideration Rosalind Franklin or Ada Lovelace. Nonetheless: you most likely take into consideration intelligence as a advantage: one tied to having the ability to assume properly. After that, peeling again the onion, you most likely perceive intelligence as a measure of one thing’s sentience. Do canine have intelligence? Do bugs?

I’ve used AI within the title of this piece, and I’m utilizing it right here, as a result of it’s the favored nomenclature. However in my expertise, AI is extra of a advertising time period than one that folks within the ML area use. I personally use “Machine Studying,” as do (so far as I can inform) most engineers, ethicists, and researchers who cope with “can we make robots assume.”

A very good callout my pal Luke made is to tell apart additional between ‘AI’ and ‘AGI.’ AGI, or Synthetic Normal Intelligence, is the sort of… alchemist-turning-lead-into-gold commonplace for this type of analysis. It’s ‘making a machine that may be taught something a human can’– a generalized synthetic intelligence. A part of the issue with utilizing “AI” is that it brings within the implications of AGI– saying “AI” makes customers consider AGI, once they’re actually coping with a flawed, particular, ML algorithm.

So what’s Machine Studying?

Machine Studying is the time period for making an attempt to get machines to determine stuff out with out being particularly programmed.

For example: you may have a pile of widgets, and also you need to type them into two piles primarily based on their attributes. A standard programming strategy would do one thing like ‘if colour=purple, left, else, proper.”

A machine studying strategy could be extra like “there are x units of widgets: utilizing the knowledge you may have type these piles.” Relying on how carefully you monitor the outcomes, you would possibly get the identical finish outcome– otherwise you would possibly get one thing utterly new.

For instance, you would practice the algorithm by labelling a smaller variety of widgets (that is what a blue one seems like, that is what a purple one seems like) after which correcting it if it goes off the overwhelmed path. (“This isn’t purple, it’s inexperienced!”) You would additionally simply put as a lot information as potential in (that is what the widgets seem like these are their variations, these are their similarities) and let the algorithm determine it out. You probably have extra information (both extra labels/descriptions, or simply extra widgets) you’ll get completely different outcomes.

One main shift within the panorama of ML has been the power to make use of MASSIVE datasets for coaching: and whereas the standard knowledge is “the extra information you may have, the extra correct your outcomes shall be,” the truth is that the extra information you may have, the extra you possibly can practice your algorithm to be fallacious.

Conventional programming depends loads on ‘knowns’– you realize what’s entering into and what you anticipate to get out of it. The trick is getting a from b. Machine studying depends on bulk info– it is a, that is b, determine it out.

Intelligence vs. thought, thought vs. being. It’s all very philosophical and never very sensible. Loads of discussions come right down to that one scene from iRobot, you realize the one:

will smith irobot meme format: will says "can you write me a sonnet on the subject of the fourth bridge". the robot says "can you". will looks thoughtful.

So how do Dall-e and GPT work?

One thing I’ve typically stated and browse is that good machine studying is unhealthy statistics. If statistics is making an attempt to explain information properly by utilizing the precise numbers, machine studying is making an attempt to make use of numbers to get the precise descriptions. In the event you don’t do statistics, that’s the alternative of the way it’s speculated to work.

For instance: I’ve a machine depend and categorize my widgets. I do know I’ve good numbers, and I anticipate there to be 50% purple widgets. I set my counting machine to depend, and get a results of ‘10% purple widgets.’ From right here, I’m at a crossroads: statistics is the apply of updating my earlier assumption, understanding that I’ve 10% purple widgets, not 50% as I began. ML could be fucking round with the inputs till you get the 50% quantity you have been anticipating– ‘10% doesn’t appear proper, there have to be one thing fallacious with the coaching information.’ (It depends upon what you’re utilizing the ML for, although!)

I feel one of many methods you possibly can actually perceive GPT is by working a extra easy model your self. You are able to do it your self without spending a dime– I like Max Woolf’s blogpost + code right here.

What can we see utilizing this and studying from this code?

For many who don’t click on the hyperlink: GPT-2 is an earlier model of GPT. Usually you will notice GPT described as a ‘black field’ due to the difficult, transformers-based ML structure. It was skilled on textual content that was gathered by Reddit hyperlinks. So the ML engineers took these hyperlinks from Reddit (which has its personal biases), and cleaned it up considerably, eradicating spam and mess. GPT then took that textual content, and seemed for patterns in it. You’ll be able to then enter your personal tokens (which is named prompting) or fantastic tune it additional to carry out particular duties.

In the event you immediate GPT with “As soon as–” it seems by way of the patterns it noticed from the textual content it was skilled on. Frequent sentences that begin with “as soon as” is likely to be “as soon as upon a time.” However in the event you had extra textual content earlier than that (“I used to be a younger little one. As soon as–”) that may change the parameters of the prediction.

Let’s take an instance exterior of textual content: I’ve an ML algorithm that claims who will win a World Cup sport. It will likely be affected by the prompts that go in. “France vs. Brazil” may have a distinct consequence primarily based on climate, beginning line up, whether or not Mbappe is taking part in, and so forth. Whereas the algorithm can’t think about all of these elements, you possibly can replace it and provides it parameters to play with. If it doesn’t have information about climate, saying “The climate is unhealthy” won’t lead to adjustments. But when it has climate information, the climate being unhealthy will have an effect on its prediction of the outcomes.

There are many methods to carry out ‘machine studying,’ however most of them are methods of in search of patterns in massive reams of information and having the ability to replicate these patterns with new prompts and information units.


GPT is a textual content model- in the event you’ve used chat GPT, or different interfaces (the openAI playground was actually well-liked on Tumblr earlier this 12 months), that is the mannequin you’ve performed with. GPT is an LLM, or a Massive Language Mannequin. And that is the place I’m going to underline some phrases for you. It’s a Language mannequin: it’s a likelihood distribution. Principally, it guesses, primarily based on the phrases round it, what the following phrase in a sentence shall be, and it’s excellent at that.

However that’s all it’s good at. Like parrots, college students earlier than 8am, and canine, GPT can put up a great act of understanding phrases, and like the previous two instances it could possibly repeat these phrases or combine them up in order that they sound good. However that’s not the identical as understanding.

People are essentially a social species– we exist to work together with different people and use their data to reinforce our personal. This implies we’ve got a bias in the direction of anthropomorphization – we need to see humanity in different issues, even inanimate objects. The issue is that whenever you usher in an LLM that may plausibly mimic human thought processes, people tend to overextend them a welcoming hand to the human race.

A Minor Instance

Let’s take a look at an instance right here.

I’ve requested GPT-3 to jot down me a paragraph with the immediate: “Please write a paragraph in regards to the human tendency to anthropomorphize non-sentient creatures and the way that impulse may cause issues as folks start to work together with LLMs en masse, embody citations. “

I’ve lower out a few of the response (it was largely about what anthropomorphizing is with examples about pets,) however that is the cash of it:

” This similar impulse might also trigger points when folks start to work together with massive language fashions (LLMs) en masse. If folks start to anthropomorphize LLMs and ascribe feelings and consciousness to them, it might result in misunderstandings and probably dangerous interactions. To keep away from these issues, it’s important for folks to concentrate on their very own tendencies to anthropomorphize and to strategy LLMs with a transparent understanding of their capabilities and limitations.”

Boris Artzybasheff illustration of the personification of machinery

Let’s take a look at and take into consideration this response to my immediate. In some methods, it’s extraordinarily spectacular: it match the temporary (aside from not together with citations.) Nevertheless it’s a shallow studying and shallow response: I requested it to jot down about these issues, and it stated “there are issues.” Whereas misunderstandings are barely extra particular, “probably dangerous interactions” is just not.

I can ask the LLM to be extra particular, and it is going to be, however this nonetheless identifies two issues with LLMs. One is that writing with out understanding is essentially weak. The second drawback is a class, one we’ll name:

Huge Knowledge Issues

LLMs are skilled on tens of millions, even billions of items of information throughout the net. This causes the identical sorts of issues you get when something is skilled on net information– from machines to human beings. Loads of the stuff on the net is obscene, or offensive, or if it’s not both of these issues, it’s unhealthy Search engine marketing-laden advertising slop. The web was designed to deliver collectively the world’s sharpest minds they usually created a playground the place you get marketed megachurches on websites the place intercourse employees are banned however Pornhub has an account the place it tweets tasteless, cheeky, company synergy. The online is usually sanitized, however not in a approach that makes anybody safer; solely a approach that makes it extra boring.

That is the soup that trains these massive language fashions. Knowledge cleansing is likely one of the greatest issues that apparently goes unsolved in ML analysis. That is the place you’re taking your information– texts, pictures, or so on– and clear it up, making it useable, sure, but in addition ensuring you don’t have something that pollutes your dataset.

Let’s take a look at a sensible instance. Amazon, traditionally, has employed builders primarily from MIT and different huge schools. They created a ML algorithm primarily based on this profile: the algorithm ended up discriminating in opposition to completely good engineers from traditionally black schools.(And girls.)

Slurs vs. Slurs (affectionate)

So perhaps a part of that’s cleansing curse phrases, porn spam, or nonsensical rubbish out. However perhaps as a step you need to keep away from your chatbot turning into a nazi, so that you eliminate something that might be thought-about offensive to a minority group. However the issue with _that_ is that human language is advanced and unusual. As an expletive-laden instance, see under:

‘Fuck off you homosexual bitch’– me to my associates after we get wine drunk and watch the bachelorette.

‘Fuck off you homosexual bitch’– the person following me residence after delight, screaming at me and throwing bottles

You and I can most likely inform which of these is a hate crime and which isn’t; however, remoted from context and whirring with out human decision-making talents, it’s virtually not possible for a LLM to inform the distinction. And that could be a drawback whenever you’re speaking in regards to the breadth of human expertise.

This can be a drawback that Google has run into traditionally as properly. Utilizing one other homosexual instance; for a very long time, in the event you Googled ‘lesbian’, all you’d get is reams of porn. That is a kind of cases the place I might complain about the way in which the homosexual lady exists in a spot of fetishization or one thing, however I’m excited about the issue Google got here up in opposition to right here. As a result of extra folks are most likely Googling to seek out pornography than there are lesbians in search of connections. There are most likely extra attractive straight males than lesbian girls (particularly as a result of lesbians use duckduckgo.) If Google desires to be a happiness engine, properly, one response will make extra folks completely happy. But when it desires to have the precise response, these folks should do extra clicking, or worse, go to a different website.

Examples of syntactic and lexical ambiguity. Lexica=l ambiguity is when the presence of two or more possible meanings is contained within a single word (I saw her duck), while syntactic is the presence of two or more possible meanings within a single sentence or sequence of words (the chicken is ready to eat)

LLMs can’t perceive the context of their sentences past how they’re programmed to: which is to say, semantically. It most likely is aware of primarily based on the big swathes of textual content it has absorbed that flat Earth is a conspiracy concept; however does it perceive that the conspiracy concept is an antisemitic canine whistle? Context is a part of language: and whereas language fashions have been skilled on tens of millions of phrases and the order of these phrases they can not have the context an individual who’s alive on the earth has.

So TLDR: GPT and different LLMs work by guessing, statistically, what the most probably responses to a immediate are and the way doubtless these phrases are to be adopted by different phrases. This could result in some unimaginable texts, enjoyable experiments, and believable sentences, however it essentially lacks the power to parse, perceive, and argue factors. That is all-important to grasp as you work together with LLMs and the area round them. I personally assume it may be attention-grabbing or helpful to make use of these fashions to reinforce human intelligence: stringing collectively an overview, writing a abstract, and rewriting textual content. However even in these instances, making an attempt to faux area data by utilizing GPT is a high-risk effort.

GPT Doesn’t ‘Know’

GPT doesn’t know what the essential elements of papers are and it doesn’t know if a paper was researched properly or not. It doesn’t learn about agendas, meta evaluation, or statistical significance.

Just a few days in the past a tweet went round encouraging folks to make use of ChatGPT to summarize a scientific paper about xylitol in a extra readable approach. All I might take into consideration was Andrew Wakefield, the person who’s the epicentre of our present vaccine hysteria. In the event you have been to place his paper in ChatGPT, you’d get an intelligent-sounding, authoritative, uncritical abstract, able to propagate antivaccine propaganda.

A case research that’s typically introduced up for GPT is code– ChatGPT has a code era characteristic that was promoted broadly. StackOverflow fairly rapidly banned GPT-generated code from getting used as a response to their questions. People on Twitter, particularly AI-positive folks, rapidly stated this was StackOverflow making an attempt to muscle out their competitors.

However guys.

The GPT code was unhealthy.

It’s fairly okay at widespread/typically written about code challenges, however the second you go off the overwhelmed path it depends on for loops, unhealthy formatting, and TODO statements that will make me blush.

The present degree of response from GPT-Chat is superb. I’ve argued that it’s most likely about the identical degree as a low-effort human being. However that’s simply it– we have already got low effort content material on the market. Don’t you need to make one thing good?


Now that we all know how GPT works, we are able to take into consideration DALL-E in that very same approach. It’s merely predicting what it thinks the following pixel over will seem like in colour, primarily based on coaching information from 1000’s of artists who didn’t consent to have their work used on this approach.

That is, I feel, the center level between two teams which have essentially the most completely different factors of view about mental property rights: for artists, signatures, colour marks, and credit score HAVE to be all the things. Whereas the web can deliver folks fame from nothing, it could actually additionally imply your work will get all of the serial numbers filed off and it finally ends up on 4chan years later closely edited as a racist meme.

Builders, alternatively, reward the almighty MIT license. Sharing code– grabbing it from stackoverflow, utilizing different folks’s modules, downloading NPM packages– these are all such main elements of recent net improvement there’s a joke about all the things counting on one bundle by a man in Omaha. There’s not typically credit score, there’s not typically fame, and there’s not typically rights. There’s, nevertheless, cash, and work, and it’s working to date, proper?

It’s a bleak factor: the continued underappreciation of artwork has led to artworks getting used to exchange the artists who created them. The result’s a mannequin that represents a sort of collective unconscious of artwork; DALL-E creates stunning issues. (Biased issues.)

Steal like an Artist

In 2012, I had a dialog with my mum that I keep in mind vividly. I used to be watching the United States of Pop 2011 mashup (made by an artist referred to as DJ Earworm) and mum requested if I assumed the mashup disrespected the unique artists. I replied that I didn’t assume so– I assumed it was cool that the constituent parts might be introduced collectively to make one thing new, vibrant, and enjoyable.

In the identical approach, to some extent, I really feel like I can’t muster up the identical rage many artists do when they give thought to DALL-E. I really feel that rage when builders and developer fanboys make enjoyable of artists for being upset, denigrate the very artwork they’ve constructed their fashions on and are typically impolite and merciless.

However the capability to generate artwork in seconds, creating a really difficult collage? I can’t hate that. I can’t hate that individuals who can’t draw can create superior drawings in a short time, in the identical approach I can’t hate that images changed portraits, in the identical approach I can’t hate that pong changed tennis, in the identical approach collages, Rothko, and Duchamp’s fountain are or aren’t artwork.

a mashup, a labor of love: the cover of Bartkira, a collaboration between hundreds of artists to recreate the graphic novel akira with simpsons characters

Nevertheless it’s all the time this form of balancing act, isn’t it? I make digital artwork: as somebody who does that, I’ve been accused of not making actual artwork: as if I press ‘management paint’ and my picture is totally produced and extant with no work of my very own.

Some of my art i made using digital painting techniques

However now folks can do this. GarageBand guitar loops haven’t stopped folks from studying the guitar, Wix hasn’t stopped net builders, however it nonetheless feels unhealthy to see somebody put no effort into one thing you’ve put effort into and get the identical (or extra) credit score.

I additionally need to draw a line between utilizing DALL-E and different image-generation platforms for pleasure and creativity and utilizing it to soullessly automate away the inventive course of. There’s a distinction between a man who can’t draw utilizing it to create a picture he has in his head or coronary heart and a man making an attempt to create the most well-liked #content material for the rolling functions of content material creation, or pretending he painted it from scratch.

A part of a really perfect world for artists is that they don’t have to create company coprolith to outlive; sadly, we’re automating away any job that would deliver a component of pleasure or creativity, leaving three lessons of Individuals: fits, service employees, and slaves.

I don’t assume McDonalds’ will ever be totally automated, as a result of a part of what folks pay for is the human interplay, an individual saying “in fact, no matter you need” and smiling. Equally, with these ML leaps ahead: there shall be some jobs, jobs with folks faces, that survive. I can’t say what’s going to occur to the remaining. (As considered one of my editors famous: there are already touchscreens to order within the US. There’s already some automation of this type of job. What does that depart us with?)

DALL-E Issues

Early on, DALL-E bought referred to as out for an absence of range of their response pictures. It will return white male docs for the enter ‘docs,’ and ladies for the enter ‘nurses,’ and so forth. Give it some thought– black writers have been speaking for years a few lack of range in inventory pictures and what that may mirror in regards to the group. You scoop in a ton of milk-white docs and stereotypes about black folks from the web, and also you get a picture mannequin that displays that– keep in mind, it’s what essentially the most statistically doubtless pixel is.

When referred to as out for this, the DALLE staff sprung into motion– not by fixing inputs or weights, however by stapling phrases like ‘feminine’ or ‘black’ to the tip of consumer prompts. This did work– it did lead to a extra various outcome. Nevertheless it additionally meant customers might show these stapled phrases by simpling including ‘an individual holding an indication that claims’ to the immediate.

Dr. Casey Fiesler tweets: "And when I asked DALL-E for 'cartoon secretary holding a sign that says" one of the images seems to have still appended "female." I did get one obviously male secretary on another prompt though (shrug emoji)

A series of 'clip art' style generated business people, with misspelled attempts at "female" or other 'diversity markers' on signs

Most software program methods are constructed like this– individuals are pushed to provide code, and produce it rapidly, which results in fast fixes. These fast fixes could be extra embarrassing/foolish than the preliminary mistake was.

Image description: A series of posts from Jason Lefkowitz dated Dec 08, 2022, 04:33, reading:It’s good that our finest minds have focused on automating writing and making art, two things human beings do simply because it brings them joy. Meanwhile tens of thousands of people risk their lives every day breaking down ships, a task that nobody is in a particular hurry to automate because those lives are considered cheap (Headline: ‘Recycling a ship is always dangerous.’ on Deutsche Welle)A world where computers write and make art while human beings break their backs cleaning up toxic messes is the exact opposite of the world I thought I was signing up for when I got into programming
Recycling a ship is all the time harmful

Can AI assume? Can AI make artwork?

With all of this comes the large query: can AI assume? Can AI create?

My private reply to this differs from what I’ve heard from many ML and AI researchers. Most good ML researchers are solidly on the aspect that LLMs, diffusion fashions, and different ML fashions should not sentient and can’t be sentient in the way in which we outline sentient.

I agree with this in most methods: nevertheless, that is the caveat I want to put ahead. LLMs are both already sentient, or they may by no means be.

We already talked about intelligence firstly of this very very lengthy piece. Sentience is a associated topic: Sentience is, based on Wikipedia, the capability to expertise emotions and sensations. So how do you discover out if one thing is sentient?

The NPC meme is an enormous one in right-wing circles; it’s a meme I fervently dislike, however it’s helpful for rationalization functions. The ‘meme’ goes like this: some folks aren’t folks, however extra like NPCs in a online game. NPCs are ‘Non participant characters,’ the characters in a sport that aren’t managed by the participant and easily observe laptop scripts of their heads. This meme applies that to human folks. They imagine this implies some folks don’t have any capability for particular person thought, no emotions– they’re philosophical zombies. They aren’t sentient.

An example of the npc meme in action.

I deliver up this repulsive reference to say we have no idea if individuals are sentient. you possibly can show people are sentient, in a bunch of various methods for a bunch of various definitions inside a variety of completely different philosophical faculties., however definitively, scientifically, there isn’t any technique to know. If somebody says “I’m sentient” how can they show it? All we are able to know is what different folks inform us: and it’s straightforward to get an LLM to answer a immediate saying it ‘feels’ one thing. You’ll be able to by no means objectively show one other human has sentience in a approach that may’t even be disproven.

Descartes thought animals have been “automatons” and never sentient. People are collections of earlier experiences and information filtered by way of a number of neural networks to make choices primarily based on possibilities. In the event you grind a GPU (Graphics processing unit) right down to silicon particulate, you received’t discover an atom of feeling: in the event you grind a mind right down to its atomic elements, you received’t discover emotions there both.

So: LLMs are both already sentient, or they may by no means be.

However often, when folks speak about sentience, they imply the tip of the world situation– Skynet, Rokos Basilisk, and different extraordinarily severe and non-silly threats!

The factor is: the instruments are already getting used for evil, merciless functions. People are utilizing AI to harm one another within the current. The variety of issues that would wish to go catastrophically fallacious for AI to be any extra harmful than most different threats is sort of ridiculous. Google has an AI on a supercomputer: how is that factor going to get its fingers on nukes?

No, the issues you might want to keep watch over with ML are those the place it exaggerates and multiplies the issues that exist already.

ML is just not harmful, in of itself. It’s when it’s used carelessly, or by unhealthy actors, that the hurt is available in. ML is just not prone to resolve one of the best ways to create world peace is to kill all people: it’s prone to present enterprise capitalists the place to purchase up homes to take advantage of cash, exacerbating the housing disaster.

Copilot wrote these previous couple of traces: “It’s prone to present advertisers the place to focus on folks to take advantage of cash, exacerbating the wealth hole. It’s prone to present police the place to focus on folks to take advantage of cash, exacerbating the jail industrial advanced.”

Yeah, buddy. You bought it.

Particular questions + misconceptions

(I’ll replace this over time as I get questions or requests for clarification.)

  1. What’s GPT?

GPT is a big language mannequin that makes use of neural networks and transformers to guess at what the most probably phrases in a sentence shall be.

An example of the GPT3 playground and percentages

  1. What’s DALL-E?

DALL-E is a mannequin that makes use of one thing referred to as Secure Diffusion to generate pictures, predicting on what the most probably place of pixels is in comparison with different pixels for the immediate

  1. What ought to I take advantage of GPT for?

Some automation (getting tokens/key phrases, fundamental automation of summaries, getting information from unstructured information), prompting you in the event you get caught writing, developing with concepts, having enjoyable.

  1. What ought to I keep away from utilizing GPT for?

Keep away from letting GPT assume for you.

  1. Is it AI?

Synthetic Intelligence is a sort of laptop science boogyman/buzzword. You’ll get much less hype in the event you speak about ML, however it’ll be extra correct.

  1. Can ML substitute people in inventive endeavours?

Sure, however it is going to be worse at it.

Due to the oldsters who learn/edited this (and supplied to) earlier than it went out: my pal Clare, my editor Ellie, AlexImanDáreMatsLuke, and Marco.


Because you’re right here… In the event you can spare 5 bucks, and revel in my work,I’d recognize it in the event you donate a bit of cash to the Sylvia Rivera Legislation Mission. Thanks!

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