Many of the most pressing concerns around AI safety will come with these features, whenever they arrive. The fear is that when you tell AI systems to do things on your behalf, they might accomplish them via harmful means. This is the fear embedded in the famous paperclip problem, and while that remains an outlandish worst-case scenario, other potential harms are much more plausible.Once you start enabling agents like the ones OpenAI pointed toward today, you start building the path toward sophisticated algorithms manipulating the stock market; highly personalized and effective phishing attacks; discrimination and privacy violations based on automations connected to facial recognition; and all the unintended (and currently unimaginable) consequences of infinite AIs colliding on the internet.

That same Copy Editor I described above might be able in the future to automate the creation of a series of blogs, publish original columns on them every day, and promote them on social networks via an established daily budget, all working toward the overall goal of undermining support for Ukraine.

Which actions is OpenAI comfortable letting GPT-4 take on the internet today, and which does the company not want to touch?  Altman’s answer is that, at least for now, the company wants to keep it simple. Clear, direct actions are OK; anything that involves high-level planning isn’t.

For most of his keynote address, Altman avoided making lofty promises about the future of AI, instead focusing on the day-to-day utility of the updates that his company was announcing. In the final minutes of his talk, though, he outlined a loftier vision.“We believe that AI will be about individual empowerment and agency at a scale we've never seen before,” Altman said, “And that will elevate humanity to a scale that we've never seen before, either. We'll be able to do more, to create more, and to have more. As intelligence is integrated everywhere, we will all have superpowers on demand.”

A person can’t mimic another voice perfectly (impressionists don’t have to pay licence fees) but they can listen to a thousand hours of music and make something in that style - a ‘pastiche’, we sometimes call it. If a person did that, they wouldn’t have to pay a fee to all those artists, so if we use a computer for that, do we need to pay them?

I think most people understand that if I post a link to a news story on my Facebook feed and tell my friends to read it, it’s absurd for the newspaper to demand payment for this. A newspaper, indeed, doesn’t pay a restaurant a percentage when it writes a review.

one way to think about this might be that AI makes practical at a massive scale things that were previously only possible on a small scale. This might be the difference between the police carrying wanted pictures in their pockets and the police putting face recognition cameras on every street corner - a difference in scale can be a difference in principle. What outcomes do we want? What do we want the law to be? What can it be?

OpenAI hasn’t ‘pirated’ your book or your story in the sense that we normally use that word, and it isn’t handing it out for free. Indeed, it doesn’t need that one novel in particular at all. In Tim O’Reilly’s great phrase, data isn’t oil; data is sand. It’s only valuable in the aggregate of billions,, and your novel or song or article is just one grain of dust in the Great Pyramid.

it’s supposed to be inferring ‘intelligence’ (a placeholder word) from seeing as much as possible of how people talk, as a proxy for how they think.

it doesn’t need your book or website in particular and doesn’t care what you in particular wrote about, but it does need ‘all’ the books and ‘all’ the websites. It would work if one company removed its content, but not if everyone did.

What if I use an engine trained on the last 50 years of music to make something that sounds entirely new and original? No-one should be under the delusion that this won’t happen.

I can buy the same camera as Cartier-Bresson, and I can press the button and make a picture without being able to draw or paint, but that’s not what makes the artist - photography is about where you point the camera, what image you see and which you choose. No-one claims a machine made the image.

Spotify already has huge numbers of ‘white noise’ tracks and similar, gaming the recommendation algorithm and getting the same payout per play as Taylor Swift or the Rolling Stones. If we really can make ‘music in the style of the last decade’s hits,’ how much of that will there be, and how will we wade through it? How will we find the good stuff, and how will we define that? Will we care?

With the comprehensive application of Artificial Intelligence into the creation and post production of images, it seems questionable if the resulting visualisations can still be considered ‘photographs’ in a classical sense – drawing with light. Automation has been part of the popular strain of photography since its inception, but even the amateurs with only basic knowledge of the craft could understand themselves as author of their images. We state a legitimation crisis for the current usage of the term. This paper is an invitation to consider Synthography as a term for a new genre for image production based on AI, observing the current occurrence and implementation in consumer cameras and post-production.

Prompt engineers not only need to get the model to respond to a given question but also structure the output in a parsable way (such as JSON), in case it needs to be rendered in some UI components or be chained into the input of a future LLM query. They scaffold the raw input that is fed into an LLM so the end user doesn’t need to spend time thinking about prompting at all.

From the user’s side, it’s hard to decide what to ask while providing the right amount of context.From the developer’s side, two problems arise. It’s hard to monitor natural language queries and understand how users are interacting with your product. It’s also hard to guarantee that an LLM can successfully complete an arbitrary query. This is especially true for agentic workflows, which are incredibly brittle in practice.

When we speak to other people, there is a shared context that we communicate under. We’re not just exchanging words, but a larger information stream that also includes intonation while speaking, hand gestures, memories of each other, and more. LLMs unfortunately cannot understand most of this context and therefore, can only do as much as is described by the prompt

most people use LLMs for ~4 basic natural language tasks, rarely taking advantage of the conversational back-and-forth built into chat systems:Summarization: Summarizing a large amount of information or text into a concise yet comprehensive summary. This is useful for quickly digesting information from long articles, documents or conversations. An AI system needs to understand the key ideas, concepts and themes to produce a good summary.ELI5 (Explain Like I'm 5): Explaining a complex concept in a simple, easy-to-understand manner without any jargon. The goal is to make an explanation clear and simple enough for a broad, non-expert audience.Perspectives: Providing multiple perspectives or opinions on a topic. This could include personal perspectives from various stakeholders, experts with different viewpoints, or just a range of ways a topic can be interpreted based on different experiences and backgrounds. In other words, “what would ___ do?”Contextual Responses: Responding to a user or situation in an appropriate, contextualized manner (via email, message, etc.). Contextual responses should feel organic and on-topic, as if provided by another person participating in the same conversation.

Prompting nearly always gets in the way because it requires the user to think. End users ultimately do not wish to confront an empty text box in accomplishing their goals. Buttons and other interactive design elements make life easier.The interface makes all the difference in crafting an AI system that augments and amplifies human capabilities rather than adding additional cognitive load.Similar to standup comedy, delightful LLM-powered experiences require a subversion of expectation.

Users will expect the usual drudge of drafting an email or searching for a nearby restaurant, but instead will be surprised by the amount of work that has already been done for them from the moment that their intent is made clear. For example, it would a great experience to discover pre-written email drafts or carefully crafted restaurant and meal recommendations that match your personal taste.If you still need to use a text input box, at a minimum, also provide some buttons to auto-fill the prompt box. The buttons can pass LLM-generated questions to the prompt box.

What is the testable hypothesis? What would falsify the hypothesis? How do we know when we are getting into a danger zone? These questions go mainly unanswered apart from “You can’t prove it won’t happen!” In fact, these Baptists’ position is so non-scientific and so extreme – a conspiracy theory about math and code – and is already calling for physical violence, that I will do something I would normally not do and question their motives as well.

In the past several months, I have come across people who do programming, legal work, business, accountancy and finance, fashion design, architecture, graphic design, research, teaching, cooking, travel planning, event management etc., all of whom have started using the same tool, ChatGPT, to solve use cases specific to their domains and problems specific to their personal workflows. This is unlike everyone using the same messaging tool or the same document editor. This is one tool, a single class of technology (LLM), whose multi-dimensionality has achieved widespread adoption across demographics where people are discovering how to solve a multitude of problems with no technical training, in the one way that is most natural to humans—via language and conversations.

I cannot recall the last time a single tool gained such widespread acceptance so swiftly, for so many use cases, across entire demographics.

there is significant substance beneath the hype. And that is what is worrying; the prospect of us starting to depend indiscriminately on poorly understood blackboxes, currently offered by megacorps, that actually work shockingly well.

If a single dumb, stochastic, probabilistic, hallucinating, snake oil LLM with a chat UI offered by one organisation can have such a viral, organic, and widespread adoption—where large disparate populations, people, corporations, and governments are integrating it into their daily lives for use cases that they are discovering themselves—imagine what better, faster, more “intelligent” systems to follow in the wake of what exists today would be capable of doing.

A policy for “AI anxiety” We ended up codifying this into an actual AI policy to bring clarity to the organisation.[10] It states that no one at Zerodha will lose their job if a technology implementation (AI or non-AI) directly renders their existing responsibilities and tasks obsolete. The goal is to prevent unexpected rug-pulls from underneath the feet of humans. Instead, there will be efforts to create avenues and opportunities for people to upskill and switch between roles and responsibilities

To those who believe that new jobs will emerge at meaningful rates to absorb the losses and shocks, what exactly are those new jobs? To those who think that governments will wave magic wands to regulate AI technologies, one just has to look at how well governments have managed to regulate, and how well humanity has managed to self-regulate, human-made climate change and planetary destruction. It is not then a stretch to think that the unraveling of our civilisation and its socio-politico-economic systems that are built on extracting, mass producing, and mass consuming garbage, might be exacerbated. Ted Chiang’s recent essay is a grim, but fascinating exploration of this. Speaking of grim, we can always count on us to ruin nice things! Along the lines of Murphy’s Law,[11] I present: Anything that can be ruined, will be ruined — Grumphy’s law

I asked GPT-4 to summarise this post and write five haikus on it. I have always operated a piece of software, but never asked it anything—that is, until now. Anyway, here is the fifth one. Future’s tangled web, Offloading choices to black boxes, Humanity’s voice fades

This is reflected in their name: a “language model” implies that they are tools for working with language. That’s what they’ve been trained to do, and it’s language manipulation where they truly excel. Want them to work with specific facts? Paste those into the language model as part of your original prompt! There are so many applications of language models that fit into this calculator for words category: Summarization. Give them an essay and ask for a summary. Question answering: given these paragraphs of text, answer this specific question about the information they represent. Fact extraction: ask for bullet points showing the facts presented by an article. Rewrites: reword things to be more “punchy” or “professional” or “sassy” or “sardonic”—part of the fun here is using increasingly varied adjectives and seeing what happens. They’re very good with language after all! Suggesting titles—actually a form of summarization. World’s most effective thesaurus. “I need a word that hints at X”, “I’m very Y about this situation, what could I use for Y?”—that kind of thing. Fun, creative, wild stuff. Rewrite this in the voice of a 17th century pirate. What would a sentient cheesecake think of this? How would Alexander Hamilton rebut this argument? Turn this into a rap battle. Illustrate this business advice with an anecdote about sea otters running a kayak rental shop. Write the script for kickstarter fundraising video about this idea.

A flaw in this analogy: calculators are repeatable Andy Baio pointed out a flaw in this particular analogy: calculators always give you the same answer for a given input. Language models don’t—if you run the same prompt through a LLM several times you’ll get a slightly different reply every time.

Coming back to the internet analogy, how did Google, Amazon etc ended up so successful? Metcalf’s law explains this. It states that as more users join the network, the value of the network increases thereby attracting even more users. The most important thing here was to make people join your network. The end goal was to build the largest network possible. Google did this with search, Amazon did this with retail, Facebook did this with social.

Collecting as much data as possible is important. But you don’t want just any data. The real competitive advantage lies in having high-quality proprietary data. Think about it this way, what does it take to build an AI system? It takes 1) data, which is the input that goes into the 2) AI models which are analogous to machines and lastly it requires energy to run these models i.e. 3) compute. Today, most AI models have become standardized and are widely available. And on the other hand, the cost of compute is rapidly trending to zero. Hence AI models and compute have become a commodity. The only thing that remains is data. But even data is widely available on the internet. Thus, a company can only have a true competitive advantage when it has access to high-quality proprietary data.

Recently, Chamath Palihapitiya gave an interview where he had this interesting analogy. He compared these large language models like GPT to refrigeration. He said “People that invented refrigeration, made some money. But most of the money was made by Coca-Cola who used refrigeration to build an empire. And so similarly, companies building these large models will make some money, but the Coca-Cola is yet to be built.” What he meant by this is that right now there are lot of companies crawling the open web to scrap the data. Once that is widely available like refrigeration, we will see companies and startups coming up with proprietary data building on top of it

Software is misunderstood. It can feel like a discrete thing, something with which we interact. But, really, it is the intrusion into our world of something very alien. It is the strange interaction of electricity, semiconductors, and instructions, all of which somehow magically control objects that range from screens to robots to phones, to medical devices, laptops, and a bewildering multitude of other things. It is almost infinitely malleable, able to slide and twist and contort itself such that, in its pliability, it pries open doorways as yet unseen.

the clearing price for software production will change. But not just because it becomes cheaper to produce software. In the limit, we think about this moment as being analogous to how previous waves of technological change took the price of underlying technologies—from CPUs, to storage and bandwidth—to a reasonable approximation of zero, unleashing a flood of speciation and innovation. In software evolutionary terms, we just went from human cycle times to that of the drosophila: everything evolves and mutates faster.

A software industry where anyone can write software, can do it for pennies, and can do it as easily as speaking or writing text, is a transformative moment. It is an exaggeration, but only a modest one, to say that it is a kind of Gutenberg moment, one where previous barriers to creation—scholarly, creative, economic, etc—are going to fall away, as people are freed to do things only limited by their imagination, or, more practically, by the old costs of producing software.

We have almost certainly been producing far less software than we need. The size of this technical debt is not knowable, but it cannot be small, so subsequent growth may be geometric. This would mean that as the cost of software drops to an approximate zero, the creation of software predictably explodes in ways that have barely been previously imagined.

Entrepreneur and publisher Tim O’Reilly has a nice phrase that is applicable at this point. He argues investors and entrepreneurs should “create more value than you capture.” The technology industry started out that way, but in recent years it has too often gone for the quick win, usually by running gambits from the financial services playbook. We think that for the first time in decades, the technology industry could return to its roots, and, by unleashing a wave of software production, truly create more value than its captures.

Software production has been too complex and expensive for too long, which has caused us to underproduce software for decades, resulting in immense, society-wide technical debt.

technology has a habit of confounding economics. When it comes to technology, how do we know those supply and demand lines are right? The answer is that we don’t. And that’s where interesting things start happening. Sometimes, for example, an increased supply of something leads to more demand, shifting the curves around. This has happened many times in technology, as various core components of technology tumbled down curves of decreasing cost for increasing power (or storage, or bandwidth, etc.).

Suddenly AI has become cheap, to the point where people are “wasting” it via “do my essay” prompts to chatbots, getting help with microservice code, and so on. You could argue that the price/performance of intelligence itself is now tumbling down a curve, much like as has happened with prior generations of technology.

it’s worth reminding oneself that waves of AI enthusiasm have hit the beach of awareness once every decade or two, only to recede again as the hyperbole outpaces what can actually be done.

Across universities, professors have been looking into ways to engage students so cheating with ChatGPT is not as attractive, such as making assignments more personalized to students’ interests and requiring students to complete brainstorming assignments and essay drafts instead of just one final paper.

This analogy to lossy compression is not just a way to understand ChatGPT’s facility at repackaging information found on the Web by using different words. It’s also a way to understand the “hallucinations,” or nonsensical answers to factual questions, to which large language models such as ChatGPT are all too prone

When an image program is displaying a photo and has to reconstruct a pixel that was lost during the compression process, it looks at the nearby pixels and calculates the average. This is what ChatGPT does when it’s prompted to describe, say, losing a sock in the dryer using the style of the Declaration of Independence: it is taking two points in “lexical space” and generating the text that would occupy the location between them

they’ve discovered a “blur” tool for paragraphs instead of photos, and are having a blast playing with it.

A close examination of GPT-3’s incorrect answers suggests that it doesn’t carry the “1” when performing arithmetic. The Web certainly contains explanations of carrying the “1,” but GPT-3 isn’t able to incorporate those explanations. GPT-3’s statistical analysis of examples of arithmetic enables it to produce a superficial approximation of the real thing, but no more than that.

In human students, rote memorization isn’t an indicator of genuine learning, so ChatGPT’s inability to produce exact quotes from Web pages is precisely what makes us think that it has learned something. When we’re dealing with sequences of words, lossy compression looks smarter than lossless compression

Generally speaking, though, I’d say that anything that’s good for content mills is not good for people searching for information. The rise of this type of repackaging is what makes it harder for us to find what we’re looking for online right now; the more that text generated by large language models gets published on the Web, the more the Web becomes a blurrier version of itself.

Can large language models help humans with the creation of original writing? To answer that, we need to be specific about what we mean by that question. There is a genre of art known as Xerox art, or photocopy art, in which artists use the distinctive properties of photocopiers as creative tools. Something along those lines is surely possible with the photocopier that is ChatGPT, so, in that sense, the answer is yes

If students never have to write essays that we have all read before, they will never gain the skills needed to write something that we have never read.

Sometimes it’s only in the process of writing that you discover your original ideas.

Some might say that the output of large language models doesn’t look all that different from a human writer’s first draft, but, again, I think this is a superficial resemblance. Your first draft isn’t an unoriginal idea expressed clearly; it’s an original idea expressed poorly, and it is accompanied by your amorphous dissatisfaction, your awareness of the distance between what it says and what you want it to say. That’s what directs you during rewriting, and that’s one of the things lacking when you start with text generated by an A.I.

People are not interested in visiting websites about a topic; they, by and large, just want answers to their questions. Google has been strip-mining the web for years, leveraging its unique position as the world’s most popular website and its de facto directory to replace what made it great with what allows it to retain its dominance.

Artificial intelligence — or some simulation of it — really does make things better for searchers, and I bet it could reduce some tired search optimization tactics. But it comes at the cost of making us all into uncompensated producers for the benefit of trillion-dollar companies like Google and Microsoft.

Search optimization experts have spent years in an adversarial relationship with Google in an attempt to get their clients’ pages to the coveted first page of results, often through means which make results worse for searchers. Artificial intelligence is, it seems, a way out of this mess — but the compromise is that search engines get to take from everyone while giving nothing back. Google has been taking steps in this direction for years: its results page has been increasingly filled with ways of discouraging people from leaving its confines.

here are five large language model applications that I find intriguing: Intelligent automation starting with browsers but this feels like a step towards phenotropics Text generation when this unlocks new UIs like Word turning into Photoshop or something Human-machine interfaces because you can parse intent instead of nouns When meaning can be interfaced with programmatically and at ludicrous scale Anything that exploits the inhuman breadth of knowledge embedded in the model, because new knowledge is often the collision of previously separated old knowledge, and this has not been possible before.

Even if chatbots were to fix their accuracy issues, Google would still have a business model problem to contend with. The company makes money when people click ads next to search results, and it’s awkward to fit ads into conversational replies. Imagine receiving a response and then immediately getting pitched to go somewhere else — it feels slimy, and unhelpful. Google thus has little incentive to move us beyond traditional search, at least not in a paradigm-shifting way, until it figures out how to make the money aspect work. In the meantime, it’ll stick with the less impressive Google Assistant.

“Google doesn’t inherently want you, at an inherent level, to just get the answer to every problem. Because that might reduce the need to go click around the web, which would then reduce the need for us to go to Google.”

Web3 has largely been technology looking for problems to solve while generative AI has been about almost too many solutions created by technology which is evolving on a seemingly daily basis. As a result, web3 has thus far been evangelists trying to convince us to re-solve old problems with their new technology