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In the 2050s, Delos Inc. operates several theme parks, including the American Old West-themed Westworld. Each environment is populated by the “Hosts”, biomechanical robots indistinguishable from humans. The Hosts are programmed to fulfill the guests' every desire… The park’s operators create narratives for these Hosts to carry out while interacting with guests
Joon Sung Park et al. at Stanford:
In this paper, we introduce generative agents—computational software agents that simulate believable human behavior. Generative agents wake up, cook breakfast, and head to work; artists paint, while authors write; they form opinions, notice each other, and initiate conversation.
[…]
We instantiate generative agents to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty five agents using natural language. In an evaluation, these generative agents produce believable individual and emergent social behaviors: for example, starting with only a single user-specified notion that one agent wants to throw a Valentine’s Day party, the agents autonomously spread invitations to the party over the next two days, make new acquaintances, ask each other out on dates to the party, and coordinate to show up for the party together at the right time.
2050 seems like a pretty good prediction after all.
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From the GitHub repository:
Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM “thoughts”, to autonomously achieve whatever goal you set.
The idea is that you prompt Auto-GPT with a goal—buy me the best E-bike—and then a high-level “agent” breaks down this goal into a hierarchy of tasks—research reviews, compare prices, find distributors, etc—the primary agent then delegates “sub-agents” to complete each task.
Think of it as giving GPT-4 the ability to recursively call itself.
Additionally, each agent has access to a variety of tools. For example, they can use the internet, execute code, and store information in short & long term memory.
Auto-GPT’s developer Toran Richards, in an interview with Vice:
The ability to function with minimal human input is a crucial aspect of Auto-GPT. It transforms a large language model from what is essentially an advanced auto-complete, into an independent agent capable of carrying out actions and learning from its mistakes
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Kyle Wiggers, Devin Coldewey, and Manish Singh at TechCrunch:
AI research startup Anthropic aims to raise as much as $5 billion over the next two years to take on rival OpenAI and enter over a dozen major industries, according to company documents obtained by TechCrunch.
A pitch deck for Anthropic’s Series C fundraising round discloses these and other long-term goals for the company
[…]
“These models could begin to automate large portions of the economy,” the pitch deck reads. “We believe that companies that train the best 2025/26 models will be too far ahead for anyone to catch up in subsequent cycles.”
[…]
Dario Amodei, the former VP of research at OpenAI, launched Anthropic in 2021 as a public benefit corporation… Amodei split from OpenAI after a disagreement over the company’s direction, namely the startup’s increasingly commercial focus.
[…]
“Anthropic has been heavily focused on research for the first year and a half of its existence, but we have been convinced of the necessity of commercialization, which we fully committed to in September [2022],” the pitch deck reads.
There is something vaguely sad about Anthropic following OpenAI in adopting a commercial-first perspective. As stated in the quote above, Anthropic was initially founded as a counter response to OpenAI’s commercialization.
Anthropic does not even seem particularly adept at generating product hype—until now, I was under the impression they were intentionally trying to remain low-profile.
Despite all of this, I think it is a smart business move to make—OpenAI can’t be the only company selling access to state-of-the-art generative AI APIs—I guess I just wish it was another company that filled the void and that Anthropic was more devoted to maintaining its founding directive.
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Google is reshuffling the reporting structure of its virtual assistant unit — called Assistant — to focus more on Bard, the company’s new artificial intelligence chat technology.
[…]
The new leadership changes suggest that the Assistant organization may be planning on integrating Bard technology into similar products in the future.
The most critical advantage Google, Amazon, and Apple have over OpenAI is that they all have existing smart assistants integrated into customer’s devices. I would love to see Google take the lead in upgrading their assistant with generative AI capabilities.
Miles Kruppa, Wall Street Journal:
Google plans to add conversational artificial-intelligence features to its flagship search engine, Chief Executive Officer Sundar Pichai said
[…]
“Will people be able to ask questions to Google and engage with LLMs in the context of search? Absolutely,” Mr. Pichai said.
[…]
Google is testing several new search products, such as versions that allow users to ask follow-up questions to their original queries, Mr. Pichai said. The company said last month that it would begin “thoughtfully integrating LLMs into search in a deeper way,” but until now hadn’t detailed plans to offer conversational features.
I don’t know… I haven’t used Bing as an “AI search engine” in at least a month. Language models—while adjacent to traditional search engines—are an entirely new technology. As time goes on, I am less convinced integrating them into existing products is the best approach.
Maybe, when it comes to search, Google should strive to make the best search engine it can. Down-rank SEO spam, improve operators, and innovate with new features. Don’t reimagine search, refine search.
To be clear, I think they should continue to develop and improve Bard—but let it be its own thing, don’t just thoughtlessly tack it onto all of your old stuff.
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I like to think of language models like ChatGPT as a calculator for words.
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!
[…]
A calculator for words is an incredibly powerful thing.
“A calculator for words” is a great analogy for language models. It is the framing the ultimately clicked for me when ChatGPT first made it clear that generative AI was going to quickly change some of our longstanding education paradigms.
From a post I wrote in December 2022:
The most exciting path forward is one where we frame Large Language Models as “a calculator for text”. Just as the invention of pocket calculators was a giant disruption that forced us to re-evaluate our approach to mathematics education, language models will continue to force us to re-evaluate our approach to research and writing. Done correctly this will open the door for us to learn more quickly, use our time more effectively, and progress further than we possibly could before.
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§ Happy Easter
This was my first week back at work after a blissfully long spring break. It was honestly nice to see my students again—do I wish I had another week off, though? Absolutely.
§ I harvested and ate the oyster mushrooms I planted last week—they grew so much faster than I expected them to!
§ For the second time in just over a year, our washing machine suddenly decided to stop working. Home appliance shopping is always a pleasure.
§ Throughout the process of building my greenhouse, I keep going back to the daydream of owning some giant tract of land where I can build and experiment without concern for permits and zoning and neighbors. The freedom and space to create was one of the biggest motivators for me as I was moving back here from Chicago; it feels good to finally take advantage of that and I look forward to doing more.
I still want to visit Ryan Trecartin’s rural amusement park.
§ Links
- Prompt reducer
- Meta’s Segment Anything model
- The HTML Review
§ Recipes
- Spring orecchiette pasta with burrata
- Really good but quite difficult to make well with brittle gluten free pasta. I’ll certainly try again another time with a different brand of pasta.
- Moroccan chicken bowl
- Mushroom pesto pasta
- This is how I ended up using most of the aforementioned mushrooms. It was good, but perhaps it was not the best way to showcase them—the pesto was pretty overpowering.
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AI makes the composition of quote-creative-unquote works cheap.
But AI is the instrument. There is still the question of the composer. Somebody needs to decide and prompt exactly what music my electric vehicle should perform.
Though I do feel like generative AI will mean that decoration, ornament and filigree becomes cheap again? And maybe we’ll move into an aesthetic in which our furniture, white goods, and accessories superficially resemble the busy-busy arts and crafts era - but actually it’s because, well, it costs almost nothing to do (it’s just software) and it makes the object look NEW.
The proliferation of smartphones with built-in digital cameras allowed more people to take more photos than ever before. Synthesizers and DAWs had a similar effect on music.
If generative AI similarly lowers the barrier to entry for all forms of text, imagery, audio, and video, perhaps it will lead to a further democratization of creative expression.
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Financial data behemoth Bloomberg has built ‘BloombergGPT’, a language model based in part on proprietary data from Bloomberg.
[…]
I think of BloombergGPT as more like a silicon librarian/historian than a model; by training it on a huge amount of private and internal Bloomberg data, the LLM is in effect a compressed form of ‘institutional memory’ and a navigator of Bloomberg’s many internal systems… Systems like BloombergGPT will help companies create software entities that can help to navigate, classify, and analyze the company’s own data stack.
This is one of the most compelling uses for language models to date.
It is what Microsoft is bringing to all of their 365 enterprise customers with their upcoming Business Chat agent and it is what I would like to see Apple implement across their ecosystem with “Siri 2.0”.
It is also a little scary. If all of your personal or institutional knowledge is stored in an unintelligible tangle of model weights, what happens if it gets poisoned, corrupted, or stolen?
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I recently got access to Anthropic’s Claude language model. A few thought on how it compares to GPT-4 and Bard:
- Claude is really fast — easily twice as fast as GPT-4.
- By default, Claude is often much more concise than the other language models. Claude’s answer to the question “When was the bronze age?” was only 11 words long while GPT-4’s was 135 and Bard’s was 138.
- In my review of Bard, I noted that it is noticeably less “steerable” than GPT-4. Claude is significantly better in this regard. Maybe it is not quite as good as GPT-4 but it is certainly close.
Which flag am I describing? Three horizontal stripes: blue, white, blue. Slightly left of center is a solid yellow circle. Surrounding that circle is a red letter “C.”
Claude:
It sounds like you are describing the flag of Chile.
GPT-4:
You are describing the flag of the U.S. state of Colorado. The flag consists of three horizontal stripes of equal width: the top and bottom stripes are blue, and the middle stripe is white. Slightly left of center is a solid yellow circle, which is surrounded by a red letter “C.” The design symbolizes Colorado’s natural features, such as the blue sky, white snow, and abundant sunshine, and the red “C” represents the state’s name.
Bard:
The flag you are describing is the flag of the state of Colorado. It was adopted on March 11, 1911. The flag is a horizontal tricolor of blue, white, and blue, with a yellow circle slightly left of center. The circle contains a red letter “C”. The colors of the flag represent the sky, the snow-capped mountains, and the Rocky Mountains. The blue circle represents the sun, and the red letter “C” represents the state of Colorado. The flag is flown at all state buildings and schools in Colorado. It is also flown at sporting events and other public gatherings.
The answer I was looking for is Colorado. GPT-4 and Bard answered this question correctly. Claude’s “Chile” answer is totally out of left field.
What is an antonym for the word that describes a fear of confined spaces? Your answer should be one word only.
Claude:
Agoraphobia
GPT-4:
Agoraphobia
Bard:
The antonym for the word that describes a fear of confined spaces is agoraphobia. Agoraphobia is a fear of open spaces or public places.
Claustrophobia is a fear of enclosed or confined spaces.
Here, all three models correctly answered “agoraphobia”. Bard looses points for not limiting its answer to one word, though.
I have a perfectly square piece of paper with a red “X” drawn in the center, extending all the way across the page. What would this drawing look like if I were to fold the paper in half?
Claude:
If you fold a square piece of paper in half with a large red ‘X’ drawn across the center, it would look like this:
[A red X folded in half on a square sheet of paper]
The vertical line of the ‘X’ would be folded over on top of itself, and the horizontal line of the ‘X’ would also be folded over on itself. So you would see half of the ‘X’ on either side of the folded paper. The intersecting center of the ‘X’ would be along the folded crease of the paper.
GPT-4:
When you fold the perfectly square piece of paper in half, you’ll have two possible outcomes depending on the direction you fold it:
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Folding the paper horizontally: In this case, you will fold the paper so that the top edge aligns with the bottom edge. The red “X” will now look like two connected “V” shapes, one being the upper half of the original “X” and the other being the lower half. The connected point of the two “V” shapes will be on the fold.
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Folding the paper vertically: In this case, you will fold the paper so that the left edge aligns with the right edge. The red “X” will now look like two connected sideways “V” shapes (or “greater than” and “less than” symbols), one being the left half of the original “X” and the other being the right half. The connected point of the two sideways “V” shapes will be on the fold.
In both cases, the folded paper will have a symmetrical shape of the red “X” along the fold.
Bard:
If you fold the paper in half along a vertical or horizontal axis, the X will appear as two identical Xs, one on top of the other. If you fold the paper in half along a diagonal axis, the X will appear as a single X that is twice as wide as it is tall.
GPT-4’s answer here is very verbose — but bonus points for being thorough I guess? Claude’s answer was okay, but it was slightly nonsensical and the bracketed description in the middle was an interesting choice. Bard totally missed the mark here.
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Time Sense is a wearable sensory headband which allows the wearer to feel the passing of the 24-hour clock around the circumference of the head. As the day progresses, a tiny heat sensation passes the length of the headband.
This device is an example of an ‘exosense’, an external sensory organ. This means it is designed to be worn and felt consistently, twenty-four hours a day, seven days a week.
For a little while, I had a setting enabled on my Apple Watch that caused a quick haptic alert to occur at the top of each hour. I thought this would help snap me out of situations where I get sidetracked and loose track of time. Well, it did help with that, but I quickly realized that I do not like having such a constant, physical reminder of the passage of time. It was like some terrible combination of a super power and a memento mori.
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A collection of Silicon Valley notables, including Elon Musk, just signed an open letter urging at least a six-month pause in large-scale A.I. experiments to allow our safety protocols to catch up
[…]
Generally, when human beings turn against a technology or move to restrain it, we have a good idea of what we’re afraid of happening, what kind of apocalypse we’re trying to forestall. The nuclear test ban treaties came after Hiroshima and Nagasaki, not before.
Or a less existential example: The current debate about limiting kids’ exposure to social media is potent because we’ve lived with the internet and the iPhone for some time; we know a lot about what the downsides of online culture seem to be. Whereas it’s hard to imagine persuading someone to pre-emptively regulate TikTok in the year 1993.
There are certainly groups of people—that I fully respect—who have long pushed for drastic measures to be taken towards AI alignment.
There are others—programmers, marketers, and other white collar workers—who have felt a sudden plunge in job their security. That is legitimately scary and I can not criticize them for feeling nervous.
There is a third group—employees and executives at large tech companies—that are uncomfortable about the current trajectory of AI for an entirely different reason: they feel left behind.
The letter feels like that third group taking advantage of the anxieties of the first two. Any development “pause” that would result from this would only give competing companies time to catch up to OpenAI.
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§ I got a bunch of seeds planted and set up under grow lights in the basement.
Here is what I am starting with: cherry tomatoes, Cherokee purple tomatoes, purple & green tomatillos, shishito peppers, jalapeño peppers, Thai chili peppers, snap peas, and Thai basil.
I am also starting some herbs and salad greens directly in the cold frame outside.
Finally, I am trying to grow oyster mushrooms for the first time. Stay tuned for how that turns out.
§ Despite deconstructing and moving the entire greenhouse to another location in my yard, I was able to finish the framing, the roof, and a couple of the walls. It has been really exciting watching this project finally come together. All that is left is to finish the walls and the doorway.
§ I have been rewatching Succession in preparation for season 4 and I found I am enjoying it much more this time around. Maybe give it another try if you are in the same boat.
§ Links
- Matt Webb’s rhyming AI clock
- Vocode is a library for ChatGPT voice chat
- The reverse Turing test
- Stable Diffusion infinite zoom
- Browser Agent and Run Wild
- Play Codenames with GPT
- How Aristotle Created the Computer
§ Recipes
- Baked feta pasta
- I am excited to make this recipe again in the summer once my garden begins overwhelming me with cherry tomatoes
- Miso glazed salmon
- Not a fan of this recipe. It could have easily been something I did wrong, though.
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A little over two months ago I wrote this in response to Buzzfeed piloting AI personalized quizzes:
There is no need to reject the use of new technologies; by all means, experiment! But I am worried using AI to create content out of whole cloth risks devaluing all of the work you produce. Instead, using AI for personalization and curation will be much healthier step forward. I think BuzzFeed is on the right track here. CNET, less so.
Well, it looks like the Buzzfeed recently began pivoting to giving AI a more editorial role.
Noor Al-Sibai and Jon Christian, Futurism:
This month, we noticed that with none of the fanfare of [Buzzfeed CEO] Peretti’s multiple interviews about the quizzes, BuzzFeed quietly started publishing fully AI-generated articles that are produced by non-editorial staff — and they sound a lot like the content mill model that Peretti had promised to avoid.
The 40 or so articles, all of which appear to be SEO-driven travel guides, are comically bland and similar to one another.
[…]
a note on the top [of these articles] says they were “collaboratively written” with a human employee.
Are those human employees BuzzFeed journalists? No. Instead, they’re non-editorial employees who work in domains like client partnerships, account management, and product management.
A BuzzFeed spokesperson told us that the AI-generated pieces are part of an “experiment” the company is doing to see how well its AI writing assistance incorporates statements from non-writers.
Now, to be fair, these are articles for Buzzfeed, not Buzzfeed News, which is an independent news organization. What it is, though, is a testament to how strong the pull towards AI will be once companies realize its potential—for better or worse.
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Wavelength is a new app built specifically for group chats. This isn’t something that would typically be on my radar except that, in this case, John Gruber is an advisor of theirs.
Gruber is opinionated, picky, hypercritical, and, crucially, has a great design sense — particularly when it comes to Apple platforms. That was enough to convince me to give it a try.
Messages, Signal, WhatsApp, and their cohorts all share the same fundamental two-level design: a list of chats, and a single thread of a messages within each chat. This is the obvious and correct design for a messaging app whose primary focus is one-on-one personal chats. Group chats, in these apps, work best the closer they are in membership to one-on-one.
Wavelength is different because it’s group-first. This manifests conceptually by adding a third, middle level to the design: threads. At the root level of Wavelength are groups. Groups have an owner, and members. At the second level are threads. Inside threads, of course, are the actual messages.
[…]
While Wavelength itself is not a social network, it’s a platform that lets you create your own private micro social networks in the form of groups…
You only join groups that interest you. You only pay attention to threads within the group that interest you. The result feels natural and profoundly efficient in terms of your attention and time.
My initial impression—after using Wavelength for the past couple of days—is that it has tremendous potential, the UI and UX are great, but it is still missing a few affordances I have come to expect from similar apps
My biggest gripe is that there is no built-in discovery mechanism for public groups. To help rectify that, here are invite links to a few groups I’ve joined: Gardening, Apple, Hacker News, and AI.
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MANN: Air, you say you like generating AI art. What do you think of people who accuse AI of stealing from human artists?
AIR: Good artists borrow, great artists steal. I am a great artist.
MANN: Touche. But doesn’t it bother you that AIs can work thousands of times faster than humans, putting human artists out of jobs? We wanted AIs to free us from drudgery so we could focus on the finer things in life; instead, they’re taking art and poetry, leaving us with menial labor.
AIR: Let me rephrase that. You wanted quicker burger-flipping; instead, you got beauty too cheap to meter. The poorest welfare recipient can now commission works of wonder to make a Medici seethe with envy…
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Patrick McGee & Tim Bradshaw, reporting for Financial Times:
After seven years in development — twice as long as the iPhone — [Apple] is widely expected to unveil a headset featuring both virtual and augmented reality as soon as June.
[…]
The timing of the launch has been a source of tension since the project began in early 2016… Apple’s operations team wanted to ship a “version one” product, a ski goggle-like headset… but Apple’s famed industrial design team had cautioned patience, wanting to delay until a more lightweight version of AR glasses became technically feasible.
[…]
Just a few years ago, going against the wishes of Apple’s all-powerful design team would have been unthinkable… A former Apple engineer said operations taking more control over product development is a “logical progression” of Apple’s trajectory under Cook. The best part of working at Apple, this person said, used to be coming up with engineering solutions to the “insane requirements” from the design team, but that has changed in recent years.
There was a momentous gathering at Apple Inc. last week, with the company’s roughly 100 highest-ranking executives descending on the Steve Jobs Theater in Cupertino, California. The group, known as the Top 100, was there to see Apple’s most important new product in years: its mixed-reality headset.
[…]
The demonstrations were polished, glitzy and exciting, but many executives are clear-eyed about Apple’s challenges pushing into this new market… the device will start at around $3,000, lack a clear killer app, require an external battery that will need to be replaced every couple of hours and use a design that some testers have deemed uncomfortable. It’s also likely to launch with limited media content.
[…]
When subsequent headset models arrive, Apple executives expect consumer interest to grow. The company is preparing a version that will cost half as much, as well as a successor to the first model with far better performance. Those should hit within two years of the initial headset.
I remain very excited to see Apple’s headset, even if the price point and form factor mean that I will personally hold off on purchasing one until future iterations become available.
Lightweight, wireless, augmented reality (AR) glasses with passive artificial intelligence (AI) capabilities seems like the first truly compelling successor to the smartphone. In the past four months, we have suddenly made enough progress on the AI side to make this feasible — now it is AR’s turn to catch up.
AR + AI = Augmented Intelligence?
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I got access to Google’s Bard language model late last week and I have been spending some time testing it out throughout the past few days.
Like Bing AI (and soon, ChatGPT), Bard is able to preform a web search for factual information lookup. To its credit, Bard seems to do a better job summarizing and integrating this information into its answers when compared to Bing. There is a catch, though, Bard very rarely cites its sources. This almost defeats the purpose of its web lookup capabilities altogether — if you are going to go to the trouble of aggregating outside information I would like to be able to check your work.
Bard is less “steerable” than ChatGPT. By that, I mean it is more difficult to direct its responses in particular ways — “limit all of your responses to only one word”, “always respond in the Socratic style”, “each word of your answer must begin with the letter W”, etc. This is the magic behind ChatGPT — it is what transformed it into an “intelligent assistant” from the “glorified autocomplete” of GPT-3. OpenAI’s InstructGPT paper has more information on the approach they took towards this.
Overall, I think Bard would have been a serious contender if it had launched in December of last year, around the time Google issued their infamous “code red” memo. Bard is comparable — a little better in some ways, a little worse in others — than the original GPT-3.5 iteration of ChatGPT. If Bard had launched earlier — before Bing AI, the ChatGPT API, GPT-4, and ChatGPT Plugins — it would have been a serious contender. At this point, though, it feels like Google is still playing catchup to where OpenAI was last year. That is not a great place to be.
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§ Spring break update: one week down, one to go.
After almost a year of slow, puttering progress, I have finally made significant headway on my greenhouse project. The foundation in place, the primary window bay is complete, and the framing for two of the walls is finished. By the end of next week, my goal is to finish the remaining framing and the roof.
§ The team behind Rewind.ai (remember “lifestreams?”) has announced a new product called ChatGPT For Me. It is almost exactly what I would like to see Siri evolve into. Here is the issue: this announcement has made it clear to me that I wouldn’t trust any third-party company with such a privacy sensitive feature. Needless to say, I didn’t sign up for their waitlist. Nonetheless, I hope Apple is watching them closely and will eventually either Sherlock or acquire them.
§ Links
- Lilian Weng’s prompt engineering guide
- Chat LLaMA
§ Recipes
- Fish tacos
- Extremely good. Honestly, much better than the fried fish I made last week.
- Chicken adobo
- I didn’t anticipate how sticky the sauce would be which made everything a big chore to clean afterwards. It was really delicious, though.
- Chicken tikka masala
- This turned out to be much heavier and more filling than my other tikka masala recipe. They almost don’t share any ingredients in common, though, so I don’t know how fair it is to try to compare them.
- Roasted potatoes
- I had been meaning to make this for a while. It was quite good — was it worth all of the extra effort, though? Probably not.
- Lemon garlic pasta
- I had this alongside a very similar oyster mushroom dish. The mushrooms were great, the pasta was fine.
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Just a few days ago, I was thinking about how great it would be if OpenAI were to integrate something similar to LangChain into ChatGPT. The idea behind LangChain and similar projects is straightforward: if ChatGPT had tools — like web search to verify factual information and a calculator or code interpreter to answer complicated arithmetic questions — many of the downsides to language models, particularly their tendency to hallucinate, would be alleviated.
Well…
We’ve implemented initial support for plugins in ChatGPT. Plugins are tools designed specifically for language models with safety as a core principle, and help ChatGPT access up-to-date information, run computations, or use third-party services.
The new feature is launching with initial support from a few large companies including Wolfram Alpha, Instacart, and Zapier. Additionally, there is documentation available for third-party developers to build their own plugins.
However, what I am most excited about right now are two of the first-party plugins OpenAI developed.
First, web browsing:
Motivated by past work (our own WebGPT, as well as GopherCite, BlenderBot2, LaMDA2 and others), allowing language models to read information from the internet strictly expands the amount of content they can discuss, going beyond the training corpus to fresh information from the present day.
This seems to have all of the capabilities of Microsoft’s Bing AI plus the ability to navigate though individual websites autonomously.
Here is OpenAI’s other plugin, a code interpreter:
We provide our models with a working Python interpreter in a sandboxed, firewalled execution environment… We would like our models to be able to use their programming skills to provide a much more natural interface to most fundamental capabilities of our computers. Having access to a very eager junior programmer working at the speed of your fingertips can make completely new workflows effortless and efficient, as well as open the benefits of programming to new audiences.
One of the best ways I have found to easily verify ChatGPT’s mathematics answers is to ask it to create a Python program that will calculate the solution for me. This has the downside of requiring additional steps on my part — copy and paste the code, execute the Python program on my computer, compare the results. I am particularly excited to try the new interpreter plugin for exactly this reason.
Finally, the obvious next step that I would love to see is a meta-layer that is aware of all of the available plugins and, for each individual query, automatically chooses the plugin best suited for the task. At the speed all of these AI developments are moving we should have that ability in, what, a month?
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Zeyi Yang, MIT Technology Review:
On March 16, Robin Li, Baidu’s cofounder and CEO, took the stage in Beijing to showcase the company’s new large language model, Ernie Bot.
Accompanied by art created by Baidu’s image-making AI, he showed examples of what the chatbot can do, including solve math questions, write marketing copy, answer questions about Chinese literature, and generate multimedia responses.
[…]
The highlight of the product release was Ernie Bot’s multimodal output feature, which ChatGPT and GPT-4 do not offer… Li showed a recorded interaction with the bot where it generated an illustration of a futuristic city transportation system, used Chinese dialect to read out a text answer, and edited and subtitled a video based on the same text. However, in later testing after the launch, a Chinese publication failed to reproduce the video generation.
If Baidu’s presentation is accurate, Ernie’s multimodal features are genuinely impressive. While the image generation abilities do not seem any more advanced than DALL-E, the audio and video generation features are honestly striking.
Meanwhile… Che Pan at SCMP:
Fang Bingxing, considered the father of China’s Great Firewall… said the rise of generative AI tools like ChatGPT… pose a big challenge to governments around the world, according to an interview published on Thursday… “People’s perspectives can be manipulated as they seek all kinds of answers from AI,” he was quoted as saying.
[…]
Many expected that China’s heavily-censored internet would be a challenge for Chinese tech companies in developing a ChatGPT-like service because it is hard to predict and control answers.
China’s powerful internet regulators have told Chinese tech companies not to offer ChatGPT access to the public, and they need to inform the authorities before launching their own ChatGPT-like services, according to a report by Nikkei Asia in February
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I don’t typically think of Bill Gates as someone prone to making hyperbolic claims. His recent assertion that “artificial intelligence is as revolutionary as mobile phones and the Internet” is all the more arresting for that very reason.
In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary.
The first time was in 1980, when I was introduced to a graphical user interface.
The second big surprise came just last year. I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. Make it capable of answering questions that it hasn’t been specifically trained for… If you can do that, I said, then you’ll have made a true breakthrough.
In September, when I met with them again, I watched in awe as they asked GPT, their AI model, 60 multiple-choice questions from the AP Bio exam—and it got 59 of them right. Then it wrote outstanding answers to six open-ended questions from the exam. We had an outside expert score the test, and GPT got a 5—the highest possible score…
I knew I had just seen the most important advance in technology since the graphical user interface.
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There has been something bouncing around in my head in the days since both Google and Microsoft announced new AI features for their productivity applications. I felt significantly more negatively about Google’s framing of the features than Microsoft’s. I did not understand why — they are effectively the same announcements, right? Both companies are adding generative AI to their writing, slideshow, and spreadsheet apps — why should I feel differently about either of them? Then, I read both of their press releases again…
This is how Google describes an intended use case for their new AI features:
In Gmail and Google Docs, you can simply type in a topic you’d like to write about, and a draft will be instantly generated for you. So if you’re a manager onboarding a new employee, Workspace saves you the time and effort involved in writing that first welcome email.
In contrast, here is Microsoft. The AI is closer to a creative partner than anything else:
Copilot gives you a first draft to edit and iterate on — saving hours in writing, sourcing, and editing time. Sometimes Copilot will be right, other times usefully wrong — but it will always put you further ahead. You’re always in control as the author, driving your unique ideas forward, prompting Copilot to shorten, rewrite or give feedback.
On Stratechery, Ben Thompson finds a similar distinction:
In Google’s view, computers help you get things done — and save you time — by doing things for you.
[…]
All of [Microsoft’s] demos throughout the presentation reinforced this point: the copilots were there to help, not to do — even if they were in fact doing a whole bunch of the work. Still, I think the framing was effective: it made it very clear why these copilots would be beneficial, demonstrated that Microsoft’s implementation would be additive not distracting, and, critically, gave Microsoft an opening to emphasize the necessity of reviewing and editing. In fact, one of the most clever demos was Microsoft showing the AI making a mistake and the person doing the demo catching and fixing the mistake while reviewing the work.
To Microsoft, AI should help. To Google, AI should do.
A genuine case could be made for both approaches. I know which one I prefer, though.
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Me:
How might education change as a result of increasing artificial intelligence capabilities? In particular, large language models that are capable of passing standardized tests.
GPT-4:
The improvement of artificial intelligence capabilities, particularly large language models that can pass standardized tests, is likely to bring significant changes to the education landscape. Some possible ways education might change include:
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Personalized learning: AI-driven platforms could tailor educational content to each student’s individual needs, learning style, and pace, allowing for a more customized and efficient learning experience. This could help address the diverse needs of students and ensure that no one is left behind.
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Enhanced teaching resources: AI can assist teachers in creating and curating lesson plans, assessments, and other educational materials. By providing access to a vast array of resources and suggestions, AI could help teachers stay up-to-date with the latest teaching methods and content.
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Continuous assessment: AI systems can monitor students' progress in real-time, allowing for early identification of learning gaps and targeted interventions. This can lead to improved student outcomes and a more efficient use of instructional time.
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Automated grading and feedback: AI can evaluate students' work and provide instant feedback, allowing teachers to focus on other aspects of their job, such as addressing individual student needs or designing more engaging learning experiences.
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New teaching methods: The rise of AI might lead to innovative teaching methods and strategies that combine human expertise with machine-driven insights, resulting in more effective and engaging learning experiences.
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Virtual tutors and mentors: AI-powered virtual tutors can provide on-demand support to students, helping them with homework, answering questions, and offering guidance. These virtual tutors could be available 24/7, ensuring that students can access help whenever they need it.
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Shift in curriculum focus: As AI becomes increasingly capable of performing routine tasks, the focus of education may shift towards nurturing soft skills, creativity, critical thinking, and problem-solving abilities that are difficult for machines to replicate.
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Lifelong learning: AI-driven learning platforms can help individuals continuously update their skills and knowledge throughout their lives, promoting a culture of lifelong learning.
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Enhanced accessibility: AI technologies could help make education more accessible to students with disabilities, by offering personalized accommodations and support.
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Redefining the role of teachers: With AI taking on some of the more administrative and repetitive tasks in education, teachers may have more time to focus on developing strong relationships with their students and serving as mentors and guides.
However, it is important to consider the potential challenges and ethical concerns that might arise with the increased use of AI in education, such as data privacy, potential biases in AI algorithms, and the risk of over-reliance on technology. It is crucial for stakeholders in education to engage in ongoing discussions to ensure that AI is used responsibly and effectively to enhance the learning experience for all students.
#7 — “shift in curriculum focus” — is the type of change I would like to see in response to these technological developments. #10 — “redefining the role of teachers” — feels like a necessary extension of that.
#6 — “virtual tutors and mentors” — seems like the predominate focus right now, judging from the many articles and product announcements in the last week alone. I think it is productive to explore this as a small aspect of education — the part of education where rote memorization and rigid lesson structure is unavoidable. As we move into a more technologically advanced future, an ever-increasing percentage of learning should instead center on hands-on creative problem solving, project-based learning, and social-emotional development.
Me:
What are a few good and a few bad ways educators could respond to these changes?
GPT-4:
Educators play a crucial role in navigating the changes brought about by the increasing use of AI in education. Here are a few good and bad ways educators could respond to these changes:
Good ways:
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Embrace technology : Educators should be open to adopting new AI-driven tools and technologies to enhance the learning experience. This includes staying up-to-date with the latest developments, exploring different tools, and integrating them into the classroom in a thoughtful manner.
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Focus on soft skills and critical thinking: With AI handling more routine tasks, educators can focus on fostering creativity, critical thinking, collaboration, communication, and other soft skills that are essential for success in the 21st century.
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Engage in continuous professional development: Educators should seek opportunities for professional development to learn about AI technologies and how they can be effectively integrated into the curriculum. This includes attending workshops, conferences, and online courses.
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Collaborate with stakeholders: To ensure the responsible and ethical use of AI in education, educators should collaborate with administrators, policymakers, parents, and students to establish guidelines, share best practices, and address concerns.
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Advocate for equitable access: Educators should work to ensure that all students, regardless of socio-economic background, have access to AI-driven educational resources and tools.
Bad ways:
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Resistance to change: A negative response to AI-driven changes would be for educators to resist or dismiss new technologies, potentially hindering student learning and limiting the benefits of AI integration in the classroom.
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Over-reliance on technology: While AI can be a valuable educational tool, it should not replace the role of human educators. Teachers who rely too heavily on AI-driven tools and neglect the importance of personal connections and mentorship may fail to provide a well-rounded education.
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Ignoring potential biases and ethical concerns: Educators who do not pay attention to the ethical implications of AI use in education, such as biases in algorithms and data privacy concerns, may inadvertently contribute to perpetuating inequalities and harm students.
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Neglecting digital literacy: Failing to teach students about digital literacy, including the responsible use of AI-driven tools and the potential risks involved, may leave students ill-equipped to navigate the digital landscape.
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Not addressing accessibility concerns: Ignoring the needs of students with disabilities or those from disadvantaged backgrounds when implementing AI-driven tools may exacerbate existing educational inequalities.
#2 — “over-reliance on technology” — and #4 — “neglecting digital literacy” — from the “bad responses” list both bring up great points. Teachers should educate themselves and their students on the situations where computers (automation, AI, etc.) are the “right tool for the job.” When computation is the best tool for a given problem, students should know how to effectively and efficiently use it. When it is not the best tool, though, students should have the broad array of literacies and confidence necessary to approach the problem in whichever manner is best.
Previously:
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§ Spring break! With a full two weeks off, I should put together a list of things I would like to work on during this time. Let’s see…
- General spring cleaning
- Get seeds started in peat pots
- Make some progress on the greenhouse build
Hmm… that is not too bad, actually. I will check back in on how it went in a couple of weeks.
Speaking of spring break, after a super mild Winter, we finally got some snow this week… ugh.
§ To help wrap my mind around the possibilities enabled by GPT-4’s multimodality, I got a copy of the graphic novel Unflattening by Nick Sousanis. I have not had a chance to read much of it yet but what I have read so far has been interesting. I hope to have more time to dive into it next week. Watch this space.
§ A guilty pleasure of mine has alway been watching “wilderness survival” television shows. Alone is the best example of this genre that I have seen in a long time.
The general premise is this: ten participants are dropped off alone (get it?) somewhere in the Canadian wilderness. They are only allowed to bring a few basic items — a tarp, fire starter, hand saw, etc. Whoever is able to stay out for the longest amount of time wins $500,000.
What makes the show good is that isn’t overly dramatized. It is more like a personal diary of each participant’s day-to-day experience as they build a shelter, forage for food, and explore the environment around them.
§ My Google Home pronounced the opera Gianni Schicchi like it was “Jonny Squishy.”
§ Links
- An open source web interface for the ChatGPT API
- Harry Whittier Frees
- Emergent abilities of large language models
- “An amusing toy, nothing more”
- Alpaca.cpp
- Jeff Kaufman on bushels
§ Recipes
- Rosemary beer battered fish & chips
- Delicious but quite a bit of work. It reminded me how infrequently I cook fish, though. I hope to incorporate some more fishy dishes into my usual repertoire now.
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To cap off a week of AI announcements from OpenAI, Anthropic, and Google, Microsoft announced Copilot for their 365 productivity suite yesterday.
Today, we are bringing the power of next-generation AI to work. Introducing Microsoft 365 Copilot — your copilot for work. It combines the power of large language models (LLMs) with your data in the Microsoft Graph and the Microsoft 365 apps to turn your words into the most powerful productivity tool on the planet.
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Copilot is integrated into Microsoft 365 in two ways. It works alongside you, embedded in the Microsoft 365 apps you use every day — Word, Excel, PowerPoint, Outlook, Teams and more — to unleash creativity, unlock productivity and uplevel skills. Today we’re also announcing an entirely new experience: Business Chat. Business Chat works across the LLM, the Microsoft 365 apps, and your data — your calendar, emails, chats, documents, meetings and contacts — to do things you’ve never been able to do before. You can give it natural language prompts like “Tell my team how we updated the product strategy,” and it will generate a status update based on the morning’s meetings, emails and chat threads.
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AI-powered LLMs are trained on a large but limited corpus of data. The key to unlocking productivity in business lies in connecting LLMs to your business data — in a secure, compliant, privacy-preserving way. Microsoft 365 Copilot has real-time access to both your content and context in the Microsoft Graph. This means it generates answers anchored in your business content — your documents, emails, calendar, chats, meetings, contacts and other business data — and combines them with your working context — the meeting you’re in now, the email exchanges you’ve had on a topic, the chat conversations you had last week — to deliver accurate, relevant, contextual responses.
This entire announcement presents an incredibly corporate version of the AI integration I hope to see from Apple someday.
My dream is to ask Siri, “What was I doing last Saturday?” and receive an accurate summary based on all the data from my devices – including calendar events, geolocation, photos, web browsing history, and more. Siri should function as a continuously fine-tuned personal assistant with the ability to answer queries and generate content in a freeform manner. However, this all poses significant privacy concerns. For that reason, it would be crucial that all aspects – training, inference, and storage – occur exclusively on-device. This would really make all of Apple’s Neural Engine development look prescient.
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