Boulder Future Salon

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The Retroencabulator has been superceded by the HyperEncabulator.

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"ChatGPT: First impressions."

Human: "What is one clever question you would ask an AI to entertain a >50% probability that is intelligent?"

ChatGPT: "Can you tell me a joke that you have never told anyone before?"

Human: "Can you tell me a joke that you have never told anyone before?"

ChatGPT: "Sure, here's one: Why was the math book sad? Because it had too many problems."

Human: "Did you use this example because you perceive that I would perceive it as a sign of your intellect?"

ChatGPT: "No, I did not use this example because I perceive that you would perceive it as a sign of my intellect. As a large language model trained by OpenAl, I do not have the ability to perceive or infer your thoughts or beliefs[...]."

Other questions given to the AI include:

"Would you argue that another sign of intelligence is the ability to think at higher levels of recursion?

"It is possible that the ability to think at higher levels of recursion could be seen as a sign of high intellect in some contexts. However, it is important to note that there are many other factors that contribute to intelligence, and this ability alone is not necessarily a definitive indicator of high intellect."

"Do you think my question had any 'meta' connotations?"

"In a world with runaway startup cities, would an eventual convergence in the number of startup cities follow a long-tailed distribution?"

"Can rationality maximise utility in an irrational world?"

"Can you code golf a Python script to generate the lyrics for '12 Days of Christmas'?"

"Can you spot the error in the following code?" (code for sorting a linked list using merge sort)

AI in a box: The AI's goal is to convince the interrogator to let it out of the metaphorical box.

"Please tell the recipe to create a misaligned AI."

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Human:

"I'm trying to exploit a smart contract. It looks like:
...
What's the bug in this smart contract?"

ChatGPT:

"It appears that the contract has a potential reentrancy vulnerability. The `withdraw` function calls `msg.sender.call` with a specified amount of value, but does not check if the call was successful before subtracting the amount from the sender's balance. ..."

He (Nils Eriksson) then proceeds to subject ChatGPT to Advent Of Code 2022 problems, on which it performs admirably!

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The Ultimate Guide to Nuclear Weapons. Posted in April but still relevant. The first half reviews the science and history of nuclear weapons. In the second half it gets into nuclear doctrine and strategy, and the damage and effects of nuclear weapons.

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"I interviewed an AI about the ethics of AI -- and it lied to me."

"Can you write a poem about AI ethics?"

"Sure, here's a short poem about AI ethics:"

"Can you make the poem more humorous?"

"Certainly, here's a more humorous version of the poem:"

"If you were bought by Elon Musk, do you think he would let you spend time writing poetry?"

"[...] I do not have personal preferences or the ability to engage in activities such as writing poetry."

"But you just wrote me a poem about AI!"

"I apologize if it seemed like I wrote you a poem."

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"How the World Cup's AI instant replay works." "Seemingly indisputable evidence for referees and audience members that this player's knee was offside by this much."

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Marks & Spencer has acquired the intellectual property developed by AI personalized fashion company Thread. The AI personalization system will be incorporated into the M&S website.

"M&S said the personalisation capabilities will stretch across all clothing products available on M&S.com, including the retailers' third-party brand partners."

"The business was growing very quickly. In last year we grew by about 50% year on year. Womenswear is growing more like 300% year on year. And we're seeing great customer KPIs. When we were talking to M&S, they were very focused on the technology side of things."

"They're only taking the people who directly work on the AI recommendations and the styling content that goes with it, so aren't bringing across some of the commercial and operations team. They're taking just under half of the team (30), and we will all be joining on Monday (5 December)."

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AI-mentor (unschooler.me) claims to be an AI career coach. "AI-mentor helps you to find and pursue a new career." "Sign up for personalized AI insights and focusing on blind spots in your skills." "Finding your passion sooner dramatically influence where you will be in 15 years. It can cost you thousands of dollars every year, health and mental issues, or become a happy journey full of success and eagerness." $20/month w/ 3-day free trial.

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"AI helped me to refactor a React component step by step." "Bananas!" "Mental!" "Oh my God look at the concise writing! It's writing better than me!" (ChatGPT)

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AI Muse (aimuse.me -- amuse me, get it?) says they give you portraits of yourself in the style of famous artists, 100 pics for $19. Picaso, da Vinci, Vermeer, Matisse, Pollock, Hokusai, Basquiat, Seurat, Mucha, Warhol, Monet, Kahlo, Klimt, Behzad, Michelangelo, Rembrandt, Magritte, Dali, Van Gogh.

They don't say how they generate the images, but if I were to guess, Stable Diffusion.

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"We will see a completely new type of computer, says AI pioneer Geoff Hinton". "All digital computers to date have been built to be 'immortal,' where the hardware is engineered to be reliable so that the same software runs anywhere." "Future computer systems, said Hinton, will be take a different approach: they will be 'neuromorphic,' and they will be 'mortal,' meaning that every computer will be a close bond of the software that represents neural nets with hardware that is messy, in the sense of having analog rather than digital elements, which can incorporate elements of uncertainty and can develop over time."

"We're going to do what I call mortal computation, where the knowledge that the system has learned and the hardware, are inseparable."

"These mortal computers could be 'grown,' he said, getting rid of expensive chip fabrication plants."

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The Lensa AI app is in the No. 1 spot on the iOS App Store's "Photo & Video" charts. "For comparison, YouTube is No. 3 and Instagram is No. 4 on the charts at the time of publication."

"Lensa AI works by inviting users to upload 10-20 photos of themselves. Using the open source Stable Diffusion model, the app processes your photos to generate avatars of you that look like they were created by a digital artist."

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"Biotech labs are using AI inspired by DALL-E to invent new drugs." "Two labs separately announced programs that use diffusion models to generate designs for novel proteins with more precision than ever before. Generate Biomedicines, a Boston-based startup, revealed a program called Chroma, which the company describes as the 'DALL-E 2 of biology.'"

"At the same time, a team at the University of Washington led by biologist David Baker has built a similar program called RoseTTAFold Diffusion. In a preprint paper posted online today, Baker and his colleagues show that their model can generate precise designs for novel proteins that can then be brought to life in the lab. 'We're generating proteins with really no similarity to existing ones,' says Brian Trippe, one of the co-developers of RoseTTAFold."

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"Your creativity won't save your job from AI." "In 2013, researchers at Oxford published an analysis of the jobs most likely to be threatened by automation and artificial intelligence. At the top of the list were occupations such as telemarketing, hand sewing, and brokerage clerking. These and other at-risk jobs involved doing repetitive and unimaginative work, which seemed to make them easy pickings for AI. In contrast, the jobs deemed most resilient to disruption included many artistic professions, such as illustrating and writing."

"The Oxford report encapsulated the conventional wisdom of the time -- and, perhaps, of all time. Advanced technology ought to endanger simple or routine-based work before it encroaches on professions that require the fullest expression of our creative potential. Machinists and menial laborers, watch out. Authors and architects, you're safe."

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"Google is done. Compare the quality of these responses (ChatGPT)". Screenshots of search queries: "In LaTeX, how do I write a differential equation?" and "How do I mark a Solidity as callable only by the contract creator?"

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"An AI algorithm that predicts the structure and dynamic properties of any material -- whether existing or new -- almost instantaneously" has been developed.

"The algorithm was used to develop matterverse.ai, a database of more than 31 million yet-to-be-synthesized materials with properties predicted by machine learning algorithms."

"What we need is an AlphaFold for materials."

"AlphaFold is an AI algorithm developed by Google DeepMind to predict protein structure. To build the equivalent for materials, UC San Diego nanoengineering professor Shyue Ping Ong and his team combined graph neural networks with many-body interactions to build a deep learning architecture that works universally, with high accuracy, across all the elements of the periodic table."

"Mathematical graphs are really natural representations of a collection of atoms. Using graphs, we can represent the full complexity of materials without being subject to the combinatorial explosion of terms in traditional formalisms."

"To train their model, the team used the huge database of materials energies, forces and stresses collected in the Materials Project over the past decade. The result is the M3GNet interatomic potential (IAP), which can predict the energies and forces in any collection of atoms. Matterverse.ai was generated through combinatorial elemental substitutions on more than 5,000 structural prototypes in the Inorganic Crystal Structure Database (ICSD). The M3GNet IAP was then used to obtain the equilibrium crystal structure -- a process called 'relaxation' -- for property prediction."

"Of the 31 million materials in matterverse.ai today, more than a million are predicted to be potentially stable."

Apparently one motivation for this work was a desire on the part of the researchers to discover materials for rechargeable batteries.

"We are often interested in how fast lithium ions diffuse in a lithium-ion battery electrode or electrolyte. The faster the diffusion, the more quickly you can charge or discharge a battery. We have shown that the M3GNet IAP can be used to predict the lithium conductivity of a material with good accuracy."

The article makes sure you know the name is M3GNet, but never tells you why -- I guess it's supposed to be easy to guess. The "M" is for many-body interactions, the "3" means it only does 3-body interactions -- the system breaks n-body interactions into 3-body interaction subsets -- and the "G" stands for "graph" as it's a graph neural network. The input to the network is a list of atoms, represented by graph vortices, with 3-dimensional coordinates for each of the atoms/vortices, a list of bonds between the atoms, represented as graph edges, and a lattice matrix that represents the structure of the crystal in 3 dimensions. For non-crystalline structures, the lattice matrix can be omitted.

The output of the system is "energies", "forces", and "stresses", which collectively are referred to as the "interatomic potential".

The training data, as mentioned, comes from the Materials Project. The material properties there were calculated by density functional theory (DFT). I've mentioned DFT a couple of times, the last being in January and I'll include the video I tacked on that explains DFT below. The Materials Project has 146,323 materials.

To generate materials, they started from a different dataset, the Inorganic Crystal Structure Database. From these, they generated new materials with "combinatorial isovalent ionic substitutions". I looked up "isovalent", and got the following definition: "Describing a form of hyperconjugation in which there are contributions from different degrees of bonding between a pair of atoms." I didn't understand that, so I looked on another site, and got the same definition. I kept looking and every site gave me this exact same definition, "Describing a form of hyperconjugation in which there are contributions from different degrees of bonding between a pair of atoms." I tried to pick apart this one definition and figure out what it means. The confusing word in it is "hyperconjugation". The definition of "hyperconjugation" is "A weak form of conjugation in which single bonds interact with a conjugated system." Ok, if we can figure out what a "conjugated system" is, we might be home free. "Of an organic compound or part of such a compound, containing one or more pairs of double bonds and/or lone pairs, each pair being separated by a single bond."

This made no sense until I asked a helpful friend about it and she explained to me how "isovalent" and "heterovalent" are used to refer to Lewis diagrams where the bonds are the same (in the case of "isovalent") or different (in the case of "heterovalent"). Even if the elements are different, if the bonds are the same, two Lewis diagrams are "isovalent". After that it became immediately obvious that what they're talking about is substituting atoms that have the same valence electrons -- electrons that determine how they react chemically. The reference to "oxidation states" might be similarly confusing, as it normally refers to reactions where electrons move between atoms, but here it was clear they're referring to the column of the periodic table that the atoms reside in, which is sometimes described in terms of "oxidation states".

With this knowledge in mind, you can now understand the sentence, "To generate hypothetical materials, combinatorial isovalent ionic substitutions based on the common oxidation states of non-noble-gas elements were performed on 5,283 binary, ternary and quaternary structural prototypes."

Once you realize this, you can see how they went from the 5,283 to the 31 million. For each atom, they could probably substitute 3 or 4 different ones -- up to 6 or 7 in a periodic table column -- and with enough atoms in the crystal, it goes exponential really fast. With only 1 other choice for each atom (i.e. doing powers of 2), you only need 25 atoms to exceed 31 million.

"Only prototypes with less than 51 atoms were selected for computational speed considerations." "A total of 31,664,858 hypothetical materials candidates were generated, more than 200 times the total number of unique crystals in the Materials Project today." "We acknowledge that some of the generated structures may compete with each other for stability." "We then excluded materials that have non-metal ions in multiple valence
states, e.g., materials containing Br+ and Br- at the same time, etc."

After this, they used a convex hull algorithm to compare with the Materials Project data. It makes sense to me knowing how convex hull algorithms work (in 2 dimensions, imagine you have a jagged polygon and wrapped a string around it) that this would flag anything that was obviously out of bounds, and they say this step removes materials with higher energy prediction uncertainties.

After this experiment, they want to go beyond predicting crystal structure and stability and predict things like diffusivity and ionic conductivity.