2. Roleplay

1. Life-like:

Furthermore, our research demonstrates that the acceptance rate rises over time and is particularly high among less experienced developers, providing them with substantial benefits.

the widespread deployment of robotics

The gap between promise and reality also creates a compelling hype cycle that fuels funding

A dynamic concept graph consisting of nodes, each representing an idea, and edges showing the hierarchical structure among them.LLMs generates the hierarchical structure automatically but the structure is editable through our gestures as we see fitattract and repulse in force between nodes reflect the proximity of the ideas they containnodes can be merged, split, grouped to generate new ideasA data landscape where we can navigate on various scales (micro- and macro views).each data entry turns into a landform or structure, with its physical properties (size, color, elevation, .etc) mirroring its attributesapply sort, group, filter on data entries to reshape the landscape and look for patterns

We can construct a thinking space from a space that is already enriched with our patterns of meaning, hence is capable of representing our thoughts in a way that makes sense to us. The space is fluid, ready to learn new things and be molded as we think with them.

a new perspective-oriented document retrieval paradigm. We discuss and assess the inherent natural language understanding challenges in order to achieve the goal. Following the design challenges and principles, we demonstrate and evaluate a practical prototype pipeline system. We use the prototype system to conduct a user survey in order to assess the utility of our paradigm, as well as understanding the user information needs for controversial queries.

Experience the Web: as an extension of your Mind

Data center emissions probably 662% higher than big tech claims. Can it keep up the ruse?Emissions from in-house data centers of Google, Microsoft, Meta and Apple may be 7.62 times higher than official tallyIsabel O'BrienSun 15 Sep 2024 17.00 CESTLast modified on Wed 18 Sep 2024 22.40 CESTShareBig tech has made some big claims about greenhouse gas emissions in recent years. But as the rise of artificial intelligence creates ever bigger energy demands, it’s getting hard for the industry to hide the true costs of the data centers powering the tech revolution.According to a Guardian analysis, from 2020 to 2022 the real emissions from the “in-house” or company-owned data centers of Google, Microsoft, Meta and Apple are probably about 662% – or 7.62 times – higher than officially reported.Amazon is the largest emitter of the big five tech companies by a mile – the emissions of the second-largest emitter, Apple, were less than half of Amazon’s in 2022. However, Amazon has been kept out of the calculation above because its differing business model makes it difficult to isolate data center-specific emissions figures for the company.As energy demands for these data centers grow, many are worried that carbon emissions will, too. The International Energy Agency stated that data centers already accounted for 1% to 1.5% of global electricity consumption in 2022 – and that was before the AI boom began with ChatGPT’s launch at the end of that year.AI is far more energy-intensive on data centers than typical cloud-based applications. According to Goldman Sachs, a ChatGPT query needs nearly 10 times as much electricity to process as a Google search, and data center power demand will grow 160% by 2030. Goldman competitor Morgan Stanley’s research has made similar findings, projecting data center emissions globally to accumulate to 2.5bn metric tons of CO2 equivalent by 2030.In threat to climate safety, Michigan to woo tech data centers with new lawsRead moreIn the meantime, all five tech companies have claimed carbon neutrality, though Google dropped the label last year as it stepped up its carbon accounting standards. Amazon is the most recent company to do so, claiming in July that it met its goal seven years early, and that it had implemented a gross emissions cut of 3%.“It’s down to creative accounting,” explained a representative from Amazon Employees for Climate Justice, an advocacy group composed of current Amazon employees who are dissatisfied with their employer’s action on climate. “Amazon – despite all the PR and propaganda that you’re seeing about their solar farms, about their electric vans – is expanding its fossil fuel use, whether it’s in data centers or whether it’s in diesel trucks.”A misguided metricThe most important tools in this “creative accounting” when it comes to data centers are renewable energy certificates, or Recs. These are certificates that a company purchases to show it is buying renewable energy-generated electricity to match a portion of its electricity consumption – the catch, though, is that the renewable energy in question doesn’t need to be consumed by a company’s facilities. Rather, the site of production can be anywhere from one town over to an ocean away.Recs are used to calculate “market-based” emissions, or the official emissions figures used by the firms. When Recs and offsets are left out of the equation, we get “location-based emissions” – the actual emissions generated from the area where the data is being processed.The trend in those emissions is worrying. If these five companies were one country, the sum of their “location-based” emissions in 2022 would rank them as the 33rd highest-emitting country, behind the Philippines and above Algeria.Many data center industry experts also recognize that location-based metrics are more honest than the official, market-based numbers reported.“Location-based [accounting] gives an accurate picture of the emissions associated with the energy that’s actually being consumed to run the data center. And Uptime’s view is that it’s the right metric,” said Jay Dietrich, the research director of sustainability at Uptime Institute, a leading data center advisory and research organization.Nevertheless, Greenhouse Gas (GHG) Protocol, a carbon accounting oversight body, allows Recs to be used in official reporting, though the extent to which they should be allowed remains controversial between tech companies and has led to a lobbying battle over GHG Protocol’s rule-making process between two factions.On one side there is the Emissions First Partnership, spearheaded by Amazon and Meta. It aims to keep Recs in the accounting process regardless of their geographic origins. In practice, this is only a slightly looser interpretation of what GHG Protocol already permits.The opposing faction, headed by Google and Microsoft, argues that there needs to be time-based and location-based matching of renewable production and energy consumption for data centers. Google calls this its 24/7 goal, or its goal to have all of its facilities run on renewable energy 24 hours a day, seven days a week by 2030. Microsoft calls it its 100/100/0 goal, or its goal to have all its facilities running on 100% carbon-free energy 100% of the time, making zero carbon-based energy purchases by 2030.Google has already phased out its Rec use and Microsoft aims to do the same with low-quality “unbundled” (non location-specific) Recs by 2030.Academics and carbon management industry leaders alike are also against the GHG Protocol’s permissiveness on Recs. In an open letter from 2015, more than 50 such individuals argued that “it should be a bedrock principle of GHG accounting that no company be allowed to report a reduction in its GHG footprint for an action that results in no change in overall GHG emissions. Yet this is precisely what can happen under the guidance given the contractual/Rec-based reporting method.”To GHG Protocol’s credit, the organization does ask companies to report location-based figures alongside their Rec-based figures. Despite that, no company includes both location-based and market-based metrics for all three subcategories of emissions in the bodies of their annual environmental reports.In fact, location-based numbers are only directly reported (that is, not hidden in third-party assurance statements or in footnotes) by two companies – Google and Meta. And those two firms only include those figures for one subtype of emissions: scope 2, or the indirect emissions companies cause by purchasing energy from utilities and large-scale generators.In-house data centersScope 2 is the category that includes the majority of the emissions that come from in-house data center operations, as it concerns the emissions associated with purchased energy – mainly, electricity.Data centers should also make up a majority of overall scope 2 emissions for each company except Amazon, given that the other sources of scope 2 emissions for these companies stem from the electricity consumed by firms’ offices and retail spaces – operations that are relatively small and not carbon-intensive. Amazon has one other carbon-intensive business vertical to account for in its scope 2 emissions: its warehouses and e-commerce logistics.For the firms that give data center-specific data – Meta and Microsoft – this holds true: data centers made up 100% of Meta’s market-based (official) scope 2 emissions and 97.4% of its location-based emissions. For Microsoft, those numbers were 97.4% and 95.6%, respectively.The huge differences in location-based and official scope 2 emissions numbers showcase just how carbon intensive data centers really are, and how deceptive firms’ official emissions numbers can be. Meta, for example, reports its official scope 2 emissions for 2022 as 273 metric tons CO2 equivalent – all of that attributable to data centers. Under the location-based accounting system, that number jumps to more than 3.8m metric tons of CO2 equivalent for data centers alone – a more than 19,000 times increase.A similar result can be seen with Microsoft. The firm reported its official data center-related emissions for 2022 as 280,782 metric tons CO2 equivalent. Under a location-based accounting method, that number jumps to 6.1m metric tons CO2 equivalent. That’s a nearly 22 times increase.While Meta’s reporting gap is more egregious, both firms’ location-based emissions are higher because they undercount their data center emissions specifically, with 97.4% of the gap between Meta’s location-based and official scope 2 number in 2022 being unreported data center-related emissions, and 95.55% of Microsoft’s.Specific data center-related emissions numbers aren’t available for the rest of the firms. However, given that Google and Apple have similar scope 2 business models to Meta and Microsoft, it is likely that the multiple on how much higher their location-based data center emissions are would be similar to the multiple on how much higher their overall location-based scope 2 emissions are.In total, the sum of location-based emissions in this category between 2020 and 2022 was at least 275% higher (or 3.75 times) than the sum of their official figures. Amazon did not provide the Guardian with location-based scope 2 figures for 2020 and 2021, so its official (and probably much lower) numbers were used for this calculation for those years.Third-party data centersBig tech companies also rent a large portion of their data center capacity from third-party data center operators (or “colocation” data centers). According to the Synergy Research Group, large tech companies (or “hyperscalers”) represented 37% of worldwide data center capacity in 2022, with half of that capacity coming through third-party contracts. While this group includes companies other than Google, Amazon, Meta, Microsoft and Apple, it gives an idea of the extent of these firms’ activities with third-party data centers.Those emissions should theoretically fall under scope 3, all emissions a firm is responsible for that can’t be attributed to the fuel or electricity it consumes.When it comes to a big tech firm’s operations, this would encapsulate everything from the manufacturing processes of the hardware it sells (like the iPhone or Kindle) to the emissions from employees’ cars during their commutes to the office.When it comes to data centers, scope 3 emissions include the carbon emitted from the construction of in-house data centers, as well as the carbon emitted during the manufacturing process of the equipment used inside those in-house data centers. It may also include those emissions as well as the electricity-related emissions of third-party data centers that are partnered with.However, whether or not these emissions are fully included in reports is almost impossible to prove. “Scope 3 emissions are hugely uncertain,” said Dietrich. “This area is a mess just in terms of accounting.”According to Dietrich, some third-party data center operators put their energy-related emissions in their own scope 2 reporting, so those who rent from them can put those emissions into their scope 3. Other third-party data center operators put energy-related emissions into their scope 3 emissions, expecting their tenants to report those emissions in their own scope 2 reporting.Additionally, all firms use market-based metrics for these scope 3 numbers, which means third-party data center emissions are also undercounted in official figures.Of the firms that report their location-based scope 3 emissions in the footnotes, only Apple has a large gap between its official scope 3 figure and its location-based scope 3 figure.This is the only sizable reporting gap for a firm that is not data center-related – the majority of Apple’s scope 3 gap is due to Recs being applied towards emissions associated with the manufacturing of hardware (such as the iPhone).Apple does not include transmission and distribution losses or third-party cloud contracts in its location-based scope 3. It only includes those figures in its market-based numbers, under which its third party cloud contracts report zero emissions (offset by Recs). Therefore in both of Apple’s total emissions figures – location-based and market-based – the actual emissions associated with their third party data center contracts are nowhere to be found.”.2025 and beyondEven though big tech hides these emissions, they are due to keep rising. Data centers’ electricity demand is projected to double by 2030 due to the additional load that artificial intelligence poses, according to the Electric Power Research Institute.Google and Microsoft both blamed AI for their recent upticks in market-based emissions.“The relative contribution of AI computing loads to Google’s data centers, as I understood it when I left [in 2022], was relatively modest,” said Chris Taylor, current CEO of utility storage firm Gridstor and former site lead for Google’s data center energy strategy unit. “Two years ago, [AI] was not the main thing that we were worried about, at least on the energy team.”Taylor explained that most of the growth that he saw in data centers while at Google was attributable to growth in Google Cloud, as most enterprises were moving their IT tasks to the firm’s cloud servers.Whether today’s power grids can withstand the growing energy demands of AI is uncertain. One industry leader – Marc Ganzi, the CEO of DigitalBridge, a private equity firm that owns two of the world’s largest third-party data center operators – has gone as far as to say that the data center sector may run out of power within the next two years.And as grid interconnection backlogs continue to pile up worldwide, it may be nearly impossible for even the most well intentioned of companies to get new renewable energy production capacity online in time to meet that demand. This article was amended on 18 September 2024. Apple contacted the Guardian after publication to share that the firm only did partial audits for its location-based scope 3 figure. A previous version of this article erroneously claimed that the gap in Apple’s location-based scope 3 figure was data center-related.

Has ChatGPTo1 just become a 'Critical Thinker'?

nobody told it what to do that's that's the kind of really amazing and frightening thing about these situations when Facebook gave uh the algorithm the uh uh aim of increased user engagement the managers of Facebook did not anticipate that it will do it by spreading hatefield conspiracy theories this is something the algorithm discovered by itself the same with the capture puzzle and this is the big problem we are facing with AI

when a open AI developed a gp4 and they wanted to test what this new AI can do they gave it the task of solving capture puzzles it's these puzzles you encounter online when you try to access a website and the website needs to decide whether you're a human or a robot now uh gp4 could not solve the capture but it accessed a website task rabbit where you can hire people online to do things for you and it wanted to hire a human worker to solve the capture puzzle

in the 21st century with AI it has enormous positive potential to create the best Health Care Systems in history to to help solve the climate crisis and it can also lead to the rise of dystopian totalitarian regimes and new empires and ultimately even the destruction of human civilization

In an age where "corporate" evokes images of towering glass buildings and faceless multinational conglomerates, it's easy to forget that the roots of the word lie in something far more tangible and human: the body.In the medieval period, the idea of a corporation wasn't about shareholder value or quarterly profits; it was about flesh and blood, a community bound together as a single "body"—a corpus.

FLUX.1

generative-AI supply chain

we've never as a normal user you and I had the opportunity to take AI artificial intelligence and train it on our own data this is the first time we're able to do that

The FTC has already outlined this principle in its recent Amazon Alexa case

Cerebras differentiates itself by creating a large wafer with logic, memory, and interconnect all on-chip. This leads to a bandwidth that is 10,000 times more than the A100. However, this system costs $2–3 million as compared to $10,000 for the A100, and is only available in a set of 15. Having said that, it is likely that Cerebras is cost efficient for makers of large-scale AI models

summary

AI’s effect on our idea of knowledge could well be broader than that. We’ll still look for justified true beliefs, but perhaps we’ll stop seeing what happens as the result of rational, knowable frameworks that serenely govern the universe.  Perhaps we will see our own inevitable fallibility as a consequence of living in a world that is more hidden and more mysterious than we thought. We can see this wildness now because AI lets us thrive in such a world.

Michele Zanini and I recently wrote a brief post for Harvard Business Review about what this sort of change in worldview might mean for  business, from strategy to supply chain management. For example, two  faculty members at the Center for Strategic Leadership at the U.S Army War College have suggested that AI could fluidly assign leadership roles based on the specific details of a threatening situation and the particular capabilities and strengths of the people in the team. This would alter the idea of leadership itself: Not a personality trait but a fit between the specifics of character, a team, and a situation.

Newton’s Laws, the rules and hints for diagnosing a biopsy — to say that they fail at predicting highly particularized events: Will there be a traffic snarl? Are you going to develop allergies late in life? Will you like the new Tom Cruise comedy? This is where traditional knowledge stops, and AI’s facility with particulars steps in.

"A few weeks ago, we hosted a little dinner in New York, and we just asked this question of 20-plus CDOs [chief data officers] in New York City of the biggest companies, 'Hey, is this an issue?' And the resounding response was, 'Yeah, it's a real mess.'" Asked how many had grounded a Copilot implementation, Berkowitz said it was about half of them. Companies, he said, were turning off Copilot software or severely restricting its use. "Now, it's not an unsolvable problem," he added. "But you've got to have clean data and you've got to have clean security in order to get these systems to really work the way you anticipate. It's more than just flipping the switch."

RAG_Techniques

When a user asks Claude to generate content like code snippets, text documents, or website designs, these Artifacts appear in a dedicated window alongside their conversation. This creates a dynamic workspace where they can see, edit, and build upon Claude’s creations in real-time, seamlessly integrating AI-generated content into their projects and workflows.

we are using set theory so a certain piece of reference text is part of my collection or it's not if it's part of my collection somewhere in my fingerprint is a corresponding dot for it yeah so there is a very clear direct link from the root data to the actual representation and the position that dot has versus all the other dots so the the topology of that space geometry if you want of that patterns that you get that contains the knowledge of the world which i'm using the language of yeah so that basically and that is super easy to compute for um for for a computer i don't even need a gpu

for example our standard english language model is trained with something like maybe 100 gigabytes or so of text um that gives it a strength as if you would throw bird at it with the google corpus so the other thing is of course uh a small corpus like that is computed in two hours or three hours on a on a laptop yeah so that's the other thing uh by the way i didn't mention our fingerprints are actually a boolean so when we when we train as i said we are not using floating points

AI and Gender Equality on Twitter

Artificial Intelligence (AI) in Robotics

Manila has one of the most dangerous transport systems in the world for women (Thomson Reuters Foundation, 2014). Women in urban areas have been sexually assaulted and harassed while in public transit, be it on a bus, train, at the bus stop or station platform, or on their way to/from transit stops.

AI for Good3, SDG AI LAB4, IRCAI5 y Global Partnership for Artificial Intelligence6

that's why the computer can never be conscious because basically he has none of the characteristics of qualia and he certainly doesn't have free will and Free Will and conscious must work together to create these fields that actually can can direct their own experience and create self-conscious entities from the very beginning

“Analysts need to be able to dissect exactly how the AI reached a particular conclusion or recommendation,” says Chief Business Officer Eric Costantini. “Neo4j enables us to enforce robust information security by applying access controls at the subgraph level.”

is it possible to teach machine values

the future future for education and this is a mega Trend that will last in the next decades is that we use artificial intelligence to tailor um educational let's say or didactic Concepts to the specific person so let's say in in the future everybody will have his or her specific let's say training or education profile he or she will run through and artificial intelligence um will will tailor the different educational environments for everybody in the future this is this is a pre this is a pretty clear Trend

this is the reason why I'm not afraid of artificial intelligence taking over

human beings don't do that we understand that the chair is not a specifically shaped object but something you consider and once you understood that concept that principle you see chairs everywhere you can create completely new chairs

the brain is Islam Islam is it is lousy and it is selfish and still it is working yeah look around you working brains wherever you look and the reason for this is that we totally think differently than any kind of digital and computer system you know of and many Engineers from the AI field haven't figured out that massive difference that massive difference yet

you can Google data if you're good you can Google information but you cannot Google an idea you cannot Google Knowledge because having an idea acquiring knowledge this is what is happening on your mind when you change the way you think and I'm going to prove that in the next yeah 20 or so minutes that this will stay analog in our closed future because this is what makes us human beings so unique and so Superior to any kind of algorithm

“For our customer base, there's a lot of folks who say ‘I don't actually need the newest B100 or B200,’” Erb says. “They don’t need to train the models in four days, they’re okay doing it in two weeks for a quarter of the cost. We actually still have Maxwell-generation GPUs [first released in 2014] that are running in production. That said, we are investing heavily in the next generation.”

Improving the living standards of all working-class Americans while closing racial disparities in employment and wages will depend on how well we seize opportunities to build multiracial, multigendered, and multigenerational coalitions to advance policies that achieve both of these goals

a dictator who wields the power of superintelligence would command concentrated power unlike 00:50:45 anything we've ever seen

this is why it's such a trap which is why like we're on this train barreling down this pathway which is super risky

getting a base model to you know make money by default it may well learn to lie to commit fraud to deceive to hack to seek power because 00:47:50 in the real world people actually use this to make money

this company's got not good for safety

the alignment problem

open AI literally yesterday published securing research infrastructure for advanced AI

this is a serious problem because all they need to do is automate AI research 00:41:53 build super intelligence and any lead that the US had would vanish the power dynamics would shift immediately

the model Waits are just a large files of numbers on a server and these can be easily stolen all it takes is an adversary to match your trillions 00:41:14 of dollars and your smartest minds of Decades of work just to steal this file

our failure today will be irreversible soon in the next 12 to 24 months we will leak key AGI breakthroughs to the CCP it will 00:38:56 be to the National security establishment the greatest regret before the decade is out

here are so many loopholes in our current top AI Labs that we could literally have people who are infiltrating these companies and there's no way to even know what's going on because we don't have any true security 00:37:41 protocols and the problem is is that it's not being treated as seriously as it is

if you have the cognitive abilities of something that is you know 10 to 100 times smarter than you trying to to outm smarten it it's just you know it's just not going to happen whatsoever so you've effectively lost at that point which means that 00:36:03 you're going to be able to overthrow the US government

whoever controls superintelligence will possibly have enough power to seize control from 00:35:14 pre superintelligence forces

military power and Technology progress have been tightly linked historically and with extraordinarily rapid technological 00:34:11 progress will come military revolutions

this is where we can see the doubling time of the global economy in years from 1903 it's been 15 years but after super intelligence what happens is it going to be every 3 years is it going be every five is it going to 00:33:22 be every year is it going to be every 6 months I mean how crazy is the growth going to be

AGI level factories are going to shift from going to human run to AI directed using human physical labor soon to be fully being run by swarms of human level robots

be able to quick Master any domain write trillions lines of code and read every research paper in every scientific field ever written

you're going to have like 100 million more AI research and they're going to be working at 100 times what 00:27:31 you are

nobody's really pricing this in

perhaps 100 million human researcher equivalents running day and night t

Sam mman has said that's his entire goal that's what opening eye are trying to build they're not really trying to build super intelligence but they Define AGI as a 00:24:03 system that can do automated AI research and once that does occur

we are on course for AGI by 2027 and that these AI 00:19:25 systems will basically be able to automate basically all all cognitive jobs think any job that can be done remotely

suppose that GPT 4 training took 3 months in 2027 a leading AI lab will be able to train a GPT 4 00:18:19 level model in a minute

by 2027 rather than a chatbot you're going to have something that looks more like an agent and more like a coworker

this is where we talk about un hobbling this is of course something that we just spoke about before but the reason that this is important is because this is where you can get gains from a model in ways that you couldn't previously see 00:15:31 before

the inference efficiency improved by nearly three orders of magnitude or 1,000x in less than 2 years

there is essentially this Benchmark 00:09:58 called the math benchmark a set of difficult mathematic problems from a high school math competitions and when the Benchmark was released in 2021 gpt3 only got 5%

having an automated AI research engineer by 2027 00:05:14 to 2028 is not something that is far far off

it is strikingly plausible that by 2027 models 00:03:36 will be able to do the work of an AI researcher SL engineer that doesn't require believing in sci-fi it just requires in believing in straight lines on a graph

he Talk of the Town has shifted from 10 billion compute clusters 00:01:16 to hundred billion do compute clusters to even trillion doll clusters and every 6 months another zero is added to the boardroom plans

quite frankly a lot of artists and 00:21:16 producers are probably using it just for that they come up with something inspiration they go they make something new

what if a band decides to take one of the udio generated songs and re-record it entirely will they own the full copy rate to that very new recording now if I 00:21:03 was udio the answer probably be like no you made that thing using our platform

the AI created Music learned from got inspiration from the hit songs and came up with a great new hit song for you and then kind of you 00:13:21 know what we'll call those those artifacts or the little similarities here and there might get picked up by Content ID on YouTube

here's a way to do direct to 00:16:46 Consumer sell and can make some money and don't just be like so worried about being on the music platform streaming and now you're diluted because the AI

there's a huge disparity between state of law application of tech and what's 00:15:42 actually happening

to your point for 00:13:46 every problem there's going to be a solution and AI is going to have it and then for every solution for that there's going to be a new problem

this is more of a unfair competition 00:10:36 issue I think as a clearer line than the copyright stuff

now there's going to be even more AI music pouring 00:09:04 into platforms which saturated Market in an already oversaturated Market

these conversations are having daily people are scrambling trying to like we're trying to keep up 00:07:32 with AI in real time scrambling to find out what we're going to do think about all the different businesses that are affected from this

Google deep mind they're coming up their new Google AI sound boox that and it is making Loops from prompts and they have wav Jean

backstory of udio like I didn't know that willim IM and United Masters were like investors in udio

deluding the general royalty pool

the volume of how much music is being created over 800,000 00:01:56 tracks a day are being created using udio

terms of service which is the contract that you sign when you get on their platform does say that you can monetize what you make so meaning you can put into distribution 00:00:41 the music that you make

These arguments are meant to present a cautionary tale of unintended consequences.

I think that Noam chsky said exactly a year ago in New York Times around a year ago that generative AI is not any 00:18:37 intelligence it's just a plagiarism software that learned stealing human uh work transform it and sell it as much as possible as cheap as possible

if we just had a big enough spreadsheet we could get the data in and then we could get you know something like AI or some you know some other computational 00:12:32 process in to help us deal with all this complexity because our little brains can't handle it and my feeling about this is that 00:12:44 actually no

normalizeddifference vegetation index (NDVI)

Die Rede der ZukunftspreisträgerinMeredith Whittaker warnt in ihrer Rede vor der Macht der Techindustrie und erklärt, warum es sich gerade jetzt lohnt, positiv zu denken.

And one way we've seen artificial intelligence used in research practices is in extracting information from copyrighted works. So researchers are using this to categorize or classify relationships in or between sets of data. Now sometimes this is called using analytical AI and it evolves processes that are considered part of text and data mining. So we know that text data mining research methodologies can but they don't necessarily need to rely on artificial intelligence systems to extract this information.

AI with intact skills, but broken goals, would be an AI that skillfully acts towards corrupted goals.

Without intuition, AI can't understand common sense or humane values. Thus, AI might achieve goals in logically-correct but undesirable ways.

Matthew van der Hoorn Yes totally agree but could be used for creating a draft to work with, that's always the angle I try to take buy hear what you are saying Matthew!

***Deep Processing***-> It's important in learning. It's when our brain constructs meaning and says, "Ah, I get it, this makes sense." -> It's when new knowledge establishes connections to your pre-existing knowledge.-> When done well, It's what makes the knowledge easily retrievable when you need it. How do we achieve deep processing in learning? 👉🏽 STORIES, EXPLANATIONS, EXAMPLES, ANALOGIES and more - they all promote deep meaningful processing. 🤔BUT, it's not always easy to come up with stories and examples. It's also time-consuming. You can ask you AI buddies to help with that. We have it now, let's leverage it. Here's a microlesson developed on 7taps Microlearning about this topic.

If students know that the AI has some responsibility for determining their grades, that AI will have considerably more authority in the classroom or in any interactions with students.

if I met a robot that looked very much like a beautiful girl and everything went fine together with her and me but

One of the key elements was "attribution is non-negotiable". OpenAI, historically, has done a poor job of attributing parts of a response to the content that the response was based on.

I feel violated, cheated upon, betrayed, and exploited.

I wouldn't focus too much on "posted only after human review" - it's worth noting that's that's worth nothing. We literally just saw a case of obviously riduculous AI images in a scientific paper breezing through peer review with noone caring, so quality will necessarily go down because Brandolini's law combined with AI is the death sentence for communities like SE and I doubt they'll employ people to review content from the money they'll make

What could possibly go wrong? Dear Stack Overflow denizens, thanks for helping train OpenAI's billion-dollar LLMs. Seems that many have been drinking the AI koolaid or mixing psychedelics into their happy tea. So much for being part of a "community", seems that was just happy talk for "being exploited to generate LLM training data..." The corrupting influence of the profit-motive is never far away.

If you ask ChatGPT to cite it will provide random citations. That's different from actually training a model to cite (e.g. use supervised finetuning on citations with human raters checking whether sources match, which would also allow you to verify how accurately a model cites). This is something OpenAI could do, it just doesn't.

There are plenty of cases where genAI cites stuff incorrectly, that says something different, or citations that simply do not exist at all. Guaranteeing citations are included is easy, but guaranteeing correctness is an unsolved problem