The blog does not detail how the cabinets are connected. Adrian said that, in future, the disaggregated power racks will allow AC inputs to be converted into 400Vdc. Current power solutions convert into 48Vdc, and Adrian argues 400V will be crucial for building more powerful and efficient AI systems. “With 400V we expect improvements and incremental evolution in improved efficiency, like what we have seen in the 48Vdc conversion space,” he said.

However, these rankings rely on indicators that cannot be fully implemented in Indonesia and other similar countries, such as utilizing English as the main academic publishing language, thereby perpetuating the dominance of traditional Western ranking metrics.

Having professors who are transparent about how they want students to use AI “encourages students a lot more to only use it how they’ve instructed,” she said.

The seamless running of an eCommerce company depends on effective control of stock levels. Through demand prediction, stock level optimization, and reordering process automation, artificial intelligence streamlines inventory control. AI-driven inventory systems examine consumer preferences, seasonal variations, and sales trends to guarantee that you always have the appropriate level of supply.

Best suited for deployment of trained AI models in Android and iOS operating systems, TensorFlow Lite provides customers with on-device machine learning capability through mobile-optimized pre-trained models. It’s efficient while having low latency and compatibility for multiple languages which makes it very versatile. Developers can leverage its lightweight and mobile-optimized models to provide on-device AI functionality with minimal latency when implementing TensorFlow Lite in mobile apps.

Stafford Beer coined and frequently used the term POSIWID (the purpose of a system is what it does) to refer to the commonly observed phenomenon that the de facto purpose of a system is often at odds with its official purpose

I’ve come to feel like human-centered design (HCD) and the overarching project of HCI has reached a state of abject failure. Maybe it’s been there for a while, but I think the field’s inability to rise forcefully to the ascent of large language models and the pervasive use of chatbots as panaceas to every conceivable problem is uncharitably illustrative of its current state.

hegemonic algorithmic systems (namely large language models and similar machine learning systems), and the overwhelming power of capital pushing these technologies on us

gravitating away from the discourse of measuring and fixing unfair algorithmic systems, or making them more transparent, or accountable. Instead, I’m finding myself fixated on articulating the moral case for sabotaging, circumventing, and destroying “AI”, machine learning systems, and their surrounding political projects as valid responses to harm

To generate text that I've edited to include in my own writing

Grammarly

Page 13 of 19Have one or more of your instructors integrated AI into your learning?

Arle LommelArle Lommel • Following • Following Senior Analyst at CSA ResearchSenior Analyst at CSA Research 3d • Edited • 3 days ago One of the most interesting aspects of writing about AI and LLMs right now is that if I say anything remotely positive, some people will accuse me of being a shill for Big AI. If I say anything remotely negative, others will accuse me of being insufficiently aware of the progress AI has made.So I will put out a few personal statements about AI that might clarify where I am on this:1. AI is not intelligent, at least not in the human sense of the word. It is a sophisticated tool for drawing inference from binary data and thus operates *below* a symbolic level.2. AI, at least in the guise of LLMs, is not going to achieve artificial general intelligence (AGI) now or in the future.3. AI is getting much better at *approximating* human behavior on a wide variety of tasks. It can be extremely useful without being intelligent, in the same way that an encyclopedia can be very useful without being intelligent.4. For some tasks – such as translating between two languages – LLMs sometimes perform better than some humans perform. They do not outperform the best humans. This poses a significant challenge for human workers that we (collectively) have yet to address: Lower-skilled workers and trainees in particular begin to look replaceable, but we aren’t yet grappling with what happens when we replace them so they never become the experts we need for the high end. I think the decimation of the pipeline for some sectors is a HUGE unaddressed problem.5. “Human parity” is a rather pointless metric for evaluating AI. It far exceeds human parity in some areas – such as throughput, speed, cost, and availability – while it falls far short in other areas. A much more interesting question is “where do humans and machines have comparative advantage and how can we combine the two in ways that elevate the human?”6. Human-in-the-loop (HitL) is a terrible model. Having humans – usually underpaid and overworked – acting in a janitorial role to clean up AI messes is a bad use of their skill and knowledge. That’s why we prefer augmentation models, what we call “human at the core,” where humans maintain control. To see why one is better, imagine if you applied an HitL model to airline piloting, and the human only stepped in when the plane was in trouble (or even after it crashed). Instead, with airline piloting, we have the pilot in charge and assisted by automation to remain safe.7. AI is going to get better than it is now, but improvements in the core technology are slowing down and will increasingly be incremental. However, experience with prompting and integrating data will continue to drive improvements based on humans’ ability to “trick” the systems into doing the right things.8. Much of the value from LLMs for the language sector will come from “translation adjacent” tasks – summarization, correcting formality, adjusting reading levels, checking terminology, discovering information, etc. – tasks that are typically not paid well.

And who, especially adjuncts, has the time and resources to run each student’s work through cumbersome software? Personally, I think there’s something questionable about using AI to detect AI.

these teammates

There’s a lot to be said for the promise that AI agents bring to organizations.

People should always be at the center of an AI application, and agents are no different

Don’t – remove the human aspect

As various agents start to take care of routine tasks, provide real-time insights, create first drafts, and more, team members can focus on more meaningful interactions, collaboration,

gents can also help build team morale by highlighting team members' contributions and encouraging colleagues to celebrate achievements through suggested notes

One of my favorite use cases for agents is related to team culture. Agents can be a great onboarding buddy — getting new team members up to speed by providing them with key information, resources, and introductions to team members.

inviting a new AI agent to join your team in service of your shared goa

One of our most popular agent use cases for a while was during our yearly performance reviews a few months back. People pointed an agent to our growth profiles and had it help them reframe their self-reflections to better align with career development goals and expectations. This was a simple agent to create an application that helped a wide range of Atlassians with something of high value to them.

Start by practising and experimenting with the basics, like small, repetitive tasks. This is often a great mix of value (time saved for you) and likely success (hard for the agent to screw up). For example, converting a simple list of topics into an agenda is one step of preparing for a meeting, but it's tedious and something that you can enlist an agent to do right away

For instance, a 'Comms Crafter' agent is specialized in all things content, from blogs to press releases, and is designed to adhere to specific brand guidelines. A 'Decision Director' agent helps teams arrive at effective decisions faster by offering expertise on our specific decision-making framework. In fact, in less than six months, we’ve already created over 500 specialized agents internally.

Decolonizing AI is a multilayered endeavor, requiring a reaction against the philosophy of ‘universal computing’—an approach that is broad, universalistic, and often overrides the local. We must counteract this with varied and localized approaches, focusing on labor, ecological impact, bodies and embodiment, feminist frameworks of consent, and the inherent violence of the digital divide. This holistic thinking should connect the military use of AI-powered technologies with their seemingly innocent, everyday applications in apps and platforms. By exploring and unveiling the inner bond between these uses, we can understand how the normalization of day-to-day AI applications sometimes legitimizes more extreme and military employment of these technologies.There are normalized paths and routine ways to violence embedded in the very infrastructure of AI, such as the way prompts (text inputs, N.d.R.) are rendered into actual imagery. This process can contribute to dehumanizing people, making them legitimate targets by rendering them invisible.

That development time acceleration of 4 days down to 20 minutes… that’s equivalent to about 10 years of Moore’s Law cycles. That is, using generative AI like this is equivalent to computers getting 10 years better overnight. That was a real eye-opening framing for me. AI isn’t magical, it’s not sentient, it’s not the end of the world nor our saviour; we don’t need to endlessly debate “intelligence” or “reasoning.” It’s just that… computers got 10 years better.

I was one of the first people to use gen-AI for data extraction instead of chatbots

AI algorithms can assist in determining which compounds have the potential by predicting the chemicals that interact with biological targets. AI is also essential to forecast the efficacy of drugs. AI models can predict the side effects of the medication before it is put to clinical testing. AI analyzes the data in prior and helps in clinical trials. Hire dedicated developers who are capable of using the predictive ability to lower the chance of failure in later phases and speed up the pharmaceutical app development process.

the bodic SAA so the bodh SATA path

around the AI is um the problem right now as I understand it as I see it is a lot of the AI has been coded from the

a new level upon which Dharma can be built

when this technology meets it that we're not that our Interiors are not completely taken over because this technology is so potent when it you know it be very easy to lose our souls right to to to to decondition to be so conditioned so quickly by the dopamine whatever these you know whatever is going to happen when we kind of when this stuff rolls

around the AI is um the problem right now as I understand it

just going back to the AI to the extent that the that the fourth turning meets the people who are actually doing the AI and informs the AI that actually the wheel goes this way don't listen to those guys it goes this way

we haven't even got to a planetary place yet really and we're about to unleash Galactic level technology you know what I'm saying like so we have a we have a lot of catchup that needs to happen in a very short period of time

Google AI Overviews is the main culprit and poses an existential threat to publishers.

confabulation

But that label has grown controversial as the topic becomes mainstream because some people feel it anthropomorphizes AI models (suggesting they have human-like features) or gives them agency (suggesting they can make their own choices) in situations where that should not be implied.

Here’s most of what I’ve used Claude Artifacts for in the past seven days. I’ve provided prompts or a full transcript for nearly all of them. URL to Markdown with Jina Reader SQLite in WASM demo Extract URLs Clipboard viewer Pyodide REPL Photo Camera Settings Simulator LLM pricing calculator YAML to JSON converter OpenAI Audio QR Code Decoder Image Converter and Page Downloader HTML Entity Escaper text-wrap-balance-nav ARES Phonetic Alphabet Converter

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