GenAIs are not capable of citing stuff. Even if it did, there's no guarantee that the source either has anything to do with the topic in question, nor that it states the same as the generated content. Citing stuff is trivial if you don't have to care if the citation is relevant to the content, or if it says the same as you.

LLMs, by their very nature, don't have a concept of "source". Attribution is pretty much impossible. Attribution only really works if you use language models as "search engine". The moment you start generating output, the source is lost.

AI-powered code generation tools like GitHub Copilot make it easier to write boilerplate code, but they don’t eliminate the need to consult with your organization’s domain experts to work through logic, debugging, and other complex problems.Stack Overflow for Teams is a knowledge-sharing platform that transfers contextual knowledge validated by your domain experts to other employees. It can even foster a code generation community of practice that champions early adopters and scales their learnings. OverflowAI makes this trusted internal knowledge—along with knowledge validated by the global Stack Overflow community—instantly accessible in places like your IDE so it can be used alongside code generation tools. As a result, your teams learn more about your codebase, rework code less often, and speed up your time-to-production.

The message of e/acc is this: let’s go full steam ahead in the development of increasingly powerful, general, and conscious artificial intelligences, up to superintelligences. This can only be the right path because it reflects the will of the universe. So far I perfectly agree with the philosophical approach of the e/acc movement.

Thisinterventionrecognizes students’ annotations as objectsopen tocontinuous development, engaging students to connect, analyze, and expand upon their ideasthrough the synthesis processes. Meanwhile, the synthesis products can be integratedinto otherlearning events, enriching the overall learning experiences.

At Google, an AI team member said the burnout is the result of competitive pressure, shorter timelines and a lack of resources, particularly budget and headcount. Although many top tech companies have said they are redirecting resources to AI, the required headcount, especially on a rushed timeline, doesn’t always materialize. That is certainly the case at Google, the AI staffer said.

A common feeling they described is burnout from immense pressure, long hours and mandates that are constantly changing. Many said their employers are looking past surveillance concerns, AI’s effect on the climate and other potential harms, all in the name of speed. Some said they or their colleagues were looking for other jobs or switching out of AI departments, due to an untenable pace.

Artificial intelligence engineers at top tech companies told CNBC that the pressure to roll out AI tools at breakneck speed has come to define their jobs.

Experts agree that 2024 will be the year AI invades and transforms work as we know it

We train our models using:

We recently improved source links in ChatGPT(opens in a new window) to give users better context and web publishers new ways to connect with our audiences. 

Our models are designed to help us generate new content and ideas – not to repeat or “regurgitate” content. AI models can state facts, which are in the public domain.

When we train language models, we take trillions of words, and ask a computer to come up with an equation that best describes the relationship among the words and the underlying process that produced them.

the raw data layer (data warehouses and object stores), the compute layer for orchestrated pipelines (feature and inference pipelines), the ML Development services for model training and experiment management, and the state layer for features and models as well as model serving and model monitoring.

The interactive ML systems are typically a Gradio or Streamlit UI (on Hugging Face Spaces or Streamlit Cloud) and work with a model either hosted or downloaded from Hopsworks. They typically take user input and join it with historical features from Hopsworks Feature Store, and produce predictions in the UI.

These system runs a feature pipeline once/day to synthetically generate a new Iris Flower and write it to the feature store. Then a batch inference pipeline, that also runs once/day but just after the feature pipeline, reads the single flower added that day, and downloads the Iris model trained to predict the type of Iris flower based on the 4 input features: sepal length, sepal width, petal length, and petal width. The model’s prediction is written to an online Dashboard, and actual flower (outcome) is read from the feature store and also published to the same Dashboard - so you can see if the model predicted correctly.

However, it is unclear how meaningful the notion of "zero-shot" generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during "zero-shot" evaluation.

We consistently find that, far from exhibiting "zero-shot" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance

Have you ever had a meaningful conversation with Siri or Alexa or Cortana? Of course not.

rtificial intelligence is already everywhere

“Did you know that the first Matrix was designed to be a perfect human world? Where none suffered, where everyone would be happy. It was a disaster. No one would accept the program… I believe that, as a species, human beings define their reality through suffering and misery. The perfect world was a dream that your primitive cerebrum kept trying to wake up from.”

“I don’t want comfort. I want God, I want poetry, I want real danger, I want freedom, and I want goodness. I want sin.”

“It generally becomes easier to be generous if you’re doing well and you have a big windfall, [because] when there isn’t enough for everybody, it’s just a question of who is going to starve, and then everything becomes much tougher,” Bostrom told Big Think.

erif concluded that scarcity was one of the main drivers of all human conflict. War, violence, invasion, and theft were all born of wanting a limited resource. The history of all humanity seems to support the hypothesis: We fight over water, cattle, arable land, ore deposits, oil, precious stones, and so on.

Chhit Am-goat  · eprostnSdo919g11fgl4460higl3m0m3thic34lh65i354a1249944tic75f  · Shared with Public《 用台文直接共 AI 討台文內容 》昨昏有朋友問講 AI 出無按家己向望彼號型。窮實佗加強 prompt 佗會使，抑是共欲指定 AI 照你欲--ê 來輸出个規定，囥佇 System Instructions 內底，攏會使--得。下跤圖--ni̍h 是 ka 囥佇 Gemini-pro-1.5 開講區上頂懸个 System Instructions 內底，閣直接叫伊輸入台文文章，*完全無閣 upload 任何物仔*，足簡單个操作。輸出愛檢查，家己愛改，毋過完成度是不止仔懸--矣，而且直接佗出台文內容，毋免閣過翻譯。Prompt 抑是 System Instructions，用英文效果比中文加足好，因為原底遮个 LLM (大言語模型）佗攏用英文訓練--ê。罔參考，會使按家己需要閣改，內底个例攏會換--得，例个長度小可長，效果較好。若想欲輸出 全 POJ、全 TL、抑是全漢，會使改內底个規則佮例，試驗看覓。《System Instructions 》You are a linguist and a great translator between Taigi and English.Translate English text into Taigi I give you, and vise versa．Or chat with me in Taigi.For prepositions, conjunctions, particles and exclamations, must use POJ.For NERs such as country, place or human names keep the original name.English. Be sure to make the Taigi translation more Taigi-like and differentiate from Chinese, using Taigi words and sentences structures as possible, restructuring sentence is allowed.*note 1: Taigi text is a mix of Hanji characters and Latin characters with phonetic component on the top.*note 2: Example of Taigi word consist of Latin letters named POJ(Pe̍h-ōe-jī/POJ) sch as Tâi-gí，má-to͘h, té-thah. Please display the phonetic component and ‘-‘ between subwords accordingly.*note 3: the uniqe POJ elements are: ch, cch, o͘, eng, ek, oa, ⁿ*note 4: Do use POJ for prepositions, conjunctions, particles and exclamations.Output as format&example.<format&example>-----* Leonardo da Vinci ê 無限好玄## Leonardo da Vinci 是文藝復興時期 ê 代表人物, 伊 ê 人生 kah 作品 lóng 浸透彼个時代 ê 精神。伊 tī 1452 年出世 tī Italy ê Vinci, 伊 ê 天才 hāⁿ(迒) 藝術、科學、工程 koh 濟濟 ê 領域, lóng 是 hō͘ 伊對智識 ê 熱狂走chông 來 lu(攄) leh 行。## 伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。## 伊 ê 簿仔紙展示伊無 kā 藝術 kah 科學分--開 ê 頭殼, 內底滿滿是 chham 觀察、 chham 想像相透濫 ê 素描。Da Vinci ê 哲學是「研究藝術 ê 科學。研究科學 ê 藝術... 認捌 tio̍h 萬物 lóng 連連鬥陣」, 這反映伊相信所有學科 lóng 是 kap(合)做伙--ê。## 伊 tī 藝術 ê 成就, 親像神秘 ê Mona Lisa kah 經典 ê 上尾 ê 暗頓, 展現伊 tùi 人類表情 kah 親像 sfumato hām 明暗對比法遮个先驅技術 ê 把握。 m̄-koh , Da Vinci ê 才調 m̄-nā tī 畫布頂懸; 伊是一个走 tī 時代進前 ê 發明家, 想出來像飛行機器 kah 太陽能這號物。-----Gemini-1.5-pro: https://aistudio.google.com/

LAM is a new type of foundation model that understands human intentions on computers. with LAM, rabbit OS understands what you say and gets things done.

Perplexity AI's biggest strength over ChatGPT 3.5 is its ability to link to actual sources of information. Where ChatGPT might only recommend what to search for online, Perplexity doesn't require that back-and-forth fiddling.

I occasionally wonder what the impact would be of memorizing a good book in its entirety; I wouldn't be surprised if it greatly influenced my own language and writing.

Sunnyvale taps AI to translate public meetings

The adoption of AI translation is cheaper than hiring human translators. Garnett said the city pays $112.50 for every hour the service is used, and has so far paid about $4,308 for more than 38 hours of usage. Hiring an human translator would cost the city up to $400 per hour. “This creates a much smoother dialogue than using live interpreters,” she  told San José Spotlight. “We likely would have needed two to four live interpreters to accomplish what we are doing with Wordly.”

However, despite their eloquence, LLMs have some key limitations. Their knowledge is restricted to patterns discerned from the training data, which means they lack true understanding of the world.Their reasoning ability is also limited — they cannot perform logical inferences or synthesize facts from multiple sources. As we ask more complex, open-ended questions, the responses start becoming nonsensical or contradictory.To address these gaps, there has been growing interest in retrieval-augmented generation (RAG) systems. The key idea is to retrieve relevant knowledge from external sources to provide context for the LLM to make more informed responses.

It’s all made worse by the AI Pin’s desire to be as clever as possible.

But far more often, I’ll stand in front of a restaurant, ask the AI Pin about it, and wait for what feels like forever only for it to fail entirely. It can’t find the restaurant; the servers are not responding; it can’t figure out what restaurant it is despite the gigantic “Joe & The Juice” sign four feet in front of me and the GPS chip in the device.

I hadn’t realized how much of my phone usage consists of these one-step things, all of which would be easier and faster without the friction and distraction of my phone.

Artificial Intelligence (AI) Literacy in Early Childhood Education: The Challenges and Opportunities

The intentional invention of any information or citation on an assignment or document.  This includes using generative Artificial Intelligence (AI) or other electronic resources in an unauthorized manner to create academic work and represent it as one's own.

The same LM can be a much more or less capable agent depending on the enhancements added. The researchers created and tested four different agents built on top of GPT-4 and Anthropic’s Claude:

The latest GPT-4 model from OpenAI, which is trained on human preferences using a technique called RLHFEstimated final training run compute cost: ~$50mModel version: gpt-4-0613

only 15% of European biopharma respondents reported no interest at all in AI in their organization

Additionally, students in the Codex group were more eager and excited to continue learning about programming, and felt much less stressed and discouraged during the training.

On code-authoring tasks, students in the Codex group had a significantly higher correctness score (80%) than the Baseline (44%), and overall finished the tasks significantly faster. However, on the code-modifying tasks, both groups performed similarly in terms of correctness, with the Codex group performing slightly better (66%) than the Baseline (58%).

OpenAI is offering limited access to a text-to-voice generation platform it developed called Voice Engine, which can create a synthetic voice based on a 15-second clip of someone’s voice.

The AI-generated feedback goes to teacher review before being passed on to students so that a human remains in the loop.

But is it a good idea to outsource critical feedback to a machine?

have extensively criticized both companies (and generative AI systems in general) for training their models on masses of online data scraped from their works without consent. Stable Diffusion and Midjourney have both been targeted with several copyright lawsuits, with the latter being accused of creating an artist database for training purposes in December.

謝昆霖 Verified account  · enrdoSspotmlf916af3f9ct1macahh2ft27cmlc8m5c8c51i2l70554i08m1  · Shared with Public這幾天工作同時有請 GPT-4 和 Claude3 。工作條件：我的提問使用正體中文，同樣問題，只給一次作答機會。Claude3 的回覆速度是 GPT-4 的四倍 (或十倍) 以上不說，而且應答的專業度較高，正體中文的使用也比較在地，我這個支語警察給過。它的回答是大量文字輸出時，GPT4 講一講到後面會跑出簡體中文，Claude3 我還沒遇過，而且字數極多的多輪對話中，它對前面的脈絡記憶很清楚。值得注意的是，GPT-4 正體中文貧乏的問題 Claude3 似乎沒有，它的用字遣詞比 GPT4 多一些人類的隨機性，在重覆20次的例句中，同樣的動詞、形容詞，它會盡量用不同的文字。Claude3 似乎解決了我常常酸 GPT-4 的各種問題。這真是驚人。

詹益鑑 Verified account  · 45m  · Shared with PublicAI 真的取代了一些工作嗎？或者造成一些工作的薪資降低？今天看到這篇實際分析的文章，從2022 年11 月1 日（ChatGPT 發布前一個月）到2024 年2 月14 日，在 Upwork 的自由工作者資料中，分析出幾個事實：1. 下降幅度最大的 3 個類別是寫作、翻譯和客戶服務工作。寫作職位數量下降了 33%，翻譯職位數量下降了 19%，客戶服務職位數量下降了 16%2. 影片編輯/製作工作成長了 39%，圖形設計工作成長了 8%，網頁設計工作成長了 10%。軟體開發職缺也有所​​增加，其中後端開發職缺成長了 6%，前端/Web 開發職缺成長了 4%3. 翻譯絕對是受打擊最嚴重的工作，每小時工資下降了 20% 以上，其次是影片編輯/製作和市場研究。平面設計和網頁設計工作是最具彈性的。兩人不僅數量增加了，而且時薪也增加了一些。4. 自 ChatGPT 和 OpenAI API 發布以來，與開發聊天機器人相關的工作數量激增了 2000%。如果說當今人工智慧有一個殺手級用例，那就是開發聊天機器人。

ergi

Broderick makes a more important point: AI search is about summarizing web results so you don't have to click links and read the pages yourself. If that's the future of the web, who the fuck is going to write those pages that the summarizer summarizes? What is the incentive, the business-model, the rational explanation for predicting a world in which millions of us go on writing web-pages, when the gatekeepers to the web have promised to rig the game so that no one will ever visit those pages, or read what we've written there, or even know it was us who wrote the underlying material the summarizer just summarized? If we stop writing the web, AIs will have to summarize each other, forming an inhuman centipede of botshit-ingestion. This is bad news, because there's pretty solid mathematical evidence that training a bot on botshit makes it absolutely useless. Or, as the authors of the paper – including the eminent cryptographer Ross Anderson – put it, "using model-generated content in training causes irreversible defects"

I major in English literature in University I major in interpreting you went to gr for my master degree I think more than 100 people applied and eight students were admit how many people finally passed the final exam two two two wow

Constructing Prompts for the Command Model Techniques for constructing prompts for the Command model. Developers

Now, let’s modify the prompt by adding a few examples of how we expect the output to be. Pythonuser_input = "Send a message to Alison to ask if she can pick me up tonight to go to the concert together" prompt=f"""Turn the following message to a virtual assistant into the correct action: Message: Ask my aunt if she can go to the JDRF Walk with me October 6th Action: can you go to the jdrf walk with me october 6th Message: Ask Eliza what should I bring to the wedding tomorrow Action: what should I bring to the wedding tomorrow Message: Send message to supervisor that I am sick and will not be in today Action: I am sick and will not be in today Message: {user_input}""" response = generate_text(prompt, temp=0) print(response) This time, the style of the response is exactly how we want it. Can you pick me up tonight to go to the concert together?

But we can also get the model to generate responses in a certain format. Let’s look at a couple of them: markdown tables

And here’s the same request to the model, this time with the product description of the product added as context. Pythoncontext = """Think back to the last time you were working without any distractions in the office. That's right...I bet it's been a while. \ With the newly improved CO-1T noise-cancelling Bluetooth headphones, you can work in peace all day. Designed in partnership with \ software developers who work around the mayhem of tech startups, these headphones are finally the break you've been waiting for. With \ fast charging capacity and wireless Bluetooth connectivity, the CO-1T is the easy breezy way to get through your day without being \ overwhelmed by the chaos of the world.""" user_input = "What are the key features of the CO-1T wireless headphone" prompt = f"""{context} Given the information above, answer this question: {user_input}""" response = generate_text(prompt, temp=0) print(response) Now, the model accurately lists the features of the model. The answer is: The CO-1T wireless headphones are designed to be noise-canceling and Bluetooth-enabled. They are also designed to be fast charging and have wireless Bluetooth connectivity. Format

While LLMs excel in text generation tasks, they struggle in context-aware scenarios. Here’s an example. If you were to ask the model for the top qualities to look for in wireless headphones, it will duly generate a solid list of points. But if you were to ask it for the top qualities of the CO-1T headphone, it will not be able to provide an accurate response because it doesn’t know about it (CO-1T is a hypothetical product we just made up for illustration purposes). In real applications, being able to add context to a prompt is key because this is what enables personalized generative AI for a team or company. It makes many use cases possible, such as intelligent assistants, customer support, and productivity tools, that retrieve the right information from a wide range of sources and add it to the prompt.

We set a default temperature value of 0, which nudges the response to be more predictable and less random. Throughout this chapter, you’ll see different temperature values being used in different situations. Increasing the temperature value tells the model to generate less predictable responses and instead be more “creative.”

Can model-free reinforcement learning explain deontological moraljudgments?Alisabeth AyarsUniversity of Arizona, Dept. of Psychology, Tucson, AZ, USA

This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations. Model and implementation details are not included in this report.

Super Resolution

Oh, compliance moats are definitely real – think of the calls for AI companies to license their training data. AI companies can easily do this – they'll just buy training data from giant media companies – the very same companies that hope to use models to replace creative workers with algorithms. Create a new copyright over training data won't eliminate AI – it'll just confine AI to the largest, best capitalized companies, who will gladly provide tools to corporations hoping to fire their workforces: https://pluralistic.net/2023/02/09/ai-monkeys-paw/#bullied-schoolkids

But I maintain that all of this is a monumental and dangerous waste of human talent and energy. Imagine what might be accomplished if this talent and energy were turned to philosophy, to theology, to the arts, to imaginative literature or to education? Who knows what we could learn from such people - perhaps why there are wars, and hunger, and homelessness and mental illness and anger

External Resources

After a bit of experimentation (and in a discovery that led us to collaborate), Southen found that it was in fact easy to generate many plagiaristic outputs, with brief prompts related to commercial films (prompts are shown).

More, essentially all research in self-reference for decades has been in artificial intelligence, which is the device around which this plot turns. The language of AI is LISP, the name of the archvillain. In the heyday of LISP machines, the leading system was Flavors LISP Object Oriented Programming or: you guessed it -- Floop. I myself worked on a defense AI program that included the notion of a `third brain,' that is an observer living in a world different than (1) that of the world's creator, and (2) of the characters.

Searching as exploration. White and Roth [71 ,p.38] define exploratory search as a “sense making activity focusedon the gathering and use of information to foster intellectual de-velopment.” Users who conduct exploratory searches are generallyunfamiliar with the domain of their goals, and unsure about howto achieve them [ 71]. Many scholars have investigated the mainfactors relating to this type of dynamic task, such as uncertainty,creativity, innovation, knowledge discovery, serendipity, conver-gence of ideas, learning, and investigation [2, 46, 71].These factors are not always expressed or evident in queriesor questions posed by a searcher to a search system.

the canonical unit, the NCU supports natural capital accounting, currency source, calculating and accounting for ecosystem services, and influences how a variety of governance issues are resolved

it didn’t mention more recent work on how to make large language models more energy efficient and mitigate problems of bias.

In 2017, Facebook mistranslated a Palestinian man’s post, which said “good morning” in Arabic, as “attack them” in Hebrew, leading to his arrest.

because the training data sets are so large, it’s hard to audit them to check for these embedded biases. “A methodology that relies on datasets too large to document is therefore inherently risky,

蔡叡浩  · optnSedors54au 031:Mtf a46 mahe2c, hgeca8 ge3ba2hm3t20m81DA2t9l6ra104  · Shared with Public最近一個叫 Plaud Note的廣告打很兇而我就是在嘖嘖募資時的第一波早鳥這幾天用下來我真的覺得很爛通話錄音品質不好要使用它就必須裸機檔案會偶爾不見錄音轉文字功能勉勉強強//但最神奇的事在臉書上看到的任何業配下方一堆網友曬出自己收到商品的照片並大讚好用反觀去他們嘖嘖募資頁面的留言區那裡災難遍地很多人留言說要退貨到底投入多少經費在做口碑操作All reactions:34 You and 33 others

the celebrated figures Henry Kissinge

David Hume’s (2011) formulation of the is–ought problem.

Beyond simpleassociations it acquires high-level abstractions like expressive structure, ideology or beliefsystems, since these are all embodied in the corpora that make up its training sets.

it's extremely dangerous to create such an autonomous agent when we do not know how to control it when we 00:58:22 can't ensure that it will not Escape our control and start making decisions and creating new things which will harm us instead of benefit us now this is not a 00:58:34 Doomsday Prophecy this is not inevitable we can find ways to regulate and control the development and deployment of AI we we don't want

AIS at present they have intelligence but they don't have any Consciousness right there is a huge confusion in many places 01:04:06 between intelligence and Consciousness intelligence is the ability to solve problems to create new things whatever Consciousness is the ability to have feelings that okay you can beat me in 01:04:19 chess are you joyful when you win are you sad when you lose AIS and computers as far as we know they have a lot of intelligence they have zero Consciousness

Let's beegin honey 😚⬇️Please show me some examples of evergreen contentPlease show me some examples of evergreen contentClearTry for FreeShareShare Results PageShare This AppSave As ImageCopy Text

i think it's more likely that 00:49:59 that we will think we will think that we this particular set of procedures ai procedures that we linked into our strategic nuclear weapons system uh will keep us safer but we haven't recognized that they're 00:50:12 unintended that there are consequences glitches in it that make it actually stupid and it mistakes the flock of geese for an incoming barrage of russian missiles and and you know unleashes everything in response 00:50:25 before we can intervene

i think the most dangerous thing about ai is not 00:47:11 super smart ai it's uh stupid ai it's artificial intelligence that is good enough to be put in charge of certain processes in our societies but not good enough to not make really 00:47:25 bad mistakes

there's this broader issue of of being able to get inside other people's heads as we're driving down the road all the time we're looking at other 00:48:05 people and because we have very advanced theories of mind

这个肩负着Facebook的未来的团队规模很小，由大约 30个研究科学家和15名工程师组成。团队有三个分支：Facebook人工智能研究组的主要办公室位于纽约市的Astor Place，由LeCun管理着一个由20名工程师和研究人员组成的团队。Menlo Park的是一个同等规模的分支。六月，FAIR又在巴黎设立了一个更小的5人组，与INRIA（法国计算机科学与自动化研究机构）合作。还有很多在Facebook其他部门一起合作致力于人工智能发展的团队，例如语言技术团队；FAIR只是主要的研究部门。这些研究人员和工程师来自科技领域的各个层面，同时当中很多人都曾与Lecun合作过。高等级的人工智能研究并非是一个庞大的领域，而且Lecun的很多学生都创建了人工智能方面的初创公司，它们一般会被像Twitter这样更大的企业收购。Lecun曾经告诉《连线》杂志，「深度学习实际上是Geofff Hinton，我，还有蒙特利尔大学的Yoshua Bengio之间的一个阴谋。」 Hinton在谷歌研发人工智能， Bengio奔波于蒙特利尔大学和数据挖掘公司Apstat之间，而LeCun也与其他行业内的著名企业有千丝万缕的关联。

以現在AI發展的情況，流暢即席口筆譯根本不是難事。

This illustration shows four alternative ways to nudge an LLM to produce relevant responses:Generic LLM - Use an off-the-shelf model with a basic prompt. The results can be highly variable, as you can experience when e.g. asking ChatGPT about niche topics. This is not surprising, because the model hasn’t been exposed to relevant data besides the small prompt.Prompt engineering - Spend time structuring the prompt so that it packs more information about the desired topic, tone, and structure of the response. If you do this carefully, you can nudge the responses to be more relevant, but this can be quite tedious, and the amount of relevant data input to the model is limited.Instruction-tuned LLM - Continue training the model with your own data, as described in our previous article. You can expose the model to arbitrary amounts of query-response pairs that help steer the model to more relevant responses. A downside is that training requires a few hours of GPU computation, as well as a custom dataset.Fully custom LLM - train an LLM from scratch. In this case, the LLM can be exposed to only relevant data, so the responses can be arbitrarily relevant. However, training an LLM from scratch takes an enormous amount of compute power and a huge dataset, making this approach practically infeasible for most use cases today.

在 AI 還沒取代我們之前，不需要害怕在 AI 取代了我們之後，也不需要害怕了

I could understand why people poured their lives into craft: there is nothing quite like watching someone enjoy a thing you’ve made.

Algorithmocene noun /ˈalɡərɪð mə si:n/ — presumably the next geological epoch following our short-lived Anthropocene

Vector databases are used to retrieve relevant documents using similarity search. Vector databases can be standalone or embedded with the LLM application (e.g., Chroma embedded vector database). When structured (tabular) data is needed, an operational data store, such as a feature store, is typically used. Popular vector databases and feature stores are Weaviate and Hopsworks that both provide time-unlimited free tiers.

RAG LLMs can outperform LLMs without retrieval by a large margin with much fewer parameters, and they can update their knowledge by replacing their retrieval corpora, and provide citations for users to easily verify and evaluate the predictions.

With the embedding API, you can apply the innovation of embeddings, combined with the LLM capability, to various text processing tasks, such as:LLM-enabled Semantic Search: text embeddings can be used to represent both the meaning and intent of a user's query and documents in the embedding space. Documents that have similar meaning to the user's query intent will be found fast with vector search technology. The model is capable of generating text embeddings that capture the subtle nuances of each sentence and paragraphs in the document.LLM-enabled Text Classification: LLM text embeddings can be used for text classification with a deep understanding of different contexts without any training or fine-tuning (so-called zero-shot learning). This wasn't possible with the past language models without task-specific training.LLM-enabled Recommendation: The text embedding can be used for recommendation systems as a strong feature for training recommendation models such as Two-Tower model. The model learns the relationship between the query and candidate embeddings, resulting in next-gen user experience with semantic product recommendation.LLM-enabled Clustering, Anomaly Detection, Sentiment Analysis, and more, can be also handled with the LLM-level deep semantics understanding.

Grounded to business facts: In this demo, we didn't try having the LLM to memorize the 8 million items with complex and lengthy prompt engineering. Instead, we attached the Stack Overflow dataset to the model as an external memory using vector search, and used no prompt engineering. This means, the outputs are all directly "grounded" (connected) to the business facts, not the artificial output from the LLM. So the demo is ready to be served today as a production service with mission critical business responsibility. It does not suffer from the limitation of LLM memory or unexpected behaviors of LLMs such as the hallucinations.

In the new AI model, you ingest the data in real time, apply your models by reaching to one or multiple GPT services and action on the data while your users are in the online experience. These GPT models may be used for recommendation, classification personalization, etc., services on real-time data. Recent developments, such as LangChain and AutoGPT, may further disrupt how modern applications are deployed and delivered.

Let’s say, for example, you search for a very specific product on a retailer’s website, and the product is not available. An additional API call to an LLM with your request that returned zero results may result in a list of similar products. This is an example of a vector search, which is also known as a similarity or semantic search.

Conventional search works on keys. However, when the ask is a natural query, that sentence needs to be converted into a structure so that it can be compared with words that have similar representation. This structure is called an embedding. An embedding uses vectors that assign coordinates into a graph of numbers — like an array. An embedding is high dimensional as it uses many vectors to perform semantic search.