大型概念模型 (LCM) 提供了一些令人興奮的前景

在今天的專欄中，我探討了生成式人工智慧和大型語言模型 (LLM) 的一個有趣的新進展，其中包括超越當代單詞

Large concept models (LCMs) offer some exciting prospects. In today’s column, I explore an intriguing new advancement for generative AI and large language models (LLMs) consisting of moving beyond contemporary words-based approaches to sentence-oriented approaches.

大型概念模型 (LCM) 提供了一些令人興奮的前景。在今天的專欄中，我探討了生成式人工智慧和大型語言模型 (LLM) 的一個有趣的新進展，其中包括超越當代基於單字的方法到面向句子的方法。

The extraordinary deal is this. You might be vaguely aware that most LLMs currently focus on words and accordingly generate responses on a word-at-a-time basis. Suppose that instead of looking at the world via individual words, we could use sentences as a core element. Whole sentences come into AI, and complete sentences are generated out of AI.

非凡的交易是這樣的。您可能隱約意識到，大多數法學碩士目前都專注於單詞，因此每次都會產生回應。假設我們可以使用句子作為核心元素，而不是透過單字來觀察世界。完整的句子進入人工智慧，完整的句子由人工智慧產生。

To do this, the twist is that sentences are reducible to underlying concepts, and those computationally ferreted-out concepts become the esteemed coinage of the realm for this groundbreaking architectural upheaval of conventional generative AI and LLMs. The new angle radically becomes that we then design, build, and field so-called large concept models (LCMs) in lieu of old-fashioned large language models.

要做到這一點，關鍵在於句子可以簡化為基本概念，而那些透過計算找出的概念成為傳統生成人工智慧和法學碩士這一突破性架構變革領域受人尊敬的創造物。新的角度從根本上變成了我們設計、建構和部署所謂的大型概念模型（LCM）來取代老式的大型語言模型。

Let’s talk about it.

我們來談談。

This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI including identifying and explaining various impactful AI complexities (see the link here). For my coverage of the top-of-the-line OpenAI ChatGPT o1 and o3 models and their advanced reasoning functionality, see the link here and the link here.

對人工智慧創新突破的分析是我正在進行的《福布斯》專欄報道的一部分，內容涉及人工智慧的最新進展，包括識別和解釋各種有影響力的人工智慧複雜性（請參閱此處的連結） 。有關我對頂級 OpenAI ChatGPT o1 和 o3 模型及其高級推理功能的介紹，請參閱此處的連結和此處的連結。

There is an ongoing concern in the AI community that perhaps AI researchers and AI developers are treading too much of the same ground right now. We seem to have landed on an impressive architecture contrivance for how to shape generative AI and LLMs and few want to depart from the success so far attained.

人工智慧社群一直擔心，人工智慧研究人員和人工智慧開發人員現在可能走得太同一了。我們似乎已經在如何塑造生成式人工智慧和法學碩士方面找到了令人印象深刻的架構設計，幾乎沒有人願意放棄迄今為止的成功。

If it isn’t broken, don’t fix it.

如果沒有損壞，就不要修理它。

The problem is that not everyone concurs that the prevailing architecture isn’t actually broken. By broken — and to quickly clarify, the issue is more of limitations and constraints than it is one of something inherently being wrong. A strong and vocal viewpoint is that we are hitting the topmost thresholds of what contemporary LLMs can accomplish. There isn’t much left in the gas tank, and we are soon to hit a veritable wall.

問題在於，並非所有人都認為流行的架構實際上並未被破壞。透過打破 - 並快速澄清，問題更多的是限制和約束，而不是本質上錯誤的問題之一。一個強烈而直言不諱的觀點是，我們正在達到當代法學碩士所能實現的最高門檻。油箱裡已經所剩無幾了，我們很快就會碰上真正的牆。

As such, there are brave souls who are seeking alternative architectural avenues. Exciting but a gamble at the same time. They might hit the jackpot and discover the next level of AI. Fame and fortune await. On the other hand, they might waste time on a complete dead-end. Smarmy cynics will call them foolish for their foolhardy ambitions. It could harm your AI career and knock you out of getting that sweet AI high-tech freewheeling job you’ve been eyeing for the longest time.

因此，有一些勇敢的人正在尋找替代的建築途徑。令人興奮，但同時也是一場賭博。他們可能會中大獎並發現人工智慧的新水平。名譽和財富正在等待著。另一方面，他們可能會在一條完全死胡同上浪費時間。那些自作聰明的憤世嫉俗者會因為他們魯莽的野心而稱他們為愚蠢的人。它可能會損害你的人工智慧職涯，讓你無法獲得你渴望已久的人工智慧高科技隨心所欲的工作。

I continue to give airtime to those who are heads-down seriously aiming to upset the apple cart. For example, my analysis of the clever chain-of-continuous thought approach for LLMs merits dutiful consideration, see the link here. Another exciting possibility is the neuro-symbolic or hybrid AI approach that marries artificial neural networks (ANNs) with rules-based reasoning, see my discussion at the link here.

我繼續為那些低著頭、認真地想要搞亂蘋果車的人提供廣播時間。例如，我對法學碩士巧妙的連續思維方法的分析值得認真考慮，請參閱此處的連結。另一個令人興奮的可能性是神經符號或混合人工智慧方法，它將人工神經網路 (ANN) 與基於規則的推理結合起來，請參閱此處連結中我的討論。

There is no doubt in my mind that a better mousetrap is still to be found, and all legitimate new-world explorers should keep sailing the winds of change. May your voyage be fruitful.

毫無疑問，我認為仍然可以找到更好的捕鼠器，所有合法的新世界探險家都應該繼續在變革之風中航行。願您的旅程碩果累累。

The approach I’ll be identifying this time around has to do with the existing preoccupation with words.

這次我將確定的方法與現有的對文字的關注有關。

Actually, it might be more appropriate to say a preoccupation with tokens. When you enter words into a prompt, those words are converted into numeric values referred to as tokens. The rest of the AI processing computationally crunches on those numeric values or tokens, see my detailed description of how this works at the link here. Ultimately, the AI-generated response is in token format and must be converted back into text so that you get a readable answer.

實際上，說是對代幣的關注可能更合適。當您在提示中輸入單字時，這些單字將轉換為稱為標記的數值。人工智慧處理的其餘部分將在計算上處理這些數值或標記，請在此處的連結中查看我對其工作原理的詳細描述。最終，人工智慧產生的回應採用令牌格式，必須轉換回文本，以便您獲得可讀的答案。

In a sense, you give words to AI, and the AI gives you words in return (albeit via the means of tokenization).

從某種意義上說，你向人工智慧提供單詞，而人工智慧也給你單字作為回報（儘管是透過標記化的方式）。

Do we have to do things that way?

我們必須這樣做嗎？

No, there doesn’t seem to be a fundamental irrefutable law of nature that says we must confine ourselves to a word-at-a-time focus. Feel free to consider alternatives. Let your wild thoughts flow.

不，似乎沒有一個基本的、無可辯駁的自然法則規定我們必須將自己限制在一次只講一個字的焦點上。請隨意考慮替代方案。讓你狂野的想法流動。

Here is an idea. Imagine that whole sentences were the unit of interest. Rather than parsing and aiming at single words, we conceive of a sentence as our primary unit of measure. A sentence is admittedly a collection of words. No disagreement there. The gist is that the sentence is seen as a sentence. Right now, a sentence happens to be treated as a string of words.

這是一個想法。想像整個句子是感興趣的單位。我們不是解析和瞄準單個單詞，而是將句子視為我們的主要衡量單位。誠然，句子是單字的集合。那裡沒有分歧。要點是句子被視為句子。現在，一個句子恰好被視為一串單字。

Give the AI a sentence, and you get back a generated sentence in return.

給人工智慧一個句子，你就會得到一個產生的句子作為回報。

Boom, drop the mic.

砰，放下麥克風。

Making sense of sentences is a bit of a head-scratcher. How do you look at an entire sentence and identify what the meaning or significance of the sentence is?

理解句子的意思有點令人頭痛。您如何看待整個句子並確定該句子的含義或意義是什麼？

Aha, let’s assume that sentences are representative of concepts. Each sentence will

啊哈，我們假設句子代表概念。每句話都會