AI芯視界

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谷歌正在開發一種名為 Gemini 的新型 AI，這是一種通用的多模式智能網絡，可以處理多種類型的數據和任務，包括文本、圖像、音頻、視頻、3D 模型和圖形。 Gemini 使用了一種新的架構，它融合了多模式編碼器和解碼器，並且可以根據編碼輸入和手頭的任務以不同的模式生成輸出。與其他大型語言模型相比，Gemini 更具適應性、效率更高，並且可以從任何領域和數據集中學習，而不受預定義類別或標籤的限制。 Gemini 還可以擴展到更大的數據集和模型，而不會影響其性能或質量。人們在衡量大型語言模型時最常見的事情之一就是它的參數數量，谷歌表示 Gemini 有四種尺寸：壁虎、水獺、野牛和獨角獸。


Q:Gemini 相對於其他大型語言模型的優勢是什麼？
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與 GPT-4 等其他大型語言模型相比，Gemini 具有多項優勢。 首先，它的適應性更強，可以處理任何類型的數據和任務，而無需專門的模型或微調。 其次，它比其他需要單獨處理多種模態的模型更高效，使用更少的計算資源和內存。 第三，Gemini 可以從任何領域和數據集中學習，而不受預定義類別或標籤的限制。 最後，Gemini 可以擴展到更大的數據集和模型，而不會影響其性能或質量。


Q:Gemini 如何處理多種類型的數據和任務？
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Gemini 通過使用通用的多模態智能網絡來處理多種類型的數據和任務。 它合併了一個多模式編碼器和解碼器，允許它根據編碼輸入和手頭的任務生成不同模式的輸出。 Gemini 使用基於 transformer 的架構，這是一個可以處理多個輸入並同時生成多個輸出的神經網絡。 它可以處理各種類型的數據，例如文本、圖像、音頻、視頻、3D 模型和圖形，並且可以通過結合多種模態的技能來執行不同的任務，例如摘要、翻譯和生成。 這意味著 Gemini 可以理解和分析來自不同來源的信息，並生成與上下文和用戶偏好相關的有意義的輸出。


Q:Gemini 能否生成不受現有數據或模板約束的新穎多樣的輸出？
Chat YouTube:
是的，Gemini 可以生成不受現有數據或模板約束的新穎多樣的輸出。 由於 Gemini 不局限於預定義的類別或標籤，它可以從任何領域和數據集中學習，並可以生成創造性的原始輸出。 Gemini 有能力通過組合不同的模式並應用其對輸入的理解來生成新的相關輸出來創建獨特的內容。 例如，Gemini 可以通過分析電影的不同方面（例如情節、角色互動和隱藏信息）來生成全新的、不同於任何現有數據或模板的電影劇本。 這使得 Gemini 比其他語言模型更具交互性和創造性，並且可以在內容生成、創意寫作等領域帶來令人興奮的可能性。


Q:Gemini跟AGI差別
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AGI代表通用人工智能，是一個較為廣泛的概念，指的是一種人工智能系統，能夠像人類一樣理解、學習和解決多種任務，而不需要事先設計特定的任務或領域。 Gemini是Google正在開發的一種AI技術，利用新的架構結合多模態編碼器和解碼器，可以處理多種類型的數據和任務，包括文本、圖像、音頻、視頻、3D 模型和圖表等。 但是，Gemini目前還沒有達到通用人工智能的境界，它仍然需要針對不同的任務和領域進行訓練和優化。 因此，Gemini與AGI還是存在一定的差異。


0:00 so today I've got something that's just 0:02 gonna blow your mind Google is gearing 0:04 up to completely revolutionize the 0:06 industry with this new AI they've been 0:08 working on and it goes by the name of 0:10 Gemini it's seriously Next Level stuff 0:13 rivaling chat GPT and mighty gpt4 in 0:17 terms of understanding and generating 0:18 natural language trust me you're not 0:21 going to want to miss out on this one so 0:23 make sure you stick around till the end 0:24 of the video now what's Gemini all about 0:27 well this is Google's latest project in 0:30 the world of large language models the 0:32 full form is generalized multimodal 0:35 intelligence Network and it's basically 0:37 this Mega powerful AI system that can 0:40 handle multiple types of data and tasks 0:42 all at once we're talking text images 0:45 audio video even 3D models and graphs 0:48 and the tasks like question answering 0:50 summarization translation captioning 0:52 sentiment analysis and so on but here's 0:55 the deal Gemini isn't just one single 0:58 model it's an entire network work of 1:00 models all working together to deliver 1:02 the best results possible alright now 1:05 how Gemini works so basically Gemini 1:08 uses a brand new architecture that 1:10 merges two main components a multimodal 1:13 encoder and a multimodal decoder the 1:16 encoder's job is to convert different 1:18 types of data into a common language 1:20 that the decoder can understand then the 1:23 decoder takes over generating outputs in 1:26 different modalities based on the 1:27 encoded inputs and the task at hand say 1:30 for instance the input is an image and 1:33 the task is to generate a caption the 1:35 encoder would turn the image into a 1:37 vector that captures all its features 1:39 and meaning and the decoder would then 1:41 generate a text output that describes 1:43 the image now what sets Gemini apart and 1:46 makes it special is that Gemini has 1:48 several advantages when compared to 1:50 other large language models like gpt4 1:53 first off it is just more adaptable it 1:56 can handle any type of data and task 1:58 without needing specialized models or 2:00 any sort of fine tuning plus it can 2:02 learn from any domain and data set 2:04 without being Boxed In by predefined 2:07 categories or labels so compared to 2:09 other models that are trained on 2:11 specific domains or tasks Gemini can 2:14 tackle new and unseen scenarios much 2:17 more efficiently then there's the fact 2:19 that Gemini is just more efficient in 2:21 general it uses fewer computational 2:24 resources and memory than other models 2:26 that need to deal with multiple 2:27 modalities separately also it uses a 2:31 distributed training strategy which 2:33 means it can make the most out of 2:35 multiple devices and servers to speed up 2:37 the learning process and honestly the 2:40 best part is that Gemini can scale up to 2:43 larger data sets and models without 2:45 compromising its performance or quality 2:48 which is pretty impressive if you ask me 2:50 if we talk about size and complexity one 2:53 of the most common things people look at 2:55 to measure a large language model is its 2:58 parameter count right so basically 3:01 parameters are numerical variables that 3:04 serve as the Learned knowledge of the 3:06 model enabling it to make predictions 3:08 and generate text based on the input it 3:10 receives generally speaking more 3:13 parameters means more potential for 3:14 learning and generating diverse and 3:16 accurate outputs but having more 3:18 parameters also means you need more 3:20 computational resources and memory to 3:23 train and use the model now gpt4 has one 3:27 trillion parameters which is about six 3:29 times bigger than GPT 3.5 with its 175 3:33 billion parameters that makes gpt4 one 3:36 of the biggest language models ever made 3:38 for Gemini Google has said that it comes 3:41 in four sizes gecko otter bison and 3:44 unicorn they haven't given us the exact 3:47 parameter count for each size but based 3:49 on some hints we can guess that unicorn 3:51 is the largest and probably similar to 3:53 gpt4 in terms of parameters maybe a bit 3:56 less oh and by the way I gotta mention 3:58 this before I show you few examples of 4:00 what it can do I must say that Gemini is 4:03 more interactive and creative than other 4:05 llms it can churn out outputs in 4:07 different modality is based on what the 4:09 user prefers and it can even generate 4:12 novel and diverse outputs that aren't 4:14 Bound by existing data or templates for 4:17 example Gemini could whip up original 4:18 images or videos based on text 4:21 descriptions or sketches it could also 4:23 create stories or poems based on images 4:25 or audio clips now let's talk about how 4:28 does it not exactly outsmart but perform 4:31 tasks that are more varied and longer 4:33 than gpt4 alright let me give you a few 4:36 examples one thing Gemini can do is 4:38 multi-modal question answering this is 4:41 when you ask a question that involves 4:43 multiple types of data like text and 4:45 images for instance you might ask who is 4:48 the author of this book while showing an 4:50 image of a book cover or perhaps what is 4:53 the name of this animal while showing an 4:55 image of some creature Gemini can answer 4:58 these questions by combining its skills 5:00 in understanding both text and visuals 5:03 another cool thing it can do is 5:05 multi-modal summarization imagine you've 5:08 got a piece of information that's made 5:09 up of different types of data like text 5:12 and audio for example you might want to 5:14 summarize a podcast episode or a news 5:16 article by generating a short text 5:18 summary or an audio summary Gemini can 5:22 do all that by putting together its 5:24 skills in textual and auditory 5:26 comprehension a third thing is 5:28 multimodal translation this is when you 5:30 need to translate a piece of information 5:31 that involves multiple types of data 5:34 like text and video suppose you have a 5:36 video lecture or a movie trailer that 5:38 you need to generate subtitles for or in 5:41 another language Gemini can pull that 5:43 off by combining its skills in textual 5:46 and visual translation and then there's 5:48 multimodal generation this is when you 5:50 want to generate a piece of information 5:52 that involves multiple types of data 5:54 like text and images for example you 5:57 might want to generate an image based on 5:59 a text description or a sketch or maybe 6:01 you want to generate a text based on an 6:03 image or a video clip again Gemini can 6:06 do this by combining its skills in 6:08 textual and visual generation but to me 6:10 honestly the most impressive thing that 6:13 Gemini can perform is multimodal 6:15 reasoning which basically means it can 6:17 combine information from different data 6:19 types and tasks to make assumptions for 6:23 example let's say you show it a clip 6:24 from a movie and using the multimodal 6:27 reasoning Gemini can now answer complex 6:30 questions like what is the main theme of 6:32 this movie by synthesizing information 6:34 from multiple modalities so it allows 6:37 Gemini to notice patterns that happen 6:39 again and again under understand how 6:41 characters interact with each other and 6:43 find hidden messages or meanings in a 6:45 movie by doing all of this Gemini can 6:48 give you a complete understanding of 6:50 what the movie is really about and what 6:52 its main idea or message is and honestly 6:55 I'm seriously Blown Away by that so 6:57 these are just a couple of things Gemini 6:59 can do there's a ton more potential here 7:01 that I just can't cover in this video 7:03 but I hope you're starting to see just 7:05 how incredibly powerful and versatile 7:07 this technology really is so where does 7:10 this leave us in terms of the future of 7:12 AI well it's pretty obvious to me that 7:15 Google is likely going to give gpt4 and 7:18 maybe even gpt5 a real challenge in the 7:21 coming years with this multimodal 7:23 approach this also means we're likely to 7:25 see more applications and services that 7:28 use Gemini's capabilities to provide 7:30 better user experiences and solutions 7:32 for instance we could see more 7:34 personalized assistance that can 7:36 understand and respond to us in 7:38 different modalities or maybe more 7:40