国外：GPT-4等效智能在过去18个月内价格下降240倍  国内：大模型「亏本」卖，可以「白嫖」大模型API能力 19政企、创业者必读 DeepSeek出现之前的十大预判 之七 多模态越来越重要  由文本生成迈向图像、视频、3D内容与世界模拟  多模态模态在能力变强的同时，规模正在变小 20政企、创业者必读 21 DeepSeek出现之前的十大预判 之八 智能体推动大模型快速落地  能够调用各种工具，具有行动能力 过去如何做蛋白质研究 AlphaFold 1. X射线晶体衍射 2. 核磁共振 3. 冷冻电子显微镜 1. 利用Transformer的预测能力， 2. 直接从蛋白质的氨基酸序列 3. 中预测蛋白质的3D结构 靠肉眼观察，几年才能发现一个复杂蛋 白质结构，半个世纪预测了20多万种 从数年缩短到几分钟，解开了生物学密码 成功预测了地球存在的2亿种蛋白质结构 45政企、创业者必读  DeepS

接下来，我们会讨论随着 Rust 程序增大时如何以惯用的方式对问题进行建模和对解决方案进 行结构化。此外我们还会讨论 Rust 的惯用写法如何与你可能已经熟悉的面向对象编程惯例相 对应。 424/562Rust 程序设计语言 简体中文版 面向对象编程特性 面向对象编程（Object-Oriented Programming，OOP）是一种对程序进行建模的方式。对 象（Object）作为一个编程概念来源于 20 允许我们在只有一个匹配分支的match 中使用不可反驳模式，不过这么 做不是特别有用，并可以被更简单的 let 语句替代。 目前我们已经讨论了所有可以使用模式的地方，以及可反驳模式与不可反驳模式的区别，下面 让我们一起去把可以用来创建模式的语法过目一遍吧。 452/562Rust 程序设计语言 简体中文版 模式语法 在本节中，我们收集了模式中所有有效的语法，并讨论为什么以及何时你可能要使用这些语 法。 匹配字面值 如第六

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3);CHAPTER row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________ 4 = elements in each 4d slice (4,) ⇒ shape = ((2, 1, 1), (3, 1), (4,), IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3);CHAPTER row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________ 4 = elements in each 4d slice (4,) ⇒ shape = ((2, 1, 1), (3, 1), (4,), IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3);CHAPTER row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________ 4 = elements in each 4d slice (4,) ⇒ shape = ((2, 1, 1), (3, 1), (4,), IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________ 4 = elements in each 4d slice (4,) ⇒ shape = ((2, 1, 1), (3, 1), (4,), IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________CHAPTER 48. ARRAYS 1088 4 = elements in each 4d slice (4,) ⇒ shape = ((2 IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________CHAPTER 48. ARRAYS 1087 4 = elements in each 4d slice (4,) ⇒ shape = ((2 IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________CHAPTER 48. ARRAYS 1087 4 = elements in each 4d slice (4,) ⇒ shape = ((2 IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1

julia> cat(a, b; dims=(1, 2)) 2×6 Matrix{Int64}: 1 2 3 0 0 0 0 0 0 4 5 6 Extended Help Concatenate 3D arrays: julia> a = ones(2, 2, 3); julia> b = ones(2, 2, 4); julia> c = cat(a, b; dims=3); julia> row (2, 1, 1) _______ _ 3 1 = elements in each column (3, 1) _____________ 4 = elements in each 3d slice (4,) _____________CHAPTER 47. ARRAYS 1079 4 = elements in each 4d slice (4,) ⇒ shape = ((2 IndexLinear to the extent that it is possible. Index replacement Consider making 2d slices of a 3d array: julia> A = rand(2,3,4); julia> S1 = view(A, :, 1, 2:3) 2×2 view(::Array{Float64, 3}, :, 1