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\u7b54\u3048\u3068\u306a\u308b\u30c7\u30fc\u30bf\uff08\u6559\u5e2b\u30c7\u30fc\u30bf\uff09\u3092\u4e00\u7dd2\u306b\u30e2\u30c7\u30eb\u306b\u5b66\u7fd2\u3055\u305b\u308b\u65b9\u6cd5\u3067\u3059\u3002\u4f8b\u3048\u3070\u3001\u7537\u6027\u306e\u753b\u50cf\u3068\u300c\u7537\u6027\u300d\u3068\u3044\u3046\u30e9\u30d9\u30eb\u3092\u30bb\u30c3\u30c8\u3067\u5b66\u7fd2\u3055\u305b\u308b\u3053\u3068\u3067\u3001AI\u306f\u7537\u6027\u3068\u5973\u6027\u3092\u898b\u5206\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002\u6570\u5024\u306e\u4e88\u6e2c\uff08\u56de\u5e30\uff09\u3084\u30ab\u30c6\u30b4\u30ea\u306e\u4e88\u6e2c\uff08\u5206\u985e\uff09\u306b\u7528\u3044\u3089\u308c\u307e\u3059\u3002<\/li>\n<li>**\u6559\u5e2b\u306a\u3057\u5b66\u7fd2:** \u7b54\u3048\u304c\u306a\u3044\u72b6\u614b\u3067\u3001\u4e0e\u3048\u3089\u308c\u305f\u30c7\u30fc\u30bf\u306e\u7279\u5fb4\u3084\u6cd5\u5247\u3092\u81ea\u52d5\u7684\u306b\u62bd\u51fa\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002\u9867\u5ba2\u306e\u30b0\u30eb\u30fc\u30d7\u5206\u3051\uff08\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\uff09\u3084\u3001\u30c7\u30fc\u30bf\u306e\u91cd\u8981\u306a\u60c5\u5831\u3092\u62bd\u51fa\u3057\u3066\u6b21\u5143\u3092\u524a\u6e1b\u3059\u308b\uff08\u6b21\u5143\u524a\u6e1b\uff09\u306a\u3069\u306b\u6d3b\u7528\u3055\u308c\u307e\u3059\u3002<\/li>\n<li>**\u5f37\u5316\u5b66\u7fd2:** AI\u304c\u81ea\u3089\u8a66\u884c\u932f\u8aa4\u3092\u7e70\u308a\u8fd4\u3057\u306a\u304c\u3089\u3001\u6700\u9069\u306a\u884c\u52d5\u3092\u5b66\u7fd2\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002\u304a\u6383\u9664\u30ed\u30dc\u30c3\u30c8\u306e\u30eb\u30f3\u30d0\u3084\u3001\u56f2\u7881\u306e\u4e16\u754c\u30c1\u30e3\u30f3\u30d4\u30aa\u30f3\u3092\u6253\u3061\u7834\u3063\u305fAI\u300cAlphaGo\u300d\u306a\u3069\u304c\u305d\u306e\u4ee3\u8868\u4f8b\u3067\u3059\u3002<\/li>\n<\/ul>\n<p>\u3053\u308c\u3089\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306f\u3001\u305d\u308c\u305e\u308c\u7570\u306a\u308b\u554f\u984c\u89e3\u6c7a\u306b\u9069\u3057\u3066\u304a\u308a\u3001\u79c1\u305f\u3061\u306e\u751f\u6d3b\u306e\u69d8\u3005\u306a\u5834\u9762\u3067\u6d3b\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<h2>\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\uff1aAI\u306e\u98db\u8e8d\u3092\u652f\u3048\u308b\u6280\u8853<\/h2>\n<p>\u6a5f\u68b0\u5b66\u7fd2\u306e\u4e2d\u306b\u3001\u8fd1\u5e74AI\u30d6\u30fc\u30e0\u306e\u706b\u4ed8\u3051\u5f79\u3068\u306a\u3063\u305f\u300c\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u300d\u304c\u3042\u308a\u307e\u3059\u3002\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u306e\u624b\u6cd5\u306e\u4e00\u3064\u3067\u3042\u308a\u3001\u7279\u306b\u753b\u50cf\u3084\u81ea\u7136\u8a00\u8a9e\u306a\u3069\u306e\u8907\u96d1\u306a\u30c7\u30fc\u30bf\u3092\u6271\u3046\u3053\u3068\u306b\u9577\u3051\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306f\u3001\u4eba\u9593\u306e\u8133\u306e\u795e\u7d4c\u56de\u8def\u3092\u6a21\u3057\u305f\u300c\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u300d\u3092\u591a\u5c64\u306b\u91cd\u306d\u308b\u3053\u3068\u3067\u3001\u3088\u308a\u9ad8\u5ea6\u306a\u5b66\u7fd2\u3092\u53ef\u80fd\u306b\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001AI\u306f\u3053\u308c\u307e\u3067\u4eba\u9593\u304c\u624b\u4f5c\u696d\u3067\u884c\u3063\u3066\u3044\u305f\u7279\u5fb4\u91cf\u306e\u62bd\u51fa\u3092\u81ea\u52d5\u3067\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u3001\u305d\u306e\u6027\u80fd\u306f\u98db\u8e8d\u7684\u306b\u5411\u4e0a\u3057\u307e\u3057\u305f\u3002<\/p>\n<p>\u4f8b\u3048\u3070\u3001\u753b\u50cf\u8a8d\u8b58\u306e\u5206\u91ce\u3067\u306f\u3001\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u767b\u5834\u306b\u3088\u308a\u3001AI\u304c\u753b\u50cf\u306e\u4e2d\u304b\u3089\u7279\u5b9a\u306e\u7269\u4f53\u3084\u4eba\u7269\u3092\u9a5a\u304f\u307b\u3069\u306e\u7cbe\u5ea6\u3067\u8b58\u5225\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u81ea\u52d5\u904b\u8ee2\u3084\u533b\u7642\u8a3a\u65ad\u306a\u3069\u3001\u69d8\u3005\u306a\u5206\u91ce\u3067\u9769\u65b0\u7684\u306a\u9032\u6b69\u304c\u751f\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<h2>\u5b9f\u8df5\uff01AI\u3092\u52d5\u304b\u3059\u30b3\u30fc\u30c9\u306e\u4e16\u754c<\/h2>\n<p>\u3053\u3053\u304b\u3089\u306f\u3001\u5b9f\u969b\u306bAI\u3092\u52d5\u304b\u3059\u305f\u3081\u306e\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u30b3\u30fc\u30c9\u3092\u5c11\u3057\u3060\u3051\u8997\u3044\u3066\u307f\u307e\u3057\u3087\u3046\u3002Python\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u300cscikit-learn\u300d\u3092\u4f7f\u3048\u3070\u3001\u8907\u96d1\u306aAI\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3082\u9a5a\u304f\u307b\u3069\u7c21\u5358\u306b\u5b9f\u88c5\u3067\u304d\u307e\u3059\u3002<\/p>\n<h3>\u91cd\u56de\u5e30\u5206\u6790\u3067\u4f4f\u5b85\u4fa1\u683c\u3092\u4e88\u6e2c\u3059\u308b<\/h3>\n<p>\u307e\u305a\u306f\u3001\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u306e\u300c\u56de\u5e30\u300d\u306e\u4f8b\u3068\u3057\u3066\u3001\u4f4f\u5b85\u4fa1\u683c\u306e\u4e88\u6e2c\u306b\u6311\u6226\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u3053\u3053\u3067\u306f\u3001\u30dc\u30b9\u30c8\u30f3\u8fd1\u90ca\u306e\u4f4f\u5b85\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u3001\u8907\u6570\u306e\u8981\u56e0\u304b\u3089\u4f4f\u5b85\u4fa1\u683c\u3092\u4e88\u6e2c\u3059\u308b\u300c\u91cd\u56de\u5e30\u5206\u6790\u300d\u3092\u5b9f\u88c5\u3057\u307e\u3059\u3002<\/p>\n<p>&#8220;`python<br \/>\n# \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<br \/>\nimport numpy as np<br \/>\nimport pandas as pd<br \/>\nimport matplotlib.pyplot as plt<br \/>\nimport seaborn as sns<br \/>\nfrom sklearn.datasets import load_boston<br \/>\nfrom sklearn.model_selection import train_test_split<br \/>\nfrom sklearn.linear_model import LinearRegression<\/p>\n<p># \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6e96\u5099<br \/>\nboston = load_boston()<br \/>\nX = boston.data<br \/>\ny = boston.target<br \/>\nfeature_names = boston.feature_names<\/p>\n<p># \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u5909\u63db<br \/>\ndf = pd.DataFrame(X, columns=feature_names)<br \/>\ndf[&#8216;PRICE&#8217;] = y<\/p>\n<p># \u8a13\u7df4\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306b\u5206\u5272<br \/>\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)<\/p>\n<p># \u30e2\u30c7\u30eb\u306e\u5b9a\u7fa9\u3068\u5b66\u7fd2<br \/>\nmodel = LinearRegression()<br \/>\nmodel.fit(X_train, y_train)<\/p>\n<p># \u4e88\u6e2c\u7cbe\u5ea6\u306e\u8a55\u4fa1<br \/>\ntrain_score = model.score(X_train, y_train)<br \/>\ntest_score = model.score(X_test, y_test)<\/p>\n<p>print(f&#8221;\u8a13\u7df4\u30c7\u30fc\u30bf\u306e\u6c7a\u5b9a\u4fc2\u6570: {train_score:.3f}&#8221;)<br \/>\nprint(f&#8221;\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u6c7a\u5b9a\u4fc2\u6570: {test_score:.3f}&#8221;)<br \/>\n&#8220;`<\/p>\n<p>\u3053\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001\u8a13\u7df4\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u305d\u308c\u305e\u308c\u306e\u4e88\u6e2c\u7cbe\u5ea6\uff08\u6c7a\u5b9a\u4fc2\u6570\uff09\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002\u6c7a\u5b9a\u4fc2\u6570\u306f1\u306b\u8fd1\u3044\u307b\u3069\u7cbe\u5ea6\u304c\u9ad8\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002\u3082\u3057\u8a13\u7df4\u30c7\u30fc\u30bf\u306e\u7cbe\u5ea6\u306f\u9ad8\u3044\u306e\u306b\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u7cbe\u5ea6\u304c\u4f4e\u3044\u5834\u5408\u3001\u305d\u308c\u306f\u300c\u904e\u5b66\u7fd2\u300d\u3068\u547c\u3070\u308c\u308b\u73fe\u8c61\u304c\u8d77\u304d\u3066\u3044\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002\u307e\u308b\u3067\u3001\u904e\u53bb\u554f\u3070\u304b\u308a\u89e3\u304d\u3059\u304e\u3066\u3001\u5fdc\u7528\u554f\u984c\u306b\u5bfe\u5fdc\u3067\u304d\u306a\u3044\u53d7\u9a13\u751f\u306e\u3088\u3046\u306a\u72b6\u614b\u3067\u3059\u3002<\/p>\n<h3>\u6c7a\u5b9a\u6728\u3067\u30a2\u30e4\u30e1\u306e\u7a2e\u985e\u3092\u5206\u985e\u3059\u308b<\/h3>\n<p>\u6b21\u306b\u3001\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u306e\u300c\u5206\u985e\u300d\u306e\u4f8b\u3068\u3057\u3066\u3001\u30a2\u30e4\u30e1\u306e\u82b1\u306e\u7a2e\u985e\u3092\u5206\u985e\u3059\u308b\u300c\u6c7a\u5b9a\u6728\u300d\u3092\u5b9f\u88c5\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\u30a2\u30e4\u30e1\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u306f\u3001\u82b1\u3073\u3089\u3084\u304c\u304f\u306e\u9577\u3055\u30fb\u5e45\u3068\u3044\u3063\u305f\u60c5\u5831\u304c\u542b\u307e\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>&#8220;`python<br \/>\n# \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<br \/>\nimport numpy as np<br \/>\nimport pandas as pd<br \/>\nfrom sklearn.datasets import load_iris<br \/>\nfrom sklearn.model_selection import train_test_split<br \/>\nfrom sklearn.tree import DecisionTreeClassifier<\/p>\n<p># \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6e96\u5099<br \/>\niris = load_iris()<br \/>\nX = iris.data<br \/>\ny = iris.target<br \/>\nfeature_names = iris.feature_names<br \/>\ntarget_names = iris.target_names<\/p>\n<p># \u8a13\u7df4\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306b\u5206\u5272<br \/>\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)<\/p>\n<p># \u30e2\u30c7\u30eb\u306e\u5b9a\u7fa9\u3068\u5b66\u7fd2<br \/>\nmodel = DecisionTreeClassifier(random_state=0)<br \/>\nmodel.fit(X_train, y_train)<\/p>\n<p># \u4e88\u6e2c\u7cbe\u5ea6\u306e\u8a55\u4fa1\uff08\u6b63\u89e3\u7387\uff09<br \/>\ntrain_accuracy = model.score(X_train, y_train)<br \/>\ntest_accuracy = model.score(X_test, y_test)<\/p>\n<p>print(f&#8221;\u8a13\u7df4\u30c7\u30fc\u30bf\u306e\u6b63\u89e3\u7387: {train_accuracy:.3f}&#8221;)<br \/>\nprint(f&#8221;\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u6b63\u89e3\u7387: {test_accuracy:.3f}&#8221;)<br \/>\n&#8220;`<\/p>\n<p>\u6c7a\u5b9a\u6728\u306f\u3001\u307e\u308b\u3067\u30d5\u30ed\u30fc\u30c1\u30e3\u30fc\u30c8\u306e\u3088\u3046\u306b\u6761\u4ef6\u5206\u5c90\u3092\u7e70\u308a\u8fd4\u3057\u3066\u5206\u985e\u3092\u884c\u3044\u307e\u3059\u3002\u3053\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f\u3001\u3069\u306e\u7279\u5fb4\u91cf\u304c\u5206\u985e\u306b\u91cd\u8981\u306a\u306e\u304b\u3092\u8996\u899a\u7684\u306b\u7406\u89e3\u3057\u3084\u3059\u3044\u3068\u3044\u3046\u5f37\u307f\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<h3>k-means\u6cd5\u3067\u9867\u5ba2\u3092\u30b0\u30eb\u30fc\u30d7\u5206\u3051\u3059\u308b<\/h3>\n<p>\u6700\u5f8c\u306b\u3001\u6559\u5e2b\u306a\u3057\u5b66\u7fd2\u306e\u300c\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u300d\u306e\u4f8b\u3068\u3057\u3066\u3001\u30b3\u30f3\u30d3\u30cb\u30a8\u30f3\u30b9\u30b9\u30c8\u30a2\u306e\u8cfc\u8cb7\u30c7\u30fc\u30bf\u304b\u3089\u9867\u5ba2\u3092\u30b0\u30eb\u30fc\u30d7\u5206\u3051\u3059\u308b\u300ck-means\u6cd5\u300d\u3092\u5b9f\u88c5\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<p>&#8220;`python<br \/>\n# \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30a4\u30f3\u30dd\u30fc\u30c8<br \/>\nimport pandas as pd<br \/>\nfrom sklearn.cluster import KMeans<\/p>\n<p># \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6e96\u5099\uff08\u67b6\u7a7a\u306e\u30b3\u30f3\u30d3\u30cb\u8cfc\u8cb7\u30c7\u30fc\u30bf\uff09<br \/>\ndata = {<br \/>\n    &#8216;Number&#8217;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],<br \/>\n    &#8216;Bento_Noodle&#8217;: [25000, 1000, 30000, 500, 28000, 1200, 29000, 800, 27000, 1500],<br \/>\n    &#8216;Sweets&#8217;: [1000, 20000, 800, 22000, 900, 18000, 700, 21000, 1100, 19000],<br \/>\n    &#8216;Salad&#8217;: [500, 1500, 600, 1800, 700, 1300, 800, 1600, 900, 1400],<br \/>\n    &#8216;Drink&#8217;: [2000, 10000, 1500, 12000, 1800, 9000, 1300, 11000, 1600, 9500]<br \/>\n}<br \/>\ndf = pd.DataFrame(data)<\/p>\n<p># \u9867\u5ba2ID\u3092\u9664\u5916<br \/>\nX = df.drop(&#8216;Number&#8217;, axis=1).values<\/p>\n<p># k-means\u30e2\u30c7\u30eb\u306e\u5b9a\u7fa9\u3068\u5b66\u7fd2<br \/>\nkmeans = KMeans(n_clusters=3, random_state=0) # 3\u3064\u306e\u30b0\u30eb\u30fc\u30d7\u306b\u5206\u3051\u308b<br \/>\nkmeans.fit(X)<\/p>\n<p># \u5404\u9867\u5ba2\u304c\u3069\u306e\u30b0\u30eb\u30fc\u30d7\u306b\u5c5e\u3059\u308b\u304b\u3092\u4e88\u6e2c<br \/>\nclusters = kmeans.predict(X)<\/p>\n<p># \u7d50\u679c\u3092\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306b\u8ffd\u52a0<br \/>\ndf[&#8216;Cluster&#8217;] = clusters<\/p>\n<p>print(df)<br \/>\n&#8220;`<\/p>\n<p>k-means\u6cd5\u306f\u3001\u4e8b\u524d\u306b\u30b0\u30eb\u30fc\u30d7\u306e\u6570\uff08n_clusters\uff09\u3092\u6307\u5b9a\u3057\u3001\u30c7\u30fc\u30bf\u9593\u306e\u8ddd\u96e2\u306b\u57fa\u3065\u3044\u3066\u9867\u5ba2\u3092\u30b0\u30eb\u30fc\u30d7\u5206\u3051\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u4f8b\u3048\u3070\u300c\u30b9\u30a4\u30fc\u30c4\u597d\u304d\u30b0\u30eb\u30fc\u30d7\u300d\u300c\u5f01\u5f53\u30fb\u9eba\u985e\u4e2d\u5fc3\u30b0\u30eb\u30fc\u30d7\u300d\u3068\u3044\u3063\u305f\u9867\u5ba2\u306e\u8cfc\u8cb7\u884c\u52d5\u306e\u7279\u5fb4\u3092\u628a\u63e1\u3057\u3001\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u6226\u7565\u306a\u3069\u306b\u6d3b\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<h2>AI\u306e\u672a\u6765\u3001\u305d\u3057\u3066\u3042\u306a\u305f\u306e\u5f79\u5272<\/h2>\n<p>AI\u306f\u3001\u79c1\u305f\u3061\u306e\u60f3\u50cf\u3092\u8d85\u3048\u308b\u30b9\u30d4\u30fc\u30c9\u3067\u9032\u5316\u3092\u7d9a\u3051\u3066\u3044\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u3069\u3093\u306a\u306bAI\u304c\u9032\u5316\u3057\u3066\u3082\u3001\u305d\u306e\u6839\u5e95\u306b\u306f\u4eba\u9593\u306e\u77e5\u6075\u3068\u5275\u9020\u6027\u3001\u305d\u3057\u3066\u502b\u7406\u89b3\u304c\u4e0d\u53ef\u6b20\u3067\u3059\u3002<\/p>\n<p>AI\u306e\u6280\u8853\u3092\u7406\u89e3\u3057\u3001\u305d\u308c\u3092\u793e\u4f1a\u306b\u5f79\u7acb\u3066\u308b\u305f\u3081\u306b\u306f\u3001\u79c1\u305f\u3061\u4e00\u4eba\u3072\u3068\u308a\u304cAI\u306b\u3064\u3044\u3066\u5b66\u3073\u3001\u8003\u3048\u3001\u8b70\u8ad6\u3057\u3066\u3044\u304f\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u8a18\u4e8b\u304c\u3001\u3042\u306a\u305f\u304cAI\u306e\u4e16\u754c\u306b\u8db3\u3092\u8e0f\u307f\u5165\u308c\u308b\u304d\u3063\u304b\u3051\u3068\u306a\u308a\u3001\u672a\u6765\u3092\u5275\u9020\u3059\u308b\u4e00\u54e1\u3068\u306a\u308b\u305f\u3081\u306e\u4e00\u6b69\u3068\u306a\u308c\u3070\u5e78\u3044\u3067\u3059\u3002<\/p>\n<p>\u3055\u3042\u3001AI\u306e\u7121\u9650\u306e\u53ef\u80fd\u6027\u3092\u3001\u79c1\u305f\u3061\u3068\u4e00\u7dd2\u306b\u63a2\u6c42\u3057\u3066\u3044\u304d\u307e\u3057\u3087\u3046\uff01<\/p>\n<div style=\"text-align: right\"><a href=\"https:\/\/www.youtube.com\/watch?v=okpRV08-svw\">\u30bd\u30fc\u30b9<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>AI\u306e\u6249\u3092\u958b\u304f\uff01\u6a5f\u68b0\u5b66\u7fd2\u3001\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3001\u305d\u3057\u3066\u3042\u306a\u305f\u306e\u672a\u6765 \u300cAI\u300d\u3068\u3044\u3046\u8a00\u8449\u3092\u805e\u3044\u3066\u3001\u3042\u306a\u305f\u306f\u3069\u3093\u306a\u30a4\u30e1\u30fc\u30b8\u3092\u62b1\u304d\u307e\u3059\u304b\uff1fSF\u6620\u753b\u306e\u3088\u3046\u306a\u672a\u6765\u306e\u4e16\u754c\uff1f\u305d\u308c\u3068\u3082\u3001\u79c1\u305f\u3061\u306e\u751f\u6d3b\u3092\u4fbf\u5229\u306b\u3059\u308b\u6700\u65b0\u6280\u8853\uff1f \u5b9f\u306f\u3001AI\u306f\u3059\u3067 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[600],"tags":[372,1180,738,678,727],"class_list":["post-3586","post","type-post","status-publish","format-standard","hentry","category-ai","tag-ai","tag-1180","tag-738","tag-678","tag-727"],"_links":{"self":[{"href":"https:\/\/wtpmj.com\/index.php?rest_route=\/wp\/v2\/posts\/3586","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wtpmj.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wtpmj.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wtpmj.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wtpmj.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3586"}],"version-history":[{"count":0,"href":"https:\/\/wtpmj.com\/index.php?rest_route=\/wp\/v2\/posts\/3586\/revisions"}],"wp:attachment":[{"href":"https:\/\/wtpmj.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3586"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wtpmj.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3586"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wtpmj.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3586"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}