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https://media.wired.jp/photos/62fd7947b44dc86459866ae0/master/w_2240,c_limit/PaLM-SayCan-Robot-Still-Business.jpg 2240w" sizes="100vw"/><img alt="PaLM robot picking up a dish sponge in a kitchen" class="ResponsiveImageContainer-dkDswF jdxiQR responsive-image__image" src="https://media.wired.jp/photos/62fd7947b44dc86459866ae0/master/w_2560%2Cc_limit/PaLM-SayCan-Robot-Still-Business.jpg"/></picture></div></div></span><div class="CaptionWrapper-bpWYxk LlrFc caption ContentHeaderLeadAssetCaption-geFkyV wcfnB"><span class="BaseWrap-sc-SJwXJ BaseText-fEohGt CaptionCredit-cUoKHu deUlYF kdWwXS khKWGS caption__credit">Courtesy of Google</span></div></div></figure><div data-testid="ContentHeaderLeadRailAnchor" class="ContentHeaderLeadRailAnchor-efccTA hLxPXU"/></div><div class="ContentHeaderHedAccreditationWrapper-hBVoLr knxLSX"><div data-testid="ContentHeaderTitleBlockWrapper" class="ContentHeaderTitleBlockWrapper-jplBVR ekhtxs"><div data-testid="ContentHeaderRubric" 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data-testid="ContentHeaderPublishDate" datetime="2022-08-18T17:00:00+09:00" class="BaseWrap-sc-SJwXJ BaseText-fEohGt ContentHeaderTitleBlockPublishDate-kkCQlD deUlYF dxTjrJ eXEinX">2022.08.18</time></div></div><h1 data-testid="ContentHeaderHed" class="BaseWrap-sc-SJwXJ BaseText-fEohGt ContentHeaderHed-kojVvG deUlYF boZsvw ivjNFd">グーグルの新しいAIロボットは、“プログラミングなし”で人間の命令に対処する</h1></div><div class="ContentHeaderAccreditation-gTueYh dCYdyd content-header__accreditation" data-testid="ContentHeaderAccreditation"><div class="ContentHeaderDek-tfbwp clYhYW">人工知能（AI）で動作するロボットが、プログラミングなしで人間の指示を理解して行動する──。そんなデモを、このほどグーグルが披露した。中核をなすのは、ウェブ上にある膨大なテキストから言葉と動作の関係を機械学習で習得する大規模な言語モデルだ。</div><div class="SocialIconsWrapper-izisLx beigaT social-icons social-icons--standard ContentHeaderAccreditationSocialIcons-eNsQce dlNnwJ content-header__social-share"><ul data-testid="socialIconslist" class="SocialIconsList-PfiWk eLQdyJ social-icons__list"/></div></div></div></div><aside class="PersistentAsideWrapper-NHZeN KksnM persistent-aside persistent-aside--align-left" style="top:auto;height:auto" data-testid="PersistentAsideWrapper"><div class="StickyBoxWrapper-esWinU eA-DNAL sticky-box ContentHeaderPersistentAside-cTbFNg jMrVkm"><div class="StickyBoxPrimary-fFrXmn hYtIfu sticky-box__primary"><div class="SocialIconsWrapper-izisLx beigaT social-icons social-icons--has-background ContentHeaderSocialIcons-caSQAw gCrRQL social-icons--share"><ul data-testid="socialIconslist" class="SocialIconsList-PfiWk eLQdyJ social-icons__list"/></div></div><div class="StickyBoxPlaceholder-jMFsiI gEPiHy"/></div></aside></header></div><div data-attribute-verso-pattern="article-body" class="ArticlePageContentBackGround-eDCECR kgkaAb"><div class="ArticlePageChunksContent-hrTfkf hqmKvI"><div data-testid="ArticlePageChunks" class="ArticlePageChunks-leZTjq gIXpw"><div class="GridWrapper-uChIO jPANBg grid grid-margins grid-items-2 ArticlePageChunksGrid-kSsjNq bgKfPe grid-layout--adrail narrow wide-adrail"><div class="GridItem-btGJuM efCVnM grid--item grid-layout__content"><div class="BodyWrapper-cuyyBm jQxFiM body body__container article__body" data-journey-hook="client-content" data-testid="BodyWrapper"><div class="body__inner-container"><p>グーグルのリサーチサイエンティストのシァ・フェイ（夏斐）は、明るく開放感のあるキッチンの中心に座り、ノートPCにコマンドを入力した。そのノートPCは1本のアームと車輪を備え、大きなフロアランプのような姿をした<a href="https://wired.jp/tag/robot/">ロボット</a>に接続されている。</p><div><div class="ConsumerMarketingUnitThemedWrapper-kjAKNf hvESZJ consumer-marketing-unit consumer-marketing-unit--article-mid-content" role="presentation" aria-hidden="true"><div class="consumer-marketing-unit__slot consumer-marketing-unit__slot--article-mid-content consumer-marketing-unit__slot--in-content"/><div class="journey-unit"/></div></div><p>「わたしは空腹です」と、シァは入力した。すると、そのロボットはすぐさま部屋を横切って近くのカウンターへと向かう。そしてペンチのような形の大きなプラスチックのパーツでマルチグレインのチップスが入った袋を慎重につまみ上げ、シァのもとへと車輪で移動し、このスナックを差し出した──。</p><p>カリフォルニア州のマウンテンビューにあるグーグルのロボティクス研究所で2022年8月に実施されたデモンストレーションで最も印象的だったのは、そのロボットが人間のコーダーによるプログラミングなしで、シァの命令にどのように対応すればいいのか理解していたことだった。そのロボットを制御するソフトウェアは、ウェブから集めた何百万ページ分の文章をから、人間が話した言葉を一連の動作へと翻訳する方法を学習していたのである。</p><div class="GenericCalloutWrapper-lpinlz fzuDpR callout--has-top-border" data-testid="GenericCallout"><p><a href="https://wired.jp/membership/about"><figure class="AssetEmbedWrapper-iJUDRx jzCma asset-embed"><div class="AssetEmbedAssetContainer-fmVyIF fAvqua asset-embed__asset-container"><span class="SpanWrapper-kFvfwm ezrazJ responsive-asset AssetEmbedResponsiveAsset-fBMxW gbuarq asset-embed__responsive-asset"><picture class="ResponsiveImagePicture-jJVAmS laQysq AssetEmbedResponsiveAsset-fBMxW gbuarq asset-embed__responsive-asset responsive-image"><noscript><img 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asset-embed__caption"><span class="BaseWrap-sc-SJwXJ BaseText-fEohGt CaptionText-cPRdAa deUlYF qNUNG DkJeI caption__text">毎週開催のイベントに無料参加できる！『WIRED』日本版のメンバーシップ会員募集中！</span></div></figure></a></p></div><p>それはすなわち、<a href="https://wired.jp/tag/alexa/">「Alexa」</a>や<a href="https://wired.jp/tag/siri/">「Siri」</a>のようなバーチャルアシスタントとは違って、命令をする際にあらかじめ承認された特定の表現を用いる必要がないことを意味している。ロボットに「のどがカラカラなんだ」と言えば、飲み物を見つけようとしてくれるだろう。「おっと、飲み物をこぼしてしまった」と言えば、スポンジを取ってきてくれるはずだ。</p><div class="Container-inRyLO VAZYn"/><p>「現実世界のさまざまな状況に対処するために、ロボットは適応能力と経験から学習する能力を得る必要があります」と、グーグルの上級リサーチサイエンティストのカロル・ハウスマンはデモンストレーションの際に説明している。デモンストレーションでは、ロボットがスポンジを取ってきて、こぼした飲み物を拭きとる様子も披露された。</p><figure data-testid="IframeEmbed" class="IframeEmbedWrapper-sc-fhZlDM gJpraj iframe-embed"><div data-testid="IframeEmbedContainer" class="IframeEmbedContainer-hLMZbV kCKgwc"><div class="IframeEmbedAspectRatioWrapper-fsaboU jmelwV"><iframe class="IframeEmbedContent-gXzCvz kyJkei IframeEmbedContent lazyload" height="300px" sandbox="allow-scripts" title="Embedded Frame" data-src="https://media.wired.com/clips/62fadc4523d2b53441872fb0/360p/pass/PaLM-SayCan-Robot-Video-Business.mp4" allow="autoplay *; encrypted-media *; clipboard-write; autoplay; fullscreen; picture-in-picture"/></div><div class="CaptionWrapper-bpWYxk LlrFc caption"><span class="BaseWrap-sc-SJwXJ BaseText-fEohGt CaptionText-cPRdAa deUlYF qNUNG DkJeI caption__text"><p>COURTESY OF GOOGLE</p> </span></div></div></figure><p>機械が人間と交流するには、言葉がいかにして数々の組み合わせによってさまざまな意味を生み出しうるのかを、機械が理解できなければならない。「言語の微妙なニュアンスの違いや複雑さをすべて理解できるかどうかは、ロボットにかかっているのです」と、ハウスマンは言う。</p><h2>大規模な言語モデルの問題だったこと</h2><p>グーグルによるデモンストレーションは、複雑な環境において人間と交流できるロボットをつくるという長期的な目標に向けた第一歩だった。</p><div class="AdWrapper-fGHyEL ejxrQx ad ad--in-content"><div class="ad__slot ad__slot--in-content" data-node-id="88dcas"/></div><p>この数年で研究者たちは、書籍やウェブから得た大量の文章を大規模な<a href="https://wired.jp/tag/machine-learning/">機械学習モデル</a>に与えれば、<a href="https://wired.jp/tag/openai/">OpenAI</a>の文章生成プログラム<a href="https://wired.jp/2020/08/17/ai-text-generator-gpt-3-learning-language-fitfully/">「GPT-3」</a>のような優れた言語スキルをもったプログラムを生み出せることを発見している。オンラインでさまざまな形式の文書を処理することにより、ソフトウェアは文章を要約したり、文章に関する質問に答えたりする能力や、特定の主題に関する記事を生成する能力、人間と適切に会話する能力すら得ることができるのだ。</p><div data-attr-viewport-monitor="inline-recirc" class="inline-recirc-wrapper inline-recirc-observer-target-1 viewport-monitor-anchor"/><p><strong>関連記事：</strong><a href="https://wired.jp/article/chatgpt-fluent-bs/">対話型AI「ChatGPT」が生み出す“流暢なデタラメ”が、説得力をもって読めてしまう理由</a></p></div></div></div><div class="GridItem-btGJuM bGetkH grid--item grid-layout__aside"><div class="StickyBoxWrapper-esWinU bSxsLn sticky-box"><div class="StickyBoxPrimary-fFrXmn hYtIfu sticky-box__primary"><div class="Container-inRyLO VAZYn"/><div class="AdWrapper-fGHyEL ejxrQx ad ad--rail"><div class="ad__slot ad__slot--rail" data-node-id="afa2io"/></div><div class="ConsumerMarketingUnitThemedWrapper-kjAKNf hvESZJ consumer-marketing-unit consumer-marketing-unit--display-rail" 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2023」で暗号資産やNFTの話題が“影を潜めた”現実から見えたこと</div></div></div></a><div class="SummaryItemBylineWrapper-bcuBna lmhpbV summary-item__byline-date-icon"><div class="SummaryItemBaseByline-cUjuuc fKunmU summary-item__byline"><div class="summary-item__byline__content"><div data-testid="BylinesWrapper" class="BylinesWrapper-hlQKwg jcjUNa bylines"><p class="BylineWrapper-ikeNdb eKFOik byline bylines__byline" data-testid="BylineWrapper" itemprop="author" itemtype="http://schema.org/Person"><span itemprop="name" class="BylineNamesWrapper-dcvWHf ixVaxb"><span data-testid="BylineName" class="BylineName-cMiZYb ksslzD byline__name"><span class="BaseWrap-sc-SJwXJ BaseText-fEohGt BylinePreamble-ihZoJc deUlYF hcfGua jRnpHk byline__preamble">By </span>Eric Ravenscraft</span></span></p></div></div></div></div></div></div></li><li><div class="AdWrapper-fGHyEL ejxrQx RecircMostPopularSummaryItem-brNZRs drQZgc ad ad--aside"><div class="ad__slot ad__slot--aside" data-node-id="32ywge"/></div></li></ul><div class="RecircMostPopularFooter-dTrmjU dEaYZJ"/></div></div></div><div class="StickyBoxPlaceholder-jMFsiI gEPiHy"/></div></div></div><div class="RowWrapper-efswrE cfsZjl full-bleed-ad row-mid-content-ad"><div class="AdWrapper-fGHyEL ejxrQx ad ad--mid-content should-hold-space"><div class="ad__slot ad__slot--mid-content" data-node-id="b6hppo"/></div></div><div class="GridWrapper-uChIO jPANBg grid grid-margins grid-items-2 ArticlePageChunksGrid-kSsjNq bgKfPe grid-layout--adrail narrow wide-adrail"><div class="GridItem-btGJuM efCVnM grid--item grid-layout__content"><div class="BodyWrapper-cuyyBm jQxFiM body body__container article__body" data-journey-hook="client-content" data-testid="BodyWrapper"><div class="body__inner-container"><p>グーグルなどの巨大テック企業は、こうした大規模言語モデルを検索と広告の用途で幅広く活用している。数多くの企業がこの技術をクラウドAPIを通して提供しており、<a href="https://wired.jp/tag/ai/">人工知能（AI）</a>の言語能力をコード生成や広告のコピーの執筆といったタスクに応用した新しいサービスも登場した。</p><div class="GenericCalloutWrapper-lpinlz jXZVrJ callout--has-top-border" data-testid="GenericCallout"><p><a href="https://wired.jp/about/thursday-editors-lounge/"><figure class="AssetEmbedWrapper-iJUDRx jzCma asset-embed"><div class="AssetEmbedAssetContainer-fmVyIF fAvqua asset-embed__asset-container"><span class="SpanWrapper-kFvfwm ezrazJ responsive-asset AssetEmbedResponsiveAsset-fBMxW gbuarq asset-embed__responsive-asset"><picture class="ResponsiveImagePicture-jJVAmS laQysq AssetEmbedResponsiveAsset-fBMxW gbuarq asset-embed__responsive-asset responsive-image"><noscript><img alt="「WIRED Thursday Editor's Lounge」本格始動！いまいちばん会いたいゲストに“公開インタビュー”。毎週木曜夜のオンラインイベントをチェック！（詳細はこちら）" class="ResponsiveImageContainer-dkDswF jdxiQR responsive-image__image" src="https://media.wired.jp/photos/61e64331705e9457064ef705/master/w_1600%2Cc_limit/top-2400-WIRED_MEETUP_-Thursday.jpg" srcset="https://media.wired.jp/photos/61e64331705e9457064ef705/master/w_120,c_limit/top-2400-WIRED_MEETUP_-Thursday.jpg 120w, https://media.wired.jp/photos/61e64331705e9457064ef705/master/w_240,c_limit/top-2400-WIRED_MEETUP_-Thursday.jpg 240w, https://media.wired.jp/photos/61e64331705e9457064ef705/master/w_320,c_limit/top-2400-WIRED_MEETUP_-Thursday.jpg 320w, https://media.wired.jp/photos/61e64331705e9457064ef705/master/w_640,c_limit/top-2400-WIRED_MEETUP_-Thursday.jpg 640w, https://media.wired.jp/photos/61e64331705e9457064ef705/master/w_960,c_limit/top-2400-WIRED_MEETUP_-Thursday.jpg 960w, https://media.wired.jp/photos/61e64331705e9457064ef705/master/w_1280,c_limit/top-2400-WIRED_MEETUP_-Thursday.jpg 1280w, https://media.wired.jp/photos/61e64331705e9457064ef705/master/w_1600,c_limit/top-2400-WIRED_MEETUP_-Thursday.jpg 1600w" sizes="100vw"/></noscript></picture></span></div><div class="CaptionWrapper-bpWYxk LlrFc caption AssetEmbedCaption-eZjZkg extAwN asset-embed__caption"><span class="BaseWrap-sc-SJwXJ BaseText-fEohGt CaptionText-cPRdAa deUlYF qNUNG DkJeI caption__text">「WIRED Thursday Editor's Lounge」本格始動！いまいちばん会いたいゲストに“公開インタビュー”。毎週木曜夜のオンラインイベントをチェック！（詳細はこちら）</span></div></figure></a></p></div><p>グーグルのエンジニアだった<a href="https://wired.jp/membership/2022/07/01/blake-lemoine-google-lamda-ai-bigotry/">ブレイク・レモイン</a>は最近、こうした言語モデル<a href="https://wired.jp/article/plaintext-lamda-soul-machine-learning-system/">「LaMDA」</a>と呼ばれる<a href="https://wired.jp/article/lamda-sentient-ai-bias-google-blake-lemoine/">チャットボットには意識がある</a>かもしれないと警告を発したことで解雇されている。いまもグーグルに勤めるバイスプレジデントは、LaMDAとチャットをしたら「知性をもった何かと話している」ような気分になったと、英国の週刊誌『The Economist』への<a href="https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-are-making-strides-towards-consciousness-according-to-blaise-aguera-y-arcas" target="_blank">寄稿に記していた</a>。</p><p>こうした進歩にもかかわらず、AIのプログラムには混乱したり、意味不明な言葉をオウム返ししたりする傾向がいまだにある。ウェブの文章で訓練した言語モデルは事実を理解することもできず、訓練用データ内にある偏見や憎悪に満ちた言葉を再生産することもよくあるのだ。</p><p>このことは、ロボットを暴走させずに確実に指導するには、注意深くエンジニアリングすることが必要になるかもしれないことを示唆している。</p><h2>進化した言語モデル「PaLM」の実力</h2><p>ハウスマンがデモンストレーションしたロボットは、グーグルがこれまで発表したなかで最も強力な言語モデル「PaLM（Pathways Language Model）」を利用している。</p><p>PaLMはさまざまな“芸当”をこなすことが可能で、質問に答えるときにいかにその結論に至ったのかを自然な言葉遣いで説明するようなこともできる。ロボットが与えられたタスクをこなす際にも、同じアプローチを用いることで一連のステップを生成し、それを遂行しているわけだ。</p><p>グーグルの研究者らが用いたハードウェアは、Everyday Robotsという企業が開発したものだ。Everyday Robotsはグーグルの親会社である<a href="https://wired.jp/membership/2020/05/29/ten-years-of-google-x/">アルファベットの「X」という部門</a>から独立した企業で、“ロボット執事”の開発を目指した意欲的な研究プロジェクトに取り組んでいる。</p><div data-attr-viewport-monitor="inline-recirc" class="inline-recirc-wrapper inline-recirc-observer-target-2 viewport-monitor-anchor"/><p>グーグルの研究者らが開発した新しいプログラムは、PaLMの言語処理能力を用いている。これにより、聞いた言葉や命令を「引き出しを開ける」や「チップスを拾う」といったロボットが実行できる一連の適切な動作へと翻訳するわけだ。</p><p>ロボットがこなせる動作のライブラリーは、人間がロボットを遠隔操作して物を拾うといった動きを実演して教えるという、別の訓練プロセスで構築されたものだ。ロボットがその環境内において実行できるタスクには限りがあるので、言語モデルが誤解してもおかしな行動をしないようになっている。</p><p>PaLMの言語スキルにより、ロボットは比較的曖昧な命令も理解することができる。例えば、ロボットアームに色の付いたブロックと椀を動かすタスクを課す際に、グーグルのリサーチサイエンティストのアンディ・ツォンは、ロボットに対して「自分の妻が青いブロックで、わたしが緑のブロックであると想像してください。わたしたちを近づけてください」と頼んだ。ロボットは青いブロックを緑のブロックの隣に置いて、その要求に応えるといった具合だ。</p><p>「大規模言語モデルをロボット工学に応用することは、とても刺激的な目標です」と、ロボットの学習やロボットと人間の共同作業を専門とするブラウン大学助教授のステファニー・テレックスは言う。だが、ロボットが実行可能なタスクの幅を広げ、人間の頼みごとをより多くこなせるようにすることは、いまだに「大きな未解決の問題」であるともテレックスは指摘する。</p></div></div></div><div class="GridItem-btGJuM bGetkH grid--item grid-layout__aside"><div class="StickyBoxWrapper-esWinU khiSnd sticky-box"><div class="StickyBoxPrimary-fFrXmn hYtIfu sticky-box__primary"><div class="AdWrapper-fGHyEL ejxrQx ad ad--rail"><div class="ad__slot ad__slot--rail" data-node-id="frmlzh"/></div><div 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summary-item__image"><div data-test="aspect-ratio-container" class="AspectRatioContainer-dibUNr bkfcOt"><div class="aspect-ratio--overlay-container"><picture class="ResponsiveImagePicture-jJVAmS laQysq SummaryItemResponsiveAsset-hNRpTQ ghdATc summary-item__image responsive-image"><noscript><source media="(max-width: 767px)" srcset="https://media.wired.jp/photos/642275ac8374f4462decfc0e/16:9/w_120,h_450,c_limit/undefined 120w, https://media.wired.jp/photos/642275ac8374f4462decfc0e/16:9/w_240,h_450,c_limit/undefined 240w, https://media.wired.jp/photos/642275ac8374f4462decfc0e/16:9/w_320,h_450,c_limit/undefined 320w, https://media.wired.jp/photos/642275ac8374f4462decfc0e/16:9/w_640,h_450,c_limit/undefined 640w" sizes="100vw"/><source media="(min-width: 768px)" srcset="https://media.wired.jp/photos/642275ac8374f4462decfc0e/16:9/w_120,h_450,c_limit/undefined 120w, https://media.wired.jp/photos/642275ac8374f4462decfc0e/16:9/w_240,h_450,c_limit/undefined 240w, 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class="ClampWrapper-btLCQd dOWbxo clamp SummaryItemHedBase-eaxFWE dMnQNe summary-item__hed"><div class="ClampContent-gsqnzT wOLoC"><div class="SummaryItemHedTag-fkbLLv kghXHk" data-testid="SummaryItemHed">「SXSW 2023」で暗号資産やNFTの話題が“影を潜めた”現実から見えたこと</div></div></div></a><div class="SummaryItemBylineWrapper-bcuBna lmhpbV summary-item__byline-date-icon"><div class="SummaryItemBaseByline-cUjuuc fKunmU summary-item__byline"><div class="summary-item__byline__content"><div data-testid="BylinesWrapper" class="BylinesWrapper-hlQKwg jcjUNa bylines"><p class="BylineWrapper-ikeNdb eKFOik byline bylines__byline" data-testid="BylineWrapper" itemprop="author" itemtype="http://schema.org/Person"><span itemprop="name" class="BylineNamesWrapper-dcvWHf ixVaxb"><span data-testid="BylineName" class="BylineName-cMiZYb ksslzD byline__name"><span class="BaseWrap-sc-SJwXJ BaseText-fEohGt BylinePreamble-ihZoJc deUlYF hcfGua jRnpHk byline__preamble">By </span>Eric Ravenscraft</span></span></p></div></div></div></div></div></div></li><li><div class="AdWrapper-fGHyEL ejxrQx RecircMostPopularSummaryItem-brNZRs drQZgc ad ad--aside"><div class="ad__slot ad__slot--aside" data-node-id="wqtalf"/></div></li></ul><div class="RecircMostPopularFooter-dTrmjU dEaYZJ"/></div></div></div><div class="StickyBoxPlaceholder-jMFsiI gEPiHy"/></div></div></div><div class="RowWrapper-efswrE cfsZjl full-bleed-ad row-mid-content-ad"><div class="AdWrapper-fGHyEL ejxrQx ad ad--mid-content should-hold-space"><div class="ad__slot ad__slot--mid-content" data-node-id="jnkeae"/></div></div><div class="GridWrapper-uChIO jPANBg grid grid-margins grid-items-2 ArticlePageChunksGrid-kSsjNq bgKfPe grid-layout--adrail narrow wide-adrail"><div class="GridItem-btGJuM efCVnM grid--item grid-layout__content"><div class="BodyWrapper-cuyyBm jQxFiM body body__container article__body" data-journey-hook="client-content" data-testid="BodyWrapper"><div class="body__inner-container"><p>グーグルのリサーチサイエンティストで今回のプロジェクトに携わっているブライアン・イクターは、グーグルのキッチンロボットがいまだに「たくさんのこと」に混乱することがあると認めている。単に照明を変えたり物を動かしたりするだけで、ロボットは物体を正確に把握できなくなることがあるのだ。このことは、いかに人間にとってはささいなタスクが、ロボットにとって困難なものになりうるかを示している。</p><p>また、このシステムがデモンストレーションでは短い命令にスムーズに対応していたように、複雑な文や命令も同じくらいスムーズに処理できるかどうかは明らかではない。</p><p>AIの進歩によって、すでにロボットはこれまで以上にさまざまなことをこなせるようになった。例えば、工業用ロボットは工場内で製品を認識したり、不良箇所を見つけたりすることができる。また多くの研究者らが、現実世界やシミュレーション内での実践や観察からロボットが学習する方法を模索している。だが、素晴らしく見えるデモンストレーションも、限定的な状況でしかうまくいかないこともよくある話だ。</p><h2>現実世界をよりうまく理解する方法は生まれるか</h2><p>このプロジェクトによって、言語モデルが現実世界をよりうまく理解する方法が生まれるかもしれないと、イクターは語る。AIの言語ソフトウェアが過ちを犯すのは、常識的な知識を欠いていることが要因である場合が多い。人間なら、そうした知識を用いて言語の曖昧さを理解できるはずだ。</p><div data-attr-viewport-monitor="inline-recirc" class="inline-recirc-wrapper inline-recirc-observer-target-3 viewport-monitor-anchor"/><p>「言語モデルは現実世界をまったく経験したことがないのです。言語モデルは、ただインターネットで読んだ言葉の集合的なデータを反映しているだけなのです」と、イクターは言う。</p><p>グーグルの研究プロジェクトが実際の製品になるまでには、まだまだ時間がかかるだろう。だが、グーグルのライバル企業の多くが最近になって家庭用ロボットに新たな関心を抱いている。</p><p>アマゾンは21年9月に<a href="https://wired.jp/2021/10/05/plaintext-amazon-astro-dog-that-will-have-its-day/">「Astro」という家庭用ロボット</a>を披露したが、これはグーグルのロボットと比べてかなり限られた能力しかもっていないものだ。そしてアマゾンはこの8月、人気のロボット掃除機「ルンバ」の開発元である<a href="https://wired.jp/article/amazon-irobot-roomba-acquisition-data-privacy/">アイロボットを買収すると発表</a>している。</p><p>イーロン・マスクはテスラがヒト型ロボットを開発すると約束しているが、そのプロジェクトに関する詳細はあまり多くは明かされていない。それは製品の発表というよりは、人材募集のためのピッチのようなものなのかもしれない。</p><p>（<a href="https://www.wired.com/story/google-robot-learned-to-take-orders-by-scraping-the-web/" target="_blank">WIRED US</a>/Edit by Daisuke Takimoto）</p><p>※『WIRED』による人工知能（AI）の関連記事は<a href="https://wired.jp/tag/ai/">こちら</a>。ロボットの関連記事は<a href="https://wired.jp/tag/robot/">こちら</a>。</p><hr/><div role="heading" aria-level="3" class="heading-h3"><strong>Related 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fAvqua asset-embed__asset-container"><span class="SpanWrapper-kFvfwm ezrazJ responsive-asset AssetEmbedResponsiveAsset-fBMxW gbuarq asset-embed__responsive-asset"><picture class="ResponsiveImagePicture-jJVAmS laQysq AssetEmbedResponsiveAsset-fBMxW gbuarq asset-embed__responsive-asset responsive-image"><noscript><img alt="グーグルの新しいAIロボットは、“プログラミングなし”で人間の命令に対処する" class="ResponsiveImageContainer-dkDswF jdxiQR responsive-image__image" src="https://media.wired.jp/photos/61de2e1c705e9457064ef62e/master/w_1600%2Cc_limit/sz_og.png" srcset="https://media.wired.jp/photos/61de2e1c705e9457064ef62e/master/w_120,c_limit/sz_og.png 120w, https://media.wired.jp/photos/61de2e1c705e9457064ef62e/master/w_240,c_limit/sz_og.png 240w, https://media.wired.jp/photos/61de2e1c705e9457064ef62e/master/w_320,c_limit/sz_og.png 320w, https://media.wired.jp/photos/61de2e1c705e9457064ef62e/master/w_640,c_limit/sz_og.png 640w, https://media.wired.jp/photos/61de2e1c705e9457064ef62e/master/w_960,c_limit/sz_og.png 960w, https://media.wired.jp/photos/61de2e1c705e9457064ef62e/master/w_1280,c_limit/sz_og.png 1280w, https://media.wired.jp/photos/61de2e1c705e9457064ef62e/master/w_1600,c_limit/sz_og.png 1600w" sizes="100vw"/></noscript></picture></span></div></figure></a></div><inline-embed name="align-center" attrs="[object Object]" childtypes="" contenttype="callout:align-center"><p><strong>毎週のイベントにも無料参加できる！</strong><br/><strong>『WIRED』日本版のメンバーシップ会員募集中！</strong></p></inline-embed><p>次の10年を見通すためのインサイト（洞察）が詰まった選りすぐりのロングリード（長編記事）を、週替わりのテーマに合わせてお届けする会員サーヴィス「WIRED SZ メンバーシップ」。毎週開催のイベントにも無料で参加可能な刺激に満ちたサービスは、無料トライアルを実施中！<a href="https://wired.jp/membership/about">詳細はこちら</a>。</p><hr/></div></div></div><div class="GridItem-btGJuM bGetkH grid--item grid-layout__aside"><div class="StickyBoxWrapper-esWinU khiSnd sticky-box"><div class="StickyBoxPrimary-fFrXmn hYtIfu sticky-box__primary"><div class="AdWrapper-fGHyEL ejxrQx ad ad--rail"><div class="ad__slot ad__slot--rail" data-node-id="yoz7wf"/></div><div 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