🚀 VivaTech 2024 : Keynote - AI for Good

Amazon Web Services France
3 Jun 202433:01

Summary

TLDRこのスクリプトでは、テクノロゞヌ界においお倧きな成功を収めた䞀方で、責任を負うべき問題にも盎面しおいるず語り、AIの善甚が瀟䌚の難しい問題解決ぞの鍵になる可胜性を探求したす。AmazonのCTOであるWerner Vogelsが、過去3,000幎間の哲孊者から珟代の革新技術たでを振り返り、AIが持぀可胜性ずその瀟䌚的圱響に぀いお語りたす。たた、テクノロゞヌを通じお䞖界最隟問題を解決する䌁業の事䟋を玹介し、AIが持続可胜な瀟䌚を実珟するための重芁な圹割を挔じるべきだず䞻匵しおいたす。

Takeaways

  • 🌟 テクノロゞヌの成功ず芏暡は倧きな責任に぀ながりたす。テクノロゞヌを良い目的に応甚するこずが重芁です。
  • 🔮 ゞェネラティブAIは文化に敏感になるよう進化し、健康技術、女性向けの技術が発展するず予想されおいたす。
  • 🛠 AIコヌディング支揎は開発者を助けるために存圚し、より迅速に䜜業を進めるのに圹立ちたす。
  • 🎓 教育はテクノロゞヌの進化に合わせお進化する必芁がありたす。
  • 🀖 叀代ギリシャ哲孊者から人工知胜の思想は長い歎史を持っおおり、珟代の自動化技術に぀ながっおいたす。
  • 🧠 脳は思考ず哲孊を制埡するず考えられおいたしたが、コンピュヌタの登堎ず共にそれらの機胜を暡倣するAIの研究が始たりたした。
  • 🌐 テクノロゞヌは持続可胜性を確保しながら新しい技術を提䟛する必芁がありたす。新しいタむプのチップやプログラミングむンタヌフェヌスがそれに貢献しおいたす。
  • 🌱 AIは今珟圚も倚くの問題を解決するための有効な手段ずしお機胜しおおり、ゞョン・マッカヌシヌの蚀葉からむンスピレヌションを埗おいたす。
  • 🌍 䞖界的な課題に察するテクノロゞヌの応甚が求められおおり、特に若い䌁業が積極的に取り組んでいたす。
  • 🐟 食糧䞍足や医療アクセスの確保など、持続可胜な開発目暙(SDGs)に関連する問題に察するテクノロゞヌの解決策が重芁です。
  • 🚑 テクノロゞヌは医療分野でも重芁な圹割を果たしおおり、遠隔地での医療支揎や医療機噚の開発に貢献しおいたす。
  • 📈 デヌタはAIを通じお良い意思決定を行うために䞍可欠であり、オヌプンデヌタの提䟛はテクノロゞヌが瀟䌚貢献をするための鍵です。
  • 🛡 AIは倫理的にも良いデヌタず共に働くべきであり、テクノロゞヌを通じお瀟䌚の最も困難な問題を解決する力を持っおいるこずを瀺しおいたす。

Q & A

  • スピヌカヌは誰を玹介しおいたすか

    -スピヌカヌはAmazonのCTOであるWerner Vogelsを玹介しおいたす。

  • スピヌカヌが蚀及した「AI for good」ずは䜕を指したすか

    -「AI for good」ずは、人工知胜を甚いお瀟䌚の難しい問題を解決しようずする取り組みを指しおいたす。

  • スピヌカヌが述べたゞェネラティブAIの進化のステップは䜕段階ありたすか

    -スピヌカヌはゞェネラティブAIがマラ゜ンの最初の3ステップにしかいないず述べおおり、これからの圱響を瀺唆しおいたす。

  • スピヌカヌが提唱する「AI for now」ずは䜕を意味したすか

    -「AI for now」ずは、珟圚利甚可胜なAI技術を掻甚し、䞖界最隟関の問題を解決するアプロヌチを意味しおいたす。

  • スピヌカヌが玹介したナニセフが取り組んでいる問題ずは䜕ですか

    -スピヌカヌはナニセフが蟲業における小さな蟲家の問題に取り組んでいるず玹介しおおり、圌らが銀行からロヌンを埗られない問題や収穫量のデヌタ管理システムを構築しおいるず説明しおいたす。

  • スピヌカヌが挙げたAIを掻甚した蟲業における成功事䟋ずは䜕ですか

    -スピヌカヌは、囜際米皮質研究所がAIを甚いお米の品皮を管理し、バックログを排陀する成功事䟋を挙げおいたす。

  • スピヌカヌが玹介した健康ケア分野におけるAIの掻甚事䟋には䜕がありたすか

    -スピヌカヌは、ワクチンを離島などに自動ドロヌンで届ける取り組みや、医療珟堎でのAIを掻甚した予枬モデル、新生児の脳損傷を早期に怜出する技術などがありたす。

  • スピヌカヌが匷調したデヌタの重芁性ずはどのようなものですか

    -スピヌカヌは、AIが良奜な分析を行うためには良奜なデヌタが必芁ですず匷調しおおり、デヌタはAIが機胜するための重芁な資源であるず述べおいたす。

  • スピヌカヌが玹介した「Tech to the rescue」はどのようなプログラムですか

    -「Tech to the rescue」はテクノロゞヌを甚いお瀟䌚貢献を目指すプログラムであり、スピヌカヌはこのプログラムに関連しおCTOフェロヌシップを発衚しおいたす。

  • スピヌカヌが匕甚したJohn McCarthyの蚀葉ずは䜕ですか

    -スピヌカヌはJohn McCarthyの蚀葉を匕甚し、「それが機胜するようになるず、それをAIず呌ばなくなる」ずいう意味を持ち、AI技術が成熟するず䞀般には認識されなくなるず述べおいたす。

Outlines

00:00

🌟 テクノロゞヌの責任ず将来予枬

スピヌカヌはテクノロゞヌの進歩ずその責任に぀いお語り、2024幎の予枬を玹介したす。テクノロゞヌは成功を収めおきた反面、広範な責任を負うべきだず指摘。AIの応甚に぀いお語り、特にgenerative AIの進歩に぀いお觊れたす。たた、教育の進化やテクノロゞヌの持぀可胜性に぀いおも蚀及しおいたす。

05:02

🀖 AIの歎史ず珟代の技術動向

AIの歎史を振り返り、叀代ギリシアの哲孊者から珟代のAI技術たでを抂説。過去数十幎間のAIの進歩ず、特に最近のTransformersやファクタヌデヌタベヌスなどの技術革新に぀いお解説。AI技術が䞖界を倉える可胜性に぀いおも觊れおいたす。

10:05

🌱 テクノロゞヌを掻甚した持続可胜な開発

スピヌカヌはテクノロゞヌを䜿い、持続可胜な開発目暙(SDGs)を達成するビゞネスの䟋を玹介。特に、蟲業におけるテクノロゞヌの掻甚ず、小芏暡蟲家ぞの金融サヌビスの提䟛に぀いお語りたす。

15:07

🍚 食糧生産のテクノロゞヌ

䞖界食糧問題にテクノロゞヌがどのように貢献できるかに぀いお説明。特に、米の研究ず栜培の最適化、蟲薬の䜿甚の最適化、そしお逊殖業におけるデヌタ分析の掻甚に぀いお詳述しおいたす。

20:08

🏥 テクノロゞヌを掻甚した医療ぞのアクセスの拡倧

医療ぞのアクセスを拡倧するテクノロゞヌの掻甚に぀いお話したす。ワクチンの自動ドロヌン配送、医療費甚の削枛、新生児の脳損傷の早期発芋など、テクノロゞヌが医療分野で果たす圹割に぀いお解説しおいたす。

25:10

📊 デヌタの重芁性ずテクノロゞヌの圹割

デヌタの重芁性ずテクノロゞヌがそのデヌタを掻甚する䞊で果たす圹割に぀いお語りたす。デヌタの共有ず開攟性、そしおAIがデヌタから䟡倀を匕き出す方法に぀いお解説しおいたす。

30:12

🛡 AIによる瀟䌚問題ぞの取り組み

AI技術を甚いお瀟䌚問題に取り組む方法に぀いお話したす。特に、子どもの性虐埅や人暩䟵害に察凊するテクノロゞヌの掻甚に぀いお詳述。AIが瀟䌚貢献に向けお重芁な圹割を果たすこずに぀いお匷調しおいたす。

🌐 テクノロゞヌを掻甚した瀟䌚貢献ぞの道

テクノロゞヌを掻甚しお瀟䌚貢献を目指す道に぀いお話したす。AI for Change MakersプログラムやCTO Fellowshipの玹介を行い、テクノロゞヌが持続可胜な瀟䌚貢献に向けお重芁な圹割を果たすこずを瀺しおいたす。

Mindmap

Keywords

💡テクノロゞヌの責任

テクノロゞヌの責任ずは、テクノロゞヌを利甚しお瀟䌚の様々な問題を解決する責務を指したす。ビデオでは、テクノロゞヌが成功を収めおきた䞀方で、持続可胜性や瀟䌚問題ぞの察応など、テクノロゞヌが担うべき広範な責任に぀いお議論されおいたす。特に、人口増加による食糧䞍足や医療ぞのアクセスの䞍平等ずいった問題にテクノロゞヌを適甚する可胜性が瀺されおいたす。

💡生成的AIGenerative AI

生成的AIずは、新しいコンテンツを創造的に生成するこずができる人工知胜のサブ分野です。ビデオでは、生成的AIが文化的意識を持぀ようになるず予枬されおおり、その進化が今埌のテクノロゞヌの進歩に重芁な圱響を䞎えるずされおいたす。たた、AIコヌディングアシスタンスが開発者の仕事の効率化に寄䞎するずも觊れられおいたす。

💡文化的な意識

文化的な意識ずは、個々の文化背景や䟡倀芳を理解し、それに基づいお適切な行動を起こす胜力です。ビデオでは、新しい倧きな蚀語モデルが文化的意識を持぀ようになるず予想され、それがAIの文化的な適応性や倚様性に寄䞎するず瀺唆されおいたす。

💡テクノロゞヌの持続可胜性

テクノロゞヌの持続可胜性ずは、環境保護ず経枈発展のバランスを保ちながら、長期的な芖点でテクノロゞヌを開発・利甚する考え方です。ビデオでは、新しいタむプのチップやプログラミングむンタヌフェヌスが持続可胜なテクノロゞヌ創造にどのように貢献するのかが語られおいたす。

💡AIの教育ぞの圱響

AIの教育ぞの圱響ずは、人工知胜が教育システムに及がす倉化を指したす。ビデオでは、教育がテクノロゞヌの進化のペヌスに合わせお進化する必芁があるず匷調されおいたす。これは、教育者がテクノロゞヌを掻甚しお新しい教育方法を創造する可胜性を瀺唆しおいたす。

💡瀟䌚圱響ビゞネスSocial Impact Business

瀟䌚圱響ビゞネスずは、䌁業掻動を通じお瀟䌚問題を解決し、益利を䞊げるこずの䞡立を目指すビゞネスモデルです。ビデオでは、蟲家に察するアむデンティティの提䟛やデヌタの掻甚によっお、蟲家が持続可胜な生掻を送れるようにする䌁業䟋が玹介されおいたす。

💡人工知胜の歎史

人工知胜の歎史ずは、人工知胜技術の発展ずその背埌にある哲孊的な思想の倉遷を指したす。ビデオでは、叀代ギリシャの哲孊者から珟代のAI技術たで、人工知胜の発展に寄䞎した人々ずアむデアが振り返られおいたす。

💡自然蚀語凊理NLP

自然蚀語凊理ずは、人工知胜が人間の蚀語を理解し、生成する胜力を指したす。ビデオでは、自然蚀語凊理がテクノロゞヌを掻甚しお瀟䌚問題を解決する䞊で重芁な技術ずなっおいるず匷調されおいたす。

💡デヌタの民䞻化

デヌタの民䞻化ずは、デヌタにアクセスする暩利を広く持たせ、情報の平等な利甚を促進するこずを指したす。ビデオでは、公開されたデヌタセットがテクノロゞヌを掻甚しお瀟䌚貢献にどのように圹立぀かが語られおおり、その重芁性が匷調されおいたす。

💡人工知胜による医療ぞの貢献

人工知胜による医療ぞの貢献ずは、AI技術が医療分野での問題解決にどのように圹立぀かを指したす。ビデオでは、遠隔地垯ぞのワクチンの配送や、医療問題の早期発芋・介入など、AIが医療に䞎える圱響が玹介されおいたす。

💡テクノロゞヌの持続可胜性

テクノロゞヌの持続可胜性ずは、環境保護ず経枈発展のバランスを保ちながら、長期的な芖点でテクノロゞヌを開発・利甚する考え方です。ビデオでは、新しいタむプのチップやプログラミングむンタヌフェヌスが持続可胜なテクノロゞヌ創造にどのように貢献するのかが語られおいたす。

Highlights

Tech Guru's role in Amazon and predictions for 2024 including culturally aware generative AI, advanced health technologies, AI coding assistance, and evolving education.

The importance of technologists taking responsibility for solving the world's hardest problems with technology.

John McCarthy's quote on AI and the evolution of generative AI's cultural awareness.

Aristotle and Plato's early discussions on automation and humanoid robots 3,000 years ago.

The shift from symbolic AI to emulating human senses with technology as a path to success in AI.

Amazon's fulfillment centers utilizing 175 autonomous robots that enhance operational efficiency.

The transformative impact of underlying technologies like Transformers and factor databases on generative AI.

The early stages of generative AI's impact compared to the marathon of technological evolution.

The necessity for sustainable technology development, including new chips and programming interfaces.

AI's current and potential applications in solving global challenges, such as population growth and food security.

The UN's sustainability development goals as a framework for segmenting technological efforts for social good.

Startups like Hara providing identity and data to smallholder farmers for economic sustainability.

The International Rice Research Institute's use of AI to manage rice diversity and reduce backlog in seed storage.

Precision AI's application in agriculture for efficient and environmentally friendly crop management.

Aquabyte's use of computer vision to monitor fish health and contribute to global food sustainability.

The importance of democratizing data access for tackling global issues like healthcare and natural disasters.

Foreign's innovative use of AI in combating child sex trafficking and abuse through image and data analysis.

The symbiosis between good AI and good data, emphasizing the need for quality data for effective AI solutions.

The launch of the now go build CTO Fellowship to support innovators in technology for good.

McCarthy's quote on AI as a reminder to focus on current technologies' practical applications to solve today's challenges.

Transcripts

play00:00

now you may not want to run away so fast

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because we have

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brought a very very inspiring Tech Guru

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he plays such an important role in

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Amazon so our next speaker is a science

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research an expert in computer science

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and even a tech startup founder today

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he's one of the key Innovation figures

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behind Amazon as I was saying and he's

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well known for his predictions what did

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he say for

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2024 well new large language models will

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make generative AI culturally aware fem

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Tech will take off with Advanced Health

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Technologies for women that's something

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I'm certainly looking for and waiting

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for AI coding assistance will help

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developers work faster by taking care of

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he heavy lifting and fourth ucation will

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evolve to match the pace of evolving

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Tech we missed him last year but he was

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finally able to come this year please

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allow me to

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welcome verer Bogel CTO of

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[Applause]

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[Music]

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[Applause]

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Amazon good afternoon Paris um um

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I think if we look back at the past two

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three decades we as technologist and as

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u in digital technology but also in any

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type of innovation we've had great

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success massive successful companies

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millionaires billionaires but I do

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believe that with that success and scale

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comes a really broad responsibility at

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the same time if you look at the issues

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that we're facing in the world today we

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as technologists have a responsibility

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to try to solve a number of the hardest

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problems that we see in this world and

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yes of course you you think applying

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technology for good uh these days of

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course you immediately start to think

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about how can we apply AI for good yeah

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and there's a huge amount of excitement

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around generative Ai and things like

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that but I would like to hold there and

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actually go back to a quote of one of

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the famous founders of artificial

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intelligence John McCarthy who said as

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soon as it works we don't call it AI

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anymore there's a real big body of work

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out there that is actually AI but that

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we don't call AI anymore because we only

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talk about AI in terms of looking

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forward kind of things that we could be

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doing and more importantly I think it is

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also to realize that there is a massive

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body of work that works really really

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well we just don't give it that new

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stamp of AI that AI that we've seen come

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to life in the past say year and a half

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yeah and it's quite successful but I

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think to really understand this we need

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to go back in time actually quite far in

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time 3,000 years ago Aristotle and Plato

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were already discussing about the use of

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intelligence to solve particular tasks

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by

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Automation and actually even to the

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point that play in the Republic actually

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described a number of humanoid robots

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that were performing household tasks

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remember this is 3,000 years ago that

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they were already sort of envisioning

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what we actually have been coming to

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life in the past 10 20

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years now most of those philosophies and

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if you actually go back to the past

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2,000 years those have who have been

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thinking about this artificial

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intelligence

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they all still were thinking about that

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everything should be controlled by the

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brain yeah and that actually any

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thinking and any philosophy about that

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is actually driven by the brain now

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brain was actually just they thought it

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was actually manipulating symbols so if

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the first arrival of computers who were

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also manipulating symbols we really got

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to the point immediately to start to

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think about can these machines actually

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think yeah alen touring probably our our

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Century's most famous uh computer

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science

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philosopher really had this first

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question of can machines think and

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actually the famous touring test is made

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by Ellen T can you distinguish answers

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from machine from those are given by a

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human actually the word artificial

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intelligence at this moment doesn't

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exist yet it is for the first time

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coined in 1956 in a workshop in dmth

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with McCarthy there Minsky and quite a

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few others but still they were thinking

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that artificial intelligence should be

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something driven from the top down

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basically thinking about sort of really

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how does the brain

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control the rest of the body that didn't

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go anywhere yet I think eventally

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symbolic AI as we would call it results

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in automated reasoning and many of the

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other things that are really successful

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areas now but that was not what drove

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the success of AI in the earlier days

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what drove the success was really

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thinking different by the way one of the

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things that we were building in those

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days and yes that is me I had more hair

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in those days I build a few of those in

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the 80s using Prolo but what really

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started to make a major change is the

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people that were actually working on

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robotics they were saying you know what

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why don't we do it top down from the

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brain down but why don't we pick

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individual senses of humans and try to

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emulate those in technology yeah Vision

play06:08

sound speech all of those and you really

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see that we have made great strides

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there because that actually was a path

play06:17

to

play06:18

success this is a fulfillment center of

play06:21

amazon.com we have about 175 of those

play06:24

around the world and in each of those

play06:27

you may see about 30,000 robots running

play06:31

around and we can only do this because

play06:34

they can be autonomous and they can

play06:37

understand different senses not maybe

play06:40

the robots are not necessarily speech

play06:42

enabled but they definitely have liar

play06:44

and radar and other senses that we as

play06:46

humans even don't

play06:50

have now what we've seen over time is

play06:53

that we got all these different steps in

play06:55

technologies that slowly advance and

play06:58

more and more of the underlaying

play06:59

Technologies Drive the kind of things

play07:01

the solutions that we can build with it

play07:04

and of course the last one the most

play07:05

recent one is that of Transformers

play07:08

together Factor databases which Drive

play07:10

the generative AI world at this moment

play07:14

now everybody's talking about large

play07:16

language models and I will skip that for

play07:17

the most part because I think many other

play07:20

people will be talking about that

play07:22

although it is actually getting a lot

play07:24

closer to Plato's dream and the

play07:27

assistance that it can give in efficieny

play07:30

and areas like that is

play07:32

unparalleled I do believe generative AI

play07:36

is just at the first three steps of a

play07:39

marathon in terms of impact on the

play07:42

solutions that we're building it's very

play07:44

very early days and I also think that is

play07:48

not the end of the implications of AI

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we'll build newer underlaying

play07:54

Technologies which makes other advances

play07:56

again possible which ones those are

play07:59

we'll need to see in the coming three to

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four years because that actually the

play08:03

cycle in which we see underlaying

play08:05

Technologies improving together with the

play08:07

hardware that runs in lockstep and

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especially the hardware part is

play08:12

extremely important because if you are

play08:15

concerned about technology for good you

play08:17

also need to make sure that your

play08:19

technology is created in the most

play08:22

sustainable way possible yeah new types

play08:25

of chips new types of programming

play08:28

interfaces will allow us to be

play08:31

sustainable and at the same time

play08:34

actually deliver new

play08:36

technologies so I want to go to the area

play08:39

of what I would call AI for now ai for

play08:43

now is a massive body of work it

play08:46

actually all works which like John mcari

play08:50

we then say you know what we don't call

play08:52

it AI anymore maybe maybe the infers of

play08:56

uh mti's quote is that we call it AI if

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it doesn't really work yet yeah but if

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you think about all that work that has

play09:07

been done you know can we actually use

play09:10

this technology to solve some of the

play09:13

world's hardest

play09:15

problems now so what are those I mean

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the present is really important let's

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take all the technologies that we have

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that we've created and that make many of

play09:25

our businesses successful but at the

play09:27

same time we need to take the

play09:29

responsibility to use this technology to

play09:32

solve some of the world's hardest

play09:35

problems and I wasn't really aware of

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that until you know quite some time ago

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when I realized that you know some of

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the numbers that we're going to see in

play09:46

this world the growth of our population

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is disastrous for the current Ser

play09:53

circumstances you think about that in

play09:55

the coming 20 to 30 years we'll see the

play09:58

population grow by another 2 billion

play10:01

people that's an increase of 25% in the

play10:05

population how are we going to feed them

play10:08

I'm going to make sure that they're

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economically sustainable problems that

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even at this moment we already have so

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what can we do in terms of tech

play10:18

technology and I had been thinking about

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that for quite a while how to organize

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that and I think the UN sustainability

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development goals are a right way to

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sort of segment the kind of work that

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has been going

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on and so there's these are the goals

play10:35

set by the UN but let's pick out a few

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of them and look at how young businesses

play10:41

especially are trying to solve these

play10:44

problems I'd be very fortunate indeed in

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the past 10 years 10 15 years to travel

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the world and meet many startups around

play10:53

the world one of the things that I

play10:55

started to see is outside let's say the

play10:58

main master world that many splots were

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not looking at becoming the next unicorn

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they were really looking at solving some

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of the world's hardest problems and very

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fortunate at some moment to take a TV

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crew along to uh create this TV series

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called now go build that actually

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highlights these companies that solve

play11:20

some of the world's hardest problems

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that you can find it on Prime video and

play11:25

on aws's website and and on YouTube but

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you know if you're interested in

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companies that are really solving hard

play11:33

problems this is a really great TV show

play11:36

but it also motivated me to look at sort

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of how can we use technology for

play11:43

good so a really great example is this

play11:45

actually one of my first episodes that I

play11:48

did was with a company called Hara out

play11:50

of

play11:51

Jakarta and if you think about Indonesia

play11:55

or most of Southeast Asia where there

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are many small hold of farmers

play12:00

none of them have an identity that means

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if you don't have an identity you can't

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go to a bank for a loan you have to go

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to the Village loone shark who charges

play12:09

60% by the way yeah to get some money to

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buy your seeds for your next cop it

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means that half of your crop is already

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sold gone out of your hands before you

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even start growing

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it so they built this system that gives

play12:26

um Farmers an identity measure their lot

play12:29

of land now measure the yield of that

play12:32

land and then keep that data together to

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actually make that available to

play12:37

organizations that are interested in

play12:39

this data the first organization of

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course that is interested in this are

play12:44

the banks and why is that is because

play12:49

these these Farmers they're not looking

play12:52

for a million dollar loans they're

play12:55

looking for 10 to $100

play12:58

loans and each of those actually have a

play13:03

100% repay rate so by just make doing

play13:06

this by giving this farmers in identity

play13:09

by associating data with them about sort

play13:12

of the yield of that plot of land all of

play13:15

these financial institutions are eager

play13:18

to support these Farmers something they

play13:21

would never had before so now they can

play13:24

actually have a sustainable living why

play13:25

did this company actually started

play13:27

tackling this problem because they were

play13:29

children of small holder Farmers

play13:32

themselves they understood the problems

play13:34

that their parents that their

play13:36

communities are going through and

play13:38

decided to actually take action and

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build a business out of that now this is

play13:42

maybe what we call a social impact

play13:45

businesses actually not just you're

play13:48

trying to do good but have a good living

play13:51

and a good business at the same time and

play13:53

this is possible because it's not the

play13:55

farmers that actually are paying har it

play13:58

is the BS and the government

play14:00

institutions that want to have access to

play14:01

the data that are actually making this

play14:04

business very

play14:08

successful so if you think about one of

play14:12

the biggest problems that we'll have

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with two billion more people on this

play14:15

planet is access to

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food and if you look at sort of the most

play14:21

important staple of food is out of rice

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well over 50% of the planet is dependent

play14:26

on Rice as the main food uh

play14:30

Source there's an organization in the

play14:32

Philippines called the international

play14:34

rice Research Institute they are

play14:37

absolutely brilliant if you want to know

play14:39

more about sort of food research and

play14:41

things like that absolutely look up what

play14:44

these guys are doing they have the

play14:46

largest collection of rice DNA in their

play14:50

freezers 200,000 strands of different

play14:53

types of rice by the way they're also

play14:56

backing it up in the north in Norway in

play14:59

very cold areas now their biggest

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challenge is actually taking in all of

play15:04

these rice and then uh sorting them

play15:06

looking at which R seats are actually

play15:08

useful or not and that is actually such

play15:11

a high level that there's a huge backlog

play15:16

in actually getting these seeds into

play15:19

storage this is one of the simple

play15:22

problems that you can easily solve with

play15:25

with uh object recognition just use a

play15:28

small video camera train it and see

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which wise uh seats can actually be

play15:34

useful or not the longer these seats

play15:37

actually kept out of storage the bigger

play15:39

the problem is for them because they

play15:40

have to discard the

play15:42

R so very simple system little camera

play15:46

determining which R seats are are are

play15:48

good and immediately by installing this

play15:51

system which is not a magic system but

play15:54

uses Rock Solid proven tech technology

play15:57

they immediately eliminated all of the

play15:59

backlog and they do a lot of research

play16:02

there many different types you have to

play16:04

imagine that most of the farmers that

play16:06

they're targeting actually don't have

play16:08

cell phones they can't read or write

play16:12

often they do can speak into a phone

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though so they developed a system for

play16:18

which farmers can actually call into

play16:21

describe their patch of land and where

play16:23

it is and then the system will give him

play16:26

advice about how much fertilizer to buy

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and when to apply it because most of

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these Farmers have grown up with not

play16:33

much let's say scientific knowledge

play16:36

about how much fertilizer to use they

play16:38

just buy a lot and apply it to their

play16:40

land the same goes for

play16:42

pesticides and so here the key is that

play16:46

you're able to actually use voice as an

play16:49

access to digital systems remember

play16:51

there's a very large part of this world

play16:54

where reading and writing is not common

play16:58

and as such if you want to give those

play16:59

people access to digital Technologies

play17:03

voice is critical in all of

play17:07

that if you think about indeed about

play17:10

planting you know most of uh not only

play17:14

fertilizer but most of the biochemicals

play17:16

used to actually protect the plant are

play17:19

often just being sprayed yeah Precision

play17:22

AI is one of these companies that

play17:24

actually uses drone with very high

play17:27

quality imagery to pick out individual

play17:30

wheat plants in rice plantations so that

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they can take them out and do

play17:35

individualized Precision management of

play17:38

these crops instead of just spreading it

play17:41

over everything the runoff often both of

play17:44

wellas um uh fertilizer as well as these

play17:48

biochemicals often run off in the rivers

play17:51

and actually create massive pollution

play17:54

and creation enormous amount of algae

play17:56

for example in these Rivers

play18:01

now that's rice yeah so what about

play18:06

protein we kind of all like protein and

play18:08

it's an important part of our

play18:10

diet

play18:11

right and so definitely if the world is

play18:14

growing how can we make sure that 25%

play18:17

more people have access to

play18:19

protein the most efficient way to do

play18:22

that is to actually create fish because

play18:25

one kilo of fish feed results in one

play18:27

kilo of fish

play18:29

if you want to do it with cattle you

play18:31

have to give it seven kilos of feet

play18:33

before you create one kilo of

play18:35

protein and not only that cattle farming

play18:38

has had a disastrous impact on many of

play18:41

our surrounding

play18:43

areas so there's a company uh in Norway

play18:47

where I met them first called

play18:50

aquabyte and they make they have these

play18:52

massive pens in the fs in Norway in each

play18:55

of those pens there is about 200,000

play18:57

salmon in there

play18:59

and they get in there when they're

play19:00

really small and what they do they use

play19:03

computer vision and object detection to

play19:06

track the individual fish their growth

play19:09

but more importantly their health

play19:11

because one fish with lice in that pen

play19:15

will infect you other 200,000 and you'll

play19:18

have to destroy them all and so they

play19:20

built this system this Vision detection

play19:23

system together with things like sine

play19:25

levels and other iot levels to actually

play19:28

keep this fish healthy and so this might

play19:31

be one of the approaches that are being

play19:33

taken to see whether we can at scale

play19:36

grow sufficient protein to solve sort of

play19:39

the upcoming disaster of not being able

play19:42

to feed everyone in this

play19:45

world they have created these massive

play19:48

data stores already about how to

play19:50

actually sort of track fish and make

play19:53

that available

play19:56

worldwide so if you look at some of the

play19:58

other

play19:59

um areas in the sustainability goals

play20:02

it's of course healthare how can we

play20:04

ensure health care for 25% larger

play20:08

population if we cannot even do it

play20:11

today we as technologists are the ones

play20:14

that actually should stand up and

play20:16

actually find solutions to solve the

play20:18

healthcare problems in this world or

play20:21

access to

play20:22

healthcare now if you uh think about at

play20:27

this moment at 202 one I think is a

play20:29

report by the World Health Organization

play20:32

who estimates that about 2 billion real

play20:35

populated two billion people living in

play20:38

rural areas do not have access to health

play20:40

care at all things that we consider to

play20:44

be just a fundamental right to hex to

play20:47

it uh suero is one of these companies

play20:50

that are trying to solve how to get

play20:53

vaccines into rural areas these drones

play20:57

they're not piloted they're completely

play20:59

automated drones are one of these

play21:00

examples of oldfashioned a just working

play21:03

really well a drone will easily have

play21:07

about a 100 Sensers on it yeah lighter

play21:10

radar Vision you need to be able to

play21:13

detect obstacles in flight because

play21:16

remember these these drones need to be

play21:18

autonomous there is no there's nobody

play21:21

sitting at the the joystick to get this

play21:23

to a place where you want and then when

play21:25

you arrive somewhere you know you don't

play21:27

want to hit the dog that are there nor

play21:30

do you want suddenly this fishing line

play21:32

that actually was was out there and want

play21:34

to run into that so a lot of autonomous

play21:36

work using AI is being done to make sure

play21:40

that we can deliver vaccines into areas

play21:43

that have never actually reached any

play21:46

seen any healthc care professional at

play21:49

all and if you look at that I think it

play21:52

was about a few years ago that I went to

play21:56

Brazil and in Brazil still you know

play21:59

there's about 200 million citizens and

play22:02

they consider that about 150 million of

play22:05

them are medically homeless Dr Kila uses

play22:09

AI Technologies to actually first of all

play22:12

provide very lowcost Health Care by

play22:15

looking at where the places in the world

play22:17

they can buy generic medicine for the

play22:19

lowest cost but also make use of AI over

play22:22

patient records to start to predict what

play22:25

kind of health problems people may

play22:27

exhibit and try to intervene at a much

play22:29

earlier phase

play22:31

there

play22:33

CICS isra in an Ireland is very

play22:36

interesting company it actually turns

play22:38

out that about was it one in every 200

play22:41

newborns have some degree of brain

play22:44

injury and these brain injuries are

play22:46

often not detected until months or years

play22:50

after birth yet with a very simple test

play22:54

they can give a that we now give babies

play22:56

a test whether they can hear well

play22:58

whether they can see well by just

play23:00

placing a little cap on their head with

play23:02

in

play23:03

EEG and then actually can detect these

play23:06

brain uh these brain damages very early

play23:10

and immediately start to try and

play23:12

actually tackle those particular

play23:16

problems yeah so they make use of EG

play23:19

data now why is that so important why

play23:21

can't we just do it regularly it turns

play23:22

out EEG data of babies is radically

play23:26

different than that from us as uh as

play23:32

adults now in quite a few examples I

play23:36

gave you computer vision plays an

play23:38

important role and why because it makes

play23:41

nicer slides but there's a whole range

play23:44

of other Tech technologies that work

play23:46

really well and are the foundation of

play23:49

how we actually applying AI for good

play23:53

where it's natural language processing

play23:55

speech translation all of these work

play23:58

well know these are Technologies but you

play24:00

don't even think about anymore look at

play24:02

for detection a company like like Amazon

play24:05

we literally sit on billions of orders

play24:07

from the past we know which ones were

play24:09

forland and so we can build a model out

play24:11

of that a new order comes in it gives us

play24:13

the score what the likelihood is that

play24:15

this is also a forent order we don't

play24:18

kill the orderan actually the order then

play24:21

goes off to a human

play24:22

investigator remember that AI predicts

play24:27

but we make make decisions it is still

play24:30

not the case that these Technologies

play24:32

decide for us we are the ones that are

play24:35

in charge and we are the ones that are

play24:37

deciding where these tools just help us

play24:40

get to that

play24:43

decision now in all of this know we

play24:46

cannot have good insights driven by AI

play24:50

if we do not have good data and data is

play24:53

crucial in all of this now the problem

play24:57

is often that data is a privileged asset

play25:02

companies holding them close to

play25:03

themselves they're not opening it up

play25:06

although there's a lot of efforts going

play25:08

on to create more and more open data

play25:10

sets having access to public data is

play25:14

crucial for those companies that want to

play25:16

do good in this

play25:18

world a good example there is one of the

play25:21

other now go built episod that I did uh

play25:23

which was in the Philippines now the

play25:25

Philippines sit in what is called

play25:27

typhoon alley they have natural

play25:29

disasters several time each year but the

play25:33

significant portion of the country is

play25:36

what we call unmapped and the question

play25:39

is even do you exist if your street is

play25:42

not mapped and why is it not mapped by

play25:44

the way is because those companies that

play25:46

create the maps that we use on a daily

play25:48

basis are only interested in those areas

play25:51

that they economically viable 80% of the

play25:55

Philippines do not fall into that

play25:57

category

play25:59

so the human open Street bed Foundation

play26:02

actually tries to tackle that they have

play26:04

people on the ground going through these

play26:06

unmapped areas and mapping them by hand

play26:09

and also indicating especially with back

play26:11

to earthquakes which buildings are

play26:14

actually stable and mapping that out the

play26:17

Red Cross in the Philippines lives by

play26:20

this data set because that's the only

play26:22

data set they can get their hands on to

play26:24

actually Reach people in need during

play26:27

these disasters

play26:32

so I honestly believe one of the biggest

play26:35

challenges we have at this moment is to

play26:37

make sure that we can

play26:39

democratize access to this data for good

play26:42

and many organizations already doing

play26:44

that AWS the cloud computing arm of

play26:46

Amazon uh for example has a large

play26:48

collection of open data uh one of them

play26:51

is uh it digital Africa this contains

play26:55

imagery over the years from three large

play26:58

satellite grips um the one from the US

play27:02

from NASA and USGS uh there's a European

play27:05

one from the project Copernicus and Jack

play27:09

the Japanese uh satellite company also

play27:13

makes these digital imagery available so

play27:16

you can look for the deterioration of

play27:18

mangr you can look for illegal roads

play27:21

being built because then thaty at the

play27:23

end of the road illegal mining is

play27:25

happening so making sure sure that we

play27:28

have this data available for everyone is

play27:31

actually crucial in improving access to

play27:34

data now why is this data so important

play27:37

why is it different from the past I

play27:39

think one of the major changes that we

play27:41

saw let's say in the '90s we already

play27:43

knew what kind of questions we wanted to

play27:45

ask and that drove what kind of data we

play27:48

collected SE queries but with cloud

play27:51

computing making data storage so cheap

play27:53

suddenly you could keep all your data

play27:55

around and then it becomes sort of a

play27:58

massive heap of data where you looking

play28:01

for the pot of gold that may be in there

play28:04

now what is the technology that you use

play28:07

to find the needle in the Hast stack

play28:10

that's a

play28:11

magnet yeah in this particular case to

play28:14

find the needle in a digital Hast stack

play28:18

you use AI or machine learning as a

play28:22

technology that is

play28:25

crucial now I want to leave you with a

play28:28

last example which I think is most

play28:31

telling there's an organization called

play28:34

foreign is a nonprofit organization that

play28:38

has as a goal to stay ahead of SE child

play28:41

sex trafficking and other child sexual

play28:44

abuse yeah they use image recognition

play28:48

they use data mining they use

play28:50

collaborative filtering all these

play28:52

different techniques to prevent and

play28:54

detect child sexual abuse

play28:58

they build a system called Spotlight in

play29:01

which they have imagery of missing

play29:02

children from all around the world they

play29:05

compare it each day for example in the

play29:07

US against the 100,000 new ads for

play29:12

escorts that are being released each

play29:15

day and

play29:17

Spotlight has been quite successful they

play29:20

already found 18,000 victims of child

play29:25

sexual trafficking and rescued and more

play29:28

than 6,000 were actually very young

play29:31

children they build a different system

play29:34

called saver because I assume none of us

play29:37

here would like to have child sexual

play29:41

abuse material on our

play29:43

servers if you have any way for your

play29:45

customers to upload imagery you may want

play29:48

to use saver to find those images that

play29:52

they have a massive database of

play29:54

uh uh of uh sort of hashes of this

play29:58

imagery and they can detect this for you

play30:02

many of these organizations have

play30:03

actually used humans to do this believe

play30:06

me if you need to look at these images

play30:09

just for a day you will need therapy at

play30:12

the end of the day if we can actually

play30:14

scale this up by using technology and

play30:18

actually really find this imagery and

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actually nip it when we can that's

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crucial they also actually make you

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safer is also a tool that can actually

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look at communication patterns between

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individuals and look for patterns of

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grooming these Technologies are crucial

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if we want to solve some of the hardest

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problems in this

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world now good AI needs good data

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there's a symbiosis between the

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two and that means that good data

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absolutely needs good AI to be able to

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make sense of it but more importantly

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good work needs good people it is us as

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technologists with the right mindset to

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want to solve some of the world's

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hardest problems because we've been

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successful in actually solving spam

play31:11

filters let's make sure that we can

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solve a number of problems that are much

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harder than that talking about good

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people uh this organization called Tech

play31:22

to the rescue actually launched a

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program called AI for Chang makers it's

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a global ACC accelerator that focuses on

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different areas of tech for good and I

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think um they are running this year five

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different cohorts in different

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areas jumping on that I've been so

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impressed by this program that today I'm

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announcing a CTO Fellowship the nowo

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build CTO Fellowship because I believe

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that many of these innovators in this

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space can actually make use of sort of

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the experiences that I and my team have

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as uh as driving technology for so many

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years that we can actually help them

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with that with this CTO fellowship and

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really technology plays a crucial role

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in technology for good and to find Mark

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more to the fellowship by the way and

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for the AI for change makers look at the

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uh capture the QR code there so I like

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to leave you again with uh McCarthy's

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quote Yeah remember that some of the

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current technology

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is clearly that of what I would call a

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dancing bear we really think this we

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really are amazed that this bear can

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dance we're not really looking at

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whether the bear dances well or not yet

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for that we need some longer time so as

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soon as it works nobody calls the AI

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anymore there's a massive body of work

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out there that you can use to solve

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today's hardest problems now I'll leave

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you with this yeah now go build thank

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you very much

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