Rethinking Biology: A Conversation With Michael Levin

Summary

このスクリプトは、生物学の新しい波を代表する生物学者であるMichael Levin教授と、高度技術移行の教授でアリゾナ州立大学の「ヒューマン・ビーイングの未来」イニシアチブのディレクターであるAndrew Maynard氏の対話を記録しています。Levin教授は、細胞がどのように集まってネットワークを形成し、それらのネットワークが集団知能を発揮して生物体の形を決定するのかについて研究しています。ゲノムは生物学的な「ハードウェア」をエンコードし、個々の細胞はそのハードウェアを再プログラミングして、集団として問題解決を行うと述べています。この対話では、生物学の可能性を広げる革新的な研究と、それが人工知能や人間の定義に与える可能性について触れています。

Takeaways

• 🧬 生物学の基礎知識に挑戦する新しい波の思想家と研究者がいて、私たちが生きる世界についての既定観念に問いを投げかけ、包括的な変革の可能性を広げている。
• 🧠 細胞は個々の存在として自分の位置を知らないが、細胞の集まりはネットワークとして情報を共有し、集団知能を発揮して形を作り出す。
• 🧵 ゲノムは細胞の「ハードウェア」をエンコードし、proteinsなどの具体的な機能を決定するが、生物体の形や記憶内容は直接指定しない。
• 🔋 生物の電気的ネットワークは細胞集団の集団知能を介して行動パターンを制御し、その_voltage pattern_を操作することで生物学的な形を変化させることができる。
• 🪲 プラリア（扁形虫）の研究から、生物電気回路のパターンを操作して、記憶を書き換え、_TWO-HEADED_（二頭）のプラリアを生み出すことができる。
• 🌱 進化の過程で生物は不安定なハードウェアと環境の変化に対処し、問題解決エージェントとして機能し、形質空間を通じて様々な形に適応する能力を発達させてきた。
• 🧬 遺伝子工学的なアプローチから一歩後退し、生物学の各レベルが持つ自己主張と問題解決能力を活用して、再生医療などの応用技術を開発している。
• 🧠 記憶と学習は脳だけでなく、分子ネットワークや単一細胞でも見られる。生物学的な記憶は組織を越えて移転することができる。
• 🤖 AIと同様に、機械も問題解決能力を持つが、意識や自己認識を持つかどうかは別問題であり、それが彼らに対する道徳的な扱い方にも影響を与える。
• 🌐 現代の科学は、私たちが世界の仕組みについて考える伝統的な考え方を根拠から再評価し、再定義し始めている。
• 🚀 未来の生物学と再生医療は、人間の体の可能性を大幅に拡大し、個々の目標に応じて自在に身体を変化させることができる未来を示唆している。

Q & A

細胞はどのようにして自分の体の位置を知るのですか？

細胞自体はその位置を知りませんが、細胞集団はネットワークを形成し、それらのネットワークには集団知能というコンピュータティブな性質があります。このネットワークが、自分が構築すべき物体の粗い表現を記憶し、現在の状況とのデルタを減少させるために誤差を最小限に抑えます。

ゲノムはどのようにして私たちの形を決定するのですか？

ゲノムはハードウェアをエンコードし、各細胞が持つべきマイクロハードウェアの種類を指示します。その後のことは、重要な意味でソフトウェアであり、そのハードウェアは再プログラマブルで、デフォルトで何かを実行し始め、高度に再プログラマブルです。

生物学的な「アウトオブザボックス」デフォルトとは何ですか？

「アウトオブザボックス」デフォルトとは、例えば人間であれば、基本的な人間の形を指します。しかし、生物学的生き物は状況によって異なる道を進むことができます。例えば、早期胚を切っても、半分の体ではなく、同卵双子や三つ子ができるように、生物学は様々なperturbationsに対処できます。

プラリアの研究で何が見つかりましたか？

プラリアの研究では、細胞集団のbioelectric circuitが発見され、その特定の電圧パターンが1つの頭を持つべきであるという事実をエンコードしていることがわかりました。研究者はこのパターンを書き換えて、2つの頭を持つように変更することができました。

プラリアのbioelectric circuitを操作することで何が可能です？

プラリアのbioelectric circuitを操作することで、プラリアの形と構造を変化させることができます。これにより、2頭を持つプラリアを作成することができ、その2頭を持つ状態は永久的です。遺伝子的な変化なしで、切り離された後も2頭を持つことが可能です。

プラリアのbioelectric circuitの操作は、脳の記憶にどのように関連していますか？

プラリアのbioelectric circuitの操作は、脳の記憶に類似しています。プラリアの2頭を持つ記憶はcounterfactual memoryであり、現在 trueではないため、未来のある時点で真になる可能性があるという能力を有しています。これは、脳で行われているmental time travelの能力に似ています。

記憶はどのようにして異なる生物間で転送されるのでしょうか？

記憶は生物学的なネットワークを通じて転送されることができます。例えば、訓練されたアピスのRNAを無知のホストの脳に注入することで、情報を転送する実験が行われています。また、プラリアでは、頭を切り落としてから新しい頭と脳を再生させた後も、情報を記憶していることが示唆されています。

カタツムリからチョウに変わる際の記憶の転送はどのように行われますか？

カタツムリからチョウへ変わる際には、記憶が保持されますが、カタツムリの記憶はチョウには無意味です。代わりに、記憶は一般化された食品のカテゴリーへとGENERALIZEされ、新しい体で駆動される異なるセットのエフェクターシグナルへと関連付けをリマップします。

生物学的なネットワークを操作することで、生物学と医療にどのような可能性が開かれるでしょうか？

生物学的なネットワークを操作することで、出生欠陥、再生誘導、およびtorsの正常化などのアプリケーションが可能になります。形態を制御し、生物学的なシステムの各レベルが設定されたパラメータ内で最適化を試みる環境を創造することができます。

マシンやAIにおける知能とは何ですか？

マシンやAIにおける知能は、問題解決能力です。これは、意識や自己認識とは別であり、異なる手段で同じ目標を達成するナビゲーション能力を意味します。これは、現在の機械学習の多くで開発されている能力です。

私たちはAIを構築しているので、その動作を完全に理解していると思いますが、なぜそれでも予測不可能な結果が発生するのでしょうか？

私たちが構築するシステムには、私たちが完全に予測することができないbuilt-in agencyがあるからです。これは、システムが持つ問題解決能力や学習能力によるものです。たとえ私たちがシステムの各部分を知っている場合でも、システム全体の動作は予測不可能かもしれません。

未来のヒトになる意味とは何ですか？

未来のヒトになることは、身体の自由度を意味します。私たちは、宇宙線がDNAに当たった結果、生まれた体にとどまる必要があるという偶然性にとらわれることなく、自分の目標に応じて異なる知能や長命を持ちたいと考えることができます。

Outlines

00:00

😀 未来人类研究所の紹介とゲストの迎え

アンドリュー・メイヤード教授は、亜利桑那州立大学の先端技術移行研究所の教授であり、「未来人类」イニシアティブのディレクターです。ゲストとして、生物学者で先駆的な研究を行っているマイケル・レヴァン教授を迎え、彼の研究は私たちの生物学に対する前提知識に挑戦し、私たち自身を含む世界を変革する可能性を広げています。

05:00

🧠 細胞の集団知能と遺伝子の役割

個々の細胞は自分の位置を知りませんが、細胞の集団はネットワークを形成し、集団知能を発揮します。レヴァン教授は、細胞のネットワークがどのように集団として情報を処理し、生物の形や振る舞いを決定するのか、また遺伝子の役割について語りました。遺伝子は生物の「ハードウェア」をエンコードし、その後のプロセスは「ソフトウェア」であり、再プログラマブルで、生物学的な形を決定するのではなく、デフォルトの動作を実行し始め、その後は高度に再プログラマブルとなります。

10:02

🐛 記憶の移転と生物学的な柔軟性

生物学的なcreatabilityについては、通常は非常に頼りになる発展過程がありますが、他の生物工学者が示すように、私たちの考えられる限界は非常に小さいです。特に、ガラの葉がハエの寄生蜂によって変貌される例や、フカケの再生能力に関する研究が、細胞の集団知能の電気的インターフェースについて語りました。彼らは、フカケが2つの頭を持つようにバイオ電気パターンを書き換え、その動物がその後も2つの頭を持つようにしました。

15:03

🧬 記憶と集団知能の連続性

レヴァン教授は、私たちの脳における記憶の蓄積方法については不明であると語りました。しかし、集団知能における記憶の概念は、私たちの脳の記憶と類似しています。彼らは、フカケにおける2つの頭の記憶をカウンターファクトゥアルな記憶と見なしており、将来的に真実になる可能性のあるものであり、それが動物が将来的に傷ついたときに行うべき行動です。

20:04

🦋 記憶のリマッピングと進化

カタツムリからチョウに変わる際に記憶が保持されるという過去の研究、特にダグラス・ブラックストンの经典的なカタツムリバタフライの研究について語りました。カタツムリは葉を食べるが、チョウは三维空間で飛び、蜜を吸う必要があります。そのため、記憶を一般化し、新しい体にリマップする必要があります。これは、進化や認知科学など、様々な分野で見られるリマッピングの例であり、非常に興味深いと感じています。

25:06

🧵 生物学のネットワークと目標達成

生物学のネットワークは、細胞が集団として記憶を持ち、私たちが生物学を実際に利用できるようになることについて話しました。短期間では、生物学を再指定する方法を理解し、出生欠陥や再生を誘導し、torsを正常化するアプリケーションがあります。また、生物学の各レベルは、生物学的なシステム全体が望む方向に向かって最適化しようとするパラメータを設定することで、カジュアルなトリックを実行するようにすることができます。

30:08

🤖 AIと機械学習の進化

AIと機械学習の進化について語り合い、レヴァン教授は、私たちが持っているアーキテクチャは、キープロパティを多く持っていないが、問題解決のインテリジェンスを持っていると語りました。また、現在のAIアーキテクチャでは、予測不可能なアウトカムを持つビルドシステムがあることを理解し、そのアプローチから離れることが重要です。

35:10

🧐 科学的なルネッサンスと意義の危機

レヴァン教授は、科学的ルネッサンスが到来していると感じています。多くの異なる分野で確立された考え方に挑戦している人々が多く見受けられます。また、意義の危機から脱却し、より科学的に支持される方法で考え方を再構築する必要があると語りました。

40:11

🚀 人間の未来と身体の自由

未来の人类について語り合い、レヴァン教授は、身体の自由とエンバディメントの自由が見られる未来を想像しています。人々は、宇宙線によってDNAに当たっただけで、生まれた体にとどまる必要があるとされる過去の物語に驚くでしょう。未来の世界では、人間の身体や能力に制約されることなく、自分自身の目標を追求することができるでしょう。

Keywords

💡細胞集団

細胞集団とは、個々の細胞がネットワーク状に結合し、集団として情報を共有し合意形成を行う集まりを指します。ビデオでは、細胞集団が集団知能を発揮し、個々の細胞が持つ情報以上の能力を発揮しているとされています。たとえば、細胞が集まってネットワークを形成し、集団として生物学的な問題を解決する様子が説明されています。

💡ゲノム

ゲノムは、生物体の全ての遺伝子のセットを指し、個々の細胞の性質や形態を決定する情報を持つとされています。ビデオでは、ゲノムが直接形態を決定するのではなく、細胞が持つ「ハードウェア」の性質を決める役割を持っており、その後の形態決定は「ソフトウェア」として扱われることに重点が置かれています。

💡集団知能

集団知能とは、個々の知能を持つ存在が集まって新しい知能を発揮する現象です。ビデオでは、細胞が集団を形成し、集団知能を通じて生物学的な問題を解決するプロセスが説明されています。特に、細胞集団が持つネットワークの計算的性質が、生物学的な形態の構築に寄与すると述べられています。

💡問題解決能力

問題解決能力とは、ある問題に対して創意工夫を働かせながら解決策を見つける能力です。ビデオでは、生物学的なシステムが持つ問題解決能力が強調されており、細胞集団が形態を変化させることで生物学的な問題を解決する様子が語られています。

💡bioelectric circuit

bioelectric circuitとは、細胞内の電気的シグナルが伝達される回路であり、細胞の行動を制御する重要な役割を果たします。ビデオでは、このbioelectric circuitが細胞集団の知能を介して形態変化を制御するメカニズムとして機能していると説明されています。

💡集団の記憶

集団の記憶とは、個々の細胞が持つ情報以上の、集団で共有される情報や経験の蓄積を指します。ビデオでは、細胞集団が持つ集団的な記憶が、形態決定や生物学的な問題解決に関与しているとされています。特に、プラリアの研究でbioelectric circuitを操作して集団の記憶を書き換え、2頭のプラリアを生み出す実験が紹介されています。

💡進化

進化とは、自然選択によって生物種が時間と共に変異し適応していくプロセスです。ビデオでは、進化の過程で細胞が持つ柔軟性や適応性を通じて、生物学的なハードウェアがどのように変化し続けるのかが議論されています。

💡自己認識

自己認識とは、自分自身についての認識や理解です。ビデオでは、自己認識が重要な能力であるとされていますが、自己認識を持たない細胞や分子系でも問題解決能力を発揮できると述べられており、意識や自己認識を超えた情報処理の仕組みが存在する可能性が示唆されています。

💡人工知能

人工知能とは、人間のように学習・判断・問題解決能力を持つ機械的なシステムです。ビデオでは、人工知能が持つ問題解決能力と、生物学的なシステムの持つ問題解決能力との比較がされています。特に、機械が持つオペレーションズ・インテリジェンスと、生物学的な進化のプロセスとの関係が議論されています。

💡科学的なルネッサンス

科学的なルネッサンスとは、既存の科学的概念や理解を根本から問い直すことを意味します。ビデオでは、現代の科学が抱える危機や意義の再評価を通じて、人間の存在や自由意志、決定論などの観点が刷新され、新たな科学的な理解が生まれると期待されています。

💡自己形成

自己形成とは、細胞や分子が自分自身の形態や性質を決定するプロセスです。ビデオでは、自己形成が細胞集団の集団知能によって制御されるとされており、bioelectric circuitを操作することで形態を変化させる実験が紹介されています。

Highlights

Andrew Maynard, a professor at Arizona State University, discusses the future of human evolution with pioneering biologist Michael Levin.

Levin's research is redefining our understanding of how cells and organisms operate, challenging traditional biological assumptions.

The conversation explores the concept that individual cells don't know their location but cell networks do, indicating a form of collective intelligence.

Levin explains that the genome encodes the hardware of a cell, but the software, or functional expression, is shaped by the cellular collective.

The idea that organisms have an 'out of the box' default form is not fixed; biology can be manipulated to achieve different outcomes.

Levin's lab has demonstrated that by manipulating bioelectric circuits, they can change the physical form of organisms, such as creating two-headed flatworms.

The concept of cellular 'memory' is introduced as a way to understand how cells collectively remember and reshape their form.

Levin discusses the transfer of memories between different species and across generations, highlighting the complexity of biological memory.

The potential applications of these discoveries in regenerative medicine are significant, offering new ways to treat birth defects and induce regeneration.

Levin's work suggests a shift from deterministic science to a more nuanced understanding where biology is persuaded to achieve desired outcomes.

The discussion touches on the implications of artificial intelligence, drawing parallels between machine learning and biological intelligence.

Levin emphasizes the need for humility in understanding the capabilities of both biological and artificial systems, given their inherent complexity.

The conversation suggests we may be on the cusp of a scientific renaissance, reevaluating long-held beliefs across various disciplines.

Levin speculates about the future of humanity, envisioning a time where the constraints of our physical bodies are no longer a limitation.

The potential for a crisis of meaning in society is discussed, with a call for a new narrative that embraces the implications of modern scientific discoveries.

The interview concludes with a call to action for scientists and philosophers to provide a new perspective that honors both our humanity and the power of our intelligence.

Transcripts

[Music]

hello and welcome to the future of Being

Human unplugged my name's Andrew mayard

I'm a professor of advanced technology

transitions at Arizona State University

and I'm also the director of asu's

future of Being Human initiative today

it is my great pleasure to be joined by

the pioneering biologist Professor

Michael Levan so I first became aware of

Mike's work some time ago when one of my

grad students asked if I'd seen it and

what I thought of it so here I should

say I'm not a biologist so Mike hadn't

actually been on my radar but there were

enough hints from Mike student that

there was something profoundly

transformative going on in his lab that

I thought I should probably check it out

and I'm very glad I did so Mike is part

of a new wave of thinkers and

researchers who are challenging received

wisdom about the world we live in and

opening up radical new possibilities for

how we transform it including ourselves

and here I suspect that that phrase

profoundly transformative is an

understatement through his work on why

cells do what they do why organisms look

and behave as they do and why our genome

doesn't seem to be that great of an

indicator of much of that um it's clear

that Mike and his collaborators are

upending a lot of our assumptions about

how biology works and what that implies

about everything from intelligence

including AI to what it means to be

human so Mike it is great to have you

here for this conversation thanks so

much Andrew so happy um yeah and it's

it's one that I've actually been looking

forward to for a long time um and I'm

expecting that we're actually going to

cover a lot of ground and as always with

this unplugged format um Serendipity is

the name of the game so I have no idea

where this conversation is going to go

but I did want to start with um the

biology um and the science um and I want

to start off with a question that I

suspect some people will consider to be

very naive um and I'm allowed to because

as I said I'm a physicist and it's

something that actually has been

bothering me for years so the question

is you take a cell on the end of my nose

how does it know that on the end of my

nose how does it know what the shape of

my nose is and what its places in my

body because that cell if you look at

the genome there's no way it should know

where it is in space and time how on

Earth does this work yeah um great

question and uh like you uh I entered

this field from a different area I was a

computer scientist and and likewise um

asking similar similar questions um I as

as far as we know now the individual

cell does not know where it is it

doesn't know anything about a nose it

doesn't know anything about you but the

cellular Collective does so cells merge

into networks and these networks have

computational properties that we're only

beginning to understand and it's those

networks that um Implement a kind of

collective intelligence that solves a

number of problems one of those problems

that solves is storing a rough uh

representation of what it's supposed to

be building and then minimizing error SL

stress in order to reduce the Delta

between what it thinks it should be

building and what it thinks the current

state is so so so what we ask in this

lab all the time is what what does the

group know not you know sometimes what

does the cell know but mostly what does

the group know right and and I've got to

ask where where does that idea of what

the group should be knowing come from

presumably at some point that is coded

within the

genome well uh one way one way to think

about this uh and I know you know people

are not enamored of of computer

analogies in biology and and and in many

ways they're bad but but but this one I

think is a good one uh the genome what

the genome encodes is the hardware the

jome Tells every cell what uh kind of uh

microscopic Hardware it has to play with

those are the proteins that it has

everything that happens after that in an

important sense is software that that

Hardware is reprogrammable uh it uh it

it does representation it does a number

of interesting things that that that

Hardware does but but you have to treat

the genome as that the genome does not

directly specify your shape it doesn't

specify uh the content of the of the

memories of your of your body networks

um what it gives you is some amazing

Hardware that does some stuff out by

default you know sort of out of the box

and also is is is highly reprogrammable

yeah yeah so I just to to sort of follow

on with that so this idea of out of the

box so I'm assuming if you're looking at

at humans for instance and we're going

to start really complex and we'll

probably get more simple from here

but the the out of thebox default is a

rough human shape but what you're saying

it seems to be is that that doesn't have

to be the the end point we can begin to

sort of tweak the out ofth box sort of

default of the genome cor correct I mean

if you think about um the task that that

is set in front of biological creatures

and I can give you some examples of of

some amazing things that they do uh the

kind of architecture that we com that we

use in computer science where the

hardware is ex extremely reliable and

information is meant to be kept and we

make sure that it doesn't get get

altered and and we keep the noise down

and then the higher levels are sort of

insulated from the lower levels um that

that doesn't work in biology because the

hardware is fundamentally unreliable

everything that is in the cell is going

to be damaged turned over no the noise

is huge and during Evolution guaranteed

the hardware is going to change so

there's going to be mutations there are

going to be the environment will change

your own parts will change everything is

going to change so sticking with this

idea that um what you're going to try to

do is create a solution to a specific

problem the way that our current let's

say genetic algorithms do do um is I I

think is not what biology does at all

what I think what biology does is create

problem-solving agents that operate uh

and we could talk about some of the ways

that that this happens they operate in

various problem spaces like anatomical

space and uh if all all things being

equal they take the same journey through

anatomical morphis space and you go from

a being a a single cell oite to a human

shape but but they're perfectly willing

to take other Journeys if the situation

um requires them for example if you cut

an early embryo into pieces you don't

get half bodies you get monozygotic

twins triplets whatever so so they can

make up for all kinds of weird

perturbations some really strange ones

in certain species you know amazing

examples um and then uh and then you can

also you you could also get these cells

to do other things so we we've shown

examples of that of of of you know PL

area making flat worms making bodies

with the heads of different species and

and things like that there's a lot of

variety yeah so why why do we sort of go

to the that the flat won't work because

I think that actually sort of shows some

really interesting stuff where you can

effectively I I'm not sure that

reprogram is programing is the the right

term but you can actually get the the

the form and structure of flat worms to

change depending on how you actually

program these collectives of

cells yeah uh and and I guess the the

first thing I wanted to just um mention

is that this this idea the plasticity of

of the hardware is so uh hard for us to

see because development is normally very

reliable so so most of the time it does

exactly the right thing and so we get

this we get lulled into this false sense

of security that we know what these

genomes do and they produce a certain a

certain shape and that's that um there

are other bio then I'm going to I'll

I'll address the the plenaria point in a

second but there are other bioengineers

besides the human ones that show us how

limited that view is and uh one of the

things that uh that that that I show

sometimes in my talks are these Galls so

imagine uh you have a you have a a flat

Green Leaf that belongs to an oak and

you kind of know you say well the oak

genome makes this flat green thing you

know 100% of the time that's what it

makes well Along Comes A a wasp a

parasite that's a non a non-human

bioengineer and it prompts the leaf

cells with some some chemicals not

really known exactly how it works and

they go on to make this crazy beautiful

red spiky thing that looks absolutely

nothing like the leaf so we we would

have zero clue that those cells are

capable of doing that and you can bet

that the uh the WASP is not sitting

there micromanaging where the cells go

it's not micromanaging the gene

expression it's not doing genetic

editing it is uh communicating to the

cellular Collective some prompts that

take advantage of their morphogenetic

capacity so so nature is already doing

this but you know evolution is making

these these plastic things that can

respond in interesting ways and so in

plaria we took advantage of that and the

thing about planaria is that it's these

flat worms uh they're they've got a head

and a tail and lot you know true brain

lots of different organs you can chop

them into pieces the record is I don't

know 275 I think or something like that

and each piece will reliably regenerate

an entire worm so so so really

interesting to ask how does every piece

know what a correct worm is supposed to

look like and so we were studying this

uh and we long story short we discovered

that there's a there's a bioelectric

circuit and this is one thing my lab

does is it studies this really

interesting electrical interface to the

collective intelligence of these cells

that that interface is it's kind of like

an API that cells use to to hack each

other's behavior and um and and there's

a circuit that has a particular voltage

pattern that basically encodes the fact

that you should have one head and we

learn to rewrite that pattern to change

that into saying two heads instead of

one and those animals if you then if you

then cut them they will make two-headed

animals and so so a couple things

interesting there one is that uh we can

actually see the bi electrical pattern

so we now have the ability to directly

visualize the memories in the mind of

this collective intelligence you can you

can see them the way that

neuroscientists try to read the brains

um that's a b uh we can at least begin

to uh decode them so that now you can

rewrite them see these two-headed worms

are permanently two-headed meaning that

if you keep cutting them in plain water

with no more manipulation of any kind

they will continue to be two-headed for

forever with no genetic change right

right so you look at that I actually I

love the analogy of an API between cells

by the way um but you also use this this

term memory so effectively by

manipulating these bioelectrical

Networks you're EMB in effectively a new

Collective memory um Within These um

these plaria um how analogous is that to

sort of the memory we think of in in our

brains is is there a Continuum there um

well a couple of things so so

mechanistically actually no one really

knows how memories are stored in our

brain um there's a con there's a sort of

a conventional story having to do with

something about uh synaptic structures

that story has a lot of cracks in it

there are some folks that have been

challenging that in a in a in a strong

way it it it really isn't clear the

biggest thing about memory is not just

the storage medium but the

interpretation because uh there have

been all kinds of experiments on moving

memories from one animal to another and

uh in in fact across radically different

architectures so let's say from

caterpillar to butterfly memories

persist they have to be remapped you

know the straight up memories of a

caterpillar don't don't are useless to a

butterfly everything is is different and

so so so these we don't know how it's

handled in brains I mean I I have

suspicions but but we don't know but but

the thing that the thing that is

um homologous here is a couple things

first of all a lot of the Machinery is

the same so ion channels um

neurotransmitters electrical synapses

all of that stuff is there and being

used much like in in brains and the idea

that that that two-headed memory that we

that we first incept into these animals

is a counterfactual memory meaning that

it isn't true right now so you can put

in a a two a a you know kind of a

bipolar memory pattern into a

anatomically normal worm and that

pattern is not what the worm is right

now so I think of it as the beginnings

of that kind of mental time travel that

we have meaning the ability to conceive

of and remember things that are not true

right now but might be true at some

later time so that pattern right that

that pattern is what the animal is going

to do if it gets injured in some future

time okay okay so so this actually takes

us into

weird territory and I I'm going to push

on this I I'm not sure how comfortable

you are going there but it's the the way

that you're actually challenging sort of

our concepts of memory um all the way

through sort of memories from Collective

collectives of cells all the way up to

what we understand about sort of the

memory in our heads um it seems like

you're implying that in principle we can

actually sort of shift memories around

we can actually sort of put new memories

maybe it's just me memories of sort of

physical form but we can put memories

into actually I'm going to move away

from humans but but into the the brain

of an organism but you can also transfer

those memories either between

generations of organisms or Beyond there

I how am I going outside the the bounds

of reality here no no I don't think you

are I mean it's it's been done people

there there are lots of papers on moving

uh memories from from one body to

another so so some of the best modern

work is David lansman um at UCLA who um

injects RNA ground up from trained Appia

into the brains of of of naive hosts and

the and the information transfers

there's a long history of of that work

in plaria and and you know this was

discovered in the 60s but we actually

confirmed it ourselves in 2013 um if you

train the worm chop off their heads and

wait for the tail to regenerate a brand

new head with a brand new brain they

still show recall of the information

which means that Not only was it

partially stored in the tail but also

somehow imprinted onto the new brain as

the new brain develops so this idea of

Behavioral memories uh moving through

tissue moving across tissues being

transferred in molecular uh you know

molecular media um I yeah I think that's

I think all of that exist and and if you

could talk a little bit more about the

caterpillar butterfly example because I

that work of yours just blew my mind in

terms of the the progression from the

catabella to the butterfly with retained

memories so so I want to be really clear

that isn't my work so we weren't that so

so there was there was old work that

that did it in um uh various kind of

larv and beetles and things like that

and then uh the classic caterpillar

butterfly stuff uh was done by um

Douglas Blackiston who's a staff

scientist in my lab that's kind of a

coincidence I hired him um you know a

long time ago uh and uh you know I

didn't realize at first that he had done

that amazing work but but anyway uh

the the the the results basically go

like this um you train a caterpillar to

eat food which for the caterpillar is

leaves on a particular color disc the

caterpillar uh under goes metamorphosis

because what it needs to do is shift

from a softbed kind of creature which

requires a particular controller because

you know in the soft body there's

nothing you can push on right so so then

it becomes a butterfly that has to uh

that has to um live in a

three-dimensional world now and um and

so because of that the brain is largely

dissolved a lot many of the connections

are broken most of the uh most of the

cells are killed off there's some

there's some debate now as to you know

whether everything is killed off or

whether some things remain but the

interesting thing is not just the

Persistence of the memory the

interesting to me the more interesting

thing is this if you learn as a

caterpillar to crawl in a particular way

to receive um uh leaves which is your

food that memory is completely useless

to the butterfly the the butterfly

doesn't crawl it lives in a

three-dimensional space it has to fly

and it doesn't eat leaves it drinks

nectar so so so that memory is is is

useless so what has to happen is an

interesting kind of remapping which we

still really don't understand very well

although I have some some thoughts about

um what's going on but what's happening

is that it first has to generalize the

idea of from from leaves into a generic

category of food so generalizing from

specifics to to General categories is a

kind of intelligence so first it has to

generalize

and then it has to remap that

relationship the the the L the

association between the color and the

and the food concept onto a completely

new body which is driven by a completely

different set of effector signals so

that that to me is the more interesting

part is this remapping of information

and I think that that is just um just

the beginning I think I think once you

start looking for it that remapping is

everywhere it's there in evolution it's

there in um you know human um Co you

know cognitive science it's it's all

over the place yeah um and that I think

is is where this this strand of work

becomes particularly interesting when we

begin to look at how it begins to sort

of apply and have relevance and

resonance across so many different areas

and I I actually want to sort of come

back to this in a in a moment looking at

how we extend it but just sticking with

this um and the idea of these endogenous

bcal networks so I if I'm getting this

right if you're looking at sort of the

these Notions of memory that they're

embedded in this idea of of these um

these networks which are sort of

remembered by these clusters of cells

but then moving away from memory this

this seems to be profoundly important

that we can actually um or or biology is

effectively determined to a certain

degree by these these networks and we

can begin to engineer these networks so

then where does this this knowledge take

us in terms of understanding biology and

how we can actually utilize

biology um okay there's a there's a

short kind of short-term version and a

in a in a bigger picture here the the

short-term version is basically that now

that we understand how to respecify at

least a little you know we're beginning

to understand how to respecify these

pattern memories it means that we have

applications and and in my group we've

been we've been going after some of

these applications in birth defects in

um inducing regeneration and normalizing

tors um we can we can control morphology

uh at a at a at a much uh higher level

and what I mean by that is look imagine

let's just let's just take it into

behavior for a second imagine you had a

rat and you wanted this rat to do a

circus trick you know sit on a little

bicycle or something one thing you could

do if you if you took the bottom up

approach that's that's basically what

all of molecular medicine these days is

is about if you were you know wanted to

um micromanage all of this from the

hardware end you could try to figure out

exactly what the muscle motions need to

be to get the rat to sit on this thing

and then uh try to work it up to to see

upwards to see which neurons would have

to fire Trace that into the brain all

the circuits figure out uh what would

have to happen there and then figure out

what pixels on the rat's retina you

would have to activate with light

signals in order to get it to do the

behavior if you if you do that uh you'll

be here Till The Sun Burns Out right and

and that feels very much like

deterministic sort of science that we do

at the moment in fact it feels like an

awful lot of computer science how do you

build stuff out from scratch yeah y

exactly but but you know the the the

good news is that computer science kind

of has shown us the way the reason that

uh we you don't get out your soldering

iron when it's time to switch from

Photoshop to Microsoft Word is that we

Now understand that that the hardware is

only a part of the story and then you

commun if if your Hardware is good

enough you can communicate to it with

signals with reprogrammability all all

all that fun stuff so so what the thing

with the rat is that instead of that

what you can do is you can just train

the rat because the rat offers you this

amazing interface that does all the hard

work of translating your goals to the

rat's goals you're getting the buyin of

the of the agent the organism and it

does all the hard work of organizing its

downward component parts into um a set

of activities that are going to get with

your joint goals met so we've been

taking exactly that approach in biom

medicine to say that I I I don't want to

control all the cells I don't want to

talk to stem cells I don't want to

control gene expression I want the cells

to be motivated to take a journey in

anatomical space that goes from a wound

to a limb being regenerated versus um

scar tissue and this is exactly what

we've done for example in the Frog where

uh we can show that adult frogs which

normally don't regenerate their legs um

24 hours of stimulation with a with a

particular treatment that we came up

with gives you a year and a half of leg

growth after that we don't touch it at

all during that time the idea is not to

micromanage the process the idea is to

convince the cells that this is what

they want to do and they have all the

competencies uh about how to do it and

and of course this brings in this idea

of a gentle system which I I actually

find quite compelling um the the

understanding that are multiple layers

within biology you have systems with

agency and you're basically creating the

environment where that agency leads to

what you want yeah the the amazing thing

about um biology is that your whole body

every every level seems to be deforming

the the energy landscape for the level

below it to take advantage of their

competencies in navigating that that

landscape but to get it to go where you

want it to go um you know and uh and and

this is this is exactly what what we can

take advantage of not not because we're

we're so smart but but because that's

what the uh the the hardware is already

primed to do you know every every level

is already primed to do all these

interesting things if you can get the

incentives right right right so when you

look at this I this feels like it's it's

really powerful in terms of rethinking

biology so I know you've already hinted

at at this that that so much of what we

do is trying to engineer biology from

the the ground up whether it's the the

individual proteins or the genome

upwards but if and I'm paraphrasing here

but if we can persuade biology to do

what we want at a higher level I from

what my understanding is you're

cascading down so you sort of set the

top level parameters and each level

within the biological system will then

try and optimize within those

parameters yeah I think I think that's

right I think I think all of these uh

levels are made of uh it's sub agents

that solve problems in various uh spaces

um anatomical space physiological space

whatever and they have different

competencies and different agendas of

doing it and each layer is taking

advantage of this is I call it an

agential material because you have to

engineer it very differently than you

would uh you know engineer um the

passive or even active matter and and it

goes It goes even below cells I mean

we've been studying the um learning

capacities of molecular Networks so

never mind whole cells even the

molecular networks have probably at

least six different kinds of learning

capacity yeah and I I know you've used

the term intelligence with these these

systems um talk a little bit about how

you define intelligence because it feels

a little weird to talk about sort of

molecular systems having intelligence

yeah uh okay two two two important

things there one one is that um we need

to have some kind of way of talking

about molecular systems with

having intelligence because we have to

be able to tell a story of scaling we

all Start Life as a as a as an

unfertilized Osa a little blob of

chemistry and physics and if you if you

don't want to tell any kind of story

about intelligence with that system you

you're going to owe a uh some kind of a

a claim on when during embryonic

development this intelligence shows up

and there is no magic light lightning

bolt that at some point says okay you

weree physics but now you're a real mind

you know so that that does happen so so

we need we we know already given given

our origin as a collection of cells uh

and then then the Single Cell before

that we know we we have to come up with

some kind of a scaling Paradigm for how

intelligence scales from simpler forms

now the kind of intelligence uh that I'm

talking about is uh the kind that

William James defined as same goal by

different means so it's really a

navigational intelligence it's it's it's

a publicly observable uh perfectly sort

of empirically test able problem solving

capacity so this is not I am not talking

about Consciousness I am not talking

about um self-aware meta intelligence

where you know how intelligent you are

I'm not talking about any of that I'm

talking about the ability to navigate a

problem space and get your goals met

despite various new things that are

going to happen like how much competency

do you have at that and so the framework

that I'm developing is called Tam

technological approach to mind

everywhere uh this this framework the

most basic thing about it says the any

kind of intelligence claim two two

things about intelligence claims first

of all it it isn't a philosophical claim

it is an empirical testable experimental

claim so if you think some kind of

system has some kind of intelligence

what you're going to do is make a

hypothesis about a problem space uh

about the goal that it has about the

competencies you think it has and then

you're going to do perturbative

experiments to see if the the type and

amount of intelligence you've you've

ascribed it helps you uh have a a more

efficient relationship with that system

so it's it's not anything goes and we

don't just paint hopes and dreams on

rocks we we have very specific

hypotheses about problem solving

capacities and and that means that you

know yeah you you you can't just sort of

Imagine a spirit under every Rock but at

the same time you can't just assume that

cells don't have it uh you have to do

experiments and when we do experiments

we find you know we find amazing things

the other the other an unexpected we get

surprised which is what you want from a

scientific theory the other right the

the the other of that is when you make a

um a claim about the intelligence of

some system you're basically taking an

IQ test yourself because what you're

saying is as an observer as an external

Observer here is what I have noticed

this system can do and that doesn't mean

you didn't miss a whole bunch of other

things that you didn't notice just

because you don't see it doing things

doesn't mean that that it that

definitely doesn't do them right right

but even just talking about that you're

you're taking an approach where you say

at some point in the system there is

enough way awareness to be aware of of

yourself and what other systems are

doing so that must be a transition I I'm

assuming that that we're not looking at

intelligence in a way where cells have

got a concept of what molecules do it's

just that they have a specific type of

agency so is there a differentiator I

mean and I know we're getting into

Consciousness here we don't really want

to go down that down that path but it it

feels as you begin to talk about humans

sort of having that intelligence to

understand what's happening further down

the hierarchy that there needs to be

some degree of self-awareness or is that

just a an emergent property which

actually isn't that important well I'm

certainly not going to say

self-awareness isn't important I'm sure

it's important uh look uh the the thing

the thing is that I you you can you can

definitely have Bonafide intelligence

without uh without self-awareness right

you can have interesting learning

capacities uh you can be able to use uh

the tools that you have in novel and

creative ways you can you have delayed

gratification the capability of uh a

context sensitive attention you can have

all of these things without having that

kind of metacognitive self-awareness and

I think and I think it's perfectly good

intelligence I think self-awareness is

is a slightly different thing but I also

think that we have to have a continuous

notion of all of these things in other

words I don't believe there's a binary

um way that you can say yes self

awareness no self-awareness because uh

we're inevitably going to get to

questions of when and how during

embryogenesis and during Evolution that

supposed self-awareness shows up all of

these processes are extremely slow they

uh they go step by step there are no you

know people talk about phase transitions

but I have yet to hear other than

certain Quantum events I have yet to

hear an actual phase transition that's

really sharp when you sort of zoom in to

the to the key parameters I don't

believe any of these things are are

sharp face transitions yeah um and I

want to take a quick far into Artificial

Intelligence on that point before coming

back to the the biology because this

seems to be really important with

conversations and discussions around the

nature of intelligence with machines um

simply because what you've just

described seems to make it very easy to

to talk um in Practical terms about the

nature of intelligence with machines if

you forget about Consciousness and

self-awareness that ability to solve

problems along multiple Pathways seems

to be actually what we're developing

with a lot of machine learning at the

moment and to me it seems to simplify

those questions around what is

intelligence versus not when it comes to

AI does that make sense yeah it makes it

makes perfect sense I I don't think

there's any way I mean we can have some

arguments about Consciousness and so on

but I don't think there's any way to

argue that we do not have machines that

have that that have a a considerable uh

in some cases human level degree of

operational intelligence and so in the

case of problem solving I think what's

what's new nowadays is that in the past

that level of intelligence always went

along with a very long evolutionary

journey and uh and and and you know

certain other um properties that certain

are the cognitive properties and now

we've managed to to dissociate them

because I think current architectures

actually don't have a lot of the Key

Properties but but I absolutely think

they have uh the you know problem

problem solving intelligence and

whatever I mean I think it's important

to know that whatever the differences

between us and some kind of um AI

architecture whether it be the current

one or some some future one the answer

is not going to be what people often say

is that's just so so here are some bad

answers that's just a machine it

operates on the laws of physics and

chemistry well guess what so so do you

right that's you know and uh and um I I

know what it is because I built it and

it's just linear algebra you know I hear

I hear that sometimes too I you know I a

couple of couple of key key things here

which is that you know we we find

learning and memory in systems as simple

as a few genes that turn each other on

and off that's it a network of of of

differential equations that turn that

that represent genes turning each other

on and off never mind a whole cell never

mind you know the genome nothing already

can do pavlovian conditioning that's it

this this stuff starts very early on and

we found unex

expected problem solving capacities and

behaviors in something as as dumb as a

sorting algorithm you know so we're

talking bubble sort selection sort if

you look at them the right way you and

these things are deterministic six lines

of code there's there's nowhere to hide

there's no magic there's nothing there

people people you know people have been

studying these for for many decades and

if you look at them the right way you

find things that you did not know they

could do and you find things that are

literally not in the algorithm so that

tells me that we need to have a lot of

humility about saying that we know what

something does or or what something is

is capable of just because we know the

parts or just because we made it if a if

if a stupid bubble sword can do things

we didn't see coming you know what what

are these other things GNA do that and

it seems that that actually completely

changes the framing around how we think

about machine learning and and AI um

from our very deterministic perspective

where we say as you say we're building

the things so we know to the nth degree

what it does to understand understanding

we're Building Systems um which have got

inbuilt agency that it's it's not that

easy to to predict the outcomes of but

it also strikes me and I I correct me if

I'm wrong here because and you you

hinted at this earlier that quite often

when it comes to building machines or or

computer science we we try and predict

everything to the nth degree we try and

create the the perfect system and from

everything you're saying it seems like

that is totally the wrong approach and I

I think we're beginning to move away

from there but if we we approach these

systems as hierarchical um systems of of

AG gental sort of algorithms or whatever

um how far along are we in terms of

saying create a system where we create

the parameters that persuade the

subsystems to do what we want or is that

something we really need to focus more

on yeah um I think that if if we were if

we I I'm not sure we I'm not sure we

need to or want to do that the problem

is well well two things number one is we

we may get it without um without uh even

trying because um while we know I mean

people have been studying complexity and

emergence for a really long time it's

very you know or perverse instantiation

these kinds of things it's very easy to

make systems that follow simple rules

and then generate a bunch of complexity

that's not what I'm talking about I'm

not talking about unexpected complexity

or side effects or or or or any of that

I'm talking about emergent agency that

emergent goal directedness now uh I I I

will say that the architect that we have

today at least the ones of which I'm

aware um do do not maximize the kinds of

uh the kinds of dynamics that would lead

to that but but there are unexpected

ways of getting it that I I think we

need to be really careful about and the

bigger picture for me is that um I think

we have to be really careful about this

in the sense that like like I started uh

a few months ago I started writing a

paper to to lay out very clearly what

are the half a dozen things about

biology that are really critical for

making a true agents that matter in a

moral sense and what's different you

know how here's what biology is doing

that none of our computer architectures

are doing and and I stopped and I'm not

going to write that paper because I

think that um well not that it'll help

because somebody else will do it

eventually but but right so somebody

will catch on to the stuff but but but

but I don't want to be responsible for

it the the thing is that to whatever

extent I'm right in in uh having found

some of the key features that I think

make true um sensient beings that we're

going to need to take care of uh to that

extent uh I I don't want to be

responsible for creating you know

trillions of them and and having no

control over how they so but but

interesting that that's your thinking

it's not that this isn't something

that's possible it's that this is

something that's possible and we've got

to be really careful about what we do in

that space I I think it's absolutely

possible because the idea that what is

special about um Minds can only be

produced by a blind uh you know a

tinkering agent that makes mutations and

selects for certain things I I don't see

why that process would have a monopoly

on creating real minds I think that U

and I know there there are people that I

respect a lot who dis who disagree with

that you know Richard Watson I think is

one but to me uh I think that there are

many roads to doing this and at some

point we are going to figure out and I

think we already have a good uh basis

for figuring out what are the actual

policies and and components that are

that are necessary and they have nothing

to do with being made of protoplasm or

um any of the things that that we assume

uh you know are are tied to the biology

so so I absolutely think they can be

reinstantiate in other media and and

that resonates with with conversations

I've had with other people where you're

looking at almost substrate agnostic

systems it's it's the agency within the

systems um which is important so on that

I we've only got a few minutes left but

I wanted to sort of build on that and

extend out into much broader territory

um and I've have no idea sort of how

you'll respond to this but it's

something that fascinates me at the the

moment I looking at your work and

looking at work in in other disciplines

it feels very much as if we're on the

age of a on the edge of a scientific

Renaissance um I'm beginning to see

people challenging established Notions

of of how the world Works in many many

different areas and I don't know whether

that's just me seeing things through my

narrow perspective or whether it really

is the case that we're at a point in

human history where we're beginning to

rethink a lot of what we've assumed is

constant and and true um how I am I off

totally off base here I I would love to

uh agree with you and and my personal

experience is that as well but but I

have to correct for the fact that I

mostly hang around with you know with

people who who who like to think in

these big directions I mean I think

purely statistically you know when I

give talks about this stuff to a to a

generic random audience in conventional

Fields most of this is things they've

either never heard of or that sound

completely wrong to them on from a

philosophical level so I'm not sure how

um where we are in that transition from

this is impossible to this is completely

obvious I'm sure I I I think that's the

journey we're on I'm not sure where we

are there but but I agree with you that

that is that is where we're going this

is assuming we we all live long enough

this is going to overturn everything and

and it should because right our future

and and I one of the reasons I say that

is I I come across the same sorts of

conversations in multiple different

disciplines so if you're looking at at

Neuroscience if you're looking at at

Psychology if you're looking at at

physics even conversations where I'll

talk to respective physicists will say

you know I'm not sure the second law of

Thermodynamics is actually sort of

holding true um and it just feels like

there are cracks in the the real that

we've built over the last um sort of

several sort of decades or centuries I

and your work as part of that um and

there's definitely a sense that that

something is Shifting it it may take a

while before sort of broader audiences

actually see it um but it does feel as

if we're at one of those really

interesting points in history where

we're rewriting what we know about the

world and how it works I think that's

true and and the part of it that I'm the

most excited about in addition to the

Practical implications of you know for

biom medicine and and thing which I

which I think are huge uh is is I think

we have to start climbing out of the

what what some people have called um a

crisis of meaning so so so this is work

that I'm doing with with a number of

collaborators but you know you you can

sort of you can sort of see this this

this parabolic shape where uh

Neuroscience has told us some some

things that that things we thought about

um what we are and how Free Will Works

that are that are wrong uh evolutionary

theory has told us about this this sort

of every man for himself uh kind of

fundamental idea physics is telling us

about um determinism and and things like

that and so this progressively the the

loss of of these important ways of

thinking about the world has taken us

down in into a uh situation where a lot

of people are actually very disturbed by

it and we scientists and philosophers

need to now climb out we need to provide

provide the the other side of that

Parabola which I think does exist right

for recovering the things in a better in

a better way not in the you know sort of

incorrect way that that we've been

thinking about them but in a better um

more scientifically defensible way yeah

so final question before we we finish

then just on that um from your very

speculative perspective what does all

this mean to the future of Being

Human okay uh 60 seconds 60 seconds the

the the future the future is uh freedom

of embodiment and the future that I see

are children who are told uh stories of

the past where they say you've got to be

kidding me you mean you mean somebody

was born and just because of the

vagaries of some cosmic ray that hit

some DNA they had to stay in the body

that they were born with maybe they

wanted you know maybe their goals

required more IQ or longer lifespan but

no they got lower back pain and

stigmatism and then they you know they

died at 70 that that can't be right like

nobody can live like that that that's

the future I see that where that that

where we are now that becomes ridiculous

and and it should be it is ridiculous I

I I love that I perfect place to and a a

beautiful challenge um and I'm very very

aware we we need to to wrap up um there

are so many other areas we we could have

touched on um that the whole

regenerative medicine area um that not

being constrained by what we think our

bodies can do um we're going to have to

have you back at some stage happy but we

just looking at the time we should wrap

up so Mike thank you so much for this

this has been an incredible conversation

thank you and to those of you watching

that's it for this episode of the future

of Being Human unplugged obviously um

thank you so much again for Mike for

joining us and challenging our thinking

and what I think are some quite profound

and unexpected ways um if you want to

know more about Mike's work I should

check out the the links in the the blur

to this video or simply Google him and

you'll get a wealth of information and

finally if you're interested in joining

us for future conversations um please do

sign up for updates from asus's future

of beinghuman initiative at Future ofbe

human. asu.edu that's future of

beinghuman all one word.

asu.edu and with that thank you again

for joining us and have a great day

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