शैडोइंग अभ्यास: AI Designed Viruses That Never Existed Before - वीडियो के साथ अंग्रेजी बोलना सीखें
अभ्यास मोड चुनें
हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।
141 वाक्य
यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।
1
Antibiotic resistant bacteria killed an estimated 1 .27 million people in 2019.
2
More than HIV slash AIDS or malaria.
3
And what I find really fascinating is that number is projected to keep climbing.
4
In fact, 40 million more deaths by 2050 if nothing changes.
5
Now there are some interesting things happening to hopefully change this.
6
I mean phage therapy it's using viruses to kill specific bacteria instead of broad -spectrum antibiotics.
7
People have been very hopeful about it, but to be frank, it's been slow for decades.
8
And the reason for this is because designing effective phages takes years.
9
But essentially, if a model can generate working candidates in days, that's a real medical breakthrough.
10
But it comes with kind of an unexpected security problem.
11
So let's take a step back here.
12
Scientists have used artificial intelligence to create brand new viruses, 16 of them.
13
Researchers at Stanford University and the Arc Institute say they trained an AI model called Evo on huge amounts of DNA data.
14
Then that information was used to create the new synthetic viruses.
15
On August 5th, a peer -reviewed study by Stanford was released
16
that showed how they trained a model to write DNA the same way, at its core, that GPT writes text.
17
The Stanford -based team is led by Brian He, who's working with the ARC Institute and bioengineering grad student Samuel King.
18
And it was he who built EVO2, which is a genome language model.
19
You can predict the next token, except the tokens are base pairs and the training data is the genetic code of viruses,
20
bacteria, plants and humans.
21
So the team used EVO2 to design candidate genomes for 5X1 74,
22
which is a virus that infects E coli, and the first DNA genome ever fully sequenced.
23
They essentially synthesized close to 300 of the model's designs.
24
Now out of those 300, 16 worked which meant new viruses that don't exist anywhere in nature.
25
I mean, some killing E coli more effectively than the original.
26
Now, the virus itself that was created is harmless.
27
It really only infects E coli.
28
But the paper, the Stanford paper that came out of it,
29
proving this came out to what I found very fascinating and a lot of others did.
30
It was from biosecurity researchers at John Hopkins and they basically call the findings a source of urgent biosafety and biosecurity questions.
31
Yeah, here is the actual technical problem.
32
DNA screening systems catch sequences by matching them against a database of known threats, things that already exist.
33
But this sequence has no match because nothing like it existed before it was created.
34
And to be fair, there's an industry -led screening system already.
35
I mean, the International Gene Synthesis Consortium has more than 40 gene synthesis companies that agreed to a shared screening protocol.
36
And even more than 175 developers of AI biodesign tools have
37
even signed commitments to only use the synthesis providers who follow those standards, which I think is pretty exciting.
38
People are seeing that this could be a real threat and taking action, hopefully ahead of time.
39
But all of that infrastructure really answers one question.
40
Does this sequence look like something we already know is dangerous?
41
Adoption is essentially voluntary and uneven, especially among smaller providers.
42
And none of it was built to catch a sequence that resembles nothing in any database.
43
I mean it's making something that doesn't exist yet.
44
And that's the real hole of Evo 2 exposed.
45
It's a mismatch between the rules that were set in place to catch these things and what something can do,
46
what a generative model can do and now produce something that hasn't existed before.
47
How do we put guardrails for that?
48
Running Evo 2, which by the way is open source right now, at full scale takes some serious hardware easily.
49
It's open source but it probably takes a quarter million dollars or more in compute.
50
It's really out of reach for someone working alone
51
but well within reach for a university lab or a startup
52
or a state program and
53
that barrier tends to drop over time
54
now one caveat worth knowing evo2 in its raw form couldn't reliably design a specific phage like phi x1 74.
55
it really doesn't roll off the tongue easy
56
it couldn't design one of those on its own
57
it has to take an extra step supervised to narrow the model's focus on that phage family.
58
So this was a deliberate targeted process by people who really understood what exactly the model needed to be doing.
59
Not a case of typing a prompt in and a virus comes out.
60
Much more complicated than that.
61
But it is still a real shift.
62
I mean, a predictive model tells you about biology that already exists, whereas a generative model invents biology that never did.
63
How does something like EVO2 actually work?
64
And why is this different from a normal biology AI tool?
65
I mean, they've been around for a while now.
66
Most AI biology tools are narrow.
67
AlphaFold predicts how an existing protein folds, which, yes, is extremely useful, but it's answering a question about something that already exists,
68
whereas EVO2 is generative.
69
For scale, to put this in perspective for you,
70
9 trillion nucleotides is enough genetic material to cover the entire human genome about 3 ,000 times over.
71
And EVO2 gets there with just 4 billion parameters, which is smaller than GPT -3s, going back a while now,
72
$170 billion, which should really tell you the power here came from what it was trained on, not from the size of the model itself.
73
Three years ago, four years ago, an LLM writing usable code was cool.
74
It's a cool party trick.
75
Here's how I can develop some code.
76
But now, I mean, it produces full production ready software.
77
Capability curves in AI don't move straight.
78
They move in jumps, in leaps.
79
And we've watched this happen again and again, especially when it comes to language and code.
80
And we're watching the opening move of the same jump when it comes to genome design in biotech.
81
Okay, I want to go back to the hit rate because I feel like we skimmed over that earlier.
82
16 out of 300.
83
It's not high for the 16 out of 300 viruses that actually took.
84
I mean, that puts this closer to the GPT -2 era than the GPT -4 era of the technology.
85
It's on the same curve though.
86
Every generative model improves on more data, better outputs, better architecture.
87
But let's zoom out to say two to five years.
88
What's going to happen with this?
89
I really don't think anyone's saying something like EVO2 is designing a human genome next year
90
but every capability curve
91
we've watched has moved toward the harder version of the problem
92
not away from it now bacteriophage run a few thousand base
93
pairs bacteria runs into the millions humans into the billions the gap between 16 working phage gmones
94
and a model that reliably designs genomes for more complex organisms is an engineering gap
95
and i really think it's not a a fundamental one
96
that will come with time all right
97
but this isn't the first time biology has hit a moment
98
like this back in february 1975 around 140 scientists actually gathered
99
to figure out what to do about a brand new capability
100
which at the time was recombinant dna
101
which is engineering genes across the species for a first time
102
now paul berg who was the organizer of it looked for an existing framework to follow and found nothing.
103
So instead the scientists wrote their own voluntary safety guidelines before any regulations forced them to.
104
And those guidelines really shaped biotech policy for the next 50 years.
105
How was 70s by the way 50 years ago?
106
It's wild.
107
But 50 years later scientists reconvened in the same convention center for a 50th anniversary meeting
108
and stated the reason was for the same kind of moment was currently happening
109
that happened in the 70s but this time with AI driven biotech.
110
Basically a call for the people building this to set guardrails before anyone makes them.
111
Evo2's own paper does exactly that.
112
It argues for oversight.
113
The open question I think on everyone's minds is whether
114
that instinct spreads to the next lab
115
that picks up an open source genome model
116
or what guidelines will they follow whether it will stay limited
117
to the one team careful enough in this case thank goodness to publish
118
it now i want to be clear here none of this necessarily makes ai designed medicine dangerous by default
119
i think there are so many incredible breakthroughs that we are seeing
120
because of this phage therapy against resistant infections could save lives
121
and that's the outcome his team is actually working towards
122
but it also the flip side you have to talk about
123
that because a technology
124
that can do enormous good can also do enormous harm from the same capability
125
and that is something
126
that we need to really have conversations around how to put guardrails for how to govern it before it scales not after.
127
A lot of coverage jumped straight to the scariest version of this story.
128
Researchers in the US have for the first time used artificial intelligence to design new viruses.
129
What could possibly go wrong?
130
There's also concern that the technology could create new and yet unheard of bioweapons.
131
Let's be honest here.
132
If somebody actually wanted to cause harm with a pathogen today, training a genome model from scratch is definitely not the easy path.
133
The big deal here isn't that phage is dangerous.
134
It's that the thing standing between a generative model
135
and a dangerous sequence just got proven not to work while the regulatory conversation is still stuck on chatbots.
136
Now, I'm not asking if this specific phage we spoke about is scary.
137
I'm asking whether you're comfortable with what it proves is now possible.
138
Leave in the comments your thoughts.
139
Do you like topics like this around biotech?
140
I'm kind of obsessed lately with biotech, so let me know.
141
All right, I'll see you in the next video.
ऐप डाउनलोड करें
धाराप्रवाह बोलने के लिए ज़रूरी सब कुछ
AI उच्चारण स्कोरहर वाक्य का स्कोर
IPA अभ्यासहर ध्वनि में महारत
शब्दावलीअपना शब्द भंडार बनाएँ
शब्द गेमखेलते हुए सीखें
FOR YOU
अनुशंसित वीडियो
C11
67 वाक्य
Viruses: Components & Shapes - Microbiology Acellular Pre-Nursing, Pre-Med | @LevelUpRN
Daily ConversationUniversity Students
C12
37 वाक्य
I read Anthropic's 92-page risk factor document, so you don't have to | Explainer
Daily ConversationUniversity StudentsWorking Adults
C16
32 वाक्य
What if quantum physics could eradicate illness? | Jim Al-Khalili for Big Think
Daily ConversationUniversity StudentsWorking Adults
C136
14 वाक्य
Why Ebola Keeps Coming Back
Daily ConversationUniversity StudentsWorking Adults
C11
97 वाक्य
Explaining how AI is killing the PhD in less than 10 minutes
Daily ConversationUniversity Students
C11
163 वाक्य
Session #1: Life Sciences in the 21st Century – A Pathway to Scientific Discovery - Rotem Sorek
Daily ConversationUniversity Students
C13
171 वाक्य
This Woman Cured Her Cancer with an Insane Method
Daily ConversationUniversity StudentsWorking Adults
C11
130 वाक्य
3 Ways AI Will Completely Change Your Life by 2029
Daily ConversationUniversity Students
C12
182 वाक्य
We Let $200 GPT-Pro Think for 7 Days on Unsolved Math
Daily ConversationUniversity StudentsWorking Adults
C12
685 वाक्य
Editing Your DNA, with Nobel Prize Winner Dr. Jennifer Doudna
Daily ConversationUniversity Students
C12
28 वाक्य
Explanation of Vaccine Adjuvants
Daily ConversationUniversity Students
C18
75 वाक्य
What AI can (and can't) currently do in a real lab | Jennifer Doudna
Daily ConversationUniversity StudentsWorking Adults
इस पाठ की शब्दावली और बोलने के सुझाव
यह स्पीकिंग पाठ वीडियो “AI Designed Viruses That Never Existed Before” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: model, Evo, genome, viruses, design। इस वीडियो में शैडोइंग के लिए 6 वाक्य और 1694 शब्द हैं। बोला गया हिस्सा 10:54 लंबा है। वक्ता लगभग 155 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। केवल 78% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| virus संज्ञा | /ˈvaɪ.ɹəs/ | विषाणु, वाइरस |
| genome संज्ञा | /ˈd͡ʒiː.noʊm/ | संजीन |
| biology संज्ञा | /baɪˈɑ.lə.d͡ʒi/ | जीवविज्ञान, जैविकी |
| gene संज्ञा | /d͡ʒiːn/ | जीन, वंशाणु |
| scientist संज्ञा | /ˈsaɪəntəst/ | वैज्ञानिक, आलिम |
| capability संज्ञा | /ˌkeɪ.pəˈbɪl.ɪ.ti/ | क्षमता |
| curve संज्ञा | /kɚv/ | चक्कर, घुमाव |
| researcher संज्ञा | /ˈɹiˌsɝ.t͡ʃɚ/ | शोधकर्ता, शोधक |
| harm संज्ञा | /hɑːm/ | चोट, घाव |
| narrow विशेषण | /ˈnæɹəʊ/ | तंग |
| scary विशेषण | /ˈskɛə.ɹi/ | भयानक, डरावना |
| genetic विशेषण | /d͡ʒəˈnɛt.ɪk/ | अनुवांशिक |
| database संज्ञा | /ˈdeɪtəˌbeɪs/ | डेटाबेस |
| artificial विशेषण | /ˌɑː.tɪˈfɪʃ.əl/ | कृत्रिम |
| provider संज्ञा | /pɹəˈvaɪ.də(ɹ)/ | प्रदाता, दाता |
वीडियो में आने वाली फ़्रेज़ल वर्ब
| शब्द | अर्थ |
|---|---|
| pick up क्रिया | उठाना |
दोहराने लायक वाक्य
वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:
- Does this sequence look like something we already know is dangerous?
- I mean, it's making something that doesn't exist yet.
- How do we put guard rails for that?
- I mean, they've been around for a while now.
- Do you like topics like this around biotech?
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | is projected · was released · is led |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | have been · have used · hasn't existed |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | people who really · parameters, which is |
ध्यान देने योग्य उच्चारण
वक्ता 19 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, I'm, couldn't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: synthesis /ˈsɪnθəsɪs/, breakthrough /ˈbɹeɪk.θɹuː/, gather /ˈɡæðɚ/, synthetic /sɪnˈθɛtɪk/, pathogen /ˈpæθəd͡ʒn̩/
- “sh” और “zh” की ध्वनियाँ: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, regulation /ˌɹɛɡjʊˈleɪʃən/, infection /ɪnˈfɛkʃən/, publish /ˈpʌblɪʃ/
- लंबे शब्द — बलाघात सही रखें: biology /baɪˈɑ.lə.d͡ʒi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/
हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:
- /v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: virus /ˈvaɪ.ɹəs/, sequence /ˈsiː.kwəns/, curve /kɚv/, whereas /(h)wɛɹˈæz/, provider /pɹəˈvaɪ.də(ɹ)/
- /z/ — इसे “ज” (/dʒ/) न बनाएँ: whereas /(h)wɛɹˈæz/, resistant /ɹəˈzɪstənt/, expose /ɪkˈspoʊz/, zoom /zuːm/, resemble /ɹɪˈzɛmbəl/
- शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: scary /ˈskɛə.ɹi/, scratch /skɹæt͡ʃ/, startup /ˈstɑɹtˌʌp/, skim /skɪm/
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- 0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर virus, genome, biology जैसे शब्दों पर ध्यान दें।
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।