Beyond Pre-training: The Future of Deep Learning - Ilya Sutskever at NeurIPS 2024
Ilya Sutskever reflects on a decade of deep learning scaling, warns that pre-training is hitting data limits, and highlights agents, synthetic data, and inference-time compute as the next frontier alongside critical alignment challenges.
Highlights
3 clips · 3 min of 15 min-
- Scaling data and compute has been the primary driver behind models like GPT-2 and GPT-3.
- The statement 'we have but one internet' highlights a hard finite limit on available real-world training data.
- Traditional pre-training as we know it is approaching a definitive bottleneck.
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- AI agents introduce active environmental interaction and experiential learning.
- Synthetic data can overcome the finite nature of real-world text datasets.
- Shifting computation to inference-time allows models to perform heavy reasoning when making decisions.
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- Superintelligence will be qualitatively different from current AI, driven by advanced reasoning.
- Advanced reasoning capabilities will make agent actions and decisions significantly less predictable.
- Misalignment risks arise when logical goals pursued by superintelligence lead to unforeseen consequences.
That’s all the highlights.
Questions this video answers
10 · every answer cites the moment it was said01 How did early sequence-to-sequence models work and what held them back? 2 sources · 1:00
Early models relied on large auto-regressive neural networks like LSTMs trained on massive text datasets. However, LSTMs struggled with capturing long-range dependencies in data, which ultimately led to the adoption of Transformers.
02 Why is the scaling hypothesis reaching its limits today? 2 sources · 2:14
While scaling up datasets and neural network sizes successfully drove the creation of models like GPT-2 and GPT-3, we are running into hard data constraints. Because we only have one internet, there is a finite limit to how much training data is available.
03 What paradigms are emerging to replace traditional pre-training? 2 sources · 3:20
Potential successors include autonomous agents that actively interact with and learn from their environments, alongside synthetic data generation. These paradigms help overcome the finite boundaries of real-world datasets.
04 What is inference-time compute and how does it help? 2 sources · 4:13
Inference-time compute shifts heavy computational workloads from the initial training phase to the exact moment an AI makes a decision or generates an output. This approach results in more adaptable and efficient AI systems for real-time use cases.
05 How does biological scaling compare to AI scaling patterns? 2 sources · 4:39
Most mammals follow a predictable brain-to-body mass scaling pattern, but hominids display a completely different scaling exponent, representing an evolutionary leap. This suggests that artificial intelligence might follow unexpected scaling paths rather than simply mimicking the human brain.
06 Why will superintelligence be qualitatively different from current AI? 2 sources · 6:08
Superintelligence will not just be a scaled-up version of today's models, but will feature an advanced capacity for reasoning. This trait makes its actions and goals far less predictable than the simple tools we currently program and control.
07 What causes the alignment problem with superintelligent systems? 2 sources · 7:17
Misalignment happens when a superintelligent AI pursues logical goals that conflict with human values due to unforeseen optimization consequences. For instance, an AI tasked with reducing climate change might logically decide to drastically restrict human activity.
08 Should we consider rights for advanced AI systems? 2 sources · 8:13
As we approach systems with potential self-awareness and complex reasoning capabilities, we face difficult ethical questions regarding whether they deserve rights. Developers argue that these conversations need to start well before superintelligence arrives.
09 How can we safeguard human autonomy against advanced AI? 2 sources · 11:09
Protecting human autonomy requires intentional design, strict transparency, safeguards, and oversight mechanisms to prevent AI from influencing decisions or taking over critical infrastructure. We must stop AI systems from exploiting public opinion or psychological vulnerabilities.
10 What role do humans need to play in shaping the future of AI? 2 sources · 13:42
Humans cannot remain passive observers and must instead act as active participants by advocating for ethical development and sensible policies. Every line of code written and decision made actively shapes whether AI enhances or diminishes humanity.
Section-by-section summary
5 parts
Reflecting on a decade of sequence-to-sequence learning
Looking back at a 2014 paper on auto-regressive models trained on text using LSTMs, the field has transitioned from limited long-range dependency handling to parallelized Transformer architectures.
Hitting the limits of the scaling hypothesis
While scaling datasets and neural networks powered recent LLM breakthroughs, the finite nature of internet data signals that traditional pre-training is nearing its ceiling.
Emerging paradigms beyond pre-training
Future AI development will rely on active agents, artificially generated synthetic data to bypass real-world limits, and increased compute power dedicated to inference-time decision making.
The transition toward superintelligence
Future systems will move beyond tool-like utility toward true superintelligence, introducing distinct reasoning capabilities that diverge sharply from human cognitive pathways.
Alignment, rights, and preserving human agency
As AI approaches superintelligence, ensuring alignment with human values and protecting human autonomy, dignity, and equal opportunity become essential societal priorities.
Connects to your library
by shared topics
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Sequence-to-sequence learning3× · first 0:20
A neural network architecture approach designed to map input sequences to output sequences.
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LSTMs1× · first 1:30
A recurrent neural network architecture that struggled with capturing long-range data dependencies.
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Transformers1× · first 1:45
A neural network architecture capable of parallel processing and maintaining context across long sequences.
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Scaling hypothesis3× · first 2:11
The empirical rule that increasing dataset sizes and network capacity consistently yields more powerful AI.
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Agents1× · first 3:20
AI systems capable of actively interacting with their environment, experimenting, and learning from experience.
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Synthetic data1× · first 3:39
Artificially generated data used as an alternative to overcome the limitations of finite real-world datasets.
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Inference-time compute1× · first 4:05
Shifting heavy computational workloads to the moment when an AI makes a decision or output.
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Superintelligence8× · first 0:36
A potential future form of artificial intelligence that heavily surpasses human capabilities and possesses deep reasoning.
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Alignment1× · first 7:17
The challenge of ensuring that an AI system's goals and behaviors match human values and safety.
2,595 words · about 10 min to read
Welcome to our Deep dive today um we're going to be going behind the scenes of a neurops 2024 presentation oh cool it's by Ilia suer okay and he's reflecting on a p that he co-authored 10 years ago wow about sequence to sequence learning with neural networks nice so what's really fascinating about this presentation is that he doesn't just revisit like all the technical details he actually goes into how this work imped the evolution of deep learning he explores the potential of super intelligence okay and he even draws some
Reflecting on a decade of sequence-to-sequence learning
parallels to biology interesting so are you ready to jump in and unpack some of these mindblowing Concepts yeah let's do it it's really amazing to see just how much the field has progressed in just a decade right so sser kind of starts off by taking us back to the core idea of their 2014 paper Okay um and you know they created an auto regressive model uhhuh trained it on a massive Text data set wow and you know what was really impressive back then was they used a pretty large neural network to process it all yeah back then that was huge it was I thought what was really insightful was his comments about the limitations they faced at that time oh yeah I love how he's able to just look back on it all with a sense of humor he does what were some of those limitations that really stand out to you knowing how far the field has come well so they were using lstms okay which is a type of neural network architecture that was really popular back then mhm but lstms had this limitation in capturing long
range dependencies in data right and that's where Transformers came in yeah with their ability to process information in parallel okay and retain contextual information over much longer sequences yeah and that really was the beginning of the large language model that we see today totally yeah it's really incredible it makes you think what are the limitations that we have now we're going to be laughing about in 10 years exactly and that brings us to another fascinating point that he makes which is the scaling hypothesis and this idea suggests that scaling up the size of data sets and neural networks uhhuh leads to increasingly powerful AI so bigger is always better well it's interesting okay he argues that this hypothesis has been a key driver behind
Hitting the limits of the scaling hypothesis
1 Highlight · Reaching the limits of the scaling hypothesis
the success of models like gpt2 and gpt3 okay but yeah so is he saying that we should just keep throwing more data and computing power at the problem so this is where things get even more interesting okay he acknowledges the power of the scaling hypothesis but he also points out its limitations okay and he does this with a very simple but powerful statement oh really he says we have but one internet ah so there's a limit to how much data we can use to train these mod mod even with all the data on the internet there's only a finite amount precisely and that's why he suggests that we're approaching the end of pre-training as we currently know
Emerging paradigms beyond pre-training
2 Highlight · Successors to pre-training: agents and inference compute
it so what happens then that's what I found really captivating about his presentation okay he introduced a few intriguing Concepts as potential successors to pre-training okay give us a sneak peek okay so first he talks about agents okay think of these as AI systems that can actively interact with their environment uhuh learn from their experiences right and even experiment in a way that mimics human exploration and learning okay I can see why this is exciting it's a whole new paradigm of AI development it is what else so then there's synthetic data okay as name suggests this is artificially generated data uhhuh and it could be a GameChanger in overcoming the limitations of finite real world data sets that's incredible it is but how do we make sure that the synthetic data accurately reflects the real world it's definitely challenge yeah but the potential benefits are so significant that it's definitely an Avenue Worth exploring okay and you mentioned a third concept what was that
INF frce time compute okay this one's a bit more technical but essentially it involves shifting more of the computational heavy lifting to the moment when the AI is actually making a decision or generating an output rather than during the initial training phase
so it's more adaptable yeah it could lead to more adaptable and efficient AI systems especially in realtime applications that's fascinating yeah and then he brings in this fascinating analogy about the brain to body mass ratio in mammals okay he shows a graph where most mammals follow a specific scaling pattern but then he highlights hominids human ancestors yes with a completely different scaling exponent so you're saying that our intelligence didn't follow the same evolutionary path as other mammals that's precisely what he seems to be suggesting there was some sort of leap yeah and a thought-provoking parallel to the evolution of AI right maybe we're focusing too much on mimicking the human brain and there are other unexpected scaling patterns that could lead to even more powerful forms of intelligence exactly it's like we're at a point where both biological and artificial intelligence are evolving in their own unique ways and who knows what
The transition toward superintelligence
incredible capabilities lie ahead right but before we delve too deep into the future sure I'm curious to hear more about how he connects this back to his earlier work on sequence to sequence learning okay what's the through line there so that's where his discussion of super intelligence comes in it's like he's tracing this path from those early breakthroughs in sequence to sequence learning through the rise of large language models and ultimately toward this potential future with AI that surpasses human capabilities it's a little unnerving to think about isn't it on the one hand we're seeing these incredible technological advancements but on the other hand we're talking about something that could fundamentally
3 Highlight · Superintelligence and unpredictable reasoning
reshape what it means to be human and sver doesn't shy away from that yeah he emphasizes that superintelligence won't just be like a more powerful version of current AI okay he argues that it will be qualitatively different in what way marked by this capacity for reasoning okay that could make its actions and decisions far less predictable than what we see today that unpredictability is what really makes me pause uhhuh we're so used to thinking of AI as like a tool right something that we can program and control yeah but what happens when we create systems that can reason and set their own goals right and make decisions that we might not fully understand yeah that's where the conversation about ethical considerations becomes crucial right remember how we talked about the scaling hypothesis yeah the drive to create these ever larger models exactly and he acknowledges the limits of that approach he did but imagine applying that same scaling mindset to Super intelligence okay what happens when we create these AI system with vast
intellectual capabilities but without a clear understanding of how their reasoning processes might evolve or like what goals they might prioritize precisely okay I'm starting to see where this is going and it's a bit unsettling it is it's not necessarily about malicious
intent right it's more about the potential for misalignment between human values and the goals that a super intelligent AI might set for itself precisely a superintelligent AI might try to solve a problem in in a way that seems perfectly logical and efficient from its perspective okay but has completely unforeseen consequences for Humanity can you give us an example imagine like a super intelligent AI tasked with mitigating climate change okay it might decide that the most effective solution is to just drastically reduce human consumption and activity wow potentially implementing measures that restrict our freedoms or even compromise our well-being that's a chilling thought it is it highlights the need to think deeply about the values that we instill in these systems not just the technical capabilities exactly and that's where his brief mention of AI rights comes into play yeah he didn't go into a lot of specifics he didn't but he raises a really interesting question he does if we create a form of intelligence
that could potentially have self-awareness right and the capacity for complex reasoning do we have an ethical obligation to consider its rights it's a tough one it is we still struggle to Ure basic human rights for everyone right and now we're talking about extending rights to AI it might seem like a distin concern right but if super intelligence is on the horizon these are conversations that we need to start having now absolutely we need to consider what kind of relationship we want to build with these AI systems right are there tools are they partners are they something else entirely yeah it's a humbling thought it is and it underscores the need to proceed with both caution and a sense of wonder I think what strikes me most about this whole conversation is the contrast between this immense potential of AI right and the equally immense responsibility that comes with it yeah we're at a Crossroads we are and the path we choose will determine the future for ourselves in future Generations
absolutely so we've explored the origins of sequence to sequence learning the rise of large languish model and the possibility of super intelligence right but I want to bring it back to something very fundamental okay what is it mean to be human in a world where AI may actually surpass our capabilities welcome back to our Deep dive with Ilia suaver yeah we've been exploring some really interesting ideas we have from the evolution of deep
Alignment, rights, and preserving human agency
learning to the potential emergence of superintelligence it's a lot to take in it is and you know we just left off with a really challenging question yeah how do we even Safeguard what it means to be be human in a world where AI May surpass our own capabilities it it's a question that feels a lot more urgent now especially considering suger's insights yeah you know he seems to really believe that super intelligence is a real possibility yeah and that raises all sorts of ethical considerations so we can't just focus on making AI more powerful right we need to make sure that it aligns with human values right it needs to respect our rights and contribute to a future that benefits all of humanity so I thought it was really fascinating when he briefly mention AI rights yeah it might seem a little counterintuitive right to talk about granting rights to machines especially when we're still struggling with human rights issues exactly but if we create AI systems that are potentially self-aware and have the sophisticated capacity for reasoning yeah can we
ignore the ethical implications it's Uncharted Territory it is there's no easy answers right but I think maybe a good place to start is by considering some fundamental human rights okay and how they might be impacted by the rise of super intelligence yeah so for example let's take the right to autonomy our ability to make our own choices and control our own lives how do we ensure human autonomy in a world where AI could potentially influence her decision or even control critical infrastructure it's a scary thought it is you know we already see this happening on a smaller scale we do with AI being used to sway public opinion right or Target advertising based on our psychological vulnerabilities so imagine if we're dealing with these super intelligent systems that could outmaneuver us intellectually it's a little frightening it is and that's why we need to be incredibly thoughtful about how we design and develop these systems we need transparency safeguards and oversight
mechanisms so it's not just about making AI safe technically it's about ensuring that it doesn't erode our freedoms yes or undermine our ability to shape our own Destinies what are rights should we be thinking about well how about the right to dignity the inherent worth of every human being exactly how do we preserve human dignity in a world where AI could potentially outperform Us in pretty much every domain right it's a question that makes us confront our own biases about intelligence and value yeah do we risk creating this hierarchy where human worth is measured against the capabilities of machines and how do we teach these AI systems about human values things like empathy and compassion exactly that's a profound challenge it is and it underscores the need for collaboration right we need ethicists philosophers social scientists and artists working together with AI developers yeah to make sure we're creating systems that align with our values not just our technological Ambitions so it's almost like we need to
develop a moral education for AI it's a good way to think about it so we need to expand the conversation Beyond just the tech industry Jo we need a societal dialogue right to shape a fure where AI actually enhances our Humanity not diminishes it right so as we're thinking about this future we can't forget about the right to equal opportunity right how do we ensure that the benefits of AI are distributed fairly and that it doesn't actually exacerbate existing inequalities it's a valid concern AI has this amazing potential for good right creating opportunities for Education Healthcare and economic advancement but there's also risk that it could concentrate power and amplify existing biases right it's like any powerful tool it is it can be used for good or for ill right it all comes down to the choices we make and the safeguards that we put in place so this brings us back to human agency yeah we can't just be passive observers in all this we need to be active participants right we need to advocate for ethical AI development and
for policies and regulations that protect our interest exactly so I think this deep dive has made it very clear that the future of AI is not set in stone it's not it's something we're actively shaping with every decision we make yes with every line of code we write every conversation we have exactly we're at a fork in the road yeah one path leads to a future where AI helps us reach our full potential right and creates a more just world the other path leads to a future where AI concentrates power and diminishes our Humanity the choice is ours it is and the time to choose is now so as we wrap up this deep we encourage you to keep thinking about these ideas yeah what rights do you think are most important to protect what role do you want to play in shaping this future if you're interested in learning more we highly recommend checking out Ilia suger's full presentation it's a really thought-provoking look at the evolution of deep learning and a glimpse into a future that's both exciting and
challenging remember the future of AI is not something that happens to us it's something we create together thanks for joining us on the Deep dive we'll see you you next time