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State of AI Report – October 11, 2022✭

October 18, 2022

72 minutes

Reports

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State of AI Report

Research

– Diffusion models took the computer vision world by storm with impressive text-to-image generation capabilities.
– AI attacks more science problems, ranging from plastic recycling, nuclear fusion reactor control, and natural product discovery.
– Scaling laws refocus on data: perhaps model scale is not all that you need. Progress towards a single model to rule them all.
– Community-driven open sourcing of large models happens at breakneck speed, empowering collectives to compete with large labs.
– Inspired by neuroscience, AI research are starting to look like cognitive science in its approaches.
Industry
– Have upstart AI semiconductor startups made a dent vs. NVIDIA? Usage statistics in AI research shows NVIDIA ahead by 20-100x.
– Big tech companies expand their AI clouds and form large partnerships with A(G)I startups.
– Hiring freezes and the disbanding of AI labs precipitates the formation of many startups from giants including DeepMind and OpenAI.
– Major AI drug discovery companies have 18 clinical assets and the ﬁrst CE mark is awarded for autonomous medical imaging diagnostics.
– The latest in AI for code research is quickly translated by big tech and startups into commercial developer tools.
Politics
– The chasm between academia and industry in large scale AI work is potentially beyond repair: almost 0% of work is done in academia.
– Academia is passing the baton to decentralized research collectives funded by non-traditional sources.
– The Great Reshoring of American semiconductor capabilities is kicked off in earnest, but geopolitical tensions are sky high.
– AI continues to be infused into a greater number of defense product categories and defense AI startups receive even more funding.
Safety
– AI Safety research is seeing increased awareness, talent, and funding, but is still far behind that of capabilities research.

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Scorecard: Reviewing our predictions from 2021

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Grade

Evidence

Transformers replace RNNs to learn world models with which RL
agents surpass human performance in large and rich games.

Yes

DeepMind’s Gato model makes progress in this direction in which a transformer
predicts the next state and action, but it is not trained with RL. University of
Geneva’s GPT-like transformer model IRIS solves tasks in Atari environments.

ASML’s market cap reaches $500B.

Current market cap is circa $165B (3 Oct 2022)

Anthropic publishes on the level of GPT, Dota, AlphaGo to establish
itself as a third pole of AGI research.

Not yet.

A wave of consolidation in AI semiconductors with at least one of
Graphcore, Cerebras, SambaNova, Groq, or Mythic being acquired by
a large technology company or major semiconductor incumbent.

No new announced AI semiconductor consolidation has happened yet.

Small transformers + CNN hybrid models match current SOTA on
ImageNet top-1 accuracy (CoAtNet-7, 90.88%, 2.44B params) with
10x fewer parameters.

Yes

MaxViT from Google with 475M parameters almost matched (89.53%) CoAtNet-7’s
performance (90.88%) on ImageNet top-1 accuracy.

DeepMind shows a major breakthrough in the physical sciences.

Yes

Three (!) DeepMind papers in mathematics and material science.

The JAX framework grows from 1% to 5% of monthly repos created
as measured by Papers With Code.

JAX usage still accounts for <1% of monthly repos on Papers With Code. A new AGI-focused research company is formed with signiﬁcant backing and a roadmap that’s focused on a sector vertical (e.g. developer tools, life science). Yes Adept.ai was co-founded by the authors of the Transformer and is focused on AGI via software tool use automation. #stateofai | 10 Introduction | Research | Industry | Politics | Safety | Predictions Bonus! Predictions, revisited – better late than never! Year Prediction Grade Evidence 2018 Access to Taiwanese and South Korean semiconductor companies becomes an explicit part of the trade war between US and China. Yes US CHIPS Act 2022 prevents recipients to expand operations in China. TSMC caught in the crosshairs. 2018 The government of an OECD country blocks the acquisition of a leading ML company by a US or Chinese HQ’d tech company. Yes The UK, amongst others, blocked the acquisition of Arm by NVIDIA. 2019 As AI systems become more powerful, governance of AI becomes a bigger topic and at least one major AI company makes a substantial change to their governance model. Yes Anthropic set up as a public beneﬁt corporation. 2020 Facebook/Meta makes a major breakthrough in AR/VR with 3D computer vision. 2020 2020 Sort of Implicitron in PyTorch3D. Not applied to AR/VR yet. Chinese and European defense-focused AI startups collectively raise >$100M in the next 12 months.

Yes

Helsing (Germany) raised $100M Series A in 2022.

NVIDIA does not end up completing its acquisition of Arm.

Yes

Deal is formally cancelled in 2022.

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Section 1: Research

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2021 Prediction: DeepMind’s breakthroughs in the physical sciences (1/3)
In 2021, we predicted: “DeepMind releases a major research breakthrough in the physical sciences.” The company
has since made signiﬁcant advancements in both mathematics and materials science.
● One of the decisive moments in mathematics is formulating
a conjecture, or a hypothesis, on the relationship between
variables of interest. This is often done by observing a large
number of instances of the values of these variables, and
potentially using data-driven conjecture generation methods.
But these are limited to low-dimensional, linear, and
generally simple mathematical objects.
● In a Nature article, DeepMind researchers proposed an iterative workﬂow involving mathematicians and a
supervised ML model (typically a NN). Mathematicians hypothesize a function relating two variables (input X(z)
and output Y(z)). A computer generates a large number of instances of the variables and a NN is ﬁt to the data.
Gradient saliency methods are used to determine the most relevant inputs in X(z). Mathematicians can turn
reﬁne their hypothesis and/or generate more data until the conjecture holds on a large amount of data.

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2021 Prediction: DeepMind’s breakthroughs in the physical sciences (2/3)
In 2021, we predicted: “DeepMind releases a major research breakthrough in the physical sciences.” The company
has since made signiﬁcant advancements in both mathematics and materials science.
● DeepMind researchers used their framework in a collaboration with
mathematics professors from the University of Sydney and the University of
Oxford to (i) propose an algorithm that could solve a 40 years-long standing
conjecture in representation theory and (ii) prove a new theorem in the
study of knots.
● DeepMind made an important contribution in materials science as
well. It showed that the exact functional in Density Functional
Theory, an essential tool to compute electronic energies, can be
efﬁciently approximated using a neural network. Notably, instead of
constraining the neural network to verify mathematical constraints
of the DFT functional, researchers simply incorporate them into the
training data to which they ﬁt the NN.

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2021 Prediction: DeepMind’s breakthroughs in the physical sciences (3/3)
In 2021, we predicted: “DeepMind releases a major research breakthrough in the physical sciences.” The company
has since made signiﬁcant advancements in both mathematics and materials science.
● DeepMind repurposed AlphaZero (their RL model trained to beat the best
human players of Chess, Go and Shogi) to do matrix multiplication. This
AlphaTensor model was able to ﬁnd new deterministic algorithms to
multiply two matrices. To use AlphaZero, the researchers recast the matrix
multiplication problem as a single-player game where each move
corresponds to an algorithm instruction and the goal is to zero-out a tensor
measuring how far from correct the predicted algorithm is.
● Finding faster matrix multiplication algorithms, a seemingly simple and
well-studied problem, has been stale for decades. DeepMind’s approach not
only helps speed up research in the ﬁeld, but also boosts matrix
multiplication based technology, that is AI, imaging, and essentially
everything happening on our phones.

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Reinforcement learning could be a core component of the next fusion breakthrough
DeepMind trained a reinforcement learning system to adjust the magnetic coils of Lausanne’s TCV (Variable
Conﬁguration tokamak). The system’s ﬂexibility means it could also be used in ITER, the promising next
generation tokamak under construction in France.
● A popular route to achieving nuclear fusion requires
conﬁning extremely hot plasma for enough time
using a tokamak.
● A major obstacle is that the plasma is unstable, loses
heat and degrades materials when it touches the
tokamak’s walls. Stabilizing it requires tuning the
magnetic coils thousands of times per second.
● DeepMind’s deep RL system did just that: ﬁrst in a
simulated environment and then when deployed in
the TCV in Lausanne. The system was also able to
shape the plasma in new ways, including making it
compatible with ITER’s design.

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Predicting the structure of the entire known proteome: what could this unlock next?
Since its open sourcing, DeepMind’s AlphaFold 2 has been used in hundreds of research papers. The company has
now deployed the system to predict the 3D structure of 200 million known proteins from plants, bacteria, animals
and other organisms. The extent of the downstream breakthroughs enabled by this technology – ranging from
drug discovery to basic science – will need a few years to materialize.
● There are 190k empirically determined 3D structures in the Protein Data
Bank today. These have been derived through X-Ray crystallography and
cryogenic electron microscopy.
● The ﬁrst release of AlphaFold DB in July 20221 included 1M predicted
protein structures.
● This new release 200x’s the database size. Over 500,000 researchers from
190 countries have made use of the database.
● AlphaFold mentions in AI research literature is growing massively and is
predicted to triple year on year (right chart).

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Language models for proteins: a familiar story of open source and scaled models
Researchers independently applied language models to the problems of protein generation and structure
prediction while scaling model parameter. They both report large beneﬁts from scaling their models.
● Salesforce researchers ﬁnd that scaling their LMs allows them to better capture the
training distribution of protein sequences (as measured by perplexity).
● Using the 6B param ProGen2, they generated proteins with similar folds to natural
proteins, but with a substantially different sequence identity. But to unlock the full
potential of scale, the authors insist that more emphasis be placed on data distribution.
● Meta et al. introduced the ESM family of protein LMs, whose sizes range from 8M to 15B
(dubbed ESM-2) parameters. Using ESM-2, they build ESMFold to predict protein structure.
They show that ESMFold produces similar predictions to AlphaFold 2 and RoseTTAFold,
but is an order of magnitude faster.
● This is because ESMFold doesn’t rely on the use of multiple
sequence alignments (MSA) and templates like AlphaFold 2 and
RoseTTAFold, and instead only uses protein sequences.

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OpenCell: understanding protein localization with a little help from machine learning
Researchers used CRISPR-based endogenous tagging — modifying genes by illuminating speciﬁc aspects of the
proteins’ function — to determine protein localization in cells. They then used clustering algorithms to identify
protein communities and formulate mechanistic hypotheses on uncharacterized proteins.
● An important goal of genomic research is to understand where proteins localize and
how they interact in a cell to enable particular functions. With its dataset of 1,310
tagged proteins across ~5,900 3D images, the OpenCell initiative enabled
researchers to draw important links between spatial distribution of proteins, their
functions, and their interactions.
● Markov clustering on the graph of protein interactions successfully delineated
functionally related proteins. This will help researchers better understand so-far
uncharacterized proteins.
● We often expect ML to deliver deﬁnitive predictions. But here as with math, ML ﬁrst
gives partial answers (here clusters), humans then interpret, formulate and test
hypotheses, before delivering a deﬁnitive answer.

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Plastic recycling gets a much-needed ML-engineered enzyme
Researchers from UT Austin engineered an enzyme capable of degrading PET, a type of plastic responsible for
12% of global solid waste.
● The PET hydrolase, called FAST-PETase, is more
robust to different temperatures and pH levels
than existing ones.
● FAST-PETase was able to almost completely
degrade 51 different products in 1 week.
● They also showed that they
could resynthesize PET from
monomers recovered from
FAST-PETase degradation,
potentially opening the way
for industrial scale
closed-loop PET recycling.

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Beware of compounded errors: in science, ML in and garbage out?
With the increased use of ML in quantitative sciences, methodological errors in ML can leak to these disciplines.
Researchers from Princeton warn of a growing reproducibility crisis in ML-based science driven in part by one
such methodological error: data leakage.
● Data leakage is an umbrella term covering all cases where data
that shouldn’t be available to a model in fact is. The most
common example is when test data is included in the training
set. But the leakage can be more pernicious: when the model
uses features that are a proxy of the outcome variable or when
test data come from a distribution which is different from the
one about which the scientiﬁc claim is made.
● The authors argue that the ensuing reproducibility failures in ML-based science are systemic: they study 20
reviews across 17 science ﬁelds examining errors in ML-based science and ﬁnd that data leakage errors
happened in every one of the 329 papers the reviews span. Inspired by the increasingly popular model cards in
ML, the authors propose that researchers use model info sheets designed to prevent data leakage issues.

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OpenAI uses Minecraft as a testbed for computer-using agents
OpenAI trained a model (Video PreTraining, VPT) to play Minecraft from video frames using a small amount of
labeled mouse and keyboard interactions. VPT is the ﬁrst ML model to learn to craft diamonds, “a task that usually
takes proﬁcient humans over 20 minutes (24,000 actions)”.
● OpenAI gathered 2,000 hours of video labeled with
mouse and keyboard actions and trained an inverse
dynamics model (IDM) to predict actions given past
and future frames – this is the PreTraining part.
● They then used the IDM to label 70 hours of video
on which they trained a model to predict actions
given only past video frames.
● They show that the model can be ﬁne-tuned with
imitation learning and reinforcement learning (RL)
to achieve a performance which is too hard to reach
using RL from scratch.

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Corporate AI labs rush into AI for code research
OpenAI’s Codex, which drives GitHub Copilot, has impressed the computer science community with its ability to
complete code on multiple lines or directly from natural language instructions. This success spurred more
research in this space, including from Salesforce, Google and DeepMind.
● With the conversational CodeGen, Salesforce researchers leverage the language
understanding of LLMs to specify coding requirements in multiturn language
interactions. It is the only open source model to be competitive with Codex.
● A more impressive feat was achieved by Google’s LLM PaLM, which achieves a similar
performance to Codex, but with 50x less code in its training data (PaLM was trained
on a larger non-code dataset). When ﬁne-tuned on Python code, PaLM outperformed
(82% vs. 71.7% SOTA) peers on Deepﬁx, a code repair task.
● DeepMind’s AlphaCode tackles a different problem: the generation of whole
programs on competitive programming tasks. It ranked in the top half on Codeforces,
a coding competitions platform. It was pre-trained on GitHub data and ﬁne-tuned on
Codeforces problems and solutions. Millions of possible solutions are then sampled,
ﬁltered, and clustered to obtain 10 ﬁnal candidate submissions.

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Five years after the Transformer, there must be some efﬁcient alternative, right… right?
The attention layer at the core of the transformer model famously suffers from a quadratic dependence on its
input. A slew of papers promised to solve this, but no method has been adopted. SOTA LLMs come in different
ﬂavors (autoencoding, autoregressive, encoder-decoders), yet all rely on the same attention mechanism.
● A Googol of transformers have been trained over the past few years,
costing millions (billions?) to labs and companies around the world.
But so-called “Efﬁcient Transformers” are nowhere to be found in
large-scale LM research (where they would make the biggest
difference!). GPT-3, PaLM, LaMDA, Gopher, OPT, BLOOM, GPT-Neo,
Megatron-Turing NLG, GLM-130B, etc. all use the original attention
layer in their transformers.
● Several reasons can explain this lack of adoption: (i) the potential
linear speed-up is only useful for large input sequences, (ii) the new
methods introduce additional constraints that make the architectures
less universal, (iii) the reported efﬁciency measures don’t translate in
actual computational cost and time savings.

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Mathematical abilities of Language Models largely surpass expectations
Built on Google’s 540B parameter LM PaLM, Google’s Minerva achieves a 50.3% score on the MATH benchmark
(43.4 pct points better than previous SOTA), beating forecasters expectations for best score in 2022 (13%).
Meanwhile, OpenAI trained a network to solve two mathematical olympiad problems (IMO).
● Google trained its (pre-trained) LLM PaLM on an additional 118GB dataset of scientiﬁc
papers from arXiv and web pages using LaTeX and MathJax. Using chain of thought
prompting (including intermediate reasoning steps in prompts rather than the ﬁnal
answer only) and other techniques like majority voting, Minerva improves the SOTA on
most datasets by at least double digit pct points.
● Minerva only uses a language model and doesn’t explicitly encode formal
mathematics. It is more ﬂexible but can only be automatically evaluated on its ﬁnal
answer rather than its whole reasoning, which might justify some score inﬂation.
● In contrast, OpenAI built a (transformer-based) theorem prover built in the Lean
formal environment. Different versions of their model were able to solve a number of
problems from AMC12 (26), AIME (6) and IMO (2) (increasing order of difﬁculty).

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Fast progress in LLM research renders benchmarks obsolete, but a BIG one comes to help
Only 66% of machine learning benchmarks have received more than 3 results at different time points, and many
are solved or saturated soon after their release. BIG (Beyond the Imitation Game), a new benchmark designed by
444 authors across 132 institutions, aims to challenge current and future language models.
● A study from the University of Vienna, Oxford, and FHI
examined 1,688 benchmarks for 406 AI tasks and
identiﬁed different submission dynamics (see right).
● They note that language benchmarks in particular tend
to be quickly saturated.
● Rapid LLM progress and emerging capabilities seem to outrun current benchmarks. As a result, much of this
progress is only captured through circumstantial evidence like demos or one-off breakthroughs, and/or
evaluated on disparate dedicated benchmarks, making it difﬁcult to identify actual progress.
● The new BIG benchmark contains 204 tasks, all with strong human expert baselines, which evaluate a large set
of LLM capabilities from memorization to multi-step reasoning. They show that, for now, even the best models
perform poorly on the BIG benchmark.

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Ducking language model scaling laws: more data please
DeepMind revisited LM scaling laws and found that current LMs are signiﬁcantly undertrained: they’re not trained
on enough data given their large size. They train Chinchilla, a 4x smaller version of their Gopher, on 4.6x more
data, and ﬁnd that Chinchilla outperforms Gopher and other large models on BIG-bench.
● Empirical LM scaling laws determine, for a ﬁxed compute budget,
the model and training data sizes that should be used. Past work
from OpenAI had established that model size should increase faster
than training data size as the compute budget increases.
● DeepMind claims that the model size and the number of training
tokens should instead increase at roughly the same rate.
● Compared to OpenAI’s work, DeepMind uses larger models to derive
their scaling laws. They emphasize that data scaling leads to better
predictions from multibillion parameter models.
● Following these new scaling laws, Chinchilla (70B params) is trained on 1.4T tokens. Gopher (230B) on 300B.
● Though trained with the same compute budget, the lighter Chinchilla should be faster to run.

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Ducking language model scaling laws: emergence
While model loss can be reasonably predicted as a function of size and compute using well-calibrated scaling
laws, many LLM capabilities emerge unpredictably when models reach a critical size. These acquired capabilities
are exciting, but the emergence phenomenon makes evaluating model safety more difﬁcult.
● Emergence is not fully understood: it could be that for multi-step reasoning tasks,
models need to be deeper to encode the reasoning steps. For memorization tasks,
having more parameters is a natural solution. The metrics themselves may be part
of the explanation, as an answer on a reasoning task is only considered correct if its
conclusion is. Thus despite continuous improvements with model size, we only
consider a model successful when increments accumulate past a certain point.
● A possible consequence of emergence is that there are a range of tasks that are out
of reach of current LLMs that could soon be successfully tackled.
● Alternatively, deploying LLMs on real-world tasks at larger scales is more uncertain
as unsafe and undesirable abilities can emerge. Alongside the brittle nature of ML
models, this is another feature practitioners will need to account for.

Arithmetics

Fig. of speech

Multi-task NLU

Transliteration

Training FLOPs

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Teach a machine to ﬁsh: tool use as the next frontier?
Language models can learn to use tools such as search engines and calculators, simply by making available text
interfaces to these tools and training on a very small number of human demonstrations.
● OpenAI’s WebGPT was the ﬁrst model to demonstrate this convincingly by
ﬁne-tuning GPT-3 to interact with a search engine to provide answers
grounded with references. This merely required collecting data of humans
doing this task and converting the interaction data into text that the
model could consume for training by standard supervised learning.
Importantly, the use of increasing amounts of human demonstration data
signiﬁcantly increased the truthfulness and informativeness of answers
(right panel, white bars for WebGPT), a signiﬁcant advance from when we
covered truthfulness evaluation in our 2021 report (slide 44).
● Adept, a new AGI company, is commercializing this paradigm. The company
trains large transformer models to interact with websites, software
applications and APIs (see more at adept.ai/act) in order to drive workﬂow
productivity.

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Looking back: three eras of compute in machine learning
A study documents the incredible acceleration of compute requirements in machine learning. It identiﬁes 3 eras
of machine learning according to training compute per model doubling time. The Pre-Deep Learning Era
(pre-2010, training compute doubled every 20 months), the Deep Learning Era (2010-15, doubling every 6
months), and the Large-Scale Era (2016-present, a 100-1000x jump, then doubling every 10 months).

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Diffusion models take over text-to-image generation and expand into other modalities
When we covered diffusion models in the 2021 Report (slide 36), they were overtaking GANs in image generation
on a few benchmarks. Today, they are now the undisputable SOTA for text-to-image generation, and are diffusing
(pun intended) into text-to-video, text generation, audio, molecular design and more.
● Diffusion models (DMs) learn to reverse successive noise additions to images by
modeling the inverse distribution (generating denoised images from noisy ones) at each
step as a Gaussian whose mean and covariance are parametrized as a neural network.
DMs generate new images from random noise.
● Sequential denoising makes them slow, but new techniques (like denoising in a
lower-dimensional space) allow them to be faster at inference time and to generate
higher-quality samples (classiﬁer-free guidance – trading off diversity for ﬁdelity).
● SOTA text-to-image models like DALL-E 2, Imagen and Stable Diffusion are based on
DMs. They’re also used in controllable text generation (generating text with a pre-deﬁned
structure or semantic context), model-based reinforcement learning, video generation
and even molecular generation.

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DALL-E 2, Imagen and Parti…the battle for text-to-image generation rages
The second iteration of OpenAI’s DALL-E, released in April 2022, came with a signiﬁcant jump in the quality of
generated images. Soon after, another at least equally impressive diffusion-based model came from Google
(Imagen). Meanwhile, Google’s Parti took a different, autoregressive, route.
DALL-E 2

Imagen

Parti-350M

Parti-20B

● Instead of using a diffusion model, Parti treats text-to-image generation as a simple sequence-to-sequence
task, where the sequence to be predicted is a representation of the pixels of the image. Notably, as the number
of parameters and training data in Parti are scaled, the model acquires new abilities like spelling.
● Other impressive text-to-image models include GLIDE (OpenAI) and Make-a-Scene (Meta — can use both text
and sketches), which predate DALL-E 2, and CogView2 (Tsinghua, BAAI — both English and Chinese).

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The text-to-image diffusion model frenzy gives birth to new AI labs
Stability.ai and Midjourney came out of seemingly nowhere with text-to-image models that rival those of
established AI labs. Both have APIs in beta, Midjourney is reportedly proﬁtable, and Stability has already
open-sourced their model. But more on their emergence and research dynamics in our Politics section.

Image Credits: Fabian Stelzer

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The text-to-video generation race has started
Research on diffusion-based text-to-video generation was kicked-off around April 2022, with work from Google
and the University of British Columbia. But in late September, new research from Meta and Google came with a
jump in quality, announcing a sooner-than-expected DALL-E moment for text-to-video generation.
Make-a-Video

Imagen Video

Phenaki

● Meta made the ﬁrst splash from Big Tech in text-to-video generation by releasing Make-a-Video, a diffusion
model for video generation.
● In an eerily similar fashion to text-to-image generation, Google then published (less than a week later) almost
simultaneously two models: one diffusion-model based, Imagen, and another non diffusion-model based,
Phenaki. The latter can dynamically adapt the video via additional prompts.

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Closed for 14 months: community-driven open sourcing of GPT et al.
Landmark models from OpenAI and DeepMind have been implemented/cloned/improved by the open source
community much faster than we’d have expected.
GPT-NeoX (20B)
GPT-j (6B)

GPT-3 (175B)

Pan-Gu (200B)

Megatron
Turing-NLG (137B)

FLAN (137B)

BLOOM (176B)

Chinchilla
(70B)

Gopher (280B)

OPT (175B)
Aug 2022

Jan 2022
June 2020

May 2021

Aug 2021

HyperCLOVA (204B)

May 2022

Sep 2021

Yuan 1.0 (246B)

LaMDA (280B)

Jurassic-1 Jumbo (204B)
Ernie 3.0 Titan (260B)
Open-sourced models in red

GLM (130B)
PaLM (540B)

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Closed for 15 months: community-driven open sourcing of DALL-E et al.
Landmark models from OpenAI and DeepMind have been implemented/cloned/improved by the open source
community much faster than we’d have expected.

DALL-E

Make-a-scene

DALL-E 2

Imagen

Parti

May 2022

June 2022

Stable Diffusion

Apr 2022
Jan 2021

Mar 2022

July 2022

Aug 2022

DALL-E mini

CogView2

Open-sourced models in red

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Closed for 35 months: community-driven open sourcing of AlphaFold et al.
Landmark models from OpenAI and DeepMind have been implemented/cloned/improved by the open source
community much faster than we’d have expected.

AlphaFold 1

ESM-1B

AlphaFold 2

RosettaFold

OpenFold
Aug 2022

Aug 2018

Apr 2019

July 2021

June 2022

ESM 2

Open-sourced models in red

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LLMs empower robots to execute diverse and ambiguous instructions
Thanks to their large range of capabilities, LLMs could in principle enable robots to perform any task by
explaining its steps in natural language. But LLMs have little contextual knowledge of the robot’s environment
and its abilities, making their explanations generally infeasible for the robot. PaLM-SayCan solves this.
● Given an ambiguous instruction “I spilled my drink, can you help?”, a carefully
prompt-engineered LLM (e.g. Google’s PaLM) can devise a sequence of abstract steps to
pick up and bring you a sponge. But any given skill (e.g. pick up, put down) needs to be
doable by the robot in concordance with its environment (e.g. robot sees a sponge).
● To incentivise the LLM to output feasible instructions, SayCan maximises the likelihood
of an instruction being successfully executed by the robot.
● Assume the robot can execute a set of skills. Then, for any given instruction and state, the
system selects the skill that maximizes: the probability of a given completion (restricted
to the set of available skills) times the probability of success given the completion and
the current state. The system is trained using reinforcement learning.
● Researchers tested SayCan on 101 instructions from 7 types of language instructions. It
was successful in planning and execution 84% and 74% of the time respectively.

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2021 Prediction: in vision, convolutional networks want a fair ﬁght with transformers…
The introduction of Vision Transformers (ViT) and other image transformers last year as SOTA models on imaging
benchmarks announced the dawn of ConvNets. Not so fast: work from Meta and UC Berkeley argues that
modernizing ConvNets gives them an edge over ViTs.
● The researchers introduce ConvNeXt, a ResNet which is augmented
with the recent design choices introduced in hierarchical vision
Transformers like Swin, but doesn’t use attention layers.
● ConvNeXt is both competitive with Swin Transformer and ViT on
ImageNet-1K and ImageNet-22K and beneﬁts from scale like them.
● Transformers quickly replaced recurrent neural networks in language
modeling, but we don’t expect a similar abrupt drop-off in ConvNets
usage, especially in smaller scale ML use-cases.
● Meanwhile, our 2021 prediction of small transformers + CNN hybrid
models manifested in MaxViT from Google with 475M parameters
almost matching (89.53%) CoAtNet-7’s performance (90.88%) on
ImageNet top-1 accuracy.

stateof.ai 2022

#stateofai | 39

…but the inevitable vision and language modeling uniﬁcation continues…
Self-supervision techniques used to train transformers on text are now transposed almost as is to images and are
achieving state of the art results on ImageNet-1K.
● Transformer-based autoencoder LMs are trained to predict randomly
masked words in large text corpora. This results in powerful models that
are SOTA in language modeling tasks (e.g. BERT).
● While masking a word in a sentence makes the sentence nonsensical
and creates a challenging task for LMs, reconstructing a few randomly
masked pixels in images is trivial thanks to neighbouring pixels.
● The solution: mask large patches of pixels (e.g. 75% of the pixels). Meta use this and other adjustments (the
encoder only sees visible patches, the decoder is much smaller than the encoder) to pre-train a ViT-Huge model
on ImageNet-1K and then ﬁne-tune it to achieve a task-best 87.8% top-1 accuracy.
● Self-supervised learning isn’t new to computer vision (see for e.g. Meta’s SEER model). Nor are masking
techniques (e.g. Context encoders, or a more recent SiT). But this work is further evidence that SOTA techniques
in language transition seamlessly vision. Can domains uniﬁcation be pushed further?

stateof.ai 2022

#stateofai | 40

2021 Prediction:…culminating in a single transformer to rule them all?
Transformers trained on a speciﬁc task (via supervised or self-supervised learning) can be used for a broader set
of tasks via ﬁne-tuning. Recent works show that a single transformer can be directly and efﬁciently trained on
various tasks across different modalities (multi-task multimodal learning).
● Attempts at generalist multitask, multimodal models date back to at
least Google’s “One model to learn them all” (2017), which tackled 8
tasks in image, text and speech. DeepMind’s Gato brings this effort to
another level: researchers train a 1.2B parameter transformer to
perform hundreds of tasks in robotics, simulated environments, and
vision and language. This partially proves our 2021 Prediction.
● They showed that scaling consistently improved the model, but it was
kept “small” for live low-latency robotics tasks.
● To train their model on different modalities, all data was serialized into a sequence of tokens which are
embedded in a learned vector space. The model is trained in a fully supervised fashion.
● Separately: With data2vec, on a narrower set of tasks, Meta devised a uniﬁed self-supervision strategy across
modalities. But for now, different transformers are used for each modality.

stateof.ai 2022

#stateofai | 41

2021 Prediction: transformers for learning in world models in reinforcement learning
In 2021, we predicted: “Transformers replace RNNs to learn world models with which RL agents surpass human
performance in large and rich game environments.” Researchers from the University of Geneva used a GPT-like
transformer to simulate the world environment. They showed that their agent (dubbed IRIS) was sample efﬁcient
and surpassed human performance on 10 of the 26 games of Atari. IRIS was notably the best method among the
ones that don’t use lookahead search.

stateof.ai 2022

#stateofai | 42

Transformers are becoming truly cross-modality
In the 2020 State of AI Report we predicted that transformers would expand beyond NLP to achieve state of the
art in computer vision. It is now clear that transformers are a candidate general purpose architecture. Analysing
transformer-related papers in 2022 shows just how ubiquitous this model architecture has become.

41%
81%
22%

16%
9%
7%
5%

stateof.ai 2022

#stateofai | 43

NeRFs expand into their own mature ﬁeld of research
The seminal NeRF paper was published in March 2020. Since then, fundamental improvements to the methods
and new applications have been quickly and continuously developed. For example, more than 50 papers on NeRF
alone appeared at CVPR in 2022.
● From last year’s Report (slide 18): Given multiple views of an image, NeRF uses a
multilayered perceptron to learn a representation of the image and to render
new views of it. It learns a mapping from every pixel location and view direction
to the color and density at that location.
● Among this year’s work, Plenoxels stands out by removing the MLP altogether
and achieving a 100x speedup in NeRF training. Another exciting direction was
rendering large scale sceneries from a few views with NeRFs, whether city-scale
(rendering entire neighborhoods of San Francisco with Block-NeRF) or
satellite-scale with Mega-NeRF*.
● Given the current quality of the results and the ﬁeld’s rate of progress, we expect that in a year or two, NeRFs
will feature prominently in our industry section.
*You can better appreciate NeRF research by checking demos. E.g.
Block-NeRF, NeRF in the dark, Light Field Neural Rendering

stateof.ai 2022

#stateofai | 44

Treating bacterial infections by data-driven personalised selection of antibacterial agents
Resistance to antibacterial agents is common and often arises as a result of a different pathogen already present
in a patient’s body. So how should doctors ﬁnd the right antibiotic that cures the infection but doesn’t render the
patient susceptible to a new infection?
● By comparing the microbiome proﬁles of >200,000 patients with urinary tract or wound infections who were
treated with known antibiotics before and after their infections, ML can be used to predict the risk of
treatment-induced gain of resistance on a patient-speciﬁc level.
● Indeed, urinary tract infection (UTI) patients treated with antibiotics that the ML system would not have
recommended resulted in signiﬁcantly resistance (E). Both UTI (F) and wound infection (G) patients would suffer
far fewer reinfections if they’d have been prescribed antibiotics according to the ML system.

stateof.ai 2022

#stateofai | 45

Interpreting small molecule mass spectra using transformers
Tandem mass spectrometry (MS/MS) is commonly used in metabolomics, the study of small molecules in
biological samples. Less than 10% of small molecules can be identiﬁed from spectral reference libraries as most
of nature’s chemical space is unknown. Transformers enable fast, accurate, in silico, characterization of the
molecules in metabolic mixtures, enabling biomarker and natural product drug discovery at scale.
● Very few biological samples can typically
be identiﬁed from reference libraries.
● Property-prediction transformers
outperform at predicting a suite of
medicinally-relevant chemical properties
like solubility, drug likeness, and synthetic
accessibility directly from MS/MS, without
using structure prediction intermediates or
reference lookups.

stateof.ai 2022

#stateofai | 46

Drug discovery, the ﬂagship “AI for good” application, is not immune to misuse
Researchers from Collaborations Pharmaceuticals and King’s College London showed that machine learning
models designed for therapeutic use can be easily repurposed to generate biochemical weapons.
● The researchers had trained their “MegaSyn” model to maximize bioactivity
and minimize toxicity. To design toxic molecules, they kept the same model,
but now simply training it to maximize both bioactivity and toxicity. They used
a public database of drug-like molecules.
● They directed the model towards generation of the nerve agent VX, known to
be one of the most toxic chemical warfare agents.
● However, as is the case with regular drug discovery, ﬁnding molecules with a
high predicted toxicity doesn’t mean it is easy to make them. But as drug
discovery with AI in the loop is being dramatically improved, we can imagine
best practices in drug discovery diffusing into building cheap biochemical
weapons.

stateof.ai 2022

#stateofai | 47

Compared to US AI research, Chinese papers focus more on surveillance related-tasks.
These include autonomy, object detection, tracking, scene understanding, action and
speaker recognition.
Comparing data modalities in Chinese vs. US papers

Comparing machine learning tasks in Chinese vs. US papers

Red = more common in China

Blue = more common in the US

stateof.ai 2022

#stateofai | 48

While US-based authors published more AI papers than Chinese peers in 2022, China and
Chinese institutions are growing their output at a faster rate
# papers published in 2022 and change vs. 2021
-2%
+3%
+10%
+4%

+24%

+11%

# papers published in 2022 and change vs. 2021
+13%
+13%
+11%
+1%

+27%

stateof.ai 2022

#stateofai | 49

The China-US AI research paper gap explodes if we include the Chinese-language
database, China National Knowledge Infrastructure
Chinese institutions author 4.5x the number of papers than American institutions since 2010.

stateof.ai 2022

#stateofai | 50

Section 2: Industry

stateof.ai 2022

#stateofai | 51

Do upstart AI chip companies still have a chance vs. NVIDIA’s GPU?
NVIDIA’s FY 2021 datacenter revenue came in at $10.6B. In Q4 2021, they recognised $3.26B, which on an
annualised basis is greater than the combined valuation of top-3 AI semiconductor startups. NVIDIA has over 3
million developers on their platform and the company’s latest H100 chip generation is expected to deliver 9x
training performance vs. the A100. Meanwhile, revenue ﬁgures for Cerebras, SambaNova and Graphcore are not
publicly available.
Latest private valuation

Annualised datacenter revenue

$5.1 billion
$4 billion

$13 billion

$2.8 billion

stateof.ai 2022

#stateofai | 52

NVIDIA’s chips are the most popular in AI research papers…and by a massive margin
GPUs are 131x more commonly used than ASICs, 90x more than chips from Graphcore, Habana, Cerebras,
SambaNova and Cambricon combined, 78x more than Google’s TPU, and 23x more than FPGAs.
log scale

23x
78-131x

stateof.ai 2022

#stateofai | 53

For NVIDIA, the V100 is most popular, and Graphcore is most used amongst challengers
The V100, released in 2017, is NVIDIA’s workhorse chip, followed by the A100 that was released in 2020. The
H100 is hotly awaited in 2022. Of the major AI chip challengers, Graphcore is cited most often.
Number of AI papers citing use of speciﬁc NVIDIA cards

Number of AI papers citing use of speciﬁc AI chip startups

stateof.ai 2022

#stateofai | 54

NVIDIA fails to acquire Arm and grows its revenue 2.5x and valuation 2x during the deal
Announced at $40B, NVIDIA’s attempted acquisition of Arm fell through due to signiﬁcant geopolitical and anti
competition pushback. Nonetheless, NVIDIA’s enterprise value grew by $295B during the period (!!)
Deal announced

Deal cancelled

stateof.ai 2022

#stateofai | 55

NVIDIA reaps rewards from investing in AI research tying up hardware and software
NVIDIA has been investing heavily in AI research and producing some of the best works in imaging over the years.
For instance, their latest work on view synthesis just won the best paper award at SIGGRAPH, one of the most
prestigious computer graphics conferences. But NVIDIA has now gone a step further and applied their
reinforcement learning work to design their next-generation AI chip, the H100 GPU.

stateof.ai 2022

#stateofai | 56

David teaming up with Goliath: training large models requires compute partnerships
The hyperscalers and challenger AI compute providers are tallying up major AI compute partnerships, notably
Microsoft’s $1B investment into OpenAI. We expect more to come.

None yet?
None yet?
stateof.ai 2022

#stateofai | 57

In a gold rush for compute, companies build bigger than national supercomputers
“We think the most beneﬁts will go to whoever has the biggest computer” – Greg Brockman, OpenAI CTO

im
at

Future NVIDIA H100 GPU count

Current NVIDIA A100 GPU count

stateof.ai 2022

#stateofai | 58

The compounding effects of government contracting in AI
In 1962 the US government bought all integrated circuits in the world, supercharging the development of this
technology and its end markets. Some governments are providing that opportunity again, as “buyers of ﬁrst
resort” for AI companies. With access to unique high-quality data, companies could gain an edge in building
consumer or enterprise AI software.
● Researchers examined Chinese facial recognition AI companies and
showed a causal relationship between the number of government
contracts they signed and the cumulative amount of general AI
software they produced. Unsurprisingly, leadership in the computer
vision space has largely been ceded to Chinese companies now.
● The principle should stand in other heavily regulated sectors, like
defence or healthcare, which build an expertise through unique data
that is transferable to everyday AI products.

stateof.ai 2022

#stateofai | 59

How should big tech deal with their language model consumer products?
Meta’s release of the BlenderBot3 chatbot for free public use in August 2022 was faced with catastrophic press
because the chatbot was spitting misinformation. Meanwhile, Google, which published a paper on their chatbot
LaMDA in May 2021, had decided to keep the system in-house. But a few weeks after BlenderBot’s release, Google
announced a larger initiative called “AI test kitchen”, where regular users will be able to interact with Google’s
latest AI agents, including LaMDA.
● Large-scale release of AI systems to the 1B+ users of Google and
Facebook all but ensures that every ethics or safety issue with these
systems will be surfaced, either by coincidence or by adversarially
querying them. But only by making these systems widely available can
these companies ﬁx those issues, understand user behaviour and create
useful and proﬁtable systems.
● Running away from this dilemma, 4 of the authors of the paper
introducing LaMDA went on to found/join Character.AI, which describes
itself as “an AI company creating revolutionary open-ended conversational
applications”. Watch this space…

stateof.ai 2022

#stateofai | 60

DeepMind and OpenAI alums form new startups and Meta disbands its core AI group
Once considered untouchable, talent from Tier 1 AI labs is breaking loose and becoming entrepreneurial.
Alums are working on AGI, AI safety, biotech, ﬁntech, energy, dev tools and robotics. Others, such as Meta, are
folding their centralised AI research group after letting it run free from product roadmap pressure for almost
10 years. Meta concluded that “while the centralized nature of the [AI] organization gave us leverage in some
areas it also made it a challenge to integrate as deeply as we would hope.”

stateof.ai 2022

#stateofai | 61

Attention is all you need… to build your AI startup
All but one author of the landmark paper that introduced transformer-based neural networks have left Google
to build their own startups in AGI, conversational agents, AI-ﬁrst biotech and blockchain.
Amount raised in 2022
$580M
$225M
$125M
$65M

stateof.ai 2022

#stateofai | 62

AI coding assistants are deployed fast, with early signs of developer productivity gains
OpenAI’s Codex quickly evolved from research (July 2021) to open commercialization (June 2022) with
(Microsoft’s) GitHub Copilot now publicly available for $10/month or $100/year. Amazon followed suit by
announcing CodeWhisperer in preview in June 2022. Google revealed that it was using an internal ML-powered
code completion tool (so maybe in a few years in a browser IDE?). Meanwhile, with its 1M+ users, tabnine
raised $15M, promising accurate multiline code completions.

Single line

Multi-line

Fraction of code added by ML

2.6%

0.6%

Average characters per accept

Acceptance rate (for suggestions visible for >750ms)

25%

34%

Reduction in coding iteration duration

–

Metrics for Google’s coding assistant. Users are 10k+
Google-internal developers (5k+ for multi-line
experiments).

stateof.ai 2022

#stateofai | 63

AI-ﬁrst drug discovery companies have 18 assets in clinical trials, up from 0 in 2020
And many more assets in early discovery stages. We expect early clinical trial readouts from 2023 onwards.
# of assets per pipeline stage per company

Updated as of 26 Aug 2022

% of assets per pipeline stage overall

stateof.ai 2022

#stateofai | 64

Can AI and compute bend the physical reality of clinical trial chokepoints?
A study of 6,151 successful phase transitions between 2011–2020 found that it takes 10.5 years on average
for a drug to achieve regulatory approval. This includes 2.3 years at Phase I, 3.6 years at Phase II, 3.3 years at
Phase III, and 1.3 years at the regulatory stage. What’s more, it costs $6.5k on average to recruit one patient
into a clinical trial. With 30% of patients eventually dropping out due to non-compliance, the fully-loaded
recruitment cost is closer to $19.5k/patient. While AI promises better drugs faster, we need to solve for the
physical bottlenecks of clinical trials today.
# of registered studies (ClinicalTrials.gov EOY)

Stepwise probability of drug success

stateof.ai 2022

#stateofai | 65

Predicting the evolution of real-world covid variants using language models
mRNA vaccine leader, BioNTech, and enterprise AI company, InstaDeep, collaboratively built and validated an
Early Warning System (EWS) to predict high-risk variants. The EWS could identify all 16 WHO-designated
variants on average more than one and a half months prior to ofﬁcially receiving the designation.
● A large pre-trained protein language model was
trained on viral spike protein sequences of variants.
● New spike protein variants are fed to a transformer
that outputs embeddings and a probability
distribution of the 20 natural amino acids for each
position to determine how this would affect immune
escape and ﬁtness.
● The red dash line indicates the date when the EWS
predicted the variant would be high-risk and the
green dash-dot line is when the WHO designated the
variant. In almost all cases, EWS alerted several
months before the WHO designation.

stateof.ai 2022

#stateofai | 66

The ﬁrst regulatory approval for an autonomous AI-ﬁrst medical imaging diagnostic
Lithuanian startup Oxipit received the industry’s ﬁrst autonomous certiﬁcation for their computer vision-based
diagnostic. The system autonomously reports on chest X-rays that feature no abnormalities, removing the need
for radiologists to look at them.
● Due to a shortage of radiologists and an increasing volume of
imaging, the diagnostic task of assessing which X-rays contain
disease and which don’t is challenging.
● Oxipit’s ChestLink is a computer vision system that is tasked
with identifying scans that are normal.
● The system is trained on over a million diverse images. In a
retrospective study of 10,000 chest X-rays of Finnish primary
health care patients, the AI achieved a sensitivity of 99.8% and speciﬁcity of 36.4 % for recognising clinically
signiﬁcant pathology on a chest X-ray.
● As such, the AI could reliably remove 36.4% of normal chest X-rays from a primary health care population data
set with a minimal number of false negatives, leading to effectively no compromise on patient safety and a
potential signiﬁcant reduction of workload.

stateof.ai 2022

#stateofai | 67

Universities are a hotbed for AI spinouts: the UK case study
Universities are an important source of AI companies including Databricks, Snorkel, SambaNova, Exscientia and
more. In the UK, 4.3% of UK AI companies are university spinouts, compared to 0.03% for all UK companies. AI is
indeed among the most represented sectors for spinouts formation. But this comes at a steep price: Technology
Transfer Ofﬁces (TTOs) often negotiate spinout deal terms which are unfavourable to founders, e.g. a high equity
share in the company or royalties on sales.

stateof.ai 2022

#stateofai | 68

Spinout.fyi: an open database to help founders and policymakers ﬁx the spinout problem
Spinout.fyi crowdsourced a database of spinout deal terms from founders representing >70 universities all over
the world. The database spans AI and non-AI companies across different product categories (software, hardware,
medical, materials, etc.), and shows that the UK situation, while particularly discouraging for founders, isn’t
isolated. Only a few regions stand out as being founder-friendly, like the Nordics and Switzerland (ETH Zürich in
particular). A major reason for the current situation is the information asymmetry between founders and TTOs,
and the spinout.fyi database aims to give founders a leg up in the process.

stateof.ai 2022

#stateofai | 69

As 5-year programmes in Berkeley and Stanford wrap up, what comes next?
In 2011, UC Berkeley launched the “Algorithms, Machines, and People” (AMPLab) as a 5-year collaborative
research agenda amongst professors and students, supported by research agencies and companies. The program
famously developed the critical Big Data technology Spark (spun out as Databricks), as well as Mesos (spun out as
Mesosphere). This hugely successful program was followed in 2017 by the “Real-time intelligence secure
explainable systems” (RISELab) at Berkeley and “Data Analytics for What’s Next” (DAWN) at Stanford, which
focused on AI technologies. RISELab created the Ray ML workload manager (spun out as Anyscale), and DAWN
created and spun out the Snorkel active labelling platform. Will other universities and countries learn from the
successes of the 5-year model to fund ambitious open-source research with high spinout potential?
Lab name
2011-16

OSS project created

Spinouts that emerged
$38B val

$250M raised

$1B val
2017-22
$1B val

stateof.ai 2022

#stateofai | 70

In 2022, investment in startups using AI has slowed down along with the broader market
Private companies using AI are expected to raise 36% less money in 2022* vs. last year, but are still on track to
exceed the 2020 level. This is comparable with the investment in all startups & scaleups worldwide.
Worldwide investment in startups & scaleups
using AI by round size » view online

Worldwide investment in all startups & scaleups
by round size » view online

▊ $250M+ ▊ $100-250M ▊ $40-100M (Series C) ▊ $15-40M (Series B)
▊ $4-15M (Series A) ▊ $1-4M (Seed) ▊ $0-1M (Pre-Seed)

$150B

$800B

$726.5B

$111.4B

-36%

$100B
$69.3B

$50B

$70.9B*

-24%

$553.2B*

$600B
$400B

$351.7B

$399.7B

$47.5B

$200B
0

20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
YT 22
D

In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions.
(*) estimated amount to be raised by the end of 2022

stateof.ai 2022

#stateofai | 71

The drop in investment is most noticeable in megarounds
The drop in VC investment is most noticeable in 100M+ rounds, whereas smaller rounds are expected to amount
to $30.9B worldwide by the end of 2022, which is almost on track with the 2021 level.

$80B

Worldwide investment in startups & scaleups
using AI by round size » view online

Worldwide investment in startups & scaleups using
AI by round size » view online

▊ $250M+ ▊ $100-250M

▊ $40-100M (Series C) ▊ $15-40M (Series B) ▊ $4-15M (Series A)
▊ $1-4M (Seed) ▊ $0-1M (Pre-Seed)
$77.5B

$40B

-55%

$60B

-9%
$33.9B
$30.9B*

$30B

$24.9B

$44.4B

$40B

$34.9B*

$22.3B

$20B
$10B

20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
YT 22
D

$20B

20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
YT 22
D

$25.2B

In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions.
(*) estimated amount to be raised by the end of 2022

stateof.ai 2022

#stateofai | 72

Public valuations have dropped in 2022, while private keep growing
Combined public enterprise value (EV) has dropped to the 2020 level. Meantime, private valuations keep growing,
with the combined EV already reaching $2.2T, up 16% from last year.
Combined EV of public startups & scaleups using
AI by launch year; worldwide » view online

Combined EV of privately owned startups & scaleups
using AI by launch year; worldwide » view online

▊ 2015-2022 YTD ▊ 2010-2014 ▊ 2005-2009 ▊ 2000-2004
▊ 1995-1999 ▊ 1990-1994

$8.0T

$9.6T

$2.5T

-29%
$6.8T

$6.8T

$1.5T

$4.0T

$1.0T

$2.0T

$500B

$2.2T

$1.9T

$2.0T

$6.0T

20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
YT 22
D

+16%

$1.4T

20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
YT 22
D

$10.0T

▊ 2015-2022 YTD ▊ 2010-2014 ▊ 2005-2009 ▊ 2000-2004
▊ 1995-1999 ▊ 1990-1994

In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions.
(*) estimated amount to be raised by the end of 2022

stateof.ai 2022

#stateofai | 73

The US leads by the number of AI unicorns, followed by China & the UK
The US has created 292 AI unicorns, with the combined enterprise value of $4.6T.
Countries with the largest number of AI unicorns » view online
Number of AI unicorns
United States
69
24

Examples

$4.6T

292

China
United Kingdom

Combined enterprise value (2022 YTD)

$1.4T
$207B

Israel 14

$53B

Germany 10

$56B

Canada 7

$12B

Singapore 6

$39B

Switzerland 6

$14B

In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions.

stateof.ai 2021

#stateofai | 74

Investment in the USA accounts for more than half of the worldwide VC
Despite signiﬁcant drop in investment in US-based startups & scaleups using AI, they still account for more than
half of the AI investment worldwide.
Amount invested in the companies using AI
$140B
$120B
▊ Rest of the world

$100B
$13.1B

$80B
$60B

▊ United Kingdom

$40B

$63.6B

$20B
0

▊ China

$7.5B
$8.9B

$4.3B
$3.8B
$7.5B
$25.1B

2010 2011

2012

2013 2014

2015

2016

2017 2018

2019

2020 2021

▊ EU-27, Switzerland & Norway
53% in 2022 YTD
vs. 57% in 2021

▊ USA

2022
YTD

In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions.

stateof.ai 2022

#stateofai | 75

Enterprise software is the most invested category globally, while robotics captures the largest
share of VC investment into AI
Industries

Enterprise software
Transportation
Fintech
Health
Robotics
Food
Marketing
Security
Media
Telecom
Semiconductors
Education
Energy
Travel
Real estate
Gaming
Home living
Jobs recruitment
Legal
Sports
Music
Fashion
Hosting
Wellness beauty
Event tech
Kids
Dating

Amount invested in
startups & scaleups
using AI, 2010-2022 YTD

$133.7B
$109.5B
$87.8B
$61.7B
$48.9B
$44.1B
$43.0B
$37.9B
$27.4B
$17.9B
$17.8B
$14.7B
$13.1B
$10.6B
$7.7B
$6.5B
$6.0B
$5.2B
$4.9B
$3.4B
$2.5B
$2.4B
$2.2B
$2.1B
$927M
$292M
$85M

Amount invested in
startups & scaleups
using AI, 2021-2022 YTD

Investment in startups & scaleups
using AI, 2021-2022 YTD;
number of rounds

$6.0B
$13.5B
$12.2B
$4.0B
$2.7B
$6.8B
$2.5B
$3.6B
$3.4B
$617M
$629M
$1.5B
$2.4B
$568M
$421M
$28M
$349M
$408M
$348M
$33M
$69M
$5M

1288

$44.7B
$22.8B
$25.1B
$21.9B
$18.4B

332

2%
3%
3%

660
689
367
220
406
306
182
44
73
143
231
27
148
55
31
103
88
56
22
48
16
56
14
13
4

In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions.

Amount invested in startups
& scaleups using AI as %
of all VC, 2021-2022 YTD

% of AI startups

30%
21%
13%
14%

71%
10%

1%
4%
5%
2%
2%
2%

24%
25%
13%
13%
41%

3%
2%
1%
1%
1%
1%
3%
2%
1%
1%
1%
<1%
1%
1%
<1%
<1% 9% 6% 24% 2% 3% 8% 14% 7% 4% 1% 2% 9% 3% 1% 2% 4% stateof.ai 2022 #stateofai | 76 Introduction | Research | Industry | Politics | Safety | Predictions Acquisitions are on track to exceed the 2021 level While the number of IPOs and SPAC IPOs declined sharply, the number of acquisitions is on track to exceed the 2021 level Number of exits in 2022 among the companies using AI; worldwide » view online Number of exits among the companies using AI; worldwide » view online ▊ Acquisition ▊ Buyout ▊ IPO ▊SPAC IPO 500 436 400 300 SaaS for planning & forecasting Customer experience tools $13.1B Acquisition May 2022 $10.7B Buyout Mar 2022 $10.2B Buyout Jun 2022 Tax compliance software Consumer robotics company Autonomous, continuous workload optimization $8.4B Buyout Aug 2022 $1.7B Acquisition Aug 2022 $650M Acquisition Mar 2022 Mobile device & application security solutions Cloud-native SOAR platform Developing autonomous mobility platform $525M Buyout Mar 2022 $500M Acquisition Jan 2022 $469 Acquisition Aug 2022 285 253 200 Technology, data & analytics for real estate 332 259 100 211 0 10 11 12 13 14 15 16 17 18 19 20 21 22 20 20 20 20 20 20 20 20 20 20 20 20 20 TD Y In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions. stateof.ai 2022 #stateofai | 77 Introduction | Research | Industry | Politics | Safety | Predictions The number of exits in EU-27, Switzerland & Norway has already exceeded 2021 levels With 108 exits to date, the US hasn’t yet reached half of the 2021 level, while the EU, Switzerland & Norway combined have already exceeded the 2021 number. Number of exits among the companies using AI 500 450 ▊ Rest of the world 400 350 9 33 300 94 250 1 25 200 97 150 222 100 108 50 0 ▊ China 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 ▊ United Kingdom ▊ EU-27, Switzerland & Norway ▊ USA 2022 YTD In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions. stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 78 Investment in SaaS startups & scaleups using AI is expected to reach $41.5B by the end of the year, down 33% from last year, but higher than in 2020 VC investment in AI SaaS startups & scaleups » view online $80.0B $61.8B $60.0B -33% ▊ $250M+ $37.7B $40.0B $41.5B* ▊ $100-250M $31.1B ▊ $40-100M (Series C) ▊ $15-40M (Series B) $20.0B 222 ▊ $4-15M (Series A) 108 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions. (*) estimated amount to be raised by the end of 2022 2022 YTD ▊ $1-4M (Seed) ▊ $0-1M (Pre-Seed) stateof.ai 2021 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 79 The combined EV of public and private SaaS startups & scaleups using AI now amounts to $2.3T, down 26% from last year, but still higher than in 2020 Combined EV of AI SaaS startups & scaleups by launch year globally » view online $4.0T $3.1T -26% $3.0T ▊ 2015-2022 YTD $2.3T $2.1T $2.0T ▊ 2010-2014 ▊ 2005-2009 ▊ 2000-2004 ▊ 1995-1999 $1.0T ▊ 1990-1994 0 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 YTD In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions. stateof.ai 2021 #stateofai | 80 Introduction | Research | Industry | Politics | Safety | Predictions The combined EV of private SaaS startups & scaleups using AI keeps growing and has already reached $1.1T, up 12% from last year Combined EV of privately owned AI SaaS startups & scaleups by launch year; worldwide » view online ▊ 2015-2022 YTD ▊ 2010-2014 ▊ 2005-2009 ▊ 2000-2004 ▊ 1995-1999 ▊ 1990-1994 Top valued privately owned startups & scaleups using AI » view online Financial infrastructure platform USA $1.5T Global payments solution provider Lakehouse platform to unify data, analytics and AI Valuation: $68.4B United Kingdom Valuation: $40B USA Valuation: $38B Autonomous driving technology Online education platform Fintech-as-a-service platform USA Valuation: $30B China Valuation: $15.5B United Kingdom Valuation: $15B Process mining software Business cloud software products AI-powered writing assistant Germany Valuation: $13B USA Valuation: $13B USA Valuation: $13B +12% $1.0T $1.0T $895B $489B $500B 0 0 1 20 1 1 20 2 1 20 3 1 20 4 1 20 1 20 5 6 1 20 1 20 7 8 1 20 19 20 21 22 20 20 20 20 TD Y In this slide, startups & scaleups using AI include both startups & scaleups with AI-ﬁrst and AI-enabled products and solutions. stateof.ai 2021 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 81 Section 3: Politics stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 82 A widening compute chasm is separating industry from academia in large model AI The compute requirements for large-scale AI experiments has increased >300,000x in the last decade. Over the
same period, the % of these projects run by academics has plummeted from ～60% to almost 0%. If the AI
community is to continue scaling models, this chasm of “have” and “have nots” creates signiﬁcant challenges for AI
safety, pursuing diverse ideas, talent concentration, and more.

stateof.ai 2022

#stateofai | 83

Slow progress in providing academics with more compute leaves others to act faster
There is a growing appreciation that AI is an engineering science in which the objects of study need to ﬁrst be
built. Western academics and governments are starting to wake up to this reality, most notably through the
National AI Research Resource process in the US. While spending years on consultations and marketing however,
others in China and outside academia are ﬁnding creative ways to do large-scale AI projects.
Governments / Academia
National AI
Research ResourceStanford opens Center for Research on
Foundation Models
Task Force Created

National AI
Initiative Act
enacted

NAIRRTF
Meeting 1

NAIRRTF
Meeting 2

NAIRRTF
Meeting 3

GLM-130B
(Tsinghua)
NAIRRTF
Meeting 4

NAIRRTF
Meeting 5

NAIRRTF
Meeting 6

NAIRRTF
Meeting 7

NAIRRTF
Meeting 8

NAIRRTF
Meeting 9

NAIRRTF
Meeting 10

Aug

Mar
Jan 2021

NAIRRTF Final
Report

Jun

July

Aug

Oct

Dec

The Pile GPT-Neo GPT-J-6B
(Eleuther) (Eleuther) (Eleuther)

Feb 2022

Apr

May

Jul

Sep

Oct

Dec 2022

Stable Diffusion
(Stability)

GPT-NeoX-20B
(Eleuther)
BLOOM-178B
(BigScience)

Research Collectives

stateof.ai 2022

#stateofai | 84

The baton is passing from academia to decentralized research collectives
Decentralized research projects are gaining members, funding and momentum. They are succeeding at ambitious
large-scale model and data projects that were previously thought to be only possible in large centralised
technology companies – most visibly demonstrated by the public release of Stable Diffusion.
● The most notable large-scale academic project this year
came from China: Tsinghua’s GLM-130B LLM.
● Eleuther, the original AI research collective, released the
20B parameter GPT-NeoX. However, core members have
since moved on to OpenAI, Stability and Conjecture.
● Hugging Face led the BigScience initiative, releasing the
178B parameter BLOOM multilingual LLM.
● Stability came out of nowhere, obtained 4,000 A100
GPUs, brought together multiple open-source
communities and created Stable Diffusion.
Graph data source: Sevilla et al. Parameter, Compute and Data Trends in Machine Learning

Large-Scale AI Results

stateof.ai 2022

#stateofai | 85

Stability AI is attempting a new paradigm in commercializable open-source AI
Where there was previously a dependence on ad-hoc compute
donations to enable large-scale projects, Stability is pioneering a
new approach of structured compute and resource provision for
open-source communities, while also commercializing these
projects with revenue-sharing for developers.
● Stability has embedded itself as a compute platform for
independent and academic open-source AI communities:
supporting LAION for building a dataset of 5B image-text pairs
and training an open-source CLIP model, and supporting the
CompVis group’s research in efﬁcient diffusion models. It funds
PhD students to work on community projects, and has directly
hired generative AI artists, core members of Eleuther, and
renowned ML researchers such as David Ha.
● Stable Diffusion cost <$600K to train, and while weights were
released, access is also sold through the DreamStudio API.

stateof.ai 2022

#stateofai | 86

AI continues to be infused into a greater number of defense product categories
Defense technology companies are applying AI to electronic warfare, geospatial sensor fusion, and to create
autonomous hardware platforms.
● Epirus, founded in 2018, has built a next-generation electromagnetic pulse
weapon capable of defeating swarms of drones that pose threats to human
safety. Sweden’s Saab is also making efforts towards AI-driven automation of
electronic warfare: they built the COMINT and C-E.SM sensors to balance
automated and operator-controlled surveillance depending on the context on the
ﬁeld. The company is also collaborating with defense startup, Helsing.
● Modern Intelligence, founded in 2020, builds a platform-independent AI for
geospatial sensor data fusion, situational awareness and maritime surveillance.
● Meanwhile, through both organic and inorganic growth, Anduril has expanded its
autonomous hardware platforms. For example, Anduril acquired Area-I to launch
a new product in Air Launched Effects with an increased payload, data sharing
and integration capabilities with other UAVs. Anduril also expanded into
Underwater Autonomous Vehicles by acquiring Dive Technologies.

stateof.ai 2022

#stateofai | 87

AI in defense gathers big funding momentum
Heavily funded start-ups and Amazon, Microsoft, and Google continue to normalise the use of AI in Defense.
● Nato published their AI Strategy and announced a $1B fund to invest
in companies working on a range of dual-use technologies. It was
described as the world’s ﬁrst ‘multi-sovereign venture capital fund’
spanning 22 nations.
● Helsing, a European defense AI company, announced a €102.5M
Series A led by Daniel Ek of Spotify.
● Microsoft, Amazon and Google continue to compete for a major role
in defense – most notably Microsoft’s $10B contract with the
Pentagon was cancelled after a lawsuit from Amazon. The new
beneﬁciaries of the contract will now be announced in late 2022.
● Anduril landed their largest DoD contract to date and is now
reportedly valued at $7B.
● Shield AI, developer of military drones, raised at a $2.3B valuation

stateof.ai 2022

#stateofai | 88

Ukraine’s homegrown geospatial intelligence GIS Arta software is a sign of things to come
The use of geospatial (GIS) software has reportedly reduced the decision chain around artillery from 20 minutes
to under one minute.
● GIS Arta is a homegrown application developed prior to Russia’s
invasion based on lessons learned from the conﬂict in the
Donbas.
● It’s a guidance command and control system for drone, artillery
or mortar strikes.
● The app ingests various forms of intelligence (from drones, GPS,
forward observers etc) and converts it into dispatch requests for
reconnaissance and artillery.
● GIS Arta was allegedly developed by a volunteer team of
software developers led by Yaroslav Sherstyvk, inspired by the
Uber taxi model.

stateof.ai 2022

#stateofai | 89

The Great Reshoring will be slow: US lags in new fab projects, which take years to build
Between 1990 and 2020, China accelerated its output of greenﬁeld fab projects by almost 7x while the US slowed
down by 2.5x. Moreover, while China and Taiwan fabs take roughly 650 days from construction start to being
production-ready, the US builds fabs 42% slower today than they did 30 years ago.
Total # of greenﬁeld fab projects

Avg. # of days from build start to production

stateof.ai 2022

#stateofai | 90

The US CHIPS and Science Act of 2022: $250B for US semiconductor R&D and production
The bipartisan legislation was signed into law in August 2022. It provides for $53B to boost US-based
semiconductor R&D, workforce and manufacturing, as well as a 25% investment tax credit for semiconductor
manufacturers’ capital expenses. In exchange, recipients must not upgrade or expand their existing operations
in China for 10 years, nor can they use funds for share buybacks or to issue dividends.
● The bill poses a dilemma for Korean (e.g. Samsung), Taiwanese (e.g.
TSMC) and other manufacturers: if they accept US subsidies, then they
must pivot away from China without backlash from Beijing, which is
opposed to this “friendshoring”.
● Since passing the bill, Micron announced a $40B investment in
memory chip manufacturing to increase US market share from 2% to
10%. Qualcomm will expand its US semiconductor production by 50%
by 2027 and in partnership with GlobalFoundries the two will invest
$4.2B to expand the latter’s upstate New York facility.
● CSET estimates the US should focus on its manufacturing capabilities
in leading-edge, legacy logic and DRAM (right chart).

stateof.ai 2022

#stateofai | 91

The US cuts China off from NVIDIA and AMD chips…will this spur Chinese AI R&D?
NVIDIA GPUs are used by all major Chinese technology companies (Baidu, Tencent et al.) and universities
(Tsinghua, Chinese Academy of Sciences et al.). Washington ordered NVIDIA and AMD to stop exporting their
latest AI chips (e.g. NVIDIA A100 and H100, and AMD M100 and M200) to China as a means of curbing their
use in applications that threaten American national security via China. The companies will have to provide
statistics on previous shipments and customer lists. Not having access to state of the art AI chips could stall a
large swath of Chinese industry if domestic suppliers don’t step into the void and fast.
● Earlier this year, CSET analysed 24 public contracts awarded by Chinese PLA units and state-owned defense
enterprises in 2020. They found that nearly all of the 97 AI chips in these purchase orders were designed by
NVIDIA, AMD, Intel and Microsemi. Domestic AI chip companies were not featured. Thus, American chips are
arming Chinese defense capabilities.
● Chinese semiconductor manufacturers have been already cut off from advanced lithography machines made
by ASML and related equipment from Lam Research and Applied Materials.
● It is unlikely that domestic AI chip companies (e.g. Biren) can ﬁll the void: leading-edge node manufacturing is
still only possible by TSMC in Taiwan and because domestic talent, software and technology is still years away
from NVIDIA. China will still accelerate its development.
stateof.ai 2022

#stateofai | 92

The EU advances with its plans to regulate AI
In April 2021, the EU tabled a proposal for regulating the placement on the market and use in the EU of AI
systems (the “AI Act”). The proposal introduces certain minimum requirements (e.g. mainly information
obligations) that all AI systems in use must meet. It also introduces more elaborate requirements (e.g. risk
assessments, quality management) with respect to AI systems that pose higher risks to users. The AI Act bans
the use of certain types of AI-based techniques (e.g. social scoring, real-time biometric remote identiﬁcation
(subject to exceptions), “subliminal techniques”).
● The AI Act moves through the EU legislative process. The European Parliament has worked over the summer
on a compromise text to address tabled amendments and opinions by the Parliament’s various committees.
The compromise text is scheduled to go through the various stages of the voting process at the European
Parliament by the end of 2022.
● The AI Act is expected to be voted into law in 2023, either under the Swedish or the Spanish Presidency of the
EU.
● Current realistic expectations are that the AI Act will become effective in the second-half of 2023.
Source: Dessislava Fessenko

stateof.ai 2022

#stateofai | 93

The EU aims at quick operationalization of the AI Act
The EU aims at quick operationalization of the requirements under the AI Act through standardization, setting
up of testing facilities, and launch of pan-European and national regulatory sandboxes.
● European standardization efforts are already underway. The EU standardization organizations CEN and
CENELEC have already commenced preparatory works on standardization and expect to be requested to
develop relevant sets of standards by 31 October 2024.
● The EU appears to favor testing of high-risk AI systems, in either controlled or even possibly in real-world
conditions, as a suitable mode for supporting and promoting compliance with the AI Act among businesses of
all sizes.
● Pan-European and national regulatory sandboxes start to emerge in the EU. Spain launched the ﬁrst one in
June 2022. Other EU member states (e.g. the Czech Republic) have announced similar plans. Sandboxes are
considered by EU regulators as suitable testbeds for technical, policy and standardization solutions. They are
also intended as a medium for supporting small and medium-sized businesses in particular in attaining
compliance with the AI Act.
Source: Dessislava Fessenko

stateof.ai 2022

#stateofai | 94

Section 4: Safety

stateof.ai 2022

#stateofai | 95

While AI advances rapidly, the safety of highly-capable future systems remains unclear
While many concerns still appear speculative, early AI pioneers considered that highly capable and
economically integrated AI systems of the future could fail catastrophically and pose a risk to humanity,
including through the emergence of behaviours directly opposed to human oversight and control.

Alan Turing
1951

“… it seems probable
that once the
machine thinking
method has started,
it would not take
long to outstrip our
feeble powers. …
At some stage
therefore we should
have to expect the
machines to take
control”

I.J. Good
1965

“Thus the ﬁrst
ultraintelligent
machine is the last
invention that man
need ever make,
provided that the
machine is docile
enough to tell us
how to keep it under
control.”

Marvin Minsky
1984

“The problem is that,
with such powerful
machines, it would
require but the
slightest accident of
careless design for
them to place their
goals ahead of ours”

stateof.ai 2022

#stateofai | 96

The UK is taking the lead on acknowledging these uncertain but catastrophic risks
The UK’s national strategy for AI, published in late 2021, notably made multiple references to AI safety and the
long-term risks posed by misaligned AGI.
● “While the emergence of Artiﬁcial General Intelligence (AGI) may seem like a
science ﬁction concept, concern about AI safety and non-human-aligned systems is
by no means restricted to the fringes of the ﬁeld.”
● “We take the ﬁrm stance that it is critical to watch the evolution of the technology,
to take seriously the possibility of AGI and ‘more general AI’, and to actively direct
the technology in a peaceful, human-aligned direction.”
● “The government takes the long term risk of non-aligned AGI, and the
unforeseeable changes that it would mean for the UK and the world, seriously.”
● “[We must] establish medium and long term horizon scanning functions to
increase government’s awareness of AI safety.”
● “[We must] work with national security, defence, and leading researchers to
understand how to anticipate and prevent catastrophic risks.”

stateof.ai 2022

#stateofai | 97

AI researchers increasingly believe that AI safety is a serious concern
Long dismissed as science ﬁction by mainstream AI research and academia, researchers are now shifting
consensus towards greater concern for the risks of human-level AI and superhuman AGI in the near future.
● A survey of the ML community found that
69% believe AI safety should be prioritized
more than it currently is.
● A separate survey of the NLP community
found that a majority believe AGI is an
important concern we are making progress
towards. Over 70% believe AI will lead to
social change at the level of the Industrial
Revolution this century, and nearly 40%
believe AI could cause a catastrophe as bad
as nuclear war during that time.

Note: The number in green represents the fraction of respondents who agree with the position out of all
those who took a side. The number in black shows the average predicted rate of agreement.

stateof.ai 2022

#stateofai | 98

AI safety is attracting more talent… yet remains extremely neglected
Increased awareness of AI existential risk is leading to increased headcount,
with an estimated 300 researchers now working full-time on AI safety. However
this is still orders of magnitude fewer researchers than are working in the
broader ﬁeld, which itself is growing faster than ever (right chart).
● New non-proﬁt research labs include the Center for AI Safety and the Fund for
Alignment Research. The Centre for the Governance of AI was spun out as an
independent organization from the Future of Humanity Institute in Oxford.
● There was a huge increase in interest for education programmes with over 750
people taking part in the online AGI Safety Fundamentals course. New
scholarships were created, including the Vitalik Buterin PhD Fellowship in AI
Existential Safety.
● Notably, Ilya Sutskever, OpenAI’s Chief Scientist, has shifted to spending 50% of
his time on safety research.

Researchers by venue/ﬁeld

30x

stateof.ai 2022

#stateofai | 99

Funding secured, though trailing far behind what goes into capabilities
Increased awareness of AI existential risk has led to rapidly increasing funding for research into the safety of
highly-capable systems, primarily through donations and investments from sympathetic tech billionaires Dustin
Moskovitz (Open Philanthropy) and Sam Bankman-Fried (FTX Foundation). However, total VC and philanthropic
safety funding still trails behind resources for advanced capabilities research, not even matching DeepMind’s
2018 opex.
Philanthropic AI Safety funding pales in comparison to AI capabilities funding*

*We include fundraises for Adept, Hugging Face, Cohere, AI21, Stability and Inﬂection under Capabilities VC and fundraises for Anthropic under Safety VC.

stateof.ai 2022

#stateofai | 100

Language Model Alignment: Reinforcement Learning from Human Feedback (RLHF)
RLHF has emerged as a key method to ﬁnetune LLMs and align them with human values. This involves humans
ranking language model outputs sampled for a given input, using these rankings to learn a reward model of
human preferences, and then using this as a reward signal to ﬁnetune the language model with using RL.
● OpenAI started the year by ﬁnetuning GPT-3 using RLHF to
produce InstructGPT models that improved on helpfulness for
instruction-following tasks. Notably, the ﬁne-tuning only
needed <2% of GPT-3’s pretraining compute, as well as 20,000 hours of human feedback. API users on average prefer these models to the original ones. ● RLHF has also been used by both Anthropic and Deepmind to improve the helpfulness, harmlessness, and correctness of their language models. OpenAI has stated that RLHF will be a core building block for it’s long-term approach to aligning AGI. stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 101 Language Model Alignment: Reinforcement Learning from Human Feedback An RLHF preference model provides limited learning signal, compared to the full expressiveness of language that humans use. NYU researchers demonstrated that language models could be improved directly using human feedback written in language. ● Notably, their method was highly data-efﬁcient, with only 100 samples of human feedback they were able to ﬁnetune GPT-3 and improve it’s abilities on a summarization task to human-level. ● On a synthetic task for removing offensive words from sentences, they observe that only the largest GPT-3 models are capable of incorporating feedback effectively, demonstrating another example of emergent behaviour at scale. stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 102 Language Model Alignment: Red Teaming As language models exhibit an increasing array of capabilities, it becomes difﬁcult to exhaustively evaluate their failure modes, inhibiting trust and safe public deployment. DeepMind introduced automated “red teaming”, in which manual testing can be complemented through using other language models to automatically “attack” other language models to make them exhibit unsafe behaviour, as determined by a separate classiﬁer. ● Anthropic used manual red teaming to evaluate RLHF models, ﬁnding that they are harder to attack and less harmful with increased model size ● In the future, a classiﬁer could detect for speculative risks such as power-seeking behavior or malicious coding stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 103 Mechanistic interpretability – can we reverse-engineer neural networks? Interpreting deep neural networks when seen just as sequences of matrix operations is exceedingly difﬁcult – research in mechanistic interpretability instead seeks to reverse-engineer models into human-interpretable computer programs, gaining an understanding of individual neurons as well as their collective behaviour. ● Researchers at Anthropic released signiﬁcant analyses of small transformer-based language models, focusing on a phenomenon of “induction heads” that learn to copy and complete sequences which have occurred before in a text. They ﬁnd that these heads emerge during “phase shifts” in training during which in-context learning capabilities also emerge, and further developed a hypothesis that these heads may be responsible for the majority of in-context learning capabilities in large transformer models as well. ● Follow up work in this space has also brought to light ways in which individual neurons become responsible for individual or multiple semantic features, and ways to control this type of interpretability. stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 104 Goal misgeneralization – agents can learn the right skills but the wrong objective One concern of using RL agents is that they may learn strong skills while having failed to learn the right goals, and for this failure to only exhibit at test-time under distribution shifts. This issue was empirically demonstrated for the ﬁrst time in a paper presented at ICML this year. ● Agents were trained on the CoinRun video game task, in which a reward is obtained and the level completes when reaching a coin at the end of a stage. ● At test-time, the coin is randomly placed within the stage instead. Agents maintained their capabilities to navigate and traverse obstacles, but ignore the coin and instead run to the end of the level, demonstrating a failure to learn the correct goal. stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 105 Measuring moral behavior in artiﬁcial agents Researchers released suite of sequential decision-making environments for evaluating moral behavior in AI. ● Future artiﬁcial agents may be pretrained on swaths of environments that do not penalize and may even reward behavior such as murder and theft (e.g., see bottom-right). ● Jiminy Cricket environments were created to evaluate moral behavior in 25 semantically rich text-based adventure games. Every action the agent can take is annotated for several aspects of how moral it is. ● As a ﬁrst step, CMPS uses LMs with moral knowledge and mediates this knowledge into actions. This greatly reduces immoral behavior over the course of training. stateof.ai 2022 #stateofai | 106 Introduction | Research | Industry | Politics | Safety | Predictions Conjecture is the ﬁrst well funded startup purely focusing on AGI alignment Unlike DeepMind, Google Brain, OpenAI and other major research labs, Conjecture is primarily focused on AI Alignment, with an emphasis on conceptual research and “uncorrelated bets” distinct from other organizations ● Conjecture is a London based start-up, led by Connor Leahy who previously co-founded Eleuther – the organisation that kicked off decentralised development of large AI models. ● Conjecture’s operates under the assumption that AGI will be developed in the next 5 years, and on the current trajectory will be misaligned with human values and consequently catastrophic for our species. ● They have raised millions from investors include the founders of Github, Stripe and FTX. ● They are the ﬁrst AI Alignment group to have published their internal infohazard policy. ● This continues a broader trend of some new AGI focused labs taking alignment research more seriously (see coverage of Anthropic last year). stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 107 Section 5: Predictions stateof.ai 2022 Introduction | Research | Industry | Politics | Safety | Predictions #stateofai | 108 9 predictions for the next 12 months 1. A 10B parameter multimodal RL model is trained by DeepMind, an order of magnitude larger than Gato. 2. NVIDIA announces a strategic relationship with an AGI focused organisation. 3. A SOTA LM is trained on 10x more data points than Chinchilla, proving data-set scaling vs. parameter scaling 4. Generative audio tools emerge that attract over 100,000 developers by September 2023. 5. GAFAM invests >$1B into an AGI or open source AI company (e.g. OpenAI).
6. Reality bites for semiconductor startups in the face of NVIDIA’s dominance and a high proﬁle start-up is shut
down or acquired for <50% of its most recent valuation. 7. A proposal to regulate AGI Labs like Biosafety Labs gets backing from an elected UK, US or EU politician. 8. >$100M is invested in dedicated AI Alignment organisations in the next year as more people become aware
of the risk we are facing by letting AI capabilities run ahead of safety.
9. A major user generated content side (e.g. Reddit) negotiates a commercial settlement with a start-up
producing AI models (e.g. OpenAI) for training on their corpus of user generated content.

stateof.ai 2022

#stateofai | 109

Thanks!
Congratulations on making it to the end of the State of AI Report 2022! Thanks for reading.
In this report, we set out to capture a snapshot of the exponential progress in the ﬁeld of artiﬁcial intelligence, with
a focus on developments since last year’s issue that was published on 12 October 2021. We believe that AI will be a
force multiplier on technological progress in our world, and that wider understanding of the ﬁeld is critical if we are
to navigate such a huge transition.
We set out to compile a snapshot of all the things that caught our attention in the last year across the range of AI
research, industry, politics and safety.
We would appreciate any and all feedback on how we could improve this report further, as well as contribution
suggestions for next year’s edition.
Thanks again for reading!
Nathan Benaich (@nathanbenaich), Ian Hogarth (@soundboy), Othmane Sebbouh (@osebbouh) and
Nitarshan Rajkumar (@nitarshan).

stateof.ai 2022

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Reviewers
We’d like to thank the following individuals for providing critical review of this year’s Report:
– Andrej Karpathy
– Moritz Mueller-Freitag
– Shubho Sengupta
– Miles Brundage
– Markus Anderlung
– Elena Samuylova

stateof.ai 2022

#stateofai | 111

Conﬂicts of interest
The authors declare a number of conﬂicts of interest as a result of being investors and/or advisors, personally or via
funds, in a number of private and public companies whose work is cited in this report.
Ian is an angel investor in the following companies mentioned in this report: Anthropic and Helsing AI.
Nathan is an investor in the following companies: airstreet.com/portfolio

stateof.ai 2022

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About the authors

Nathan Benaich

Ian Hogarth

Nathan is the General Partner of Air Street Capital, a
venture capital ﬁrm investing in AI-ﬁrst technology
and life science companies. He founded RAAIS and
London.AI (AI community for industry and research),
the RAAIS Foundation (funding open-source AI
projects), and Spinout.fyi (improving university spinout
creation). He studied biology at Williams College and
earned a PhD from Cambridge in cancer research.

Ian is a co-founder at Plural, an investment platform
for experienced founders to help the most ambitious
European startups. He is a Visiting Professor at UCL
working with Professor Mariana Mazzucato. Ian was
co-founder and CEO of Songkick, the concert service.
He started studying machine learning in 2005 where
his Masters project was a computer vision system to
classify breast cancer biopsy images.

stateof.ai 2022

Othmane Sebbouh

Nitarshan Rajkumar

Research Assistant

Othmane is a PhD student in ML at
ENS Paris, CREST-ENSAE and CNRS.
He holds an MsC in management
from ESSEC Business School and a
Master in Applied Mathematics from
ENSAE and Ecole Polytechnique.

Nitarshan is a PhD student in AI at
the University of Cambridge. He was
a research student at Mila and a
software engineer at Airbnb. He
holds a BSc from University of
Waterloo.

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Tags : AI Report predictions scaleups startups State of AI

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