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AI全景报告——文本生成图像掀起新风暴(英) 剑桥 2022.pdf
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2022-12-20
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AI全景报告——文本生成图像掀起新风暴(英) 剑桥 2022.pdf
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About the authors
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.
Nathan Benaich Ian Hogarth
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.
Introduction | Research | Industry | Politics | Safety | Predictions
#stateofai | 2
stateof.ai 2022
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is
because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its fifth year. Consider this report as a compilation of the most interesting things we’ve seen
with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
- Research: Technology breakthroughs and their capabilities.
- Industry: Areas of commercial application for AI and its business impact.
- Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
- Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
- Predictions: What we believe will happen in the next 12 months and a 2021 performance review to keep us honest.
Produced by Nathan Benaich (@nathanbenaich), Ian Hogarth (@soundboy), Othmane Sebbouh (@osebbouh) and
Nitarshan Rajkumar (@nitarshan).
stateof.ai 2022
#stateofai | 3
Introduction | Research | Industry | Politics | Safety | Predictions
Thank you!
Othmane Sebbouh
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.
#stateofai | 4
Nitarshan Rajkumar
Research Assistant
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.
Introduction | Research | Industry | Politics | Safety | Predictions
stateof.ai 2022
Definitions
stateof.ai 2022
#stateofai | 5
Artificial intelligence (AI): a broad discipline with the goal of creating intelligent machines, as opposed to the natural
intelligence that is demonstrated by humans and animals.
Artificial general intelligence (AGI): a term used to describe future machines that could match and then exceed the full
range of human cognitive ability across all economically valuable tasks.
AI Safety: a field that studies and attempts to mitigate the catastrophic risks which future AI could pose to humanity.
Machine learning (ML): a subset of AI that often uses statistical techniques to give machines the ability to "learn" from data
without being explicitly given the instructions for how to do so. This process is known as “training” a “model” using a learning
“algorithm” that progressively improves model performance on a specific task.
Reinforcement learning (RL): an area of ML in which software agents learn goal-oriented behavior by trial and error in an
environment that provides rewards or penalties in response to their actions (called a “policy”) towards achieving that goal.
Deep learning (DL): an area of ML that attempts to mimic the activity in layers of neurons in the brain to learn how to
recognise complex patterns in data. The “deep” refers to the large number of layers of neurons in contemporary models that
help to learn rich representations of data to achieve better performance gains.
Introduction | Research | Industry | Politics | Safety | Predictions
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