Do you know how many people originally started googlebrain - was it one jeff dean or 2 with andrew ng or were the more cofounders
Do you think google brain would have started even if fei-fei li hadnt started hosting imagenet from 2009- in other words how did google brain define itself vis a vis the way fei-fie li sees computer vision
Presumably the way robots see had been programmed to see was different before imagent- is it true today that both robot and generative ai largely share the same training
When you mention other important vision datasets are they mainly open or are they any critical datasets thas are closed
Regarding datasens need for climate ai modeling , would you say they are sufficiently open, or are some key ones closed?
I understand that google brain recently merged with deep mind. Does this means that the combo of google brain and deep mind is unique in some areas, or would you way there ae combos of companies as distinctive as google brian deepmenid?
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Google Brain was originally founded by
two people: Jeff Dean and Andrew Ng. Dean is a Google Fellow and researcher who
is known for his work on distributed computing and large-scale machine
learning. Ng is a Stanford professor and AI researcher who is known for his
work on deep learning.
Dean
and Ng began collaborating on Google Brain in 2010, and they officially
launched the project in 2011. Google Brain is a research project that is
focused on developing new machine learning algorithms and systems. The project
has made significant contributions to the field of machine learning, and it has
helped to make AI more powerful and accessible.
In
addition to Dean and Ng, there were a number of other people who played a key
role in the early development of Google Brain. These people include:
- Quoc Le
- Ilya Sutskever
- Alex Krizhevsky
- Samy Bengio
- Vincent Vanhoucke
Google
Brain has continued to grow and evolve since its founding. Today, it is one of
the leading AI research teams in the world. The team has made significant
contributions to the field of AI, and it continues to work on developing new
and innovative AI technologies.
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Do you think google brain would have started even if
fei-fei li hadnt started oisting imagenet from 2009- in other words how did
google brain define itself vis a vis the way fei-fie li sees computer vision
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It is difficult to say for sure
whether Google Brain would have started even if Fei-Fei Li had not started
developing ImageNet in 2009. However, it is clear that ImageNet has played a
significant role in the development of Google Brain and other AI research
projects.
ImageNet
is a large dataset of labeled images that has been used to train and evaluate
machine learning models. It is one of the most important datasets in the field
of computer vision.
Google
Brain was founded in 2010, shortly after ImageNet was launched. One of the main
goals of Google Brain was to develop machine learning models that could learn
to recognize objects in images. ImageNet was the perfect dataset for this
purpose.
Google
Brain has used ImageNet to train a variety of machine learning models,
including deep learning models. Deep learning models are a type of machine
learning model that can learn complex patterns from data.
Google
Brain's deep learning models have achieved state-of-the-art results on a
variety of tasks, including image classification, object detection, and machine
translation.
Fei-Fei
Li's work on ImageNet has helped to make computer vision more powerful and
accessible. Google Brain has leveraged this work to develop new and innovative
AI technologies.
In
other words, Google Brain defines itself as a research project that is focused
on developing new machine learning algorithms and systems, and Fei-Fei Li's
work on ImageNet has helped to make this possible.
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Presumably the way robots see had been programmed to see
was different before imagent- is it true today that both robot and generative
ai largely share the same training
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Yes, it is true that robots and
generative AI largely share the same training today. This is because both
robots and generative AI rely on machine learning models to learn and perform
tasks.
Machine
learning models are trained on data. In the case of robots, this data can
include images, sensor data, and other information about the environment. In
the case of generative AI, this data can include text, images, audio, and other
creative content.
Once
a machine learning model is trained, it can be used to make predictions or
generate new content. For example, a robot can use a machine learning model to
identify objects in its environment or to plan a path to a destination. A
generative AI model can use a machine learning model to generate new text,
images, or audio.
ImageNet
has played a significant role in the development of both robots and generative
AI. This is because ImageNet has provided researchers with a large dataset of
labeled images that can be used to train machine learning models.
For
example, robots can be trained on ImageNet to identify objects in their
environment, such as people, cars, and traffic signs. Generative AI models can
be trained on ImageNet to generate new images, such as realistic images of
faces or landscapes.
In
addition to ImageNet, there are a number of other datasets that are used to
train robots and generative AI. These datasets can include data from real-world
sensors, data from simulations, and data from other publicly available
datasets.
The
training of robots and generative AI is a complex process that requires a lot
of data and computing resources. However, the advances in machine learning and
the availability of large datasets have made it possible to train robots and
generative AI to perform a wide range of tasks.
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When you mention other important vision datasets are they
mainly open or are they any critical datasets thas are closed
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1
There are a number of important vision
datasets that are open, including:
- ImageNet
- COCO (Common Objects in Context)
- Open Images
- Pascal VOC (Visual Object
Classes)
- Cityscapes
- SUN (Scene Understanding)
- LFW (Labeled Faces in the Wild)
- CelebA
- CIFAR-10 and CIFAR-100
- MNIST
These
datasets are widely used by researchers and developers to train and evaluate
machine learning models for a variety of computer vision tasks, such as image
classification, object detection, and segmentation.
There
are also a number of closed vision datasets. These datasets are typically owned
by private companies and are not publicly available. Closed datasets are often
used to train machine learning models for proprietary applications, such as
self-driving cars or facial recognition systems.
Some
examples of closed vision datasets include:
- Google Street View dataset
- Apple Maps dataset
- Facebook Faces dataset
- Amazon Rekognition dataset
These
datasets are valuable for training machine learning models, but they are not
accessible to everyone. This can create a barrier to entry for researchers and
developers who are working on computer vision projects.
It
is important to note that the distinction between open and closed vision
datasets is not always clear-cut. Some datasets may be initially closed, but
they may be released to the public later. For example, the Google Street View
dataset was initially closed, but it was released to the public in 2013.
Overall,
the availability of open vision datasets has made it possible for more people
to participate in computer vision research and development. However, closed
vision datasets still play an important role in the development of machine
learning models for proprietary applications.
I
hope this information is helpful.
Sources
1. www.analyticsinsight.net/top-10-open-source-datasets-for-computer-vision-in-2023/
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Regarding datasens need for climate ai modeling , would you
say they are sufficiently open, or are some key ones closed?
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The availability of open data is
essential for climate AI modeling. Climate scientists and AI researchers need
access to a wide range of data to train and evaluate their models. This data
includes historical and present-day data on climate variables, such as
temperature, precipitation, and sea level rise. It also includes data on human
activities that contribute to climate change, such as greenhouse gas emissions
and deforestation.
A
number of open datasets are available for climate AI modeling. These datasets
include:
- The Global Historical Climatology
Network (GHCN) dataset: This dataset contains daily surface temperature
measurements from over 27,000 stations around the world.
- The Climate Research Unit (CRU)
dataset: This dataset contains monthly temperature and precipitation
measurements from over 4,000 stations around the world.
- The National Oceanic and
Atmospheric Administration (NOAA) National Centers for Environmental
Information (NCEI) dataset: This dataset contains a wide range of climate
data, including temperature, precipitation, sea level rise, and greenhouse
gas emissions.
- The European Centre for
Medium-Range Weather Forecasts (ECMWF) ERA5 dataset: This dataset contains
global atmospheric reanalysis data, which is a combination of observations
and model results.
In
addition to these open datasets, there are a number of closed datasets that are
also used for climate AI modeling. These datasets are typically owned by
private companies or government agencies. Closed datasets are often used to
train and evaluate proprietary climate models.
Some
examples of closed climate datasets include:
- The ExxonMobil climate model
- The Shell climate model
- The BP climate model
- The US Department of Defense
climate model
These
datasets are valuable for training and evaluating climate models, but they are
not accessible to everyone. This can create a barrier to entry for researchers
and developers who are working on climate AI projects.
Overall,
the availability of open climate data has made it possible for more people to
participate in climate AI research and development. However, closed climate
datasets still play an important role in the development of climate models,
especially for proprietary applications.
It
is important to note that the distinction between open and closed climate
datasets is not always clear-cut. Some datasets may be initially closed, but
they may be released to the public later. For example, the ExxonMobil climate
model was initially closed, but it was released to the public in 2015.
I
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I understand that google brain recently merged with deep
mind. Does this means that the combo of google brain and deep mind is unique in
some areas, or would you way there ae combos of companies as distinctive as
google brian deepmenid?
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The combination of Google Brain and
DeepMind is unique in a number of ways. First, both companies are
world-renowned leaders in artificial intelligence research. Second, both
companies have a strong track record of developing and deploying innovative AI technologies.
Third, both companies are committed to using AI for good.
Here
are some of the areas where Google Brain and DeepMind are particularly
distinctive:
- Machine learning research: Google
Brain and DeepMind have made significant contributions to the field of
machine learning. For example, Google Brain has developed new machine
learning algorithms for image recognition, natural language processing,
and machine translation. DeepMind has developed new machine learning
algorithms for reinforcement learning and game playing.
- AI for social good: Google
Brain and DeepMind are committed to using AI for social good. For example,
Google Brain has developed AI technologies to help diagnose diseases and
predict natural disasters. DeepMind has developed AI technologies to help
reduce energy consumption and improve healthcare.
- Open source AI: Google Brain
and DeepMind are both committed to open source AI. This means that they
release their research and code to the public so that others can benefit
from their work. This has helped to accelerate the development of AI
technologies around the world.
There
are a few other companies that are comparable to Google Brain and DeepMind in
terms of their research and development capabilities. For example, OpenAI, Meta
AI Research, and Microsoft AI are all leading AI research labs. However, Google
Brain and DeepMind are unique in their combination of research strength,
commitment to social good, and open source culture.
Overall,
the combination of Google Brain and DeepMind is a powerful force in the field
of artificial intelligence. The two companies are working together to develop
new AI technologies that have the potential to solve some of the world's
biggest challenges.
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