Lila Ibrahim
help businesses innovate, grow & create jobs
keep people safe & build public trust
I sat down with @DeepMind to talk about the exciting future of AI in the UK
gov.uk/government/new
On #BlazingTrails, @DeepMind's dives into the benefits, risks, and questions associated with AI and how businesses can prevent it from creating biases.
Watch now: sforce.co/3Fvhz4N
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correspondence with bard oct 2023 Do you know how many people originally started googlebrain - was it one jeff dean or 2 with andrew ng or were thetremore cofounders
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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.
I hope this information is helpful.
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Do you think google brain would have started even if fei-fei li hadnt arrived stanford and 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
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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.
I hope this information is helpful.
share
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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.
I hope this information is helpful.
share
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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 hope this information is helpful.
share
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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?
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.
I hope this information is helpful other drafts
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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.
I hope this information is helpful.
share
more_vert
Do you think google brain would have started even if fei-fei li hadnt arrived stanford and 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
volume_up
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.
I hope this information is helpful.
share
more_vert
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
volume_up
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.
I hope this information is helpful.
share
more_vert
When you mention other important vision datasets are they mainly open or are they any critical datasets thas are closed
volume_up
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/
share
more_vert
Regarding datasens need for climate ai modeling , would you say they are sufficiently open, or are some key ones closed?
volume_up
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 hope this information is helpful.
share
more_vert
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?
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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