Monday, September 4, 2023

The Unintended Benefit of AI Bias in Educating People About Social Biases

 I gave Bard the following command:

Write an article that talks about the fact that the biases we see coming out of AIs have an unintended benefit of educating people about social biases and is garnering more conversation around bias and in turn addressing the negative aspects of bias.

And this is mostly what Bard responded with. Some minor tweaks from me.

One of the unintended benefits of AI bias is that it is raising awareness of social biases. When people see AI systems making biased decisions, it can help them to understand how these biases exist in the real world. This can lead to more conversations about bias and how to address it.

For example, in 2019, a facial recognition algorithm was found to be more accurate at identifying white people than black people. This revelation sparked a lot of discussion about the role of race in facial recognition technology.

Another example is the case of COMPAS, a risk assessment tool used by judges to determine whether defendants should be released from jail before their trial. COMPAS was found to be biased against black defendants, who were more likely to be classified as high-risk and denied bail. This led to calls for reforms to the criminal justice system.

The unintended benefit of AI bias is not always positive. In some cases, it can reinforce existing biases and make it more difficult to address them. However, overall, it is clear that AI bias is having a positive impact on raising awareness of social biases. This is an important step towards addressing these biases and creating a more just and equitable society.

Here are some ways to address the negative aspects of AI bias:

Use more diverse data sets: AI systems are trained on data, so it is important to use data that is as diverse as possible. This will help to reduce the likelihood that AI systems will learn to reflect biases that are present in the data.

Use fairer algorithms: There are a number of algorithms that are designed to be fairer than traditional algorithms. These algorithms can be used to reduce the impact of bias in AI systems.

Be transparent about the data and algorithms: It is important to be transparent about the data and algorithms that are used to train AI systems. This will help to make it easier to identify and address any biases that may be present.

Encourage human oversight: AI systems should not be used to make decisions without human oversight. This will help to ensure that any biases that are present in the AI system are not allowed to have a negative impact on people's lives.

By taking these steps, we can help to ensure that AI is used for good and that it does not perpetuate the negative aspects of social bias.

Tuesday, August 15, 2023

The Parallels between the evolution of self driving cars and generative AI

Generative AI and driver-less cars are two technologies that are rapidly evolving and have the potential to change the world in a major way. Both technologies rely on artificial intelligence (AI) to perform complex tasks, but they do so in very different ways.

Generative AI is a type of AI that can create new content, such as images, text, and music. It does this by learning from existing data and then using that data to generate new variations.

Self driving cars are vehicles that can navigate roads and highways without the need for a human driver. They use a variety of sensors, such as cameras, radar, and lidar, to collect data about their surroundings. This data is then used by AI algorithms to make decisions about where to go and how to avoid obstacles.

The generative AI industry and the driver-less car industry have both been growing rapidly in recent years. Here is a timeline of some of the key milestones in each industry:

Generative AI

  • 2012: Generative adversarial networks (GANs) are first introduced. GANs are a type of generative AI that have been used to generate realistic images, text, and music.
  • 2014: DeepDream is released. DeepDream is a software program that uses neural networks to generate psychedelic images from regular photos.
  • 2017: OpenAI Five defeats a team of professional Dota 2 players. This is a major milestone for generative AI, as it shows that AI can be used to create systems that can outperform humans in complex tasks.
  • 2020: Nvidia releases StyleGAN2. StyleGAN2 is a generative AI model that can generate photorealistic images of people, animals, and objects.
  • 2023: Google AI releases Imagen. Imagen is a generative AI model that can generate images that are indistinguishable from real photos.

Driver-less Cars

  • 2004: The first self-driving car is built by Stanford University. This car is able to navigate a small course without human input.
  • 2010: Google begins testing self-driving cars on public roads.
  • 2014: Uber launches a self-driving car pilot program in Pittsburgh.
  • 2016: Tesla releases its Autopilot feature, which allows cars to drive themselves on highways.
  • 2020: Waymo launches a commercial self-driving car service in Phoenix, Arizona.
  • 2023: Several major automakers announce plans to release self-driving cars in the next few years.

Both the generative AI industry and the driverless car industry are rapidly evolving. It is still too early to say when either technology will become mainstream. It is clear that they have the potential to change the world in a major way.

One of the most exciting things about generative AI is its potential to be used to create realistic simulations of driving situations. This could be used to train driver-less cars to be more safe and efficient. For example, a generative AI model could be used to create a simulation of a busy intersection, and then driver-less cars could be trained to navigate this intersection safely and efficiently.

Generative AI could also be used to create new features for driver-less cars. A generative AI model could be used to create new navigation apps that are more intuitive and easier to use. The future of generative AI and driver-less cars is very bright. These two technologies have the potential to revolutionize transportation and make our lives safer, easier, and more enjoyable.