As further examples are provided, the code behind the model is rewritten, and the probabilities tweaked.At a certain point, the AI program takes over, and models are created on their own.Much like a teenager’s hormones, there isn’t necessarily an off button.
ML stands for “machine learning,” whereby computer algorithms can learn to perform various tasks on their own by analyzing data.Auto ML is a machine-learning algorithm that learns to build other machine-learning algorithms.In large part this stems from the fact that there are only a small group of people who possess the education, experience, and talent necessary to create the algorithms underpinning AI.While AI is a key part of scalable new technologies, there is a human bottleneck with only 300,000 AI engineers on the planet, according to Chinese technology company Tencent. If we want to see cost reductions and productivity gains in the industry any time soon, AIs that can teach themselves are a necessary force for the creation of the AI-driven economy.While we look forward to a future where humans use AI to enhance our existence, we need to consider what steps are being taken to get us there.
In particular, we should be concerned with the fact that AI is being developed to replicate itself, potentially embedding biases into the algorithms that will underpin and drive our tomorrow—and repeating them, writ large. Because while to err is to be human, to truly foul things up requires a computer.
Our history shows innovation and technology advancements are replete with unintended consequences.
Who knew that widespread social-media adoption would lead to disinformation campaigns aimed at undermining liberal democracy, when it was originally thought it would increase civic engagement?
If humanity is no longer to be the core of our civilization, we should spend some time considering what our future is to be.
We may not have Copernicus’s luxury of waiting for a future generation to solve the problem—as our future appears to be happening right here, right now.
To train a deep-learning algorithm to recognize a cat with a cat-fancier’s level of expertise, you first must feed it tens or even hundreds of thousands of images of felines, capturing a huge amount of variation in size, shape, texture, lighting, and orientation.