Quotes About Machine learning

Robots... I think that is a hot topic.

Deep learning has instead given us machines with truly impressive abilities but no intelligence. The difference is profound and lies in the absence of a model of reality.

I think there are a lot of industries that are collecting a lot of data and have not yet considered the implications of machine learning but will ultimately use it.

You need to use data science and machine learning to get the ground truth of what's happening inside of a company.

All you need is lots and lots of data and lots of information about what the right answer is, and you'll be able to train a big neural net to do what you want.

AIs are only as good as the data they are trained on.

The irony across all self-protection is that at the same time as we're worrying about machine learning and artificial intelligence taking jobs and dehumanizing work, we're intentionally or unintentionally creating cultures that, instead of leveraging the unique gifts of the human heart like vulnerability, empathy, and emotional literacy, are trying to lock those gifts away.

Things like chatbots, machine learning tools, natural language processing, or sentiment analysis are applications of artificial intelligence that may one day profoundly change how we think about and transact in travel and local experiences.

Particularly over the past decade, there've been many advances in the art of training neural nets. And, yes, it is basically an art.

And—as we'll discuss later—these weights are normally determined by "training" the neural net using machine learning from examples of the outputs we want.)

Ultimately, every neural net just corresponds to some overall mathematical function—though it may be messy to write out. For the example above, it would be: The neural net of ChatGPT also just corresponds to a mathematical function like this—but effectively with billions of terms.

It would be great to have every engineer have at least some amount of knowledge of machine learning.

My Ph.D. is in computer vision and machine learning. I developed software that can read your emotions from your face as part of my doctorate work.

Every problem is super-interesting and has its own nuances, and you solve it today, but you try to solve it with an architecture. You build a machine to solve the problems that are like it later. And then you move on to the next.

Instead of learning from one mind at a time, the search engine learns from the collective human mind, all at once. Every time an individual searches for something, and finds an answer, this leaves a faint, lingering trace as to where (and what) some fragment of meaning is. The fragments accumulate and, at a certain point, as Turing put it in 1948, "the machine would have 'grown up.

A good question may be the last job a machine will learn to do. A good question is what humans are for.

By 2026, Google's main product will not be search but AI.

I'm a learning machine and this is the place to learn.

Ultimately, I hypothesize that technology will one day be able to recreate a realistic representation of us as a result of the plethora of content we're creating converging with other advances in machine learning, robotics and large-scale data mining.

Intel's first problem was artificial intelligence.

Deep neural networks are responsible for some of the greatest advances in modern computer science.

Can man-made machines learn and can they reproduce themselves? We shall try to show in this chapter that in fact they can learn and can reproduce themselves, and we shall give an account of the technique needed for both these activities.

We don't use the word 'intelligence' with software. We regard that as a naive idea. We say that it's 'complex.' Which means that we don't always understand what it's doing.

A lot of the progress in machine learning - and this is an unpopular opinion in academia - is driven by an increase in both computing power and data. An analogy is to building a space rocket: You need a huge rocket engine, and you need a lot of fuel.