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When Will Computers Have Common Sense? Ask Facebook

#artificialintelligence

Facebook is well known for its early and increasing use of artificial intelligence. The social media site uses AI to pinpoint its billion-plus users' individual interests and tailor content accordingly by automatically scanning their newsfeeds, identifying people in photos and targeting them with precision ads. And now behind the scenes the social network's AI researchers are trying to take this technology to the next level--from pure data-crunching logic to a nuanced form of "common sense" rivaling that of humans. AI already lets machines do things like recognize faces and act as virtual assistants that can track down info on the Web for smartphone users. But to perform even these basic tasks the underlying learning algorithms rely on computer programs written by humans to feed them massive amounts of training data, a process known as machine learning.


When Will Computers Have Common Sense? Ask Facebook

#artificialintelligence

Facebook is well known for its early and increasing use of artificial intelligence. The social media site uses AI to pinpoint its billion-plus users' individual interests and tailor content accordingly by automatically scanning their newsfeeds, identifying people in photos and targeting them with precision ads. And now behind the scenes the social network's AI researchers are trying to take this technology to the next level--from pure data-crunching logic to a nuanced form of "common sense" rivaling that of humans. AI already lets machines do things like recognize faces and act as virtual assistants that can track down info on the Web for smartphone users. But to perform even these basic tasks the underlying learning algorithms rely on computer programs written by humans to feed them massive amounts of training data, a process known as machine learning.


Generative adversarial networks: What GANs are and how they've evolved

#artificialintelligence

Perhaps you've read about AI capable of producing humanlike speech or generating images of people that are difficult to distinguish from real-life photographs. More often than not, these systems build upon generative adversarial networks (GANs), which are two-part AI models consisting of a generator that creates samples and a discriminator that attempts to differentiate between the generated samples and real-world samples. This unique arrangement enables GANs to achieve impressive feats of media synthesis, from composing melodies and swapping sheep for giraffes to hallucinating footage of ice skaters and soccer players. In point of fact, it's because of this prowess that GANs have been used to produce problematic content like deepfakes, which is media that takes a person in existing media and replaces them with someone else's likeness. The evolution of GANs -- which Facebook AI research director Yann LeCun has called the most interesting idea of the decade -- is somewhat long and winding, and very much continues to this day.


The Next Frontier Of Artificial Intelligence Is Here, And Its A Bit Eerie

#artificialintelligence

Using our imagination is easy. We can all close our eyes, and think of ice cream, or cake, or even better, cake and ice cream. But teaching AI to imagine things has been very difficult up until a few years ago, with the advent of Dueling Neural Networks, also known as Generative Adversarial Networks or GANS. GANs is the next frontier in Machine Learning and it involves using two Neural Networks with opposing objectives to train one another, resulting in mind-blowing results. The first network is the generator which is programmed to generate images from random noise, with the goal to fool the other network called the discriminator, which is programmed to detect whether images are real or fake.


Using GANs to Create Anime Faces via Pytorch

#artificialintelligence

Most of us in data science have seen a lot of AI-generated people in recent times, whether it be in papers, blogs, or videos. We've reached a stage where it's becoming increasingly difficult to distinguish between actual human faces and faces generated by artificial intelligence. However, with the current available machine learning toolkits, creating these images yourself is not as difficult as you might think. In my view, GANs will change the way we generate video games and special effects. Using this approach, we could create realistic textures or characters on demand.