It can no longer be denied that Artificial Intelligence is having a growing impact in many areas of human activity. It is helping humans communicate with each other--even beyond linguistic boundaries--, finding relevant information in the vast information resources available on the web, solving challenging problems that go beyond the competence of a single expert, enabling the deployment of autonomous systems, such as self-driving cars or other devices that handle complex interactions with the real world with little or no human intervention, and many other useful things. These applications are perhaps not like the fully autonomous, conscious and intelligent robots that science fiction stories have been predicting, but they are nevertheless important and useful, and most importantly they are real and here today. The growing impact of AI has triggered a kind of'gold rush': we see new research laboratories springing up, new AI start-up companies, and very significant investments, particularly by big digital tech companies, but also by transportation, manufacturing, financial, and many other industries. Management consulting companies are competing in their predictions on how big the economic impact of AI is going to be and governments are responding with strategic planning to see how their countries can avoid staying behind. Although all of this is good news, it cannot be denied that the application of AI comes with certain risks. Several initiatives have been taken in recent years to better understand the risks of AI deployment and came up with legal frameworks, codes of conduct, and value-based design methodologies.

It's accomplishing this with an artificial intelligence-powered system that uses "conversational memory" to help users complete tasks.

One group of researchers at The University of Texas at Austin is developing new "scientific machine learning" methods to address this challenge.

One group of researchers at The University of Texas at Austin is developing new "scientific machine learning" methods to address this challenge.

A machine learning model is first trained on a synthetically generated dataset with the intention of transfer learning to real data.

This week, the 2019 remake of horror movie "Child's Play" is trending on Netflix. Testament to the advances in artificial intelligence since the original movie was made in 1988, we now see the psychotic Chucky doll, re-imagined, inline with modern technology. Chucky can now control household appliances, connect to the internet, record, stream, and playback what he hears and sees, and most importantly, he can learn, and adapt to his environment. The original Chucky was far fetched back in 1988. Less than 30 years later, the prospect of rogue AI is increasingly believable.

Artificially intelligent algorithms that create images are notoriously difficult to predict and control. But now researchers at Adobe have devised AI-controlled software that lets you transform the shape of objects in images, and adjust the lighting and perspective, with a few simple controls. Given, say, a photograph of a car, a person can use a combination of several control sliders in the program to adjust different components, including its shape and style, background, lighting conditions, and colour. Dragging a slider can instantly change a convertible into a hatchback, for example.

Disclaimer: My highest qualification is currently an MSc in Computer Vision and Machine Learning. Do be aware I'll attempt to approach this article/ …