“It is a demonstration of AI’s ability to understand language well enough to construct and represent a really persuasive point of view about a complex topic.” – Talia Gershon, IBM’s director of research strategy
Using a training data set of approximately 52,000 written recipes, along with images showing the completed foods, the researchers were able to devise a system that can read a recipe and then generate a picture showing what the end result is likely to look like.
It played like no computer ever has, intuitively and beautifully, with a romantic, attacking style. It played gambits and took risks.
Artificial intelligence (AI), with its vast computational power, predictive analytics and ability to rapidly sift through drug-discovery data, could help speed the discovery of new therapies and offer relief to people impacted by brain disorders.
IBM is steadily gaining ground in blockchain technology. Lately, blockchain technology has gained significant acceptability among financial providers due to its speed and security.
Google’s big play at CES this year is a bevy of upgrades to Google Assistant (and feel free to read “big play” as a child might, as its chief attraction was an actual roller coaster). Leading the pack of said upgrades is Google’s new Interpreter feature, which allows real-time translation in 27 languages.
“…mastered not only chess but shogi, or Japanese chess, and Go. The algorithm started with no knowledge of the games beyond their basic rules. It then played against itself millions of times and learned from its mistakes. In a matter of hours, the algorithm became the best player, human or computer, the world has ever seen.”
Remove.bg is a single-purpose website that uses AI to do the hard work for you. Just upload any image and the site will automatically identify any people in it, cut around the foreground, and let you download a PNG of your subject with a transparent background.
“We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis.”