AI is comprised of two ingredients: algorithms and data, and "Both of these are man-made," said Tony Fernandes, founder of HumanFocused.AI and CEO of UEGroup, a design company that employs artists. "People are fascinated to see what happens when both are allowed to run on their own and to look at what they generated,'' Fernandes said, adding that creating art requires inherent human influence. "To say that AI creates art is to say that anyone can replicate the work of Picasso," he said. "What makes Picasso's work relevant is the human spirit, imagination, and desire for expression. Picasso experimented with cubism to achieve something new.

By any estimation, Minecraft is impossibly successful. The bestselling video game ever, as of last December it had 204 million monthly active players. Since it was first released in 2011, it has generated over 3bn ( 2.3bn) in revenue. What's more, its players have always been eager to demonstrate their fandom outside the boundaries of the game itself. In 2021, YouTube calculated that videos related to the game – tutorials, walk-throughs, homages, parodies – had collectively been viewed 1tn times. In short, it is a phenomenon.

When scenes created by the AI image generator DALL-E started circulating online earlier this year, it seemed inevitable that someone would turn the technology to fashion. DALL-E is part of a new crop of AI capable of creating extraordinarily detailed and realistic imagery from a text prompt, making it easy for anyone to use. Artists have quickly begun applying these programs to creating digital art, with one piece conjured up by the program Midjourney even beating out its human-generated competition for a prize. The same power could easily be used to whip up clothing designs. The idea is already becoming reality.

Andrew Leakey, Jiayang (Kevin) Xie and their colleagues developed an improved method for analyzing features of plant leaves that contribute to water-use efficiency in crops like corn, sorghum (pictured) and Setaria. They used advanced statistical approaches to identify regions of the genome and lists of genes that contribute to these traits. Scientists have developed and deployed a series of new imaging and machine learning tools to discover attributes that contribute to water-use efficiency in crop plants during photosynthesis and to reveal the genetic basis of variation in those traits. The findings are described in a series of four research papers led by University of Illinois Urbana-Champaign graduate students Jiayang (Kevin) Xie and Parthiban Prakash, and postdoctoral researchers John Ferguson, Samuel Fernandes and Charles Pignon. The goal is to breed or engineer crops that are better at conserving water without sacrificing yield, said Andrew Leakey, a professor of plant biology and of crop sciences at the University of Illinois Urbana-Champaign, who directed the research.

Those were some of the questions posed by John Zimmer, president and co-founder of U.S. rideshare firm Lyft, at the recent Rakuten Optimism 2019 conference in Yokohama, Japan. Lyft became the first ridesharing company to go public earlier this year when it completed an IPO with a valuation of $24 billion. It has also been pursuing autonomous driving technology: in partnership with Aptiv, Lyft recently notched 50,000 rides in Las Vegas in just a year, and has recently launched Waymo autonomous vehicles on the Lyft platform in Phoenix, Arizona. Against that background, Zimmer spoke about the future of transport with Mickey Mikitani, CEO of early Lyft investor, Rakuten. "We have to think about what is the right infrastructure to support (the future of transport)," Zimmer said during his second appearance at Optimism since speaking at the inaugural conference last year in San Francisco.

A neural network looks at a picture of a turtle and sees a rifle. A self-driving car blows past a stop sign because a carefully crafted sticker bamboozled its computer vision. An eyeglass frame confuse facial recognition tech into thinking a random dude is actress Milla Jovovich. The hacking of artificial intelligence is an emerging security crisis. Pre-empting criminals attempting to hijack artificial intelligence by tampering with datasets or the physical environment, researchers have turned to adversarial machine learning.

The aim of this paper is to discuss the use of Haar scattering networks, which is a very simple architecture that naturally supports a large number of stacked layers, yet with very few parameters, in a relatively broad set of pattern recognition problems, including regression and classification tasks. This architecture, basically, consists of stacking convolutional filters, that can be thought as a generalization of Haar wavelets, followed by non-linear operators which aim to extract symmetries and invariances that are later fed in a classification/regression algorithm. We show that good results can be obtained with the proposed method for both kind of tasks. We have outperformed the best available algorithms in 4 out of 18 important data classification problems, and have obtained a more robust performance than ARIMA and ETS time series methods in regression problems for data with strong periodicities.

India's logistics sector is booming, and is touted to be worth $307 billion by 2020. Retail, e-commerce and manufacturing have propelled its rapid growth, but so far it has been largely unorganized, mainly thanks to outdated processes and limited technological intervention. This technological gap has created multiple opportunities for startups to provide innovative solutions to mitigate the industry's connectivity problems, service e-commerce players better and provide a seamless customer experience. Shadowfax is one of India's first logistics startups planning to run a pilot project using a drone. Gurgaon-based hyperlocal logistics startup Shadowfax is one such startup.