Probing the ethical implications surrounding the use of AI, Franklin Ritch's debut feature hinges on a high-concept premise: an entirely digital avatar of a young girl named Cherry (Tatum Matthews) is used as bait to trap paedophiles in online chatrooms. Without the signature spectacle of the sci-fi genre, The Artifice Girl is a markedly low-key and small-scale endeavour, steeped in philosophical musings that ultimately seem stagey rather than cinematic. It starts in a police interrogation room where Ritch's Gareth, Cherry's creator, is questioned by Deena (Sinda Nichols) and Amos (David Girard), members of a taskforce combatting child sexual abuse. Once Gareth reveals Cherry is a virtual being, concerns arise as to whether she can meaningfully consent to interacting with men on a daily basis. As Cherry grows increasingly sentient, the same talking points are reiterated in the second section of the film, as Gareth advocates to transfer Cherry's intelligence into a physical form.

Combining a set of phylogenetic trees into a single phylogenetic network that explains all of them is a fundamental challenge in evolutionary studies. Existing methods are computationally expensive and can either handle only small numbers of phylogenetic trees or are limited to severely restricted classes of networks. In this paper, we apply the recently-introduced theoretical framework of cherry picking to design a class of efficient heuristics that are guaranteed to produce a network containing each of the input trees, for datasets consisting of binary trees. Some of the heuristics in this framework are based on the design and training of a machine learning model that captures essential information on the structure of the input trees and guides the algorithms towards better solutions. We also propose simple and fast randomised heuristics that prove to be very effective when run multiple times. Unlike the existing exact methods, our heuristics are applicable to datasets of practical size, and the experimental study we conducted on both simulated and real data shows that these solutions are qualitatively good, always within some small constant factor from the optimum. Moreover, our machine-learned heuristics are one of the first applications of machine learning to phylogenetics and show its promise.

You wake up, pour a cup of coffee, and eventually make your way to one or more crossword puzzles, word games and other brain twisters. The test of banked knowledge and problem-solving ability can boost your ego, or deflate it. It's the "use it or lose it" theory in action, and as I get older, I'd like to believe these mental exercises can help keep my mind sharp and maybe even ward off memory loss, even if my wife usually beats me at all these games. But is there any science behind that, or is it wishful thinking? I am trying to solve that riddle, because since launching the Golden State column two months ago, I've heard from a lot of readers who -- like me --put at least a bit of faith in the value of mental gymnastics.

Tree fruit breeding is a long-term activity involving repeated measurements of various fruit quality traits on a large number of samples. These traits are traditionally measured by manually counting the fruits, weighing to indirectly measure the fruit size, and fruit colour is classified subjectively into different color categories using visual comparison to colour charts. These processes are slow, expensive and subject to evaluators' bias and fatigue. Recent advancements in deep learning can help automate this process. Objective data can be generated for consistent characterization of germplasm, with greater speed and higher accuracy. A method was developed to automatically count the number of sweet cherry fruits in a camera's field of view in real time using YOLOv3. A system capable of analyzing the image data for other traits such as size and color was also developed using Python. The YOLO model obtained close to 99% accuracy in object detection and counting of cherries and 90% on the Intersection over Union metric for object localization when extracting size and colour information. The model surpasses human performance and offers a significant improvement compared to manual counting.

Prokaryotic viruses, which infect bacteria and archaea, are key players in microbial communities. Predicting the hosts of prokaryotic viruses helps decipher the dynamic relationship between microbes. Experimental methods for host prediction cannot keep pace with the fast accumulation of sequenced phages. Thus, there is a need for computational host prediction. Despite some promising results, computational host prediction remains a challenge because of the limited known interactions and the sheer amount of sequenced phages by high-throughput sequencing technologies. The state-of-the-art methods can only achieve 43\% accuracy at the species level. In this work, we formulate host prediction as link prediction in a knowledge graph that integrates multiple protein and DNA-based sequence features. Our implementation named CHERRY can be applied to predict hosts for newly discovered viruses and to identify viruses infecting targeted bacteria. We demonstrated the utility of CHERRY for both applications and compared its performance with 11 popular host prediction methods. To our best knowledge, CHERRY has the highest accuracy in identifying virus-prokaryote interactions. It outperforms all the existing methods at the species level with an accuracy increase of 37\%. In addition, CHERRY's performance on short contigs is more stable than other tools.

Self-supervised learning is one of those recent ML methods that have caused a ripple effect in the data science network, yet have so far been flying under the radar to the extent Entrepreneurs and Fortunes of the world go; the overall population is yet to find out about the idea yet lots of AI society consider it progressive. The paradigm holds immense potential for enterprises too as it can help handle deep learning's most overwhelming issue: data/sample inefficiency and subsequent costly training. Yann LeCun said that if knowledge was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake and reinforcement learning would be the cherry on the cake. We realize how to make the icing and the cherry, however, we don't have a clue how to make the cake." Unsupervised learning won't progress a lot and said there is by all accounts a massive conceptual disconnect with regards to how precisely it should function and that it was the dark issue of ...

The University of Adelaide plans to achieve substantial growth in its student population within five years, and one of the teams responsible for achieving this very aggressive goal has a new staff member this year: a chatbot. It helps answer the critical question, "Am I eligible to attend the university?" Catherine Cherry, the school's director of prospect management, is putting innovative technologies to work to help meet that goal. The University of Adelaide uses a chatbot to let prospective students know whether they're eligible to apply. Prior to the introduction of the chatbot, the university's admissions office couldn't easily answer the eligibility question for prospective students from outside of Australia who were curious about whether they could attend.

Last Wednesday, researchers at Caltech announced that they created an artificial neural network from synthetic DNA that is able to recognize numbers coded in molecules. It's a novel implementation of a classic machine learning test that demonstrates how the very building blocks of life can be harnessed as a computer. This is pretty mind blowing, but what does it all mean? For starters, "artificial intelligence" here doesn't refer to the superhuman AI that is so beloved by Hollywood. Instead, it refers to machine learning, a narrow form of artificial intelligence that is best summarized as the art and science of pattern recognition.

"If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don't know how to make the cake." By 2016, Yann LeCun began to hedge with his use of the term "unsupervised learning". In NIPS 2016, he started to call it in even more nebulous terms "predictive learning": I have always had trouble with the use of the term "Unsupervised Learning". In 2017, I had predicted that Unsupervised Learning will not progress much and said "there seems to be a massive conceptual disconnect as to how exactly it should work" and that it was the "dark matter" of machine learning.

Caltech scientists have recently developed an AI made out of DNA that can tackle a classic machine learning problem by precisely recognizing written by hand numbers. The work is a critical advance in showing the ability to program AI into engineered biomolecular circuits. Lulu Qian, assistant professor of bioengineering at Caltech said, "Though scientists have only just begun to explore creating artificial intelligence in molecular machines, its potential is already undeniable. Similar to how electronic computers and smartphones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come." Scientists' goal behind this study is to program intelligent behaviors (the ability to compute, make choices, and more) with artificial neural networks made out of DNA.