We present an Artificial Neural Network (ANN) model of photospheric lithium depletion in cool stars (3000 < Teff / K < 6500), producing estimates and probability distributions of age from Li I 6708A equivalent width (LiEW) and effective temperature data inputs. The model is trained on the same sample of 6200 stars from 52 open clusters, observed in the Gaia-ESO spectroscopic survey, and used to calibrate the previously published analytical EAGLES model, with ages 2 - 6000 Myr and -0.3 < [Fe/H] < 0.2. The additional flexibility of the ANN provides some improvements, including better modelling of the "lithium dip" at ages < 50 Myr and Teff ~ 3500K, and of the intrinsic dispersion in LiEW at all ages. Poor age discrimination is still an issue at ages > 1 Gyr, confirming that additional modelling flexibility is not sufficient to fully represent the LiEW - age - Teff relationship, and suggesting the involvement of further astrophysical parameters. Expansion to include such parameters - rotation, accretion, and surface gravity - is discussed, and the use of an ANN means these can be more easily included in future iterations, alongside more flexible functional forms for the LiEW dispersion. Our methods and ANN model are provided in an updated version 2.0 of the EAGLES software.

Organizations implementing AI applications have several considerations to ponder in choosing the proper infrastructure. But one critical consideration is making a distinction between the training portion of AI and inferencing. This is the view of Michael Lang, solutions architecture manager at NVIDIA, speaking on a panel discussion on implementing AI at the recent NexGen Connectivity Forum. The forum comprised both industry participants and solution providers. The training and learning piece of AI, said Lang, is very different and often requires a different infrastructure environment to the one used for inferencing with AI. "The training and learning piece is about HPC and data-intensive needs," said Lang. "That means big data centers and infrastructure and big capability."

Widely regarded as the father of marketing science, he developed algorithms to automatically analyse scanner data--sales information obtained by scanning product barcodes at the cash register--and provide managers with informed insights. This problem-driven approach--a departure from early computer programmes, which mostly used classical statistics to analyse data--is also what underpins the artificial intelligence (AI) and machine learning strategies in use today, said Professor Phil Parker, chaired professor of management science at INSEAD. "Today, if you don't start with a very concrete objective, you may find yourself in a situation where you invest a lot of money in big data, and two years later, you wonder how to monetise it," said Professor Parker. "The best way is to start with a problem and then reverse engineer the proper algorithms." Professor Parker was speaking on 4 December 2017 at the Artificial Intelligence and Machine Learning Festival, a three-day event organised by INSEAD, SGInnovate and Impact Hub.

The University of Southern California has made available one of the largest open-source datasets of brain scans from stroke patients in a push to spur the development of machine learning to automatically process MRI images and identify lesions. The Anatomical Tracings of Lesion After Stroke (ATLAS) dataset, which contains 304 manually segmented MRI scans that took more than 500 hours to create, is now available for download to researchers around the world. "The unique thing is that we have manually traced the lesions on all of these brains--304 brains in total," says Sook-Lei Liew, assistant professor with joint appointments at the USC Mark and Mary Stevens Neuroimaging and Informatics Institute, the Chan Division of Occupational Science and Occupational Therapy, the Division of Biokinesiology and Physical Therapy, and the USC Viterbi School of Engineering. According to Liew, manually traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are time consuming and require neuroanatomy expertise. And while algorithms that employ machine-learning techniques hold promise for automating the process, it requires large training datasets to optimize performance, she contends.

Machines have already conquered many aspects of finance, from prediction markets to high frequency trading. Could algorithms also do a better job of picking and trading volatile cryptocurrencies, the way some software already surpasses experienced stock traders? So far, experts remain skeptical. Nvidia deep learning consultant Michelle Gill told International Business Times current machine learning tools aren't a great way to predict cryptocurrency markets because this new phenomena is full of infrequent events. AI is only as good as the data sets we feed it.