Syngenta and the AI for Good Foundation have partnered to launch the Syngenta AI Challenge, a new international competition focused on leveraging Artificial Intelligence (AI) tools for use in seed breeding. The competition is accepting submissions from applicants who are ready to put their programming skills to the test for the chance to win $7,500. "This new competition will give entrants the chance to use their talents to take on the extraordinary complexity of seed genetic data," said Joseph Byrum, Ph.D., MBA, PMP and senior R&D strategic marketing executive with Syngenta. "In the face of a rising global population, we need to grow plants that can adapt and thrive in changing conditions – especially as vital resources like water and land are finite. The Syngenta AI Challenge is about creating models that can help solve this puzzle and ensure world food security."

The telecommunications industry is highly competitive, which means that the mobile providers need a business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal level of cost in marketing activities. Machine learning applications can be used to provide guidance on marketing strategies. Furthermore, data mining techniques can be used in the process of customer segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling according to their billing and socio-demographic aspects. Results have been experimentally implemented.

Health Catalyst has used healthcare.ai to build predictive models that drive its clients' outcomes improvement efforts and span across the company's product lines. Models include but are not limited to a predictive model for central line associated blood stream infection (CLABSI), readmission models for COPD and other chronic conditions, schedule optimization, and financial predictions such as patient propensity to pay. "Machine learning and artificial intelligence are going to transform healthcare. We are seeing amazing results and yet we are barely getting started. We are applying it to the reduction of patient harm events, care management, hospital acquired infections, revenue cycle management, patient risk stratification, and more," said Dale Sanders, Executive Vice President of Health Catalyst.

Last week's World Economic Forum in snowy Davos, Switzerland, brought a blizzard of proclamations about the disruptive impact of artificial intelligence, along with an avalanche of debate over its job-killing potential. The good news for us humans is that the current generation of AI technologies being used to automate data collection and processing -- such as machine-learning software that amasses more expertise as it analyzes data or neural networks modeled after the human brain -- are more likely to augment the human workforce rather than replace it. Indeed, almost two-thirds of the business executives responding to a survey released last week by IT consulting firm Infosys (PDF) said they believed AI would "bring out the best in their organization's people." The rise of AI, the Infosys poll respondents suggest, will place a premium on skills such as creativity and logical reasoning. Noted IBM chairwoman and CEO Ginni Rometty, in remarks Wednesday at Davos, said: "History has taught us many things. When you [have] powerful technologies, you have a responsibility that they're introduced in the right way." "This pivotal investment will empower our team to accelerate R&D and enhance our proprietary technology with the latest innovations in machine learning and natural language processing while broadening our expertise in CSR analysis to foster environmental, social and ethical performance at a global scale," said EcoVadis co-CEO Frederic Trinel, in a statement.

Viewing the trajectory of a patient as a dynamical system, a recurrent neural network was developed to learn the course of patient encounters in the Pediatric Intensive Care Unit (PICU) of a major tertiary care center. Data extracted from Electronic Medical Records (EMR) of about 12000 patients who were admitted to the PICU over a period of more than 10 years were leveraged. The RNN model ingests a sequence of measurements which include physiologic observations, laboratory results, administered drugs and interventions, and generates temporally dynamic predictions for in-ICU mortality at user-specified times. The RNN's ICU mortality predictions offer significant improvements over those from two clinically-used scores and static machine learning algorithms.

The first step of the partnership will see Nuritas, a biotech and R&D start-up that uses artificial intelligence and new technologies for the discovery of novel food and health ingredients, grant an exclusive royalty-based license to BASF to commercialise one of its existing peptides. A second part of the deal will focus on the discovery of new functional peptides, based on health areas that are strategically important to BASF, using Nuritas' technological expertise and AI platform. According to BASF, peptide networks of focus in the collaboration will be natural, food-derived, patented and of significant benefit to health – including peptides that bring about anti-inflammatory responses. "Cooperating with an innovative and agile start-up like Nuritas enables us to further expand our already broad portfolio of health solutions," commented Saori Dubourg, head of BASF's Nutrition & Health Business. Nuritas' unique platform combines DNA analysis and artificial intelligence (AI) to predict, unlock, and validate peptides from natural sources.

Born in the vicarage in Lenham, Kent, he was the second of the parish priest's three children. He joined The Pilgrim's school, Winchester, in 1932 and became a senior chorister at the cathedral. Three years later, he won the top entrance scholarship to Winchester College, where his precocious talents in mathematics and music flourished. In 1941, he won a scholarship to Balliol College, Oxford, to read chemistry, but at the end of his first year also took part one of the music tripos, and was appointed Balliol organ scholar. In his second year, Christopher performed what Dr John Jones has described as "probably the greatest intellectual feat by a Balliol undergraduate ever": he proposed, with convincing arguments, the correct structure of the chemical compound diborane (B2H6) - a compound that defied contemporary chemical valency principles.

In the last 20 years, business executives have used these all-too-familiar terms to describe their world. The near-constant interruptions from ringing telephones, buzzing pagers, the web, and an unceasing stream of email messages were thought to be overwhelming people with information, forcing them to switch attention from one task to the next, again and again. People were becoming multitaskers who couldn't concentrate for more than a few seconds at a time. Today, the impact of the information barrage has been termed cognitive overload--the information itself doesn't cause problems, but having to think about it does.1,2 In almost all situations, the amount of information that comes at people exceeds their cognitive capacity to handle it, and their performance could be adversely affected if they miss important details or have difficulty understanding the information.

We propose a generic framework based on a new stochastic variance-reduced gradient descent algorithm for accelerating nonconvex low-rank matrix recovery. Starting from an appropriate initial estimator, our proposed algorithm performs projected gradient descent based on a novel semi-stochastic gradient specifically designed for low-rank matrix recovery. Based upon the mild restricted strong convexity and smoothness conditions, we derive a projected notion of the restricted Lipschitz continuous gradient property, and prove that our algorithm enjoys linear convergence rate to the unknown low-rank matrix with an improved computational complexity. Moreover, our algorithm can be employed to both noiseless and noisy observations, where the optimal sample complexity and the minimax optimal statistical rate can be attained respectively. We further illustrate the superiority of our generic framework through several specific examples, both theoretically and experimentally.

Bonirob is more than 90 percent effective in destroying weeds in carrot cultivation trials. While the world's first fully-robotic farm will operate indoors, traditional outdoor farms aren't immune to the coming robotic revolution. Bonirob, developed by Bosch's Deepfield Robotics, is billed to eliminate some of the most tedious tasks in modern farming, plant breeding, and weeding. The autonomous robot is built to be a mobile plant lab, able to decide which strains of plant are most apt to survive insects and viruses and how much fertilizer they would need, and then smash any weeds with a ramming rod. Bonirob employs a type of machine learning (a stab at artificial intelligence) called decision tree learning. Researchers show Bonirob lots of pictures of healthy leaves that are tagged to be good, and pictures of weeds that are tagged to be bad, and the machine makes a series of choices based on observed in new data to judge whether a plant in the field is good or bad.