We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step towards solving the challenging problem of computational retrosynthetic analysis.

When we go grocery shopping, we often have a standard list of things to buy. Each shopper has a distinctive list, depending on one's needs and preferences. A housewife might buy healthy ingredients for a family dinner, while a bachelor might buy beer and chips. Understanding these buying patterns can help to increase sales in several ways. While we may know that certain items are frequently bought together, the question is, how do we uncover these associations? Besides increasing sales profits, association rules can also be used in other fields.

But the mandate has drawn strong and sustained opposition from social conservatives, who see it as an infringement on freedom of conscience. The Obama administration exempted houses of worship, and set up a workaround for religiously affiliated nonprofits, such as hospitals, universities and social service organizations. The Supreme Court later ruled that closely held private companies were also eligible for the workaround, through which the government arranges contraceptive coverage for the affected women employees.

A machine-learning algorithm has the capability to identify hospitalized patients at risk for severe sepsis and septic shock using data from electronic health records (EHRs), according to a study presented at the 2017 American Thoracic Society International Conference. Sepsis is an extreme systemic response to infection, which can be life-threatening in its advanced stages of severe sepsis and septic shock, if left untreated. "We have developed and validated the first machine-learning algorithm to predict severe sepsis and septic shock in a large academic multi-hospital healthcare system," said lead author Heather Giannini, MD, of the Hospital of the University of Pennsylvania. "This is a breakthrough in the use of machine learning technology, and could change the paradigm in early intervention in sepsis." Machine learning is a type of artificial intelligence that provides computers with the ability to learn complex patterns in data without being explicitly programmed, unlike simpler rule-based systems.

MACHINE-LEARNING is beginning to shake up finance. A subset of artificial intelligence (AI) that excels at finding patterns and making predictions, it used to be the preserve of technology firms. The financial industry has jumped on the bandwagon. To cite just a few examples, "heads of machine-learning" can be found at PwC, a consultancy and auditing firm, at JP Morgan Chase, a large bank, and at Man GLG, a hedge-fund manager. From 2019, anyone seeking to become a "chartered financial analyst", a sought-after distinction in the industry, will need AI expertise to pass his exams.

Machine Learning, Artificial Intelligence and Natural Language Processing (NLP) are transforming the technological landscape in a wide range of applications. Three primary uses are predictive analytics, deductive reasoning and natural language understanding. Interfaces for domains such as search and geolocation are increasingly natural-language-like instead of using rigid menu-driven, or programming-language-like interfaces. The task of understanding the user's intention requires complex systems based on machine learning, training data, NLP algorithms modeling theoretical linguistics, or a combination of these techniques. Secondly, machine learning allows us to predict user intention based off of previous user data and tendencies.

Retrieving a music file from a large database is a non-trivial task. To support this task, many mechanisms have been developed over the years. However, indexing files remains one of the most popular mechanisms. Several algorithms allow feature extraction from audio signals. Usually, these features are used to describe music content. In this paper, we demonstrate that associations between content-based descriptors can be used as well. We have developed a processing chain which uses association rules mining to find significant relations between content-based descriptors of music files. The significant relations are used to index music files. Experiments conducted demonstrates that the proposed approach can yield interesting results especially with classical music.

Automation and artificial intelligence are poised to free up millions of hours of manpower and save billions of dollars across all levels of government, according to a recent report from Deloitte University Press. The 28-page report, titled AI-augmented Government, examines several case studies, provides a taxonomy of AI systems, and concludes that in the federal government alone, automation with "high investment" could free up as many as 1.2 billion hours of work and save up to $41.1 billion annually. Through the use of rules-based systems, machine translation, computer vision, machine learning, robotics and natural language processing, the report notes the unusual but "tantalizing" paradigm presented by AI in which speed is increased, quality is improved, and cost is reduced -- all in parallel. Researchers said they identified a potential 30 percent savings in government worker time that could happen within five to seven years of implementing an AI solution. The report concludes that AI will fundamentally change how every level of government works, and will do so much sooner than most people believe.

Microsoft's Fall Creators Update for Windows 10 might have an ironically uncreative name, but the upgrade itself is flush with artistic potential and useful features. It will give users a timeline to manage complex work sessions, APIs that tie all of Microsoft's services together and, notably, a new design paradigm intended to radically overhaul the flat rectangle user interface it's known for. Microsoft's Fluent Design System focuses on five core tenets to help developers build more creative and engaging user interfaces: Depth, Material, Light, Scale and Motion. These philosophies are intended to draw a line in the sand between Microsoft's stiff, old design and a new future of interactive user experiences. "It's time to move beyond mere rectangles confined to a plane," Microsoft's Joe Belfiore declared as he introduced the design language at Build.

Machine learning is being put to use in all sorts of areas today. From smart cars and homes and beyond, the use of artificial intelligence (AI) and machine learning (ML) are becoming a larger part of how many companies conduct business. As more and more businesses are hit with cyber crime rather than physical crimes, there has been a needed shift from commercial surveillance systems towards cyber security systems to protect confidential data. More recently, we've seen ML sink its teeth into anti money laundering (AML) with big potential impacts there. Most current AML systems are founded on an extensive list of rules.