R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R. Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationship. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction. Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a startup company that mainly focuses on providing big data and machine learning products.

NLP Engine: Natural Language Processing (NLP) is an integral part of developing heuristic rule-based chatbots. In the context of chatbots, NLP classifies the intent of the chat conversation and once the intent is identified, it routes the flow to appropriate dialogue handlers. Language Understanding Intelligent Service (LUIS) is part of Azure cognitive service that abstracts complex NLP models and helps developers to create apps that identifies the correct intent and entities. LUIS also provides GUI based interface for assigning standard responses to intents, training and retraining the intents with utterances etc.

From medicines to materials, small organic molecules are indispensable for human well-being. To plan their syntheses, chemists employ a problem solving technique called retrosynthesis. In retrosynthesis, target molecules are recursively transformed into increasingly simpler precursor compounds until a set of readily available starting materials is obtained. Computer-aided retrosynthesis would be a highly valuable tool, however, past approaches were slow and provided results of unsatisfactory quality. Here, we employ Monte Carlo Tree Search (MCTS) to efficiently discover retrosynthetic routes. MCTS was combined with an expansion policy network that guides the search, and an "in-scope" filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on 12 million reactions, which represents essentially all reactions ever published in organic chemistry. Our system solves almost twice as many molecules and is 30 times faster in comparison to the traditional search method based on extracted rules and hand-coded heuristics. Finally after a 60 year history of computer-aided synthesis planning, chemists can no longer distinguish between routes generated by a computer system and real routes taken from the scientific literature. We anticipate that our method will accelerate drug and materials discovery by assisting chemists to plan better syntheses faster, and by enabling fully automated robot synthesis.

Use various add-ons available within Orange to mine data from external data sources, perform natural language processing and text mining, conduct network analysis, infer frequent itemset and do association rules mining. Additionally, bioinformaticians and molecular biologists can use Orange to rank genes by their differential expression and perform enrichment analysis.

Suspected money laundering enterprises are in the headlines again, ensnaring the state-controlled Industrial and Commercial Bank of China as well as . Yet for financial institutions that want to identify, track and stop money laundering and other financial crimes, the technological tools of banking's yesteryear no longer cut it. A new wave of tech tools can help banks and other financial firms discover dispersed and complex webs of illegal activity. Increasingly, financial firms are turning to artificial intelligence and sophisticated network analysis to detect such criminal enterprises. The legacy tools banks use to find suspicious dispersals of money often rely on logical "if-then" rules to spot criminal activity, as Wired reports.

Like many industry buzzwords, Artificial Intelligence (AI) has become a hot topic that RegTech technologists often write or speak about. But the reality is – AI has become an overloaded and misused term, often mistaken for Machine Learning (ML). This blog aims to clarify the difference between the two, explain some of the complexities of implementing these solutions today, and highlight how ML can immediately add value in financial compliance applications. In simple terms, Artificial Intelligence enables computer systems to perform tasks that require human intelligence. Intelligence is the key word.

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to retrieve (by derivation) multiple statements (sometimes assumed) or results from the literature.

Before breakfast, I check my Facebook and LinkedIn newsfeeds for a quick synopsis of the day. As I jump in the shower, I hit "download" on a recommended movie on Netflix, knowing I have a long flight this evening. While wolfing down my cereal, I click once to buy a gift for a friend's birthday next week. My iPhone pings to tell me that I need to leave now if I want to make that early meeting 54 miles away. And as I get in my car, I use voice activation to play my favorite Spotify playlist, and Apple Maps informs me it will take five minutes to drive to the train station this morning.

We typically think of artificial intelligence (AI) within our industry in terms of processes and calculations. Media buying, for example, is ripe for intervention by sophisticated algorithms and machine learning systems. The commonly-held assumption is that developments such as these will free up humans to spend more time on creative tasks, like campaign strategy and content production. But as we move from rule-based automation to true AI, should we believe that creativity will remain a singularly human pursuit? How close is artificial intelligence to being able to carry out the role of a content marketer?

Amazon Prime Day (APD) was a huge success, they say. At an estimate 60 percent increase in sales over 2016 and nearly $2 billion in revenue, it's hard to argue otherwise. If you want to talk numbers though, let's consider this. What would you say if you were told that Amazon could lose nearly 5 percent of that revenue, or $100 million, due to fraud? And it's not just Amazon on its Prime Day, it's every online retailer that is exposed to online fraud every single day.