The future capabilities of Facebook and IBM chatbots


Chatbots of the future will have advanced capabilities in five key areas: natural language processing (NLP), natural language understanding, contextual awareness, anticipate customer needs, and sentiment analysis. Natural Language Processing is the process a machine goes through when translating, summarizing, contextualizing, and analyzing text – or the same process that Google Translate uses to translate text. With natural language understanding, developers can analyze semantic features of text input such as categories, concepts, emotion, entities, keywords, metadata, relations, semantic roles, and sentiment. Facebook recently launched its own Facebook messenger chatbot called Assistant M, as well as a open source developer toolkit for bots.

Why Companies Should Want to Be Held Liable for Their Artificial Intelligence


The liability question comes into play in almost every type of business, especially as leaders move toward sophisticated solutions powered by artificial intelligence. For some software-reliant businesses, however, accepting liability is part of providing high-quality customer service. One company that exemplifies this approach is Signifyd, an artificial intelligence platform that protects e-commerce merchants against credit card fraud. From autonomous cars to financial advice to fraud risk, artificial intelligence is making a serious impact on the way businesses serve their customers.

Big Data, Data Mining and Machine Learning: Deriving Value for Business


However, before we look at how the recommendation engine works and its effectiveness as part of the business forecasting model, let's look at what machine learning is. Its primary tenet is based on algorithms that can look at input data and use statistical analysis to predict trends and values based on the input data. However, before we look at how the recommendation engine works and its effectiveness as part of the business forecasting model, let's look at what machine learning is. Essentially, the predictive model is logical; thus, it uses statistical analysis to build a model of user personas, including what clothing styles and colours each visitor to the site will like.

Top 10 Essential Books for the Data Enthusiast


The true data enthusiast has a lot to read about: big data, machine learning, data science, data mining, etc. There are a lot of lists available of the top books in particular categories related to data. In fact, KDnuggets has previously, and rather recently, put together such lists on data mining, databases & big data, statistics, AI & machine learning, and neural networks. This inclusive list of essential books for the data enthusiast (or practitioner) recommends a top paid and free resource in each of 10 categories.

10 Examples of How Brands Are Using Chatbots to Delight Customers


Fandango's Facebook Messenger bot lets you watch movie trailers, find local theaters, and see what's trending this week. Spotify's Facebook Messenger bot makes it easy for its customers to search for, listen to, and share music. Mastercard's Facebook Messenger bot makes it easy for customers to check on account transactions (e.g., just ask "how much did I spent on restaurants in May?"). Staples' Facebook Messenger bot can answer common customer questions, which tend to be about orders -- tracking and returns -- and whether specific items are in stock.

10 Real-World Examples Of Machine Learning And AI You Can Use Today


The world's biggest search engine offers recommendations and suggestions based on previous user searches. In 2012, Google introduced Knowledge Graph – an algorithm used to decipher the semantic content of a search query. Lyst is an ecommerce fashion site working with a new breed of model – the machine learning model. To match customer searches with relevant recommendations, Lyst uses meta-data tags to make visual comparisons between items of clothing.

5 Types of Recommenders


This type of statistical analysis relies on only the simplest of calculations to find items that are frequently consumed together. Most CB systems use vector factorization and begin by creating a feature vector describing the user (products and features identified as interesting, size and frequency of prior purchases, etc.). In more advanced CB systems (combined CB/CF systems) feature vectors are also constructed for the products (author, genre, features, etc.). Cosine similarity calculations are made against the feature vectors to identify similar customers and similar products.

10 Amazing use of Artificial Intelligence (AI) in your daily life - KnowStartup


"Artificial Intelligence" today includes a variety of technologies and tools, some time-tested, others relatively new. Just like a human, self-driving cars need to have sensors to understand the world around them and a brain that collects, processes and chooses specific actions based on information gathered. Surveillance systems that include video analytics analyze video footage in real-time and detect abnormal activities that could pose a threat to an organization's security. Essentially, video analytics technology helps security software "learn" what is normal so it can identify unusual, and potentially harmful, behavior that a human alone may miss.

Eliminating the Human

MIT Technology Review

Online ordering and home delivery: Online ordering is hugely convenient. Robot workforce: Factories increasingly have fewer and fewer human workers, which means no personalities to deal with, no agitating for overtime, and no illnesses. Big data: Improvements and innovations in crunching massive amounts of data mean that patterns can be recognized in our behavior where they weren't seen previously. Automated high-speed stock buying and selling: A machine crunching huge amounts of data can spot trends and patterns quickly and act on them faster than a person can.

AI gets down to business


Already, many of the 2017 CIO 100 leaders are piloting AI and machine learning projects, taking a do-it-yourself approach to building predictive models and open platforms, working with consultants, or taking advantage of new AI-infused capabilities increasingly popping up in core enterprise systems like ERP and CRM. While AI isn't exactly a newcomer -- it's been around for at least a couple of decades -- the technology has taken off this year for a number of reasons: Relatively cheap access to cloud-based computing and storage horsepower; unlimited troves of data; and new tools that make it more accessible for mere mortals, not just research scientists, to develop complex algorithms, notes David Schubmehl, research director for cognitive and AI systems at IDC. "It's really the idea that programs or applications can self-program to improve and learn and make recommendations and make predictions." Read ahead to learn how six 2017 CIO 100 leaders are transforming their enterprises to capitalize on AI and machine learning.