Rule-Based Reasoning

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.

There's a parallel between a software developer applying the most applicable software pattern and the machine performing pattern detection. Companies today are building new algorithms based on AI models: detecting fraud in financial transactions, finding cancer cells in images, or listening to a newborn's cry to identify birth asphyxia. Could we replace the application's routing table with an ML algorithm? As we stitch together AI model after AI model, we slowly replace every bit of predictable code with an impenetrable decision tree.

By capturing both decision logic, as well as the data and analytics that informed a business decision or process, an enterprise can create an auditable paper trail that endures as individuals or knowledge workers come and go. In addition, creating more powerful analytics and decision process by connecting otherwise disparate parts of organizations increasingly improves an organizations ability to connect with, and impress, customers (i.e., connecting new customer onboarding with customer lifecycle marketing, fraud detection, upsell marketing, etc.). About the author: Scott Zoldi is Chief Analytics Officer at FICO responsible for the analytic development of FICO's product and technology solutions, including the FICO Falcon Fraud Manager product which protects about two thirds of the world's payment card transactions from fraud. Scott is actively involved in the development of new analytic products utilizing Artificial Intelligence and Machine Learning technologies, many of which leverage new streaming artificial intelligence innovations such as adaptive analytics, collaborative profiling, deep learning, and self-learning models.

Fuelling this rapid advancement in machine learning are two key factors: a) an explosion and availability of data and b) the ability to get cost-effective compute power that can run powerful algorithms inexpensively and process massive amount of data. Financial services are also at the cusp of AI-fuelled disruption as some key macro factors are coming into play: a) digital and cloud are becoming more accepted at financial institutions b) Financial firms have a massive amount of historical customer, market and third-party data enabling them to gain greater insight c) With the possibility of a reduced regulatory burden, there is the potential to redirect dollars to focus more on building a competitive advantage through digital capabilities and d) AI use cases, which provide benefits to customers and processes within banks, are showcasing the value of AI across the business. When we hear the term AI, we usually think of technology companies such as Google, Apple, Microsoft, IBM and start-ups working on deep learning problems or building tools and platforms. The technology scans a vast amount of trading data and creates a strategy based on learning from market patterns.

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.

Together, we are enabling businesses and organizations in remote and autonomous locations to tap into the combined power of IBM's Watson IoT Cloud and business analytics technologies and Cisco's edge and fog capabilities to more deeply understand and act on critical data on the network edge. Watson IoT is a leader in the Internet of Things, using cognitive computing and analytics to process IoT data and other contextual inputs, redefine data exploration and uncover patterns and insights previously unattainable. Combining that delivery capability with IBM's analytics and cognitive solutions delivers an IoT solution that is much greater than the sum of its parts. Cisco and IBM--leaders in networking and cognitive analytics respectively--can deliver a solution that solves today's most critical IoT operational and business challenges.

Businesses large and small are being lured in by the potential of artificial intelligence (AI), machine learning (ML), deep learning and cognitive computing, while others are still trying to figure out how to tell them apart. Most companies aren't ready for any form of AI, deep learning or cognitive computing because their data is in such poor shape. Defining matching rule sets is a challenge because it takes time and a deep understanding of data profiles. As the number of sources increases, and the format and data types grow, defining rules grows in complexity, as simple rules-based matching may not be sufficient.

"What to Do When Machines Do Everything" offers deep insight on how emerging technologies like artificial intelligence and the Internet of Things will change our labor force and production industries. The new machine ushering in the new technology wave will be a system of intelligence that combines software (algorithms, business rules, machine-learning code, predictive analytics), hardware (servers, sensors, mobile devices, connectivity), data (contextualized and real-time), and lastly, the input of human operators. Automate: Using the modern technology tools to create automated processes such as computer software, robots, sensors, etc. These targets should include tasks that demand low human judgement (repeatable manual actions), low levels of empathy (robots do not need empathy to be repeatable and accurate in a task, but do for dealing with customer service), and anything that requires the handling or generating of data.

In simple terms, Artificial Intelligence enables computer systems to perform tasks that require human intelligence. Today, ML is used in many narrow compliance applications, including risk detection models, and other event classification use cases. Most artificially intelligent systems use a combination of machine learning applications and techniques along with rule-based systems (to be fully interactive). And this is a good thing, because while smart machines and complex algorithms can process a lot of data to automate and perform some human tasks, faster, there are limitations.

In the face of relentlessly rising customer expectations, leading marketers are investing in AI-based tools – a category that encompasses everything from personalization tools that "learn" from individuals' online behavior to recommend content more effectively, to tools that can detect minute patterns across massive consumer data sets and predict future behavior. Since the major marketing suites have yet to fully deploy or productize their AI offerings, adopting AI usually requires a blend of point solutions and data sets. They appreciate that gaining and acting on deeper customer understanding build competitive advantage. The cohort of businesses Forrester defines in this category – fast-growing companies innovating based on customer understanding and experience – should be truly terrifying to incumbents.