Unifying Logical and Statistical AI With Markov Logic

As AI practitioners in the enterprise we understood this early on.   You need to know the results of statistical analysis AND the ability to link them usefully to logical decision making.   Sometimes easy,  sometimes not   Thus approaches like decision trees based on statistical data became popular for our team.   We understood too that Markov methods could provide the framework for providing this, so we experimented with them.  In both cases the results were also relatively transparent.    This unification can also outline way that humans will interact with the AI.  Research on the idea was going on then and is still now.  Below gives you a good update.  Starts basic and gets technical.

Unifying Logical and Statistical AI with Markov Logic
By Pedro Domingos, Daniel Lowd 
Communications of the ACM, July 2019, Vol. 62 No. 7, Pages 74-83    10.1145/3241978

For many years, the two dominant paradigms in artificial intelligence (AI) have been logical AI and statistical AI. Logical AI uses first-order logic and related representations to capture complex relationships and knowledge about the world. However, logic-based approaches are often too brittle to handle the uncertainty and noise present in many applications. Statistical AI uses probabilistic representations such as probabilistic graphical models to capture uncertainty. However, graphical models only represent distributions over propositional universes and must be customized to handle relational domains. As a result, expressing complex concepts and relationships in graphical models is often difficult and labor-intensive.  .... "   (  Full Technical paper)

Video intro to the concept (technical): 





Alchemy Language, mentioned in the above talk:

https://alchemy.cs.washington.edu/
Alchemy: Open Source AI
Welcome to the Alchemy system! Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to easily develop a wide range of AI applications, including: .... " 

Emotion Analysis API

More companies are looking at text analysis ....

AlchemyLanguage Emotion Analysis API is Generally Available, and It’s Getting Better

Many in the field of Cognitive AI research and development speak of the importance of context. Context could be visualized similar to that of an onion, with multiple levels of nested, related and non-related context. But perhaps one of the most important layers is Emotional context, as it has the power to transform dynamic decision making internal to the intelligence.”

—Brennon Williams, Chief Executive Officer & Founder of Iridium Systems and Robotics Corporation

On July 1st, 2016, the Emotion Analysis capability in AlchemyLanguage became Generally Available for production use. Now, with our latest updates, you can use Sentiment & Emotion Analysis to understand social data at a deeper level than ever before.

AlchemyLanguage users take their Sentiment Analysis one step deeper to detect five distinct emotions in text – joy, fear, sadness, anger, and disgust. Users employ our sentiment and emotion capabilities to discover emotional trends in social media, prioritize inbound social data, and more. ... " 

Forrester on Knowledge Discovery

A long time topic of interest.  You can get the full report from Forrester with Registration.  They cover a number of well known vendors in the space:   " ... In Forrester’s 31-criteria evaluation, they've identified nine big data search and knowledge discovery solutions providers — Attivio, Coveo Solutions, Google, HP, IBM, Lexmark International, Lucidworks, Mindbreeze, and Sinequa — and researched, analyzed, and scored their current market offerings. ... " .

Is it best to think of such discovery as a form of search?  Or should such a system be attentive rather than directed?

Recall just last year I investigated IBM's Watson Developer Cloud and Alchemy.    Which includes more cognitive aspects of discovery.   This ranks high in Forrester's analysis.

I would also again point to local vendor Zakta, which I have worked with as well.  Worth a look.

Alchemy API Language

Brought Back to my attention.

Build Smarter Apps With AlchemyLanguage
12 Semantic Text Analysis APIs Using Natural Language Processing

Pioneering Easy-to-build Smart Apps for 
Understanding Customer Needs and Predicting Their Behavior The AlchemyAPI cloud platform makes it easy to create smart apps that deeply understand the world's conversations, reports and photos so you can align your business with customer preferences and intent. We help you take action. Boost revenues. Cut costs. All by quickly transforming vast numbers of web pages, tweets, emails and images into facts and knowledge on how people feel about your product, campaign, offer or service. ... " 

Example Use of Watson for Social Benchmarking

Always looking for good, simple examples of the use of cognitive methods, and thus also Watson. Just recently connected with Eric Santos, of Benchmark Intelligence,  and he writes how they use Watson for their social intelligence bench marking and trending. A good example of what can be done.

" ... Benchmark is a product suite that helps retail chains understand why certain locations perform better than others. Benchmark discovers the factors (customer service, product quality, cleanliness, etc) that affect unit performance. Benchmark Intelligence is a proud IBM Watson ecosystem partner. 

Currently Benchmark collects its data through various ways which includes social media listening, SMS comments, surveys and field audits. A good portion of this data is qualitative and unstructured. We needed a way to run analysis on this data and identify trends, that’s why we turned to IBM Watson. 

Benchmark is leveraging Watson’s Alchemy languages, specifically their sentiment analysis and keyword extraction technologies. We are using these cognitive technologies to analyze this unstructured data and discover the variables (customer service, cleanliness, etc.) that affect performance at each location. 

Watson looks at thousands of open-ended data points (social media reviews, SMS comments, etc.) on our platform for any given chain. For each data point Watson defines whether the statement as a whole is positive, negative or neutral. Watson also identifies the key words that make up the statement. That way as locations gather more data points, we can identify the trends that are going on at each location in the chain. 

Example use cases of this include knowing that customers complained about cockroaches at a specific location 5 times in one week and customers at another location in the same chain complained about a cashier named Bryan 4 times in one week.

Once Benchmark understand what these trends are, we can surface actionable insights that retail chains can use to improve the performance across their portfolio of locations.   .... "