My Dad’s favorite advice to me growing up was “Be a Gentleman and a Scholar.” If I didn’t hear that daily, I heard it at least once a week. During the 22 years of my kindergarten-through-college student life, it was a very quick way of saying:
I’ve collected sports cards ever since I was 11 years old. Something about amassing and organizing a collection of cards that chronicled the bios and stats of my favorite sports stars always appealed to me. Included early on in this collection pursuit was a bonus lesson on the basic laws of supply and demand – generally speaking, the tougher the sports card is to pull from a pack of unopened cards, the higher the market value. The most popular sports stars create the most demand, also driving market value higher. I still remember the exact moment 25 years ago in 1993 when I pulled a rare (at the time) card of Michael Jordan from a pack of basketball cards – the perfect combination of scarcity and star power! The card immediately vaulted to the top of my collection.
Where is Kingland focused within the overall Cognitive / AI / Machine Learning space? Our passion and investment are in two primary areas:
- Extracting data / important information out of unstructured content
- Learning from vast amounts of data to make discoveries and decisions
Examples of Enterprise Data Management Requests
Regulatory documents, government registries, corporate sites, news and industry trade journals, product documents, you name it – they all represent important sources that contain an incredible amount of valuable data, insight, sentiment and analysis that is ripe for processing. Clients approach Kingland because of a great opportunity for efficiency – they typically already staff teams of data / business subject matter expert analysts to manually extract data and information out of these types of unstructured sources, yet they continue to pursue higher quality, better timeliness, and more productivity from these teams when managing data from such sources.
Historical data from the enterprise data management programs within our clients offer a level of quality management and predictive capabilities, if harnessed appropriately. In one example, legal entity data that has been classified with NAICS codes can offer machine learning opportunities to build models that can verify the classification accuracy of millions of entities, including additionally predicting the NAICS classifications of newly onboarded entities. In another example, retailers benefit from recommendations learned from past consumer behavior, for the benefit of timely product and complementary offerings.
Cognitive, AI, Machine Learning … to Deep Learning, Neural Nets, Natural Language Processing and Entity Recognition … to Siri, Alexa, Cortana, Google Home, Watson … and just for fun, to Skynet, The Matrix, Westworld, and my personal favorite, Iron Man’s J.A.R.V.I.S.
With the onslaught of (enter your favorite term(s) from the above themes) nearly everywhere we turn in business, home and entertainment, have you ever found yourself trying to make sense of it all? Trying to figure out how it all relates – if it even does?
Last week leading banks, broker dealers, and regulators gathered in New York for DTCC’s third annual Fintech Symposium. This is always a must attend event for Kingland as its incredibly well run and covers many of the emerging trends in the industry in an afternoon. DTCC’s perspective is critical as they sit at the cross roads of the sell side, buy side, and global regulatory environment, so if an issue is on the table, there’s a strong reason.
For years and years, regulatory and legal requirements have created hundreds if not thousands of processes that require companies to capture information in documents or records. This information includes details about their customers, suppliers, market activity, and many other facets of their business. I don't know a company on the planet that really values all of this documentation. However, in the era of Artificial Intelligence and Natural Language Processing, we're discovering that all of this documentation may be a goldmine of data for these large enterprises.
At Kingland, we use a variety of Artificial Intelligence (AI) techniques for text analysis as we build cognitive and enterprise data management solutions. One of the most sophisticated is the neural network. Inspired by (but certainly not the same as) human biology, neural networks can learn to answer questions about complex data by observing and storing examples of subtle patterns in the connections between nodes, which are loosely analogous to neurons in the human brain. Most neural networks have multiple layers, with information flowing from one layer to the next (and back again in some cases), often transforming the input into more and more abstract representations as it progresses through the layers.
In the last 30 days I've had many conversations with executives about cognitive, or artificial intelligence (AI) capabilities. I think we've made tremendous progress with our cognitive computing suite at Kingland over the last 7 years and it's fun to show people what we've done and where we're headed. The surprising thing to me, though, is the amount of skepticism I still hear from these leaders. When it comes to AI, there's almost a "too good to be true" cautiousness in the discussions. I think this is driven from the sheer volume of attention AI is getting as these leaders are asked daily to join the "AI Club". At the same time, though, I hear an incredible amount of optimism. Executives are thinking "maybe AI is one of the key investments I need to make to really make a difference." The efficiency, the automation, the chance to deliver something innovative for their company; all of these breed optimism.
I'd like to explore both sides of this skepticism / optimism coin. Let's look at one of the most popular questions I get: "So...is that really AI"?
There is a lot of hype around Cognitive Computing. In the past month, a Google search discovered nearly 16,000 articles devoted to the topic. For perspective on the hype, Forbes contributor Bernard Marr wrote, "Today another revolution is underway with potentially even further reaching consequences... Cognitive computing, machine learning, natural language processing - different terms have emerged as development of the technology has progressed in recent years. But they all encapsulated the idea that machines could one day be taught to learn how to adapt by themselves..."