Sep 042017
 

CogTechIn part one I examined the connection of KM and AI and how this connection has lead the way for cognitive computing; while in part two I examined those industries that will or are soon to be disrupted by Cognitive Computing; and in this post I will examine those technologies that will lead in the disruption brought to many industries by the way of cognitive computing.

Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems (Artificial Neural Network machine learning algorithms) that use data mining, pattern recognition and natural language processing to imitate how humans think. The goal of cognitive computing systems is to accelerate our ability to create, learn, make decisions and think.

According to Forbes, “cognitive computing comes from a mashup of cognitive science and computer science.” However, to understand the various aspects of this mashup we must peel back the various components of cognitive computing. These components are centered within AI and KM. The components of cognitive computing enable these applications to be trained in order to recognize images and understand speech, to recognize patterns, and acquire knowledge and learn from it as it evolves producing more accurate results over time.

Cognitive Technologies

Cognitive technologies have been evolving since I started developing AI applications (Expert Systems and Artificial Neural Networks) in the late 1980’s and early 1990’s. Cognitive technologies are now a prominent part of the products being developed within the field of artificial intelligence.

Cognitive computing is not a single technology: It makes use of multiple technologies and algorithms that allow it to infer, predict, understand and make sense of information. These technologies include Artificial Intelligence and Machine Learning algorithms that help train the system to recognize images and understand speech, to recognize patterns, and through repetition and training, produce ever more accurate results over time. Through Natural Language Processing systems based on semantic technology, cognitive systems can understand meaning and context in a language, allowing deeper, more intuitive level of discovery and even interaction with information.

The major list of cognitive technologies solutions include:

Expert Systems, Neural Networks, Robotics, Virtual Reality, Big Data Analytics, Deep Learning, Machine Learning Algorithms, Natural Language Processing, and Data Mining

Various cognitive technologies or applications are being developed by many organizations (large, small, including many startups). When it comes to cognitive technologies, IBM Watson has become the most recognized. IBM Watson includes a myriad of components that comprise the Watson eco system of products.

Companies Delivering Cognitive Solutions

Here are a few companies delivering cognitive solutions that take advantage of the cognitive technologies mentioned above as well as the industry they focus on.

Industry: Healthcare

Welltok: Welltok offers a cognitive powered tool called CaféWell Concierge that can process vast volumes of data instantly to answer individuals’ questions and make intelligent, personalized recommendations. Welltok offers CaféWell Concierge to health insurers, providers, and similar organizations as a way to help their subscribers and patients improve their overall health.

Industry: Finance

Vantage Software : provides reporting and analytics capabilities to private equity firms and small hedge funds. The company’s latest product, Coalesce, is powered by IBM Watson’s cognitive computing technology. This is an example of a company developing a software platform and using IBM Watson’s API’s to provide cognitive capabilities. This product addresses the need to absorb and understand huge volumes of information and use that information to make split-second, reliable decisions about where and when to invest client funds in a highly volatile market.

Industry: Legal

One of the major impediments to quality, affordable legal representation is the high cost of legal research. The body of law is a growing mountain of complex data, and requires increasingly more hours and manpower to parse. Lawyers are constantly analyzing data to find answers that will benefit their clients. For law firms to stay competitive they must find ways to cut cost and streamlining legal research is one way to do just that.

ROSS Intelligence: software is built on the Watson cognitive computing platform, ROSS has developed a legal research tool that will enable law firms to slash the time spent on research, while improving results.

AI & Blockchain

Detailing AI, KM and Cognitive computing would not be complete without adding blockchain to the technologies that will disrupt several industries. Functionally, a blockchain can serve as “an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way. The ledger itself can also be programmed to trigger transactions automatically. AI & Blockchain come together when analyzing digital rights. For example, AI will learn the rules by identifying actors who break copyright law. The use of AI applications will be extended by incorporating blockchain technology. When blockchains scale to encompass big-data, AI will provide the query and analysis engine to extract insights from the blockchain of data.

Cognitive technology solutions can be found in a number of applications across many industries. These industries include but are not limited to legal, customer service, oil & gas, healthcare, financial and automotive just to name a few. Cognitive technologies have the potential to disrupt Every industry and Every discipline — Stay Tuned!!

 

Jan 132016
 

K15968-v2As we move into 2016 it is time to reflect on knowledge management and look at the future of this discipline. In my latest book: Knowledge Management in Practice I address what I believe is the future of KM in 2016 and beyond. An excerpt from chapter 18 follows.

Future of Knowledge Management:

One of the major areas in which KM will make an impact is within the customer service industry. Customer Service is the area in which most customers will have their only connection and interaction with your organization. It is this area where customers will form their opinions about the organization and determine if they remain a customer or move on to another competitor. Due to this scenario (and others) organizations invest a major portion of their revenue and attention to improving their customer service.

In an August 2014 Harvard Business Review article by Peter Kriss entitled “The Value of Customer Experience, Quantified” he states “Intuitively, most people recognize the value of a great customer experience. Brands that deliver them are ones that we want to interact with as customers that we become loyal to, and that we recommend to our friends and family”. Also, he states that the “value of delivering such an experience is often a lot less clear, because it can be hard to quantify” Delivering consistent and concise knowledge to provide answers to customer inquiries in an efficient way leads to providing value to the customer and improving the overall customer experience.

In support of this trend of KM in customer service, Forrester’s “Top Trends For Customer Service in 2015”, author Kate Leggett points out in trend #4, knowledge management’s impact when she states “Knowledge Will Evolve From Dialog To Cognitive Engagement. Organizations will look at ways to reduce the manual overhead of traditional knowledge management for customer service. They will start to explore cognitive engagement solutions, interactive computing systems that use artificial intelligence to collect information, automatically build models of understanding and inference, and communicate in natural ways. These solutions have the potential to automate knowledge creation, empower agents with deeply personalized answers and intelligence, scale a company’s knowledge capability, and uncover new revenue streams by learning about customer needs.”

IBM Watson is playing a significant role in the evolution of applications that automate knowledge creation by providing deeply personalized answers and intelligence. This technology will not only effect customer service, but a multitude of industries with its capability to extract knowledge from Big Data sources. The IBM Watson ecosystem will provide deep content analytics and intensive scientific discovery that will lead to improve cognition contributing to an organization’s knowledge capabilities. This supports Kate Leggett’s research and points out that KM will continue to play a significant role in delivering knowledge and decision making capabilities to the customer service industry for the foreseeable future (2016 and beyond).

Global View of KM

In reviewing the 2015 Global Knowledge Management Observatory Report, authors David Griffiths, Abi Jenkins and Zoe Kingston-Griffiths state “The Knowledge Management function in many organizations is in a state of general decline”. This as they indicate is due to the following factors:

  • “Satisfaction in Knowledge Management’s contribution to strategic and operational objectives within organizations is often poor.”
  • “Knowledge Management lacks maturity and integration within the vast majority of organizations.”
  • “Knowledge Management continues to be predominantly seen as a technology-led function.”
  • “Satisfaction with technology-led Knowledge Management solutions is not improving.”
  • “Many Knowledge Management professionals do not appear to have the necessary awareness and/or permissions required to respond to unmet demand for KM activities in organizations.”
  • “Knowledge Management, as a field or area of practice, is argued to be suffering from a lack of specialist practitioners.”
  • “The value and/or significance of Knowledge Management activities is still not being appropriately recognized or reported within most organizations.”

Solutions that address many of the findings of the 2015 Global Knowledge Management Observatory Report are essential for KM success in 2016 and as KM evolves as a discipline. This includes producing a comprehensive KM strategy, KM education options, adopting KM programs, project and systems, and addressing why KM programs/projects fail.

All of these aspects from the 2015 Global KM Observatory Report are addressed in Knowledge Management in Practice. This book should be leveraged as a reference/guide and presents a tremendous resource to support the growth of KM at your organization.