The virtues and drawbacks of cognitive computing are being discussed in mainstream television, newspapers, magazines and blogs, with questions like:
What will it bring, what will be the effect on our jobs, and how we make decisions.
According to Reinoud Kaasschieter, an analyst with Capgemini, the view on the future – Technovision 2017 – states that capturing and leveraging collective knowledge as well as decreasing the dependency on undocumented, individual expertise and experience, is an important aspect of artificial intelligence (AI).
One of the impacts of AI, according to Capgemini’s Technovision, is: cost effectiveness, improved productivity and higher quality through “smart” automation of human tasks. But will this be the case for creative computing?
Will it become economically viable to create automated computing systems for creative processes?
Cognitive computing, like IBM Watson, can help suggesting solutions and ideas that are unknown to the user, based on collective knowledge. Cognitive computing can find the applicable jurisprudence for any given law case or diagnose the right type of cancer from the patient’s syndromes. And cognitive computing help people resolve problems by creating new ideas.
According to Rob High, ‘A dystopian future?’, this notion of creating ideas and inspiring new thoughts and new ways of asking questions is critical to so many things people do in the professional world with this.
The goal of “Strong AI” is to develop artificial intelligence to the point where the machine’s intellectual capability is functionally equal to a human’s. At a certain point in time, computers will be able to take over all human tasks, including reasoning, creativity and self-control. These machines could make us redundant.
This is not only the vision of science-fiction writers and movie makers, but also renowned people like Stephen Hawking and Bill Gates.
To the contrary, according to Hod Lipson, professor of engineering at Columbia University computers will never be able to take over all human tasks. From a technical and scientific standpoint, creativity is “one of these last frontiers of AI,” says. Because non-creative machines cannot design creative machines.
“Thus, humanity’s last stronghold, the walls of which separate our capabilities from machine capabilities, will only fall if we (…) do our best to open its gates.”
According to Reinoud Kaasschieter, every month some new case is published where cognitive computers helps with creative processes; film trailers, thinking sculptures, music etc. So let’s take a closer look what needs to be done to build a digital assistant for the creative process and how humans are still needed to get the desired results:
Though computers are very good in analyzing and interpreting large amounts of data, humans still have to judge the results that computers give. Tools like IBM Content Curator have the ability to collect data from different sources, filter out the candidate documents and control the selection process. Ask Google what happens when you don’t check sources on trustworthiness.
The human factor is still very evident for the tasks that need to be done to create computer assisted design, art, innovation and so on. But as real life examples have proven, the result of these suggestions can be inspiring, surprising and innovative. And touches on what we see as truly creative.
“AI works best by collaborating with humans rather than replacing them.”
But what is cognitive computing and how can it be used in the organization?
“ Cognitive computing offers fundamental differences in how systems are built and interact with humans. Cognitive-based systems, such as IBM Watson, are able to build knowledge and learn, understand natural language, and reason and interact more naturally with human beings than traditional systems.”
How companies are using IBM Watson’s cognitive capabilities?
eyeQ specializes in digital content presentation at the point of sale; they use Watson to help customers make onsite product selection decisions. Their recommendations are based on analysis of the shoppers’ age, gender, and personality based on their Twitter feed.
Sellpoints (noticed) that 69% of consumers use search engines to make purchase decisions, but they drop off rapidly with each additional step (click) in the process. By searching on intent rather than simply by keyword matching, they reduce the number of steps and thus increase sales.
In addition to the above, IBM has put Watson to use for oncology, or general health, where it helps doctors diagnose patients and propose treatment plans. It will in the near future help lawyers find applicable texts and jurisdiction for their cases. It can help you improve your customer engagements with automated self-service Question-and-Answer applications.
All these and other applications take unstructured documents, websites, blog posts, books and so on as their source base of knowledge. And that the knowledge base should be complete, or as complete as can be.
That’s one of the reasons that cognitive products find their first use in sectors where completeness of information is necessary to be compliant.
Based on this corpus of knowledge, and the processing power to know and interpret all the information at hand, Watson and other cognitive systems can give you more complete and relevant answers than a human being. Or through discovery by any advanced search engine. 24/7 and almost anywhere with an internet connection.
Computers can make increasingly better decisions, often even better than humans. But, will the person allow that?
According to research by the Dutch company Motivaction 75 percent want to stay in control, 20 percent will occasionally have computers decide some things and only 5 percent would leave all decisions to computers.
Experts and professionals know they don’t know everything and are struggling to get to know what they don’t know:
“The professionals don’t know what they need to know. For instance, even attorneys that specialize don’t know the majority of the laws that define their specialty, let alone all of them. At the core of the IBM Watson effort and its general analytics push is to fix that problem. The time is coming when any professional that doesn’t have access to, and knowhow to use a tool like Watson will have inadequate skills to gain employment in the developed world.”
But first, you have to be aware of the value of this knowledge for your organization. That this knowledge is constantly changing and you and your employees have to learn constantly to keep up; at the same time being aware that you still don’t know enough.
When your organization is focused on adding value based on knowledge, cognitive computing will give you a competitive edge.
You don’t have to be a research institute to work with knowledge. When you’re a retailer, cognitive computing will help you understand and communicate with your customers. For instance, you can use cognitive computing to improve your customer engagement by leveraging your previous experiences with your customers. You can even automate your customer interaction by using automated self-service Question-and-Answer applications.
Today’s consumers want to seamlessly shop whenever and wherever they are. In order to meet these demands, retailers must bring the physical and digital shopping worlds into one omnichannel experience. Cognizant helps retailers turn these possibilities into new business opportunities.
Based upon what you know about your customers and their interactions with you, a system like the Watson Engagement Advisor can be used which promises “to automate customer interaction by fielding questions in natural language with informed, evidence-based reasoning”. This system can take information residing within your organization and use it, may be together with public information, to create a knowledge base tuned to your line of business.
In oil and gas exploration, it helps companies decide where to drill.
It can assist lawyers in building legal arguments and police detectives in solving cold cases. Almost all organizations today use knowledge. Using knowledge from unstructured sources in and outside the organization will open new opportunities.
Cognitive computing can provide you with an unparalleled opportunity to leverage information and learning, to both grow your profits and provide value added services.
The question is not what can cognitive computing do, but rather how will you put cognitive computing to work for you.
Challenges and Opportunities
According to Gary Cokins, predictive analytics is getting much attention. This is because senior executives appear to be shifting away from a command-and-control style of management – reacting after the fact to results – to a much more anticipatory style of managing.
With predictive analytics executives, managers and employee teams can see the future coming at them, such as the volume and mix of demands to be placed on them. As a result they can adjust their resource capacity levels and types, such as number of employees needed or spending amounts. They can also quickly address small problems before they become big ones. They can transform their mountains of raw data into information to test hypothesis, see trends, and make better decisions.
However, according to Reinoud Kaasschieter, the tools for collecting and maintaining knowledge are underused, to say the least. Documents are only stored when used in operational processes. Wiki’s are out-of-date because it takes too much time and effort to update them with information. Social media feeds contain too many bogus messages to be useful as an information source.
So how can you use cognitive computing to tap into the knowledge from inside your organization?
The answer is Content Curation: collecting and evaluating all documents and other data that contains knowledge, about the topics I want to use cognitive computing for.
If you want to supply your employees with better knowledge and insights, automate customer interaction, or gain a competitive edge through better quality information and decisions, you’ll have to have your information in good shape: managed and maintained.
Start by making your unstructured data accessible for use within cognitive computing. Use tools for content curation to access the information sources. And valuate the information for these sources, to make sure you’ll use that information that will help you to gain knowledge.
Encourage people to collect and share information. And to store this information in an accessible and retrievable way.
Combining the information at hand, analyzing the data and building hypothesis around it is the way to start learning.
Aggregating information into new facts and insights and using these in company policies is a good step forward in building a new culture of learning and sharing.
“Harnessing the data generated by your customers, and your own operations and supply chain gives you the opportunity to provide new levels of performance. The right insight into this data can transform your supply processes, use of assets, and performance of services, strengthening your customer relationships and enabling you to out-perform the market. ”
This implies that executives must create an organizational culture for metrics and analytics.
For the last few decades many executives and strategic consulting firms have followed the framework of the popular Harvard Business School professor, Michael S. Porter. Porter has basically advocated three types of generic strategies.
However according to Gary Cokins, with today’s real time data access and technology-driven markets and economies, each generic strategy is vulnerable:
- Cost leadership strategy. This is accomplished via improving process efficiencies, unique access to low-cost inputs (e.g., labor, materials), vertical integration, or avoiding certain costs. Think Walmart. But today other firms using lean management techniques and data analysis methods can quickly lower their costs.
- Differentiation strategy. This is accomplished via developing products and/or services with unique traits valued by customers. But today there can be imitation or replication of products and services by competitors (e.g., smart phones) or changes in customer tastes.
- Focus strategy. This is accomplished via concentrating on a narrow customer segment with entrenched customer loyalty. Think Tiffany jewelry. But today broad market cost leaders or micro-segmenters can invade a supplier’s space and erode its customers’ loyalty.
According to Gary Cokins, in Stephen Bungay’s book The Art of Action he addresses how an organization can implement and achieve the formulated strategy and plans of its executive team. In his book he draws on battle tactics of the 19th century Prussian army.
Bungay’s premise is that the leaders of almost all organizations can define reasonably good strategies. Where executives often fall down is leading their organization to execute their strategy. Bungay describes this problem as gaps and advises how to close the gaps.
His assertion is that similar to military campaigns in war when a strategy encounters the real world then three types of gaps appear. He describes gaps in terms of expected results and reality: outcomes, actions, plans. Gaps result from the complex and difficult to predict environments that all organizations deal with and are made more severe with globalization – the reduction of international borders for commerce and information. The three gaps are:
1. The knowledge gap – the difference between what we would like to know and what we actually know.
2. The Alignment Gap – the difference between what we want people to do and what they actually do.
3. The Effects Gap – the difference between what we expect our actions to achieve and what they actually achieve.
Based on Bungay’s deep knowledge as a historian of military practices, he observes that a key to successful strategy execution is delegating more decision making authority to managers and employee teams.
Empowering managers and employee teams
Bungay describes lessons from the 19th century Prussian army in this way. Following an unexpected military defeat the Prussian military’s tactics were reformed. Lower level officers were given more flexible command to make decisions. What mattered is that they fully understood the battle mission. By providing more decision rights to the officer corps, this resolved a problem that the higher the military leaders are from the battlefield, then the less they are aware of the current situation. Officers could pursue local actions as they saw fit.
In today’s terms of managerial methods, the parallels of the Prussian army reforms are applying the strategy map and its associated balanced scorecard method with its key performance indicators (KPIs).
The balanced scorecard’s primary feature is the development of a strategy map that visually displays on a single page a dozen or more cause-and-effect linked strategic objectives. Using four sequenced components (referred to as “perspectives”) the linkages move from employee learning, growth and innovation to process improvement initiatives to customer loyalty objectives which result in the financial objectives’ outcomes.
The key performance indicators (KPIs) reported in the balanced scorecard are derived from the strategy map. The KPIs monitor the progress toward accomplishing the strategic objectives, and by each KPI having targets assigned, the foundation for accountability is established and alignment with the mission and strategy are achieved.
The message is that granting decision rights to managers but measuring their performance and holding them accountable with consequences is effective at closing the three gaps.
According to Stan Christiaens, the way ahead may be the’Amazon-ification’ of data.
Imagine those who rely on information to do their jobs, could search for data, and get it in context, with the same ease as a product search on Amazon.
For instance, when we visit Amazon, we expect to enter a few simple search terms that quickly serve up the items we need. A simple click drops our selection into an online shopping basket, and those products are delivered to our doorstep almost immediately.
It’s a model that works because it’s reliable, convenient and fast. Business users are consumers too, and we’ve come to expect a similarly easy and seamless experience in our work lives.
This can create business value, but has yet to become reality.
A sophisticated data catalog can incorporate machine learning functionality to go a step further and learn from the past user behaviors to make specific recommendations for “data purchases,” much like Amazon does for frequent shoppers.
It provides a collaborative framework to ensure data accountability and ownership to deliver high-quality data that is easily and consistently accessible to users.
Some of the key benefits that can be achieved thanks to a sophisticated data catalog include:
Simpler, intuitive data search that allows a data citizen to apply filters to search results, progressively narrowing terms to pinpoint the right data.
Insightful information uncovered through metadata and crowdsourcing
Using a catalog, recommendations are based on several variables, including users’ past “data purchases” as well as the data purchasing behaviors of people across the organization – using the power of the crowd to see which data has proven the most useful to whom.
Delivering a complete view of data for business intelligence
The catalog can make recommendations based on previous searches and data sets to return more holistic, and immediately usable, results. Power users can run sophisticated business intelligence reports using catalog functionality to manipulate different combinations of data across various datasets to see personalized views of information.
Simplified and trusted onboarding
A data catalog helps to easily onboard new data from various sources with structured workflows and roles-based approvals. A sophisticated catalog includes a template for what information is required before data can be onboarded from the various silos of information buried in the enterprise, in addition to outside sources.
Finding greater meaning from data
A modern data catalog has a shopping basket to hold the data for which a user has searched. The data basket is a way to easily request access to data from multiple sources across the organization and understand the data’s meaning, view relationships, issues, and sources through lineage and traceability diagrams.
With technology models borrowed from the consumer world, businesses are finally gaining the power to find the right data quickly, evaluate its lineage and enrich its value.
As a result, they’re unlocking the power of data to serve as an actionable tool for competitive advantage.
