The ultimate goal of work in cognitive architecture is to provide the foundation for a system capable of general intelligent behavior. That is, the goal is to provide the underlymg structure that would enable a system to perform the full range of cognitive tasks, employ the full range of problem solving methods and representations appropriate for the tasks, and learn about all aspects of the tasks and its performance on them.
– from Laird et al., "SOAR: An architecture for general intelligence"
For robots to successfully perceive and understand their environment, they must be taught to act in a goal-directed way. While mapping environments geometry is a necessary prerequisite for many mobile robot applications, understanding the semantics of the environment will enable novel applications, which require more advanced cognitive abilities. Sven Behnke, Head of Autonomous Intelligent Systems Group at the University of Bonn, is tackling this area of robotics by combining dense geometric modelling and semantic categorization. Through this, 3D semantic maps of the environment are built. Sven's team have demonstrated the utility of semantic environment perception with cognitive robots in multiple challenging application domains, including domestic service, space exploration, search and rescue, and bin picking.
It's never been more important for life insurers to offer their policyholders high-quality service. Digitisation and intensified competition with other financial service verticals have blunted prospects for growth and made customer acquisition and retention ever more challenging. Meanwhile, consumers' overall service expectations continue to rise as retailers in other industries set a higher bar. Insurers have struggled to keep up because of the inherent complexity of both their products and their systems' environments. Bringing cognitive computing to customer service will allow insurers to move past these challenges and deliver a level of digital service comparable to that of any other industry.
What Is the Business Imperative for Cognitive Computing? Cognitive Computing is becoming the foundation for a new generation of solutions that have the potential to transform business. Unlike traditional approaches to building solutions, a cognitive computing approach allows the data to help determine the way applications are designed. This contrasts with conventional software development that begins with defining logic based on the current way a business operates. In her session at 18th Cloud Expo, Judith S. Hurwitz, President and CEO of Hurwitz & Associates, Inc., put cognitive computing into perspective with its value to the business.
The challenge of preparing back-end data resources to support cognitive computing initiatives may be the single greatest factor that hinders progress, some observers warn. "Data scientists spend up to 70% of their time on data collection and preparation, rather than building and deploying predictive models," said Jeff Healey, senior director of product marketing, Vertica, Micro Focus. "While the need for data preparation for model-building would probably remain, having analytic platforms in place that support this critical step of data cleaning and preparation helps to shorten the predictive analytics lifecycle. Additionally, many data science and business analyst teams are being challenged to prove the value of their work in order to secure the time and resources needed to develop and operationalize ML-driven applications. Multiple data sources and different open source systems that require considerable maintenance and resources further complicates the time to value equation."
Although the interest for leveraging artificial intelligence in business is high, implementation is still low and as far as artificial intelligence in business is concerned we're about to see a growth in'meaningful' artificial intelligence deployments, Gartner said in February 2018. Do note the term meaningful. Gartner states that according to its 2018 CIO Agenda Survey four percent of CIOs have implemented artificial intelligence thus far. However, 46 percent of CIOs developed plans for AI implementation. At the same time, however, ample concerns remain for CIOs.
Although cognitive computing, which is many a times referred to as AI or Artificial Intelligence, is not a new concept, the hype surrounding it and the level of interest pertaining to it is definitely new. The combination of hype surrounding robot overlords, vendor marketing and concerns regarding job losses has fueled the hype into where we stand now. But, behind the cloud of hype that is surrounding the technology currently, there lies a potential for increased productivity, the ability to solve problems deemed too complex for the average human brains and better knowledge based transactions and interactions with consumers. I recently got a chance to catch up with Dmitri Tcherevik, who is the CTO of Progress, about this disruption and we had a healthy discussion which led to the following insights. Cognitive computing is considered a marketing jargon by many, but in layman terms it is used to define the ability of computers to replicate or stimulate human thought processes.
Maintaining regulatory compliance is a daunting task. Up to 200 regulatory changes occur every day, varying from large scale regulation like Dodd Frank, to minute changes to the font and size of footnotes in regulation text. The cost of not being compliant is astronomical – since 2008, more than $50 billion in fines have been paid. Banks and financial institutions are looking for any advantage they can get to streamline operations and reduce compliance costs. IBM Think, the three-and-a-half day event that emphasizes'mankind machine', highlights using IBM's vast background in cognitive computing to, in this instance, help compliance professionals to play by the rules and maintain compliance, avoid sizeable penalties and leverage machine learning and artificial intelligence to make their jobs easier.
People regularly use objects in the environment as tools to achieve their goals. In this paper we report extensions to the ICARUS cognitive architecture that let it create and use combinations of objects inthis manner. These extensions include the ability to represent virtual objects composed of simpler ones and to reason about their quantitative features. They also include revised modules for planning and execution that operate over this hybrid representation, taking into account both relational structures and numeric attributes. We demonstrate the extended architecture's behavior on a number of tasks that involve tool construction and use, after which we discuss related research and plans for future work.
When it comes to revolutionary technology, the blockchain and cognitive computing are two at the top of the list in 2018. With these technologies finally being put to use in practical applications, we're learning more and more about what they can do on their own--and together. Let's take a look at how some industries can take advantage of this powerful combination. Before we can discuss what these two technologies can accomplish together, it's important to understand them separately. Cognitive computing is essentially using advanced artificial intelligence systems to create a "thinking" computer.
Cognitive computing is an emerging area, with numerous use cases in manufacturing, education, commerce, and customer service. According to IDC, 90% of organizations will leverage cognitive computing by 2021 and 40% of digital transformation initiatives will use cognitive services by 2019. With use cases beginning to bear fruit in success, our customers have begun to see success using cognitive. As such, IBM selected us to present at Think, their largest user conference of the year happening March 19-22 in Las Vegas. Join us for both events and see how cognitive can transform your business goals for the months and year ahead.