Exploring cognitive services
Whether this wave will lead to true and continuing business momentum however remains to be seen despite “good signs”, as is the next wave and the increasing number of dicussions on the “ethics”, security and “place” of AI in the future. The “AI and robots taking over mankind view” and superintelligence evolutions – instead of AI as mimicking possibilities of the human brain for a purpose – are real concerns and deserve attention. A third wave took place at the end of the nineties and early 2000 when there was more attention from the perspective of specific applications of AI across diverse domains. On top of that, there was the success of the Internet, which also led to quite some hype and predictions that didn’t really live up to their promises.
With the speed of adoption and technology evolution, it will likely happen faster than many might expect. Once you start connecting everything you need APIs, connectors, information analysis technologies and “embedded intelligence”, essentially code that makes it all possible. Each of these sets of technologies are technological drivers of digital transformation as such. Artificial intelligence is being used faster in many technological and societal areas although there is quite some hype about what “it” can do from vendors. Still, the increasing attention and adoption of forms of AI in specific areas triggers debates about how far we want it to go in the future.
What are cognitive systems?
Are there other hidden representations used by experts, but not in general use? What were Gleason and Thurston imagining when they thought about mathematical groups? Can we tease those ideas out, and use them to inspire interface ideas? Perhaps we can take some of the representationsmathematicians use for cognitive technology definition thinking about high-dimensional geometry, and turn those into an interface? I’ve used examples from physics and mathematics because that’s my training, but I believe that for most subjects of any depth, experts have hidden representations that could inspire interfaces reifying those representations.
Cognitive intelligence is consistent with psychologist Phillip L. Ackerman’s concept of intelligence as knowledge, which posits that knowledge and process are both part of the intellect. Cognitive computing seeks to design computer systems that can perform cognitive processes in the same way that the human brain performs them. You’ve probably crossed paths with cognitive computing applications already, perhaps without even knowing it. Currently, cognitive computing is helping doctors to diagnose disease, weathermen to predict storms, and retailers to gain insight into how customers behave. However, these are just a few of the many ways that cognitive systems can be used to the advantage of businesses and consumers alike.
Some people are really speaking about machine learning when they talk about AI or about deep learning or about text mining, the list goes on. Others essentially talk about analytics and in doomsday movie scenarios everything gets mixed, including robotics and superintelligence. Have you wondered why so many people find it hard to stop watching Netflix?
One hallmark of a true cognitive computing system is its ability to generate thoughtful responses to input, rather than being limited to prescribed responses. Cognitive technology, another term for cognitive computing or cognitive systems, enables us to create systems that will acquire information, process it, and act upon it, learning as they are exposed to more data. Data and reasoning can be linked with adaptive output for particular users or purposes. The way cognitive computing works differs greatly from other, related forms of technology and computational sciences.
In fact, if you look at the page of IBM’s famous Watson platform you’ll read that, qote, “IBM Watson is a technology platform that uses natural language processing and machine learning to reveal insights from large amounts of unstructured data”. In this paper, I approach this issue from a different angle, inquiring whether language can be viewed as a “cognitive technology”, employed by humans as a tool for the performance of certain cognitive tasks. I propose a definition of “cognitive technology” that encompasses both external (or “prosthetic”) and internal cognitive devices. A number of parameters in terms of which a typology of cognitive technologies of both kinds can be sketched is also set forth. Some tasks that require human or near-human levels of speech recognition or vision can now be performed automatically or semi-automatically by cognitive technologies. Examples include first-tier telephone customer service, processing handwritten forms, and surveillance.
— Taxshila Teachers (@Learnography) January 25, 2017
Many of today’s applications (e.g., search, ecommerce, eDiscovery) exhibit some of these features, but it is rare to find all of them fully integrated and interactive. Because of automation, the task done by the users is now taken care of by the computers. Whereas, a cognitive assistant suggests potential career paths to the job seeker, besides furnishing the person with important details like additional education requirements, salary comparison data, and open job positions. However, in this case, the final decision must be still taken by the job seeker. Now that you know what is cognitive computing, let’s move on and see how cognitive AI works.
Language as a cognitive technology
The cognitive systems have vast repositories of structured and unstructured data. These have the ability to develop deep domain insights and provide expert assistance. The models build by these systems include the contextual relationships between various entities in a system’s world that enable it to form hypotheses and arguments.
Historically, new technology has tended to push people into higher-value jobs rather than do away with jobs, some researchers say. Ideally, a layer of core concepts and terminology common to all domains should be used to anchor domain-specific models. This allows inference engines to traverse across domain borders and draw conclusions from all constituent domain models.
It comes in many shapes and forms and from several sources and is often text-intensive. From paper documents that need to get digitized to Twitter messages or email, also a major source of unstructured data/content. Other providers of cognitive solutions for enterprises include Cognitive Scale, which attempts to integrate intelligence based on machine learning into business processes by way of actionable insights, recommendations, and predictions. HPE Haven OnDemand provides what is in essence a big-data, cognitive-powered, curated search feature that allows users to access multiple structured and unstructured data formats.
- With cognitive computing, that distinction does not exist because these systems can teach and educate themselves.
- When exposed to that work, other people can internalize those new cognitive technologies, and so expand the range of their own visual thinking.
- But as of now we can say that it is a field of computer science that mimics functions of the human brain through various means, including natural language processing, data mining and pattern recognition.
In fact, our visual thinking is done using visual cognitive technologies we’ve previously internalized. In extreme cases, to use such an interface is to enter a new world, containing objects and actions unlike any you’ve previously seen. But as they become familiar, you internalize the elements of this world. Eventually, you become fluent, discovering powerful and surprising idioms, emergent patterns hidden within the interface. You begin to think with the interface, learning patterns of thought that would formerly have seemed strange, but which become second nature. The interface begins to disappear, becoming part of your consciousness.
The book explains the ideas of classical mechanics through the medium of Lisp programs. In our terms, the book implements a Lisp-based interface to classical mechanics, complete with many new elements of cognition. It thus provides new ways of thinking about classical mechanics, but is not a visual interface. If experts often develop their own representations, why do they sometimes not share those representations? To answer that question, suppose you think hard about a subject for several years – say, cyclic subgroups of a group, to use Thurston’s example.
It uses ambient noise, facial recognition, and sentiment analysis to determine which video clips generated the most crowd excitement. While work is aimed at computers that can solve human problems, the goal is not to push human beings’ cognitive abilities out of the picture by having computing systems replace people outright, but instead to supplement and extend them. Analysis may be initiated and guided by the user, while the cognitive computing system is capable of testing hypotheses, collecting evidence, and learning.
With the increase in volumetric data, and rise in cyber attacks, and the shortage of skilled cybersecurity experts we need modern methods like cognitive computing to deal with these cyber threats. Major security players in the industry have already introduced cognitive-based services for cyber threats detection and security analytics. Such cognitive systems not only detect threats but also assess systems and scan for vulnerabilities in the system and propose actions. Thus, besides AI, ML and NLP, technologies such as NoSQL, Hadoop, Elasticsearch, Kafka, Spark etc should form a part of the cognitive system. This complete solution would be capable of handling dynamic real-time data and static historical data. The enterprises looking to adopt cognitive solutions should start with a specific business segment.
In his dual role as the US and Global Innovation leader, Ragu is responsible for collaborating with each of the US businesses and across member firms to help increase the innovation and digital coefficient of the firm. He works with member firm leaders and global business leaders to drive strategic growth offerings and cross border commercialization of assets. Ragu is also a principal in the Strategy & Analytics practice of Deloitte Consulting, focusing on the Technology, Media, and Telecommunications sector. He has a unique blend of operational, principal investing, and advisory experience in the technology and telecom sectors.