The current approach to tackling such vulnerabilities is a forensic one. "We look at error logs, attack files, and issue patches but it is essentially a rear view mirror approach," says Zach. But a network with cognitive agents residing in it can provide real-time assessments of the vulnerabilities in the network and other meaningful insights through a continuous dialogue with the network operations center. "This could fundamentally change how we run our networks," he says.
IBM hopes to extend what has been learned with analytics, extend that to human-system interactions using speech as the medium, and take away the need to code a software tool. "Why have structured programs to deal with information when the underlying data is not?" asks Zach.
Gartner explains that 'humans versus machines' is not a binary decision, there are times when machines working alongside humans is a better choice. IBM's Watson does background research for doctors, just like a research assistant, to ensure they account for all the latest clinical, research and other information when making diagnoses or suggesting treatments.
At a time when humans are clearly reaching the limits of what we can absorb and understand, the main benefit, according to Gartner, of having machines working alongside humans is the ability to access the best of both worlds (that is, productivity and speed from machines, emotional intelligence and the ability to handle the unknown from humans).
As machines get smarter and start automating more human tasks, humans will need to trust the machines and feel safe. IBM's Watson provides "confidence" scores for the answers it provides to humans.
Zach admits that the evaluation of the proficiency of a system and the reliability of system to gain trust in the dialogue is a challenge. "We see this issue of trust and proficiency in our human interactions. Transforming that set of ideas into a man machine computer user interaction is really what the technical challenges are about," he says.
It's the Watson jeopardy system that led to the further research and the announcement of the IBM Watson Engagement advisor which is delivered from the cloud and into the hands of mobile consumers, with the ability to crunch big data and provide fast, personalized advice. But a key factor with its predecessor in the game show was the ability to decide whether or not to answer and building a level of confidence in its advice. "Evidence behind the answer is a critical element of what we are exploring," says Zach.
Gartner feels that machines and systems can only benefit from a better understanding of human context, humans and human emotion. This understanding leads to simple context-aware interactions, such as displaying an operational report for the location closest to the user; to better understanding customers, such as gauging consumer sentiment for a new product line by analyzing Facebook postings; to complex dialoguing with customers, such as virtual assistants using natural language question and answering (NLQA) to interact on customer inquiries. NLQA is one of the technologies on Gartner's Hype Cycle that represent these capabilities.
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