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AI Definitions: Machine Learning vs. Deep Learning vs. Cognitive Computing vs. Robotics vs. Strong AI….

Nastel Technologies®
June 15, 2019
AI Definitions: Machine Learning vs. Deep Learning vs. Cognitive Computing vs. Robotics vs. Strong AI….

Cognitive Computing – AI comes in many forms, each at its own stage of development with its own definition, techniques and capabilities. Some forms – such as Artificial General Intelligence, AI super-intelligence or Strong AI, the kind of AI that might someday automate all work and that we might lose control of – live in the future and may never (some say will never) happen. Other forms of AI are doing valuable work today and are driving growth in the high performance sector of the technology industry.

There’s a key distinction is between “strong” and “narrow” AI. The latter is limited in scope to handling specific tasks and specific problems; that’s the current state of AI. Strong AI would enable machines and robots to handle multiple tasks and to integrate the learning from multiple disciplines simultaneously, what’s referred to as the “emergent property.” Strong AI could, theoretically, take on human-like powers of intuition, emotion and empathy. R&D groups around the world are working on breakouts into early forms of strong AI, but for now, narrow AI is the rule of the day.

Another interesting point: AI has undergone several hype cycles since its emergence 60 years ago that, at each cycle’s end, left AI discredited, the bane of entrepreneurs and investors. As hot as AI is today, we hear talk that another “AI winter” may be in the offing. Some of these predictions refer to the unlikelihood of AI super-intelligence, but it’s also true that AI’s relative lack of technological maturity makes it extremely difficult to implement, requiring the specialized and expensive capabilities of data scientists under the direction of AI-savvy IT managers experienced in guiding AI projects to fruition. AI democratization, the integration of tools, techniques and technologies, combined with support services, that help overcome AI complexities, is critical to forestalling a modified, limited form of AI winter.  It’s incumbent on technology vendors to bring AI within the skill level of more companies.

This article originally appeared on To read the full article, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

  • Advanced predictive anomaly detection, Bayesian Classification and other machine learning algorithms
  • Raw information handling and analytics speed
  • End-to-end business transaction tracking that spans technologies, tiers, and organizations
  • Intuitive, easy-to-use data visualizations and dashboards

If you would like to learn more, click here.

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


The Nastel i2M Platform provides:


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