Knowledge Representation in AI is the X-factor for Next-gen Hybrid Cloud Modelling
In the COVID-19 times, the role of AI and Hybrid Clouds can only be fathomed from their abilities to accelerate the adoption of remote working capabilities in real quick time. Knowledge representation in AI makes this space so much more exciting for everyone involved, including analysts and business intelligence teams who have to keep a track of where this technology is heading in the near future.
One of the key subfields within Artificial Intelligence (AI) development is Knowledge Representation. We have reviewed countless projects and research papers on Knowledge Representation in AI where analysts and data scientists are constantly digging through the database to come up with new capabilities within Cloud and Automation businesses. We have covered how Big Data management and analytics rely so much on Software as a Service and Cloud services. Experts believe Knowledge Representation in AI could advance the adoption of Multi Cloud and Hybrid AI Cloud capabilities for customer services, unified communications, IT operations, security and privacy, and human resources management.
What is a Hybrid AI Model?
An AI model when combined with one or more different families of technologies for novel purposes makes up for a hybrid AI infrastructure. When nanotechnology is developed with AI at its core, that’s hybrid AI for you right there. And, when these capabilities are available for customers / users over the cloud services, it’s packaged as Hybrid AI Cloud. These’ could bring in additional capabilities from the AI families, such as Voice AI, NLPs, CNN, KKR, MLOps, and much more. In fact, from the IT industry’s perspective, we should be heavily focused on the way ML Ops enrich Hybrid AI Cloud offerings.
Let’s understand why we should be laser focused on ML and Knowledge Representation in AI for Cloud markets.
What Hybrid AI applications offer more than just enrichment opportunities to ML models. Using hybrid AI, engineers can customize the way organizations adopt and use various infrastructure to match their existing AI environment and demanding IT processes.
KR models to Advance in Application modernization
Application modernization is the pinnacle of IT business that brings together the capabilities from the Computer science, coding, and AI worlds. The app modernization business is a trillion dollar economy and is dominating the current Cloud analytics ecosystem with its ability to refine efforts that go into building new software, new infrastructures, and new capabilities, all using the standard tools such as coding, search, testing, and verification. It basically allows anyone to build modern apps for business that can be built and deployed from anyplace, anytime, with a minimum of IT support and management. In the remote scenario, app modernization techniques work in favor of large sized data management teams who simply embrace the simplified workflows to securely co-develop and co-deploy Cloud environments in a hybrid manner. Knowledge Representation in AI that is used at IBM is quantum inspired and therefore demonstrates the advanced levels to which Hybrid AI models could be used to train machines to process, store, analyze and repurpose data using computational algorithms. The whole concept of ‘embedded systems’ applies to IBM’s Knowledge Representation in AI models, driven by quantum embedding of logical frameworks such as FOL, WOL, and Frame Problems solving language.
AI for Security and Compliance
When we talk of Cloud adoption, security and compliance management remain the top issues for any company. Experts believe AI KR models could solve a bulk of the issues associated with this critical area of IT Ops. In fact, in recent times, the maturity of AI Knowledge Representation has ushered in the arrival of a new technique called Information Security or InfoSec.
One security incident costs a company $3.6 million, on an average. In 50% of the security incidents, companies are unable to accurately ascertain the extent of damage despite the deployment of best security and compliance frameworks and working with top security professionals. The reason they lose out on money and time is their inability to tap Knowledge Representation in AI ML.
In best AI courses online, you would come across case studies on how Security and Quantum Logic works for symbolic reasoning and logical restructuring to make deductions from KR in AI models.
