The Gartner Hype Cycle for Artificial Intelligence, 2019, examines the stream of innovations and trends in the AI sector and scopes AI plans. Gartner says for early adopters, AI scalability is the next frontier. As I said in my previous post on 29/03/18 any proposed Augmented Intelligence (IA) solution should have each of following three features:
Scalability: To search across all possible publically available data sets.
Data Quality & Enrichment: Use currently available components of Artificial Intelligence (AI) to sort through all of that data, enriching it in a way that makes it more searchable and effective.
Simple Interface: Front-end search and analysis interface with broad design target to cover every possible application domain.
The term “Artificial Intelligence” (AI) brings to mind to the notion of replacing human intelligence with something synthetic. "Augmented Intelligence" (IA) describes a combination of the human brain and the most reliable facets of artificial intelligence. This means that IA aims to build systems that enhance and scale human expertise and skills rather than replacing them.
An approach using IA plays to the strengths of both human analysts and machines, assuming that, between the two, humans tend to be the better decision makers while machines tend to be faster analysts. It also assumes that the human analyst should always be in the driver’s seat, and that the machine exists to assist, not replace. In IA the human analyst is fundamental to the final output, but is now able to deliver at a speed and scope that could only be achieved with the aid of intelligent machines.
IA is comprehensive set of capabilities based on technologies which include AI, but that go far beyond it. It comprises the fields of machine learning, reasoning and decision technologies, language, speech and vision recognition and processing technologies, human interface technologies, distributed and high-performance computing, and new computing architectures and devices. When purposefully integrated, these capabilities are designed to solve a wide range of practical problems, boost productivity, and foster new discoveries across many industries.
As AI becomes a part of everything (software, hardware, consumer devices) and autonomously communicates with other AI, new policies and governance will emerge to protect consumers, citizens and businesses from unethical AI usage and new patterns.Increasingly, we will see new AI solutions emerge that are explicitly designed to undermine other AI solutions in order to protect consumer/employee privacy.
The quality of the data sets used is paramount to the performance of AI systems. Data sets used by AI systems (both for training and operation) may suffer from the inclusion of inadvertent historic bias, incompleteness and bad governance models. AI systems need to must instantly identify potential issues and avoid exposing dirty, inaccurate or incomplete data. This implies that even if there is a sudden problematic situation resulting in poor-data quality entries, the AI will be able to handle the quality issue and proactively notify the right users; depending on how critical the issues are, it might also deny serving data or serve data while flagging the potential issues.