Artificial Intelligence needs no introduction. This technology never fails to astound people with its amazing nature to perform complex tasks. But, users want more from AI than what it is offering currently. Well, this is what Gartner’s Hype Cycle for Artificial Intelligence 2021 report says. Analysts at Gartner described 34 AI technologies and stated that the AI Hype Cycle is fast-paced with more innovations reaching mainstream adoption in the next two to five years.
Gartner found more innovations in the Hype Cycle than usual which means that end-users are looking for something which AI is not delivering right now.
Gartner Analysis: 4 Megatrends For AI
- Organizations are looking to use AI to enable reusability, scalability and governance. AI orchestration and automation platforms reflect this trend.
- Innovation means all resources are being used efficiently which includes data and models. Composite AI, generative AI are few examples of this trend.
- Responsible AI offers risk management and AI ethics for increased trust and transparency.
- Small data approaches allow more robust analytics and deliver situational awareness.
Six Technologies Are Expected To Hit The Plateau Of The Innovation Trigger Phase Of The Hype Cycle.
- Composite AI
- AI orchestration and automation platform
- AI governance
- Generative AI
- Human-centered AI
- Synthetic data AI
Let’s Hit The Discussion!
The Composite AI approach combines several techniques to expand the knowledge representation and provide solutions to business problems more efficiently. The goal is that we need to build AI solutions that need fewer data and energy to learn. This approach will help in making the tech available for the companies that don’t have the large amounts of data but possess the human expertise required. This is an emerging technology that has penetrated 5 to 20% of the market.
This technique is best suited to organizations where there is very little data for traditional analysis or when the required intelligence is hard to represent in Artificial neural networks.
AI Orchestration And Automation Platform
Organizations are now using AIOAP to standardize DataOps and deployment pipelines. This technology unifies development, delivery, and operational contexts and particularly round reusing components like features and model stores, monitoring, experiment management, model performance, and lineage tracking.
This trend is driven by the problems that are being created by traditional siloed approaches to data management and analysis.
To implement AIOAP, Gartner recommends companies audit current data, simplify data, and use an environment that is cloud-based.
AI governance means establishing accountability for the risks that come with AI. Government leaders in Japan, The US, and Canada are setting guidelines and binding for AI. Analysts say that AI without governance is dangerous and putting some rules can surely create accountability.
Governance efforts should address-
- Ethics, fairness, and safety so that the business and its reputation are always protected.
- Trust and transparency.
AI governance has reached 1% to 5% of the audience.
Companies should set risk guidelines and make sure that humans stay in the loop to diminish AI deficiencies.
Generative AI applies everything it has learned to create new content like text, images, videos, and audio files. Generative AI has widely been used in life sciences, healthcare, manufacturing, material science, media, entertainment, automotive, aerospace, defense, and energy industries.
The analysts also predict that generative AI will disrupt software coding and automate 70% of the work done. It can be used for fraud, malware, disinformation, and motivation.
The analysts also recommend paying attention to generative AI as they are expecting rapid adoption. Companies need to be prepared for deep fakes and think about data can speed up the development cycle.
This AI approach is often termed augmented intelligence and assumes that people and technology are working together. This means that few tasks will be completed by humans and few will be completed by algorithms. It also says that people can anytime take over the process when they feel that AI has reached the limits of its capabilities. HCAI helps companies in managing AI risks and be more ethical with automation.
HCAI has reached 5% to 20% of the audience. Gartner also recommends establishing HCAI as a key factor and creating AI oversight boards.
Real-world data comes with many challenges and Artificially generated data is the solution to obtaining real-world data and labeling it to train AI models. Synthetic data also removes personally identifiable information from live data. This data is less expensive and travels faster and also reduces the cost and time. There is also one drawback with this data, it can have bias problems and anomalies.
This technology has reached 1% to 5% of the target audience. Companies are advised to work with specialist vendors.
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