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4 ‘must-know’ AI Innovations of the Future

The main innovations will happen in Data & Analytics where Synthetic Data, Composite AI, Decision Intelligence and Edge AI will become mainstream in 3 years, finds leading research body: Gartner.

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AI is already powerful, but the true potential of this discipline is yet to be witnessed. Recently, it’s uncovered that four innovations in AI are to impact business in a transformational manner. These are to work at the level of business applications, devices and tools.

This blog presents the very four innovations in AI, as explored in the Gartner’s Hype Cycle 2022, that identifies topics in AI that’ll gain popularity in the near-long term, 2-5 years. 

Business leaders and the AI community are advised to focus on these innovative technologies and techniques in AI to enable an ever improved and expanded business decision making. 

The four innovations in AI that are to gain high attention by engineers and executives are:

  • Synthetic Data: Creating enhanced and enriched data for training of algorithms.
  • Composite AI: Fusing multiple AI techniques to get the best decision outcome.
  • Decision Intelligence: Creating technology that makes better business decisions.
  • Edge AI: Embedding AI in IoT devices, near users. 

Synthetic Data

Synthetic data is a data-centric AI innovation, which means instead of tweaking AI models for better outcomes, the focus is put on quality aspects of data. It’s defined as a class of data that’s artificially created and is not obtained from real world observations. 

Real data is difficult to get. Often the data is expensive, contains bias and personally identifiable information, and is unapproachable. But as the innovation in synthetic data is gaining pace, it has become extremely easy to create enhanced and enriched data for training of algorithms. 

The technology is so remarkable that synthetic data appears as real as one would obtain from the real world. It can be created using many methods– for example, using generative adversarial networks (Gen AI), that’s capable of creating unlimited amounts of unique data in response to user prompts. 

The following key benefits are obtained:

  1. Reduced costs and time in ML development.
  2. Improved performance of ML algorithms.
  3. Creation of data for scenarios where real world data is not available– such as for training crash detection algorithms and self-driving cars.

Outlook- Synthetic data has already been adopted for applications such as computer vision and NLP, and will see accelerated adoption in 3 years.

In the longer term, synthetic data is predicted to completely substitute real world data, by 2030.

Composite AI

Composite AI is a model-centric innovation. It fuses many AI techniques together to improve: model performance, which is the reduction in computations it takes for learning, and solve a wider range of business problems. 

It combines the hyped AI: Machine Learning, with other traditional AI applications such as graph analytics, rule-based approach, and optimization methods, to deeply analyze data, improving the overall capability of the AI.

Note, Composite AI is not merely a combination of various techniques, rather the purpose is to improve model aspects such as: learning efficiency, and ability to deeply analyze data.

While composite AI still remains a mystery, Gartner and other expert organizations state that it has the capability to create applications that rely on less training data, uses models that are more efficient in training, and solves a wider range of business problems.

Outlook– Composite AI will reach mainstream in 3 years and Gartner expects it’ll bring transformational benefits for businesses.

Decision Intelligence

Decision making in business has become tough over time. With the evolving landscape– changing customer needs, technology disruption and competition among enterprises, leaders today find decision making more difficult than 2 years ago. Moreover, there’s more pressure to explain how a particular decision is reached. 

Decision Intelligence is the class of AI technology that understands and explains how effective decisions are made.

It’s an application-centric AI, which means that it creates complete AI systems– starting from designing, to modeling, aligning, executing, monitoring and tuning of the decision models and processes.

In the designing stage, DI specifically focuses on the understanding of how decisions are made, evaluated, and improved via feedback. It then uses this knowledge to create AI applications that are powerful in outcome.

DI also brings multiple traditional and advanced techniques together, to power AI applications. This includes, for example, decision management ( that uses non-deterministic techniques such as agent based systems), and decision support techniques (such as descriptive, diagnostic, prescriptive and predictive analytics). 

Outlook– According to Gartner prediction, work on decision intelligence has already accelerated and will reach its maximum level in 3 years.

Edge AI

IoT sensors or edge devices (as they are commonly called) essentially need cloud access to perform processing and decision making on data. However, as AI and devices have advanced, IoT finds a better opportunity in AI.

Edge AI brings AI technology in IoT devices, making the processes faster and better. 

The application of AI in IoT devices has many benefits that were earlier not possible in the absence of AI.

These benefits include:

  • Access to analytics is faster with Edge AI, as AI is installed within the IoT device, there’s no need to transmit data to the cloud server, saving users time.
  • Networking costs are reduced as the IoT devices perform analytics within them, without having to leave and connect with the centralized cloud processing.
  • Model accuracy is enhanced as newer data arrives at the IoT sensors and consumed by the AI model, to make it learn and make more effective decisions.
  • High availability is achieved from an offline edge device that doesn’t need network connectivity to perform data analytics.

Outlook– Edge AI is expected to reach mainstream in 3 years.

Conclusion

Gartner’s Hype Cycle 2022 analyzes the AI landscape for the near-term future. The results show four areas will come to the mainstream AI-scape in 3 years. These include: Synthetic Data, Composite AI, Decision Intelligence and Edge AI. 

If you are an AI engineer, or a business leader, keep these AI innovations at the top of your AI strategies so that you can help yourself in creating more powerful AI than what we see today.

Ayesha
Ayesha
I engineer the content and acquaint the science of analytics to empower rookies and professionals.
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Please enter your comment!
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4 ‘must-know’ AI Innovations of the Future

The main innovations will happen in Data & Analytics where Synthetic Data, Composite AI, Decision Intelligence and Edge AI will become mainstream in 3 years, finds leading research body: Gartner.

AI is already powerful, but the true potential of this discipline is yet to be witnessed. Recently, it’s uncovered that four innovations in AI are to impact business in a transformational manner. These are to work at the level of business applications, devices and tools.

This blog presents the very four innovations in AI, as explored in the Gartner’s Hype Cycle 2022, that identifies topics in AI that’ll gain popularity in the near-long term, 2-5 years. 

Business leaders and the AI community are advised to focus on these innovative technologies and techniques in AI to enable an ever improved and expanded business decision making. 

The four innovations in AI that are to gain high attention by engineers and executives are:

  • Synthetic Data: Creating enhanced and enriched data for training of algorithms.
  • Composite AI: Fusing multiple AI techniques to get the best decision outcome.
  • Decision Intelligence: Creating technology that makes better business decisions.
  • Edge AI: Embedding AI in IoT devices, near users. 

Synthetic Data

Synthetic data is a data-centric AI innovation, which means instead of tweaking AI models for better outcomes, the focus is put on quality aspects of data. It’s defined as a class of data that’s artificially created and is not obtained from real world observations. 

Real data is difficult to get. Often the data is expensive, contains bias and personally identifiable information, and is unapproachable. But as the innovation in synthetic data is gaining pace, it has become extremely easy to create enhanced and enriched data for training of algorithms. 

The technology is so remarkable that synthetic data appears as real as one would obtain from the real world. It can be created using many methods– for example, using generative adversarial networks (Gen AI), that’s capable of creating unlimited amounts of unique data in response to user prompts. 

The following key benefits are obtained:

  1. Reduced costs and time in ML development.
  2. Improved performance of ML algorithms.
  3. Creation of data for scenarios where real world data is not available– such as for training crash detection algorithms and self-driving cars.

Outlook- Synthetic data has already been adopted for applications such as computer vision and NLP, and will see accelerated adoption in 3 years.

In the longer term, synthetic data is predicted to completely substitute real world data, by 2030.

Composite AI

Composite AI is a model-centric innovation. It fuses many AI techniques together to improve: model performance, which is the reduction in computations it takes for learning, and solve a wider range of business problems. 

It combines the hyped AI: Machine Learning, with other traditional AI applications such as graph analytics, rule-based approach, and optimization methods, to deeply analyze data, improving the overall capability of the AI.

Note, Composite AI is not merely a combination of various techniques, rather the purpose is to improve model aspects such as: learning efficiency, and ability to deeply analyze data.

While composite AI still remains a mystery, Gartner and other expert organizations state that it has the capability to create applications that rely on less training data, uses models that are more efficient in training, and solves a wider range of business problems.

Outlook– Composite AI will reach mainstream in 3 years and Gartner expects it’ll bring transformational benefits for businesses.

Decision Intelligence

Decision making in business has become tough over time. With the evolving landscape– changing customer needs, technology disruption and competition among enterprises, leaders today find decision making more difficult than 2 years ago. Moreover, there’s more pressure to explain how a particular decision is reached. 

Decision Intelligence is the class of AI technology that understands and explains how effective decisions are made.

It’s an application-centric AI, which means that it creates complete AI systems– starting from designing, to modeling, aligning, executing, monitoring and tuning of the decision models and processes.

In the designing stage, DI specifically focuses on the understanding of how decisions are made, evaluated, and improved via feedback. It then uses this knowledge to create AI applications that are powerful in outcome.

DI also brings multiple traditional and advanced techniques together, to power AI applications. This includes, for example, decision management ( that uses non-deterministic techniques such as agent based systems), and decision support techniques (such as descriptive, diagnostic, prescriptive and predictive analytics). 

Outlook– According to Gartner prediction, work on decision intelligence has already accelerated and will reach its maximum level in 3 years.

Edge AI

IoT sensors or edge devices (as they are commonly called) essentially need cloud access to perform processing and decision making on data. However, as AI and devices have advanced, IoT finds a better opportunity in AI.

Edge AI brings AI technology in IoT devices, making the processes faster and better. 

The application of AI in IoT devices has many benefits that were earlier not possible in the absence of AI.

These benefits include:

  • Access to analytics is faster with Edge AI, as AI is installed within the IoT device, there’s no need to transmit data to the cloud server, saving users time.
  • Networking costs are reduced as the IoT devices perform analytics within them, without having to leave and connect with the centralized cloud processing.
  • Model accuracy is enhanced as newer data arrives at the IoT sensors and consumed by the AI model, to make it learn and make more effective decisions.
  • High availability is achieved from an offline edge device that doesn’t need network connectivity to perform data analytics.

Outlook– Edge AI is expected to reach mainstream in 3 years.

Conclusion

Gartner’s Hype Cycle 2022 analyzes the AI landscape for the near-term future. The results show four areas will come to the mainstream AI-scape in 3 years. These include: Synthetic Data, Composite AI, Decision Intelligence and Edge AI. 

If you are an AI engineer, or a business leader, keep these AI innovations at the top of your AI strategies so that you can help yourself in creating more powerful AI than what we see today.

Ayesha
Ayesha
I engineer the content and acquaint the science of analytics to empower rookies and professionals.
RELATED ARTICLES

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Most Popular