We are on the point of a fourth AI winter, and faith that AI will create enough tangible value to justify its cost is starting to fade.
While the articles from Goldman Sachs and other research institutes are falling like leaves from the scene, there remains to be time to forestall the subsequent AI winter. And the reply has been right in front of us for years.
Something is missing
In most scientific disciplines, breakthroughs are made within the laboratory after which passed on to engineers who translate them into practical applications.
When a team of chemical researchers discovers a brand new method for producing an adhesive bond, the invention is passed on to chemical engineers who use it to develop products and solutions.
The findings of mechanical physicists are passed on to mechanical engineers so as to develop technical solutions.
However, when a breakthrough in AI is made, there is no such thing as a standalone discipline for applied artificial intelligence. This results in corporations investing in hiring data scientists who earned their PhDs with the goal of creating scientific breakthroughs in AI to attempt to develop real-world solutions as a substitute.
The result? 87% of AI projects fail.
This is where artificial intelligence comes into play
Engineered intelligence (present participle: intelligence engineering) is an emerging discipline that focuses on the sensible application of AI research based on engineering—the discipline of using scientific breakthroughs along with raw materials to design and construct secure, practical assets. This enables material experts, scientists, and engineers to develop intelligent solutions without having to change into data scientists.
Industry leaders are starting to rebuild the links between research and development, form latest partnerships with academia and technology providers, and create the ecosystem conditions mandatory for AI research to be handed over to intelligence engineers, just as chemical research is handed over to chemical engineers.
The result?
Breakthrough applications in concrete use cases that add value, make it into production, and that might not have been discovered by data scientists or technology providers based on data alone.
5 steps to introduce intelligence engineering in your organization
Expertise is at the guts of intelligence engineering and is expressed in skills – specialized knowledge learned through practical application. Theory and training can speed up skill acquisition, but you possibly can't have skills (and due to this fact expertise) without practical experience. Assuming your organization already has experts, these are the five practical steps you possibly can follow to introduce the discipline of intelligence engineering and the way it differs from the standard approach to using AI:
The traditional approach to adopting AI (which is accountable for the 87% error rate) is:
- Make a listing of problems.
Or
- Examine your data;
- Select a set of potential use cases.
- Analyze use cases by way of return on investment (ROI), feasibility, cost and schedule;
- Select a subset of use cases and spend money on executing them.
The intelligence engineering approach to introducing artificial intelligence is:
- Create a heatmap of experience across your existing processes;
- Assess which expertise is most useful to the organization and evaluate how common or rare that expertise is.
- Select the five most useful and rarest areas of experience in your organization.
- Analyze ROI, feasibility, cost and schedule to develop smart solutions;
- Select a subset of value cases and spend money on execution.
Creating a brand new wave of value creation with AI
Once the intelligent technology has been introduced into your organization and the intuitive applications have been developed and put into production, this latest capability may be used to transcend existing expertise and create latest opportunities for developing secure, practical value across the enterprise and ecosystem.
As organizations, industries and academic institutions develop engineering intelligence programs, organizations, individuals and our society will reap the advantages of AI's otherwise untapped economic and societal potential, making a latest class of jobs and ushering in a brand new wave of value creation.
