AI is trending topic these days. AI enabled startups are everywhere and you can find many publications using term AI to express different aspects of innovation. Engineering software is not an exclusion from the trend. My attention was caught by Design World article – The Self-taught design system. Read the article and draw your own opinion.
The article brings few interesting opinions about the role of AI in design, CAD, PLM, IoT and related fields of engineering and manufacturing software. The one I captured is from Jon Hirschtick, founder and CEO of Onshape:
Hirschtick believes CAD programs will make use of AI, but in a more limited way in the near future, by using information the designer has entered to offer suggestions about design parameters and inputs.
Future programs might offer to the design default values for a shape based on the objects that person has designed in the past. AI would essentially learn what types of products the designer mostly works on and the inputs he or she has regularly used for those products. The suggested values may appear on the user’s screen in a dialog box, Hirschtick says.
“Or AI could offer something like: ‘Gee, I noticed you’ve done pattern of activity several times in a row, do you want that or was that a mistake?’” And AI could make engineers’ search for needed and necessary parts easier. Hirschtick envisions a program, much like that which appears for Amazon shoppers, in which engineers could type in information about a part they’re searching for “and the program says ‘a lot of times people looking for that part also look at this one,’” he says.
The ideas of search and data recognition is also coming from the interview with Mike Haley of Autodesk. The introduction of Design Graph and related search interface by Autodesk can demonstrate a continuous interest into collecting and analyzing data.
Introduced last summer, Autodesk’s Design Graph is another machine learning system that helps users manage 3D content, offering Google search-like functionality for 3D models, says Mike Haley, who leads the machine intelligence group at Autodesk. “Machine learning and artificial intelligence are starting to make the first inroads into daily life, but to our knowledge this is its very first application for industrial design and mechanical engineering,” Haley says.
Design Graph algorithms extract large amounts of 3D design data from an engineering company’s designs. It then creates a catalog by categorizing each component and design using a classification and relationship system. Designers and engineers search across all of their files for a part type, such as a bolt or a bike seat, with the tool returning dozens or hundreds of pertinent options. So how does machine learning come into play? The system teaches computers to identify and understand designs based on their inherent characteristics–their shape and structure–rather than by tags or metadata, Haley says
At the same time, Design World article made me think about huge mixed salad of terms and acronyms used in AI-related field. These amount of abbreviation is making many people confused about what is actually AI and what specific capabilities can be delivered.
AI scope is very disputed these days. Coming back from 1980s as something describing “intelligence” exhibited by machines, these days is different in my view. It is combing few specific technologies and trends I observed in the past 5-7 years. Here is a brief description of technologies related today to AI:
1- Big Data: Technology used to bring multiple islands of data in one place, allowing to look at the data and various characteristics of the data.
2- Analytic and BI: Technology and activity on top of Big Data to counting various aspects of collected data.
3- Data Science: Even more sophisticated counting in the way that will allow to model and predict some data or behavior.
4- Machine Learning: Technology to make smart counting including feedback loops.
So, what is AI? In my view, AI (artificial intelligence) as a overarching discipline used for all things I mentioned above. It certainly drives a lot of interest, but it is fundamentally related to the ability of software to collect and manage significant amount of data from a specific systems and data sources. The last thing is one that I believe drives some skepticism in engineering domain. This skepticism is probably driven by the reality in which majority of design data as of today is still managed by legacy desktop and in house hosted PDM systems. There is no easy way to collect information from CAD, PLM and related engineering and manufacturing customer data sources.
However, one thing is really excites me when I think about future of AI-enabled design system – a capability of a specfic system to know about design and product more than an individual user or specific company can do.
Therefore, I can see cloud technology as a significant enabler for the future of CAD AI-zation and machine learning. MIT Technology review article – AI Is Taking Over the Cloud is a very good example how it might happen. It speaks about how Box.com is using Google Vision API to make their service “smarter”. Here is a passage that can give you an idea of new features:
Cloud storage company Box announced today that it is adding computer-vision technology from Google to its platform. Users will be able to search through photos, images, and other documents using their visual components, instead of by file name or tag. “As more and more data goes into the cloud, we’re seeing they need more powerful ways to organize and understand their content,” says CEO Aaron Levie.
What is my conclusion? Data is a new oil. And AI is a new electricity. One of the most fundamental aspects of AI (artificial intelligence) activity is data collecting. Without real data about design, parts and their usage is very hard to think about bright future of self taught design system. To collect this data can be a tricky challenge, since it is hidden behind firewalls and CAD formats. Cloud (and especially multi-tenant data management) cloud-based systems have an opportunity to serve as a founding elements of AI platforms serving engineering and manufacturing communities. Just my thoughts…