Data from my two years at Software Engineering Radio indicates that technical software podcast listeners are an under-served market. If you are a software engineer who doesn't listen to many podcasts about software, this should be self-evident. Technical knowledge about software will help you work more intelligently. The scope of software engineering is growing. If you use WordPress, Ableton or PhotoShop, you are a software engineer.
Tier-1 behemoths still dominate the enterprise software landscape with transaction-based platforms for data management, business intelligence (BI), and reporting. But new best-of-breed cloud applications are pushing into this space, winning customers and market share, and amping up overall investment in cloud enterprise software. Market research firm Gartner last week projected the biggest worldwide IT splurge in a decade for 2018, attributing increased outlays to the rise of cloud computing, and therefore to fatter spending on cloud-based enterprise software. By 2021, 28 percent of all IT spending will be dedicated to cloud-based infrastructure, software, and services, Gartner says. "It's more of the same of the market moving from a license model, with on-premises software, to a service model in the cloud," Gartner research VP, John-David Lovelock, told Barrons.
You may recall from previous articles that Autodesk has been fairly active in the realm of generative design. Autodesk's cloud-based Generative Design service is now available to users of Autodesk's flagship Fusion 360 Ultimate software. What first started as a Skunkworks-esque project known as Project Dreamcatcher deep in the Autodesk labs has evolved into the commercial version of Generative Design. The service gives engineers the power of cloud-based simulation and design, meaning that designers are no longer constrained to the old ways of doing things and instead are free to explore designs that were hitherto impossible. Let's just recap what Autodesk's Generative Design service is, and what it means for designers.
ADM: Why aren't traditional ways of developing, testing, and releasing software equipped to support today's demands? ADM: What is continuous testing, and how does it differ from traditional testing methods? ADM: How can organizations implement automated continuous testing into their software development processes? ADM: What areas of software testing can AI benefit? ADM: What challenges of incorporating AI into software testing should organizations be prepared for?
Most people who get into AI have a computer science education, so then it should be pretty easy to get into software engineering. If you come to AI from e.g. a cognitive science background, it may be harder. The other way around is a bit trickier in my opinion. You may be able to find work as a software engineer at an AI institute, where you support the researchers by realizing their vision. If you want to become an AI researcher yourself, that kind of experience would probably help.