MIT Media Lab is open-sourcing its own efforts to create learning software from other machine learning programs, and this should help with industry-wide efforts to make this a practical way to create new software.ĪI industry experts are quick to point out that developing machine learning requires an immense human effort at the outset, but offloading some of that work to other machine learning systems could drastically reduce the human input required at the beginning of the process and throughout.
This would help greatly with endeavours like self-driving automobile systems, for which even millions of miles driven is little more than a good start in terms of delivering real-world usable results. It could also free up human researchers to work on more important problems, rather than spending their time on more rote or mundane training of AI systems using massive data sets.ĪI tuning AI has another potential benefit – improving the learning curve for AI systems so that the volume of data required to produce meaningful results is also cut down.
Offloading machine learning development to machines would help address the serious shortfall in human expertise in the area, for instance, which is currently leading to a land grab for startups and academic talent with any kind of AI chops. MIT looks at the most recent work done by a range of different organizations, including Google Brain, who are working on AI that can develop machine learning software – and finds that in many cases, the results that come from machines coding other machines match or even exceed equivalent work done by humans.ĭoes that mean even machine learning programmers are facing employment extinction? Not exactly, and not yet – efforts to create machine learning programs that best their human-designed equivalent require a lot of computing firepower thrown at the problem Google Brain’s person-besting experiment in building image recognition systems via AI development used 800 ugh-powered graphics processors working together, which is a costly endeavor to be sure.īut the advantages are clear, and there’s a path towards lessening the resource burden in creating these systems, too. There is no one-size-fits-all answer here, but there are enough examples to suggest some shift away from the traditional office setup, even for well-established companies.Who programs the programmers? Soon enough, it might not be people behind the development of advanced machine learning and artificial intelligence tech, but other AI. I spoke to a variety of people in the tech world, from consultants and investors to startup founders, to try and get a grip on exactly what the next phase of work is going to look like, and without a doubt, tech companies have at least become a lot more flexible when it comes to face time at the office. Suddenly, we had a giant laboratory to experiment with everyone working from home, and while there are certainly some problems, depending on your business, your job, and, frankly, your living situation, it showed that whole categories of workers didn’t need to be sitting in a cubby farm inside a big building to get their jobs done - certainly not five days a week. But when the pandemic hit in March 2020, it changed how we think of work, possibly forever. They built them to house their workers and show off their sheer economic power. Salesforce, Microsoft, Google, Meta, Amazon and Apple didn’t build sprawling campuses or skyscrapers across the world just to abandon them for no reason.
This was certainly true for larger tech companies. Sure, there have been fully remote companies like GitLab for some time now, but the conventional wisdom prior to the pandemic was that you mostly needed to be in the same place to get serious work done. What’s to become of the tech company office, and how do companies function without the structure that working together in the same building has traditionally provided us? That’s a monumental question facing tech companies today as they struggle to define their approach to work in a post-pandemic world.