![]() ![]() Example: Investment GuidanceĪnother one of our clients makes a large volume of really small investments that individually come with a lot of metadata. Overall, it has created a much more productive working environment without removing the human factor from a very artistic enterprise. Both the client and the client's customers understand that machine learning is a fuzzy process that can't be expected to produce perfect results all the time, so they happily (rather than begrudgingly) fall back on the manual editing tools when they need to. ![]() ![]() The best we can hope for is that the photos are excellent 80% to 90% of the time, such that we produce a considerable time-save for businesses processing thousands of photos per day.Īs a consequence, part of this software we built includes Photoshop-like manual image editing tools as fallbacks for when the automagically produced result is not perfect. In their own right, the multiple machine learning models that went into this product are impressive, but the results can't be perfect 100% of the time. One of our clients sells a photo-editing software that attempts to create perfect single-subject pictures, including background replacement, shadowing, and color correction, all from screenshots collected from a brief video recording. Businesses, now more than ever, understand the relationship between statistical analysis, machine learning, and AI, and they are taking very measured and conservative steps towards implementing it in their everyday business processes. But, they also said that it would reach a "plateau of productivity" in 2 to 5 years.īased on what I hear, talking to businesses every day about machine learning solutions, I think this prediction rang true. In July 2017, Gartner said that machine learning was at the "peak of inflated expectations", as in, overly hyped. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |