If we would stop calling this stuff "AI" it would make all our lives a lot easier, but people can't resist. When computers first came on the scene a lot of peo

If we would stop calling this stuff "AI" it would make all our lives a lot easier, but people can't resist. When computers first came on the scene a lot of people had a very poor conception of what it was the human mind did, computationally. So when computers turned out to be good at things that were challenging "intellectual" tasks for humans like chess and calculus many were duped into thinking that computers were somehow on a similar level to human brains and "AI" was just around the corner. The reality was that one of the most important tasks that the human brain performs: contextualization, categorization, and abstraction was taken for granted. We've since discovered that task to be enormously computationally difficult, and one of the key roadblocks towards "true AI" development. Now, of course, we're at it again. We have the computational muscle to make inference engines that work nothing like the human brain good at tasks that are difficult to program explicitly (such as image and speech recognition) and we've built other tools that leverage huge data sets to produce answers that seem very human or intelligent (using bayesian methods, for example). We look at this tool and too many say "Is this AI?" No, it might be related to AI, but it's just a tool. Meanwhile, because of all the AI hype people overpromise on neural networks / "deep learning" projects and people get lazy about programming. Why bother sitting down for 15 minutes to figure out the right SQL queries and post processing when you can just throw your raw data at a neural network and call it the future? One of the consistently terrible aspects of software development as a field is that it continues to look for shortcuts and continues to shirk the basic responsibilities of building anything (e.g. being mindful of industry best practices, understanding the dangers and risks of various technologies and systems and being diligent in mitigating them, etc.) Instead the field consistently and perversely ignores all of the hard-won lessons of its history. Consistently ignores and shirks its responsibilities (in terms of ethics, public safety, etc.) And consistently looks for the short cut and the silver bullet that will allow them to shirk even the small vestiges of responsibility they labor under currently. There's a great phrase on AI that goes: "machine learning is money laundering for bias", which points to just one facet among so many of what's wrong with "AI" as it's practiced today. We see "AI" used to sell snake oil. We see "AI" used to avoid responsibility for the ethical implications inherent in many software projects. We see "AI" integrated into life critical systems (like self-driving cars) without putting in the effort to ensure it's robust or protect against its failures, with the result being loss of life. AI is just the latest excuse by software developers to avoid responsibility and rigor while cashing checks in the meantime. At some point this is going to become obvious and there is going to be a backlash. Responsible developers should be out in front driving for accountability and responsibility now instead of waiting until a hostile public forces it to happen.