In Rebooting AI, Ernie Davis and I made six recommendations, each geared towards how readers – and journalists – and researchers might equally assess each new result that they achieve, asking the same set of questions in a limit section in the discussion of their papers:
Stripping away the rhetoric, what does the AI system actually do? Does a “reading system” really read?
How general is the result? (Could a driving system that works in Phoenix work as well in Mumbai? Would a Rubik’s cube system work in opening bottles? How much retraining would be required?)
Is there a demo where interested readers can probe for themselves?
If AI system is allegedly better than humans, then which humans, and how much better? (A comparison is low wage workers with little incentive to do well may not truly probe the limits of human ability)
How far does succeeding at the particular task actually take us toward building genuine AI?
How robust is the system? Could it work just as well with other data sets, without massive amounts of retraining? AlphaGo works fine on a 19×19 board, but would need to be retrained to play on a rectangular board; the lack of transfer is telling.
These types of articles seem to come down to the insatiable need for writers to sensationalize things that they don’t necessarily understand.
For example, in the scenario outlined in the article, it is unlikely that the ‘AI’ (aka computer algorithm) was self aware and said to itself “hey, I have a comprehensive understanding of humans and their capabilities, so I will modify myself to ‘cheat’ at this task in a way that a human would find difficult to detect”.
More likely is that the algorithm was poorly defined and the brute force computational model (aka ‘AI’) found a way to ‘solve’ the problem in a way that wasn’t contemplated by the software developer.
I knew that flickr has been on the decline for a while. IMHO, Yahoo’s acquisition was the beginning of the end. SmugMug’s heavy handed idiocy of late was the last straw for me.
After a few arrogant email demands from SmarmMug, I had had enough so I requested all of my data from flickr and it only took them a week and a half to provide the requested files. I happily downloaded my content and deleted my account after 13 years of use.
There is a well worn axiom in business that ‘data should be treated as a corporate asset’. This is, of course, very true and the advances in data science and ‘big data’ are giving the potential for that data to become even more valuable.
This got me thinking about how personal data should be thought about in the same way. Think about all the data generated from what you watch, what you listen to, where you visit, what you review, data from wearables, etc. All of this data is consumed and analyzed by 3rd parties currently, but what if individuals were able to take control of, what is, after all, their data.
Would this give rise to data science companies marketing algorithms directly to consumers (much like pharmaceutical companies market drugs directly)? Could it also give rise to the equivalent ‘data quackery’ similar to the natural supplements and homeopathic industry? That is, junk algorithms that, at their most benign, do no harm and at their worst incent you to dangerous courses of action?
Would there also be a new industry for ‘personal data scientists’ (like financial councilors or tax advisers) that would help you assess all of the data assets you have and how to best combine or leverage them with third parties to your best benefit (and not just the benefit of 3rd parties)? Wouldn’t it be great to have some control over the hundreds of arbitrage-like transactions that go on behind the scenes when you are waiting for a page to load on a commercial web site via browser setting that allow you to control what information about you gets shared (and with companies).
Microservices Need Architects – An excellent article on the complexity of something with ‘micro’ in it’s name. And, yes, I know and I am here to help with over a decade of experience in service design and enterprise integration skills.
For the past two years, microservices have been taking the software development world by storm. Their use has been popularized by organizations adopting Agile Software Development, continuous delivery and DevOps, as a logical next step in the progression to remove bottlenecks that slow down software delivery. As a result, much of the public discussion on microservices is coming from software developers who feel liberated by the chance to code without the constraints and dependencies of a monolithic application structure. While this “inside the microservice” perspective is important and compelling for the developer community, there are a number of other important areas of microservice architecture that aren’t getting enough attention.
Specifically, as the number of microservices in an organization grows linearly, this new collection of services forms an unbounded system whose complexity threatens to increase exponentially. This complexity introduces problems with security, visibility, testability, and service discoverability. However, many developers currently treat these as “operational issues” and leave them for someone else to fix downstream. If addressed up front—when the software system is being designed—these aspects can be handled more effectively. Likewise, although there is discussion on techniques to define service boundaries and on the linkage between organizational structure and software composition, these areas can also benefit from an architectural approach. So, where are the architects?