or Artificial Intelligence Bull Shitake

There are a lot of claims being made, and as this article points out, not many of them are supported by strong evidence/math.

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.

OpenCV Speed Cam

I really need to find the time to build this DIY speed cam. From my home office window, I have an excellent view of an intersection where I would estimate about 70% of the cars don’t even stop at the posted Stop sign. Further, I would guess that close to 90% of them are going faster than the 25 MPH speed limit. Data is good.

Computer vision itself isn’t anything new, but it has only recently reached a point where it’s practical for hobbyists to utilize. Part of that is because hardware has improved dramatically in recent years, but it also helps that good open-source machine learning and computer vision software has become available. More software options are becoming available, but OpenCV is one that has been around for a while now and is still one of the most popular. Over on PyImageSearch, Adrian Rosebrock has put together a tutorial that will walk you through how to detect vehicles and then track them to estimate the speed at which they’re traveling.

Rosebrock’s guide will show you how to make your very own DIY speed camera. But even if that isn’t something you have a need for, the tutorial is worth following just to learn some useful computer vision techniques. You could, for instance, modify this setup to count how many cars enter and exit a parking lot. This can be done with affordable and readily-available hardware, so the barrier to entry is low — perfect for the kind of project that is more of a learning experience than anything else.

Problems with AI Transparency

As more and more business decisions get handed over (sometime blindly) to computer algorithms (aka ‘AI’), companies are very late to the game in considering what the consequences of that delegation will yield. As a buffer against these consequences, a company may want to be more transparent about how it’s algorithms work but that is not without it’s challenges.

To start, companies attempting to utilize artificial intelligence need to recognize that there are costs associated with transparency. This is not, of course, to suggest that transparency isn’t worth achieving, simply that it also poses downsides that need to be fully understood. These costs should be incorporated into a broader risk model that governs how to engage with explainable models and the extent to which
information about the model is available to others.

Second, organizations must also recognize that security is becoming an increasing concern in the world of AI. As AI is adopted more widely, more security vulnerabilities and bugs will surely be discovered, as my colleagues and I at the Future of Privacy Forum recently argued. Indeed, security may be one of the biggest long-term barriers to the adoption of AI.

Clever ‘AI’ or Poor Definition?

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.

This clever AI hid data from its creators to cheat at its appointed task

Feed Shark

flickr OFF

Joined 2005… Left 2018…

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.

Personal Data as an Asset

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

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?