2019: New Year, Same Problems.

Experimenting with proximity and remote control with @anki Vector.I’ve managed to avoid the deluge of end of year posts by people, as well as their bright and shiny posts of what they expect in 2019. After you’ve seen enough of them, you know the recipe and you can make your own – even if it’s not a very good recipe, even if it typically doesn’t stand the test of time.

A ‘New Year’ is just another date on the calendar for me these days – and truth be told, it has been for some time. So I spent this ‘holiday’ running some experimental code associated with the Anki Vector I picked up.

As a way of tracking what changes and what doesn’t, years are fickle. As an example, when it comes to code, the thing we sent that is furthest is still running 8-bit code, and it still seems to be working well. Looks like hunspell (that’s what you call it for pip) is the droid I was looking for, though the documentation on that… well…

Things that haven’t changed that much is the acceleration of technology – because it continues to accelerate, and documentation on it is simply horrible in some areas. I spent roughly an hour delving into replacements for PyEnchant, as an example, reading all sorts of the same thing that Google thought would be useful – and which wasn’t.

And this is, sadly, the sort of detritus that software projects leave behind. As a friend mentioned today, a lack of documentation is better than bad/misleading documentation – and when it comes to documentation, a lack of date tagging condemns people to whatever algorithm the search engine uses when college students are trying to find hardly known authors to plagiarize from.

It goes beyond that. There’s a trend where technology gets disposed of so fast that there is almost no documentation on any of it, or if there is, it’s dated and/or misleading.

This is why we’re not fixing things as much, those of us that have that mindset – because there are always a few people, statistically, that can fix things – remember repair shops? And then there are the people who pay to fix things. The way intellectual property – really, copyright – has gone in a legal sense keeps a space between people who would repair and the owners of copyright. And the contracts, threats about warranty… even more space, starving the ability for products to be supported by third parties.

Heaven forbid you reverse engineer something to fix it. That can get you in trouble with people have chain-linked bracelets and lawyers who love killing trees.

That’s where Open Source and Free Software were supposed to step in, at least in the context of software – but after a few decades, it’s all relatively young and the documentation is done largely in crayon hieroglyphics. The successful projects are documented, at least to some degree.

If there’s one thing that I’d like to see change this year, it’s people getting better at documentation. It’s as if they think what they do isn’t worth that investment.

And when they don’t, it isn’t.

Artificial Intelligences and Responsibility.

AI-NYC_2017-1218MIT Technology Review has a meandering article, “A.I Can Be Made Legally Responsible for It’s Decisions“. In it’s own way, it tries to chart the territories of trade secrets and corporations, threading a needle that we may actually need to change to adapt to using Artificial Intelligence (AI).

One of the things that surprises me in such writing and conversations is not that it revolves around protecting trade secrets – I’m sorry, if you put your self-changing code out there and are willing to take the risk, I see that as part of it – is that it focuses on the decision process. Almost all bad decisions in code I have encountered have come about because the developers were hidden in a silo behind a process that isolated them… sort of like what happens with an AI, only two-fold.

If the decision process is flawed, the first thing to be looked at is the source data for the decisions – and in an AI, this can be a daunting task as it builds learning algorithms based on… data. And so, you have to delve into whether the data used to build those algorithms was corrupt or complete – the former is an issue we get better at minimizing, the latter cannot be solved if only because we as individuals and more so as a society are terrible at identifying what we don’t know.

So, when it comes to legal responsibility of code on a server, AI or not, who is responsible? The publishing company, of course, though if you look at software licensing over the decades you’ll find that software companies have become pretty good at divesting themselves of responsibility. “If you use our software we are not responsible for anything”, is a good short read that most end user license agreements and software licenses have in there, and by clicking through the OK, you’re basically indemnifying the publisher. That, you see, is the crux of of the problem when we speak of AI and responsibility.

In the legal frameworks, camped Armies of Lawyers wait on retainer for anything to happen so that they can defend their well paying client who by simply pointing at a contract that puts all responsibility on the user. Lawyers can argue that point, but they get paid to and I don’t. I’m sure there are some loopholes. I’m sure that when pushed into a corner by another company with similar or better legal resources, ‘settle’ becomes a word used more frequently.

So, if companies can’t be held responsible for their non-AI code, how can they be held responsible for their AI code?

Free Software and Open Source software advocates such as myself have made these points more often than not in so many ways – but this AI discussion extends into data as well, which pulls the Open Data Initiative into the spotlight as well.

The system is flawed in this regard, so to discuss whether an AI can be responsible for it’s decisions is silly. The AI won’t pay a fine, the AI won’t go to jail (what does ‘life’ mean for an AI, anyway?). Largely, it’s the court of public opinion that guides things – and that narrative is easily changed by PR people who have a side door to the legal department.

So let’s not discuss AI and responsibility. Let’s discuss code, data and responsibility – let’s go back to where the root of the problem exists. I’m not an MIT graduate, but I do understand Garbage In, Garbage Out (GIGO).

Economy and Collaboration (2015)

Bee and Flower macros

Colin Shaw’s post on LinkedIn, “Collaboration Is Dead: Long Live Symbiosis“, indirectly addresses one of the key problems with understanding Open Source and Free Software, not to mention Open Content.

Sure, with Free Software/Open Source and Open Content there is collaboration – but if you look at the most successful projects, you’ll find one major thing: An economy surrounding it. Linux grew its economy by decreasing the cost of servers and other hardware by ‘removing’ the cost of software licensing. The software is still paid for but it costs the end user less. WordPress has an economy around it, as does Drupal – though the economies are quite different between the two. The point is that all of these well known open source projects have an economy that reinvests in the projects. Sure, we can call it collaboration and do a happy dance – that has been done for at least 10 odd years (in both senses of the word), but the reality transcends collaboration and is more accurately called symbiosis. It’s something that has bothered myself and some other people for some time but… for some reason I never jumped at ‘symbiosis’. Shame on me.

Symbiosis is truly key in any business sense and far too often open source, Free Software and Open Content advocates do not acknowledge that. ‘Free as in Freedom‘ has to take into account TANSTAAFL – where the Free Lunch was never about the freedom to eat lunch. You can only write so much free software/open source before the bill collectors say, “Hey man. Love your code. Pay me.” The same applies to open content.

The same applies to Social Media – or just about anything except, hopefully, childhoods. In the context of social media, it’s about getting at least as much as you give to your network.

Symbiotic relationships where the profit is mutually exclusive are seen throughout nature, much like the bee in the photograph. The bee gets something. The flowering plant gets something. Everyone’s happy.

I think it’s time to put collaboration in the rightful place – decremented – while symbiosis is really the goal.

The sound of one hand clapping is a very lonely sound.

Image at top left is my own; you can view more of my pictures either through my Flickr photostream or in my Photography Showcase.

 

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The Future Is Not The Enterprise You Know

Greetings from Guyana to New YorkStory time.

When I was in Georgetown, Guyana in 20051, I snapped that picture of the television in my room. People called in to the station to relay messages to expatriates in New York so that they wouldn’t incur the cost of a phone call from the local phone company.

In the developed world, or the Global North, or the West, or whatever you want to call it, VoIP had already shaken telecommunications by 2005 and won. In the other parts of the world, state-owned or subsidized telephone companies fought to stay relevant.

In the context of Guyana, it took until 2015 for the Caribbean Court of Justice to rule against the telephone company. And I imagine that battle isn’t over. It’s certainly not the first time I’ve seen it in my travels, and it’s not the first technology either. It also won’t be the last.

Having now worked a bit directly with telephony and SIP trunks, spending late nights catching up to where the company was and then studying beyond it2, I have a good feel for what is possible. And I also know that the future is global, that infrastructure is subject to licensing across geopolitical lines, and that technology waits for nothing but ideas whose time has come. Having been involved at the World Summit for Information Society level at first directly and now vicariously, to get global the industries built around Enterprise have to change. Having been a part of a Pre-twitter clone (we copied them when we grabbed a flux capacitor?), we saw things change a year before Twitter. That change is coming regardless of how much people are in love with the present enterprise. Evolution awaits no one.

So, what’s the future?

We hear a lot about the Internet of Things (IoT). How disruptive it is. Most of that is marketing hype to get all of us to buy things that we really have no need for – and, to be fair, people usually buy it for reasons that I might write about on my other blog3. Behind all of that is an undeniable force of change that goes beyond the buzzwords.

A few data-points to draw the line for you.

Data-point 1: Telecommunications infrastructure is no longer the product it once was because of SIP (which most of you know as VoIP, but it’s bigger than that). It’s about the on demand use of the telecommunications infrastructure. You can think of it as time-sharing real estate without the need to worry about the last person leaving dishes in the sink.

Data-point 2: The laws governing telecommunication infrastructure vary across geopolitical lines and proceed at the rate of the internal geopolitical bureaucracy. That’s a nasty factor that everyone should know, but most people don’t.

Data-point 3: Oh, that little Internet of Things has spawned all manner of things, like the 10 Pine64s I have coming next month. I’ll be clustering those for my own purposes – but imagine those as part of a solution that, for less than $500. Do you think I’ll be paying Oracle or Microsoft for licensing for a database? If you think so, you’re nutty and should have your head examined. Even open source DBAs are cheaper.

Data-point 4: Bitcoin brought the block chain to light.  Think of a client as part of a Peer to Peer network where the client deals with licensing within their geopolitical sphere (see 2), thus avoiding licensing fees across geopolitical boundaries wherever possible, and otherwise diminishing them. Take a look at this post on blockchain, posted by Arvind Krishna, Senior Vice President and Director, IBM Research. Or consider how Microsoft has been rolling Windows 10 out… peer-to-peer.

Data-point 5: Open source software has come so far that the cost of the software itself for applications has diminished significantly – you don’t pay for software, you pay for the changes to it if you want the changes… or you pay for people to configure it for you.

Data-point 6: ‘Big data’, another overblown marketing phrase, is a driving force that will not be stopped – it will hopefully be curtailed for reasons of privacy, but again and again the world has shown that it abhors censorship and will – at the cost of individuals, corporations, or entire governments, if necessary – be had.

What does it all mean?

It means that a company’s infrastructure, unless it’s spread out over a large area, is pretty much going to be an antique soon. People espousing details on the how of Software Engineering will develop are likely to completely miss the what of the development; the what of development should be defining the how (Software Process 101).

The ‘data-center’ will not die. It will become less important and probably used to roll out continuous integration to a peer-to-peer network of SaaS. Data will make its way to whatever monstrosity of a database that’s out there, and if I were a betting man I’d go with Oracle more than Microsoft on that since they’re acquisition of MySQL probably wasn’t an accident. Sure, it’s not an RDBMS, but what’s the most used database on the Internet? And who now has a thermometer?

It’s happening. Now.

1: Doing some volunteer work with St. Joseph Mercy Hospital. IBM had quoted a million Guyanese dollars to do the local network for the hospital; we got a group of volunteers around the hospital to do it for the price of a pizza on a weekend. Sadly, because of internal bureaucracy, the network was not used when I left, but I do hope that changed in my absence.

2: Trying to plan for the future like any good engineer.

3: RealityFragments.com, where I focus on more creative and opinionated writing on things that aren’t technology.