Category Archives: ideas

What drives A.I. development? Better data

This article, Datasets Over Algorithms — Space Machine, makes a good point, namely

…perhaps many major AI breakthroughs have actually been constrained by the availability of high-quality training datasets, and not by algorithmic advances.

Looking at this chart they provide illustrates the point:

I’d argue that it isn’t solely datasets that drive A.I. breakthroughs. Better CPUs, improved storage technology, and of course new ideas can also propel A.I. forward. But if you ask me now, I think A.I. in the future will need better data to make big advances.

The Real Bias Built in at Facebook <- another bad I.T. story in the New York Times (and my criticism of it)

There is so much wrong in this article, The Real Bias Built In at Facebook – The New York Times, that I decided to take it apart in this blog post. (I’ve read  so many bad  IT stories in the Times that I stopped critiquing them after a while, but this one in particular bugged me enough to write something).

To illustrate what I mean by what is wrong with this piece, here’s some excerpts in italics followed by my thoughts in non-italics.

  • First off, there is the use of the word “algorithm” everywhere. That alone is a problem. For an example of why that is bad, see section 2.4 of Paul Ford’s great piece on software,What is Code? As Ford explains: ““Algorithm” is a word writers invoke to sound smart about technology. Journalists tend to talk about “Facebook’s algorithm” or a “Google algorithm,” which is usually inaccurate. They mean “software.” Now part of the problem is that Google and Facebook talk about their algorithms, but really they are talking about their software, which will incorporate many algorithms. For example, Google does it here: https://webmasters.googleblog.com/2011/05/more-guidance-on-building-high-quality.html At least Google talks about algorithms, not algorithm. Either way, talking about algorithms is bad. It’s software, not algorithms, and if you can’t see the difference, that is a good indication you should not be writing think pieces about I.T.
  • Then there is this quote: “Algorithms in human affairs are generally complex computer programs that crunch data and perform computations to optimize outcomes chosen by programmers. Such an algorithm isn’t some pure sifting mechanism, spitting out objective answers in response to scientific calculations. Nor is it a mere reflection of the desires of the programmers. We use these algorithms to explore questions that have no right answer to begin with, so we don’t even have a straightforward way to calibrate or correct them.” What does that even mean? To me, I think it implies any software that is socially oriented (as opposed to say banking software or airline travel software) is imprecise or unpredictable. But at best, that is only slightly true and mainly false. Facebook and Google both want to give you relevant answers. If you start typing in “restaurants” or some other facilities in Google search box, Google will start suggesting answers to you. These answers will very likely to be relevant to you. It is important for Google that this happens, because this is how they make money from advertisers. They have a way of calibrating and correcting this. In fact I am certain they spend a lot of resources making sure you have the correct answer or close to the correct answer. Facebook is the same way. The results you get back are not random. They are designed, built and tested to be relevant to you. The more relevant they are, the more successful these companies are. The responses are generally right ones.
  • If Google shows you these 11 results instead of those 11, or if a hiring algorithm puts this person’s résumé at the top of a file and not that one, who is to definitively say what is correct, and what is wrong?” Actually, Google can say, they just don’t. It’s not in their business interest to explain in detail how their software works. They do explain generally, in order to help people insure their sites stay relevant. (See the link I provided above). But if they provide too much detail, bad sites game their sites and make Google search results worse for everyone. As well, if they provide too much detail, they can make it easier for other search engine sites – yes, they still exist – to compete with them.
  • Without laws of nature to anchor them, algorithms used in such subjective decision making can never be truly neutral, objective or scientific.” This is simply nonsense.
  • Programmers do not, and often cannot, predict what their complex programs will do. “ Also untrue. If this was true, then IBM could not improve Watson to be more accurate. Google could not have their sales reps convince ad buyers that it is worth their money to pay Google to show their ads. Same for Facebook, Twitter, and any web site that is dependent on advertising as a revenue stream.
  • Google’s Internet services are billions of lines of code.” So what? And how is this a measure of complexity?  I’ve seen small amounts of code that was poorly maintained be very hard to understand, and large amounts of code that was well maintained be very simple to understand.
  • Once these algorithms with an enormous number of moving parts are set loose, they then interact with the world, and learn and react. The consequences aren’t easily predictable. Our computational methods are also getting more enigmatic. Machine learning is a rapidly spreading technique that allows computers to independently learn to learn — almost as we do as humans — by churning through the copious disorganized data, including data we generate in digital environments. However, while we now know how to make machines learn, we don’t really know what exact knowledge they have gained. If we did, we wouldn’t need them to learn things themselves: We’d just program the method directly.” This is just a cluster of ideas slammed together, a word sandwich with layers of phrases without saying anything. It makes it sound like AI has been unleashed upon the world and we are helpless to do anything about it. That’s ridiculous. As well, it’s vague enough that it is hard to dispute without talking in detail about how A.I. and machine learning works, but it seems knowledgeable enough that many people think it has greater meaning.
  • With algorithms, we don’t have an engineering breakthrough that’s making life more precise, but billions of semi-savant mini-Frankensteins, often with narrow but deep expertise that we no longer understand, spitting out answers here and there to questions we can’t judge just by numbers, all under the cloak of objectivity and science.” This is just scaremongering.
  • If these algorithms are not scientifically computing answers to questions with objective right answers, what are they doing? Mostly, they “optimize” output to parameters the company chooses, crucially, under conditions also shaped by the company. On Facebook the goal is to maximize the amount of engagement you have with the site and keep the site ad-friendly.You can easily click on “like,” for example, but there is not yet a “this was a challenging but important story” button. This setup, rather than the hidden personal beliefs of programmers, is where the thorny biases creep into algorithms, and that’s why it’s perfectly plausible for Facebook’s work force to be liberal, and yet for the site to be a powerful conduit for conservative ideas as well as conspiracy theories and hoaxes — along with upbeat stories and weighty debates. Indeed, on Facebook, Donald J. Trump fares better than any other candidate, and anti-vaccination theories like those peddled by Mr. Beck easily go viral. The newsfeed algorithm also values comments and sharing. All this suits content designed to generate either a sense of oversize delight or righteous outrage and go viral, hoaxes and conspiracies as well as baby pictures, happy announcements (that can be liked) and important news and discussions.” This is the one thing in the piece that I agreed with, and it points to the real challenge with Facebook’s software. I think the software IS neutral, in that it is not interested in the content per se as it is how the user is responding or not responding to it. What is NOT neutral is the data it is working off of. Facebook’s software is as susceptible to GIGO (garbage in, garbage out) as any other software. So if you have a lot of people on Facebook sending around cat pictures and stupid things some politicians are saying, people are going to respond to it and Facebook’s software is going to respond to that response.
  • Facebook’s own research shows that the choices its algorithm makes can influence people’s mood and even affect elections by shaping turnout. For example, in August 2014, my analysis found that Facebook’s newsfeed algorithm largely buried news of protests over the killing of Michael Brown by a police officer in Ferguson, Mo., probably because the story was certainly not “like”-able and even hard to comment on. Without likes or comments, the algorithm showed Ferguson posts to fewer people, generating even fewer likes in a spiral of algorithmic silence. The story seemed to break through only after many people expressed outrage on the algorithmically unfiltered Twitter platform, finally forcing the news to national prominence.” Also true. Additionally, Facebook got into trouble for the research they did showing their software can manipulate people by….manipulating people in experiments on them! It was dumb, unethical, and possibly illegal.
  • Software giants would like us to believe their algorithms are objective and neutral, so they can avoid responsibility for their enormous power as gatekeepers while maintaining as large an audience as possible.” Well, not exactly. It’s true that Facebook and Twitter are flirting with the notion of becoming more news organizations, but I don’t think they have decided whether or not they should make the leap or not. Mostly what they are focused on are channels that allow them to gain greater audiences for their ads with few if any restrictions.

In short, like many of the IT think pieces I have seen the Times, it is filled with wrong headed generalities and overstatements, in addition to some concrete examples buried somewhere in the piece that likely was thing that generated the idea to write the piece in the first place. Terrible.

Glitches as a design pattern for fabric

The good folks at Glitchaus have taken an oddity of the digital world – glitches – and used it as the basis of their designs of scarves and wraps. If you are in need of either, or you’d just like to see some innovative fashion, it’s worth visiting their site.

Houses aren’t homes: they’re capital

And in the richest cities, like London, they are greatly appreciating capital, as this shows:
Media preview

With some reflection, this makes sense, if you take as a given that:

  •   Stocks and bonds and even wages are fairly stagnant in terms of return on investment
  • Urbanization means homes in cities that are desirable to live in are becoming more scarce

The result is homes becoming one of the forms of capital that can has the means to greatly appreciate in value.

To reverse this will require a greater supply of homes on the market, either through greater density in desirable cities or through more cities becoming desirable to live in. I can see both of these occurring. What I don’t see occurring is other forms of capital becoming more capable of great growth.

It will be interesting to see what happens in 10 years. But right now, bet on homes in key cities to continue to do this.

 

This article about body cameras is asking the wrong questions, which is not surprising, since everyone is.

This article,  Will Body Cameras Work? – The Atlantic, is asking the wrong questions. The wrong questions are occurring because the initial answer to the question of “how do we deal with bad policing?” was often, “body cameras”. The better question to repeatedly ask: “how can we make police more accountable?” because if “body cameras” is the first answer to that question, the next question should be concerning the information captured by those body camera. How are police accountable for that? Which should then lead to another question: how are police accountable for information they capture generally? Because with new technology, police will soon be able to capture alot more information about you than just images. It will soon be possible for police to look at you or your vehicle and have that information feed back to centralized computer systems, essentially collecting information about you without you even knowing it. How will police be accountable for that?

Police accountability will come, likely through the courts. In the meantime, we will likely struggle with the fallout of police forces capturing more information.

If you feel you are stuck in the Procrastination Doom Loop, there’s help (by the Atlantic and yours truly)

Do you ever get stuck in this loop?

If so, then the Atlantic has an article for you. According to this article, The Procrastination Doom Loop—and How to Break It – The Atlantic,

Delaying hard work is all about your mood.

And it goes on to talk about how to defeat this.

Seven additional suggestions I have on defeating this doom loop:

  1. set a regular schedule of tackling difficult tasks and stick with it.
  2. dilute the difficulty by giving yourself a ridiculous amount of time to do it. If it will likely take 20 minutes, schedule 2 hours and just sit there and do nothing else until you get it done.
  3. set up a reward for getting it done.
  4. set up significant negative consequences for not getting it done. You might need help from a friend or coach here.
  5. log the positive feelings and thoughts you feel after you get it done. Review that often.
  6. log the negative feelings and thoughts you have before you do it. After you do it, analyse what you wrote and revisit your thinking and feeling. You will likely find it wasn’t as bad as you had expected.
  7. have a list of things you are procrastinating on. For example, if you have two things you are avoiding, try to avoid doing one of them by doing the other. It’s better to get one thing done than getting none done

This may just be the stupidest defense of Amazon’s workplace practices

This piece may be the stupidest defence of Amazon’s workplace practices: Replace Just 2 Words in the New York Times Amazon Article and Something Amazing Happens | Inc.com.

Amazon employees are not entrepreneurs. There is nothing in the NYTimes.com article that gives any inkling that they are. If anything, they have all the downside of being an entrepreneurs with little if any of the upside.  If someone can point out an article showing how Amazon consistently rewards employees as if they are true entrepreneurs, I’d love to read it.

There’s nothing wrong with being an entrepreneur. In fact, for some people, being an entrepreneur is the best type of work there is. Everything about it appeals to them, and working for a large corporation would kill them.

The Amazon employees are not entrepreneurs.  If you want to be an entrepreneur, be one. Don’t try to be one working at a large corporation. That is antithetical to what being an entrepreneur is.