another

But what sort of traffic?

See, e.g., Arvind's comments on StumbleUpon visitors:

http://arvindn.livejournal.com/133249.html

To quote the takeaway, "most of the traffic generated by StumbleUpon users to any given site is going to be low quality because the dopamine junkies make 100x more clicks".

That's literally true, but in practice D3 makes for (and is intended as) an effective Protovis replacement. The set of primitive marks in Protovis is close to that of SVG, in any case.

These libraries' primary author (Mike Bostok) views D3 as the successor to protovis:

https://groups.google.com/d/msg/d3-js/UAEmdL4XArw/CfB4S-60u9...

In particular:

"I would recommend switching to D3. I have no current plans to release a new Protovis version."

I'm sorry, but there is a qualitative difference between, eg, a Java application running on a modern JVM and a Python application executing through the CPython runtime, neither of which are toys.

(Although it's true that CPython is one of the few extant members of a dying breed.)

The connotations of "compiled" and "interpreted" might have plenty to do with vague expectations, but the words still mean something in the context of language implementation: even though it is reasonable to call the situations you list "compilation", it would not be accurate to call a language that was compiled to LLVM IR, then interpreted with lli, "compiled".

I'm not sure of your needs, but there's now reasonable support for efficient interaction with numpy arrays; see, eg

http://docs.cython.org/src/tutorial/numpy.html

and/or

http://conference.scipy.org/proceedings/SciPy2009/paper_2/fu...

Some types of array addressing will take the slower---but still correct---invocation path through Python, but the common form is compiled to direct (albeit strided) array access.

Unfortunately untrue.

Many military robots are purely remote controlled, but many others integrate often substantial levels of autonomy. Most UAVs provide autonomous navigation and stationkeeping, for example, and often much more; other military robots operate with no human in the loop whatsoever (eg, AUVs).

It's also interesting to consider the autonomy of fast-react weapons-in-the-loop systems such as AA/SAM sites and ABM installations (eg, Patriot), and the contribution of that autonomy to associated friendly-fire incidents. Methods normally considered AI---probabilistic filters, reasoning, and data fusion, in particular---are fundamental to such systems.

... and as someone whose research involves both SAT and statistical learning, I'd agree: putting a variety of specialties under the AI umbrella can make it easier to connect different areas in useful ways.

I understand the sentiment... but completely disagree.

_Rainbows End_ is less fun than his other novels, but it deals directly with near-future issues in a way that only a few other SF authors are doing well (Stross, Stephenson, recent Gibson, and who else?). It changed how I think---much more than did, eg, _A Deepness in the Sky_. In terms of relevance to the times, I'd rank it next to _True Names_.

Thanks, enjoyed your response.

Your "tackling nastier problems" point is well-taken, and one reason I have a great deal of sympathy for work in these research areas.

I think that you're painting an overly-rosy picture of the benchmark situation, but you're right in that I was likely too critical. The attention paid to synthetic fitness landscapes, for example, is also a positive sign that that many researchers do care about issues of comparison and analysis.

(That said, I obviously believe that the field can and must do much better---and, since it relies so heavily on empirical evidence, its need for consistent methodology is somewhat higher.)

Absolutely---it's a matter of degree, and judgments thereof are bound to be subjective. That said, I'd still claim that the EC community, broadly taken, appears particularly prone to tribalism.

I'm far from a zealot on the "theoretical foundation" question, and it's possible to point to fields that have been, perhaps, even harmed by their possession of such a foundation (eg, reinforcement learning). That said, you need at least one or the other---theory or methodology. You mention DPB, and it's a good example: a lot of value was gained from one line of research centered on that single benchmark, despite its flaws. Pull together a curated suite of such problems, release a common implementation, get it widely used, and the field would benefit immensely. Some equivalent of Caruana's grand comparison, from the supervised ML world, could also do much good.

In any case, it's an interesting topic, but we could go back and forth on it forever. GAs and their progeny (including ACO and NEAT) are fun to consider and lend themselves to accessible explanations, so they get a fair amount of press. Whether or not that's justified is something we'll likely disagree on---but I wanted to push back a bit against the implication, intended or not, that they're where the action is in AI today.

To disagree on a side issue, it's been a long time since NIPS dealt much with classic ANNs (which never had much to do with human brains, in any case). Most of the action there, as in AI and ML at large these days---eg, the focus also at ICML and at other venues---is in statistical methods.

(On the other hand, neuroscience and explicitly biological neural modeling are exciting areas, reasonably well-represented at NIPS. Those topics, however, are almost entirely different from neural networks of the multilayer perceptron / [Hyper]NEAT varieties.)

But your criticism of the article seems accurate. ACO isn't new, and there's little evidence that it will solve any of the major outstanding problems in AI.

Less generously, however, I'd suggest that much of the research related to the family of population-based stochastic search methods, ACO, PSO, and HyperNEAT included, is prone to the same risk: the lack of a field-wide theoretical foundation, coupled with the absence of a field-wide standard methodology and benchmark set for empirical comparison (as opposed to, say, the situation in supervised learning), makes it temptingly easy for a particular researcher to believe too strongly in the capabilities of that researcher's pet algorithm. This situation seems to have balkanized the field (page through a recent GECCO proceedings, for example), and holds back wider progress.

That's not to say that HyperNEAT can't do great things. It's a fun approach, and Stanley et al are running far with it. But your boosterism of it, and the boosterism of ACO that you're objecting to, seem closely related.

(For contrast, I'd suggest, eg, the natural gradient work at IDSIA. It's unlikely to be the ultimate method, but may be a good model for solid research in this area.)

... or they acquired a PhD.

(in other words, exactly what you said)

And another review in The Guardian:

http://www.guardian.co.uk/film/2010/apr/22/agora-review

Good luck finding it in theatres, of course.

Bolden's speech yesterday is (IMO) worth reading, especially the middle section regarding Constellation:

http://www.nasa.gov/pdf/434022main_bolden_WSBR_20100316.pdf

Note also the $6B increase in NASA's budget.

(I made a similar comment regarding a previous article along these lines. The same point applies.)

Even though the "United States gives up on space" narrative is temptingly simple, it's also (in my opinion) simply wrong, and substance-free commentary like this is disappointing. (If you base your opinions on the unsupported statements of old astronauts, you can look at Buzz Aldrin's comments for a different perspective, but I'd recommend doing your own research instead.)

Much of the gloomy commentary on this announcement is uninformed. Canceling the Constellation program is a forward step and an essential step---it makes possible the very dream, bold human spaceflight, that this article mourns. At the very least, read the Augustine Commission's summary report; it's only 12 pages. Note their statement of the goal of space exploration: "human expansion into the solar system". That's cause for excitement, not mourning.

Much of the gloomy commentary on this announcement, Cringely's included, is uninformed. (And statements like "NASA has had zero success in relying on outsourced systems" are just bizarre.) Canceling the Constellation program is a positive step, and an essential one---it makes possible the very dream, bold human spaceflight, that Cringely seems to be mourning. At the very least, read the Augustine Commission's summary report; it's only 12 pages. Note their statement of the goal of space exploration: "human expansion into the solar system". That's cause for excitement, not mourning.