Cybiote

I've never met a coherent definition of symbolic AI so I'll just focus on what it contained: some type of search or inference algorithm (iterative deepening, depth first search being major ones) often combined with heuristics written in a programming language like prolog or lisp. Specification + Inference ≊ Specification + Control flow. That means Probabilistic models written in a language like Stan, which is Inference + Specification, fit neatly into so called symbolic AI (which is basically just programming with search/inference).

These search and sampling algorithms still play key roles in game playing AI (chess, poker, Go) and natural language generation. It is the human knowledge, specification and heuristics, part that tends to be more readily replaceable. A lot of control flow and data-structures that powered old AI approaches can be found in databases, compilers, type inference, computer algebra and even the autodiff libraries neural nets are written in.

Video game AI, constraint solving and business rules engines are probably closest to still using the full symbolic approach rather than merely extracting the control flow and structures portion.

We can therefore make a compact prediction: learned approaches replace human written computer programs (specifications, rules systems or heuristics) whenever human contribution is not valuable or is somehow harmful to robustness/generalization.

Sociality is not the only (and probably not an initial) driver of the evolution of intelligence in animals. Ecological and particularly foraging competency in less predictable environments is another and likely more primary stimulator for intelligence.

Coordinating complex movements is another one that appears to drive intelligence.

Vulnerability in a hostile environment. Together with foraging and coordinating complex movement plans, it is hypothesized that predation pressure when combined with the loss of their shell drove cephalopod intelligence. Trading defense for agility and int would also have provided a competitive advantage when competing for prey.

That makes sense but my argument is since further material (some examples which I listed) assumes and builds upon the standard approach, you'll likely be better off taking that path.

By standard approach I mean the typical material covered for someone studying vector calculus properly. This will be stuff like differential forms and the basics of tensors, manifolds and multilinear maps at the undergrad level. Differential geometry and cohomology are examples of courses which build on them.

I agree with you that pseudovectors, cross products and vector calculus are a terribly adhoc way to teach this stuff but a course covering linear algebra with differential forms elegantly unifies, corrects and generalizes them. Standard is also in contrast to the geometric algebra/calculus alternate path.

spekcular is correct. One could draw the following graph very roughly capturing how these concepts developed:

Grassman’s exterior algebra -> Differential forms

Cartan’s Exterior Derivatives -> Differential forms

Quaternions -> Clifford Algebra

exterior algebra -> Clifford Algebra -> geometric algebra

Differential forms are a particular kind of tensor and tensors can be defined in terms of multilinear maps. As spekcular says, the standard curriculum covers differential forms, tensors and vectors. This entails becoming familiar with multivectors, the wedge product and multilinear algebra, making geometric algebra a relatively small delta to pick up.

On the other hand, the standard course will also prepare you for mathematical topics like lie derivatives, differential geometry and de Rham cohomology.

Other than physics, the standard approach equips you with the mathematical machinery underlying many topics in machine learning and statistics like Hamiltonian monte carlo, automatic differentiation, information geometry and geometric deep learning.

The central advantage of geometric algebra over the standard approach isn't that it's better or more general, it's that pedagogical material for it is generally leagues and magnitudes better than those for the standard course.

I've played many of the author's games and consider them to have better stories than most. I’d have to reach for the likes of Planescape or Disco Elysium to find something I’d consider more compelling.

As you imply, there are many games whose brief summary might sound weird, cheesy or cliche but are actually very well crafted story experiences when played. For me, some would be: Life is Strange, Soma, Deus Ex, Human Revolution, Alpha Centauri, Alan Wake, Control, Quantum Break and Detroit Become Human.

Then there are the Soul Reaver games with middling stories but whose written dialogue and delivery easily put them at the level of great literature for me.

Storywise, some of the author's games would not be out of place if ranked highly amongst such a list. It's like Berkson's paradox, his studio has had staying power but you certainly couldn't point to graphics or unique game mechanics as explanations for why.

>This article seems like a self-congratulating piece

I'd go as far as to argue the author undersells his abilities. His RPG world building and stories are a great deal more entertaining if compared to many fantasy books.

> That doesn't mean AAA studios can't make a good game story,

They could but rarely do. Meanwhile, the majority of those few attempts get workshopped to death (it's easy to tell when a game with potential got derailed in this way). The author isn't saying good game stories don't exist, only that they're rare and most often from smaller studios.

I don't think it's a stupid question, just oddly phrased (more on that later). I think there are two stable interpretations. "When did grass evolve" or "when were grass lawns invented".

Later:

Why isn't it an obviously stupid question? Because I think the accompanying "who (or what) invented grass" is validly answered as "by evolution". I feel the act of invention requires no intentionality and is simply the output of learning processes where generated artifacts have material and dynamic properties embodying deep knowledge of physical laws and help achieve some goal relative to an environment. Evolution learns in the sense that the mathematics of natural selection mirror that of bayesian filters.

Certain dolphins have larger prefrontal cortices with more neurons, are highly intelligent with the ability to predict and long term memory yet seem to lack what we think is a unique human capacity for first person introspective meta-cognition. Which is essentially what most and this article mean when talking about the hard problem or qualia.

We can also look at chimpanzees. They have a prefrontal cortex, future prediction and long term memory. While their prefrontal cortex is smaller, it is also the most human like. Yet, they too seemingly lack capacity for that human style of conscious being. There is alas, something more your definition has failed to touch upon.

> No exploding coal plant will render a whole region unlivable for >30 years

Unlivable for humans. The exclusion zone around Chernobyl became a de facto wild-life sanctuary. To the extent that some environmentalists now argue benefits of officially maintaining it as one indefinitely. Human supporting activities such as farming, mining and habitat expansion often come at a serious cost to local ecosystems. Nature ends up flourishing over time in humans excluded regions exactly because they are unlivable for > 30 years.

As pointed out by others, one effect of burning coal is the concentration of its radioactive elements in fly ash. And since less care is taken, more radioactive material ends up released into the environment by coal plants than nuclear plants. It's worth pointing out however, when contrasted with background, risks from exposure to radioactivity is not significantly raised by living near coal plants. The real killer from coal is pollution. Having said that, accumulation of fly ash over time could be a concern, especially wherever it gathered under non-uniform dispersal. As far as I know, this is yet to be shown.

Perhaps a stronger example of society's inconsistent reasoning about radioactivity exposure is inhalation of tobacco smoke. It's curious that information on the significant amounts of radioactive material in tobacco smoke did not percolate widely and probably would not have changed habits anyway, given its known carcinogenic nature was already not enough to do so.