I tend to not bring up parents and relationships with parents for at least several dates. I discovered to avoid this kin
Chomsky correctly pointed out that ANNs are useful for highly specialised tasks, but these tasks must be sharply constrained (although their scope can appear vast given the memory and speed of modern computers). He compared ANNs to a massive crane working on a high rise building; while certainly impressive, both tools exist in systems with fixed bounds. This line of reasoning is congruent with my observation that all of the deep learning breakthroughs I have witnessed have occurred in very specific domains and we do not appear to be approaching anything like artificial general intelligence (whatever that means). Chomsky also pointed to mounting evidence that ANNs do not accurately model human cognition, which is so comparatively rich that the computational systems involved may even extend to the cellular level.
This theory of a genetically hard-coded language faculty became widely accepted in the scientific community, but the obvious next question was “what does this Universal Grammar actually look like?”. Intrepid researchers soon set out to discover shared properties across all human languages but there remains no consensus on what form our innate linguistic capacities take. It’s safe to assume that Universal Grammar does not consist of concrete syntactic rules, but is more likely to be a fundamental cognitive function. Chomsky has postulated that at some point in our history, humans developed the ability to perform a simple, recursive process called “Merge” and this is responsible for the properties and constraints of the syntactic structures we see within human languages. It’s a little bit abstract (and too involved to address properly here), but essentially “Merge” is the process of taking two objects and combining them to form a new object. While seemingly prosaic, the ability to mentally combine concepts, and to do this recursively, is deceptively powerful and allows us to construct an “infinite variety of hierarchically structured expressions”. Not only may this small but crucial genetic leap forward explain our aptitude for verbal communication, it also follows that it could be responsible (at least in part) for our mathematical talents and human creativity more broadly. This “Merge” mutation that occurred in one of our ancestors ~100k years ago, might be one of the key things that separate humans from other animals.
The ethical use of AI is a salient concern for modern data scientists, but at times this domain can feel vague and subjective in an otherwise concrete field. Not only does Chomsky’s work provide a unique technical perspective on the future of deep learning, Universal Grammar also has profound moral implications since language is how we discuss and interpret the world. For example, Chomsky’s view is that the aforementioned innate neural structures preclude moral relativism and that there must exist universal moral constraints. There are many different flavours of moral relativism, but the core tenet is that there can be no objective basis for ethical determinations. Moral relativists assert that while we might believe deeply in statements such as “slavery is immoral”, we have no empirical way of proving this to somebody who disagrees since any proof will necessarily rely on value judgements and our values are ultimately exogenous and determined by culture and experience.
The primary reason I got in touch with Professor Chomsky, was because I wanted to hear his views on Artificial Neural Networks (a topic I know materially more about than linguistics). ANNs are a subset of machine learning models that are loosely modelled on the human brain and learn in a similar way (by seeing lots of examples). These models require very little hard-coding and can perform quite a broad array of complex tasks (e.g. image tagging, voice recognition, text generation) with relatively simple architectures. An instructive example of this approach is the AlphaGo Zero model developed by Google, which learnt to play the game Go (a complex and challenging board game) and ultimately became unbeatable by human world champions. Most impressively, it was trained to do all of this with no hard-coding or human intervention, that is “tabula rasa”. While ANNs are certainly not a perfect analogy for the human brain, I asked Professor Chomsky whether these models suggest that in fact we do not need hard-coded cognitive structures to learn from scattered data.
This article also puts an end to the myth that we are modeling the human brain. While it’s certainly possible to build interesting applications that outperform humans in certain tasks, it won’t be possible to build better, robot-like, humans anytime soon.
This article also puts an end to the myth that we are modeling the human brain. While it’s certainly possible to build interesting applications that outperform humans in certain tasks, it won’t be possible to build better, robot-like, humans anytime soon.
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Called my sister to ask her to come stay with the kids. Walked home and packed up a bag with a button-down shirt and granola bars, so we’d have something to snack on in the ER and he’d have a shirt to put over his sling, once his shoulder got popped back in. Painful, but no biggie.With mortgage rates at historic lows and hordes of mostly urban, stir-crazy Americans desperate for more space, The Great Housing Boom of 2020 has been one of the hottest pandemic-fueled market trends of the year — and that’s good news for Zillow. In fact, an entire digital community of aspirational home buyers, or Zillow surfers, are flocking to popular home buying sites as a form of escapism, reports Taylor Lorenz of the New York Times. “People bond over listings on Discord servers, group chats, and ‘Zillow Twitter,’” she writes, “and their obsession has made many strange and obscure listings go viral.” It sure beats doom scrolling.
I had a close friend, married to her second husband when I met her. I later learned she’d cheated on her first husband with this man. It didn’t surprise me when she started to cheat on him with someone else. What took me by surprise was just how upset the second husband was. Karma takes no prisoners.
This article explained why it’s worth having a look at Deep Learning and move away from traditional Machine Learning methods, especially when you’re dealing with a lot of data.
There are commonalities between all human languages. This shows that even when languages have developed independently, there are universal features that manifest due to common structures in all human brains.
If Chomsky is right (and for what it’s worth I think he is) what are the implications for deep learning research moving forward? Ultimately there is nothing magical about the human brain. It is simply a physical structure composed of atoms and therefore it is entirely rational to believe that at some point in the future we may be able to create an artificial version that is capable of general intelligence. With that said, current ANNs offer only a simulacrum of this kind of cognition and by Chomsky’s logic, we won’t reach this next frontier without first improving our understanding of how organic neural networks operate.
Zillow Surfing Is the Escape We All Need Right Now Scrolling through real estate listings in far-flung destinations is a way to visualize an alternate life, whether…medium.com
Children are not hard-wired to learn a specific language. If you take a child born in Kenya and raise them in Germany, they will acquire the German language just as easily as a German child.
That’s not just from my own personal experience and from friendship relationship insights. It’s a proven fact. According to a study conducted by the University of Denver, someone who reported cheating during their first relationship was over three times likely to cheat during their subsequent relationship.