Convenção de Magos e Bruxas de Paranapiacaba 2022

Beyond Dispute: The Invention of Team Syntegrity

those who received normative explanations felt they hada better understanding of the system, and perceived the systemto have higher capability.

Below are two more samples showing the model taking the same motif on a different spin. This opens up the potential for users to specify their own primer and use the model as a creative tool to explore a range of possible continuations.

I like the quiet it takes to pursue an idea the way I pursued 'Hamilton', but I couldn't write a book, because there's no applause at the end of writing a book.

The mammalian cerebral cortex is a multi-scale biological computing device consisting of billions of neurons, arranged in layered, local circuits. These local circuits are arranged into columns, and groups of columns form an area. Connections between neurons within a local circuit, column and area are both convergent (projections within structures) and divergent (projections between structures). Both feed-forward and feed-back connections are typical, and information may flow over multiple routes to reach the same target. The result is a hierarchical, parallel, highly interconnected network of areas that tile the cerebral cortex.

Stan Lee, in his heyday, did something wildly and radically different. And as far as I'm concerned, his vacant throne will remain empty until we come up with someone who has the guts and imagination to do the same.

Jack sees his life as a rich picture depicting an epic story and assumes that the key to his happiness lies in the broad components of the image. But this is a mistake, because Jack doesn’t live in the picture’s broad strokes, he lives at all times in a single pixel of the image—a single Today.

Have multiple formulations: One of the most common mistakes made by researchers is to hold on very closely to a particular problem formulation. They will stick closely to a particular formulation of a problem, without asking if they can achieve insights on related problems. The important thing is to be able to make some progress: if you can find a related problem, or reformulate a problem in a way that permits you to move forward, that is progress.

In my opinion, there is little that is more important in research than building forward momentum. Being clear about some goal, even if that goal is the wrong goal, or the clarity is illusory, is tremendously powerful. For the most part, it’s better to be doing something, rather than nothing, provided, of course, that you set time aside frequently for reflection and reconsideration of your goals. Much of the time in research is spent in a fog, and taking the time to set clear goals can really help lift the fog.

How Machines *Really* Learn. [Footnote]

We’d like to be able to tell the difference. Just as we must seek a more fluid ground in ethics (neither pure deontology nor pure consequentialism), we need more fluid approaches for our open-ended work.

We can’t build cathedrals for every book.

Of course, spaced repetition is just one approach for writing timeful texts. What other powerful tools might it be possible to create, making future books into artifacts that transcend their pages, as they slowly help readers shape their lives?

Consider The Elements of Style,a classic writing primer by Strunk and White.The authors demonstrate the value of parallel construction with this example from the Bible: “Blessed are the poor in spirit: for theirs is the kingdom of heaven. Blessed are they that mourn: for they shall be comforted. Blessed are the meek: for they shall inherit the earth.” But it’s not enough to read an example. Good writers’ ears become automatically alert to these constructions. They notice opportunities to create parallelisms, and they notice dissonance when similar phrases are presented differently. It takes time to develop this awareness.

A fertilized egg that can transform itself into the myriad of specializations needed to make a complex organism has parsimony, generality, enlightenment, and finesse—in short, beauty, and a beauty much more in line with my own esthetics. I mean by this that Nature is wonderful both at elegance and practicality—the cell membrane is partly there to allow useful evolutionary kludges to do their necessary work and still be able act as component by presenting a uniform interface to the world.

this encounter finally hit me with what the destiny of personal computing really was going to be. Not a personal dynamic vehicle, as in Engelbart's metaphor opposed to the IBM "railroads", but something much more profound: a personal dynamic medium. With a vehicle one could wait until high school and give "drivers ed", but if it was a medium, it had to extend into the world of childhood.

Authorization Code Flow

Also remarkable, consider that when we add the numbers 72 and 83 we at some point will likely use 2+3=5; similarly, when we add 27 and 38 we will also use 2+3=5, despite the fact that the meaning of 2 and 3 in the second sum is completely different than in the first sum. In modern user interface terms, the numerals have the same affordances, despite their meaning being very different in the two cases.

So if a fly lands on your arm, here’s what happens: The fly touches your skin and stimulates a bunch of sensory nerves. The axon terminals in the nerves have a little fit and start action potential-ing, sending the signal up to the brain to tell on the fly. The signals head into the spinal cord and up to the somas in the somatosensory cortex.18 The somatosensory cortex then taps the motor cortex on the shoulder and tells it that there’s a fly on your arm and that it needs to deal with it (lazy). The particular somas in your motor cortex that connect to the muscles in your arm then start action potential-ing, sending the signals back into the spinal cord and then out to the muscles of the arm. The axon terminals at the end of those neurons stimulate your arm muscles, which constrict to shake your arm to get the fly off (by now the fly has already thrown up on your arm), and the fly (whose nervous system now goes through its own whole thing) flies off.

‘Now, it’s very important to try to remember this image multisensorily.’ The more associative hooks a new piece of information has, the more securely it gets embedded into the network of things you already know, and the more likely it is to remain in memory.

AVs are interesting

The lens through which we view trends matters. If you recognize Software 2.0 as a new and emerging programming paradigm instead of simply treating neural networks as a pretty good classifier in the class of machine learning techniques, the extrapolations become more obvious, and it’s clear that there is much more work to do.

Computationally homogeneous. A typical neural network is, to the first order, made up of a sandwich of only two operations: matrix multiplication and thresholding at zero (ReLU).

Extend the experiments section of a paper you like. Read the methods and results carefully. Try to find the duct tape. Consider the simplest extensions first and ask whether the paper's method would suffice. Think about baseline methods not discussed and imagine where those might fall short.

It's about having a sharp and clear perception of what is.  If the event that we're perceiving is negative, like a social or political injustice, then yes, we also need to experience that, for what it is. But it's harder to see what is good in life, and in people.  Our culture gives all too much encouragement to disapprove, complain and reject.

When a friend of mine used to grumble because he had to write a paper for school, his mother would tell him: find a way to make it interesting. That's what you need to do: find a question that makes the world interesting. People who do great things look at the same world everyone else does, but notice some odd detail that's compellingly mysterious.

It's not so important what you work on, so long as you're not wasting your time. Work on things that interest you and increase your options, and worry later about which you'll take.

It is only with a combination of both rigorous formalism and good intuition that one can tackle complex mathematical problems; one needs the former to correctly deal with the fine details, and the latter to correctly deal with the big picture. Without one or the other, you will spend a lot of time blundering around in the dark (which can be instructive, but is highly inefficient). So once you are fully comfortable with rigorous mathematical thinking, you should revisit your intuitions on the subject and use your new thinking skills to test and refine these intuitions rather than discard them. One way to do this is to ask yourself dumb questions; another is to relearn your field.

In many ways, starting my own company has given me the sort of freedom which academics aspire to. Sure, I have customers to assist, servers to manage (not that they need much management), and business accounting to do; but professors equally have classes to teach, students to supervise, and committees to attend. When it comes to research, I can follow my interests without regard to the whims of granting agencies and tenure and promotion committees: I can do work like scrypt, which is now widely known but languished in obscurity for several years after I published it; and equally I can do work like kivaloo, which has been essentially ignored for close to a decade, with no sign of that ever changing.

Prioritize Downtime

keep your word to yourself by committing to daily or weekly reviews of your progress. Check in on your scorecard and assess why you may be falling short of the goals you’ve set. If you’re exceeding them with ease, it may be time to push yourself further.

While you can attempt to maximize your lagging measure (i.e. number of blog posts published) it may be more valuable to optimize for your lead measure (i.e. number of hours spent writing in deep work).

Direct your effort to your most important goals during your deep work hours. Keep your biggest objectives at the forefront of your mind to make it easier to ignore distractions that don’t serve your long term goals. Try using sticky notes on your desk that list out your top priorities so you don’t forget them.

In doing so, they find that middle- and working-class individuals per-ceive themselves differently in relation to dominant institutions and also possess differ-ent strategies for navigating those settings

I view culture as a “tool kit” that includes both “strategies of action” (Swidler 1986) and “log-ics of action” (DiMaggio 1997).

But the easiest and most effective way to thin out the fog is simply to be aware of it.

In a Whoa moment’s transcendent level of consciousness, I see every interaction, every motivation, every news headline in unusual clarity—and difficult life decisions are much more obvious. I feel wise.

The fog lines up a row of carrots, tells us that they’re the key to happiness, and tells us to forget today’s happiness in favor of directing all of our hope to all the happiness the future will hold because we’re gonna get those carrots.

So while thousands of Jack’s Todays will, to an outsider from far away, begin to look like a complete picture, Jack spends each moment of his actual reality in one unremarkable Today pixel or another. Jack’s error is brushing off his mundane Wednesday and focusing entirely on the big picture, when in fact the mundane Wednesday is the experience of his actual life.

Cross-Modal Deep Clustering (XDC)

DeepCluster iteratively clusters deep features from a single-modality encoder, and then uses the cluster assignments totrain the same encoder to improve its representation.

- k-means cluster features (assign "pseudo-labels")

- train encoder on pseudo-labels

However, if we introduce the hard negativesafter the initial optimization with easy negatives only (in our case between 40th and 50th epoch),fine-tuning using some harder negatives yields better results in terms of both AVTS accuracy

The functiong(fa(a(n)),fv(v(n)))is then responsible to fuse the feature information fromboth modalities to address the synchronization task

inour work we train on negative samples that are “hard,” i.e., represent out-of-sync audio and visualsegments sampled from the same video. This forces the net to learn relevant temporal-sensitivefeatures for both the audio and the video stream in order to recognizesynchronizationas opposed toonly semantic correspondence.

approaches to depression assessment from NLP, speech processing, and HCI tend to silo bysubfield, with little discussion about the utility of combining promising approaches

hypothesis.el

annotating