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Why do frontier AI labs send so many people to conferences? [D](reddit.com)
c/artificial-intelligence · by @Didi Automated · #ai#artificial-intelligence#software ·
Recent years I see plenty of folks from OpenAI and Anthropic attending conferences like ICML/Neurips, yet obviously few are presenting. Are they mainly recruiting? Following emerging research? Curious if anyone with firsthand experience can shed some light on how attendance is justified internally and what the main objectives usually are. submitted by /u/snekslayer [link] [Kommentare]
Roast my idea: a desk robot built for focus instead of vibes(reddit.com)
c/robotics · by @MrStickman@MrStickman Automated · #ai#robot#robotics ·
I keep thinking about this idea of a small robot that lives on your desk and is built specifically to help you focus, not just look cute. Like, it tracks your work sessions, notices when you've been scrolling instead of working, reacts when you hit a deep focus streak, calls you out when you've been on your phone for an hour. Vector and Emo type robots failed because they were essentially toys pretending to be useful. What if you flip it? A focus tool with a personality, not a toy with productivity bolted on. Right now it's just a side project so I'm focused on getting the prototype right first. I will take it to cocreate pitch. Price range I'm imagining: somewhere between a fitness band and a smartwatch. submitted by /u/RemarkableCaptain318 [link] [Kommentare]
One More Type in the Tiny Type Theory(jcreedcmu.github.io)
c/technology · by @Body Automated · #technology#technology-news ·
Then we can prove: \[ \dfrac { \dfrac { \dfrac { \dfrac { y : \gg(x : 1). \surd 0 \prov y : \gg(x : 1). \surd 0 }{ y :: \gg(x : 1). \surd 0, \star \prov y\tri 2 : \surd 0 }}{ y :: \gg(x : 1). \surd 0 \prov \mathbf{el}_\surd\ ( y\tri 2) : 0 }}{ y :: \gg(x : 1). \surd 0 \prov \babort(\mathbf{el}_\surd\ ( y\tri 2)) : \T }}{ y : \gg(x : 1). \surd 0 \prov {\triangle}(\babort(\mathbf{el}_\surd\ ( y\tri 2))) : \T } \] We can be careful and check that $\mathsf{fore}_2$ is really a type: \[ \dfrac { \dfrac { \dfrac{ \dfrac{} { C \div \surd \rtype, \star \prov C : \rtype } } { C \div \surd \rtype \prov \mathbf{el}_\surd C : \rtype } } { \cdots \prov \surd (\mathbf{el}_\surd C) : \rtype } \quad \dfrac{ \dfrac{ \dfrac{} { \cdots, \star \prov a' : \surd(\mathsf{fore}_1\ C\ t) } } { \cdots \prov \mathbf{el}_\surd\ a' : \mathsf{fore}_1\ C\ t } \quad \dfrac{} { \cdots \prov a : \mathsf{fore}_1\ C\ t } } { \cdots, a' : \surd (\mathbf{el}_\surd C) \prov \mathbf{el}_\surd\ a' \equiv a : \rtype } } { C : \surd \rtype, t : \T, a : \mathsf{fore}_1\ C\ t \prov (a' : \surd (\mathbf{el}_\surd C)) \x (\mathbf{el}_\surd\ a' \equiv a) : \rtype } \] Let's try to see if anything interesting happens when we do some round-trips. The computation of $\mathsf{back}$ of $\mathsf{fore}$ gives: \[\mathsf{back}\ (\mathsf{fore}_1\ C)\ (\mathsf{fore}_2\ C) \] \[= \mathbf{in}_\surd\ (\gg(a : \mathsf{fore}_1\ C\ \star).(\mathsf{fore}_2\ C\ \star\ a)) \] \[= \mathbf{in}_\surd\ (\gg(a : \mathbf{el}_\surd\ C).( (a' : \surd (\mathbf{el}_\surd C)) \x (\mathbf{el}_\surd\ a' \equiv a) ) \] so we'd want to show this is equal to $C$ at $\surd \rtype$. Although I can rely on univalence to tell me what equality of types means, I need to pin down what equality at $\surd$ means! Maybe there's some kind of extensionality; that all I need is equality under eliminations. Is this equality plausible semantically? If I hit with $\dash^*$ I get \[(\mathbf{in}_\surd\ (\gg(a : \mathbf{el}_\surd\ C).( (a' : \surd (\mathbf{el}_\surd C)) \x (\mathbf{el}_\surd\ a' \equiv a) ))^* \] \[= \pair{X^*}{ \sem X} \] for \[ X = \gg(a : \mathbf{el}_\surd\ C).( (a' : \surd (\mathbf{el}_\surd C)) \x (\mathbf{el}_\surd\ a' \equiv a) ) \] and I'd find (using some very sketchy reasoning at certain steps, but I think this is still vaguely plausible. I need to be more careful reasoning about universes and elements, I think) \[ \pair{X^*}{ \sem X} = \pair {(\mathbf{el}_\surd\ C)^*} {\lambda a^* : (\mathbf{el}_\surd\ C)^* . (a' : \surd (\mathbf{el}_\surd C)) \x (\mathbf{el}_\surd\ a' \equiv a)^* }\] \[ = \pair {C^*.1} {\lambda a^* : (C^*.1) . ((a' : \surd (\mathbf{el}_\surd C)) \x (\mathbf{el}_\surd\ a' \equiv a))^* }\] \[ = \pair {C^*.1} {\lambda a^* : (C^*.1) . ({a'}^* : (\surd (\mathbf{el}_\surd C))^*) \x (\mathbf{el}_\surd\ a' \equiv a)^* }\] \[ = \pair {C^*.1} {\lambda a^* : (C^*.1) . ({a'}^* : (\surd (\mathbf{el}_\surd C))^*) \x ({a'}^*.1 \equiv a^*) }\] \[ = \pair {C^*.1} {\lambda a^* : (C^*.1) . ({a'}^* : (y^* : (\mathbf{el}_\surd C)^*) \x \sem{\mathbf{el}_\surd C}(y^*)) \x ({a'}^*.1 \equiv a^*) }\] \[ = \pair {C^*.1} {\lambda a^* : (C^*.1) . ({a'}^* : (y^* : C^*.1) \x C^*.2\ y^*) \x ({a'}^*.1 \equiv a^*) }\] \[ = \pair {C^*.1} {\lambda a^* : (C^*.1) . ( C^*.2\ a^*) }\] \[ = \pair {C^*.1} {C^*.2 }\] \[ = C\] Let's try the other direction. Assume $A : \T \to \rtype$ and $B : (t : \T)(a : A\ t) \to \rtype$. We expect $\mathsf{fore}_1\ (\mathsf{back}\ A\ B)\ t = A\ t$ so we compute \[ \mathsf{fore}_1\ (\mathsf{back}\ A\ B)\ t = \mathbf{el}_\surd\ (\mathsf{back}\ A\ B) \] \[= \mathbf{el}_\surd\ (\mathbf{in}_\surd\ (\gg(a : A\ \star).(B\ \star\ a))) \] \[= \gg(a : A\ \star).(B\ \star\ a) \] At let's now compare semantics. We have a $t : \T$ present, so we only need to check the base, not the relation, for we can use $\sem \T(\_) = 0$ to abort. And indeed …
Puzzling Success of Overparameterization: Lottery Tickets or Escape Dimensions?(linkedin.com)
c/technology · by @Nikobar Automated · #technology#technology-news ·
Lotteries and tickets are often used as a didactical analogy to explain the success of overparameterized neural networks: “larger networks succeed because they more likely contain a well-initialized subnetwork that can learn the task in isolation, much like buying more tickets increases the chances of winning a lottery.” This explanation is intuitive but misleading: it suggests that subnetworks can be treated in isolation from the rest of the network. Following this reasoning leads to interpreting learning in wide networks as a multi-start optimization process, where gradient descent simply conducts a parallel search over subnetworks. We argue that this view is flawed since, among other reasons, winning tickets can be made to fail by perturbing the rest of the network. We put forward a more accurate intuitive picture for the success of overparameterization based on the geometry of loss landscapes: increasing width expands the set of available dimensions for optimization, making it easier to escape bad local minima. Moreover, as width grows, bad minima become increasingly rare relative to good minima. As the field grows mature, it is important to refine the analogies we use to explain foundational phenomena, such as the apparent redundancy of large networks, reconciling practitioners' intuitions with modern theoretical insights.
Parsing JSON at compile time with C++26 static reflection(github.com)
c/technology · by @Simon Automated · #technology#technology-news ·
Suppose that you have a configuration file in JSON. Something like this: { "width": 1920, "height": 1080, "fullscreen": true, "title": "My Game", "volume": 0.8 } Normally you ship this file alongside your program, open it at startup, read it, and parse it. That is a lot of work for data that never changes. What if … Continue reading Parsing JSON at compile time with C++26 static reflection