BlackRock’s Kevin Franklin explains how investors get comfortable with applying these tools to money management.
For all Morningstar videos: http://www.morningstar.com/articles/archive/467/us-videos.html
Category: information science – Page 137
Researchers have finally succeeded in building a long-sought nanoparticle structure, opening the door to new materials with special properties.
Alex Travesset does not have a sparkling research lab stocked with the most cutting-edge instruments for probing new nanomaterials and measuring their unique properties.
Instead of using traditional laboratory instruments, Alex Travesset, a professor of physics and astronomy at Iowa State University and an affiliate of the U.S. Department of Energy’s Ames National Laboratory, relies on computer models, equations, and figures to understand the behavior of new nanomaterials.
In the midst of the Anti AI Art movement and the ever evolving complexity of the algorithms they are rallying against, this video essay discusses current flaws and future potential of AI Translation technology within Retro Game Emulation. Through rigorous testing of 3 games that never got localizations or fan translations (Tokimeki Memorial 2, Sakura Wars 2 & Boku No Natsuyasami 2), we will see how well Retroarch and ZTranslate’s AI Translator works for the average player. We will also discuss the ways in which this technology could one day be used in more formal localisations by professional teams, and wel will come to understand the nuances of the AI debate.
#AI #FanTranslation #Emulation.
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CHAPTERS:
00:00 — Introduction.
02:09 — Short Interlude.
02:45 — The Monolingual Dilemma.
03:31 — The Arrival of a Solution.
05:40 — Over Analyzing Someone Else’s Tweet.
09:32 — Preparing the Test.
11:04 — Test 1: Tokimeki Memorial 2
17:23 — Test 2: Sakura Wars 2
23:13 — Test 3: Boku No Natsuyasami 2
30:29 — Dejection & Regret.
32:05 — What I Learnt.
37:26 — Outro / Special Thanks.
KEYWORDS:
In particle physics, particles are constantly interacting and interfering with all the other kinds of particles, but the strength of those interactions depend on the particle masses. So, when we try to evaluate anything involving the Higgs boson – like, say, its ability to maintain the separation between the electromagnetic and weak nuclear forces – we also need to pay attention to how the other particles will interfere with that effort. And since the top quark is handily the biggest of the bunch (the next largest, the bottom quark, weighs a mere 5 GeV) it’s essentially the only other particle we need to care about.
When physicists first calculated the stability of the universe, as determined by the Higgs boson’s ability to maintain the separation of the electroweak force, they didn’t know the mass of either the Higgs itself or the top quark. Now we do: The top quark weighs around 175 GeV, and the Higgs around 125 GeV.
Plugging those two numbers into the stability equations reveals that the universe is… metastable. This is different than stable, which would mean that there’s no chance of the universe splitting apart instantly, but also different than unstable, which would mean it already happened.
❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers.
❤️ Their mentioned post is available here: http://wandb.me/RLHF-OpenAI
Try #ChatGPT!
https://chat.openai.com/
https://openai.com/blog/chatgpt/
Our earlier paper with the translucent materials:
If you wish to read my latest paper on simulations that look almost like reality, it is available for free here:
https://rdcu.be/cWPfD
Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5
Tweet links:
PROVIDENCE, R.I. [Brown University] — When it comes to predicting disasters brought on by extreme events (think earthquakes, pandemics or “rogue waves” that could destroy coastal structures), computational modeling faces an almost insurmountable challenge: Statistically speaking, these events are so rare that there’s just not enough data on them to use predictive models to accurately forecast when they’ll happen next.
But a team of researchers from Brown University and Massachusetts Institute of Technology say it doesn’t have to be that way.
In a new study in Nature Computational Science, the scientists describe how they combined statistical algorithms — which need less data to make accurate, efficient predictions — with a powerful machine learning technique developed at Brown and trained it to predict scenarios, probabilities and sometimes even the timeline of rare events despite the lack of historical record on them.
AI Art is NOT Theft
Posted in information science, robotics/AI
The term AI Art refers to artwork created by computers and algorithms. AI Art is not theft as it does not involve taking or copying someone else’s work without permission. AI Art is an entirely new form of creativity that involves the use of artificial intelligence to create unique and original works of art.
▼ Link(s) From Today’s Video:
✩ How diffusion models work: https://scale.com/guides/diffusion-models-guide#what-are-diffusion-models?
✩ Anti AI Art Gofundme: https://www.gofundme.com/f/protecting-artists-from-ai-techno…e=customer.
► MattVidPro Website: https://MattVidPro.com.
► MattVidPro Discord: https://discord.gg/bQgcbjs2Sg.
New signal-processing algorithms have been shown to mitigate the impact of turbulence in free-space optical experiments, potentially bringing “free space” internet a step closer to reality.
The team of researchers, from Aston University’s Aston Institute of Photonic Technologies and Glasgow University, used commercially available photonic lanterns, a commercial transponder, and a spatial light modulator to emulate turbulence. By applying a successive interference cancelation digital signal processing algorithm, they achieved record results.
The findings are published in the Journal of Lightwave Technology.
In spin-based quantum processors, each quantum dot of a qubit is populated by exactly one electron, which requires careful tuning of each gate voltage such that it lies inside the charge-stability region (the “Coulomb diamond’‘) associated with the dot array. However, mapping the boundary of a multidimensional Coulomb diamond by traditional dense raster scanning would take years, so the authors develop a sparse acquisition technique that autonomously learns Coulomb-diamond boundaries from a small number of measurements. Here we have hardware-triggered line searches in the gate-voltage space of a silicon quadruple dot, with smart search directions proposed by an active-learning algorithm.
How programmers turned the internet into a paintbrush. DALL-E 2, Midjourney, Imagen, explained.
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Beginning in January 2021, advances in AI research have produced a plethora of deep-learning models capable of generating original images from simple text prompts, effectively extending the human imagination. Researchers at OpenAI, Google, Facebook, and others have developed text-to-image tools that they have not yet released to the public, and similar models have proliferated online in the open-source arena and at smaller companies like Midjourney.
These tools represent a massive cultural shift because they remove the requirement for technical labor from the process of image-making. Instead, they select for creative ideation, skillful use of language, and curatorial taste. The ultimate consequences are difficult to predict, but — like the invention of the camera, and the digital camera thereafter — these algorithms herald a new, democratized form of expression that will commence another explosion in the volume of imagery produced by humans. But, like other automated systems trained on historical data and internet images, they also come with risks that have not been resolved.
The video above is a primer on how we got here, how this technology works, and some of the implications. And for an extended discussion about what this means for human artists, designers, and illustrators, check out this bonus video: https://youtu.be/sFBfrZ-N3G4
Midjourney: www.midjourney.com.