chronicles

Feb 13

Summarizey the story of invention of Blue LED.

The invention of the blue LED was a significant breakthrough in lighting technology, for which Isamu Akasaki, Hiroshi Amano, and Shuji Nakamura were awarded the Nobel Prize in Physics in 2014. Shuji Nakamura, a Japanese-born American electronic engineer, played a key role in this innovation. He worked at the Nichia Corporation, where he developed a bright blue LED using indium gallium nitride (InGaN) and a low-temperature heating method, significantly increasing its efficiency. This blue LED was nearly 100 times brighter than existing ones at the time, enabling its use in various applications. By combining the bright blue LED with fluorescent material, Nakamura enabled the creation of white light. Additionally, this development led to the production of power-efficient screens used in televisions, computer monitors, and smartphones. The blue LED also paved the way for the invention of the blue laser, which significantly increased storage capacity and led to the creation of new industry standards. Nakamura's invention of the blue LED was a game-changer in the lighting industry, with far-reaching implications for energy-efficient lighting and display technologies. Despite the immense impact of his invention, it is worth noting that Nakamura initially received only 180 for his discovery.

  • https://www.nobelprize.org/prizes/physics/2014/press-release/
  • https://www.invent.org/blog/inventors/shuji-nakamura-blue-led-lights
  • https://en.wikipedia.org/wiki/Shuji_Nakamura

How pigment works ?

Pigments work by selectively absorbing certain wavelengths of light and reflecting or transmitting others, which determines their color. This process is known as subtractive color, and it's what happens with paints and dyes. The absorbed colors are the ones you don't see, and you see only the colors that come bouncing back to your eye. Pigments are insoluble and chemically unreactive substances that are used to add color or change the visual appearance of materials. They can be organic or inorganic, with inorganic pigments being brighter and longer-lasting than organic ones. The color of pigments arises because they absorb only certain wavelengths of visible light, and this absorption is the result of charge transfer between a metal for inorganic pigments, or conjugated double bonds for organic pigments. In summary, pigments work by selectively absorbing and reflecting light to produce the colors we see.

Is there blue pigment in the nature ? And how the blue pigment is different from others ?

Natural blue pigments are rare in nature. The color blue found in foods, plants, and animals often lacks a chemical compound that makes them blue, making natural blue pigment uncommon. For instance, the majority of natural blue colors in food are derived from the purple compound "anthocyanin," which can also produce rich red and black shades in addition to blue and purple. Blue colors in animals are often achieved through the manipulation of light, such as the layered nanostructures on the wing scales of blue-winged butterflies or the light-scattering structures in blue birds. The only known animal to produce a true blue pigment is the Obrina Olivewing butterfly, which uses a bile pigment called biliverdin to achieve its blue color. Therefore, true blue pigments are rare in nature, and the blue color in living organisms is often achieved through structural or optical means rather than through a chemical pigment.

Feb 10

Tweet

Elad Hazan: most exciting paper ever from our @GoogleAI lab at @Princeton : @naman33k @danielsuo @XinyiChen2 https://arxiv.org/abs/2312.06837 Convolutional filters predetermined by the theory, no learning needed!

Yann Lecun: Hi Elad, basically, you are backpedaling to the old days of statistical pattern recognition when the entire literature was devoted to designing fixed feature extractors by exploiting the properties of the signal.
Pretty much every signal classification system (including speech recognition) used to use spectral features of some kind (computed by banks of wavelet-like filters), followed by some sort of non-linearity (e.g. square complex modulus) and temporal pooling.
9 years ago, Stéphane Mallat made a bet with me: he claimed that he could use features from his scattering transform followed by a simple trainable classifier and approach the performance of convnets on ImageNet. The scattering transform architecture looks very much like a convnet (and is inspired by it), except that the kernels are fixed wavelets, whereas in convnets, the kernels are all learned. The prize was a meal at a 3-star restaurant. He admitted defeat after 5 years. He said, "I learned something while trying to understand why it didn't work."

Elad Hazan: Hi Yann, is this is an invitation for another bet? I'll take it, worst case I have to pay for dinner w. you ;-) These particular filters have a nice property: they can learn a symmetric LDS without dependence on the condition number (effective memory). The hope is they can help, but not replace, the learned component. My bet would be that this improves performance, in tasks requiring long memory. But there is no claim that it replaces learning completely.
You would agree that in certain cases, pre-fixed structures can help? That is the case, for example, in adaptive gradient methods for optimization, where a predefined rule is helpful.

  • https://twitter.com/ylecun/status/1756249264500380129
  • https://arxiv.org/abs/2312.06837

Feb 9

MiniCPM

  • 2B/3B
  • Deployment on Mobile Phone, ~10 tokens/s
  • 面壁智能
  • https://github.com/OpenBMB/MiniCPM

compare fine-tuned version v.s. sft version