This kind of activity spurs 'The Dawn of $1B ICO's' https://hackernoon.com/the-dawn-of-the-1b-ico-a0486f6587a2
61 karma · joined November 19, 2016
- Adapt. Even though it seems like we're all in a boat thats capsized and the stove is above your head along with the sing while the cups are floating in the water and in the dark, there's now new opportunity across the board.
- Make an observation on a "product" and then take that "product" off the table of a competitor and do it better. These are the rules. In 2020 the "product" could be this new incoming administration.
A variety of engineers and QA testers are needed for projects related to machine learning, AI, NLP and vector space approaches to analyzing user intent via bots e.g. https://slack.com/apps/A2B487WT0-sumbot https://slack.com/apps/A26G72726-quantbot
Languages include: Python, js, and Tcl.
If interested in applying please contact cymetica@gmail.com
1. You create a hypothesis
2. Prove your theory
3. Publish your theory (and maybe discovery)
Most of the time it ends here. It should continue this way:
4. Practically apply your theory
5. Create a product or widget founded on your theory
6. Put this product into the hands of consumers
7. Make observations and watch how customers/consumers use it in ways you could never predict
8. Use this data to go back and refine your original theory and start the cycle over again.
https://www.kaggle.com/c/word2vec-nlp-tutorial/forums/t/1234...
"That interim also saw dedicated attempts on the part of Google’s competitors to catch up. (As Le told me about his close collaboration with Tomas Mikolov, he kept repeating Mikolov’s name over and over, in an incantatory way that sounded poignant."
"Just as the chip-design process was nearly complete, Le and two colleagues finally demonstrated that neural networks might be configured to handle the structure of language. He drew upon an idea, called “word embeddings,” that had been around for more than 10 years. When you summarize images, you can divine a picture of what each stage of the summary looks like — an edge, a circle, etc. When you summarize language in a similar way, you essentially produce multidimensional maps of the distances, based on common usage, between one word and every single other word in the language. The machine is not “analyzing” the data the way that we might, with linguistic rules that identify some of them as nouns and others as verbs. Instead, it is shifting and twisting and warping the words around in the map. In two dimensions, you cannot make this map useful. You want, for example, “cat” to be in the rough vicinity of “dog,” but you also want “cat” to be near “tail” and near “supercilious” and near “meme,” because you want to try to capture all of the different relationships — both strong and weak — that the word “cat” has to other words. It can be related to all these other words simultaneously only if it is related to each of them in a different dimension. You can’t easily make a 160,000-dimensional map, but it turns out you can represent a language pretty well in a mere thousand or so dimensions — in other words, a universe in which each word is designated by a list of a thousand numbers. Le gave me a good-natured hard time for my continual requests for a mental picture of these maps. “Gideon,” he would say, with the blunt regular demurral of Bartleby, “I do not generally like trying to visualize thousand-dimensional vectors in three-dimensional space.”
A senior software developer understands what it means to scale and align software engineers, as a unit or team. This along with understanding that the code is not what matters but rather what the algorithmic design, innovation, invention or discovery that can be done with a particular language or code (or notation in mathematics or music).