Tweetovi
- Tweetovi, trenutna stranica.
- Tweetovi i odgovori
- Medijski sadržaj
Blokirali ste korisnika/cu @EmilWallner
Jeste li sigurni da želite vidjeti te tweetove? Time nećete deblokirati korisnika/cu @EmilWallner
-
Prikvačeni tweet
I’m thrilled to share my latest deep learning project: Turning a design mockup into code.
1) Give the trained neural network a design image
2) The network converts the image into HTML markup and renders it ↴
Article: http://bit.ly/2FoqJQy
Code: http://bit.ly/2mkkWDZ pic.twitter.com/dF0nU6O0iDPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Emil Wallner proslijedio/la je Tweet
My Interview with The Creator of DeOldify, Jason Antic
@citnaj is live: We talk all about@fastdotai, NoGANs, ML and Software Engineering along with Jason's journey with DeOldify. Audio: https://anchor.fm/chaitimedatascience/episodes/Interview-with-Jason-Antic--DeOldify--Fast-ai--NoGAN--Machine-Learning--Software-Engineering-eaj1t4 … Video:https://www.youtube.com/watch?v=A5Cq8SWudts …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Emil Wallner proslijedio/la je Tweet
Emil’s Story as a Self-Taught AI Researcher An interview with
@EmilWallner with useful tips on structuring a curriculum, creating a portfolio, getting involved in research, and finding a job.
https://blog.floydhub.com/emils-story-as-a-self-taught-ai-researcher/ …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Emil Wallner proslijedio/la je Tweet
“At large companies, less than a few hires are self-taught in ML. Of those, most don’t come through the classic hiring channels. Due to the volume of applicants a large company faces, it’s hard for them to adjust to portfolio-centric hiring.”
@EmilWallner https://blog.floydhub.com/emils-story-as-a-self-taught-ai-researcher/ …https://twitter.com/EmilWallner/status/1219327982864609280 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
How to get into machine learning without a degree, alternatives to traditional education, building a portfolio, applying for ML jobs, interviews, getting into research, and indie research ideas. I had an ace chat with
@GGozzoli at@FloydHub_ . Enjoy!https://blog.floydhub.com/emils-story-as-a-self-taught-ai-researcher/ …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Emil Wallner proslijedio/la je Tweet
Some personal news: I moved to San Francisco and joined the OpenAI Fellows Program! Super excited to learn from all the amazing people here. It's a dream come true to be able to do machine learning full-time!pic.twitter.com/8Q07ComGUy
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Emil Wallner proslijedio/la je Tweet
After getting published in ICLR as an Independent Researcher, I have received nearly 100 messages from others who are looking to do the same. So I wrote a blog post on why I decided to do it and my advice to others.https://medium.com/@andreas_madsen/becoming-an-independent-researcher-and-getting-published-in-iclr-with-spotlight-c93ef0b39b8b?source=friends_link&sk=d930cb061089bb4428f1e6ef06958662 …
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
"I’m going to work on artificial general intelligence (AGI) ... For the time being at least, I am going to be going about it “Victorian Gentleman Scientist” style, pursuing my inquiries from home, and drafting my son into the work."https://twitter.com/ID_AA_Carmack/status/1194754916293722114 …
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
'The Measure of Intelligence' contradicts the compute-is-all-we-need narrative, adds historical context, and is accessible to a broad audience. Hats off to
@fchollet for creating clear and actionable suggestions to advance AGI. It's a must-read! https://arxiv.org/pdf/1911.01547.pdf …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The ARC dataset has 400 training tasks and 600 evaluation tasks. Key features: - Only novel tasks in the evaluation set - Highly abstract - Similar to human IQ tests - 3 demonstrations per task - Fixed/limited training data - An explicit set of priorshttps://github.com/fchollet/ARC
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Chollet created a dataset according to the best practice he outlined, the ARC. The dataset mimics the abstraction and reasoning portion in an IQ test (fluid intelligence). Examples:pic.twitter.com/Sh1YRgOfu4
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
To avoid local-generalization systems that artificially "buy" performance on a specific task, Chollet restricts priors to 'Core Knowledge' found in developmental science theory: such as elementary physics, arithmetic, geometry and a basic understanding of intentions.
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Chollet uses Algorithmic Information Theory to quantify the programs and interactions, expressed below. 'The intelligence is the rate at which a learner turns its experience and priors into new skills at valuable tasks that involve uncertainty and adaptation.'pic.twitter.com/kZXmvtIezR
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Below is an overview of skill-acquisition efficiency. A task could be chess, the situation is a board position, and the skill program is a frozen chess engine. Think of the intelligent system as a program synthesis engine for different tasks.pic.twitter.com/dHzhS9D2TQ
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
In section II.2, Chollet formalizes his central idea: "The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty."
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
When humans are tested in different cognitive tests the results correlate with each other. This indicates that we an underlying meta-skill to learn skills, the g-factor. These are the abilities Chollet want to measure in the context of AI, starting with Broad Generalization.pic.twitter.com/QyDND5nBTH
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Chollet thinks our task-centric idea of intelligence is a bottleneck to advance AGI. Instead, we should adopt Hernandez-Orallo take: “AI is the science and engineering of making machines do tasks they have never seen and have not been prepared for beforehand”.
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Similarly, Chollet argues that AlphaZero, DeepMind's program synthesis engine for board games, is not flexible and general. He compares it to a hashtable that uses a locality-sensitive hash function. With unlimited simulation, you can map board positions with actions.pic.twitter.com/LGOOTB9t81
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
IBM's DeepBlue is not intelligent, but we consider humans who master chess intelligent. It's because we associate chess with a meta-skill, an aptitude for logical tasks such as math and reasoning. We often anthropomorphize AI systems in a similar way: task mastery = AGI.
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
In the 1970s, many thought that chess reflected the entire scope of rational human thought. Solving chess with computers would lead to major leaps in cognitive understanding. But after IBM's DeepBlue, they realized they didn't have a better understanding of human thinking.
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
François Chollet’s core point: We can't measure an AI system's adaptability and flexibility by measuring a specific skill. With unlimited data, models memorize decisions. To advance AGI we need to quantify and measure ***skill-acquisition efficiency***. Let’s dig in
pic.twitter.com/Y3GjoG9n4e
Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
Čini se da učitavanje traje već neko vrijeme.
Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.