Tuesday, October 30, 2018
Monday, October 29, 2018
New schemes teach the masses to build AI
https://www.economist.com/business/2018/10/27/new-schemes-teach-the-masses-to-build-ai
https://news.ycombinator.com/item?id=18320927
VER THE past five years researchers in artificial intelligence have become the rock stars of the technology world. A branch of AI known as deep learning, which uses neural networks to churn through large volumes of data looking for patterns, has proven so useful that skilled practitioners can command high six-figure salaries to build software for Amazon, Apple, Facebook and Google. The top names can earn over $1m a year.
The standard route into these jobs has been a PhD in computer science from one of America’s elite universities. Earning one takes years and requires a disposition suited to academia, which is rare among more normal folk. Graduate students are regularly lured away from their studies by lucrative jobs.
That is changing. This month fast.ai, an education non-profit based in San Francisco, kicked off the third year of its course in deep learning. Since its inception it has attracted more than 100,000 students, scattered around the globe from India to Nigeria. The course and others like it come with a simple proposition: there is no need to spend years obtaining a PhD in order to practise deep learning. Creating software that learns can be taught as a craft, not as a high intellectual pursuit to be undertaken only in an ivory tower. Fast.ai’s course can be completed in just seven weeks.
Demystifying the subject, to make it accessible to anyone who wants to learn how to build AI software, is the aim of Jeremy Howard, who founded fast.ai with Rachel Thomas, a mathematician. He says school mathematics is sufficient. “No. Greek. Letters,” Mr Howard intones, thumping the table for punctuation.
It is working. A graduate from fast.ai’s first year, Sara Hooker, was hired into Google’s highly competitive AI residency programme after finishing the course, having never worked on deep learning before. She is now a founding member of Google’s new AI research office in Accra, Ghana, the firm’s first in Africa. In Bangalore, some 2,400 people are members of AI Saturdays, which follows the course together as a gigantic study group. Andrei Karpathy, one of deep learning’s foremost practitioners, recommends the course.
Fast.ai’s is not the only alternative AI programme. AI4ALL, another non-profit venture, works to bring AI education to schoolchildren in the United States that would otherwise not have access to it. Andrew Ng, another well-known figure in the field, has started his own online course, deeplearning.ai.
Mr Howard’s ambitions run deeper than loosening the AI labour market. His aim is to spread deep learning into many hands, so that it may be applied in as diverse a set of fields by as diverse a group of people as possible. So far, it has been controlled by a small number of mostly young white men, almost all of whom have been employed by the tech giants. The ambition, says Mr Howard, is for AI training software to become as easy to use and ubiquitous as sending an email on a smartphone.
Some experts worry that this will serve only to create a flood of dodgy AI systems which will be useless at best and dangerous at worst. An analogy may allay those concerns. In the earliest days of the internet, only a select few nerds with specific skills could build applications. Not many people used them. Then the invention of the world wide web led to an explosion of web pages, both good and bad. But it was only by opening up to all that the internet gave birth to online shopping, instant global communications and search. If Mr Howard and others have their way, making the development of AI software easier will bring forth a new crop of fruit of a different kind.
This article appeared in the Business section of the print edition under the headline "Learning, fast and deep"
https://news.ycombinator.com/item?id=18320927
VER THE past five years researchers in artificial intelligence have become the rock stars of the technology world. A branch of AI known as deep learning, which uses neural networks to churn through large volumes of data looking for patterns, has proven so useful that skilled practitioners can command high six-figure salaries to build software for Amazon, Apple, Facebook and Google. The top names can earn over $1m a year.
The standard route into these jobs has been a PhD in computer science from one of America’s elite universities. Earning one takes years and requires a disposition suited to academia, which is rare among more normal folk. Graduate students are regularly lured away from their studies by lucrative jobs.
That is changing. This month fast.ai, an education non-profit based in San Francisco, kicked off the third year of its course in deep learning. Since its inception it has attracted more than 100,000 students, scattered around the globe from India to Nigeria. The course and others like it come with a simple proposition: there is no need to spend years obtaining a PhD in order to practise deep learning. Creating software that learns can be taught as a craft, not as a high intellectual pursuit to be undertaken only in an ivory tower. Fast.ai’s course can be completed in just seven weeks.
Demystifying the subject, to make it accessible to anyone who wants to learn how to build AI software, is the aim of Jeremy Howard, who founded fast.ai with Rachel Thomas, a mathematician. He says school mathematics is sufficient. “No. Greek. Letters,” Mr Howard intones, thumping the table for punctuation.
It is working. A graduate from fast.ai’s first year, Sara Hooker, was hired into Google’s highly competitive AI residency programme after finishing the course, having never worked on deep learning before. She is now a founding member of Google’s new AI research office in Accra, Ghana, the firm’s first in Africa. In Bangalore, some 2,400 people are members of AI Saturdays, which follows the course together as a gigantic study group. Andrei Karpathy, one of deep learning’s foremost practitioners, recommends the course.
Fast.ai’s is not the only alternative AI programme. AI4ALL, another non-profit venture, works to bring AI education to schoolchildren in the United States that would otherwise not have access to it. Andrew Ng, another well-known figure in the field, has started his own online course, deeplearning.ai.
Mr Howard’s ambitions run deeper than loosening the AI labour market. His aim is to spread deep learning into many hands, so that it may be applied in as diverse a set of fields by as diverse a group of people as possible. So far, it has been controlled by a small number of mostly young white men, almost all of whom have been employed by the tech giants. The ambition, says Mr Howard, is for AI training software to become as easy to use and ubiquitous as sending an email on a smartphone.
Some experts worry that this will serve only to create a flood of dodgy AI systems which will be useless at best and dangerous at worst. An analogy may allay those concerns. In the earliest days of the internet, only a select few nerds with specific skills could build applications. Not many people used them. Then the invention of the world wide web led to an explosion of web pages, both good and bad. But it was only by opening up to all that the internet gave birth to online shopping, instant global communications and search. If Mr Howard and others have their way, making the development of AI software easier will bring forth a new crop of fruit of a different kind.
This article appeared in the Business section of the print edition under the headline "Learning, fast and deep"
Sunday, October 28, 2018
ESP-WHO
ESP-WHO is a face detection and recognition platform that is currently based on Espressif Systems' ESP32 chip. ESP-WHO is within Espressif Systems AI framework, with the focus on face detection and recognition so far.
https://github.com/espressif/esp-who
https://github.com/espressif/esp-who
Sunday, October 21, 2018
Three Principles for Successful AI Solutions
https://blog.f-secure.com/three-principles-successful-ai-solutions/
Everyone is talking about Artificial Intelligence, but what’s really going on? Is AI going to solve all your problems? And what even IS AI? What are the key things you need to take into account when building AI solutions? In practice AI is often used as an umbrella term, and most of the solutions we refer to are Machine Learning – and it has been around for some time. For example, the first artificial neural networks emerged in the mid 1900s.
Sure, methods and algorithms have evolved a lot, and perhaps even more significantly computing power has increased drastically, but we have been using and developing machine learning models for quite some time. Even though there have been fantastic recent advances in ease of use through ready toolkits like TensorFlow or manged cloud services like Sagemaker, I do think many of the fundamental things learned through experience over the years still apply, but have sometimes seen some of the fundamentals can get forgotten in all the excitement. I have been working in the field for a while now, and just wanted to share a few principles that I think everyone should keep in mind when designing and building – or evaluating, if you are sitting on that side of the fence – successful AI/ML solutions.
Everyone is talking about Artificial Intelligence, but what’s really going on? Is AI going to solve all your problems? And what even IS AI? What are the key things you need to take into account when building AI solutions? In practice AI is often used as an umbrella term, and most of the solutions we refer to are Machine Learning – and it has been around for some time. For example, the first artificial neural networks emerged in the mid 1900s.
Sure, methods and algorithms have evolved a lot, and perhaps even more significantly computing power has increased drastically, but we have been using and developing machine learning models for quite some time. Even though there have been fantastic recent advances in ease of use through ready toolkits like TensorFlow or manged cloud services like Sagemaker, I do think many of the fundamental things learned through experience over the years still apply, but have sometimes seen some of the fundamentals can get forgotten in all the excitement. I have been working in the field for a while now, and just wanted to share a few principles that I think everyone should keep in mind when designing and building – or evaluating, if you are sitting on that side of the fence – successful AI/ML solutions.
Saturday, October 20, 2018
Raster Vision: A New Open Source Framework for Deep Learning on Satellite and Aerial Imagery
An open source framework for deep learning on satellite and aerial imagery.
https://docs.rastervision.io
https://github.com/azavea/raster-vision
What is Raster Vision?
Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets, including non-georeferenced data like oblique drone imagery. It allows engineers to quickly and repeatably configure experiments that go through core components of a machine learning workflow: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment.The input to a Raster Vision workflow is a set of images and training data, optionally with Areas of Interest (AOIs), that describe where the images are labeled. Running a workflow results in evaluation metrics and a packaged model and configuration that enables easy deployment. Raster Vision also supports running multiple experiments at once to find the best model (or models) to deploy.
Tuesday, October 16, 2018
Monday, October 15, 2018
Travelling Salesman Challenge 2.0
Travelling Salesman Challenge 2.0
https://travellingsalesman.kiwi.com/
Win a trip around the world based on an algorithm you write!
This is your chance to get your hands on something that’s normally
purely theoretical and have some fun with it. The Travelling Salesman
Challenge is returning for 2018, and this time you’re finding the
cheapest route between whole areas.
Monday, October 8, 2018
AI and the News: An Open Challenge
https://aiethicsinitiative.org/challenge
Artificial intelligence — and its subfield of machine learning — is reshaping the landscape of news and information. From the algorithms filtering what we see on social media, to the use of machine learning to generate news stories and online content, AI has and will continue to play a major role in shaping what and how information is distributed and consumed.
As researchers and companies continue to advance the technical state of the art, we believe that it is necessary to ensure that AI serves the public good. This means not only working to address the problems presented by existing AI systems, but articulating what realistic, better alternatives might look like.
This open challenge, which will award up to $750,000 to a range of projects, is seeking fresh and experimental approaches to four specific problems at the intersection of AI and the news:
- Governing the Platforms: Ensuring that AI serves the public good requires the public to know how the platforms are deploying these technologies and how they shape the flow of information through the web today. However, as many others have pointed out, the level of transparency and accountability around these decisions has been limited, and we’re seeking ideas that help to raise it. This might be new policies in the form of draft legislation, or technical tools that help keep an eye on the information ecosystem.
- Stopping Bad Actors: AI might be applied by a variety of actors to spread disinformation, from powering believable bots on social media to fabricating realistic video and audio. This exacerbates a range of existing problems in news and information. We’re seeking approaches we can take to detect and counter this next generation of propaganda.
- Empowering Journalism: Journalists play a major role in shaping public understanding of AI, its impact on the information ecosystem, and what we should do to ensure the technology is used ethically. But it can be hard to keep up with the latest developments in the technical research and communicate them effectively to society at large. We’re seeking ideas that will help bolster this community in this important work, and give them the tools they need to effectively communicate about AI and its impact.
- Reimagining AI and News: It is easy to find a lot of things to critique about the influence that automation and AI have on the news and information space. More challenging is articulating plausible alternatives for how these platforms should be designed and how they should deploy these technologies. We’re interested in ideas that paint a picture of the future: How might platforms from smartphones and social media sites to search engines and online news outlets be redesigned in part or entirely to better serve the public good?
We believe there are a diverse range of communities that can bring their expertise to bear on these issues but are frequently left out of the conversation. This challenge is open to anyone: We’re looking for journalists, designers, technologists, activists, entrepreneurs, artists, lawyers from a variety of communities around the world — anyone who thinks they have a good idea about addressing these problems that may not have been tried before.
Saturday, October 6, 2018
How to deliver on Machine Learning projects
https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0
How to deliver on Machine Learning projects
A guide to the ML Engineering Loop
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