policy change index - machine learning on corpus of text to identify and predict policy changes in China

simple explanation of AI bias - sources etc.

normalization

McDonald's are getting into machine learning. Beware.

dummy encoding

In other words: culture. I'm pretty sure that Engelbart would agree with the statement that someone who could order a book from a library would likely not know the best way to find a nearby water source, as the right kind of aborigine would know. Collective intelligence is a monotonically increasing store of knowledge that is maintained through social learning -- not just social learning, but teaching. Many species engage in social learning, but humans are the only primates with visible sclera -- the whites of our eyeballs -- which enables even infants to track where their teacher/parent is looking. I think this function of culture is what Engelbart would call "C work"

A Activity: 'Business as Usual'. The organization's day to day core business activity, such as customer engagement and support, product development, R&D, marketing, sales, accounting, legal, manufacturing (if any), etc. Examples: Aerospace - all the activities involved in producing a plane; Congress - passing legislation; Medicine - researching a cure for disease; Education - teaching and mentoring students; Professional Societies - advancing a field or discipline; Initiatives or Nonprofits - advancing a cause.

B Activity: Improving how we do that. Improving how A work is done, asking 'How can we do this better?' Examples: adopting a new tool(s) or technique(s) for how we go about working together, pursuing leads, conducting research, designing, planning, understanding the customer, coordinating efforts, tracking issues, managing budgets, delivering internal services. Could be an individual introducing a new technique gleaned from reading, conferences, or networking with peers, or an internal initiative tasked with improving core capability within or across various A Activities.

C Activity: Improving how we improve. Improving how B work is done, asking 'How can we improve the way we improve?' Examples: improving effectiveness of B Activity teams in how they foster relations with their A Activity customers, collaborate to identify needs and opportunities, research, innovate, and implement available solutions, incorporate input, feedback, and lessons learned, run pilot projects, etc. Could be a B Activity individual learning about new techniques for innovation teams (reading, conferences, networking), or an initiative, innovation team or improvement community engaging with B Activity and other key stakeholders to implement new/improved capability for one or more B activities.

In other words, human culture, using language, artifacts, methodology, and training, bootstrapped collective intelligence; what Engelbart proposed, then was to apply C work to culture's bootstrapping capabilities.

This explains my personal experience here: https://hyp.is/EIfMfivUEem7SFcAiWxUpA/epic.org/privacy/global_entry/default.html (Why someone who had Global Entry was flagged for a police incident before he applied for Global Entry).

Intelligence checks is very concerning here as it suggests pretty much what has already been leaked, that the US is running complex autonomous screening of all of this data all the time. This also opens up the possibility for discriminatory algorithms since most of these are probably rooted in machine learning techniques and the criminal justice system in the US today tends to be fairly biased towards certain groups of people to begin with.

This re-affirms the previous annotation that the set of training data for the intelligence checks the US runs on global entry data is biased towards certain groups of people.

sfsdf

So then we know that most people will use supervised learning that requires less computational power and knowledge.

specialization

key insight into Gaussian processes

The new learned optical correlator uses existing light to save energy costs over an optoelectronic two-layer Combines Convolutional neural network (CNN). https://www.sciencedaily.com/releases/2018/08/180802130750.htm

AI and machine learning

Predictive analytics encompasses a variety of statistical techniques from data mining, predictive modelling, and machine learning, that analyze current and historical facts to make predictions about future or otherwise unknown events.

This is a very interesting take on "evidence" as being moral if not legal by Sergeant Cuff. It makes me question exactly what he means by that if there is a way to use computational analysis to find out. We could perhaps start by parsing out "evidence" throughout the text with a machine learning algorithm to help he define evidence and then, going forward, device a way (maybe with sentiment analysis) to determine moral evidence from legal evidence.

~32:00 What about the domain of the function being effectively lower dimensional, rather than a strongly regularity assumption? That would also work, right? Could this be the case for images? (what's the dimensionality of the manifold of natural images?)

Nice. I like the idea of regularity <> low dimensional representation. I guess by that general definition, the above is a form of regularity..

He comments about this on 38:30

Explicación gráfica de artificial intelligence, machine learning y deep learning

Most of the recent advances in AI depend on deep learning, which is the use of backpropagation to train neural nets with multiple layers ("deep" neural nets).

Neural nets consist of layers of nodes, with edges from each node to the nodes in the next layer. The first and last layers are input and output. The output layer might only have two nodes, representing true or false. Each node holds a value representing how excited it is. Each edge has a value representing strength of connection, which determines how much of the excitement passes through.

The edges in an untrained neural net start with random values. The training data consists of a series of samples that are already labeled. If the output is wrong, the edges are adjusted according to how much they contributed to the error. It's called backpropagation because it starts with the output nodes and works toward the input nodes.

Deep neural nets can be effective, but only for single specific tasks. And they need huge sets of training data. They can also be tricked rather easily. Worse, someone who has access to the net can discover ways of adding noise to images that will make the net "see" things that obviously aren't there.

Why does this have to be done in a different system?

Matthews correlation coefficient

Back in 2012, Zeynep Tufecki pointed out that election campaigns driven by big-data and social media could be bad for democracy.

Học cơ bản

financial inclusion startup

Excellent overview. I found the papers a little hard to grasp, and this cleared a lot of that up.

A very accessible explanation of the bias-variance trade-off in ML.

great examples and interactive viz

Detection of fake news in social media based on who liked it.

we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who "liked" them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts.

Obviously, in this situation whoever controls the algorithms has great power. Decisions like what is promoted to the top of a news feed can swing elections. Small changes in UI can drive big changes in user behavior. There are no democratic checks or controls on this power, and the people who exercise it are trying to pretend it doesn’t exist

On Facebook, social dynamics and the algorithms’ taste for drama reinforce each other. Facebook selects from stories that your friends have shared to find the links you’re most likely to click on. This is a potent mix, because what you read and post on Facebook is not just an expression of your interests, but part of a performative group identity.

So without explicitly coding for this behavior, we already have a dynamic where people are pulled to the extremes. Things get worse when third parties are allowed to use these algorithms to target a specific audience.

any system trying to maximize engagement will try to push users towards the fringes. You can prove this to yourself by opening YouTube in an incognito browser (so that you start with a blank slate), and clicking recommended links on any video with political content.

...

This pull to the fringes doesn’t happen if you click on a cute animal story. In that case, you just get more cute animals (an experiment I also recommend trying). But the algorithms have learned that users interested in politics respond more if they’re provoked more, so they provoke. Nobody programmed the behavior into the algorithm; it made a correct observation about human nature and acted on it.

Really cool venue for publishing online, interactive articles for ML

AUC能够在CTR应用中有指导意义的原因

Interesting!

Robert Mercer, Steve Bannon, Breitbart, Cambridge Analytica, Brexit, and Trump.

“The danger of not having regulation around the sort of data you can get from Facebook and elsewhere is clear. With this, a computer can actually do psychology, it can predict and potentially control human behaviour. It’s what the scientologists try to do but much more powerful. It’s how you brainwash someone. It’s incredibly dangerous.

“It’s no exaggeration to say that minds can be changed. Behaviour can be predicted and controlled. I find it incredibly scary. I really do. Because nobody has really followed through on the possible consequences of all this. People don’t know it’s happening to them. Their attitudes are being changed behind their backs.”

-- Jonathan Rust, Cambridge University Psychometric Centre

The team on Google Translate has developed a neural network that can translate language pairs for which it has not been directly trained. "For example, if the neural network has been taught to translate between English and Japanese, and English and Korean, it can also translate between Japanese and Korean without first going through English."

Ground truth in machine learning

2013 article about Douglas Hofstadter, who has continued to pursue an understanding of the human mind through experiments with AI.

In machine learning it's also important to evaluate not just against random, but against how well other methods (e.g. parole boards) do. That kind of analysis would be nice to see.

We should have control of the algorithms and data that guide our experiences online, and increasingly offline. Under our guidance, they can be powerful personal assistants.

Big business has been very militant about protecting their "intellectual property". Yet they regard every detail of our personal lives as theirs to collect and sell at whim. What a bunch of little darlings they are.

What do n and d mean?

UT Austin SDS 348, Computational Biology and Bioinformatics. Course materials and links: R, regression modeling, ggplot2, principal component analysis, k-means clustering, logistic regression, Python, Biopython, regular expressions.

Python interface to the R programming language.<br> Use R functions and packages from Python.<br> https://pypi.python.org/pypi/rpy2

https://twitter.com/open_ai

They're hiring: https://openai.com/about/

The study.

https://github.com/rhiever/tpot TPOT (Tree-based Pipeline Optimization Tool) Built on numpy, scipy, pandas, scikit-learn, and deap.

penn state

I have the feeling we do not need to use models as complicated as some outlined in the text; we can (and finally will have to) abstract from most of the issues we can imagine. I expect that "magic" (an undisclosed heuristic, perhaps in combination with machine learning) will deal with the issues, a black box that will be considered inherently flawed and practical enough at the same time. The results from experimental ethics can help form the heuristic while the necessity for easy implementation and maintainability will limit the applications significantly.

This is a really useful visualization of the Multi Class SVM loss

Someday, maybe projects like Hypothesis will help teach computers to read, too.