Sentiment detection with Keras, word embeddings and LSTM deep learning networks
- Read this blog post to get an overview over SaaS and open source options for sentiment detection. Learn an easy and accurate method relying on word embeddings with LSTMs that allows you to do state of the art sentiment analysis with deep learning in Keras.
Which research results will generalize?
- One approach to AI research is to work directly on applications that matter — say, trying to improve production systems for speech recognition or medical imaging. But most research, even in applied fields like computer vision, is done on highly simplified proxies for the real world. Progress on object recognition benchmarks — from toy-ish ones like MNIST, NORB, and Caltech101, to complex and challenging ones like ImageNet and Pascal VOC — isn’t valuable in its own right, but only insofar as it yields insights that help us design better systems for real applications.
- Belief Space – A subset of information space that is associated with opinions. For example, there is little debate about what a table is, but the shape of the table has often been a source of serious diplomatic contention
- Medium – the technology that mediates the communication that coordinates the group. There are properties that seem to matter:
- Reach – How many individuals are connected directly. Evolutionarily we may be best suited to 7 +/- 2
- Directionality – connections can be one way (broadcast) or two way (face to face)
- Transparency – How ‘visible’ is the individual on the other side of the communication? There are immediate perception and historical interaction aspects.
- Friction – How difficult is it to use the medium? For example in physical space, it is trivial to interact with someone nearby, but becomes progressively difficult with distance. Broadcasting makes it trivial for a small number of people to reach large numbers, but not the reverse. Computer mediated designs typically try to reduce the friction of interaction.
- Dimension Reduction – The process by which groups decide where to coordinate. The lower the dimensions, the easier (less calculation) it takes to act together
- State – a multidimensional measure of current belief and interest
- Orientation – A vector constructed of two measures of state. Used to determine alignment with others
- Velocity – The amount of change in state over time
- Diversity Injection – The addition of random, factual information to the Information Retrieval Interfaces (IRIs) using mechanisms currently used to deliver advertising. This differs from Serendipity Injection, which attempts to find stochastically relevant information for an individual’s implicit information needs.
- Level 1: population targeted – Based on Public Service Announcements (PSAs), information presentation should range from simple, potentially gamified presentations to deep exploration with citations. The same random information is presented by the IRIs to the using population at the same time similarly to the Google Doodle.
- Level 2: group targeted – based on detecting a group’s behaviors. For example, a stampeding group may require information that is more focussed on pointing at where flocking activity is occuring.
- Level 3: individual targeted – Depending on where in the belief space the individual is, there may be different reactions. In a sparsely traveled space, information that lies in the general direction of travel might be a form of useful serendipity. Conversely, when on a path that often leads to violent radicalization, information associated with disrupting the progression of other individuals with similar vectors could be applied.
- Map – a type of diagram that supports the plotting of trajectories. In this work, maps of belief space are constructed based on the dimension reduction used by humans in discussion. These maps are assumed to be dynamic over time and may consists of many interrelated, though not necessarily congruent, layers.
- Herding – Deliberate creation of stampede conditions in groups. Can be an internal process to consolidate a group, or an external, adversarial process.
Trump as Enron (Twitter)
7:00 – 5:00 ASRC MKT
- From zero to research — An introduction to Meta-learning
- Thomas Wolf Machine Learning, Natural Language Processing & Deep learning – Science Lead @ Huggingface (We’re on a journey to build the first truly social artificial intelligence. Along the way, we contribute to the development of technology for the better.)
Over the last months, I have been playing and experimenting quite a lot with meta-learning models for Natural Language Processing and will be presenting some of this work at ICLR, next month in Vancouver 🇨🇦 — come say hi! 👋 In this post, I will start by making a very visual introduction to meta-learning, from zero to current research work. Then, we will code a meta-learning model in PyTorch from scratch and I will share some of the lessons learned on this project.
- Google veteran Jeff Dean takes over as company’s AI chief
- Add some MB framing words to the game theory part of the lit review – done
- Work on the PSA writeup
Our research has indicated that an awareness of nomadic/explorer activity in belief space may help nudge stampeding groups away from a terminal trajectory and back towards “average” beliefs. Tajfel states that groups can exist “in opposition”, so providing counter-narratives may be ineffective. Rather, we think that a practical solution to online polarization is the injection of diversity into user’s feeds, be they social media, search results, videos, etc. The infrastructure exists for this already in platform’s support of advertising. The precedent is the Public Service Announcement (PSA).
US Broadcasters since 1927, have been obligated to “serve the public interest” in exchange for spectrum rights. One way that this has been addressed is through the creation of the PSA, “the purpose of which is to improve the health, safety, welfare, or enhancement of people’s lives and the more effective and beneficial functioning of their community, state or region”
We believe that PSAs can be repurposed to support diversity injection through the following:
- Random, non-political content designed to expand information horizons, analogous to clicking the “random article” link on Wikipedia.
- Progressive levels of detail starting with an informative “hook” presented in social feeds or search results. Users should be able to explore as much or as little as they want.
- Simultaneous presentation to large populations. Google has been approximating this with their “doodle” since 1998, with widespread positive feedback, which indicates that there may be good receptivity to common serendipitous information.
- Format should reflect the medium, Text, images and videos.
- Content should be easily verifiable, recognizable, and difficult to spoof.
We believe that such diversity injection mechanisms as described above can serve as a “first do no harm” first step in addressing the current crisis of misinformation. By nudging users towards an increased awareness of a wider world, which in turn interferes with the processes that lead to belief stampedes by increasing the number of dimensions, the awareness of different paths that others are taking. As we gain understanding of the mechanisms that influence group behaviors, it may be possible to further refine our designs and interfaces so that they no longer promote extremism while still providing value.
- Done with first draft? Nope. Going to rework the implications section some more.