We show that BigBird is a universal approximator of sequence functions and is Turing complete,

Fukuyama's answer is no. Middleware providers will not see privately shared content from a user's friends. This is a good answer if our priority is privacy. It lets my cousin decide which companies to trust with her sensitive personal information. But it hobbles middleware as a tool for responding to her claims about vaccines. And it makes middleware providers far less competitive, since they will not be able to see much of the content we want them to curate.

Facebook deploys tens of thousands of people to moderate user content in dozens of languages. It relies on proprietary machine-learning and other automated tools, developed at enormous cost. We cannot expect [End Page 169] comparable investment from a diverse ecosystem of middleware providers. And while most providers presumably will not handle as much content as Facebook does, they will still need to respond swiftly to novel and unpredictable material from unexpected sources. Unless middleware services can do this, the value they provide will be limited, as will users' incentives to choose them over curation by the platforms themselves.

Suppose Google were to change what’s on that page, or reorganize its website anytime between when I’m writing this article and when you’re reading it, eliminating it entirely. Changing what’s there would be an example of content drift; eliminating it entirely is known as link rot.

The problem is, algorithms were never designed to handle such tough choices. They are built to pursue a single mathematical goal, such as maximizing the number of soldiers’ lives saved or minimizing the number of civilian deaths. When you start dealing with multiple, often competing, objectives or try to account for intangibles like “freedom” and “well-being,” a satisfactory mathematical solution doesn’t always exist.

many other systems that are already here or not far off will have to make all sorts of real ethical trade-offs

The insertion of an algorithm’s predictions into the patient-physician relationship also introduces a third party, turning the relationship into one between the patient and the health care system. It also means significant changes in terms of a patient’s expectation of confidentiality. “Once machine-learning-based decision support is integrated into clinical care, withholding information from electronic records will become increasingly difficult, since patients whose data aren’t recorded can’t benefit from machine-learning analyses,” the authors wrote.

TRAILBLAZER-WORKFLOW is another dream ‘o mine come true. It allows creating long-term processes (or state machines) based on BPMN diagrams that can be modeled using our editor.

propose to create a new academic discipline called “machine behavior.” It approaches studying AI systems in the same way we’ve always studied animals and humans: through empirical observation and experimentation

‘Programs are meant to be read by humans and only incidentally for computers to execute.’

rethink the intricate, and increasingly inti-mate, configurations of the human and the machine.

take as my focus the study ofhow the effect of machines-as-agents is generated and the latter’s impli-cations for theorizing the human.

De meeste ongevallen worden door menselijk falen veroorzaakt en kunnen dus vermeden worden. 2.4.5 Apparatuuralarmering Bloedlekdetector: membraanlek; vuile detector; hemolyse als gevolg van: te hoge dialysaattemperatuur; hardwatersyndroom; koperintoxicatie; bloedtransfusie; medicamenteus; dialyse met water (concentraat op). Alarm voor hoge veneuze druk: veneuze lijn geknikt; positie naald niet goed, door het vat heen; stolling in de naald of de bloedlijnen; arm te sterk gebogen; vaatspasme; trombosering van de vene; onjuiste instelling van het alarm; limieten. Alarm voor lage veneuze druk: lucht in het extracorporele circuit; membraanruptuur; lekkage in de bloedlijnen; arteriële naald zuigt vacuüm; tensiedaling; arteriële lijn afgeknikt; bloedpomp staat stil; veneuze bloedlijn is van de naald losgeraakt; veneuze naald is uit het vat gegaan; onjuiste instelling van het alarm; begrenzing; filter van drukmeterlijn is nat. Alarm voor arteriële druk: bloedpomp staat te snel afgesteld; arteriële drukmeting niet goed aangesloten; infuus via arterieel druksysteem; drukmeetsysteem foutief afgesteld; hoge druk in het vat; bloeddrukdaling; arteriële bloedlijn afgeknikt; insufficiëntie van de shunt door: trombose; stenose; hematoom; infectie; positie naald of katheter niet goed; arteriële pool scribner afgeknikt of gestold; vaatspasme; ernstige hypotensie. Geleidbaarheidsmeter: temperatuur dialysaat te laag; concentraattank leeg; temperatuurunit van de machine defect; mengsysteem defect; onjuiste instelling. Luchtbeldetector: schuim in de luchtvanger (bij platzuigen pompsegment of slechte aanvoer); lek in de bloedslangen voor de bloedpomp; losraken van de arteriële lijn; uit het vat gaan van de arteriële naald; verkeerde positie van de luchtvanger in de houder; defect van de luchtbeldetector; afsluiten met lucht; infuus doorgeschoten; onjuiste instelling. Temperatuurmonitor: dialysaatflow is veranderd; onjuiste instelling; defecte thermostaat; defect verwarmingssysteem; stroomuitval; te veel lucht in het dialysaat. Stroomuitvalalarm: zekering doorgebrand; kortsluiting; overschakeling op noodaggregaat. Ultrafiltratiedrukalarm: te hoge ultrafiltratie; te lage ultrafiltratiedruk bij een highfluxnier (positieve TMP); altijd enige ultrafiltratie aanhouden om backfiltration te voorkomen.

I present the Data Science Venn Diagram… hacking skills, math and stats knowledge, and substantive expertise.

Zappos created models to predict customer apparel sizes, which are cached and exposed at runtime via microservices for use in recommendations.

No one, not even Mark Zuckerberg, can control the product he made. I’ve come to realize that Facebook is not a media company. It’s a Doomsday Machine.

“There is no chance of human intervention, control, and final decision,” wrote the military strategist Herman Kahn in his 1960 book, On Thermonuclear War, which laid out the hypothetical for a Doomsday Machine.

Nye Warburton 47:36

Moises Sanabria 1:19

I want the process list of a systemd unit - just like systemctl status $unitname does - but in machine-readable output, to look for specific processes.

Some of the verbs implemented by systemctl are designed to provide a high-level overview in a human readable format. All that information is available over dbus, and/or journalctl, systemctl show. We could provide that information in json format, but there's a second problem. Information and format of information printed by e.g. systemctl status is not stable. Since the output is not suitable for programmatic consumption anyway, there's no need to provide it in a machine readable format.

systemctl show --no-page iptables \ | jq --slurp --raw-input \ 'split("\n") | map(select(. != "") | split("=") | {"key": .[0], "value": (.[1:] | join("="))}) | from_entries'

We can treat navigation as a first-class concept, even encoding state transitions in links.

f′(2)

Meanwhile, politicians from the two major political parties have been hammering these companies, albeit for completely different reasons. Some have been complaining about how these platforms have potentially allowed for foreign interference in our elections.3 3. A Conversation with Mark Warner: Russia, Facebook and the Trump Campaign, Radio IQ|WVTF Music (Apr. 6, 2018), https://www.wvtf.org/post/conversation-mark-warner-russia-facebook-and-trump-campaign#stream/0 (statement of Sen. Mark Warner (D-Va.): “I first called out Facebook and some of the social media platforms in December of 2016. For the first six months, the companies just kind of blew off these allegations, but these proved to be true; that Russia used their social media platforms with fake accounts to spread false information, they paid for political advertising on their platforms. Facebook says those tactics are no longer allowed—that they've kicked this firm off their site, but I think they've got a lot of explaining to do.”). Others have complained about how they’ve been used to spread disinformation and propaganda.4 4. Nicholas Confessore & Matthew Rosenberg, Facebook Fallout Ruptures Democrats’ Longtime Alliance with Silicon Valley, N.Y. Times (Nov. 17, 2018), https://www.nytimes.com/2018/11/17/technology/facebook-democrats-congress.html (referencing statement by Sen. Jon Tester (D-Mont.): “Mr. Tester, the departing chief of the Senate Democrats’ campaign arm, looked at social media companies like Facebook and saw propaganda platforms that could cost his party the 2018 elections, according to two congressional aides. If Russian agents mounted a disinformation campaign like the one that had just helped elect Mr. Trump, he told Mr. Schumer, ‘we will lose every seat.’”). Some have charged that the platforms are just too powerful.5 5. Julia Carrie Wong, #Breaking Up Big Tech: Elizabeth Warren Says Facebook Just Proved Her Point, The Guardian (Mar. 11, 2019), https://www.theguardian.com/us-news/2019/mar/11/elizabeth-warren-facebook-ads-break-up-big-tech (statement of Sen. Elizabeth Warren (D-Mass.)) (“Curious why I think FB has too much power? Let's start with their ability to shut down a debate over whether FB has too much power. Thanks for restoring my posts. But I want a social media marketplace that isn't dominated by a single censor. #BreakUpBigTech.”). Others have called attention to inappropriate account and content takedowns,6 6. Jessica Guynn, Ted Cruz Threatens to Regulate Facebook, Google and Twitter Over Charges of Anti-Conservative Bias, USA Today (Apr. 10, 2019), https://www.usatoday.com/story/news/2019/04/10/ted-cruz-threatens-regulate-facebook-twitter-over-alleged-bias/3423095002/ (statement of Sen. Ted Cruz (R-Tex.)) (“What makes the threat of political censorship so problematic is the lack of transparency, the invisibility, the ability for a handful of giant tech companies to decide if a particular speaker is disfavored.”). while some have argued that the attempts to moderate discriminate against certain political viewpoints.

It would allow end users to determine their own tolerances for different types of speech but make it much easier for most people to avoid the most problematic speech, without silencing anyone entirely or having the platforms themselves make the decisions about who is allowed to speak.

As an American and a staunch defender of the First Amendment, I’m allergic to the notion of forbidden speech. But if government is going to forbid it, it damned well better clearly define what is forbidden or else the penumbra of prohibition will cast a shadow and chill on much more speech.

Many of the book’s essayists defend freedom of expression over freedom from obscenity. Says Rabbi Arthur Lelyveld (father of Joseph, who would become executive editor of The New York Times): “Freedom of expression, if it is to be meaningful at all, must include freedom for ‘that which we loathe,’ for it is obvious that it is no great virtue and presents no great difficulty for one to accord freedom to what we approve or to that to which we are indifferent.” I hear too few voices today defending speech of which they disapprove.

A statistician is the exact same thing as a data scientist or machine learning researcher with the differences that there are qualifications needed to be a statistician, and that we are snarkier.

numerically evaluate the derivative of a function specified by a computer program

no gradient tapes, no graph definitions requires

use Xstate which offers a finite state machine that adheres to the SCXML spec­i­fi­ca­tion and provides a lot of extra goodness, including vi­su­al­iza­tion tools, test helpers and much more

cross-validation is sometimes not valid for time series models

For example, the one- pass (hardware) translator generated a symbol table and reverse Polish code as in conven- tional software interpretive languages. The translator hardware (compiler) operated at disk transfer speeds and was so fast there was no need to keep and store object code, since it could be quickly regenerated on-the-fly. The hardware-implemented job controller per- formed conventional operating system func- tions. The memory controller provided

I suspect that most people who aren't avid users of social media and aren't super technical don't even think to change their username. Why would they? Twitter works perfectly well, and shows their chosen name in conversations, without ever touching the username setting.

RDFa is intended to solve the problem of marking up machine-readable data in HTML documents. RDFa provides a set of HTML attributes to augment visual data with machine-readable hints. Using RDFa, authors may turn their existing human-visible text and links into machine-readable data without repeating content.

It does, however, provide the --porcelain option, which causes the output of git status --porcelain to be formatted in an easy-to-parse format for scripts, and will remain stable across Git versions and regardless of user configuration.

Parsing the output of git status is a bad idea because the output is intended to be human readable, not machine-readable. There's no guarantee that the output will remain the same in future versions of Git or in differently configured environments.

Determine if who is using my computer is me by training a ML model with data of how I use my computer. This is a project for the Intrusion Detection Systems course at Columbia University.

By honoring the mammae as sign and symbol of the highest class ofanimals, Linnaeus assigned a new value to the female, especially women’s unique rolein reproduction

on a stage two feet high, along which she was led by her keeper, and exhibited like awild beast; being obliged to walk, stand, or sit as he ordered her.”6

reast shapes amonghumans

Our membership inference attack exploits the observationthat machine learning models often behave differently on thedata that they were trained on versus the data that they “see”for the first time.

data leakage (data from outside of your test set making it back into your test set and biasing the results)

The easiest way I've found to manage that is to copy hardware-configuration.nix and a minimal version of configuration.nix and import it into the NixOps config for the corresponding machine. (I keep them in a git submodule, but keeping them in the same repo could also make sense.) 1 Pick your reaction

the network typically learns to useh(t)as a kind of lossysummary of the task-relevant aspects of the past sequence of inputs up tot

Parameter sharingmakes it possible to extend and apply the model to examples of different forms(different lengths, here) and generalize across them. If we had separate parametersfor each value of the time index, we could not generalize to sequence lengths notseen during training, nor share statistical strength across different sequence lengthsand across different positions in time. Such sharing is particularly important whena specific piece of information can occur at multiple positions within the sequence.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed

Answerbag (http://www.answerbag.com)

Python contributed examples¶ Mic VAD Streaming¶ This example demonstrates getting audio from microphone, running Voice-Activity-Detection and then outputting text. Full source code available on https://github.com/mozilla/DeepSpeech-examples. VAD Transcriber¶ This example demonstrates VAD-based transcription with both console and graphical interface. Full source code available on https://github.com/mozilla/DeepSpeech-examples.

Python API Usage example Edit on GitHub Python API Usage example¶ Examples are from native_client/python/client.cc. Creating a model instance and loading model¶ 115 ds = Model(args.model) Performing inference¶ 149 150 151 152 153 154 if args.extended: print(metadata_to_string(ds.sttWithMetadata(audio, 1).transcripts[0])) elif args.json: print(metadata_json_output(ds.sttWithMetadata(audio, 3))) else: print(ds.stt(audio)) Full source code

DeepSpeech is an open source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. NOTE: This documentation applies to the 0.7.0 version of DeepSpeech only. Documentation for all versions is published on deepspeech.readthedocs.io. To install and use DeepSpeech all you have to do is: # Create and activate a virtualenv virtualenv -p python3 $HOME/tmp/deepspeech-venv/ source $HOME/tmp/deepspeech-venv/bin/activate # Install DeepSpeech pip3 install deepspeech # Download pre-trained English model files curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.pbmm curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/deepspeech-0.7.0-models.scorer # Download example audio files curl -LO https://github.com/mozilla/DeepSpeech/releases/download/v0.7.0/audio-0.7.0.tar.gz tar xvf audio-0.7.0.tar.gz # Transcribe an audio file deepspeech --model deepspeech-0.7.0-models.pbmm --scorer deepspeech-0.7.0-models.scorer --audio audio/2830-3980-0043.wav A pre-trained English model is available for use and can be downloaded using the instructions below. A package with some example audio files is available for download in our release notes.

Speech & Machine Learning

import all the necessary libraries into our notebook. LibROSA and SciPy are the Python libraries used for processing audio signals. import os import librosa #for audio processing import IPython.display as ipd import matplotlib.pyplot as plt import numpy as np from scipy.io import wavfile #for audio processing import warnings warnings.filterwarnings("ignore") view raw modules.py hosted with ❤ by GitHub View the code on <a href="https://gist.github.com/aravindpai/eb40aeca0266e95c128e49823dacaab9">Gist</a>. Data Exploration and Visualization Data Exploration and Visualization helps us to understand the data as well as pre-processing steps in a better way. 

TensorFlow recently released the Speech Commands Datasets. It includes 65,000 one-second long utterances of 30 short words, by thousands of different people. We’ll build a speech recognition system that understands simple spoken commands. You can download the dataset from here.

Learn how to Build your own Speech-to-Text Model (using Python) Aravind Pai, July 15, 2019 Login to Bookmark this article (adsbygoogle = window.adsbygoogle || []).push({}); Overview Learn how to build your very own speech-to-text model using Python in this article The ability to weave deep learning skills with NLP is a coveted one in the industry; add this to your skillset today We will use a real-world dataset and build this speech-to-text model so get ready to use your Python skills!

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU. Read the documentation at Keras.io. Keras is compatible with: Python 2.7-3.6.

Installation in Windows Compatibility: > OpenCV 2.0 Author: Bernát Gábor You will learn how to setup OpenCV in your Windows Operating System!

Here you can read tutorials about how to set up your computer to work with the OpenCV library. Additionally you can find very basic sample source code to introduce you to the world of the OpenCV. Installation in Linux Compatibility: > OpenCV 2.0

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 18 million. The library is used extensively in companies, research groups and by governmental bodies. Along with well-established companies like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota that employ the library, there are many startups such as Applied Minds, VideoSurf, and Zeitera, that make extensive use of OpenCV. OpenCV’s deployed uses span the range from stitching streetview images together, detecting intrusions in surveillance video in Israel, monitoring mine equipment in China, helping robots navigate and pick up objects at Willow Garage, detection of swimming pool drowning accidents in Europe, running interactive art in Spain and New York, checking runways for debris in Turkey, inspecting labels on products in factories around the world on to rapid face detection in Japan. It has C++, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. OpenCV leans mostly towards real-time vision applications and takes advantage of MMX and SSE instructions when available. A full-featured CUDAand OpenCL interfaces are being actively developed right now. There are over 500 algorithms and about 10 times as many functions that compose or support those algorithms. OpenCV is written natively in C++ and has a templated interface that works seamlessly with STL containers.