The colours of the sky are caused by a complex interplay between our sun and our planet’s atmosphere. Artists have long painted the sky in all its guises, and storytellers have often taken inspiration from it. Ancient Greeks like Plato and Aristotle were the first to write their theories about colour and its relation to meteorological phenomena. Yet, it took scientists many centuries to unravel the science behind the colours we see in the sky.
Machine learning models learn their behaviour from data. So, finding the right data is a big part of the work to build machine learning into your products.
Exactly how much data you need depends on what you’re doing and your starting point. There are techniques like transfer learning to reduce the amount of data you need. Or, for some tasks, pre-trained models are available. Still, if you want to build something more than a proof-of-concept, you’ll eventually need data of your own to do so.
That data has to be representative of the machine learning task, and its collection is one of the places where bias creeps in. Building a dataset that’s balanced on multiple dimensions requires care and attention. Data for training a speech recognition system has to represent aspects like different noisy environments, multiple speakers, accents, microphones, topics of conversation, styles of conversation, and more. Some of these aspects, like background noise, affect most users equally. But some aspects, like accent, have an outsized impact on particular groups of users. Sometimes, though, bias is built deeper into the data than in the composition of the dataset. Text scraped from the web, for example, results in a dataset that embeds many of society’s stereotypes because those are present in text from the web and can’t be scrubbed. …
Since the launch of Alexa, Siri, and Google Assistant, we’re all becoming much more used to talking to our devices. Beyond these virtual assistants, voice technology and conversational AI have increased in popularity over the last decade and are used in many applications.
One use of Natural Language Processing (NLP) technology is to analyse and gain insight from the written transcripts of audio— whether from voice assistants or from other scenarios like meetings, interviews, call centres, lectures or TV shows. Yet when we speak, things are more complicated than a simple text transcription suggests. …
There are several Python libraries available that make it very easy to view waveforms in different ways. In this post, I’ll go through some of the ways to get started.
To begin, import the key libraries:
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import wavfile
For the audio, I used Audacity to record the short phrase “What’s today’s weather?” as a wave file. I’ll use scipy to read in that wave file, and matplotlib to visualise it.
fs, x = wavfile.read(wavfn)
y = np.linspace(0,len(x)/float(fs), len(x))
ya = np.max(np.absolute(x))
plt.plot(y, x, color="#004225")
The majority of folks who build technology don’t intend to be biased. Yet we all have our own unique perspective on the world, and we can’t help but bring that into our work. We make decisions based on our views and our experiences. Those decisions may each seem small in isolation, but they accumulate. And, as a result, technology often reflects the views of those who build it.
Here are a few of the places I’ve seen where bias creeps into technology.
With the recent success of machine learning (ML) and AI algorithms, data is becoming increasingly important. ML algorithms learn their behaviour from a dataset. …
During my time working at Amazon Alexa, I designed and ran an Alexa Skills workshop to encourage teenagers into coding. This workshop can be adapted to all levels and ages — our youngest participant was 12 — and all you need is a computer with a browser and an internet connection. When you finish, you’ll have an Alexa skill which can tell you facts about famous folk along with some of their quotes.
Each participant should set these up before the…
Companies are investing millions of pounds to develop Artificial Intelligence (AI) technology. Many people use that AI technology daily to make their lives easier. But search Google for images of “Artificial Intelligence”, and you’ll be faced with a sea of glowing, bright blue, connected brains. The imagery used to illustrate AI is a far cry from the more mundane reality of how AI looks to its users, where it powers services like navigation, voice assistants and face recognition. The disconnect makes it hard to grasp the reality of AI.
Artificial Intelligence (AI) is a fast growing field. The 2018 AI Index report illustrates just how fast it is growing. It reports that published research papers in AI have increased 7x since 1996, university enrolment on AI courses has increased 5x since 2012, investment in AI startups in the US has increased 113% since 2012 and mentions of AI and machine learning (ML) in the earnings calls of tech companies have increased more than 100x since 2012. These statistics show how AI is growing not just in academia, but the technology is rapidly being adopted by businesses and becoming commercialised.
Yet, AI is an ambiguous term which has no well defined and agreed upon definition. It’s typically used as an umbrella term covering a variety of techniques that make computers appear to have human-like intelligence. …
In the past few years, machine learning (ML) has become commercially successful and AI firmly established as a field. With its success, more attention is being paid specifically to the gender gap in AI. Compared to the general population, men are overrepresented in technology. While this has been the case for several decades, the opposite was true in the early days of computing when programming was considered a woman’s job.
Diversity has been shown to lead to good business outcomes like improved revenue. …
The “long hours” culture is glorified in many industries, including the tech industry where I work. Rachel Thomas writes clearly about how this is discriminatory and counter-productive, so this post is in response to her tweet:
For anyone in a job with a healthy work culture, consider blogging about it:
- offer lessons learned & advice for other companies
- with many high profile examples of toxic overwork, it can feel like “everyone” is doing it. Helpful to see counterexamples
- good for recruiting https://t.co/MaUnA4cjK7
- Rachel Thomas (@math_rachel) January 25, 2019
As a PhD student and in my early career, I often worked long hours. At other points in my career I’ve worked fewer hours, and sometimes not at all for several months. I have never put in anything approaching 80 hours a week for a sustained period of time, and yet I still consider myself to have had a successful career in technology. …