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Tabitha’s Future Manual

In her book, How to Talk to Robots, Tabitha shares her view of how the world is being transformed by technology. She shares the vast potential that exists within AI for positive change, and as well the trials that may be coming our way. She says, “this new wave of technology could be a tsunami that knocks you down, or it could be the wave that we ride together to a brighter future.” Together, we must cease to view tech as ‘boring’ or scary. Let’s start learning.

THE HISTORY OF AI © MARTIN BROWN

TABITHA’S INTRODUCTION TO AI

Key Words & Phrases

A few of the words and phrases key to know, particularly when reading Tabitha’s book. And take it from Tabitha: “If anything else is unclear, why not ask Siri, who will use a combination of speech recognition and natural-language processing to help you out.”

Artificial Intelligence (AI)

This is a tricky one because the definition of AI is hotly debated. At its
most basic, AI describes a computer system able to perform tasks normally
associated with human intelligence, like reasoning or learning. Within this wide definition of AI, there are two important distinctions.

    • 1. Artificial Narrow Intelligence (AI)

Artificial Narrow Intelligence is an umbrella term for a system that can
perform a single intelligent task. It is built for a single objective, such as automating image sorting, or text generalisation. This is the kind of
artificial intelligence researchers are currently able to build.

    • 2. Artificial General Intelligence (AGI)

Sometimes called superintelligence, AGI refers to the ideal realisation of
the field of artificial intelligence, where machines would be fully able to learn, reason and act for themselves, all at once.

Affective Computing

An area of research in robotics and AI focused on thinking about and simulating emotions and feelings in machines. In addition to computer science, affective computing can draw on ideas in information studies, psychology, philosophy and others.

Algorithm

This is a messy one! An algorithm at its most simple is just any set of rules a computer follows to solve a problem. But when I talk about algorithms in this book, I am referring to AI algorithms, or the instructions the AI takes to perform its task. These are often also called models when the algorithm has already been trained with data.

Big Data

A way of describing extremely large datasets by using a computer to analyze and reveal the patterns, trends and associations in one set.

Compiler

A compiler is a system that translates code into a mode that computers can process.

Data

Is made up of individual units of information. Although the terms ‘data’, ‘information’ and ‘knowledge’ are sometimes used interchangeably, in the case of AI it’s what people use to describe the information that an AI system is trained on or given to learn from.

Dataset

A collection of data pulled together for analysis or study.

Deep Learning

Is a type of Machine Learning where the computing system mimics the structure of the human brain to learn from experience. This is why the structure of a deep learning system is often called a Neural Network.

Expert Systems

A kind of computer system that uses databases of knowledge to make decisions, much like a human expert. This term arose in the 1970s, had its boom in the 1980s, but is used less often today.

Filter Bubble

Personalised algorithms can create a ‘bubble’ effect so that a Web user only encounters information and opinions that reinforce their existing interests or beliefs.

Gender Data Gap

Caroline Criado-Perez uses this term in her book Invisible Women to describe the ways that data sets are often male-biased. This means the conclusions drawn from that data are less likely to reflect the needs and experiences of women, and women of colour in particular.

Natural Language Processing (NLP)

A diverse field of study within artificial intelligence that asks questions about and helps machines process human language.

Machine Learning

A kind of Artificial Narrow Intelligence where a machine learns to make decisions from data and experience, instead of it being explicitly told how to perform a task. Machine learning is called Deep (Machine) Learning, when there are multiple steps in the decision- making process. There are three major types of machine learning: supervised, unsupervised and reinforcement

Stem

This is a shorthand way of referring to the fields of science, technology, engineering and mathematics.

Training Data

This is the data that contains labels for different groups so the machine can learn the characteristics of each group. This almost always forms the basis for supervised machine learning.

How do we embrace technologically induced change?

As a human, you are the most important part in all of this change. You need to constantly remind yourself why you are unique, special and more valuable than any machine–however smart it is. As you know by now, I think that because technological change is inevitable, ignoring it simply isn’t an option. The robots aren’t going away! So we need to embrace the change. But how? What does it mean to embrace change when the change might take over some of the jobs that we are used to doing ourselves? Can we embrace change in a way that doesn’t leave us vulnerable and draws on our (often hidden) strengths?

Women have a lot to teach us about how to live and work alongside machines. Throughout history a set of qualities traditionally associated with women – compassion, care, empathy, nurturing – have been dismissed or sidelined by the market. Today, care work is either amongst the lowest paid jobs, or done for free (mainly by women) in the home. But these qualities, which have always been vital, are about to become ever more necessary and much harder to undermine. Many aspects of all jobs are going to be assigned to machines, but they can’t do everything that humans can do. Imagine you’re a doctor: a robot may be able to hold all of Grey’s Anatomy in its system, predict and detect diseases invisible to the human eye. But the one thing it can’t do is connect on a human level and offer genuine care– something we know is key to patients’ comfort and recovery. Human empathy is something machines can’t do.

Women have also developed another skill that will become vital in the coming years: staying on our toes. For centuries women have faced all kinds of discrimination and prejudice. Women have had to know how to be vigilant and resilient, to anticipate change and to read subtle cues and analyse the world for risks. They have had to stay one step ahead of ‘the man’. Now, women can teach us how to stay one step ahead of ‘the machine’.

What does this mean, practically speaking? Whatever field you might be working in, or have worked in in the past, or would like to after you leave school, run a thought experiment where you consider which tasks could be automated. It’s important to note they might be automated using AI or simpler process automation.

Ask yourself – what could be done better by a smart machine? If you can predict where the machines have an edge, you can focus on honing the skills where the AI has no chance of competing. Why go head to head with a super computer?! Use your guiles to outmanoeuvre it instead. You can follow these steps to see where the moves might be

The Exercise

    • 1.

      Break down a job into a series of tasks for which you are responsible and write them in a long list. I’d use a spreadsheet, but you can use a paper if you prefer.

    • 2.

      Draw a line or use columns on either side of the sheet and on the left write ‘full automation’ while on the right, ‘humans only’.

    • 3.

      Note which tasks are repetitive, don’t require teamwork or creativity and add them to the left. On the right add the tasks which require you to deal with complex ever-changing unpredictable situations with many different people involved.

  • 4.

    Then look at the fuzzy middle. What are the tasks that might in the future be automated? Which of these do you think should be automated, and which do you think should remain solely in the human realm?

  • 5.

    Consider what’s needed to reach this level of automation and how long it might take. For example, ask yourself: is there enough data in machine-readable format (rather than lots of pieces of paper) to train a machine today? Be careful, as humans famously overestimate the change that will occur in the next ten to twenty. For example, thirty years ago they were expecting flying cars by now!

A few last words…

“I hope this exercise gives you a feel for how a job might change. It is of course almost impossible to predict exactly what will change and which skills you’ll need to prepare to adapt to that specific eventuality. What I’m really demonstrating and suggesting is that you prepare yourself for any eventuality, while all the time learning how to learn.

When I was at school, I found that my dyslexia meant I learned in a very different way to my school friends. I couldn’t learn by rote; I needed things to be visual and I could only understand new concepts if I deconstructed the idea, and then put it back together again in my own way. Struggling with the learning format at school meant I would get really frustrated and was often sent to detention for causing a nuisance. But it was also the cause of me honing my greatest skill: ‘learning to learn’. Learning how you learn, and learning how to learn, makes life a lot easier when you’re confronting something new. Be kind to yourself, and give yourself the time that you need to work out how to understand new things. One of my friends only learned by teaching something to someone else, while another had to have all her lessons on audiotape repeat. Now we are adults in work, it’s still the same, and we are using those skills to keep learning. Whether it’s podcasts or courses, events or documentaries that get your learning juices going, embrace them. It’s not too late to figure out how you best understand something and then double down on that.

It’s most likely that you’ll have many roles, jobs and even careers in your lifetime. If you are fortunate enough to make a choice, seek work for what you will learn as well as what you will earn–whatever job it is that you might be doing, or if, as is the case for many women, you are doing multiple jobs simultaneously or are a full-time mother or carer. Indeed, being able to juggle lots of things at once, where situations are evolving and people are unpredictable, is another thing that humans do better than machines. Without even realising it, you might have already developed the most sought-after skills in this new age of machine learning!
In summary: think about where you can make changes.

Recognise that you may have hidden but valuable talents. Most of all, if you can, let yourself learn. You are capable of a lot more than you might assume. All of this will give you a unique advantage over AI”.

Classroom Skye (1)


Classroom Skye (3)

  • Submit a photograph of your exercise and earn 5 points!