A beginner's guide to AI - How AI is changing the career landscape

Artificial Intelligence (AI) has captivated the minds of scientists, philosophers, and technologists for decades, evolving from a speculative vision to an integral part of our daily lives.

Perhaps you have been living under a rock or just too busy to pay more than passing attention to all the discussion on AI so here is our beginners guide to AI and how it is changing our world and careers for the busy professional.

1 What is AI and how did it begin?

AI stands for Artificial Intelligence, which refers to the ability of machines and computer systems to perform tasks that normally require human intelligence.

A genius scientist

The narrative of AI began long before the advent of computers — as far back as ancient mythologies that imagined inanimate objects endowed with intelligence. Yet, it wasn't until the mid-20th century that AI transitioned from the realm of science fiction into the domain of feasible technology.

Alan Turing, the mathematician and computer scientist who helped us win WW2, (see the film The Imitation Game with Benedict Cumberbatch), is often hailed as the father of theoretical computer science and artificial intelligence. He introduced the idea of a universal machine that could process numerical data in the late 1930s. However, it was his 1950 paper, Computing Machinery and Intelligence, that lit the fuse of the AI revolution. Turing proposed the question, can machines think? and offered the 'Turing Test' as criteria for intelligence in machines.

A brief timeline

 1960s and 1970s -AI research showed significant progress in areas like problem-solving algorithms and knowledge-based systems. Projects like ELIZA and SHRDLU demonstrated machines ability to engage in human-like dialogue and comprehend natural language.

 The 1980s heralded a resurgence of interest in AI occurred after a Winter of Disinterest where work stalled due to lack of computational power, due to new approaches and increased computational power. The introduction of machine learning algorithms allowed machines to learn from data without explicit programming.

 In parallel during the 80s and 90's  studies in neural networks also began producing promising results, moving AI towards systems capable of learning and adapting. These networks were inspired by the structure of the human brain. These developments moved AI from simple rule-based systems to those capable of learning and adapting.

 1997 IBM's Deep Blue beats world chess champion Garry Kasparov.

Then the explosion of data and computational power in the 21st century fueled an AI boom.

 2001 NASA develops an AI system for Mars exploration rovers

 2010 Google's self-driving car navigates over 140,000 miles without human intervention

 2011 IBM's Watson defeats human contestants on the game show Jeopardy!

 2016 AlphaGo, an AI program developed by Google DeepMind, beats world champion Lee Sedol in a game of Go

 2017 AI-powered virtual assistants such as Amazon's Alexa and Apple's Siri become mainstream products

 2018 The era of Chat GPT (Generative Pre-trained Transformer) and similar models began with the introduction of OpenAI's GPT-2 model. These advanced AI-powered chatbots are trained on large amounts of text data and use natural language processing techniques to generate human-like responses to user input.

 2019 Adoption of AI in healthcare for diagnosis and treatment planning increases rapidly

 2020 Use of AI in finance for fraud detection and risk management grows significantly

 2021 Introduction of AI-based chatbots for customer service becomes widespread. Other AI-powered chatbots such as Replika, Mitsuku, and Xiaoice have gained popularity for their ability to simulate human conversation.

 In 2021, AI was used in developing COVID-19 vaccines by predicting potential protein structures for the virus. This helped speed up the process of finding an effective vaccine. 

 Also in 2021 AI-based virtual assistants like Google Assistant and Microsoft's Cortana have significantly improved in their ability to understand and respond to complex queries.

 Since 2021 AI-powered translation services such as Google Translate and DeepL have become more accurate and efficient, breaking down language barriers for communication and business.

2022: The game changer: OpenAI, a company that Elon Musk helped form with Y Combinator impresario Sam Altman and a few others, released a public-facing interface to its large language model (LLM) artificial intelligence. Known as ChatGPT, this system served as an electric jolt to the nervous system of business and society at large.

Rapidly adopted by people numbering in the hundreds of millions, it revealed the potential of AI in a way people had not previously been capable of understanding. It brought the power of advanced AI to people in a fashion that was as easy to use as a search engine….ChatGPT has been the most successful new consumer technology launch in history. …it took Netflix about three and a half years to reach 1 million users, while ChatGPT got there in five days after its launch in late November 2022.

 [Source: David L Shrier, Welcome-to-ai-a-human-guide-to-artificial-intelligence, March 5, 2024]

Now, AI feels omnipresent, from search engines to voice assistants like Siri and Alexa.

Machine learning, deep learning, natural language processing, and computer vision have  matured significantly and are now underlying technologies for countless applications.

2 How does it work and how can it help your career?

AI works by using algorithms and data to learn patterns and make decisions or predictions.  This process involves three main steps: data collection, data processing, and decision-making.

AI can be broadly divided into two categories - Generative and Predictive.

Generative AI is a technology that creates new, original content like pictures, text, or videos  by studying large amounts of data. Think of it as a virtual artist that learns from various styles to come up with its own creations. It uses complex algorithms and machine learning  techniques to produce original content that mimics human creativity.  It supports innovation and applies its creativity to various problems.

Predictive AI, on the other hand, looks at past data to guess what might happen in the future, like a weather forecast for data, predicting what might come next, which helps companies  plan and make decisions.

Both types can work together in useful ways. For example, while generative AI can create designs for new products, predictive AI can estimate how popular these products will be. 

Generative AI can also create realistic examples to train predictive AI to be even smarter.

Business Applications

In the business world, these types of AI can do many things:

 Generative AI can write codes, make marketing materials, and customise educational content.

 Predictive AI can help with tasks like deciding if someone is a good fit for a loan or how credit card use might change.

Both types are powerful tools, but they require careful handling to ensure data privacy and security.

The Technology Behind Generative AI (for the techy minded)

Generative AI uses fancy algorithms for:

 Creating images or translating voices (Generative adversarial networks)

 Making structured data sets like tables (Variational autoencoders)

 Creating pictures or videos from prompts (Diffusion models)

 Writing texts, and even translating between formats like text, images, or robotic  instructions (Large language models)

Why Generative AI Can Be Good

It can:

 Generate codes for software automatically.

 Summarise large documents quickly.

 Respond to complex questions with clear answers.

When Companies Use Generative AI

It can help businesses by:

 Writing code

 Creating ads

 Personalising items for customers or staff.

 Chunking  down large topics into smaller, digestible component parts.

 Making custom guides for different job roles or skill levels.

 Adapting content for various regions or languages.

What About Predictive AI?

Predictive AI's main focus is to guide decisions by carefully analysing historical data and patterns. It helps:

 Predict future events or trends.

 Identify potential risks and opportunities.

 Optimise business processes.

It is the less sexy sibling to predictive AI, yet some suggest it is far more useful to most business settings than generative AI. 

3 How AI is changing the landscape of work and employment

Predictive AI is commonly used in industries such as finance, healthcare, retail, and marketing.

Generative AI is often utilised in industries such as technology, media and entertainment, advertising, and education. However, there may be overlap between the two when it comes to certain tasks or projects. For example, both generative AI and predictive AI can be used in marketing to personalise ads or predict customer behavior. It ultimately depends on the specific needs and goals of each individual business.

Regardless of which technology is being utilised, it is important for companies to carefully consider the potential costs and challenges associated with using AI, such as constantly updating and monitoring algorithms and addressing potential biases. Companies must also ensure that they are following ethical standards and protecting sensitive data.

As AI continues to advance and be integrated into various industries, businesses need to stay informed and adapt their strategies accordingly. By using both generative AI and predictive AI in the appropriate contexts, companies can gain valuable insights and improve decision-making processes.

While AI is creating new opportunities for businesses to streamline processes, improve efficiency, and make more informed decisions, it's also causing concerns about job automation and potential job loss. Some experts predict that certain jobs may become obsolete as AI takes over tasks that are repetitive or require a high level of data analysis.According to a study by McKinsey Global Institute, up to 800 million jobs worldwide could be displaced by automation and AI by 2030. 

This includes both blue-collar and white-collar jobs in various industries such as manufacturing, retail, healthcare, and finance. However, the same study also predicts that while some jobs may become obsolete, new ones will also be created as businesses adapt to the technology. Additionally, AI will also create new tasks and roles within existing jobs, allowing employees to focus on more creative and high-value tasks.

[McKinsey Global Institute, Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages. November 2017. https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages]

What you need to understand about AI in today's job market

To thrive in the job market today, individuals must have a basic understanding of AI and its capabilities. This knowledge can open up new opportunities for career growth and increase employability in a rapidly evolving workforce. 

Moreover, as AI continues to transform industries and job roles, having an understanding of its potential and limitations can give you a competitive edge. If you can use this knowledge to identify areas where AI can be implemented and improve processes, this will make you a more valuable asset to your organisation.

We will write more about AI and its impact on various professions and how it can be leveraged in upcoming posts. 

Has AI impacted your workplace or profession? Do you have concerns about the impact of AI on society?

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