Artificial Intelligence - is my job safe?
Jon LeeView bio
Whilst the way it was delivered in Pichai’s speech was seemingly casual, this is an extremely interesting statement. The development and advancement of Artificial Intelligence (AI) is considered of great importance for humanity, and its impact on society and the world is expected to be significant. Like the impact of electricity and fire in the past, it will change the way we live, work and communicate.
However, the comparison to fire can also be interpreted as a reference to the dangers associated with the development and use of AI. Just as fire has the potential to be incredibly useful for heating, cooking, and other purposes, it can also be extremely dangerous and destructive if not controlled and used responsibly.
AI has the potential to bring about many benefits but also poses potential risks if not developed and used ethically and responsibly. These risks include the development of autonomous weapons, perpetuating and amplifying biases, privacy violations, deepfakes - and the possibility of job displacement.
With growing appetites for the use of technology across industries as diverse as manufacturing and healthcare, as many as 5% of global jobs could completely automated in coming years. The World Economic Forum report writes that by 2025, 85 million jobs may be displaced by a shift in the division of labour between humans and machines. Digital disruption is threatening to overwhelm unprepared companies who attempt to leverage revolutionary automation technologies without a long-term plan; many global companies have therefore been seeking help on the matter, leading to a booming digital consulting market.
To better understand the risks to us as engineering consultants, we first need to understand the definition of AI.
What is AI?
AI can be defined in terms of capabilities and functionality. There are three types of AI, based on capabilities:
Artificial General Intelligence (AGI) is somewhat out of reach today and, for some, is expected to be so for the foreseeable future. The concept of Artificial Super Intelligence (ASI) is still science fiction1, so the threat to our jobs today lies in the most common forms of Artificial Narrow Intelligence (ANI) including:
- Machine learning: a method of teaching computers to learn from data, without being explicitly programmed. This includes Generative AI, where learning algorithms can be used to create new content, including audio, code, images, text, simulations, and videos. Examples include Amper Music, Open AI Codex, DALL-E, ChatGPT, AnyLogic and Pictory.
- Deep learning: an advanced field of machine learning that involves training artificial neural networks with multiple layers to recognize patterns in data. Deep learning is the logic behind the face verification algorithm on Facebook, self-driving cars, virtual assistants like Siri, Alexa and so on.
- Natural Language Processing (NLP): the use of AI to understand, interpret, and generate human language, such as in speech recognition and language translation. Twitter uses NLP to filter out terroristic language in their tweets, while Amazon uses NLP to understand customer reviews and improve user experience.
- Computer vision: the use of AI to interpret and understand images and videos, such as in facial recognition and object detection. Examples are self-driving cars, pedestrian detection and parking occupancy, digital pathology, cancer detection. In the built environment, predictive maintenance of equipment and PPE detection on site.
- Robotics: the application of AI to control and automate physical devices and machinery, such as industrial robots and drones. Sophia the humanoid is a good example.
- Expert systems: AI systems that mimic the decision-making abilities of a human expert and can be used in fields such as medicine, finance and law.
- Reinforcement learning: a type of machine learning where an agent learns to behave in an environment, by performing certain actions and observing the rewards/results which it gets from those actions.
So as an engineering consultant today, what are the risks?
I was told this recently by ChatGPT, an AI driven chatbot. It may be biased (in more ways than one) but after verifying the numbers from other sources, it got me thinking – if this is reasonably accurate what makes up this potential 40%?
We need to think about the activities that we perform in our jobs, and what proportion of those activities take up most of our time. We then need look at the potential for automating these tasks. For example;
- Jobs in the most immediate danger of high levels of automation (up to 100%) are those most heavily based around routine. These include sectors such as manufacturing, agriculture and logistics.
- On the opposite end of the scale are occupations relying on higher cognitive, social and emotional skills such as psychiatrists, social workers and legislators.
As engineering consultants, we sit somewhere in the middle of these extremes, with some activity types being more susceptible to automation such as data collection and processing and administrative tasks. However, a significant proportion of our jobs require skills that have significantly less automation potential such as:
- Managing and developing people
- Applying expertise to decision-making, planning and creative tasks
- Interfacing with stakeholders
- Performing physical activities in unpredictable environments
This is supported by a study undertaken in 2017 by McKinsey that suggests that the professional and management sectors have a large proportion of time spent in activities that are difficult to automate. See the extract below:
Source: Mckinsey Global Institute. A future that works: Automation, employment and productivity. January 2017.
So I’m clearly not going to lose my job to a robot any time soon, but how can I turn this into a positive?
Whilst our jobs as professional consultants aren’t 100% at risk, there are some inherent tasks that could incorporate AI to streamline and enhance our output. For example:
- Imagery could be generated from DALL-E, Midjourney and Stable Diffusion for use in reports, marketing material and presentations.
- Visual site surveys and 3D mapping could be carried out by autonomous drones (although this could be counter-intuitive for noise surveys!)
- Road, rail and pedestrian flow simulations can be generated by AI to assist in planning.
- Reports can be generated combining Natural Language Processing (NLP) and template creation. Natural language descriptions of what needs to be included in the report can be provided by the consultant, and an AI algorithm can generate a report template for a specific type of report, and then customize it with the data to create a report.
- AI can be used to create visually appealing reports and presentations with charts, graphs, and other visual aids. AI algorithms can identify the best way to present data visually based on the type of data being presented.
- By incorporating AI technologies, Building Management Systems (BMS) can make more informed decisions and optimize building performance in real-time, including energy optimisation, predictive maintenance, indoor environmental quality (air/light/noise, etc.), occupancy management and so on.
However, it is important to note that AI powered tools should be used with caution and best practices should be implemented such as:
- Use best practice to authenticate content of generated information, fact check and collaborate with colleagues to verify information
- Be aware of and mitigate for potential bias in the output of AI generated information
- Avoid inputting sensitive or confidential information
- Do not directly copy AI generated code without understanding the content
- Use official sources of AI and understand the terms and conditions of use
- Understand the implications of copyright infringement issues with learning datasets
And what about the future?
We’re not going to see a slowdown in the pace of technology adoption, and it may even accelerate in some areas. Individuals and businesses need to be prepared for what is round the corner.
Engineering consultants could consider moving away from document-oriented work with legacy tools and look to other industries for inspiration (such as the software industry). Here the use of tools and repositories has drastically changed for the better and the sector in general has become more agile and focused on collaboration.
Successful future engineers will need heightened communication skills, the ability to work in teams, global knowledge, a good grasp of the impact of sustainability on innovation, and technical expertise to create a futureproof service. To keep up with the pace of change, individuals need to learn the latest tools and digital trends in the industry. Upskilling is key to gaining the knowledge to accelerate careers by closing skill gaps that can help advancement.
The reality is that AI is more likely to augment and enhance the work of engineers rather than replace them. AI has the potential to automate repetitive tasks and provide engineers with powerful tools for data analysis, process automation, and predictive modelling. This can free up engineers to focus on more creative and high-value tasks, such as innovation, problem-solving, and design.
By responsibly embracing AI and adapting their skills to work alongside these new technologies, engineers can remain competitive and continue to drive innovation in their fields.