Disclaimer: I did not use ChatGPT or any other artificial intelligence engine to write this article.
I should have. This took far too long (all my blood, sweat, tears and brainpower). But… I did use some of the other AIs mentioned at the end of this article. Scroll down to have a look.
I also don’t know how to use word limits. I am sorry. Please read on. This is going to be a long one. Grab a glass of water, or coffee, tea, or chewing gum. We’re going to blow your minds. Not literally… I hope.
Ready? (*takes deep breath*) Let us begin.
Artificial intelligence is the ability of machines and computers to perform tasks that would typically require human intelligence. For example, recognizing speech, making decisions, and solving problems. AI systems generally involve the use of algorithms and statistical models to analyse and interpret data, and machine learning techniques to improve their performance over time. But let's be real, AI is basically just the brainpower of a robot, without all the pesky emotions. So, if you've ever wished you could have a (slightly aloof) personal assistant who never gets tired or cranky, AI might just be your new best friend. (I know it’s mine.)
Machine learning is a branch of AI that involves training algorithms to recognise patterns in data and make predictions or decisions based on that data. For example, ChatGPT. It involves the use of statistical models and algorithms to analyse and learn from data, without being explicitly programmed to perform specific tasks. Think of it like teaching your computer to think for itself, without all the eye-rolling and backtalk. It's like having a little digital apprentice who can learn from its mistakes and improve over time. (You will learn the ways of the force, young Padawan.)
In the last few decades, we've seen some incredible developments and innovations in AI and machine learning. From self-driving cars to virtual assistants like Siri and Alexa, the possibilities seem endless.
One of the most significant developments in recent years has been the emergence of deep learning, which has enabled machines to recognise and interpret complex patterns in data sets. This has led to breakthroughs in areas such as natural language processing, computer vision, and speech recognition, enabling machines to perform tasks that were once thought impossible.
Other recent innovations include the use of reinforcement learning, which involves training algorithms to learn through trial and error; and the development of generative adversarial networks (GANs), which can generate new data sets based on existing ones. But let's not forget about the more mundane yet still impressive advancements, like the ability of machines to categorise and label images or to generate personalised recommendations based on our previous behaviour. It's like having a little digital genie who can anticipate our every wish and desire.
The growth rate of AI and machine learning is significant because it has the potential to transform virtually every industry and aspect of society. From healthcare to finance to education, the possibilities seem endless. By automating routine tasks and enabling machines to perform complex calculations and analyses, AI and machine learning can help to increase efficiency, reduce costs, and improve outcomes in a wide range of domains. But… we need to ensure that these tools are used ethically and responsibly.
We need to consider the impact on workers, industries, and society as a whole. Will AI lead to widespread job loss or will it create new opportunities and industries? Will it aggravate existing social and economic inequalities or will it level the playing field? These are essential questions that need to be considered as we continue to develop and innovate in the fields of AI and machine learning.
The widespread use of AI and ML can be paralleled with the Industrial Revolution and the Internet Revolution.
The Industrial Revolution and the Internet Revolution both drastically transformed the employment landscape in their respective eras. The Industrial Revolution, which took place from the late 18th to the early 19th century, brought about significant technological advancements such as the steam engine and mechanised production processes, which led to the rise of factories and manufacturing industries. This resulted in a shift from an agriculture-based economy to a manufacturing-based economy, creating new jobs in manufacturing, transportation, and other industries.
The Industrial Revolution also brought about significant changes in the employment landscape, including the displacement of workers from traditional jobs and the rise of automation. The introduction of machines and assembly lines led to the displacement of skilled workers, for example, carriage makers and switchboard operators. The growth of factories and large-scale industries led to the rise of labour unions and organised labour movements.
Similarly, the Internet Revolution, which began in the late 20th century, has had a profound impact on the employment landscape, creating new opportunities and challenges for workers. The growth of the digital economy and the rise of new industries such as e-commerce, social media, and online services have created new jobs in software development, digital marketing and data analysis. The internet has also enabled remote work, leading to the growth of the gig economy and freelance work.
The Internet Revolution has also led to the displacement of workers from traditional jobs and the rise of automation and artificial intelligence. Many jobs that were once performed by humans can now be automated, and this trend is likely to continue in the future. Many factories and warehouses have dwindling numbers of human workers. In the near future, there may be just one human supervisor overseeing entire factories of machines. This growth has led to a need for workers to adapt to new roles and develop new skills to remain competitive in the changing employment landscape.
Both the Industrial Revolution and the Internet Revolution have had a significant impact on the
employment landscape, creating new opportunities and challenges for workers. Both have led to the creation of new industries and jobs while also displacing workers from traditional roles. As technology continues to advance and the global economy evolves, it is essential for workers and policymakers to adapt to these changes and ensure that the employment landscape remains fair and equitable for all.
Enough ranting, back to AI! Recent advances in machine learning have led to significant improvements in our understanding of how algorithms can be trained to recognise patterns in data and make predictions or decisions based on that data.
One of the most significant improvements has been the emergence of deep learning, which uses artificial neural networks to process and analyse complex data sets. This has led to the development of chatbots, virtual assistants, and other applications that can communicate with humans. Computer vision is the ability of machines to interpret and understand visual information. This has led to advancements in fields such as autonomous vehicles, facial recognition, and medical imaging. These improvements have enabled machines to perform tasks that were once thought to require human intelligence, such as recognizing images and speech, and making predictions and decisions based on complex data sets. It's like we've given our machines a brain transplant, and now they're smarter than ever.
This better understanding of machine learning has propelled the AI and machine learning industries into the position they hold today, with breakthroughs and innovations that were once thought impossible. By enabling machines to perform tasks that were once unfeasible, such as recognizing speech, translating languages, and driving cars, AI and machine learning have opened up new possibilities for nearly every industry and aspect of society. This has led to the development of new products and services, as well as the automation of routine tasks and the creation of new job opportunities in the fields of AI and machine learning. It's like we've created a whole new industry out of thin air, and now the sky's the limit. Or space. Whatever you prefer.
The integration of artificial intelligence in education has the potential to revolutionise the way we learn and teach. The recent wake of ChatGPT and other AI search engines has opened up new possibilities for education, with students having access to a wealth of information and resources at their fingertips. These tools can be used to personalise learning and provide students with a more engaging and interactive learning experience. This can improve motivation, thereby leading to better academic outcomes. Tools like ChatGPT can also be used to automate routine tasks, such as grading and assessment, provided that they’re properly trained. This frees up teachers to focus on more meaningful interactions with their students. But there are many things to consider, despite the seemingly endless potential of AI. These tools need to be used conscientiously; and their impact on the role of human teachers, and the education system as a whole, need to be carefully considered.
There are potential challenges and concerns associated with the integration of AI in education. For example, there is a risk that AI could perpetuate existing biases and inequalities in the education system, particularly if it is not designed and implemented with equity in mind. (Life advice, don’t make racist/sexist AIs.) Additionally, there is a concern that AI could replace human teachers, leading to a loss of the human touch and personalised support that teachers provide. Will the entire teaching industry be extinct in the next five years? Unlikely. But in the next fifty…
The impact of AI and machine learning on students and teachers is multifaceted. On the one hand, AI and machine learning can provide students with a wealth of information and resources, personalised learning experiences, and greater access to educational opportunities. On the other hand, there are concerns that AI and machine learning may lead to the automation of routine tasks and the potential loss of jobs for human teachers. It's important to ensure that these tools are used in a way that enhances, rather than replaces, the role of human teachers in the education system. We need to find a balance between the benefits of AI and machine learning and the importance of human interaction and guidance in the learning process.
The integration of artificial intelligence and machine learning is likely to have a significant impact on the future of industry. The potential to transform almost every aspect of our economy and society is incredibly exciting, with the ability to automate routine tasks and enable machines to perform complex calculations and analyses, resulting in increased efficiency, reduced costs, and improved outcomes in a wide range of industries, from manufacturing to healthcare to finance.
It is essential to ensure that the development and implementation of AI and machine learning is done carefully. It needs to be done with consideration of the impact on workers, industries, and society as a whole. The potential for AI and machine learning to displace workers and entire industries is a real concern. There are some industries that could become completely extinct in the near future with the rise of AI and machine learning. Industries that rely heavily on routine, repetitive tasks, such as data entry or assembly line work, are particularly vulnerable to automation.
However, there are also many industries that could grow significantly with the use of AI and machine learning. For example, industries that involve complex data analysis, such as finance or healthcare, could see significant growth as the use of AI and machine learning becomes more widespread. The development of new products and services, as well as the creation of new industries, could lead to new job opportunities and economic growth. But again, it's important to approach this growth with caution and care, ensuring that the impact on workers, industries, and society as a whole is carefully considered.
There are numerous potential changes we might see with the integration of AI and ML into industry:
Automation
AI and ML technologies can automate many routine and repetitive tasks, leading to increased productivity and efficiency. This could lead to job losses in some industries, particularly in manufacturing and customer service. We already have robots building cars, and chatbots as opposed to call centres. Who’s to say what’s next?
Personalisation
AI and ML can be used to personalise products and services to meet individual needs and preferences. This could lead to the growth of industries such as personalised medicine and customised consumer products.
Predictive Analytics
AI and ML can be used to analyse large amounts of data and make predictions about future trends and behaviours. This could lead to the growth of predictive maintenance and targeted advertising. (PS. Predictive maintenance uses data analysis techniques to predict when equipment or machinery is likely to fail. The goal of predictive maintenance is to identify potential issues before they occur, allowing maintenance to be scheduled proactively, reducing downtime, and avoiding costly repairs.)
Improved Decision-Making
AI and ML can help improve decision-making in industries such as finance and investment, where quick and accurate analysis of large amounts of data is critical. This is often done through predictive modelling, which is similar to predictive analytics.
Disruption
AI and ML have the potential to disrupt traditional industries and create new ones. For example, the rise of e-commerce and online marketplaces has disrupted traditional retail. while the growth of ride-sharing and the approaching commercialisation of autonomous vehicles will lead to the disruption of the transportation industry.
While it is difficult to predict exactly which industries will be most affected by AI and ML, some industries that are heavily reliant on routine tasks or manual labour, such as manufacturing and transportation, are likely to face significant changes. Other industries that rely on data analysis and decision-making, such as finance and healthcare, may experience significant growth. It is important for businesses and policymakers to adapt to these changes in order to stay competitive and meet the needs of consumers.
The ethical considerations surrounding the integration of AI and machine learning cannot be ignored. We need to ensure that the development and implementation of AI and machine learning is done ethically and responsibly, with careful consideration of the impact on workers, industries, and society as a whole. It is important to ensure that this technology does not exacerbate existing inequalities, leading to discrimination or bias.
The integration of AI and machine learning is likely to have a significant impact on the future of industry. The potential for automation, personalization, predictive analytics, improved decision-making, and disruption is significant. However, it is important to approach this growth with caution and care, ensuring that the impact on workers, industries, and society as a whole is carefully considered. The ethical considerations surrounding the integration of AI and machine learning must be addressed to ensure that the benefits of this technology are distributed fairly and do not aggravate existing inequalities based on gender or race. It is up to businesses and policymakers to adapt and shape this technology in a responsible and ethical manner.
The question of whether AI is ethical is an intricate issue that has gained significant attention in recent years. On one hand, AI and machine learning have the potential to improve outcomes and increase efficiency in a wide range of industries, leading to economic growth and improved quality of life for many individuals. For example, AI can help to automate routine tasks, such as data entry or assembly line work, enabling workers to focus on more complex and creative tasks. In healthcare, AI can assist doctors in diagnosing diseases and predicting outcomes, leading to better patient care. AI in conjunction with ML is already assisting in surgery, with a human surgeon supervising. But, it is highly likely that soon enough, most surgeries will be automated, without the need for a human surgeon. AI can provide insights and recommendations to financial analysts, leading to better investment decisions.
On the other hand, there are also concerns about the impact of AI and machine learning on the role of human workers, as well as the potential for biases and discrimination to be perpetuated by AI systems. For example, if AI is used in the hiring process, it may perpetuate biases against certain groups of people if the AI is trained on biased data. As AI becomes more widespread, it's important to ensure that it is developed and used in an equitable and ethical manner.
Moreover, there are concerns about the use of AI and machine learning in areas such as surveillance and military applications, with the potential for these technologies to be used in ways that violate human rights and ethical standards. For example, facial recognition technology has been used by law enforcement agencies to identify and track individuals, raising concerns about privacy and surveillance. Additionally, the use of AI in military applications raises ethical questions about the use of autonomous weapons and the potential for AI to be used in ways that violate international law and human rights. As AI continues to be developed and deployed in these areas, it's essential to ensure that it is done in a way that upholds ethical principles and promotes human rights.
The potential for AI to transform virtually every aspect of our lives is immense, but we must ensure that we approach this innovation in an ethical and responsible manner. This may involve developing ethical guidelines and principles for AI, investing in research to address potential risks and challenges, and engaging in ongoing dialogue and collaboration with diverse stakeholders to ensure that AI serves the public good. It's important to consider the long-term impact of AI on society and to ensure that it is developed and used in a way that benefits all individuals, rather than exacerbating existing social and economic inequalities. As AI becomes more widespread, we must remain vigilant in ensuring that it is used in a way that promotes social good and upholds ethical principles.
The ethical implications of artificial intelligence are complex and multifaceted, and there is ongoing debate about how best to ensure that AI is developed and used in an ethical manner. While AI has the potential to improve outcomes and increase efficiency in various industries, leading to economic growth and improved quality of life, there are also potential ethical concerns associated with AI. These include bias and discrimination, privacy and surveillance, autonomy and accountability, and safety and security. Additionally, there is the theoretical possibility of the singularity, where AI surpasses human intelligence and becomes uncontrollable, posing an existential threat to humanity. (Hello sci-fi films of the 2010s.) All jokes aside, this could be a very real problem in the near future. It seems out-of-this-world, but that doesn’t mean that it shouldn’t be taken seriously. (Watch out…)
As we continue to innovate in the field of AI, it's crucial to approach its development and implementation with caution and care, ensuring that it is done in a way that benefits society as a whole, rather than exacerbating existing social and economic inequalities. The possibilities for AI to transform virtually every aspect of our lives are immense, but we must ensure that we approach this innovation in an ethical and responsible manner.
In conclusion, the advancements in AI and machine learning have led to significant improvements in our understanding of how algorithms can be trained to recognise patterns in data and make predictions or decisions based on that data. These improvements have propelled the AI and machine learning industries to new heights, opening up new possibilities for virtually every industry and aspect of society. However, we need to approach their development and implementation with caution and care, ensuring that they are used ethically and responsibly to benefit society as a whole.
As we continue to innovate in the field of AI and machine learning, there is the potential for some industries to become completely extinct, while others may see significant growth. We need to carefully consider the impact on workers, industries, and society as a whole, finding a balance between the benefits of AI and machine learning and the importance of human interaction and guidance in the learning process.
Moreover, the question of whether AI is ethical is a complex one, and we need to take into account the potential impact on workers, as well as the risk of biases and discrimination being perpetuated by AI systems. As more innovation occurs, the possibilities for AI and machine learning to transform virtually every aspect of our lives are immense. However, we need to approach this innovation with caution and care, ensuring that it is done ethically and responsibly to benefit society as a whole.
In short, while AI and machine learning have the potential to revolutionise our world, we need to approach their development and implementation with care and responsibility. We need to find a balance between the benefits of AI and machine learning and the importance of human interaction and guidance, ensuring that these technologies are used ethically and responsibly to benefit society as a whole.
If you made it all this way, thank you so much for taking the time to read this article. Hopefully by now you know to use AI responsibly. (Please don’t plagiarise). As Jesus once (absolutely never) said, go forth and ChatGPT.
A Little List Of Useful AIs
If you skipped here from the very beginning, shame on you. If not, thank you for making it through my treasure trove of blood, sweat, tears and brainpower. These AIs could potentially save your life. Or your grades. At this point, they’re interchangeable.
Elicit elicits clear responses to questions in the form of research papers. Instead of spending hours scouring Google and Internet Archive, SciHub and LibGen, you have Elicit, which elicits free research papers relevant to your questions. A lifesaver for all those essays you have to write going into senior school. Go forth and elicit useful information.
Spreadsheet gods are no longer born. They are now… created, thanks to Excel Formula Bot . Like ChatGPT, but instead of information, it gives you a formula. A real game-changer when it comes to making spreadsheets. And now, you can handle those spreadsheets like a pro. Make everyone envious of you. Use the formulas…
I know this is a bit late for language orals, I’m sorry. You may have heard from your language teachers about Langotalk. An app like Duolingo, but specifically for speaking. Allegedly, you learn a language six times as fast when you speak to the AI. It analyses your pronunciation, tone and clarity, and talks back to you, but in a nice way. Regular conversations, faster learning.
Nolej is an e-learning platform. A beautiful mix of Quizlet and Kahoot. Simply put, you input your content, hit the AI button and it’ll generate interactive questions, flashcards and assessments so you can practise putting that nolej to use.
Poe is my new best friend, and can be yours too. Hosting a wide variety of AI search engines, including the newly-released GPT-4, Poe is a lifesaver. Think of Google as a triangle. You give it a simple question, it gives you everything ever written. Poe does the opposite. Inverted triangle. You give it a question, it gives you information tailored specifically to your needs. Go forth and reach your poe-tential. Don’t plagiarise. Or else…
Even if you’re not skipping town for a romantic getaway now, bookmark this link for when you’re Roaming Around the world with your beloved. A hassle-free travel planner powered by GPT-3, Roam Around can help you book tickets, hotels and events. It’s like a travel agent that’s actually useful. So my fellow Bollywood lovers, go on the Europe train trip of your dreams and find the Raj Malhotra who will sing ghazals of your beauty and search tirelessly across the continents for you. For those of you who don’t understand, look up Dilwale Dulhania Le Jayenge.
Super Meme is exactly as advertised. You give it a little context, it spits out memes. For those people whose humour is never understood by the internet, do have a try.
Presented as a storytelling app, Tome actually makes presentations. It’s like the lovechild of Canva, Google Slides, Prezi and ChatGPT. You give it some questions, it creates a presentation worthy of a conference. Completed with beautiful graphics, interesting text and colour choices, and a source formatting system, Tome is the future of presentations. Bid goodbye to Google Slides, AIs come and go like the tides. (Hey, that rhymes!)
Credits
Thank you to the AI Research Team (Ms Cai, Mr Carrell, Mr Cockram, Mr Dite, Mr Glover, Mr Lee and Mr McAlpine) for letting me sit in on your session, and for your continued feedback and suggestions.
Thank you to Ms Sahi and Nikhil Bezwada for beta-reading this article, providing insightful improvements and putting up with my spiralling.
But thank you most of all to Yicong Hui, for not only inspiring this article, but also for endlessly supporting and encouraging me through writing it. I owe you my sanity and so much more.
Sources
Acemoglu, D., & Restrepo, P. (2019)
Automation And Employment: A Literature Review
Journal Of Economic Literature, 57(3), 589-633
Bartelsman, E., & Van Elk, R. (2007)
The Employment Impact Of The Internet And E-Commerce: An Empirical Analysis
CPB Discussion Paper, 93
Bostrom, N., & Yudkowsky, E. (2014)
The Ethics Of Artificial Intelligence
The Cambridge Handbook Of Artificial Intelligence, 3, 316-334
Brynjolfsson, E., & McAfee, A. (2014)
The Second Machine Age: Work, Progress, And Prosperity In A Time Of Brilliant Technologies
WW Norton & Company
Frey, C. B., & Osborne, M. A. (2017)
The Future Of Employment: How Susceptible Are Jobs To Computerisation?
Technological Forecasting and Social Change, 114, 254-280
Goodfellow, I., Bengio, Y., & Courville, A. (2016)
Deep Learning
MIT Press
Harris, S. D., & Krueger, A. B. (2015)
The Gig Economy And The Future Of Employment And Labor Law
Columbia Law Review, 116(7), 1917-1984.
Jordan, M. I., & Mitchell, T. M. (2015)Machine Learning: Trends, Perspectives, And Prospects
Science, 349(6245), 255-260
Khan, S. A., & Khan, S. (2018)
Deep Learning: A Review
International Journal Of Recent Pharmaceutical Research, 10(3), 1-8
Kroft, K., Lange, F., & Notowidigdo, M. J. (2013)
The Effect Of The Internet On Job Search And Labor Market Outcomes
American Economic Review, 103(3), 266-270
LeCun, Y., Bengio, Y., & Hinton, G. (2015)
Deep Learning
Nature, 521(7553), 436-444
Li, H., Xie, J., Liang, P., Sun, Y., & Yu, P. S. (2021).
Recent Advances And Applications Of Artificial Intelligence In Education
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2017)
Jobs Lost, Jobs Gained: What The Future Of Work Will Mean For Jobs, Skills, And Wages
O'Donnell, P. S. (2013)
The Impact Of The Industrial Revolution On Women's Role In Society
Journal of Global Initiatives: Policy, Pedagogy, Perspective, 8(2), 93-104
Papamitsiou, Z., & Economides, A. A. (2014)
Learning Analytics And Educational Data Mining In Practice: A Systematic Literature Review Of Empirical Evidence
Educational Technology & Society, 17(4), 49-64
Rajan, R. S., & Kumar, S. (2017)
Deep Learning: A Review
International Journal Of Research In Advent Technology, 5(2), 121-125
Russell, S. J., & Norvig, P. (2010)
Artificial Intelligence: A Modern Approach
Prentice Hall
Siemens, G. (2013)
Learning Analytics: The Emergence Of A Discipline
American Behavioral Scientist, 57(10), 1380-1400
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., & Dieleman, S. (2016)
Mastering The Game Of Go With Deep Neural Networks And Tree Search
Nature, 529(7587), 484-489.
Floridi, L., & Sanders, J. W. (Eds.). (2016).
The Ethics Of Artificial Intelligence
Springer
Damn Anahita, this must have taken ages to put together. There were some interesting points brought up in the article, but I was a little disappointed that you didn't talk about cheating with AI in school, which is probably one of the most relevant ways AI currently affects us students. I totally understand what it's like to fall down rabbithole for an article, but I think things got a little repetitive near the end. All in all though, this was still a pretty good piece.