AI and the Future of Work:
The Fascinating Story of AI Tech Evolution over Decades and How You Can Prepare for Today’s AI-led Work Disruption
Be it healthcare or advanced space exploration, artificial intelligence (AI) technology is transforming the future of the modern workplace. According to the World Economic Forum, AI is poised to replace 85 million jobs globally by 2025. Further, by 2030, 65% of the existing job skills are likely to change due to AI-related development.
What’s equally concerning is the harmful impact of AI-led innovations. One such impact is the rise of deep-fake social media videos around popular celebrities. The latest case is that of actress Rashmika Mandanna. Another interesting case is that of Hollywood’s Writers Guild going on strike after 16 years over their fight against the use of AI technology.
Nevertheless, we are now at the “point-of-no-return” with regards to AI.
How does AI technology impact your future career and skill development?
To understand the future of AI, let’s first understand how it evolved over the years and how it is transforming various industries.
Evolution of AI — from the 1950s
Did you know that AI technology is not a 21st-century invention but has evolved since the 1950s? Yes, the AI journey began in the 1950s with the work of Alan Turing, who conducted the Turing test to evaluate if a machine could “mimic” human intelligence.
The 1960s saw the creation of the first AI-based programming language, LISP, by John McCarthy. In the mid-60s, Joseph Weizenbaum developed ELIZA, the computer program capable of natural language processing (NLP), or the ability for machines to understand natural human language.
The next breakthrough in AI development came in 1997, when IBM’s AI-powered Deep Blue system defeated the reigning chess world champion, Gary Kasparov.
The year 2011 saw the emergence of Apple Siri, the world’s first voice-activated assistant.
Developed by Google DeepMind, AlphaGo became the first AI-powered program to defeat a human world champion in the Go game in 2017.
Generative AI innovation opens multiple avenues for businesses to reimagine their operations and attain maximum efficiency, for example, Open AI ChatGPT and Google Bard. As AI continues to innovate, here’s an interesting look at how it can impact various industries.
How AI is impacting various industries
From improving patient diagnosis in healthcare by implementing early detection tools to reducing financial risks and improving customer experience in banking by deploying algorithms that can detect and prevent financial fraud through data anomalies or deviations.
AI has also transformed retail industry revenue and outreach by providing personalised shopping experiences to its consumers, and in a similar fashion, the ed-tech industry is leveraging generative AI to improve learning experiences and outcomes.
Why is AI technology relevant?
In modern enterprises, AI technology helps improve decision-making and the efficiency of core business processes. For instance, AI-driven foundation models can lead to various use cases (or business applications) across industries. Essentially, a foundation model is an AI model trained on large volumes of data. For example, using NLP, AI foundation models can easily interpret human language and provide a human-like response.
Previous AI systems were only accessible to technology experts and research teams. With the current democratisation of AI, this technology is now accessible to all business users and teams. AI democratisation has eliminated all technology barriers, thus providing AI capabilities and benefits to more users.
Here are some more reasons why AI is relevant to modern business:
· Higher accessibility and affordability
· Faster pace of innovation and development
· Improved business agility and scalability
· Higher ROI from AI investments
As an innovative technology, AI is already reshaping the future of work. Let’s explore four influential dimensions of AI technology in the business domain.
4 Dimensions of AI technology
Essentially, the effectiveness of AI technology is dependent on four dimensions. Let’s discuss each of these four dimensions.
1. Speed
Speed is a crucial dimension of any AI-powered system. This entails the question, “Can the AI system deliver speed or faster decision-making in real-life scenarios?” For example, self-driving or autonomous cars need instant decision-making to avoid an accident or collision to operate smoothly in any traffic condition. This is where speed is critical as an AI dimension.
2. Accuracy
Along with speed, AI systems must be reliable and accurate to drive business decision-making. As an example, AI-powered voice assistants must accurately identify spoken language (or speech) to produce relevant results. Similarly, AI in medical diagnosis needs to be highly accurate to enable doctors to prescribe the right diagnosis and treatment methods.
3. Cost
Without cost-effectiveness, AI technology cannot justify business investment. Cost is a significant dimension for companies planning to use AI systems for repetitive tasks or even creative work (like drafting an automated email or summarising a long document).
4. Compliance
The final dimension is essentially AI ethics and compliance. For example, can AI drive ethical decision-making that requires human qualities like empathy and cultural understanding? Similarly, legal compliance can differ across countries, thus requiring human professionals to address the complexities.
What skill avenues are available for you to thrive in an AI-dominated business environment?
Here’s how you can prepare yourself for the inevitable AI-led disruption.
For now, AI technology cannot replace the human workforce fully. Current AI applications still require human intervention or participation. Here are 4 AI-related avenues for gaining and improving your AI skills:
1. Data skills
No AI model or system can function properly without being fed with high-quality data. Low-quality data always leads to inaccurate predictions and outcomes. With the right data skills, you can ensure that AI systems have access only to high-quality data. Some of the common data skills include data enrichment and data quality.
2. AI algorithms and models
Exposure to general supervised and unsupervised learning models; build basic clustering, regression, decision tree, random forest, SVM, PCA, CNN, RNN, and XgBoost models; and develop specialised knowledge around vision and NLP. Developing methodologies to interface with LLM’s would require unique skills, different prompt engineering strategies, and chain of thought or tree management for concise natural language dialogue handling.
For more AI-related opportunities, you can focus on gaining an advanced understanding of the latest concepts like generative AI, GPT, prompt engineering, and chain-of-thought (CoT) prompting.
3. Continuous learning
AI skill development is all about upskilling your technical expertise. To make this possible, you need to adopt the mantra of continuous learning. This is achievable through a host of learning resources, like online workshops, AI conferences, and hackathons.
4. AI projects
This avenue is about gaining hands-on experience working on AI projects. This typically involves enrolling in an internship at an AI development firm or working on real-time AI projects.
Conclusion
This blog highlights how AI technology has evolved over the years and how it is set to transform the future of work. We have also learned about some of the common applications of AI in various industries.
With over 25 years of experience in AI technology, Nagarajan Karuppiah is the Head of AI at Tata Digital. In his Tata NeuSkills master class, he elaborates on how AI technology can disrupt the workforce and reshape the future of work. You can attend this master class for free and learn about the latest trends and opportunities in this field: https://neuskills.tatadigital.com/masterclasses/ai-and-the-future-of-work
