Age Of AI And ML: Are We There Yet?
Artificial intelligence (AI) and machine learning (ML) as terms that challenge mankind have been around for decades. One of the first successful ML systems was developed way back in 1955, by Arthur Samuel, who created a computer program for a game of checkers on an IBM 701 machine, then the most advanced computer. This program could learn by using a combination of a tree search algorithm with learned weights. In 1962, Samuel decided to have the program challenge a checkers champion in a publicised match. The program won. But with the advent of cheap personal computers in the 1970s and 1980s, mankind found better ways to use computer technology.
However, the seeds of the idea that AI will one day be able to do anything a human can do were sowed in the human mind. In the last three decades, this has been the inspiration for many movies such as the Terminator series and the Matrix series. While many of these movies depicted a breakthrough technology that overnight changed the world as we know it, scientists would tell you that most changes are generally an evolutionary process rather than a spark, and takes years to develop. In the Terminator series, the humanoid would take instructions and act on them literally. It would not understand sarcasm or human emotions, but other than that it was the perfect warrior.
Till 2010, the world was still enamoured with the wonders of the world wide web (www) and its enormous possibilities. Whatever work was being done in the AI and ML fields was more in academics or the military and it was never tried out on the common man.
Till 2010, the world was still enamoured with the wonders of the world wide web (www) and its enormous possibilities. Whatever work was being done in the AI and ML fields was more in academics or the military and it was never tried out on the common man. However, in the last decade, we have grown by leaps and bounds in this area. In April 2017, the Harvard Business Review, in its paper titled ‘Thinking Through How Automation Will Affect Your Workforce’ by Ravin Jesuthasan and John Boudreau, mentioned how companies should look at automation, how to go about in your journey, what kind of jobs are being or will be taken over by machines and how organisations should approach them. The paper divided the different work automation opportunities into three types of AI-supported automation: robotic process automation (RPA), cognitive automation, and social robotics.
RPA automates high-volume, low-complexity, routine administrative “white collar” tasks. For example, most call centre activities can be automated, but certain tasks like talking to a frustrated client still have to be handled by a human agent.
Cognitive automation takes on more complex tasks by applying things like pattern recognition or language understanding to various tasks. For example, the Amazon Go retail store has no cashiers or checkout lanes for billing. However, other elements of the “job” of store associates are still done by humans, including advising in-store customers about product features.
Between 2014 and 2016, AI took a leap in the form of AI assistants such as Alexa (Amazon), Siri (Apple), Google Assistant (Google) and Cortana (Microsoft).
Social robotics involves robots moving autonomously and interacting or collaborating with humans through the combination of sensors, AI, and mechanical robots. For example, “driverless” vehicles, where robotics and algorithms interact with other human drivers to navigate through traffic. Deconstructing the “job” reveals that a human agent still plays an important role. While human “co-pilots” no longer do the work of routine navigation and piloting, they still do things like observing the driverless operation and stepping in to assist with unusual or dangerous situations. Indeed, it is often overlooked that the human co-pilot is actually “training” the AI-driven social robotics, because every time the human makes a correction, the situation and the results are “learned” by the AI system.
Between 2014 and 2016, AI took a leap in the form of AI assistants such as Alexa (Amazon), Siri (Apple), Google Assistant (Google) and Cortana (Microsoft). Again inspired by Jarvis, the Iron Man’s assistant, these systems could understand natural language and respond. Jarvis was highly customised and understood Tony Stark’s moods, emotions and sarcasm. Within AI assistants, Siri could be witty when it comes to responding but the very nature of the AI remained consistent in that it was able to perform pre-defined activities after having understood natural language requests.
Every day, we also use multiple tools based on AI and ML. In cyber security, hackers as well as cyber security professionals use AI/ML. AI/ML-based models are being used for tracking and monitoring computer systems. In retail, most of us use some sort of map solution. All this while, AI and ML have been used for solving a specific problem or a challenge and auto-learning enables them to adapt faster.
Enter ChatGPT, an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned. GPT or Generative Pre-trained Transformers are a family of language models generally trained on a large corpus of text data to generate human-like text. The "pre-training" in its name refers to the initial training process on a large corpus of text where the model learns to predict the next word in a passage, which provides a solid foundation for the model to perform well on downstream tasks with limited amounts of task-specific data. This model was very well explained in a popular web series, Person of Interest, in 2011.
The best points about Chat GPT are that it remembers earlier conversations, doesn’t mind being corrected and declines any inappropriate requests. Its limitations include misleading information or it may provide biased content, Also, it has limited knowledge as it is still growing as a platform. Like all other AI/ML platforms before it, it does not understand sarcasm or emotions. However, even with its current functionalities, ChatGPT can revolutionise some of the industries we know.
For example:
Customer service: Customers can ask in natural language and the system would respond without asking the user to press 1 for credit card and 2 for banking for example
Education: It can provide a personalised learning experience
Health care: It can assist doctors and nurses with the latest techniques
Writing: This may certainly be the end of the writer’s block problem
Translations: From one language to another
Legal: It could provide the complete legal encyclopedia at your fingertips
Marketing: with so much segmentation and data analysis at your fingertips, the creation and maintenance of target groups would be all the simpler and many more
Will there be job losses? Maybe. But, like most technologies, there will be job enrichment. Things that used to take a long time can now be done in minutes. Google made our lives easier, but it was left to the individual to search for the right result. ChatGPT can find the right answer for you in natural languages. But it will be left to the individual to decide if the opinion is correct or biased.
Will there be job losses? Maybe. But, like most technologies, there will be job enrichment.
Technology is a great leveller, but not in the way that many people imagine it to be. So, a lot of jobs that require little deviation from the norms would get automated or disappear altogether. For example, today you go to an event planner to create a three-day event for a destination wedding and there are two people. One is the person who plans it, and he generally reports to the person who executes the plan. With AI you just need the executioner who brings in the personal and emotional touch. The creation of the plan can be done within minutes by AI for you.
My interpretation is that any new technology makes the smart person smarter, and his or her achievements become even more spectacular. Imagine an Einstein or Edison in today’s generation, a brilliant physicist and mathematician would now have the power of chemistry and biology. So, while some of us would find ways of using the new technology to enhance our productivity some would be stuck in TikTok and the fear of losing jobs.
My interpretation is that any new technology makes the smart person smarter, and his or her achievements become even more spectacular. Imagine an Einstein or Edison in today’s generation, a brilliant physicist and mathematician would now have the power of chemistry and biology.
And to answer the million-dollar question: Are we there yet? The next big task would be to conquer and understand emotions and the states of the mind of the user. Further, if it eventually starts understanding sarcasm like Jarvis, it will take us one step closer to the AI/ML-driven world. It may sound scary to some but for me the journey itself is amazing, so let’s enjoy the ride while we are on it.
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