AI and Machine Learning
February 14, 2025
“Any sufficiently advanced technology is indistinguishable from magic." – Arthur Clarke, British Fiction Writer.
Artificial Intelligence (AI) is the embodiment of this statement. From algorithms that can predict data patterns to stimulating human intelligence, AI can do everything. Things that once felt like science fiction - computers diagnosing diseases better than human doctors, self-driving cars, virtual assistants being human companions, and many more - are now a reality, all thanks to AI.
What truly makes AI magic is not its ability to do complex tasks but its ability to seamlessly integrate into our everyday lives. Artificial intelligence (AI) is getting closer and closer to the heights and depths of human intelligence. This intelligence comes from building agents that act rationally. That is where we can define the AI technique as a composite of three areas. It is a type of method built on knowledge that organizes and uses this knowledge and is aware of its complexity.
Let’s dig in to break down the AI techniques and understand what contributes to AI development.
Artificial intelligence operates on the foundation of techniques that allow machines to copy human intelligence. These techniques will enable the system to understand, learn, and improve over time. Here’s a detailed account of the methods:
Artificial intelligence (AI) agents perform a search algorithm in the background to complete their expected tasks. That’s why search is a significant building block for any artificial intelligence (AI) solution.
Any artificial intelligence (AI) has a set of states, a start state from where the search begins, and a goal state. By the use of search algorithms, the solution reaches from the start state to the goal state.
This is done through various approaches:
By utilizing these techniques, AI systems gain precise knowledge about the problem in question.
Once the search is complete, the collected information needs to be understood. To process and act upon this information, AI agents need to first understand the information, which can be achieved through knowledge representation. In knowledge representation, beliefs and facts are modeled in a manner that they assist in intelligent decision-making.
There are various forms of knowledge representation, some of which are:
Abstraction helps in simplifying the complexity of AI by making it more comprehensible and manageable. It helps in addressing the issue of the “black box”. Opacity is a massive barrier to gaining confidence and the adoption of artificial intelligence (AI).
In abstraction, the system is structured into many manageable layers, which reduces the cognitive load on developers and fosters transparency. Thus improving trust and confidence in AI.
There are various areas under which the AI techniques of search, knowledge representation, and abstraction span. Some of these are listed below:
This process is associated with the extraction of valuable patterns and insights from large datasets. The application of this process can be seen in the retail sector, where retailers use this process to understand the patterns in customer data and predict customer behavior.
It enables the system to see images and, thus, analyze and interpret visual data. Thus, with this ability, AI is able to analyze all kinds of data. Its key application can be seen in the case of autonomous vehicles, where the system can analyze the outside environment for navigation.
This system does not explicitly require programming. It can learn from the past data and its environment and improve over time. The personalized recommendations on streaming platforms and e-commerce websites are a form of machine learning, as they learn customer preferences on the basis of their search history or purchase/watch history. If you're curious to explore the concepts of machine learning, check out the IIIT Hyderabad AI course.
This enables the AI system to understand the human language. Thus, computers can understand and respond to the languages known to humans. Its application can be seen in virtual assistants and chatbots. If you're someone interested in exploring it further, you can consider enrolling in the IISc Generative AI course, which can equip you to take up the lucrative role of a Prompt Engineer. Demand for prompt engineers has increased ever since the introduction of ChatGPT, and with adequate, prompt engineering skills, you can easily acquire a high-paying job.
One of the key contributions of AI is robotics. It is the integration of AI with computer systems that enables the system to perform human tasks. Robotics play a crucial role in production/manufacturing sectors, where production can be carried out with high precision, and human accidents can be avoided.
When we think of artificial intelligence (AI), we think of machines, computer systems, algorithms, and models trying to get closer to human intelligence. Today, this space straddles many areas, like natural speech recognition, expert systems, machine learning, general AI applications, and more. The future, however, is mapped towards singularity, and there is hope that AI will exceed our expectations and assumptions and that we will enter the realm of super AI.
To get there, a lot of pieces of the AI puzzle would have to come together. Here are a few important ones.
AI is nothing but intelligence that learns, re-learns, and reinforces its learning through the data that it gets. This data can either be specifically fed into the modes or designed to collect this data independently. Hence, the volume and quality of data are extremely important in determining the efficacy and relevance of AI for any purpose.
Big data is a new form of information asset, and it requires massive processing models and the right computing muscle to take care of its flow, velocity, scale, and density. For Big Data to expand and extend human intelligence through AI, we would need to master many dimensions of Big Data. They would include, but would not be limited to, speech recognition, IoT and sensor data, image recognition, unstructured data, and real-time data of many formats.
The space of Big Data is growing rapidly and in multiple directions. According to Fortune Business Insights, the global big data market analytics market size is valued at approximately USD 348 billion in 2024, which is projected to reach USD 924.39 billion in 2032. This will help the companies increase their operational efficiency, and it will definitely help AI systems improve and grow.
Continuous innovation significantly contributes to the development and improvement of AI. AI is much more than the fundamentals of the technology that power it. Its real impact and possibility lie in the way it is augmented consistently. It has to get the right research and creative impetus to explore new models, processing modes, algorithm designs, and enablers. There are many constraints for AI as we see them today. They deter many enterprises from leveraging AI to its full potential.
This implies that continuous innovation is mandatory to make AI work to its full potential. Some expected results in the field of AI with continuous innovation include the following -
Tech professionals are at the very heart of AI development, as they are the driving force behind innovations in the field of AI. From software engineers working on the development of machine learning to data scientists collecting and curating datasets to train AI, every tech professional contributes to AI.
There are tons of examples to show the contribution of tech professionals, like the contributions of engineers in OpenAI, who created ChatGPT and revolutionized natural language processing. Additionally, the AI team at Tesla developed autonomous vehicle technology and made self-driving vehicles, which was once science fiction, a reality.
These efforts and discoveries are proof that tech professionals are transforming industries with their significant contributions to AI’s development.
Companies are still not sure about handling a lot of AI ramifications. That’s where the algorithms need to evolve and iron out these kinks. Businesses are still worried about abort risks like explainability, privacy, equity and fairness, and cybersecurity. Additionally, privacy, fairness, and ethics are among the top concerns associated with AI use.
Thus, at this moment, there is a need for algorithms that can address these issues and bolster the excitement that enterprises have towards AI. If all these ducks fall in a row, AI will swim to new shores of possibility and impact soon.
The advancements in algorithms can help AI transform industries, solve complex problems, and redefine human productivity, which can change the world of business forever.
A lot hinges on how excited the top leaders of an organization feel about AI. Investments will drive more innovation and more capabilities ahead. Businesses have realized the importance of AI in helping them increase their revenues, which is why they are keen on increasing their investments in AI research.
For instance, Google DeepMind invested billions of dollars in AI development, including AI drug discovery business with AlphaFold. On the other hand, Netflix is willing to pay a hefty salary of around USD 900,000 to machine learning project managers, as the company knows how it can benefit and increase business with AI.
Realizing the potential of further development, tech giants like NVIDIA and Google are willing to support AI start-ups. For instance, OpenAI competitor AI21 Labs was able to raise USD 155 million from its investors. These investments are expected to support AI development in the form of faster processing models and real-world applications.
AI depends a lot on how models use data, learn, and apply their intelligence. That is where AI would need higher and wider processing alternatives. Especially in the area of real-time data. The adoption of deep learning - a type of machine learning that uses neural networks and can sometimes deliver superior results.
However, for businesses, deep learning is still in the pilot stage. So, the advancements in deep learning and investments in its adoption can help businesses realize the greatest outcomes from AI.
At this point, it is clear that AI is not just for tech professionals and developers; leaders and decision-makers within the business need to understand its potential. The IIM Calcutta AI for Leaders course is perfect for senior-level managers who wish to see themselves as a business leader in the near future. This course can help them build the capability to integrate AI seamlessly into business processes.
It was once thought that AI would replace humans. However, AI’s introduction is just like any other industrial revolution, which replaced old jobs with new jobs. The adoption of AI by businesses is creating new jobs, and to be ready for these jobs, professionals are expected to acquire adequate skills and knowledge needed for these professional roles.
Businesses are increasingly investing in the development of AI, which includes investments in AI research, refining big data, subscribing to the right platforms, and acquiring the right talent for the organization. According to a 2024 LinkedIn report, demand for AI-specialized roles has increased by 74% in the last 4 years, and around 97 million new jobs will be created in 2025.
So, what are you waiting for? Get yourself enrolled in one of our AI courses to make your career future-proof.
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Why is AI the hottest buzzword in the Tech World?
AI today is a topic of conversation, a subject for corporate presentations, a space for technological debates; in general, a term uttered by many who are familiar with a machine. AI is a concept that is being adopted by many sectors; indeed the latest and hottest buzz word in the world of technology.
However, the fact is Artificial Intelligence has remained in the nascent stage of development for quite a while. When one hears the word `artificial intelligence’ the visuals of different characters from science fiction come to mind. What was a mythical concept of time travel or spacecraft with extra-terrestrial creatures is not considered a futuristic science anymore? Though still primitive in form, AI can solve unique business problems, provided one knows and decides how to leverage the technology to its potential.
James Goodnight, Co-Founder and CEO of SAS says: `AI is certainly being hyped as another buzzword, and there are many companies rushing to jump on the AI bandwagon’. Despite the hype, AI is a significant emerging technology that will continue to grow in importance. Years ago the term `cloning’ became very popular; and in course of time, was included in the general context and usage, where anything which is replicated was described as `cloned’. Dolly, the sheep (the first mammal cloned from an adult somatic cell) became a widely talked about subject. Along the same lines, people have started using AI to describe any analytics problem, be it faulty equipment or credit card fraud. The catchword now is AI, and Sophia, the Robot has replaced Dolly sheep. With everyone using the buzzword AI, one needs to see whether reality aligns with this hype.
There is so much information on the internet stored in the last few years. Wall Street Journal recently stated that the internet is filling up with good morning messages, and largely by Indians, many of whom are yet new users of the internet! On a more serious note, there are huge quantities of data collected and stored in the recent times. Almost all information is online and accessible to net browsers. A tool to statistically filter, analyze and process large data sets, not merely as a process but with intelligence, is the primary need of industries, investors, for that matter, any establishment across sectors and geographies. A growing technology like AI can unlock the value of data, and eventually help transform businesses, industries, and above all, people’s experiences. It is no surprise that AI being such a powerful concept has become not just a household term, but a word across the technology world.
Though the research in Artificial Intelligence dates back to the 1940s, it is only around the 1980s that AI progressed from research to application. In recent years, however, interest in the field of artificial intelligence has sky-rocketed as evidenced by the amount of funding invested in researching and adopting it.
To go back to sci-fi, we have seen in many robots featuring stories about how the machine ultimately overpowers the man to the point of bringing about total annihilation. This threat could be as real as the creation of artificially intelligent robots. Well, known personalities like physicist Stephen Hawking and Tesla CEO Elon Musk have been stressing on the importance of regulating artificial intelligence.
There is merit in the thought that AI technology will help society in a range of areas like transportation, education, health, manufacturing, retail, arts, medicine and so on. It can be a danger though if used to build weaponry. Leaders across the world have found this worrisome, even terrifying.
One has to admit, turning a buzzword into business is exciting, but like communications specialist Lindsay Kwan says: The danger of artificial intelligence is that we can both overestimate its power today and underestimate its capabilities tomorrow.
Which, between the two, is riskier, is a matter of conjecture.
AI has come to stay high
In the mid-eighties, Small Wonder, a sci-fi TV series entertained adult and child alike. The story revolved around an android in the form of a 10-year-old girl. Looking back, the series was indeed very amateurish and loosely structured. The point is it introduced for the first time, the machine made man to the lay person. Thirty odd years later we hear of Saudi Arabia’s decision to grant citizenship to a robot. Endowed with artificial intelligence, visual data processing and facial recognition, the robot, Sophia is designed to get smarter over time.
The citizenship as such is of hardly any consequence; what it signifies is that Artificial Intelligence (AI) has come of age. Critics have spoken at length about the absurdity of awarding citizenship and an array of rights to a robot, while Saudi ironically is yet highly restrictive about women’s rights. However, many see this as a positive move towards making the country tech savvy and progressive.
In the computing history, man and machine interfaces have only improved and enhanced over a period of time – hence both are not mutually exclusive. Satya Nadella, CEO of Microsoft, said AI, mixed reality, and quantum computing will shape the world in the coming years. The crux of the matter is that the technology world will have a blend of the real and virtual. While current technology enables in us the ability to collect data, Nadella referring to AI says, what is more important is to reason over the data to create intelligence. This is most critical as a differentiator between mechanical equipment and an AI enabled robot. In the mid-fifties the term GIGO (Garbage In Garbage Out) became a descriptive term for computers, primarily because the machines lacked the ability to think for themselves. Whatever output emerged was only as accurate as the information entered. Deep down, computers still are improvised calculators. Recent development in AI comes under the aegis of `machine learning’ and involves programming computers to learn from example data or past experiences and process information based on that.
So how does machine learning impact tasks and get independent of human influence (in terms of execution of task)? Let’s take an example: Automatic transmission in cars has been prevalent for quite some time and in India, they are getting popular by the day. With increasing traffic bottlenecks people are finding it more convenient to drive a car which does not require changing gears. The driver needs to navigate by keeping the steering in control. With AI, autonomous driving, a wide step ahead of automatic transmission, the technology required for a car to accelerate, turn, and brake on real time roads without human intervention is not anymore impossible or far into the future. The recognition that autonomous driving was a prediction problem solvable with machine learning meant that autonomous vehicles could start to become a reality in the marketplace years earlier than has been anticipated.
By breaking down tasks into components, we can begin to see how AI will affect the workplace. AI is usually framed in terms of machines versus humans, but gradually we see it more in terms of understanding the level of judgment necessary to pursue actions. In cases where whole decisions can be clearly defined with an algorithm (for example, image recognition and autonomous driving), we expect to see computers replace humans. This will take longer in areas where judgment cannot be easily described – where there is high level of ambiguity in arriving at decisions or solutions. In other words, where the task is not a mere process with anticipated outcome and the pros and cons fail to fall into previous experience; AI may not accomplish the same without human intervention.
Product companies are all uniformly using AI during their product launches. This has caused excitement as well as anxiety amongst the people. Will AI assist and enable humans to perform better and with ease, or will AI replace humans to the point of redundancy?
With AI moving into the cerebral workspace, it cannot be denied that it will soon take over many tasks hitherto performed by humans. Just as the advent of machines had a significant impact on how people performed physical labor and dispensed with many jobs handled by humans, AI is soon to be responsible for transitioning many of the tasks performed by humans to robot responsibilities.
In the medical field, AI can help improve diagnostics to a very high level, treatment and care will still require human attention and action. Online shopping has rendered many counter sales jobs redundant; but, at the same time courier and door delivery jobs have increased. Apart from these examples, AI is already playing a large role in the areas of music, architecture, education, manufacturing, gaming and just about any sector or industry. In the corporate world (as a quick example) AI has gotten into recruitment – a very important task in any company. Automatic process was only used before to sort resumes based on keywords. Now, many organizations like Ola or Accenture are using bots to streamline their recruitment processes.
As Artificial Intelligence systems evolve and become more and more sophisticated, job displacement is bound to happen. However, interestingly, many new jobs very different from the ones that are currently in existence will be created. Jobs that involve creativity and those that require advanced degrees and highly specialized skill sets will be available alongside the continuous technology transformation.
AI’s contribution to transforming society – Vehicle automation
A span of 50 years in technology’s perspective can be deemed a very long period; however, if one goes through the article called ‘The Electronic Highway’ published by IEEE spectrum in 1969, it feels like the 2 engineers Robert E. Fenton and Karl W. Olson, traveled half a century ahead to come up with specific solutions for vehicle autonomy. What is happening now in the automotive industry is what Fenton & Olson had offered as solutions. Though obsolete today, the solutions look at the future with predictions that are coming true now.
In a fast world, such as the one we live in today, commute needs to be swift, smooth with minimal effort by the commuter. Just as the autopilot system in airplanes was designed to relieve the pilot of some tasks so that he could sit back a bit, specially on long haul flights, the emerging self-driving cars require little or no effort from the driver to navigate the vehicle.
For a long time autonomous vehicles, though talked about in articles and technology forums remained a distant dream. The Electronic Highway notwithstanding, suitable technology with adequate software/hardware was a challenge. However, many automotive companies have created ready to hit the road self-driving cars that can drive, park, obey traffic signals, negotiate an intersection and navigate through busy roads. In his book `Rise of the Robot’, Martin Ford says Google’s autonomous fleet in 2012 had driven over 300,000 accident-free miles on roads ranging from freeways jammed with stop-and-go traffic to San Francisco’s famously convoluted Lombard Street. The next year, Google released data showing that its cars consistently outperformed the typical human driver not only in acceleration and braking, but as well in general defensive driving practices.
Almost every car manufacturer across the world is actively into making fully automated, or partially automated cars. The conservative, or rather cautious approach of not going in for fully automated vehicles is because decision-making – a critical aspect in driving cannot be fully owned by machines. Technology is advancing in leaps and bounds and yet every designer, manufacturer or user is aware that no technology can be 100% reliable – at least not as yet. While roads remain consistent with good conditions maintained, the surrounding environment is always dynamic. Traffic diversions, construction activities, weather conditions, sudden road blocks are situations which no system is equipped to handle end to end. And above all, the crux of the issue here lies in the fact that an autonomous car may not be able to decide when to seek the human driver’s intervention.
Having said, autonomous vehicles have some to stay. Self-driving cars’ contribution to society would be a huge reduction in the number of traffic jams and all cars will drive with equal speed and in adherence to traffic rules and lane discipline, thus minimizing the chaos that can occur due to the diverse driving styles of humans.
As Martin Ford states, after electricity, the single innovation that is central to the development of society is the automobile. Very soon, we will look back wistfully to recall the long late evening drives, steering the wheel in sheer pleasure while listening to music and enjoying the landscape.
The other side of the coin no more road rage, driving tickets or penalties!
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