AI and Machine Learning
August 20, 2024
In the age of digital transformation, innovation has become imperative to success. Businesses worldwide are embracing generative AI to revolutionize their operations, products, and services. The strategic integration of generative AI across different sectors has made it the cornerstone of competitive advantage and groundbreaking innovation.
According to Global institutions like the World Bank and the IMF, India’s economy is the fastest-growing among leading nations. The study states that India can experience a substantial spur in its GDP over seven years (2023-24 to 2029-30). This cumulative impact on GDP (Gross Domestic Product) may range from US$1.2 trillion to US$1.5 trillion, contributing an additional 0.9% to 1.1% in annual CAGR (Compound annual growth rate) (Link).
Leveraging the immense capabilities of Generative AI to boost productivity and efficiency can significantly accelerate a country’s growth trajectory. To fully capitalize on this potential, increased investment in Generative AI, education, and upskilling is essential.
Generative AI has applications in nearly every sector, thus professionals and tech enthusiasts must learn about it. Let’s dive deeper into understanding different aspects of GenAI, including popular AI jobs and the AI skills in-demand in the industry.
Generative AI, popularly abbreviated as GenAI, is a broad term that describes different types of artificial intelligence (AI) to produce images, new text, video, or audio clips. Technically, generative AI learns patterns from training the data and generates new, unique outputs with similar statistical properties.
Generative AI is a powerful technology that keeps getting better. At its core, it uses AI foundation models trained on massive amounts of unlabeled data. These models are like prediction algorithms, and they can be fine-tuned for specific tasks. One of the most common uses of generative AI today is creating content based on natural language instructions. But it goes beyond that - generative AI is also being used in various industries to develop new drugs, design chips, and create new materials.
Some examples of genAI models include Google’s Bard, ChatGPT, or DALL-E from OpenAI.
ChatGPT or DALL-E: GenAI developed by OpenAI, a Microsoft-supported, profit-limited company dedicated to advancing AI for the benefit of humanity
Google Bard: Google's GenAI, integrated with products like Google Lens and Gmail, runs on the PaLM-2 language model, which was trained on the largest dataset of any generative AI at its release
In the fascinating world of AI, generative models are like creative machines. They use different techniques to produce new data, often mimicking human-made content. These models come in various flavors, each with its own approach to crafting fresh content. Here's a look at some of the most common types of generative AI models:
GenAI has become a buzzword recently, finding its way to different sectors and positively impacting everyday operations. It has become a transformative force that has a wide range of applications. There has been a buzz around these terms related to generative AI:
Three key parameters make for a successful generative AI model, namely:
Quality: Generating high-quality outputs is crucial, especially for user-facing applications. Imagine a speech generator that stumbles over its words—not exactly easy to understand. Similarly, image generation should produce results that look like real photographs, not blurry approximations.
Diversity: An excellent generative model shouldn't just repeat the most common patterns in its data. It should also capture the less frequent variations, ensuring a diverse range of outputs. This helps avoid biases and keeps the generated content interesting.
Speed: Many interactive applications, like real-time image editing tools, rely on fast generation speeds. This allows users to see the results of their edits instantly, making the creative process smoother.
Generative AI is a type of machine learning therefore, at its core, it works by training software models based on data without being explicitly programmed.
The secret behind their learning is statistics. Generative AI models are typically based on probability. They analyze the data to understand the likelihood of certain features appearing together. This allows them to predict what elements are most likely to come next in a sequence.
Based on Neural Network Power, which means inspired by the human brain, artificial neural networks lie at the core of generative AI models. These complex structures can handle intricate patterns much better than traditional machine learning methods. They learn to mimic these patterns and use that knowledge to create entirely new content when given a prompt.
The model learns the language of the data, recognizing the grammar, style, and common phrases. Then, when prompted with a starting point, it can generate new content that follows those same rules. But unlike human supervision, generative AI models can continuously learn and improve on their own, constantly refining their ability to create.
Apart from these, generative AI can be run on a variety of models that use different mechanisms to train the AI and create outputs. These include generative adversarial networks (GANs), transformers, and Variational AutoEncoders (VAEs).
Understanding how generative AI works would be incomplete without learning about the benefits that it offers. Like any significant technological development, GenAI also opens up a world of potential.
Generative AI is a very powerful tool for streamlining the workflow of researchers, creatives, engineers, scientists, and much more. The use cases and possibilities with GenAI span all industries as well as individuals.
It is used in various industries, including healthcare, manufacturing, software development, financial services, media and entertainment, and advertising and marketing.
Here are the most popular generative AI applications:
Generative AI can offer new and disruptive opportunities to grow revenue, improve productivity and efficiency, as well as provide better risk management. In the coming years, GenAI is expected to become a differentiator and a competitive advantage.
The opportunities that generative AI brings to a business can be split into three categories:
Generative AI is set to impact an array of industries, including media, architecture, manufacturing, automotive, design, engineering, defence, aerospace, electronics, medical, interior, and energy industries by augmenting the core processes with AI models. Moreover, GenAI will impact marketing, corporate communications, design, and training and software engineering by augmenting the supporting processes spanning across organisations.
According to research it is believed that by 2025, more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today. Generative AI looks promising for the pharmaceutical industry, given the opportunity to reduce costs and time in drug discovery.
That's not all. Approximately 30% of outbound marketing messages from large businesses and organizations will be synthetically generated, up from less than 2% in 2022. However, text generators, such as GPT-3, can already be used to create marketing copy and personalized advertising.
In the manufacturing, automotive, aerospace, and defense industries, generative design optimizes designs based on specific goals and constraints, such as performance, materials, and manufacturing methods. This accelerates the design process by providing engineers with a range of potential solutions to explore.
In different businesses, though not all comprises content creation of some type. Generative AI will transform jobs by creating text, images, hardware designs, music, and video. Consequently, workers will need to shift from content creation to content editing, requiring an altogether different skill set.
The workforce's interaction with applications is expected to evolve as they become conversational, proactive, and interactive, often requiring a redesigned user experience. In the near future, generative AI models will go beyond responding to queries and start making unsolicited suggestions. Generative AI is set to increase employee productivity by imitating human thought processes to lead the process of developing strategy.
However, this change in the workforce can vary depending on different factors, including industry, location, size, and business offerings.
According to Gartner (Link), Gen AI is expected to make a very strong impact on businesses over the course of next five years. To put that into perspective, Gartner shares some statistical data to help understand the impact and integration of Gen AI across different sectors.
The demand for AI skills, particularly in Generative AI, is rising, but a talent shortage makes it challenging for organizations to find suitable candidates. Highly sought-after skills include ML Ops, Natural Language Processing, Java, Python, and more.
Leading to the importance of upskilling to stay market-ready and adapt to emerging trends. Continuous learning is crucial as roles can become obsolete over time. Job seekers as well as professionals must be prepared to learn, unlearn, and relearn to remain relevant in the future.
Some of the popular Generative AI careers include Research Scientist, Machine Learning Engineer, Data Scientist, Computer Vision Engineer, Natural Language Processing (NLP) Engineer, Game Developer, Creative AI Artist, Autonomous Systems Engineer, AI Product Manager, AI Ethicist, and many more.
With a variety of job roles in Generative AI and its growing impact across different sectors has made it important for job seekers and professionals to upskill in generative and stay ahead of the trend.
According to marketsandmarkets, the artificial intelligence market is witnessing a rapid growth trajectory, projecting a substantial growth value surge from approximately USD 214.6 billion in 2024 to USD 1339.1 billion by 2030. This reflects an upward trend characterized by a remarkable CAGR of 35.7% between 2024 and 2030. The rapid digital transformation across different industries, such as finance, manufacturing, healthcare, and retail, is now demanding AI solutions to increase efficiency, decision-making, and customer experience.
The growing demand for professionals who can offer AI solutions is also increasing.Working professionals as well as job seekers can opt for the Advanced Certification Programme in Generative AI and Prompt Engineering, an intensive course that offers the knowledge, skills and expertise required to build and deploy advanced Generative AI solutions for real-world language and vision tasks.
This well-designed course is offered in association with India’s leading institution Indian Institute of Science (IISc), Participants will master selecting and deploying Generative AI tools and industry-standard LLMOps (Large Language Model Operations) methodologies. The program designed and delivered by top-tier IISc Faculty, this four-month course includes live interactive sessions, industry-specific modules, campus visits, and capstone projects.
In the era of growing technological advancements, GenAI withholds immense potential to transform the everyday operations of enterprises. Therefore, enrolling in this program can offer learners the opportunities to start a career in GenAI as well as offer professionals the ability to transform into job roles requiring GenAI applications.
Click here for more information about this course!
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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 and its Impact on Jobs
The world, at the moment, is awash with speculations, opinions, and fears about artificial intelligence and its perceived `threat’ to human held jobs. Many researchers and scientists are still of the conviction that AI cannot, in actuality, replace human intelligence. They still maintain that AI can assist and augment but never take the place of humans at the workplace.
AI has been an area of passion and pursuit for a long time. However, it is in the recent years that it has come to stay, moving from the research desk to active field. AI is now dominant, taking on an exponential role, influencing particularly the fields of gaming, natural language processing, speech and handwriting recognition and robotics.
Since artificial intelligence is poised to take on many tasks that the human has so far been performing, is there a threat of unemployment across the globe? How is AI going to impact the job scene world over?
Leading research and advisory firm, Gartner comforts job holders in its statement: Robots are not here to take away our jobs; they’re here to give us a promotion. Instead of replacing the human at work, artificial intelligence may increase the number of job opportunities, especially in healthcare, public sector, education, to name a few. As the gig economy is gaining popularity, retail, manufacturing and transportation could possibly be the first to adopt AI for work. Industry heads are not as optimistic as Gartner. Leading personalities Elon Musk and Stephen Hawking feel AI may go beyond taking away people’s jobs. AI could eventually become a real threat to human beings. For now, it is helpful and comforting to go with Gartner’s report which states by 2020, AI will generate 2.3 million jobs, exceeding the 1.8 million that it will remove.
When one of the Big Four consulting firms, Deloitte delved into UK census data for the last 140 plus years only to discover that technology has very steadily taken over many jobs that were being handled by humans. Deloitte also quickly pointed out that jobs in the professional sector have increased multifold.
This pattern of generating new jobs as and when a machine takes over current jobs will continue in this era of AI. Emerging technologies will allow for emerging new jobs, not only for people working with new technologies but also for the enablers who need to manage the increasing requirements of content development and training to bring people up to speed.
The fact is, AI is contributing in a major way to the evolving landscape of work. Going back to the gig economy, the concept is gaining prominent space as it provides scope for professional work within a time-bound period. The big gig companies differ from traditional firms not only in conducting business online but also in allowing and encouraging individuals to freelance and engage `just in time’. As TED CEO Richard Saul Wurman sums up the online marketplace platform: For the first time in human history, individuals can design a life around the pursuit of interesting work.
AI is going definitely going to define the way work happens. How much humans will be involved in `working’ is a subject of speculation for anyone who is familiar with AI. But if we have to think about the next decade, we need to remember though AI is enabling a machine to be independent of man, it is dependent on data. The more data AI consumes the smarter it gets. But to sift data and feed AI and obtain output is a process as laborious as the creation of AI.
Google AI chief John Giannandrea explained this concept of AI’s need to be fed data by using an example involving his 4-year-old daughter. She was able to identify a 19th-century “penny-farthing” bicycle anywhere once he had told her what it was. But computers, need to be shown 100,000 penny-farthings before identifying one. Once they’d seen 100,000, they’d probably be better at identifying them than humans are.
Companies may talk at length about AI and secretly look forward to right-sizing their employee base by bringing in machines. However, they would need to clean up messy databases, work on file management and in overall make their data algorithm-friendly, before any machine can really take over a man’s job. How long will this take? Well, time will tell.
