Artificial Intelligence (AI) – What is It and Why Does it Matter?

Artificial intelligence, or AI as it’s more commonly known, has been gaining momentum for a while now. In the past few years alone we have seen its revolutionary impact on almost every industry out there and experts predict that this trend will only continue to grow in popularity over time. 

Artificial intelligence (AI) is a big change in the current technological landscape. It can be used for chatbots, financial models, predictive analytics, etc. to help companies become agile, accurate, insightful about their business model. Even if you do not like it or are ignorant about its presence, AI will be the new DNA of your reality. It is a reality that cannot be denied and one that businesses must start to reckon with soon.

As Andre NG puts it best, “Artificial Intelligence is the new Electricity.”

Artificial intelligence (AI) is supposed to augment and empower humans in what they do and how they live their lives. It is a tool, not a threat. In almost every sphere of life and work, AI injects automation, data power, and the impact of better decisions. This is how it transforms business with a staggering cut in costs and an unprecedented elevation of efficiency, agility, and customer intimacy. No wonder, AI is getting bigger and bigger every day. As IDC tracks, global revenues for the artificial intelligence (AI) market, including software, hardware, and services, are forecast to grow strongly in 2021 to $327.5 billion. It also pointed out that by 2024, the market is expected to break the $500 billion mark, with total revenues reaching a mind-blowing $554.3 billion. Interestingly, AI is gaining ubiquity across all the functional areas of a business. 

With advancements in machine learning (ML), conversational AI, and computer vision, we have entered an age where organizations are architecting converged business and IT process optimizations, predictions, and recommendations. They are, thus, enabling transformative customer and employee experiences. IDC has noted how enterprise demand for AI capabilities to support business resiliency and augment human productivity sustained double-digit expansion in 2020. This happened even as other discretionary projects experienced delays. So clearly, AI is here to stay. And its ripples are traveling far and wide.

“AI is supposed to be either the best or the worst thing to happen to humanity.” – Stephen Hawking

We used to watch them in movies just some years back but look around, and we can see them alive and kicking already. Chatbots, smart voice assistants with that special sixth sense, robots that can handle warehouses on their own, cobots helping men in factories, facial-recognition-based law enforcement, smoothly cruising self-driving vehicles, retail aisles powered with augmented reality (AR), IoT-run refrigerators and microwaves, self-driven supply chains, and whatnot. They are floating all around us.

AI has immersed its impact across a wide range of areas. It is helping us to think faster, better, and with more intelligent use of data. Let’s understand how and where AI is touching and redefining our lives.

What is artificial intelligence (AI)?

In short, AI is intelligence that computers build and give us to save time, make better decisions, and look at insights that escape the human eye. With deep learning (DL) and machine learning (ML) along its side, AI is a composite of technologies and approaches, redefining how we code, work, think, perform, and even live. The extreme flight of computing intelligence is getting closer and closer to how human intelligence works.

• Evolution of artificial intelligence (AI): The 1950s were the incipient years of AI marked by Issac Asimov’s sci-fi story and Alan Turing’s ‘Turing test’ in his paper. From there on, humanity, scientists, mathematicians, and philosophers started warming up to the very concept of AI. The 1990s and 2000s saw many milestones for AI. From IBM’s Deep Blue victory over the reigning world chess champion Garry Kasparov to Google’s Alpha Go win over Chinese Go champion, Ke Jie – AI dug its feet even deeper. Today, we have intelligent personal assistants and driverless cars – and whatnot – that signifies that AI is more than Issac’s sci-fi fiction.
• Methods of artificial intelligence (AI): AI can be differentiated into three major categories – Narrow, Strong, and Super AI. It can be narrow or higher-level AI depending on how many machines and models can learn on their own – and how close to human brains. It can be done through various approaches, ranging from computer vision to natural language processing (NLP) to machine learning (ML).
• Artificial intelligence (AI) vs. machine learning (ML) vs. deep learning (DL): AI is about building intelligent programs and machines to solve problems. Its subset is machine learning (ML), which helps systems automatically learn and improve from experience without explicit programming. Here, the hand-coding is skipped because the machine is “trained” with large amounts of data and algorithms. Then there is a subset of machine learning (ML) called deep learning (DL) that uses the biology of human brains or neural networks to analyze various factors with a structure similar to the human neural system.
• Benefits of AI for society: The power of AI is getting visible in all aspects of business and life. Like in understanding historic rain trends, clinical imaging, crime control, driverless transport, etc. It is also observed in many cognitive applications for tagging, clustering, categorization, hypothesis generation, alerting, filtering, navigation, and visualization. It is used in healthcare areas like robot-assisted surgery, dosage error reduction, virtual nursing assistants, clinical trial participant identifier, hospital workflow management, preliminary diagnosis, and automated image diagnosis. And also in BFSI areas like financial analysis, risk assessment, and investment/portfolio management solicitations. There is a clear augmentation of human capabilities in multiple areas through the effective use of AI.

As we can see – AI is pumping up humans with its horsepower for the mind. But when we say mind, there is more to it. Like a human mind, it is deep, complex, and still unexplored.

Philosophy of artificial intelligence (AI)

Just like any other abstract concept bubbling in the mind of a philosopher, AI, too, has bothered and entertained philosophers with a lot of paradoxes, assumptions, and doubts since its advent. AI can get a lot more Meta, a lot abstract, and a lot more complex than it looks on the surface. There are two ways to look at it: reasoning-based AI or behavior-based AI. One dimension is whether the goal is to match the human performance or, instead, ideal rationality. The other dimension covers the aspect of a purpose: to build systems that reason/think, or rather, systems that act.

• “Strong” versus “Weak” AI:  Weak AI is like artificial narrow intelligence (ANI). But Strong AI is artificial general intelligence (AGI), where a machine gets as smart as humans and can perform any intellectual task that a human being can.
• The Chinese Room Argument against “Strong AI”:  A human sitting inside the box is like a computer. He doesn’t understand Chinese and is mindlessly moving information around as computers would do.
• The Gödelian Argument against “Strong AI”:  Any consistent formal system that can do even simple arithmetic. There are true statements in the realm of number theory that cannot be derived from the axioms of the formal system. Hence, some statements, even if they are true, are not theorems of the formal system.

Now that we know what happens under the hood of AI, let’s see how to make the best use of it. And for that, we need to know what roads we need to drive with this new engine.

Artificial intelligence (AI) goals

AI is defined by what it is used for and how. So there is Narrow AI wherein AI can perform specific tasks the way – or better than- humans can. There is General AI, where machines become human like, make their own decisions, and learn without human input. This is where AI transcends logic and enters emotions. There is applied AI, which is tailormade for specific industry needs and problems. Cognitive stimulation creates a parallel world that mimics all constraints, opportunities, and possibilities at a cognitive level. And finally, there is the realm of Super-intelligence – where AI is way ahead of humans – from creativity to social skills. AI is complex. Whether we see its use, techniques, inner working, or its intended applications. The goals are challenging to define and measure, and that is why an organization needs to have a good understanding of what it is looking for and how it will achieve that. It is better to express AI goals as well-posed questions and hypotheses around a specific and intended benefit or outcome. 

What contributes to artificial Intelligence (AI)?

AI is not a magical or mythical beast. It is a logical creature made up of the barebones of data and the flesh of innovation and applications. When we think of AI, we think of machines, computer systems, algorithms, and models trying to get closer to human intelligence. Let us observe what essential elements shape AI into what it is.

• Big Data: 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.
• Innovation: AI needs new models, processing modes, algorithm designs, and enablers. And consistently. Especially as vendors face many barriers around complexity, integration hassles, and capabilities.
• Talent: The right skills and capabilities would maximize the outcomes expected out of any AI investment. Without the right competencies in place, AI is just a shot in the dark.
• Broader applications: To fully leverage the power of AI, we need to explore many more applications, but only when we have models and tools that give us that kind of confidence.
• Processing power: AI would need higher and wider processing alternatives. Especially in real-time data.
• Improved algorithms: AI works best when the algorithms are strong enough to address predictions, interpretability, transparency, model degradation, privacy, and fairness issues.
• Investments: AI needs the top leaders of an organization to feel excited about AI. Investments will drive more innovation and more capabilities ahead.

How about looking at AI now from both angles? There are two ways to approach its programming. And both of them work their muscles.

Programming without and with artificial intelligence (AI)

• Programming without AI: Developers who program without AI spend many resources and hours on requirement gathering, planning, designing, error-prone parts, labor-intensive code generation, deployment control, and managing vulnerabilities.
• Programming with AI: When AI enters the scene, it changes the way software is written and thought of. Today, we have tools that automate specific processes to minimize human intervention to some extent. AI can help in detecting loopholes early on before moving to design.

AI has the most significant potential to automate repetitive tasks, deliver new strategic insights, and automate areas of knowledge work. AI may make the software development part not just easy, but more beautiful and exciting. It’s time now to get a peek into how all this magic transpires. Is there a technique for it? And if yes, how does it play out?

Artificial intelligence (AI) technique

1. Search: AI agents essentially perform some kind of search algorithm in the background to complete their expected tasks. That’s why Search is a major building block of any AI solution.
2. Use of knowledge: Any AI agent has to work on some input. This work can happen only when there is some knowledge about the input or about its handling. AI, hence, has to be strong in understanding, reasoning, and interpreting knowledge.
3. Abstraction: This helps reduce complexity and achieve a simplified view of various parts and their interplay with each other. This is where we also confront the ‘black box’ effect – which is a big problem because many effective, and stellar, AI models still cannot explain how they do what they do.

AI techniques are evolving and will keep getting better and sharper to bring AI closer to human intelligence’s complexity and beauty. We need a lot of work in these areas because we need to address privacy, bias, discrimination, un-explainability, and misapplication that many AI solutions face.

It is not what shoes you wear, it is what you do with them. 

Well, the same thought applies to AI, too. All its humongous intelligence, accuracy, automation, augmentation, etc., will go unused unless these get channelized in the pipes of the rightly designed application. So every industry has a discrete application that is helping it derive value out of AI. Here’s a quick lowdown. 

Artificial intelligence (AI) applications 

• AI in the automotive industry: Autonomous cars, vehicles, and transport would completely depend on the accuracy, speed, and impact of AI models that run them.
• AI in social media:  With AI tools, personalization, location mapping, and fast responsiveness would be achieved at a large scale in the social media space.
• AI in cyber security: Risks could be mapped proactively, and zero-day vulnerabilities can be fixed thanks to the velocity and agility that AI brings in.
• AI in finance: AI is helping risk assessment, etc., and reducing data-crunching time to seconds. Now robo-advisors can help with SIP programs. And a lot more is happening in other financial areas.
• AI in e-commerce: Bots and hyper-personalization are powering many customer support and service areas.
• AI in education: AI is enabling new models like self-paced learning and application-oriented learning.
• AI in entertainment: Tune into a new world of hyper-personalized viewing choices and analytics-based viewer engagement.
• AI in agriculture: Now, agri-industry players are using AI to study crop state patterns, yield management, supply chain gaps, market pricing, and customer demand mapping.
• AI in robotics: So much is happening here in drones, software assistants, chatbots, factory floor cobots, or sophisticated virtual assistants.
• AI in travel and transport: Fragmentation and blind spots are being taken out with the visibility given by AI in supply chains and transport, apart from the revolution called self-driving vehicles.
• AI in gaming: Enter the next level of gaming with more engaging, immersive, well-monetized, and collaborative games.
• AI in healthcare: Here, AI brings in fast diagnosis, virtual nurses, personalized medicine, remote healthcare, and robotic surgeries.

Artificial intelligence (AI) myths

Like many other turning points in history, AI, too, faces a lot of skepticism and fear. There are many theories and assumptions that surround its evolution. Why not crack them with the right mindset instead of letting the fog confuse our AI strategies.

• AI is going to replace all jobs: A lot of estimates have reminded us time and again that any job losses from automation are likely to be broadly offset in the long run by new jobs which would be created because of the larger and wealthier economy made possible by these new technologies.
• AI will make human labor obsolete: AI is designed to augment humans. It is aimed to help them not only do their jobs better but also to do better jobs.
• AI, ML, and DL are all the same and can be used interchangeably: AI is about creating and using intelligence that is as close as possible to human cognitive powers.
• AI is more intelligent than people: General AI is not the same as the initial AI efforts hint at. We would need a lot of research and progress to ever come to a level where AI surpasses human intelligence.
• Companies don’t need an AI strategy: An enterprise cannot simply wake up one morning and start investing in AI. It has to know exactly what applications, goals, and differences it is aiming for. It has to chart a proper path for it. AI needs strategic mapping – and at continuous levels.

“Without data, you are blind and deaf in the middle of a freeway.”- Geoffrey Moore  

Geoffrey Moore’s words could not have been more right. In 2021 and beyond, we are looking at a world that would feel handicapped without intelligent use of data. AI is at the forefront of this data renaissance. We can make the most of the data without exerting unnecessary human labor, supervision, and time with AI. Whether it is in text, images, or digits, AI allows a model to learn with the data in and around it. AI systems are only as good as the data they train on – As companies revisit their data strategy and map short, medium, and long-term data-driven plans to make informed decisions, there would be a need for a deeper understanding of data models.

The future of AI in work and life is full of many undiscovered possibilities as of now. We could start a new phase of human growth and power with the use of AI. Doing it ethically, prudently, and with the right strategies in place – that’s what is more important to worry about now. Because there is no shortage of tools and innovations for AI, any organization can expend the power of AI today. It is getting increasingly affordable and accessible. But what will separate the men from the boys is the ‘how’. That’s why we need to address this aspect too.

AI applications would need a new set of skills and mindsets that professionals would have to prepare for. For instance, about 56 percent of respondents felt that acquiring new skills will be required to do both existing and newly created jobs, according to a Gartner Research Circle survey. There is a significant need for skills that can handle and leverage AI productively and positively. For technology professionals, there is a vast field of opportunities opening up here. Whether it is data or software, or hardware – the possibilities are endless. 

AI is the big turning point for businesses and humans. And the world needs drivers who can steer everyone in the right direction now without scratches and wrong turns. That’s why AI talent is going to see an explosive inflection point as we move ahead. That’s why you need to be ready.