Remember Sophia, the humanoid that appeared on the late-night show with Jimmy Fallon?
This revelation of Hanson robotics led to a unanimous cheer among the show spectators, and Sophia's presence of mind left Jimmy speechless and dazed.
While it's been decades since computer scientists and tech enthusiasts have been mulling over artificial intelligence, it has gained the spotlight recently. From nanorobotic technology to cancer immunotherapy to robotic chess grandmasters to creative narrators and scriptwriters, artificial intelligence has become superlative in comprehending brain mechanisms and built computer-generated responses to co-exist in today's world.
This new-age tech is powered with advanced artificial intelligence software that consists a lineage of highly supervised and reinforced machine learning algorithms that receive human queries and prompts to better their performance and lead several industrial tasks in the world today.
Artificial intelligence, also known as the fifth generation of computers, is a technology that gives "human like" thinking capability to computers. It includes advanced machine learning and deep learning techniques to analyze human input and predict an accurate response. Some examples are, robotic vaccums, language apps, speech recognition, Tesla Autopilot and so on.
From Google Deepmind's Alpha Go to Nvidia's MegaMOIBART that understood protein unfolding to create smart vaccination, the realm of artificial intelligence is progressing by leaps and bounds. Let's see why the need to have self-learning models and genetical AI systems even began in the first place.
AI can free us from monotonous tasks, make fast decisions with accuracy, act as a catalyst for boosting inventions and discoveries, and even complete dangerous operations in extreme environments.
There's no magic here. It's a collection of intelligent algorithms trying to mimic human intelligence. AI uses techniques such as machine learning and deep learning to learn from data and use the acquired knowledge to improve periodically.
And AI isn’t just a branch of computer science. Instead, it draws on aspects of statistics, mathematics, information engineering, neuroscience, cybernetics, psychology, linguistics, philosophy, economics, and much more.
The notion that reasoning could be artificially implemented on machines dates back to the 14th century when Catalan poet, Ramon Llull, published Ars generalis ultima (The Ultimate General Art). In his book, Llull discussed combining concepts to create new knowledge with the help of paper-based mechanical means.
For centuries, many mathematicians and philosophers, through a number of varying concepts, shaped the idea of artificially intelligent machines. But the field gained prominence when Alan Turing, an English mathematician, published his paper Computing Machinery and Intelligence in 1950 with a simple proposition: can machines think?
Note: To learn more about the history of AI, check out this article.
In 1956, John McCarthy coined the term "artificial intelligence" at the Dartmouth Summer Research Project on Artificial Intelligence – a conference McCarthy hosted along with Marvin Minsky. Although the conference fell short of McCarthy's expectations, the idea carried on, and AI research and development has been progressing at an incredible rate ever since.
Let's look at the entire flywheel of machine learning models and how they got better as the years progressed.
Source: G2
However, there were more innovations that were later added to the pandora's box of artificial intelligence.
2011: IBM Watson beat champions Ken Jennings and Brad Rutter at Jeopardy
2014: Microsoft Cortana was originally developed for Windows 8.1
2015: Baidu's Minwa supercomputer uses a special deep neural network known as a convolutional neural network to identify and categorize images with higher precision.
2016: Google launched its Google Neural Machine Translation algorithm to machine read a sequence and parse it with vector support for personalization of Google search.
2021: Google launches MUM, a multimodal transformer to revamp the search engine experience to increase time on search engine result pages and provide a transformational experience.
2022: Open AI's CEO Sam Altman launches "ChatGPT" as a generative AI tool for ai text generation for users.
2023: Various LLMs as a part of variational encoders are launched, including Stable Diffusion, Gemini, Google's BERT, Dall-e and Midjourney.
As a term, artificial intelligence might be easy to understand and discuss. But when considered as a concept, AI can be quite overwhelming, especially if you've just started exploring. To better understand how AI works, let's take a closer look at the six components that make the technology a reality.
Machine learning is an application of artificial intelligence that offers computers the ability to learn and improve from experience automatically without being explicitly programmed to do so.
Machine learning algorithms are capable of analyzing data, identifying patterns, and making predictions. These algorithms are designed to continually improve by learning and adapting to newer datasets exposed to them. An excellent example of the application of ML is the spam filtering algorithm in your email account.
Deep learning is a subset of machine learning. It utilizes artificial neural networks to enable machines to learn by processing data. Deep learning helps machines solve complex problems even if the dataset provided is unstructured and intensely diverse.
Here, the learning process takes place by adjusting system actions based on a continuous feedback loop. It involves learning from large amounts of data through backpropagation and gradient descent methods. Deep learning networks are a replication of human brain mechanisms and can self-teach themselves to perform accurate tasks in future.
An artificial neural network (ANN) is a component of artificial intelligence, designed to simulate the manner in which the human brain analyzes and processes information. ANN offers AI with self-learning capabilities and can also be considered as the foundation of the same technology.
Artificial neural networks are built to mimic the biological neural networks of human brains. The artificial counterparts of neurons – the fundamental units of the brain – are perceptrons. A massive number of perceptrons are stacked together to form ANNs.
Natural language processing (NLP) is a branch of AI that offers machines the ability to read, understand, and produce human language. The majority of voice assistants use NLP.
As you probably know, computers use low-level language or machine language to communicate. Such a language is composed of ones and zeros, and humans will have a hard time decoding it.
Similarly, computers will have a tough time understanding human languages - if not for NLP. NLP uses intelligent algorithms to convert unstructured language data into a form the computers can understand.
An artificial neural network (ANN) is a component of artificial intelligence, designed to simulate the manner in which the human brain analyzes and processes information. ANN offers AI with self-learning capabilities and can also be considered as the foundation of the same technology.
Artificial neural networks are built to mimic the biological neural networks of human brains. The artificial counterparts of neurons – the fundamental units of the brain – are perceptrons. A massive number of perceptrons are stacked together to form ANNs.
Natural language processing (NLP) is a branch of AI that offers machines the ability to read, understand, and produce human language. The majority of voice assistants use NLP.
As you probably know, computers use low-level language or machine language to communicate. Such a language is composed of ones and zeros, and humans will have a hard time decoding it.
Similarly, computers will have a tough time understanding human languages - if not for NLP. NLP uses intelligent algorithms to convert unstructured language data into a form the computers can understand.
Computer vision (CV) is a field of computer science that aims at replicating the human vision system to enable machines to "see" and understand the content of images and videos.
With advancements in DL, the field of CV has been successful in breaking free from its previous barriers. Computer vision grants image recognition capabilities to machines to detect and label objects. CV is a critical component that makes self-driving cars possible. With CV, such vehicles can see lane markings, signs, and other automobiles and drive safely without hitting any obstacles.
Another excellent application of computer vision is the auto-tagging feature in Google Photos. It can sort pictures based on their content and place them in albums. For instance, if you take a lot of pictures of your cat, the app will automatically group all those cat photos into a single album.
Recurrent neural networks are deep neural networks that accept the user's input in the form of a sequence or time series data and pass it through various computational layers to generate an accurate response. RNNs are used for language translation, causal language modeling, sequential modeling, and time series analysis. RNNs have three layers, namely the input layer, output layer, and hidden layer, that break down the semantics within input tokens and build cohesive logic within components to analyze the intent and process a response close to what a human brain processes.
RNNs are adaptive, flexible, and anti-discriminative throughout the computation process. They can also be used for unsupervised tasks like data labeling, data classification, dimensionality reduction, sentimental analysis, random clustering and scene recognition. .
Artificial intelligence works the same way the human brain works. It isn't coincidental at all, as AI is all about mimicking human intelligence. Although all the components discussed in the previous section significantly contribute to AI's effectiveness, machine learning takes it a step further. ML helps AI to analyze and understand information and adapt based on experience.
To better understand how artificial intelligence works, consider a standard software application that identifies the rainfall intensity based on the precipitation rate. If the precipitation rate is under 2.5 mm per hour, the rain intensity will be "light." Similarly, if it's fewer than 7.5 mm per hour but greater than 2.5 mm per hour, the rain intensity will be "moderate." You get the gist.
Since it's a standard application, a developer will have to hardcode the range of each category for the classification to be precise. If the developer makes a mistake while setting the range, the application will work, but with the wrong range, and will have no means to correct itself.
But if a developer decides to create an application powered by AI, they would just have to provide a dataset that contains precipitation rate and their classification. The AI would train using this dataset and will be able to determine the rainfall intensity without requiring any range.
AI can also scan through billions of images and sort them based on your requirements. For example, you can teach an AI to identify whether an image is that of a cat or a dog. For that, you would provide the computer with specific traits of both the animals, for example:
AI analyzes all this information using artificial neural networks. The more photos it analyzes, the better it identifies the desired object.
Not all tasks performed by an AI machine have to be complicated. You can build something as simple as an AI coffee machine that makes you a cup of coffee whenever you crave one. But such a coffee machine also has the potential to learn the exact amount of milk and sugar you'd like in your cup of coffee at a particular hour of the day.
Artificial intelligence can be classified into several categories based on its capabilities to mimic human intelligence. The easiest way to categorize them is as weak, strong, and super. To know more about how artificial intelligence works and why you don't have to be concerned about the same technology outsmarting us, let's look at its some classification types.
Source: G2
Artificial narrow intelligence (ANI) or weak AI, is the most basic and limited type of AI.
But don't be misled by the term "weak." Even though this type of machine intelligence is labeled as narrow and weak, it's pretty adept in performing the specific task it's programmed to do. ANI excels in specialized tasks.
Virtual personal assistants like Siri, Alexa, and Google Assistant are examples of weak AI. But they aren't the best examples, as weak AI can do more than that. IBM Watson, Facebook's newsfeed, Amazon's product recommendations, and self-driving cars are all powered by ANI.
Narrow AI is very good at performing monotonous tasks. Speech recognition, object detection, and facial recognition are all child's play for this kind of AI. However, this type of AI works under certain limitations and constraints—hence, it is weak.
Weak AI can also identify patterns and correlations in real time on large amounts of data, also known as big data. ANI is the only type of AI that humanity currently has access to, meaning any form of artificial intelligence you come across will be weak AI.
An AI agent said to possess artificial general intelligence would be able to learn, perceive, comprehend, and function just like a human being. AGI is also known as strong AI or deep AI, and in theory, it can do anything a human can do.
Unlike ANI, strong AI is not restricted to any form of narrow sets of limitations or constraints. It can learn, improve, and perform a variety of tasks. Achieving AGI also means that we'll be able to create computer systems capable of exhibiting multi-functional capabilities like ours.
The fear of AI enslaving the human race starts with AGI. The self-aware killer robots like the T-800 of The Terminator – if they ever exist – would possess this level of artificial intelligence.
And yes, we're years away from creating strong AI. Since this type of artificial intelligence can think, understand, and act like humans, it will also have the full set of cognitive abilities that humans take for granted.
Scientists are trying to figure out how to make machines conscious and instill the cognitive abilities that make us intelligent. If scientists succeed, we will be surrounded by machines, not just capable of improving their efficiency in performing specific tasks but also with the ability to apply the knowledge acquired through experience.
This also means that deep AI will be able to recognize emotions, beliefs, needs, and the thought processes of other intelligent systems. If you're wondering how the intelligence levels of AI systems are measured, tests like the Turing test determine whether an AI system can think and communicate like a human.
Artificial super intelligence, or ASI for short, is a hypothetical AI. ASI is also referred to as super AI, and only after achieving AGI can we even think of ASI. Super AI is where machines surpass the capacity of human intelligence and cognitive abilities.
Once we unlock ASI, machines will have a heightened level of predictive capabilities and will be able to think in a manner that is simply impossible for humans to comprehend. Machines powered by ASI will beat us at everything. Our decision-making and problem-solving capabilities will look inferior in front of a super AI.
Many experts in the industry are still skeptical about the feasibility of creating ASI. The chances are high that none of us will live to see this type of AI – unless, of course, if we unlock immortality before.
Even if we somehow manage to attain super AI and lay down rigid rules to control it, there are almost zero reasons why a machine with superior intelligence must listen to us. Even if we try to pull the plug, it would've already initiated countermeasures to nullify our actions, as its predictive abilities would be tremendous.
Self-aware AI is the branch of artificial intelligence where the computer can acquire self-realization or the highest degree of awareness to act, behave, and emote like humans. Self-aware AI can make machines exude natural expressions, like crying, anger, sadness, or happiness. These machines are powered with human-like intelligence and cognitive thinking to make critical decisions very smoothly. This level of AI will have a conscious understanding, same as the instinct powers of humans, to perceive the criticality of a situation and make an informed decision. This concept remains a plot of science fiction movies and a far-fetched goal for AI practitioners.
Theory of mind AI refers to a conceptual stage of artificial intelligence where systems that are proficient in reading data can build logic, desires, empathy, intent, motivation, sentiments, and likelihoods like humans. The name translates into transferring the prowess of a mind to the computing systems. Just as how humans can differentiate between right and wrong, control their impulses and reflexes, and salvage dangerous situations, machines would also have the same presence of mind and think twice before performing any specific task. While current AI does exhibit some level of theory of mind with the rise of generative AI and LLMs, theory of mind is a future goal.
Reactive AI is the simplest form of artificial intelligence designed to allow machines to react to human commands and accomplish a specific task instantly. These systems operate solely in the present without storing any data instances from the past computational process. They process data in response to a specific stimulus of a current situation. Deepmind's Alpha Go, IBM WatsonX, and AI chess are some examples where the algorithm is supposed to act on heels. This algorithm lacks the ability to adapt and improve its performance over time
Limited theory AI has an advanced architecture that learns from previous outputs and training samples and applies those techniques to the current set of data samples to process informed results. It is a better algorithm than reactive AI because it stores previous observations in its memory and applies learnings to new tasks. But, in a practical sense, limited theory also lacks cognitive abilities or the ability to supervise itself or learn from user inputs to better their prediction rate and accuracy.
Most of us interact with AI systems daily, even though we aren't aware of it. To shed some light on how AI is used around us, here are six applications of artificial intelligence.
Chatbots are AI software applications that can simulate conversations with users using natural language processing. You probably have encountered one while browsing the internet or trying to contact Amazon's customer support.
When was the last time you spoke to Siri, Alexa, or Google Assistant? Probably a few minutes ago. From waking you up, searching the web, and scheduling appointments, voice assistants have become a part of living in the 21st century.
They can work offline, recognize your voice with impressive accuracy, and respond to your queries almost like a fellow human would do. The more you interact with your voice assistants, the more they learn about you. As previously mentioned, intelligent personal assistants use NLP to analyze and interpret speech correctly.
Customer service has greatly benefited from conversational AI models like AI chatbots, voice assistants, and empathetic AI voices. Hume and Watson X are some prime examples of customer service. These AI models do a variety of tasks like solving helpdesk tickets, strategizing, providing quick and efficient solutions to consumer queries, providing real-time instructions, and rerouting queries to manual assistants. The AI chatbots analyze human diction and learn from the underlying machine learning algorithm to produce a contextual response and guide the consumer in the right direction.
Humanoids, robots designed to mimic human appearance and behavior, have diverse real-life applications across multiple fields. In healthcare, they assist with patient care and deliver supplies, while in customer service, they handle interactions and provide information. Educational institutions use humanoids for teaching and research, and in manufacturing, they perform repetitive or hazardous tasks to boost productivity and safety. Additionally, humanoids enhance entertainment and hospitality experiences, assist with household chores for the elderly or disabled, and perform search and rescue in disaster response and hazardous environments. These versatile applications highlight their potential to improve efficiency, safety, and quality of life in various sectors.
Robotic process automation is a subset of artificial intelligence used to create semi-autonomous or autonomous robots and systems. It follows the concept of task automation and can integrate machine learning or natural language processing to infuse more power within robotic systems and devices. RPA software is being deployed in several industries to automate supply chain, logistics, and manufacturing mechanisms.
Fuzzy logic is a conventional or boolean technique that provides mathematical reasoning for natural language processing queries. It functions on a “degree of truth” metric that lies between 0.0 and 1.0. Fuzzy logic is used to handle partial truth, particularly the “gray area” in natural language processing queries. This can range between completely true and completely false. It helps interconnect data points to make useful forecasts.
Empathetic AI voices
Empathetic AI voices have proved to be a boon for professionals who need assistance regarding their work agendas, build new strategies or prototypes, or need personal counseling. These AI voices are trained on voice data and a huge knowledge corpus to provide needed support to professionals and other audiences. These voices are designed to invoke a sense of realism in the user and give them the liberty to take action independently, with some help from the platform. The motto of these platforms is to spread empathy, listen patiently, and recover the bad mental state of users. They also play a great role in obliterating self-guilt and empowering users to take the first step toward their goals and dreams.
In healthcare, AI algorithms can analyze humongous amounts of patient data, radiology data, and pathological data to provide quick diagnosis for lab testing, consultation, and outpatient department (OPD). AI tools help with the early diagnosis of acute and chronic diseases from medical imagery such as X-rays, magnetic resource imaging (MRI), computerized tomography technology (CT) scans, and positron emission tomography (PET) scans. They also optimize hospital administration tasks by automating patient admissions, test result delivery, managing resources and providing intelligent consultation to cut on waiting times. One such technology, namely nanobots, are nano scale devices that have been monumental in cancer immunotherapies and radiation treatments for cancer patients. The nanotech devices are a faster way of recognizing cancerous cells and are painless.
AI can analyze and process images and videos to perform tasks such as object detection, image recognition, and facial recognition with high accuracy. These capabilities are applied in various domains, including autonomous vehicles for navigation and obstacle detection, healthcare for analyzing medical images, retail for enhancing shopping experiences with visual search and inventory management, and security for surveillance and identity verification. AI-driven computer vision enhances automation, accuracy, and efficiency in these applications, transforming how visual data is utilized across industries.
AI enables autonomous vehicles to navigate through traffic, handle complex situations, and steer clear of obstacles. Although fully autonomous cars are still in their testing phases, Tesla's Autopilot feature is an excellent application of AI.
With the help of AI, an autonomous vehicle can analyze and interpret the massive amount of data collected from the cameras, sensors, and GPS fitted on it. In a simpler sense, AI enables autonomous vehicles to see, hear, think, and react – just like a human driver.
One of the prominent reasons OTT platforms rose to dominance is their ability to understand the needs of their users and serve accordingly: their recommendation system. Such platforms use the watch history of other users with the same interests as yours to recommend new shows and movies you're most likely to watch.
AI algorithms power the recommendation system and can offer the right movie and show recommendations so that users stay engaged and continue their subscriptions. OTT platforms rely on AI's prowess to generate the best thumbnails to yield the highest click-through rate.
As cybercrimes grow in numbers and complexity, AI is helping companies stay ahead of threats. AI and ML-enabled computer programs can proactively detect system vulnerabilities and suggest measures to counter them.
AI can also strengthen cybersecurity systems with behavioral analysis. With behavioral analysis, AI can generate patterns of how a typical user will access and use a system. If the AI detects any abnormalities, it can notify the concerned authorities to take proactive measures.
Do you remember IBM Watson, a question-answering computer that won the first-place prize of US $1 million on the quiz show Jeopardy!? A lot has changed about Watson since it wowed the audience on the TV show.
Watson is now being extensively used in the healthcare industry and is driven by machine learning software and AI technologies. WatsonX is capable of analyzing millions of documents and suggesting alternative treatment methods in a matter of seconds, which can be quite challenging even for a group of doctors.
AI can also help pathologists make more accurate cancer diagnoses and make it possible to offer personalized medicines and treatments. AI can also take predictive analytics to the next level, which is critical in identifying disease outbreaks, among other things.
Besides saving lives, artificially intelligent machines can improve the quality and accessibility of healthcare services and reduce costs.
These tools create AI-generated music via machine learning techniques to replicate singer voices. These tools assist in producing and composing new tracks and rhythms based on pre-trained systems and a music corpus dataset. These generators can range from basic melody creation to specific genre-based music like pop, hip-hop, metal, rock, alternative, or acoustic.
They often learn patterns and styles from existing training data, which might consist of lyrics, voice notes, tempo, pitch, and instrumental sequences, to curate new music similar to what artists create. They can be used for tasks such as orchestral composition, lyrics writing, music recommendation, personalized playlists, and even production studio support.
AI text generators are transformer models that work on an encoder-decoder basis and are a form of generative AI systems that businesses use for content assistance, scriptwriting, dialogue writing, language translation, conversational AI, and email and ai content generation. These systems are based on transformer model methodology that uses a "multi-head attention mechanism" to draw relationships between tokens and generate the best possible set of textual responses. It is a hip technique used in natural language processing to declutter the training dataset and derive contextual tokens to recalibrate their responses and punch output with an exact arrangement that the user wants. These generators typically use large language models (LLM) to produce coherent and relevant strings of data that mimic human language patterns and styles.
The AI image generators are LLMs that accept text prompts to build state-of-the-art images, graphics, and product visualizations. They leverage various algorithms, including deep neural networks and generative adversarial networks (GAN), to form background images and AI-generated art. These text-to-image systems learn from user prompts and better their understanding of design thinking and graphic illustrations to build more accurate and captivating images. These systems come in various forms, such as neural style transfer, variational autoencoders, conditional generative models, model backgrounds, super-resolution generators, and AI art generators. Some examples are Adobe Firefly, Midjourney, Dall-e, Imagine Art, and so on.
Theoretically, as machine learning capabilities evolve and improve and scientists unlock AGI, there will be two possibilities: a dystopian or utopian future.
In a dystopian future, intelligent killer robots might take over the world, enslave humans, or, in the worst-case scenario, wipe out the entire human race, just like the narrative of every AI science fiction movie.
But if AI causes a utopian future, our living standards will be way beyond our current levels of comprehension. We no longer have to perform any of the monotonous tasks and can spend more time experiencing the world around us.
In a utopian world, interstellar travel would no longer be a concerning issue. Also, extracting resources from asteroids and other uninhabited planets would be possible. Artificial intelligence might also be the "key" that makes humans an interstellar species.
However, the future may not always be supportive of AI. From its inception, the pace of AI development has been severely affected multiple times when investors felt the results were unsatisfactory compared to what was promised. Such inactive cycles are called AI winters and can occur anytime in the future.
The first AI winter started around the year 1973 but lasted only a couple of years. Considering the special role artificial intelligence plays in bettering our lives, it's highly improbable that we'll ever witness an AI winter again.
Although many specialists, including Stephen Hawking and Elon Musk, fear that AI might spell the end of the human race, they are fairly supportive of the immediate benefits the same technology can grant us.
However, the distress caused by Microsoft's chatbot Tay, which posted racist tweets, and Google's racist AI algorithms that wrongly classified pictures shows that artificial intelligence needs more tweaking to become a flawless system.
If you were ever terrified of thinking AI might outsmart and enslave humans, here’s a reality check – it's not going to happen anytime soon – if ever. Although scientists have invested decades in this field, we only make baby steps. But our pace is something the forefathers of artificial intelligence technology would have always envied to achieve.
Learn about the upheaval of large language models and how they are disrupting industries today for content creation and text assistance tasks.
This article was originally published in 2023. It has been updated with new information.
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