What is Artificial Intelligence (AI)?
A common type of training model in AI is an artificial neural network, a model loosely based on the human brain.
A neural network is a system of artificial neurons—sometimes called perceptrons—that are computational nodes used to classify and analyze data. The data is fed into the first layer of a neural network, with each perceptron making a decision, then passing that information onto multiple nodes in the next layer. Training models with more than three layers are referred to as “deep neural networks” or “deep learning.” Some modern neural networks have hundreds or thousands of layers. The output of the final perceptrons accomplish the task set to the neural network, such as classify an object or find patterns in data.
Some of the most common types of artificial neural networks you may encounter include:
Feedforward neural networks (FF) are one of the oldest forms of neural networks, with data flowing one way through layers of artificial neurons until the output is achieved. In modern days, most feedforward neural networks are considered “deep feedforward” with several layers (and more than one “hidden” layer). Feedforward neural networks are typically paired with an error-correction algorithm called “backpropagation” that, in simple terms, starts with the result of the neural network and works back through to the beginning, finding errors to improve the accuracy of the neural network. Many simple but powerful neural networks are deep feedforward.
Recurrent neural networks (RNN) differ from feedforward neural networks in that they typically use time series data or data that involves sequences. Unlike feedforward neural networks, which use weights in each node of the network, recurrent neural networks have “memory” of what happened in the previous layer as contingent to the output of the current layer. For instance, when performing natural language processing, RNNs can “keep in mind” other words used in a sentence. RNNs are often used for speech recognition, translation, and to caption images.
Long/short term memory (LSTM) are an advanced form of RNN that can use memory to “remember” what happened in previous layers. The difference between RNNs and LTSM is that LTSM can remember what happened several layers ago, through the use of “memory cells.” LSTM is often used in speech recognition and making predictions.
Convolutional neural networks (CNN) include some of the most common neural networks in modern artificial intelligence. Most often used in image recognition, CNNs use several distinct layers (a convolutional layer, then a pooling layer) that filter different parts of an image before putting it back together (in the fully connected layer). The earlier convolutional layers may look for simple features of an image such as colors and edges, before looking for more complex features in additional layers.
Generative adversarial networks (GAN) involve two neural networks competing against each other in a game that ultimately improves the accuracy of the output. One network (the generator) creates examples that the other network (the discriminator) attempts to prove true or false. GANs have been used to create realistic images and even make art.
What is Artificial Intelligence? Types, Trends and Future of it?
Table of contents
What is Artificial Intelligence?
Artificial Intelligence is defined as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. AI is also defined as,
An Intelligent Entity Created By humans
Capable of Performing Tasks intelligently without being explicitly instructed.
Capable of thinking and acting rationally and humanely.
A layman with a fleeting understanding of technology would link it to robots. They’d say Artificial Intelligence is a terminator like-figure that can act and think on its own.
If you ask about artificial intelligence an AI researcher, (s)he would say that it’s a set of algorithms that can produce results without having to be explicitly instructed to do so. The intelligence demonstrated by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in today’s world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life. It’s but natural that everyone today wants to connect with AI technology somehow, may it be as an end-user or pursuing a career in Artificial Intelligence.
How do we measure if Artificial Intelligence is acting like a human?
Even if we reach that state where an AI can behave as a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity on the:
Turing Test
The Cognitive Modelling Approach
The Law of Thought Approach
The Rational Agent Approach
Let’s take a detailed look at how these approaches perform:
What is the Turing Test in Artificial Intelligence?
The basis of the Turing Test is that the Artificial Intelligence entity should be able to hold a conversation with a human agent. The human agent ideally should not be able to conclude that they are talking to an Artificial Intelligence . To achieve these ends, the AI needs to possess these qualities:
Natural Language Processing to communicate successfully.
Knowledge Representation acts as its memory.
Automated Reasoning uses the stored information to answer questions and draw new conclusions.
Machine Learning to detect patterns and adapt to new circumstances.
Cognitive Modelling Approach
As the name suggests, this approach tries to build an Artificial Intelligence model based on Human Cognition. To distil the essence of the human mind, there are 3 approaches:
Introspection : observing our thoughts, and building a model based on that
: observing our thoughts, and building a model based on that Psychological Experiments : conducting experiments on humans and observing their behaviour
: conducting experiments on humans and observing their behaviour Brain Imaging : Using MRI to observe how the brain functions in different scenarios and replicating that through code.
The Laws of Thought Approach
The Laws of Thought are a large list of logical statements that govern the operation of our mind. The same laws can be codified and applied to artificial intelligence algorithms. The issue with this approach, is because solving a problem in principle (strictly according to the laws of thought) and solving them in practice can be quite different, requiring contextual nuances to apply. Also, there are some actions that we take without being 100% certain of an outcome that an algorithm might not be able to replicate if there are too many parameters.
The Rational Agent Approach
A rational agent acts to achieve the best possible outcome in its present circumstances.
According to the Laws of Thought approach, an entity must behave according to the logical statements. But there are some instances, where there is no logical right thing to do, with multiple outcomes involving different outcomes and corresponding compromises. The rational agent approach tries to make the best possible choice in the current circumstances. It means that it’s a much more dynamic and adaptable agent.
Now that we understand how Artificial Intelligence can be designed to act like a human, let’s take a look at how these systems are built.
How does Artificial Intelligence (AI) Work?
Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using its computational prowess to surpass what we are capable of.
To understand How Artificial Intelligence actually works, one needs to deep dive into the various sub-domains of Artificial Intelligence and understand how those domains could be applied to the various fields of the industry. You can also take up an artificial intelligence course that will help you gain a comprehensive understanding.
Machine Learning : ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns and analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data saves human time for businesses and helps them make a better decisions. To learn basic concepts you can enrol on a free machine learning course for beginners.
: ML teaches a machine how to make inferences and decisions based on past experience. It identifies patterns and analyses past data to infer the meaning of these data points to reach a possible conclusion without having to involve human experience. This automation to reach conclusions by evaluating data saves human time for businesses and helps them make a better decisions. To learn basic concepts you can enrol on a free machine learning course for beginners. Deep Learning : Deep Learning is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome.
: Deep Learning is an ML technique. It teaches a machine to process inputs through layers in order to classify, infer and predict the outcome. Neural Networks: Neural Networks work on similar principles to Human Neural cells. They are a series of algorithms that captures the relationship between various underlying variables and processes the data as a human brain does.
Natural Language Processing: NLP is a science of reading, understanding, and interpreting a language by a machine. Once a machine understands what the user intends to communicate, it responds accordingly.
Computer Vision: Computer vision algorithms try to understand an image by breaking down an image and studying different parts of the object. This helps the machine classify and learn from a set of images, to make a better output decision based on previous observations.
Cognitive Computing: Cognitive computing algorithms try to mimic a human brain by analysing text/speech/images/objects in a manner that a human does and tries to give the desired output. Also, take up applications of artificial intelligence courses for free.
What are the Types of Artificial Intelligence?
Not all types of AI all the above fields simultaneously. Different Artificial Intelligence entities are built for different purposes, and that’s how they vary. AI can be classified based on Type 1 and Type 2 (Based on functionalities). Here’s a brief introduction to the first type.
3 Types of Artificial Intelligence
Artificial Narrow Intelligence (ANI)
Artificial General Intelligence (AGI)
Artificial Super Intelligence (ASI)
Let’s take a detailed look.
What is Artificial Narrow Intelligence (ANI)?
This is the most common form of AI that you’d find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. They’re able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters.
To build a strong foundation in AI, you can also upskill with the help of the free online course offered by Great Learning Academy on Introduction to Artificial Intelligence. With the help of this course, you can learn all the basic concepts required for you to build a career in AI.
What is Artificial General Intelligence (AGI)?
AGI is still a theoretical concept. It’s defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.
We’re still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. Even with the most advanced computing systems and infrastructures, such as Fujitsu’s K or IBM’s Watson, it has taken them 40 minutes to simulate a single second of neuronal activity. This speaks to both the immense complexity and interconnectedness of the human brain, and to the magnitude of the challenge of building an AGI with our current resources.
What is Artificial Super Intelligence (ASI)?
We’re almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI. An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships.
Once we achieve Artificial General Intelligence, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of. While the gap between AGI and ASI would be relatively narrow (some say as little as a nanosecond, because that’s how fast Artificial Intelligence would learn) the long journey ahead of us towards AGI itself makes this seem like a concept that lies far into the future. Check out this course on how to Build a career in ai.
Difference between Augmentation and AI
Artificial Intelligence Augmented Intelligence AI replaces humans and operates with high accuracy. Augmentation does not replace people but creates systems that help in manufacturing. Replaces human decision making Augments human decision making Robots/Industrial IoT: Robots will replace all humans on the factory floor. Robots/Industrial IoT: Collaborative robots work along with humans to handle tasks that are hard and repetitive. Real-Time Applications of AI in Customer Success
1. Automated Customer Support and Chatbots
2. Virtual Assistants Automated Workflows Real-Time Applications of IA in Customer Success
1. IA-enabled customer analytics
2. Discover high-risk/high-potential customers
3. Forecasts Sales
Strong and Weak Artificial Intelligence
Extensive research in Artificial Intelligence also divides it into two more categories, namely Strong Artificial Intelligence and Weak Artificial Intelligence. The terms were coined by John Searle in order to differentiate the performance levels in different kinds of AI machines. Here are some of the core differences between them.
Weak AI Strong AI It is a narrow application with a limited scope. It is a wider application with a more vast scope. This application is good at specific tasks. This application has incredible human-level intelligence. It uses supervised and unsupervised learning to process data. It uses clustering and association to process data. Example: Siri, Alexa. Example: Advanced Robotics
What is the Purpose of Artificial Intelligence?
The purpose of Artificial Intelligence is to aid human capabilities and help us make advanced decisions with far-reaching consequences. That’s the answer from a technical standpoint. From a philosophical perspective, Artificial Intelligence has the potential to help humans live more meaningful lives devoid of hard labour, and help manage the complex web of interconnected individuals, companies, states and nations to function in a manner that’s beneficial to all of humanity.
Currently, the purpose of Artificial Intelligence is shared by all the different tools and techniques that we’ve invented over the past thousand years – to simplify human effort, and to help us make better decisions. Artificial Intelligence has also been touted as our Final Invention, a creation that would invent ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.
That’s all in the far future though – we’re still a long way from those kinds of outcomes. Currently, Artificial Intelligence is being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and make business predictions based on hard data rather than gut feelings. As all technology has come before this, the research and development costs need to be subsidised by corporations and government agencies before it becomes accessible to everyday laymen. To learn more about the purpose of artificial intelligence and where it is used, you can take up an AI course and understand the artificial intelligence course details and upskill today.
Where is Artificial Intelligence (AI) Used?
AI is used in different domains to give insights into user behaviour and give recommendations based on the data. For example, Google’s predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increasing watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on the facial features in their images. AI is used everywhere by large organisations to make an end user’s life simpler. The uses of Artificial Intelligence would broadly fall under the data processing category, which would include the following:
Searching within data, and optimising the search to give the most relevant results
Logic-chains for if-then reasoning, that can be applied to execute a string of commands based on parameters
Pattern-detection to identify significant patterns in large data set for unique insights
Applied probabilistic models for predicting future outcomes
What are the Advantages of Artificial Intelligence?
There’s no doubt in the fact that technology has made our life better. From music recommendations, map directions, and mobile banking to fraud prevention, AI and other technologies have taken over. There’s a fine line between advancement and destruction. There are always two sides to a coin, and that is the case with AI as well. Let us take a look at some advantages of Artificial Intelligence-
Advantages of Artificial Intelligence (AI)
Reduction in human error
Available 24×7
Helps in repetitive work
Digital assistance
Faster decisions
Rational Decision Maker
Medical applications
Improves Security
Efficient Communication
Let’s take a closer look.
Prerequisites for Artificial Intelligence
As a beginner, here are some of the basic prerequisites that will help get started with the subject.
A strong hold on Mathematics – namely Calculus, Statistics and probability. A good amount of experience in programming languages like Java, or Python. A strong hold in understanding and writing algorithms. A strong background in data analytics skills. A good amount of knowledge in discrete mathematics. The will to learn machine learning languages.
History of Artificial Intelligence(AI)
Artificial Intelligence technology is much older than you would imagine and the term “AI” is not new for researchers. The term “AI” was first coined at Dartmouth college in 1956 by a scientist called Marvin Minsky.
Getting certified in AI will give you an edge over the other aspirants in this industry. With advancements such as Facial Recognition, AI in Healthcare, Chat-bots, and more, now is the time to build a path to a successful career in Artificial Intelligence. Virtual assistants have already made their way into everyday life, helping us save time and energy. Self-driving cars by Tech giants like Tesla have already shown us the first step to the future. AI can help reduce and predict the risks of climate change, allowing us to make a difference before it’s too late. And all of these advancements are only the beginning, there’s so much more to come. 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022.
Ancient Greek mythology included intelligent robots and artificial entities for the first time. The creation of syllogism and its application of deductive reasoning by Aristotle was a watershed point in humanity’s search to comprehend its own intelligence. Despite its long and deep roots, artificial intelligence as we know it today has only been around for less than a century.
Let us take a look at the important timeline of events related to artificial intelligence:
1943 – Warren McCulloch and Walter Pits published the paper “A Logical Calculus of Ideas Immanent in Nervous Activity” which was the first work on artificial intelligence (AI) in 1943. They suggested an artificial neuron model.
1949 – Donald Hebb proposed the theory for modifying connection strength between neurons in his book The Organization of Behavior: A Neuropsychological Theory
1950 – Alan Turing, an English mathematician published “Computing Machinery and Intelligence” in which he proposed a test to determine if a machine has the ability to exhibit human behavior. This test is famously knows as the Turin Test.
In the same year, Harvard graduates Marvin Minsky and Dean Edmonds built the first neural network computer named SNARC.
1956 – The “first artificial intelligence program” named “Logic Theorist” was constructed by Allen Newell and Herbert A. Simon. This program verified 38 of 52 mathematical theorems, as well as discovering new and more elegant proofs for several of them.
In the same year, the word “Artificial Intelligence” was first adopted by John McCarthy, an American scientist at the Dartmouth Conference and was coined for the first time as an academic field.
The enthusiasm towards Artificial Intelligence grew rapidly after this year.
1959 – Arthur Samuel coined the term machine learning while he was working at IBM.
1963 – John McCarthy started an Artificial Intelligence Lab at Stanford.
1966 – Joseph Weizenbaum created the first ever chatbot named ELIZA.
1972 – The first humanoid robot was built in Japan named WABOT-1.
1974 to 1980 – This period is famously knows as the first AI winter period. Lot of scientists could not pursue/continue their research to the best extent as they fell short of funding from the government and the interest towards AI gradually declined.
1980 – AI was back with a bang! Digital Equipment Corporations developed R1 which was the first successful commercial expert system and officially ended the AI winter period.
In the same year, the first ever national conference of American Association of Artificial Intelligence was organized at Stanford University.
1987 to 1993 – With emerging computer technology and cheaper alternatives, many investors and the government stopped funding for AI research leading to the second AI Winter period.
1997 – A computer beats human! IBM’s computer IBM Deep Blue defeated the then world chess champion, Gary Kasparov, and became the first computer/machine to beat a world chess champion.
2002 – The inception of vacuum cleaners made AI enter homes.
2005 – The American military started investing in autonomous robots such as Boston Dynamics’ “Big Dog” and iRobot’s “PackBot.”
2006 – Companies like Facebook, Google, Twitter, Netflix started using AI.
2008 – Google made a breakthroughs in speech recognition and introduced the speech recognition feature in the iPhone app.
2011 – Watson – an IBM computer, won Jeopardy in 2011, a game show in which it had to solve complicated questions and riddles. Watson had demonstrated that it could comprehend plain language and solve complex problems fast.
2012 – Andrew Ng, the Google Brain Deep Learning project’s founder, fed 10 million YouTube videos into a neural network using deep learning algorithms. The neural network learnt to recognise a cat without being informed what a cat is, which marked the beginning of a new era in deep learning and neural networks.
2014 – Google made the first self-driving car which passed the driving test.
2014 – Amazon’s Alexa was released.
2016 – Hanson Robotics created the first “robot citizen,” Sophia, a humanoid robot capable of facial recognition, verbal conversation, and facial emotion.
2020 – During the early phases of the SARS-CoV-2 pandemic, Baidu made its LinearFold AI algorithm available to scientific and medical teams seeking to create a vaccine. The system could anticipate the virus’s RNA sequence in just 27 seconds, which was 120 times faster than prior methods.
As each day progresses, Artificial Intelligence is making rapid advancements in all fields. AI is no longer the future, it is the present!
AI in Everyday life
Here is a list of AI applications that you may use in everyday life:
Online shopping: Artificial intelligence is used in online shopping to provide personalised recommendations to users, based on their previous searches and purchases.
Digital personal assistants: Smartphones use AI to provide personalised services. AI assistants can answer questions and help users to organise their daily routines without a hassle. Check out AI as a service here.
Machine translations: AI-based language translation software provides translations, subtitling and language detection which can help users to understand other languages.
Cybersecurity: AI systems can help recognise and fight cyberattacks based on recognising patterns and backtracking the attacks.
Artificial intelligence against Covid-19: In the case of Covid-19, AI has been used in identifying outbreaks, processing healthcare claims, and tracking the spread of the disease.
Applications of Artificial Intelligence in Business
AI truly has the potential to transform many industries, with a wide range of possible use cases. What all these different industries and use cases have in common, is that they are all data-driven. Since Artificial Intelligence is an efficient data processing system at its core, there’s a lot of potential for optimisation everywhere.
Let’s take a look at the industries where AI is currently shining.
Administration: AI systems are helping with the routine, day-to-day administrative tasks to minimise human errors and maximise efficiency. Transcriptions of medical notes through NLP and helps structure patient information to make it easier for doctors to read it.
AI systems are helping with the routine, day-to-day administrative tasks to minimise human errors and maximise efficiency. Transcriptions of medical notes through NLP and helps structure patient information to make it easier for doctors to read it. Telemedicine: For non-emergency situations, patients can reach out to a hospital’s AI system to analyse their symptoms, input their vital signs and assess if there’s a need for medical attention. This reduces the workload of medical professionals by bringing only crucial cases to them.
For non-emergency situations, patients can reach out to a hospital’s AI system to analyse their symptoms, input their vital signs and assess if there’s a need for medical attention. This reduces the workload of medical professionals by bringing only crucial cases to them. Assisted Diagnosis: Through computer vision and convolutional neural networks, AI is now capable of reading MRI scans to check for tumours and other malignant growths, at an exponentially faster pace than radiologists can, with a considerably lower margin of error.
Through computer vision and convolutional neural networks, AI is now capable of reading MRI scans to check for tumours and other malignant growths, at an exponentially faster pace than radiologists can, with a considerably lower margin of error. Robot-assisted surgery: Robotic surgeries have a very minuscule margin-of-error and can consistently perform surgeries round-the-clock without getting exhausted. Since they operate with such a high degree of accuracy, they are less invasive than traditional methods, which potentially reduces the time patients spend in the hospital recovering.
Robotic surgeries have a very minuscule margin-of-error and can consistently perform surgeries round-the-clock without getting exhausted. Since they operate with such a high degree of accuracy, they are less invasive than traditional methods, which potentially reduces the time patients spend in the hospital recovering. Vital Stats Monitoring: A person’s state of health is an ongoing process, depending on the varying levels of their respective vitals stats. With wearable devices achieving mass-market popularity now, this data is not available on tap, just waiting to be analysed to deliver actionable insights. Since vital signs have the potential to predict health fluctuations even before the patient is aware, there are a lot of live-saving applications here.
Better recommendations: This is usually the first example that people give when asked about business applications of AI, and that’s because it’s an area where AI has delivered great results already. Most large e-commerce players have incorporated Artificial Intelligence to make product recommendations that users might be interested in, which has led to considerable increases in their bottom-lines.
This is usually the first example that people give when asked about business applications of AI, and that’s because it’s an area where AI has delivered great results already. Most large e-commerce players have incorporated Artificial Intelligence to make product recommendations that users might be interested in, which has led to considerable increases in their bottom-lines. Chatbots: Another famous example, based on the proliferation of Artificial Intelligence chatbots across industries, and every other website we seem to visit. These chatbots are now serving customers in odd-hours and peak hours as well, removing the bottleneck of limited human resources.
Another famous example, based on the proliferation of Artificial Intelligence chatbots across industries, and every other website we seem to visit. These chatbots are now serving customers in odd-hours and peak hours as well, removing the bottleneck of limited human resources. Filtering spam and fake reviews: Due to the high volume of reviews that sites like Amazon receive, it would be impossible for human eyes to scan through them to filter out malicious content. Through the power of NLP, Artificial Intelligence can scan these reviews for suspicious activities and filter them out, making for a better buyer experience.
Due to the high volume of reviews that sites like Amazon receive, it would be impossible for human eyes to scan through them to filter out malicious content. Through the power of NLP, Artificial Intelligence can scan these reviews for suspicious activities and filter them out, making for a better buyer experience. Optimising search: All of the e-commerce depends upon users searching for what they want, and being able to find it. Artificial Intelligence has been optimising search results based on thousands of parameters to ensure that users find the exact product that they are looking for.
All of the e-commerce depends upon users searching for what they want, and being able to find it. Artificial Intelligence has been optimising search results based on thousands of parameters to ensure that users find the exact product that they are looking for. Supply-chain: AI is being used to predict demand for different products in different timeframes so that they can manage their stocks to meet the demand.
Building work culture: AI is being used to analyse employee data and place them in the right teams, assign projects based on their competencies, collect feedback about the workplace, and even try to predict if they’re on the verge of quitting their company.
AI is being used to analyse employee data and place them in the right teams, assign projects based on their competencies, collect feedback about the workplace, and even try to predict if they’re on the verge of quitting their company. Hiring: With NLP, AI can go through thousands of CV in a matter of seconds, and ascertain if there’s a good fit. This is beneficial because it would be devoid of any human errors or biases, and would considerably reduce the length of hiring cycles.
Robots in AI
The field of robotics has been advancing even before AI became a reality. At this stage, artificial intelligence is helping robotics to innovate faster with efficient robots. Robots in AI have found applications across verticals and industries especially in the manufacturing and packaging industries. Here are a few applications of robots in AI:
Assembly
AI along with advanced vision systems can help in real-time course correction
It also helps robots to learn which path is best for a certain process while its in operation
Customer Service
AI-enabled robots are being used in a customer service capacity in retail and hospitality industries
These robots leverage Natural Language Processing to interact with customers intelligently and like a human
More these systems interact with humans, more they learn with the help of machine learning
Packaging
AI enables quicker, cheaper, and more accurate packaging
It helps in saving certain motions that a robot is making and constantly refines them, making installing and moving robotic systems easily
Open Source Robotics
Robotic systems today are being sold as open-source systems having AI capabilities.
In this way, users can teach robots to perform custom tasks based on a specific application
Eg: small scale agriculture
What Makes AI Technology So Useful?
Artificial intelligence offers several critical benefits that make it an excellent tool, such as:
Automation – AI can automate tedious processes/tasks, without any fatigue.
– AI can automate tedious processes/tasks, without any fatigue. Enhancement – AI can enhance all the products and services effectively by improving experiences for end-users and delivering better product recommendations.
– AI can enhance all the products and services effectively by improving experiences for end-users and delivering better product recommendations. Analysis and Accuracy– AI analysis is much faster and more accurate than humans. AI can use its ability to interpret data with better decisions.
Simply put, AI helps organizations to make better decisions, enhancing product and business processes at a much faster pace.
Career Trends in Artificial Intelligence
Careers in Artificial Intelligence have shown steady growth over the past few years and will continue to grow at an accelerating rate. 57% of Indian companies are looking to hire the right talent to match the market requirements. Aspirants who have successfully transitioned into an AI role have seen an average hike in salary of 60-70%. Mumbai stands tall in competition and is followed by Bangalore and Chennai. According to WEF, 133 million jobs will be created in AI by the year 2020. Research states that the demand for jobs has increased but the workforce has not been able to keep pace with it.
AI is being used in various sectors such as healthcare, banking and finance, marketing and the entertainment industry. Deep Learning Engineer, Data Scientist, Director of Data Science and Senior Data Scientist are some of the top jobs that require AI Skills.
With the increase in opportunities available, it’s safe to say that now is the right time to upskill in this domain.
What is the relationship between AI, ML, and DL?
As the above image portrays, the three concentric ovals describe DL as a subset of ML, which is also another subset of AI. Therefore, AI is the all-encompassing concept that initially erupted. It was then followed by ML that thrived later, and lastly DL that is now promising to escalate the advances of AI to another level.
Examples of Artificial Intelligence
Facebook Watch
Facebook Friends Recommendations
Siri, Alexa and other smart assistants
Self-driving cars
Robo-advisors
Conversational bots
Email spam filters
Netflix’s recommendations
Proactive healthcare management
Disease mapping
Automated financial investing
Virtual travel booking agent
Social media monitoring
Future of Artificial Intelligence
As humans, we have always been fascinated by technological changes and fiction, right now, we are living amidst the greatest advancements in our history. Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Artificial intelligence is not only impacting the future of every industry and every human being but has also acted as the main driver of emerging technologies like big data, robotics and IoT. Considering its growth rate, it will continue to act as a technological innovator for the foreseeable future. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.
Career Opportunities in AI
AI & ML Developer/Engineer
AI & ML Engineer/Developer is responsible for performing statistical analysis, running statistical tests, and implementing statistical designs. Furthermore, they develop deep learning systems, manage ML programs, implement ML algorithms, etc.
So, basically, they deploy AI & ML-based solutions for the company. For becoming n AI & ML developer, you will need good programming skills in Python, Scala, and Java. You get to work on frameworks like Azure ML Studio, Apache Hadoop, Amazon ML, etc. If you proceed on the set ai engineer learning path, success is all yours! The average salary of an AI engineer in India is found to be ranging from INR 4 Lakhs p.a. to INR 20 Lakhs p.a.
AI Analyst/Specialist
The role of an ai analyst or specialist is similar to that of an ai engineer. The key responsibility is to cater to AI-oriented solutions and schemes to enhance the services delivered by a certain industry using the data analyzing skills to study the trends and patterns of certain datasets. Whether you talk about the healthcare industry, finance industry, geology sector, cyber security, or any other sector, AI analysts or specialists are seen to have quite a good impact all over. An AI Analyst/Specialist must have a good programming, system analysis, and computational statistics background. A bachelor’s or equivalent degree can help you land an entry-level position, but a master’s or equivalent degree is a must for the core AI analyst positions. The average salary of an ai analyst can be anywhere between INR 3 Lakhs per year and 10 Lakhs per year, based on the years of experience and company you are working for.
Data Scientist
Owing to the huge demand for data scientists, there are high chances that you are already familiar with the term. The role of a data scientist involves identifying valuable data streams and sources, working along with the data engineers for the automation of data collection processes, dealing with big data, analyzing massive amounts of data to learn the trends and patterns for developing predictive ML models. A data scientist is also responsible for coming up with solutions and strategies for the decision-makers with the help of intriguing visualization tools and techniques. SQL, Python, Scala, SAS, SSAS, and R are the most useful tools to a data scientist. They are required to work on frameworks such as Amazon ML, Azure ML Studio, Spark MLlib, and so on. The average salary of a data scientist in India is INR 5-22 Lakhs per year, depending on their experience and the company they are hired in.
Research Scientist
Research Scientist is one of the other fascinating artificial intelligence jobs. This ai job position holds responsibilities related to researching the field of Artificial Intelligence and Machine Learning to innovate and discover AI-oriented solutions to real-world problems. As we know, research in whatever streams it may be demands core expertise. Likewise, the role of a research scientist calls for mastery in various AI disciplines such as Computational Statistics, Applied Mathematics, Deep Learning, Machine Learning, and Neural Networks. A research scientist is expected to have Python, Scala, SAS, SSAS, and R programming skills. Apache Hadoop, Apache Signa, Scikit learn, H20 are some common frameworks to work on as a research scientist. An advanced master’s or doctoral degree is a must for becoming an AI research scientist. As per the current studies, an AI research scientist earns a minimum of INR 35 Lakhs annually in India.
Product Manager
Nowadays, in every leading company, the job of a product manager incorporates a significant role of artificial intelligence. Resolving challenging issues by strategically collecting data falls under the duty of a product manager. You are supposed to have the skill of identifying relevant business impeding problems and further gather related datasets for data interpretation. Once the data interpretation is made, the product manager implements effective AI strategies to evaluate the business impacts depicted by the inferences drawn from data interpretation. In view of the crucial job role, every organization needs an efficient product manager. Thus, we can say that a product manager ensures that a product is actively running. One must have good hands-on programming languages like Python, R, SQL, and other essential ones. Initially, the average pay of a product manager is around INR 7-8 Lakhs per anum, which can extend to one Crore in the later years. There is no such thing as a free lunch; similarly, for getting a job as a product manager, you must have an in-depth knowledge of AI-ML, Computer Science, Statistics, Marketing related core concepts. Ultimately, experience, skills, company and locations are the major factors that determine your salary as a product manager.
Robotics Scientist
Following the lead of global automation trends and the emergence of robotics in the field of ai, we can tell it is definitely a sign of sprouting demand for robotics scientists. In this fast-paced world where technology is becoming the pioneer, robots are indeed stealing the job of people handling manual or repetitive & boring tasks. On the contrary, it is giving employment to professionals having expertise in the field of robotics. In order to build and manage these robotic systems, we need a robotics engineer. To pursue a career as a robotics engineer, you must have a master’s degree in robotics, Computer Science or Engineering. A robotics scientist is among one of the other interesting and high paying ai careers take upon. Since we are already aware of how complicated robots are, tackling them demands knowledge in different disciplines. If the field of robotics intrigues you and you are good at programming, mechanics, electronics, electrics, sensing, and psychology and cognition to some extent, you are good to go with this career option.
Important FAQs on Artificial Intelligence (AI)
Ques. Where is AI used?
Ans. Artificial Intelligence is used across industries globally. Some of the industries which have delved deep in the field of AI to find new applications are E-commerce, Retail, Security and Surveillance. Sports Analytics, Manufacturing and Production, Automotive among others.
Ques. How is AI helping in our life?
Ans. The virtual digital assistants have changed the way w do our daily tasks. Alexa and Siri have become like real humans we interact with each day for our every small and big need. The natural language abilities and the ability to learn themselves without human interference are the reasons they are developing so fast and becoming just like humans in their interaction only more intelligent and faster.
Ques. Is Alexa an AI?
Ans. Yes, Alexa is an Artificial Intelligence that lives among us.
Ques. Is Siri an AI?
Ans. Yes, just like Alexa Siri is also an artificial intelligence that uses advanced machine learning technologies to function.
Ques. Why is AI needed?
Ans. AI makes every process better, faster, and more accurate. It has some very crucial applications too such as identifying and predicting fraudulent transactions, faster and accurate credit scoring, and automating manually intense data management practices. Artificial Intelligence improves the existing process across industries and applications and also helps in developing new solutions to problems that are overwhelming to deal with manually.
Ques. What is artificial intelligence with examples?
Ans. Artificial Intelligence is an intelligent entity that is created by humans. It is capable of performing tasks intelligently without being explicitly instructed to do so. We make use of AI in our daily lives without even realizing it. Spotify, Siri, Google Maps, YouTube, all of these applications make use of AI for their functioning.
Ques. Is AI dangerous?
Ans. Although there are several speculations on AI being dangerous, at the moment, we cannot say that AI is dangerous. It has benefited our lives in several ways.
Ques. What is the goal of AI?
Ans. The basic goal of AI is to enable computers and machines to perform intellectual tasks such as problem solving, decision making, perception, and understanding human communication.
Ques. What are the advantages of AI?
Ans. There are several advantages of artificial intelligence. They are listed below:
Available 24×7
Digital Assistance
Faster Decisions
New Inventions
Reduction in Human Error
Helps in repetitive jobs
Ques. Who invented AI?
Ans. The term Artificial Intelligence was coined John McCarthy. He is considered as the father of AI.
Ques. Is artificial intelligence the future?
Ans. We are currently living in the greatest advancements of Artificial Intelligence in history. It has emerged to be the next best thing in technology and has impacted the future of almost every industry. There is a greater need for professionals in the field of AI due to the increase in demand. According to WEF, 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022. Yes, AI is the future.
Ques. What is AI and its application?
Ans. AI has paved its way into various industries today. Be it gaming, or healthcare. AI is everywhere. Did you now that the facial recognition feature on our phones uses AI? Google Maps also makes use of AI in its application, and it is part of our daily life more than we know it. Spam filters on Emails, Voice-to-text features, Search recommendations, Fraud protection and prevention, Ride-sharing applications are some of the examples of AI and its application.
What’s your view about the future of Artificial Intelligence? Leave your comments below.
Curious to dig deeper into AI, read our blog on some of the top Artificial Intelligence books.
Further Reading
What is artificial intelligence?
Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. Register here.
The words “artificial intelligence” (AI) have been used to describe the workings of computers for decades, but the precise meaning shifted with time. Today, AI describes efforts to teach computers to imitate a human’s ability to solve problems and make connections based on insight, understanding and intuition.
What is artificial intelligence?
Artificial intelligence usually encompasses the growing body of work in technology on the cutting edge that aims to train the technology to accurately imitate or — in some cases — exceed the capabilities of humans.
Older algorithms, when they grow commonplace, tend to be pushed out of the tent. For instance, transcribing human voices into words was once an active area of research for scientists exploring artificial intelligence. Now it is a common feature embedded in phones, cars and appliances and it isn’t described with the term as often.
Today, AI is often applied to several areas of research:
Event Low-Code/No-Code Summit Learn how to build, scale, and govern low-code programs in a straightforward way that creates success for all this November 9. Register for your free pass today. Register Here
Machine vision : Which helps computers understand the position of objects in the world through lights and cameras.
: Which helps computers understand the position of objects in the world through lights and cameras. Machine learning (ML) : The general problem of teaching computers about the world with a training set of examples.
: The general problem of teaching computers about the world with a training set of examples. Natural language processing (NLP) : Making sense of knowledge encoded in human languages.
: Making sense of knowledge encoded in human languages. Robotics: Designing machines that can work with some degree of independence to assist with tasks, especially work that humans can’t do because it may be repetitive, strenuous or dangerous.
There is a wide range of practical applicability to artificial intelligence work. Some chores are well-understood and the algorithms for solving them are already well-developed and rendered in software. They may be far from perfect, but the application is well-defined. Finding the best route for a trip, for instance, is now widely available via navigation applications in cars and on smartphones.
Other areas are more philosophical. Science fiction authors have been writing about computers developing human-like attitudes and emotions for decades, and some AI researchers have been exploring this possibility. While machines are increasingly able to work autonomously, general questions of sentience, awareness or self-awareness remain open and without a definite answer.
[Related: ‘Sentient’ artificial intelligence: Have we reached peak AI hype?]
AI researchers often speak of a hierarchy of capability and awareness. The directed tasks at the bottom are often called “narrow AI” or “reactive AI”. These algorithms can solve well-defined problems, sometimes without much direction from humans. Many of the applied AI packages fall into this category.
The notion of “general AI” or “self-directed AI” applies to software that could think like a human and initiate plans outside of a well-defined framework. There are no good examples of this level of AI at this time, although some developers sometimes like to suggest that their tools are beginning to exhibit some of this independence.
Beyond this is the idea of “super AI”, a package that can outperform humans in reasoning and initiative. These are largely discussed hypothetically by advanced researchers and science fiction authors.
In the last decade, many ideas from the AI laboratory have found homes in commercial products. As the AI industry has emerged, many of the leading technology companies have assembled AI products through a mixture of acquisitions and internal development. These products offer a wide range of solutions, and many businesses are experimenting with using them to solve problems for themselves and their customers.
How are the biggest companies approaching AI?
Leading companies have invested heavily in AI and developed a wide range of products aimed at both developers and end users. Their product lines are increasingly diverse as the companies experiment with different tiers of solutions to a wide range of applied problems. Some are more polished and aimed at the casual computer user. Others are aimed at other programmers who will integrate the AI into their own software to enhance it. The largest companies all offer dozens of products now and it’s hard to summarize their increasingly varied options.
IBM has long been one of the leaders in AI research. Its AI-based competitor in the TV game Jeopardy, Watson, helped ignite the recent interest in AI when it beat humans in 2011 demonstrating how adept the software could be at handling more general questions posed in human language.
Since then, IBM has built a broad collection of applied AI algorithms under the Watson brand name that can automate decisions in a wide range of business applications like risk management, compliance, business workflow and devops. These solutions rely upon a mixture of natural language processing and machine learning to create models that can either make production decisions or watch for anomalies. In one case study of its applications, for instance, the IBM Safer Payments product prevented $115 million worth of credit card fraud.
Another example, Microsoft’s AI platform offers a wide range of algorithms, both as products and services available through Azure. The company also targets machine learning and computer vision applications and like to highlight how their tools search for secrets inside extremely large data sets. Its Megatron-Turing Natural Language Generation model (MT-NLG), for instance, has 530 billion parameters to model the nuances of human communication. Microsoft is also working on helping businesses processes shift from being automated to becoming autonomous by adding more intelligence to handle decision-making. Its autonomous packages are, for instance, being applied to both the narrow problems of keeping assembly lines running smoothly and the wider challenges of navigating drones.
Google developed a strong collection of machine learning and computer vision algorithms that it uses for both internal projects indexing the web while also reselling the services through their cloud platform. It has pioneered some of the most popular open-source machine learning platforms like TensorFlow and also built custom hardware for speeding up training models on large data sets. Google’s Vertex AI product, for instance, automates much of the work of turning a data set into a working model that can then be deployed. The company also offers a number of pretrained models for common tasks like optical character recognition or conversational AI that might be used for an automated customer service agent.
In addition, Amazon also uses a collection of AI routines internally in its retail website, while marketing the same backend tools to AWS users. Products like Personalize are optimized for offering customers personalized recommendations on products. Rekognitition offers predeveloped machine vision algorithms for content moderation, facial recognition and text detection and conversion. These algorithms also have a prebuilt collection of models of well-known celebrities, a useful tool for media companies. Developers who want to create and train their own models can also turn to products like SageMaker which automates much of the workload for business analysts and data scientists.
Facebook also uses artificial intelligence to help manage the endless stream of images and text posts. Algorithms for computer vision classify uploaded images, and text algorithms analyze the words in status updates. While the company maintains a strong research team, the company does not actively offer standalone products for others to use. It does share a number of open-source projects like NeuralProphet, a framework for decision-making.
Additionally, Oracle is integrating some of the most popular open-source tools like Pytorch and Tensorflow into their data storage hierarchy to make it easier and faster to turn information stored in Oracle databases into working models. They also offer a collection of prebuilt AI tools with models for tackling common challenges like anomaly detection or natural language processing.
How are startups approaching AI?
New AI companies tend to be focused on one particular task, where applied algorithms and a determined focus will produce something transformative. For instance, a wide-reaching current challenge is producing self-driving cars. Startups like Waymo, Pony AI, Cruise Automation and Argo are four major startups with significant funding who are building the software and sensor systems that will allow cars to navigate themselves through the streets. The algorithms involve a mixture of machine learning, computer vision, and planning.
Many startups are applying similar algorithms to more limited or predictable domains like warehouse or industrial plants. Companies like Nuro, Bright Machines and Fetch are just some of the many that want to automate warehouses and industrial spaces. Fetch also wants to apply machine vision and planning algorithms to take on repetitive tasks.
A substantial number of startups are also targeting jobs that are either dangerous to humans or impossible for them to do. Against this backdrop, Hydromea is building autonomous underwater drones that can track submerged assets like oil rigs or mining tools. Another company, Solinus, makes robots for inspecting narrow pipes.
Many startups are also working in digital domains, in part because the area is a natural habitat for algorithms, since the data is already in digital form. There are dozens of companies, for instance, working to simplify and automate routine tasks that are part of the digital workflow for companies. This area, sometimes called robotic process automation (RPA), rarely involves physical robots because it works with digital paperwork or chit. However, it is a popular way for companies to integrate basic AI routines into their software stack. Good RPA platforms, for example, often use optical character recognition and natural language processing to make sense of uploaded forms in order to simplify the office workload.
Many companies also depend upon open-source software projects with broad participation. Projects like Tensorflow or PyTorch are used throughout research and development organizations in universities and industrial laboratories. Some projects like DeepDetect, a tool for deep learning and decision-making, are also spawning companies that offer mixtures of support and services.
There are also hundreds of effective and well-known open-source projects used by AI researchers. OpenCV, for instance, offers a large collection of computer vision algorithms that can be adapted and integrated with other stacks. It is used frequently in robotics, medical projects, security applications and many other tasks that rely upon understanding the world through a camera image or video.
Is there anything AI can’t do?
There are some areas where AI finds more success than others. Statistical classification using machine learning is often pretty accurate but it is often limited by the breadth of the training data. These algorithms often fail when they are asked to make decisions in new situations or after the environment has shifted substantially from the training corpus.
Much of the success or failure depends upon how much precision is demanded. AI tends to be more successful when occasional mistakes are tolerable. If the users can filter out misclassification or incorrect responses, AI algorithms are welcomed. For instance, many photo storage sites offer to apply facial recognition algorithms to sort photos by who appears in them. The results are good but not perfect, but users can tolerate the mistakes. The field is largely a statistical game and succeeds when judged on a percentage basis.
A number of the most successful applications don’t require especially clever or elaborate algorithms, but depend upon a large and well-curated dataset organized by tools that are now manageable. The problem once seemed impossible because of the scope, until large enough teams tackled it. Navigation and mapping applications like Waze just use simple search algorithms to find the best path but these apps could not succeed without a large, digitized model of the street layouts.
Natural language processing is also successful with making generalizations about the sentiment or basic meaning in a sentence but it is frequently tripped up by neologisms, slang or nuance. As language changes or processes, the algorithms can adapt, but only with pointed retraining. They also start to fail when the challenges are outside a large training set.
Robotics and autonomous cars can be quite successful in limited areas or controlled spaces but they also face trouble when new challenges or unexpected obstacles appear. For them, the political costs of failure can be significant, so developers are necessarily cautious on leaving the envelope.
Indeed, determining whether an algorithm is capable or a failure often depends upon criteria that are politically determined. If the customers are happy enough with the response, if the results are predictable enough to be useful, then the algorithms succeed. As they become taken for granted, they lose the appellation of AI.
If the term is generally applied to the topics and goals that are just out of reach, if AI is always redefined to exclude the simple, well-understood solutions, then AI will always be moving toward the technological horizon. It may not be 100% successful presently, but when applied in specific cases, it can be tantalizingly close.
[Read more: The quest for explainable AI]
Leave a Reply