Artificial Intelligence (AI) has revolutionized various aspects of our lives, ranging from personalized recommendations to self-driving cars. One crucial element that has greatly influenced the advancement of AI is Linguistics. Language forms the basis of human communication, making it very important for AI systems to understand, analyze, and respond to human input.
This task of understanding human input is made possible by incorporating Linguistics in AI.
Linguistic Principles include components like sentence structure, meaning and usage. By adhering to these principles, machines can effectively communicate with humans.
This blog will give you an overview about the significance of linguistics in AI and how it has influenced the creation of language-based AI systems.
Natural Language Processing – Foundation of Language-Based AI
Natural Language Processing (NLP), is a branch of AI that focuses on helping computers comprehend, interpret, and produce human language. NLP essentially forms the core of Language-based AI. Recent developments in Deep Learning have now made it possible for NLP models to extract meaning, feelings, and entities by processing enormous quantities of unstructured data.
Numerous language-based AI applications have been created thanks to NLP – think Google Assistant, Apple’s Siri, and Amazon’s Alexa. Virtual Assistants use NLP to understand and carry out requests. Be it delivering information or even just playing a song!
Speech Recognition – Turning Spoken Language into Text
The use of linguistic principles in AI has led to substantial breakthroughs in speech recognition technologies. These technologies enable seamless communication between humans and machines by translating spoken words into written text. Major industries are now driving towards AI Based softwares and technologies.
For example: AI-driven speech recognition has had a significant impact on various industries, like medical transcribing services. Doctors can record patient information verbally, into a system that will note it down, saving time and lowering the possibility of an error. Additionally, voice assistants like Siri and Google Assistant use speech recognition to understand user requests, improving the usability and intuitiveness of our interactions with the technology.
Language Translation: Bridging the Gap Between Languages
Another impressive way that linguistics are used in AI is in Language translation . Advanced algorithms are used by Machine Translation (MT) systems to automatically convert text or speech from one language to another.
AI-powered translation services can assist in bridging language gaps. Users can translate individual words or whole web pages into other languages using online tools like Google Translate. People from various linguistic origins can now interact and work together successfully thanks to this transformation of cross-cultural communication. Further, these AI tools are critical to industries like Tourism, where real-time translation is required.
The Linguistic Edge: Opportunities with AI Certification
Artificial Intelligence (AI) has emerged as a transformative force across various industries, and its impact continues to grow rapidly. As a result, the importance of linguistic expertise after completing an Artificial Intelligence course cannot be overstated.
Moreover, AI language models are revolutionizing translation services, content generation, and sentiment analysis, among other applications. The demand for professionals with expertise in linguistic roles within AI is growing massively, offering immense opportunities for freshers in the fields like Healthcare, Finance and many more. By enrolling in an Artificial Intelligence and Machine Learning Program, students can land jobs in a very in-demand sector.
In conclusion, Artificial Intelligence (AI) is rapidly changing sectors around the world and offering a wealth of prospects for college students. By obtaining an AI Certification from upGrad Campus, students will be able to succeed in fields such as sentiment analysis, content development, virtual assistants, translation services, chatbots driven by AI, and more.
So don’t miss out on the chance to be part of this revolution – enroll in the upGrad Campus Artificial Intelligence and Machine Learning Certification Program and embark on a rewarding career in the field of AI.
If you are a student or a fresher you might have entered a conversation about
“Is AI going to take over human intelligence?” or “Will AI lead to mass unemployment in the marketing sector?”. Today, evolution in AI is a matter of concern for many working professionals or students choosing their career path. Especially after the introduction of ChatGPT 3.5.
In recent years, Artificial Intelligence has developed significantly shaping various industries and workplaces. AI has become a big subject in the tech industry as now it is involved in doing a lot of work which previously needed human intelligence.
AI offers a helping hand everywhere – from basic tasks like turning off the lights to more complex functions like developing apps and websites or security-led programming. This has led to speculation that AI tools will eventually replace developers, making programming a thing of the past.
The answer to these questions lies deep into understanding how AI works and how AI is going to be placed in the upcoming market. In this blog, we will explore the impact of AI on the workplace and how artificial intelligence certification can prepare developers for the future.
Most Popular AI Tools
Artificial intelligence or AI tools are introduced to make working easier and efficient for the workforce and to adroitly perform tasks which were tough and time consuming when done manually.
Gradually, these tools are undergoing massive improvements and are in great need in the current market, leading to a rise in demand for artificial intelligence courses. Many AI tools like Siri, Alexa, Grammarly, Dall-E, Jasper came in the market and became an indispensable part of everyday work. The development of ChatGPT was part of a larger effort to improve AI’s ability to process and understand natural language.
Prior to the introduction of ChatGPT, Natural Language Processing (or NLP) models were limited in their ability to understand and respond to complex natural language inputs. ChatGPT’s development represented a significant advancement in NLP, as it could generate coherent and natural language responses to a wide variety of input prompts. Its ability to generate human-like responses has made ChatGPT a valuable tool in improving the customer experience and streamlining communication between humans and machines.
However, in today’s market, ChatGPT is standing as a great question mark on its impact on the human workforce, leading to more students signing up for artificial intelligence courses. Despite its many capabilities and a great working potential, it is hard to definitively say that it can ever replace developers. Let us see why.
Can AI tools Replace Developers?
Firstly, it’s important to understand what AI tools are and what they can do.
AI tools are software programs that use machine learning algorithms to automate tasks that would normally require human intelligence.
For example, some AI tools can generate code based on natural language descriptions of what the code should do, while others can identify and fix errors in code.While AI tools can automate some aspects of software development, they cannot replace developers entirely.
Here are some reasons why:
Creativity and Problem-Solving Skills: One of the most important skills that developers possess is creativity. They can come up with innovative solutions to problems that AI tools may not be able to handle.
Developers can also understand the context of a problem and are able to apply their knowledge to find the best solution. AI tools, on the other hand, are limited by their algorithms and cannot think outside the box.
Flexibility: Developers need to be able to adapt to new situations and learn new skills quickly. They need to be able to work with new technologies and programming languages as they emerge. While AI tools can be trained on new data, they cannot adapt to new situations as quickly as humans.
Communication and Collaboration: Developers often work in teams, collaborating with designers, project managers, and other developers. They need to be able to communicate effectively and work together to achieve a common goal. While AI tools can assist in some aspects of software development, they cannot replace the human element of communication and collaboration. So, if you are going for artificial intelligence certification do remember to master soft skills with hard skills.
Ethics and Responsibility: Developers are responsible for ensuring that their code is ethical and does not harm users. They need to be aware of the social and ethical implications of their work. AI tools, however, are not capable of making ethical decisions or understanding the consequences of their actions.
AI tools can assist in some aspects of software development. For example, they can automate repetitive tasks such as testing and debugging, freeing up developers to focus on more creative work. They can also help identify security vulnerabilities and suggest improvements to code. For a better understanding, let’s check the uses and limitations of ChatGPT.
Highlights and Challenges of ChatGPT
ChatGPT, like any other AI tool, is useful in many aspects but comes with its own sets of limitations. Here are some of the key uses and limitations of ChatGPT:
Uses of ChatGPT
Chatbots and virtual assistants: ChatGPT is commonly used to develop chatbots and virtual assistants that can engage in natural language conversations with humans.
Customer service: ChatGPT can be used to provide automated customer service by responding to common inquiries and providing helpful information.
Language translation: ChatGPT can be used to translate text from one language to another, making it useful for communication across different languages.
Personalization: ChatGPT can be used to personalise the user experience by understanding user preferences and tailoring responses accordingly.
Limitations of ChatGPT
Lack of context: ChatGPT may sometimes lack the contextual understanding needed to provide accurate responses. This may lead to a non-relevant or unsatisfactory response.
Biases: ChatGPT may exhibit biases in its responses due to biases in the training data or the way the model was developed.
Limited understanding of the world: ChatGPT has limited understanding of the world outside of the data it was trained on. This can lead to a lack of understanding of certain concepts or events.
Conditioned to training data: ChatGPT purely works on training data and continuously works on self-improvements. But it significantly lacks common sense and without sufficient and diverse data, its performance may be limited.
Overall, developers are creative problem solvers who can think outside the box, understand the context of software development, adapt to new technologies, collaborate effectively with others, and consider the ethical implications of their work. Until ChatGPT or any other AI tool doesn’t imbibe these traits, it is safe to say that human software developers will still be in need. However, companies will always be on the lookout for professionals who can work hand-in-hand with AI.
If you want to be this professional, then you should go for the upGrad Campus artificial intelligence certification. This coursehelps developers stay up-to-date with the latest AI technologies and prepare for the future. Be ready for the future, act today by talking to our experts.
Being a good friend who replies late or ignores messages may not affect your identity much, but have you ever thought what if a business does the same? It can cost you a good amount of clients.
But now business has evolved a lot in this field with the introduction of Conversational AI, Chatbots and Virtual assistance. Which makes Artificial Intelligence Course a high opportunity field in today’s market. Conversational Chatbots have significantly reduced the time a business takes to respond to their customer. It has also changed the way businesses manage their internal processes.
Moreover, Conversational chatbots have taken a supreme position after the introduction of video chatbots, that use visual and audio technology to answer the queries of the customers. By leveraging AI, businesses are now able to maintain 24/7 customer support, streamline internal processes, and improve overall efficiency at a very low cost.
Let’s dig a little deeper to know how Conversational AI can improve customer and business service, and the benefits they put out to us.
What are Customer Chatbot Services and Video Chatbots?
Chatbot services are AI powered computer programs developed to mimic human interaction with the help of machine and data-based learning. These are programmed to extract answers from different articles and data present on the internet.
Besides commonly used Chatbots, the introduction of video chatbots have proved to be a massive hit. These chatbots work on audio linguistics technology and are capable of answering questions by taking audio instructions. The video graphic representation makes it more indulging and mimics real conversations perfectly.
Moreover, these services can be integrated in various platforms like websites, social media accounts or messaging apps like WhatsApp business and Instagram business profiles to make customer service convenient and accessible at any time. These chatbots are based on psychology for product management to work on self-improvisations and update themselves through client’s conversation and queries and provide more personalised assistance over time.
With a long list of benefits these chatbots have brought a big revolution for business customer services. Let’s see how.
Impact of Chatbot customer service on business
Chatbots have significantly received a warm welcome by the existing businesses and the employees. Let us explore how AI and conversational AI can improve customer and business service, and the benefits they bring to the table.
One of the most significant benefits of conversational AI is its ability to provide round-the-clock customer service support. Customers can easily interact with chatbots to get answers to frequently asked questions, place orders, and resolve issues. This can save time for both customers and businesses, and improve overall customer satisfaction.
Additionally, chatbots can collect data and analyse customer behaviour, providing valuable insights for businesses to improve their products and services.
Streamlining Internal Processes with Virtual Assistants
Conversational AI can also be used to streamline internal processes and improve employee productivity.
For example, virtual assistants can help employees with tasks such as scheduling meetings, managing emails, and accessing important information.
This can save time and increase efficiency, allowing employees to focus on more strategic tasks.
Building Chatbots and Virtual Assistants
To build a conversational AI, individuals can pursue an artificial intelligence certification or take an artificial intelligence course. These courses can provide an understanding of the fundamentals of AI, machine learning, natural language processing, and more which can be useful for building chatbots and virtual assistants.
Additionally, there are several AI platforms available that make it easy to create and deploy chatbots without any coding experience. AI and Conversational chatbots have become so commonly used and hence is a vast growing opportunity field.
Let’s check how common they are and with how many conversational AI and Chatbots you are familiar with.
Popular Conversational AIs
Conversational AI is no more about giving pre- built answers to questions asked in a certain language or manner but it has elevated itself in understanding commands in many commonly spoken languages and providing personalised responses.
There are mainly three types of conversational AI: –
AI chatbots: – These are most used chatbots in small businesses and e-commerce websites and social media and messaging apps popularly found in WhatsApp Business, Facebook, Instagram. These chatbots are very reliable for providing virtual customer assistance and are easy to install and use in your business.
Voice-activated Bots: – These bots use Interactive Voice Response technology which allows users to interact with computerised systems using voice command inputs or touch tone keypad inputs. They are mostly used to handle high volume of incoming calls and redirect it to the authorised person. Some popular examples are Ameliai and AVA.
Interactive voice assistance: – These are voice enabled devices which are programmed to mimic human conversations in many different and now even regional languages. They allow users to communicate in simple and generic language without giving any specific command.
Amazon Alexa, Google Assistant, Apple Siri, and Microsoft Cortana are most common and successful examples of Interactive Voice Assistance.
Video bots: – These chatbots use video content as means of communication instead of regular text based conversations. These are programmed on the basis of NLP which is Natural Language Processing and machine learning algorithms.
Most common and popular video bots are Meena by Google, Blender Bot by Facebook, Tay by Microsoft.
Conversational AI has a strong grasp in today’s market and hence has a great career opportunity.
If you have interest in this field and you want to pursue a course in Artificial Intelligence Course then go for upGrad campus online Artificial Intelligence certification course that provides you the complete package with Live Sessions, Mentor Support and Placement Assistance.
Artificial Intelligence (AI) and Machine Learning (ML) are no longer the stuff of science fiction movies – they are here and making an impact on the world we live in. AI & ML jobs are becoming more demanding but due to the shortage of institutions providing quality skills, only a handful of candidates are switching to this field. Hence the competition is too less and chances of success are higher.
Are you curious about this exciting, rapidly-growing field? Do you want to make a career out of it? Or even pick up some supplementary skills to boost your existing career? In any case, understanding the different types of AI & ML jobs available in the market is an important first step.
Whether you’re an experienced professional or a recent graduate, this guide will provide helpful insight into the current trends in AI & ML jobs – what salaries you can expect, what companies are hiring, and where your skills could be most in-demand. Let’s take a look at what we need to know to make sure we’re up-to-date with our job market research.
What is Artificial Intelligence and Machine Learning?
AI and ML (or Machine Learning) can be thought of as two sides of the same coin. AI is about using algorithmic models to perform tasks automatically, while ML focuses on teaching machines to solve problems by analysing huge datasets – like finding patterns or recognizing objects. In other words, AI works like your digital assistant, while ML helps you turn that data into insights that can be used to make better decisions.
AI & ML jobs are some of the most in-demand tech jobs in the market today. From working on the strategy and development of AI systems to applying ML techniques to analyse data, there are lots of opportunities for professionals with the right skillset. There are also specialised roles for building and engineering AI models, such as Machine Learning Engineers and Data Scientists – so if you’re looking for a lucrative career in tech, now might be a great time to explore these roles.
Advantages of Artificial Intelligence and Machine Learning for Industries & Job Seekers
The future of industries and job seekers stands poised to be shaped by innovation in Machine Learning and Artificial Intelligence (AI) . AI and ML are powerful tools that enable faster decision-making and allow businesses to respond more quickly to market changes. With these powerful technologies reshaping the industry, job seekers must be aware of the advantages AI and ML bring to the workplace. AI and ML offer the potential of increased efficiency and productivity to businesses. By incorporating AI into existing processes, these tools can cut costs, free up time for other tasks by automating repetitive tasks, and even increase customer satisfaction. Additionally, AI-driven automation tools can improve customer service. For example, Amazon’s suggested items based on past purchase history and its voice-activated Alexa respond to customer requests, allowing shoppers to save time and resources. AI and ML also offer better decision-making capabilities and predictive insights.
By analysing large amounts of data, AI and ML algorithms can help CEOs make more informed decisions. This data can also help forecast future trends and inform new strategies that could prove to be beneficial to the business. For example, Uber’s UberEats service uses ML to predict customer demand in areas they are less familiar with, helping them to better manage their supply and demand and improve customer service. The combination of AI and ML can also offer job seekers an edge by helping them better target the right job opportunities.
By using AI-driven job search platforms such as Monster or Indeed, jobseekers can effectively create powerful search terms that allow them to target specific jobs. Additionally, intelligent algorithms can help jobseekers assess the job opening, customise their message, and modify their uploading resume to match the search criteria. AI and ML, when used wisely, can be a powerful enabler of job opportunities and increase the efficiency and productivity of industries. By understanding and taking advantage of these valuable technologies, businesses and job seekers can create beneficial outcomes for their future and the future of the economy.
Types of AI & ML Jobs
AI & ML jobs are some of the hottest trends in tech right now, and there’s no shortage of options to choose from. Understanding the different types of roles available can help you decide which kind of job might be best suited to your skills and interests.Let’s start with AI Specialists, who work to design and build AI systems to automate processes or tasks. They often also develop algorithms, usually in Python or Java, for use with Machine Learning or Artificial Intelligence systems.
Data Analysts are responsible for searching data sets for patterns and insights using a variety of tools and techniques, from predictive modelling to natural language processing. They’ll then interpret the results and make recommendations based on their findings.
Then there are Machine Learning Algorithm engineers, who use computer programming languages such as C++ or Python to create algorithms that allow machines to learn from data—think facial recognition systems or autonomous vehicles.
Finally, there are also roles for AI & ML Architects—the people responsible for translating business objectives into AI & ML solutions using the latest technologies. They need an in-depth understanding of algorithms and software engineering principles, as well as a good knowledge of cloud computing platforms like Amazon Web Services or Microsoft Azure.
Certification Program in Artificial Intelligence & Machine Learning by upGrad Campus
Are you ready to jump into the world of AI & ML? If you’re looking to break into the field, a certification program in Artificial Intelligence and Machine Learning (AI ML certification) by upGrad Campus could be an excellent starting point. It’s one of the few courses available that offers comprehensive training in both Artificial Intelligence as well as Machine Learning.
The Artificial Intelligence and Machine Learning Course combines theoretical knowledge with real-world application and trains participants on cutting-edge skills like Artificial Intelligence automation, Python, Kera, Kaggle and more such languages, multi-agent systems, reinforcement learning, natural language processing, deep learning and more. It also provides support from mentors and industry experts to ensure students can implement their learning to solve real-world problems.
With an AI ML certification from the upGrad Campus, you can be confident that you’re equipped with the right skills and experience to help you succeed in this competitive market. What’s more, it will also open up exciting opportunities with some of the leading tech companies in the field of Artificial Intelligence & Machine Learning.
AI and ML are here to stay and the job market is in high demand for those who have completed AI ML certification and are educated around the technology. Whether you’re looking for a career in a specific field, or want to expand your skill set to include AI and ML, understanding the different types of jobs available is crucial for success.
From Machine Learning engineers creating powerful algorithms to business analysts using AI-powered tools to make better decisions, there is a variety of options for those with the right skillset. Understanding the different roles and responsibilities within these jobs, as well as staying up-to-date with the latest market trends, will help you better position yourself for a rewarding career in the most advanced, interesting, and lucrative fields of the 21st century.
Traffic Alerts on Google Map, Netflix Recommendation Engine and now ChatGPT. These are just a few applications and uses of Machine Learning.
Whether you’ve noticed it or not, Machine Learning is becoming a part of our daily lives. And with every new intelligent application cropping up, it’s a good idea to know the importance of Machine Learning and the Machine Learning career opportunities available to you.
In this article, we are going to answer all your queries starting from the most fundamental one – What is Machine Learning?
What is Machine Learning?
As the name suggests, Machine Learning is the science of equipping machines (or computers) to learn on their own. The official Machine Learning definition is that it is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” with data, without explicitly being programmed.
Now if you cannot explicitly program or tell a computer what it is supposed to do, then how does Machine Learning work?
How does Machine Learning work?
In one word? Data.
With Machine Learning techniques, you feed the computer system enough data so that it can pick on patterns and come up with meaningful insights.
(Side note: This reliance on data is what makes Data Science and Machine Learning related fields.)
Let us understand this with an example. Suppose you want to build a system that identifies whether a given animal is a dog or a cat. Here are the steps involved in the same:
Step 1) Define the problem: Your first step, like in any problem-solving case, is to define what the problem is. Our problem is to be able to distinguish between cats and dogs.
Step 2) Collect data: In this step, we will collect a large amount of images containing dogs and cats and label them appropriately.
Step 3) Prep the data: Next, we will process the images so that they are understandable by the system. For e.g., we could resize the images to be in the same dimension.
Step 4) Select a Machine Learning model: We will identify which Machine Learning model is best suited to our problems. For image classification, it is usually Convolutional Neural Networks (CNNs).
Step 5) Train the model: With the existing dataset, train the model to identify one set of images by tuning the parameters (e.g. if the image has 4 legs, 2 ears, 2 eyes, whiskers, it’s an animal).
Step 6) Evaluate the model: Feed the model another set of images to check for its accuracy.
Step 7) Fine-tune the model: Make further adjustments in the model to improve performance. For example, show the machine how to classify the different types of fur coats of dogs and cats.
Step 8) Make predictions: Test the model on completely, new unseen images and see if it has learnt enough to make predictions.
Step 9) Deploy the model: Integrate the model with a real-world application like an app or a website.
Step 10) Monitor and maintain: Continue to monitor the model functioning and make updates as needed.
Sounds simple enough right? But it needs the technical skills of a Machine Learning engineer to get this in place. And more importantly, there are variations in the above steps based on the type of Machine Learning model.
Types of Machine Learning
The three types of Machine Learning models are:
Supervised Machine Learning
Supervised learning algorithms are trained to make predictions on unseen data based on labelled data. Think of supervised learning as your 3rd standard teacher who shows you different charts to explain what things are.
The image classification model that we explained above is a perfect example of supervised machine learning.
Unsupervised Machine Learning
In unsupervised learning, the algorithms are trained to uncover patterns on unlabelled data. Think of an unsupervised machine learning model as exploring a new city without a map or a guide to help you. But only by recognising what seems familiar (like a restaurant), you can figure out the context of unfamiliar things.
For example, in fraud detection, the algorithm views various transactions and groups them. Whenever an unexpected transaction is seen, it identifies it as an anomaly and flags it to your bank.
Reinforcement Machine Learning
As the name suggests, reinforcement learning is all about positive and negative reinforcement or in other words – rewards and penalties. An example would be teaching a robot to play a game. Every time the robot makes a correct move, it wins points and every time it makes the wrong move, it loses points.
Common Machine Learning Algorithms
When you have to teach a machine learning model how to solve a problem, you don’t have to reinvent the wheel. There are already many machine learning algorithms that exist for the same.
Here is a small introduction to machine learning algorithms that are most widely used:
A neural network is based on the way our brain works – in that, our brain’s final decision is based on the smaller decision of each neuron which gets passed to the next neuron, and so forth. For example, before we eat something, there is a neuron that checks if our stomach feels empty which passes this decision to the next neuron that decides if we want to eat or not and passes this decision to the next neuron that decides whether we want to eat an apple or not and so forth.
Likewise, a neural network comprises a series of binary decision makers that come up with a consolidated output. A simple example would be a facial recognition system which looks at an image, divides it into many pixels and considers every pixel match before it finally decides whether the face matches the image or not.
Linear regression algorithm tries to determine a relationship between two entities in order to make predictions. For example, let us say a publisher wants to understand whether or not to invest in a sequel of a book. The publisher will use a linear regression algorithm to see how the sales have been of the same author’s previous novels, and based on that the possible sales of the next novel.
Similar to linear regression in that logistic regression is also used in making predictions, but the outputs are binary – a plain yes/no. The simplest example would be for banking systems to understand if a person is likely to pay their loans based on their credit history and the payment history of other customers with similar characteristics. Based on the outcome, the bank will approve or reject the loan.
As the name suggests, clustering analyses various data points to find commonalities and groups entities together. A simple example would be for a store to go through the purchase history, age, gender and other characteristics of their customers, and come up with new customer segments.
Decision Trees mimic human thinking by dividing a major decision into a series of tiny decisions. For example, let us say there is an automated game in which there is a non-playable character (NPC) that is controlled by the computer. Now this NPC may decide to shoot an actual player or not and may consider several factors. Does it have enough ammunition? Will it alert other players of its location? Based on these decisions, the final decision will be taken.
With these Machine Learning examples, we hope it is clear that the applications of Machine Learning are plenty and only growing further. You can start your career in this lucrative field by taking a Machine Learning course online. But make sure to take a course that gives you sufficient hands-on experience so that you can build Machine learning models for real-world problems.
Which Machine Learning algorithm or example fascinates you the most? Let us know in the comments below.
Artificial intelligence is the recreation of human intelligence in machines. AI systems are used to perform tasks such as recognizing speech, making decisions, and translating languages. There are different types of AI, including rule-based systems, machine learning, and natural language processing (NLP). AI has the potential to automate many tasks and improve efficiency in a wide range of industries. Moving on from the introduction of Artificial Intelligence, let’s take a look at what are the different types of Artificial Intelligence.
Different Types of Artificial Intelligence
There are several types of Artificial Intelligence. Some are already in use and run the world so to speak, and the others are still getting developed.
First, there’s Reactive Machines. These AI systems have no memory and cannot use past experiences to inform current decisions. An example of this AI is Deep Blue, the chess-playing computer developed by IBM. It simply analyses the current state of the game and makes a move based on that, without taking into account any previous moves made by the opponent.
Next, we have Limited Memory AI. These AI systems can remember past events, but they can’t use them to inform current decisions. Self-driving cars that use cameras to recognize and respond to traffic lights, make use of Limited Memory AI. The car remembers the traffic light’s previous state, but it can’t use that information to predict when the light will change.
Theory of Mind AI is a type of AI that is designed to understand mental states such as beliefs, intentions, and emotions. However, it is not yet developed and still a topic of research in the AI community.
Self-Aware AI is another type of AI that has a sense of its own consciousness and is able to reflect on its own mental states. It is also not yet developed, and it is considered as one of the challenging research areas in AI.
Strong AI, also known as Artificial General Intelligence (AGI), is the type of AI that is capable of performing any intellectual task that a human can. This is considered as the ultimate goal of AI research, but it’s still a topic of research and not yet developed.
On the other hand, Narrow or weak AI is the type of AI that is designed to perform specific tasks, such as speech recognition, image recognition, and language translation. These AI systems are currently in use in various applications such as virtual assistants, self-driving cars, and image recognition systems.
Lastly, there’s Artificial Super Intelligence (ASI), this type of AI is capable of surpassing human intelligence in every domain. It is considered as the most advanced form of AI and still a topic of research.
Applications of Artificial Intelligence
Although a lot of the AI systems we discussed in the previous section are under development, the few systems that are used dominate our daily lives. Here are a few uses of Artificial Intelligence:
Personalised shopping makes use of artificial intelligence to provide customers with a more tailored shopping experience. AI-powered recommendation systems, predictive analytics and image recognition are few of the technologies used to analyse customer browsing and purchasing history, demographic information, and interactions with the company’s website, social media, and other platforms. This helps companies recommend products that are likely to be of interest to them, thereby improving the customer’s shopping experience.
AI assistants make use of NLP and text recognition, to help users complete tasks. Famous AI assistants include Siri, Alexa, Cortana and Google Assistant. These bots are commonly used to make phone calls, answer queries, take notes, make recommendations and much more.
In order to detect fraudulent activity, AI is used to analyse transaction data and identify patterns or abnormalities using Machine Learning, Natural Language Processing, and monitoring social media or other online platforms.
AI is used to match and study the photos of humans. Deep learning algorithms are used to learn and identify patterns in images based on large amounts of training data, the aim is to be able to recognize a particular person. This technology is mostly helpful for security, identification or access control systems.
AI in Healthcare
Healthcare utilises AI to better patient outcomes, make smarter decisions and increase efficiency. AI is used to analyse medical scans, to predict whether or not there could be any potential health risks. AI is also used to automate daily routine tasks like data entry or data analysis.
AI in Robotics
AI is used in robotics to enable machines to perform tasks that would typically require human intelligence, such as interpreting and responding to sensory data, recognising patterns, and making decisions. Robotics combined with AI allows robots to learn from their environment and adapt to new situations. This means that robots with AI can be trained to do things like navigate through an unknown environment, interact with humans, and perform tasks that would otherwise be too difficult or dangerous for humans to do. This technology is applied in various fields such as manufacturing, healthcare, logistics, and the service industry.
Advantages and Disadvantages of Artificial Intelligence
AI is quite a revolutionary technology that is shaping the future as we speak. Nobody can deny that AI has helped humans in more ways than one, however, like everything else that is good, AI too has its potential advantages and disadvantages.
Advantages of AI
Improved efficiency and productivity
Better decision-making and problem-solving skills
Automation of routine and risky tasks
Understand and analyse large amounts of data fast
Increased accuracy and no chance of human error
Personalisation and customisation of products and services
Cost-effective in the long run for many businesses
Disadvantages of AI
Loss of jobs for humans
No transparency or explainability
May make biassed decisions
Increased dependency on technology
Security and privacy may get compromised
No understanding of human empathy
Initial capital needed is very high
Why is Artificial Intelligence important?
Since it’s advent AI has slowly and steadily gained importance, let’s find out why:
Efficiency and Productivity: AI can automate repetitive tasks and streamline workflows, which can boost output and reduce costs.
Better Decision-Making: AI can quickly and accurately analyse large amounts of data, revealing insightful information and assisting in better decision-making.
Personalization: AI can be used to develop individualised goods and services, such as individualised medical care or individualised shopping advice.
Problem Solving: AI can be used to tackle complicated issues that humans are unable to, like forecasting natural disasters or finding patterns in vast amounts of data.
Technology advancement: One of the primary technologies that propels innovation and development across a range of sectors, like healthcare, finance, manufacturing, and transportation, is AI.
Human Enhancement: AI can enhance human talents, enabling us to achieve activities that would otherwise be too challenging or impossible for us to complete on our own.
This is just the tip of the iceberg, compared to what AI can accomplish for humans. But where did this arise from?
History of AI
The Dartmouth Conference’s founding in the 1950s marked the beginning of AI history. Early AI programme development was the emphasis of 1950s and 1960s AI research. Due to a lack of money and advancement, AI research experienced a standstill in the 1970s this period was known as AI winter. This lasted until the 1980’s, where new technologies called for AI to be researched again. The 21st century has seen major changes in many fields thanks to advances in AI, especially deep learning and neural networks, which are employed in a wide variety of applications. The subject of Artificial Intelligence is constantly growing and evolving, and further improvements are anticipated.
The future of Artificial Intelligence
Whether we are aware of it or not, artificial intelligence technology is being used in our daily lives and has already ingrained itself into our culture. Everyone now uses AI in their daily lives, from chatbots to Alexa and Siri. Rapid advancement is taking place in this field of technology. A few of the main sectors AI will affect are:
Science – As AI technology starts to improve, it will cause rapid advancements in other technologies as well. AI will be used in large-scale projects like clinical trials or particle physics, how exciting is that?
Consumer experiences – From Metaverse to Crypto, AI will enable us to make virtual consumer experiences a reality. These virtual experiences will be largely enabled by AI. And AI algorithms too will learn in a digital environment much faster. A win-win for all!
Next-Gen Pharma care – AI has the potential to truly customise medicines. The human body is way too complex in it’s physiology for us to comprehend, AI on the hand – has the ability to synthesize personalised treatments for every individual. The best part? There will be no clinical trials required.
The future AI is definitely something we should look forward to, these kinds of advancements could eliminate a lot of current issues we’re facing as a planet and would improve the overall experience of living.
Artificial Intelligence is going to transform the social and economic states of our world, we are collectively living in what is known as the most promising era of technology. If you’re interested in being a part of this technological revolution, and are looking for an Artificial Intelligence course online, you should check out our Artificial Intelligence and Machine Learning course. Our course teaches you basics to advanced concepts from industry experts and covers 8 hands-on projects.
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Machine Learning is a subset of Artificial Intelligence that deals with teaching machines to think like humans. According to famed computer scientist, Arthur Samuel, it gives computers the ability to grasp things without the use of heavy programming. One could also call it the subset of Computer Science that teaches machines to program themselves.
It’s not plain old programming though. In Traditional Programming, data and programs are run on the computer to produce the desired output. Machine Learning works differently. Here, the data and the desired output are run on the computer to create a program which can automate a number of tasks.
But once a machine knows how to behave and work around different kinds of situations, the real-world implications are boundless! It is this trait which makes Machine Learning one of the most in-demand skills of the 21st century.
What is the difference between AI and ML?
Many people tend to use the terms Artificial Intelligence and Machine Learning interchangeably. Although Artificial Intelligence and Machine Learning are closely related, there are significant differences between the two. Artificial Intelligence is a technology that mimics human intelligence and behaviour. Machine Learning is a subset of AI which uses past data to find patterns and program itself to make predictions and respond to those patterns.
Artificial Intelligence makes use of Machine Learning to simulate human thinking. Machine Learning relies on data to complete specific tasks, while modifying itself to improve the accuracy of the results.
How do Machine Learning Algorithms work?
And here we come to the crux of the matter – how does one teach machines to think?
As we mentioned earlier, in a word, data.
By feeding machines with reliable data, one can train them to draw meaningful insights and perform tasks. The step-by-step way of implementing a Machine Learning model is:
Collecting data from reliable sources
Cleaning data by removing unwanted/missing values, formatting them and splitting them into test data and training data
Choosing a Machine Learning model (explained further below)
Training the model by analysing the patterns and making predictions based on training data (from Step 2)
Assessing the Machine Learning model with test data (from Step 2)
Tuning the model parameters to improve its accuracy
Using the model on unseen data
Machine Learning Engineers make use of programming languages like Python to execute the above steps. Now that you have a basic understanding of how Machine Learning works, let us take a closer look at the different types of Machine Learning models.
What are the 3 main types of ML models?
The 3 types of Machine Learning models are:
As the name suggests, supervised learning is when a Machine Learning algorithm or a Machine Learning model learns with the help of a supervisor. This means, there is a feedback system that explains the model whether or not it is working correctly.
Let us understand this with an example. One of the most basic ML algorithms is the image classification model, which distinguishes between whether an image is that of a cat or a dog. If the model guesses correctly, then the supervising entity has nothing to do. But if the model guesses incorrectly, then the Machine Learning Engineer has to tweak the parameters so that it works properly. A more complex example is to do sentiment analysis on a piece of text like a user’s tweets. Such a model will try to understand whether the user (or a customer) is happy with an experience.
Supervised learning algorithms are further divided into supervised classification algorithms and regression algorithms. We will explore ML classification algorithm and regression algorithm in depth in our subsequent blogs.
In the above examples, the data is labelled. In unsupervised learning, the input data for a model is unlabelled and the model has to recognise patterns in the same. For example, the demographic of customers that is likely to buy a particular product. A store may not always collect all the inputs, like age group, but based on other purchases, the model has to make an estimate and classify the user accordingly.
In a reinforcement learning model, the machine/agent to begin with understands two things – positive feedback and negative feedback. Then the agent interacts with the environment and checks the kind of feedback it has received and makes adjustments. A simple example for Reinforcement Learning would be a product recommendation system, where the model recommends a product to a customer and based on the feedback from the customer fine-tunes its recommendations.
What are some Machine Learning applications?
Machine Learning is already everywhere, and its applications in the real world are increasing by the day. Some of the popular Machine Learning applications include:
Image recognition One of the most well-known uses of Machine Learning is in social media applications which suggests users which friends to tag in which photos. Social media also makes use of other aspects of Machine Learning to suggest pages and accounts to follow and more!
Speech recognition With the advent of Siri, Alexa, Cortana and Google Assistant, speech recognition has become an everyday part of our lives. Here, these virtual assistants use Machine Learning models to follow commands based on voice instructions.
Traffic prediction Considering how rapidly the infrastructure of cities is changing, traffic prediction has become one of the essential applications of Machine Learning. By analysing the patterns of traffic on a daily basis, systems (like Google Maps) are able to accurately predict traffic at any given point on a route.
Email & Spam filtering This application of Machine Learning is something you must have already observed. The filters in our email can mark mails as important/not important/promotional/social/spam and even blacklists.
These are just the tip of the iceberg. There are several uses of ML that are cropping up every day.
It’s clear that Machine Learning is one of the most exciting fields today. Every day new breakthroughs are being made in this field unlocking new opportunities for organisations. If you want to pick up these job-ready skills, check out our Artificial Intelligence & Machine Learning certification course which teaches you basics to advanced concepts from industry experts and covers 8 hands-on projects. What are some other topics you’d like us to cover on Machine Learning? Let us know in the thoughts below.
This seems to be the general sentiment of the masses today.
And while that sounds funny, you have to acknowledge the truth in this statement.
Today, everything around us is either already automated or is in the process of being so. But automation doesn’t just happen overnight. These machines have to be constantly fed with data to help them learn and become of use. Be it Netflix recommendations or the Smart Tesla cars – we are used to having expert systems that have a brain of their own. And that ladies and gentlemen is the magic (the irony is not lost on us) of Artificial Intelligence.
Famed Computer Scientist, Alan Turing asked himself “Can machines think?”, all the way back in the 20th century. Thus, the idea of Artificial Intelligence was born. Broadly speaking, AI is the ability to program machines to mimic human intelligence and automate our tasks. To put it simply, AI is the simulation of human intelligence in machines. Artificial Intelligence, which was once just an idea in theory, is now more prevalent than ever.
AI programming focuses mainly on achieving 3 cognitive abilities:
What do these abilities entail?
To simplify this, think of machines as human babies. Babies are constantly bombarded with stimuli from the environment, that they then use to learn new things. Similarly in the case of machines, the machine is the infant and the raw data we keep feeding it, is used by the machine to ‘learn’ or pick up new skills.
Despite the occasional need for calculators, the human mind is still the most well-oiled machine out there, built to pick up skills on its own. However machines (ironically built by humans) need an extra push to pick up new skills from the data they are presented with. Hence, these machines rely on certain algorithms which enable them to understand data and draw inferences.
Once the machine draws up its own conclusions, they are then checked with the real world solutions to measure the machine’s accuracy. Depending on how wrong the solution is, the machine learns from its mistakes and draws better conclusions the next time.
The underlying principle behind all AI systems remains the same as described above, i.e. to first learn, reason and then proceed to correct itself (although humans could use a bit of self correction too).
Types of Artificial Intelligence
Artificial Intelligence is a broad term that encomposses many subsets like Machine Learning or Natural Language Processing and these subsets are known to many. Broadly speaking, there are 4 main types of Artificial Intelligence:
Reactive Artificial Intelligence
The most basic type of Artificial Intelligence, Reactive AI, does not interact with the world. It lacks imagination and will respond in the exact same way every time when presented with the same situation, making them extremely trustworthy and reliable.
One of the most famous examples of Reactive AI is Deep Blue. Deep Blue is a supercomputer that was created by IBM. This supercomputer is famous for playing and winning a chess match against chess champion Garry Kasparov.
But how did Deep Blue win the game?
In a reactive AI model, machines neither work with data nor do they have the facility to store any memory. They function depending on the way they are programmed, i.e., through a predictable output. Deep Blue made its move based on its observation from its opponent’s move.
Another example of a game-playing Reactive AI is AlphaGo. Google Inc’s brainchild, AlphaGo is unable to evaluate future outcomes. Instead it relies on its own neural network to evaluate developments of the present game.
Limited Memory AI
Limited Memory AI is being used worldwide today and is constantly experimented with.
A Limited Memory AI absorbs learning data and makes future predictions based on historical data. This form of AI automatically trains itself to evolve and become better.
An example of Limited Memory AI is the Smart Car. A smart car is a self-driving car. How do these work?
Based on the data fed, the car’s AI enhances its capabilities to understand its environment and self-drive in a safe and secure manner.
Theory of Mind AI
Although still in its development stage, Scientists claim that Theory of Mind AI will be considered successful when AI picks up the ability of decision making, similar to that of the human mind. To reach this stage, machines will have to understand human emotions and thereby act in accordance with these emotions to make decisions.
This type of AI might not yet be fully functional, but we are getting closer and closer to the day machines respond to human emotions. An example of a recent success was Sophia.
An AI built in the year 2016, it was capable of seeing human emotions and was also able to respond to these emotions. A small victory in the grand scheme of things!
Also another theory being experimented with, this type of AI is when machines reach a level of consciousness at par with humans. Artificial Intelligence experts claim that at this stage, machines will be fully aware of emotions and the state of minds of others around them. Their needs, emotions and desires will match that of human beings.
What is the difference between Artificial Intelligence and Machine Learning?
The two terms are often used interchangeably and while they do share a lot of similarities there are certain key differences between the two. Let us explore these differences,
Machine Learning deals with systems using historical data to learn. Its main aim is to capture patterns present in historical data and to gain insights that could predict an outcome. It uses algorithms to “learn” from data, and these algorithms are usually specific to the task at hand.
Just like AI, Machine Learning is also a very broad term and can be divided into 3 categories:
It’s a method where you provide assistance to help the machine learn by labelling data.
For instance, you label a picture of a dog as “A Dog” along with a picture of a cat as “A Cat” and feed this data to the ML model. This assistance that you provided will help the machine differentiate the two and identify them accurately in the future.
As the name suggests, this type of model learns from data without any guidance. In contrast to the above example, with the classification of cats and dogs, in this case you would just feed the machine unlabelled data. The idea here is for the machine to just find similarities or differences within a given dataset, as opposed to accurately labelling things. So in this case the machine would be able to tell that these two things are different but would not be able to identify them as cats or dogs.
This Machine Learning model involves machines making decisions sequentially and calculating the rewards associated with the sequence. The end goal is to determine which sequence of events is associated with the maximum reward.
Machine Learning algorithms are everywhere in today’s world. Email spam filters, search algorithms, online recommendation systems, Facebook friend suggestions, stock price forecasting, bank fraud detection, etc. are just a few applications of Machine Learning in today’s world.
AI as we discussed, is the idea of machines mimicking human intelligence to solve problems. In fact, Machine Learning and Deep Learning are subsets of AI. Unlike Machine Learning or Deep Learning, however, with Artificial Intelligence, more emphasis is placed on the success of performing a task than its accuracy.
AI includes three stages as well – learning from data, reasoning or making sense of the given data and finally making self corrections in the output if needed. AI and its applications include voice assistants like Siri & Alexa, humanoid robots like Sophia and chatbots, etc. Further, AI can be broadly classified as:
Artificial Narrow Intelligence (Weak AI)
Artificial General Intelligence (General AI)
Artificial Super Intelligence (Strong AI)
While they have their set differences, the reason AI and ML are used interchangeably, is because they are so often used together. An instance where Artificial Intelligence and Machine Learning go hand in hand includes:
Speech recognition and Natural Language Processing where AI is used to identify the things spoken by humans and NLP techniques are used to process it.
Sentiment Analysis is another such example which can determine the positive, neutral or negative attitudes that are expressed in text.
The Future of AI
Artificial Intelligence is still a relatively new field, with a lot of promise and stakes resting on the coming future. With Speech recognition already taking off, it is exciting to see what AI systems can do with Theory of Mind or Self aware AI technology and how future applications of Artificial Intelligence manifest.
In the present, we are bound by the vast infrastructure and higher computational power needed to execute Artificial Intelligence. Although Gordon Moore stated in 1965 that “every two years the number of transistors on every chip is doubled while the cost of computer’s is halved”, AI is still a costly business. However, with or without the financial backing and computational power, AI has still come a really long way from Alan Turing’s initial question “Can machine’s think?” As we have seen, the history of Artificial Intelligence has made a tremendous impact on our lives and it seems all but inevitable that the future of AI is going to make a greater impact still.
What are your thoughts on the future of Artificial Intelligence? Is a career in Artificial Intelligence worth pursuing today? Let us know in the comments below.
You might not judge a book by its cover, but you definitely watch movies based on your recommendation list. In today’s blog, we’re going to unravel the secret to Netflix’s “bingeability” and why you end up staying awake till 3 in the morning to binge-watch a show you would otherwise never be interested in.
The science behind Netflix “Recommendations”
It’s no secret that Netflix uses Machine Learning and complex algorithms to deliver the best recommendations amongst its competitors.
For those of you still new to the tech scene – an algorithm is a set of database instructions that tell the software or application what to do. Imagine the computer is Dora the Explorer. She needs a map to go about doing new things and adventures. The algorithm serves as a Maps app, the one responsible for charting out the best possible route for Dora to achieve her goals.
In order for Machine Learning to actually be facilitated, the machine needs to obviously learn something. What is that “something”? It’s the data collected from our views, searches and clicks. Every time we watch a movie, search for a title or even click on a movie but not necessarily watch it, our action informs the machine about our possible interests and preferences. The algorithm being extremely sensitive, picks this data and rewrites and adjusts itself, every time we watch Netflix and give it an insight into our tastes.
According to Todd Yellin, VP Product at Netflix, the engine takes into account information such as, what people watch, what was it that they watched before and what did they watch after, what they watched a year ago, what all they watched recently and what time of the day did they watch these things.
Netflix can’t just recommend the bestselling movies or the most cinematically advanced films to its viewers. Netflix suggestions have to be based on a viewer’s personality. Instead of just dumping their entire catalogue on a viewer’s home page, they curate lists using different algorithms present in their rankings, search bar, ratings, similarity and more.
An amalgamation of all this information is the driving force behind Netflix’s successful recommendations. It is programmed to accustom itself to the most minor changes you bring to the table. And have you ever noticed how perceptive the suggestions are? You can may have watched one episode of a whole new genre – let’s say an anime or k-drama – but the next thing you know your entire feed slowly starts to change, with suggestions such as “Other K-drama’s you may like”, “Because you watched xyz anime” “The best of East-asia”. And obviously fueled by our own binge-watching beast, we end up watching an entire genre over the course of a month.
We’ve spoken a lot about the above mentioned algorithm and how presently it tracks our viewing history. However this algorithm wasn’t born ready. And the groups you got grouped into didn’t appear out of thin air. This is the work of actual human beings, brought in to label and group movies into hyper-specific genres like “Visually striking witty comedies” “Classic feel good opposites attract romcoms” or our personal favourite, “Cynical Comedies Featuring a Strong Female Lead”.
Each of these categories is what you get grouped into by the algorithm. And it’s never just one category. Every one of us gets grouped into multiple categories, which then dictate our taste and decide what will appear on our individual home screens.
So without these hyper-specific categories made ready, the algorithm will not be able to complete its main job- analysing data and grouping people into categories.
But that’s not all what Netflix does to rope us into the binge watching cycle.
Netflix figured out that on an average, they had a golden time of 90 seconds. Only 90 seconds. In this one-and-a-half minute, their viewer would make a judgement as to whether or not they were going to watch the movie that caught their attention.
In order for their viewers to evaluate and better understand the content of a film under 90 seconds, Netflix decided to use engaging Movie posters. Neuroscientists have proven that an image can be processed and judged by a human in under 13 milliseconds. Compared to text, which takes a lot longer, an image does speak a thousand words.
The movie posters they put out originally were given to them by the studios at the time, and they were the generic movie posters that would be displayed in cinemas and on billboards. Now while these posters worked for their respective print mediums, Netflix caught on to the fact that it dampened the attractiveness of the movie on their platform for their viewers. Knowing that they had only 90 seconds to appeal to their audience they came up with a series of experiments in order to boost engagement.
They performed a series of A/B tests and explore-exploit tests, through which they tested whether the movie poster shown to the viewer would have an effect on their judgement of the movie itself.
They designed a test that displayed multiple sets of images for each title, where the original movie poster provided by the studio acts as the control in this test. Their results, overall, unanimously proved that the audience/ test subjects reacted more strongly when faced with a complex set of emotions on the posters.
A good example of this test would be Strangers Things, the hit netflix drama series. Notice how many different ways the poster is shown to all the different accounts.
In order to decide which user will be shown which poster, Netflix tracks what the user has been watching again and groups that user into certain categories (again). So consider a movie like “The Intern”. If User X happens to watch more of Anne Hathaway movies as compared to Robert De Niro, they are more likely to click on a movie poster with her face.
The same goes for genres as well. If User Y watches a lot of horror movies, they will react more strongly to a poster that depicts the horror elements of that movie.
So there you have it – the reason behind all your late night binge watching sessions. It’s a combination of machine learning, human intervention and personalised artwork that have resulted in Netflix’s 1 billion dollar algorithm for recommendations. This award winning strategy, however, is just the beginning to Netflix’s ploy to boost engagement. Don’t get too curious though, since that probably means we’ll just have to pull through more all nighters.