From freeing up humans to advancing medicine AI and ML are rapidly changing the face of everyday life. AI and ML are continuously growing.
In the past few years, the world is changing fast, because of artificial intelligence and machine learning.While both have positive impacts as well as negative impacts on society, technological field, and in our day-to-day life.
Generally, when most hear the words artificial intelligence(AI), the first thing that comes to mind is robots. However, this is not true. Artificial intelligence is a process of simulation of human knowledge that is programmed in different machines, using algorithms.
And machine learning is a branch or an application of AI where the machines learn from their experience and make predictions. It is the technology that is used to train machines to perform predictions, recommendations, estimations, etc., based on historical data or experience.
History of AI :
AI has come in the early 1950s. Artificial Intelligence derives from an area of computer science used to create intelligent programs that can problem solve, plan, learn, recognize speech, etc. Back then, when the ideas of artificial intelligence were first presented, computers were the primary source looked at to house such intellectual programs. However, in the 1950s, computers were costly and could not store commands given, which is essential in the development of the AI system. After many modifications, computers could now not only execute all sorts of commands but could store them as well. From this modernization, many were able to explore the different realms of artificial intelligence more in-depth than before.
Today, artificial intelligence is used daily. From email filters to suggestions, from social media accounts to customer service chatbots. The application of artificial intelligence is said to help make life more efficient. People can connect with friends and family, request rides using sharing apps, and navigate to places unknown. It seems that AI has made it possible for people to accomplish much more by working together with its intelligent software. The recent and trending topic of AI is ChatGPT which interacts conversationally.
For the future of artificial intelligence, AI language and self-driven cars are now in progress. AI language will cover conversations that are smoother between humans and automated machines, as well as being able to translate a conversation between two languages in the present time. On the other side of the horizon, concepts of driverless cars have been announced, and much work is being done to produce a proven concept within the next 20 years. In the beginning, artificial intelligence started as a field whose goal was to replicate human-level intelligence in a machine. The goal now of those researching and creating AI is to produce programs that surpass human capabilities cognitively in many if not all tasks.
Artificial Intelligence is given the capability to learn intelligently, by permitting machines the ability to learn from experience and in turn, perform tasks that are similar to that of humans. Some components that make up artificial intelligence are; machine learning, deep learning, and cognitive computing. These elements give AI the skills to process massive amounts of big data that is generated in the world, regularly. IT professionals write algorithms that allow AIs to sift through and analyze data analytics that could be useful for things such as figuring out consumer habits for a business. With little to no human dealings and massive amounts of data being fed to AI systems, they can take on more tasks, solve more problems, and over time, learn more to become smarter than humans.
History of machine learning :
Machine Learning was first conceived from the mathematical modeling of neural networks. A paper by logician Walter Pitts and neuroscientist Warren McCulloch, published in 1943, attempted to mathematically map out thought processes and decision-making in human cognition. In 1950, Alan Turing proposed the “Turing Test” to determine if a computer has real intelligence. To pass the test, a computer must be able to fool a human into believing it is also human. In 1952, Arthur Samuel wrote the first computer learning program i.e. The program was the game of checkers, and the IBM computer improved at the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program IBM computer improved the game the more it played, studying which moves made up winning strategies and incorporating those moves into its program. After that many machine learning algorithms were introduced to train the machine.
Why are machine learning and AI growing so fast?
ML and AI both becoming the technology of the future. Because it provides potent capabilities in a variety of industrial sectors. Among the most important are the following:
Processing Speed:
A massive amount of data is generated and accessed by almost every organization today. The data flow around us is so big that traditional tools cannot handle it. Machine Learning and AI is the solution here. It automates data generation, storage, retrieval, and analysis. ML models extract meaningful insights from large amounts of structured or unstructured data at high speeds.
Round-the-Clock Operation :
The algorithms used by machine learning are robots that do your work 24*7. With tools like MLOps and AutoML, ML plays a significant role in automating business operations. Many routine processes such as network monitoring, database management, data integration, and so on are eliminated, allowing businesses to focus on more specialized tasks.
ChatGPT is a trained AI model called which interacts conversationally. The dialogue format makes it possible to answer every query, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
Ability to Learn and Improve
Both ML and AI can learn. Most machine learning algorithms are designed to improve performance as they process more data.. Netflix, YouTube, Tinder, and Amazon are companies that use recommender systems. According to Forbes, 75% of Netflix users select movies/shows recommended by the company's machine-learning algorithms.
Applications :
Applications of AI:
1. AI in Astronomy
- Artificial Intelligence can be very useful to solve complex universal problems. AI technology can help us understand the universe such as how it works, its origin, etc.
2. AI in Healthcare
In the past few years, AI becoming more advantageous for the healthcare industry and going to have a significant impact on this industry.
Healthcare Industries apply AI to make a better and faster diagnoses than humans. It can help doctors with diagnoses and can inform them when patients are worsening so that medical help can reach the patient before hospitalization.
3. AI in Gaming
- AI can be used for gaming purposes. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places.
4. AI in Finance
- The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes with the help of AI.
5. AI in Data Security
- The security of data is crucial for every company and cyber-attacks are also growing in the digital world. AI can be used to make data safer and more secure.
6. AI in Social Media
- Social Media sites such as Facebook, Instagram, Twitter, and Snapchat contain billions of user profiles, which need to be stored and managed in a very efficient way. AI can organize and manage massive amounts of data. AI can analyze lots of data to identify the latest trends, hashtags, and requirements of different users.
7. AI in Travel & Transport
- AI is becoming highly demanding for travel industries. AI is capable of doing various travel-related work such as making travel arrangements to suggesting hotels, flights, and best routes to the customers. Travel industries are using AI-powered chatbots which can make human-like interactions with customers for better and fast responses.
9. AI in Robotics:
Artificial Intelligence has a major role in Robotics. Usually, general robots are programmed such that they can perform some repetitive tasks, but with the help of AI, we can create intelligent robots which can perform tasks with their own experiences without pre-programmed.
Human-made Robots are the best examples of AI in robotics, recently the intelligent Humanoid robot named Erica and Sophia has been developed which can talk and behave like humans.
10. AI in Entertainment
- We are currently using AI-based applications such as Netflix or Amazon. With the help of ML/AI algorithms, these services show the recommendations for programs or shows.
11. AI in Agriculture
- Agriculture is an area that requires various resources, labor, money, and time for the best result. Now a day's agriculture is becoming digital, and AI is emerging in this field. Agriculture is applying AI in agriculture robotics, solid and crop monitoring, and predictive analysis. AI in agriculture can be very helpful for farmers.
12. AI in E-commerce
- AI is becoming more demanding in the e-commerce business. AI is helping shoppers to discover associated products with recommended sizes, colors, brands or price ranges.
AI has a huge number of applications in everyday life. The discussed topics are not limited to AI being applied in education, automotive transactions etc.
Applications of ML :
1. Image Recognition:
ML is used to identify an image’s pattern and features related to objects, persons, places, digital images, etc. One popular use case of image recognition and face detection is, Automatic friend tagging suggestion:
Facebook provides us with a feature of auto friend tagging suggestions. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with a name, and the technology behind this is machine learning's face detection and recognition algorithm.
2. Speech Recognition
Speech recognition is a process of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition."
At present, machine learning algorithms are widely used in various applications of speech recognition. Google Assistant, Siri, and Alexa are using speech recognition technology to follow voice instructions.
3. Traffic prediction:
In case of visiting a new place, we take the help of Google Maps, which shows us the correct path with the shortest route and predicts the traffic conditions.
It predicts the traffic conditions such as whether traffic is cleared, slow-moving, or heavily congested with the help of two ways:
Real-Time location of the vehicle from Google Maps app and sensors and Average time taken on past days at the same time.
4. Product recommendations:
Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendations to the user.
Whenever we search for some product on Amazon, then we started getting an advertisement for the same product while internet surfing on the same browser and this is because of machine learning.
Google understands user interest using various machine learning algorithms and suggests the product as per customer interest.
When we use Netflix, we find some recommendations for entertainment series, movies, etc., and this is also done with the help of machine learning.
5. Self-driving cars:
One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on a self-driving car. It is using unsupervised learning method to train the car models to detect people and objects while driving.
6. Email Spam and Malware Filtering:
Whenever we receive a new email, it is filtered automatically as important, normal, and spam. We always receive important mail in our inbox with the important symbol and spam emails in our spam box, and the technology behind this is Machine learning. Some spam filters used by Gmail: Content Filter,Header filter, General blacklists filter, Rules-based filters, Permission filters
Some machine learning algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier are used for email spam filtering and malware detection.
7. Virtual Personal Assistant:
We have various virtual personal assistants such as Google Assistant, Alexa, Cortana, and Siri. They help us in finding the information using our voice instruction. These assistants can help us in various ways just by our voice instructions such as Play music, calling someone, Opening an email, Scheduling an appointment, etc. These virtual assistants use machine learning algorithms as an important part.
These assistants record our voice instructions, send them over to the server on a cloud, decode it using ML algorithms and act accordingly.
8. Online Fraud Detection:
Machine learning is making our online transactions safe and secure by detecting fraudulent transactions.
Whenever we perform some online transaction, there may be various ways that a fraudulent transaction can take place such as fake accounts, fake ids, and stealing money in the middle of a transaction. So to detect this, the Feed Forward Neural network helps us by checking whether it is a genuine transaction or a fraud transaction.
- Stock Market trading:
Machine learning is widely used in stock market trading. In the stock market, there is always a risk of up and downs in shares, so for this machine learning's long short-term memory neural network is used for the prediction of stock market trends.
Conclusion :
As we see AI and ML have massive applications in real life it is not possible to discuss all the things in a single blog.
AI and ML experts are already in high demand as companies look to harness advanced technologies and effectively use vast stores of data. But these skills are also in short supply. The competition to access this talent is expected only to grow in the coming years.
we can conclude our blog by saying that AI and ML are growing rapidly because of their human simulation process, reducing human work, automating different transactions and their usefulness in a business organization.