- 1 Introduction to Why Do We Need to Learn AI
- 1.1 AI is a Rewarding career option, with infinite prospects.
- 1.2 Why do we need for Artificial Intelligence?
- 1.3 Classification of Artificial Intelligence
- 1.4 Types of Artificial Intelligence
- 1.5 Applications of Artificial Intelligence
- 1.6 · Commuting
- 1.7 Google’s AI-Powered Predictions
- 1.8 Some of the applications are also present in Grading and Assessment tools online.
- 1.9 Some of the examples are in Social Networking
- 2 Conclusion
Introduction to Why Do We Need to Learn AI
Artificial Intelligence has demonstrated several glimpses of the future. Here are the main benefits of AI learning. AI can handle and process a vast volume of data to guide strategic decision making.
In almost every field, AI benefits society, be it health, education, transportation, decision making, security, smarter homes, and smarter workplaces.AI aims to enhance customer experience with greater ease of use of devices and applications.
AI is a Rewarding career option, with infinite prospects.
Overall, AI will bring more tailored solutions to support individuals and businesses in-decision.
Beginners must use these guides on artificial intellect to realize the need to incorporate this technology and how they add to the achievement development of society.
Will you be wondering what artificial intelligence is exactly? We might argue the computers can think and behave like humans. Yeah, it is just that, in a very plain language. Yet it does have more to it.
Let’s look in-depth at artificial intelligence. Artificial intelligence, known as AI, is the replication or cloning of human intelligence that enables highly intelligent output of computers, more precisely computer systems. Then what exactly is it doing? So essentially an artificially intelligent machine perceives its world in a way that makes its rational self improve its chances of success.
Why do we need for Artificial Intelligence?
Why do we need artificial intelligence anyway? Surely the query must have emerged in your mind now though. Yeah, people are a lazy race. We would love to exist in a society in which there is minimal human labor, and so we can save enough time.
The data we produce is also in big chunks. As people, we need something that processes and manages data to reduce the strenuous efforts that are being placed into its handling.
Data management and analysis are called data science. Data Science is the scientific study of data, in simple words, which stores, records and analyzes data for our gain. Now AI brings with it a new approach to handling and solving problems.
Classification of Artificial Intelligence
- Weak AI or Narrow AI
- Strong AI or General AI
An AI can be strong or weak, depending on how many tasks it may be performing. A Poor AI, also known as the Narrow AI, is a system designed to perform only one specific mission.
The appreciation of speech will serve as a good example of a poor AI. The programming is such that it can only recognize and translate spoken words & phrases into a machine-readable format.
In comparison, we can apply a Powerful AI or a General AI to perform a variety of tasks, as well as to learn and develop themselves. AlphaGo, for example, is a software application that plays Go on board games.
It trains and improvises itself according to game circumstances and has beaten Lee Sedol-a professional gamer from South Korea. There is assumed to be the third type, apart from Artificial Super Intelligence, Strong AI, and Weak AI. ASI refers to the time when machine capabilities will surpass human capabilities- this is the situation I talked about at the beginning now.
Types of Artificial Intelligence
Arend Hintze, an associate professor at the University of Michigan City, categorizes AI into 4 categories:
- Reactive Machines
- Limited Memory
- Theory of Mind
- Self Awareness
· Reactive Machines
Reactive machines do not store memories or lessons learned from past experiences. They simply respond to the current environment and select the best solution among the options available.
Based on it from its previous experiences, it cannot conclude. We all recall IBM’s DeepBlue, a supercomputer that played chess and beat international grandmaster Garry Kasparov.
DeepBlue can recognize pieces on a chessboard, and can even predict what his opponent’s next move would be. It can also select from the next possible gestures, which means that nit is unable to maintain any memory and put previous experiences into the picture when making decisions.
· Limited Memory
Restricted memory devices transiently store data; that is, their memory lasts for a limited time, just like a goldfish, haha! Erm … shh, now machines are cleverer!
Let’s not ruin them! Returning to the point, such a system could use experiences that have existed in the recent past to guide future decisions.
It can’t add those ‘experiences’ to its database or library, though. For example – self-driven car shops, and navigates based on this data, the speed, and pattern of changing lanes, etc. around those. There is, therefore, no permanent preservation of these findings.
· Theory of Mind
With mind theory, we can claim it’s the sort of AI that still has to exist. The aim behind such an AI is to have computers capable of simulating human emotions, values, and desires that will affect future decisions. It refers to the world’s perception that other beings have emotions, memories, and feelings, too.
In the future, AI systems will view human desires of how they should be handled. Isn’t that cool? Most machines today use different models to control their activities Researchers are developing Bellhop Robot for hotels, which will anticipate the demands of people who plan to stay in the hotel.
What are all this meditation and spiritual stuff amid artificial intelligence now? To answer that, let’s first understand self-awareness. When an individual has conscious knowledge of their character and their feelings, he/she becomes self-aware.
An AI is self- aware when it can form representation about itself, and thus, be conscious about itself. Once this is achieved, AI will operate as a human and start predicting its own needs and demands and start thinking of others as an equal. Such an AI does not exist yet. Guess the only thing that won’t have feelings now is rocks then? Lol.
Note: There are many types of AI these types are described by Arend Hintze, an associate professor at the University of Michigan City.
Applications of Artificial Intelligence
AI, my buddies, now aren’t in the immediate future. Right now, right now. As you are reading this post, it lurks behind your windows and is likely to recommend more such posts from now on.
In addition to being limited to personal assistants or OTT apps, Artificial Intelligence is important to a variety of facets of our everyday lives. Throughout this article, we differentiate between AI and Machine Learning (ML) where necessary.
At Emerj, based on a panel of expert input, we have established clear concepts of both artificial intelligence and machine learning. Think of AI as the wider objective of autonomous machine intelligence, and machine learning as the basic scientific methods currently in vogue to construct AI, to simplify the debate.
Both machine learning is AI, but machine learning isn’t just AI. Our enumerated AI examples are divided into Work & School and Home applications, although there is sufficient space for
As per a 2015 study by the Texas Transportation Institute at Texas A&M University, commuting times in the US have risen steadily month-over-year, culminating in 42 hours of rush-hour traffic delay per driver in 2014—more than a full week of work per year, with an estimated 160 billion dollars of missed productivity. There is a great opportunity for AI here to make a real, measurable effect in the life of each person.
Reducing commuting times is no easy issue to tackle. A single trip may involve different modes of transportation (i.e. going to drive to a railway station, riding the train to the optimal stop, and then walking or using a ride-share service from that halt to the end location), to not noting the anticipated and the unexpected: construction; accidents; road or track maintenance; and climatic conditions may restrict traffic flow with little to notice.
Besides, long-term trends may not fit historical data, depending on population count and changing demographics, local economies, and zoning policies. Here’s how AI’s helping to solve transport problems.
Google’s AI-Powered Predictions
Google Maps (Maps) will measure the pace of traffic flow at any given time using anonymized position data from smartphones. And Maps can more readily integrate user-reported traffic events such as construction and collisions with its 2013 purchase of the crowdsourced traffic app Waze.
Access to large quantities of data being fed to its proprietary algorithms means that by recommending the quickest routes to and from work, Maps can minimize commutes.
Ridesharing Apps Like Uber and Lyft
How do they describe your ride price? How are they reducing the waiting time when you hail a car? How do you optimally align these services with other passengers to reduce the detours? All these questions are answered ML.
In an NPR interview, Jeff Schneider, Engineering Leader for Uber ATC, discussed how the company uses ML to forecast rider demand to ensure that short bursts of rapid price rises to lower rider demand and increase driver supply are no longer required.
Uber’s Head of Machine Learning Danny Lange has verified Uber’s use of ETAs machine learning for trips, approximate delivery service times, pick-up locations, and fraud detection.
Commercial Flights Use an AI Autopilot
AI autopilots in commercial airlines are a relatively early application of AI technology dating back to 1914, depending on how loosely you describe the autopilot.
The New York Times estimates that a Boeing plane’s average flight only requires seven minutes of human-driven flight, usually reserved for takeoff and landing purposes only.
Note: Some Application Online on the internet like in Email services.
Your email inbox seems like an unlikely location for AI, but one of its most significant features is driven by technology: the spam filter. Simple rules-based filters.
Rather, spam filters have to learn constantly from a range of signals, including the words in the post, the metadata of the post (where it is sent from, who sent it, etc.).
The need to further tailor its results based on your understanding of what constitutes spam — maybe the regular email deals you consider spam to be a welcoming sight in others’ inboxes. Gmail effectively filters 99.9 percent of spam with the use of machine learning algorithms.
Smart Email Categorization
Gmail uses a similar approach to categorize your emails into inboxes for main, social, and promotional purposes, as well as to mark them as significant.
In a research paper entitled “The Learning behind Gmail Priority Inbox,” Google discusses its approach to machine learning and states “a large difference between user expectations for the amount of essential mail …
So we need some user manual guide to change their thresholds. When a user marks messages in a specific direction, we perform an increment to their threshold in real-time.
Know more about AI: 5 Incredible Ways -Artificial Intelligence Spikes in Demand by Digital Space
Some of the applications are also present in Grading and Assessment tools online.
· Plagiarism Checkers
Many high school students are comfortable with resources such as Turnitin, a common tool used by professors to evaluate plagiarism written by students.
While Turnitin does not specifically disclose how it detects plagiarism, research shows how ML can be used to create a plagiarism detector.
Historically, plagiarism detection for standard text (essays, books, etc.) is focused on providing a large database of primary sources to correlate with student text; however, ML can help identify plagiarism of non-database sources such as foreign language sources or older sources not digitized. For example, two authors used ML to determine when source code was plagiarized with 87 percent accuracy.
They looked at several stylistic variables that might be specific to each developer, such as average line-of-code duration, how often each line was indented, how frequent software comments were, etc.
The algorithmic key to plagiarism is the resemblance function, which gives out a numerical approximation of how documents are identical.
Not only is an ideal similarity function effective in deciding whether two documents are identical, but also efficient in doing so. A brute force check comparing every string of text to every other string of text in a database of documents would be extremely efficient, but much too costly to use in practice.
One MIT paper emphasizes the possibility of optimizing this algorithm utilizing machine learning. Most possibly the best solution requires a mixture of man and machine.
· Fraud Prevention
How to determine if a transaction is fraudulent? The regular volume of transactions is in most cases way too large for humans to manually monitor each transaction. Instead, AI is used to build systems that recognize fraudulent forms of payments.
FICO, the company that produces some well-known credit scores used to assess creditworthiness, predicts fraudulent transactions using neural networks.
Factors that can influence the final performance of the neural network include recent transaction rates, transaction size, and the type of retailer concerned.
· Credit Decisions
Whenever you apply for a loan or credit card, the major bank must quickly decide whether to approve your application and, if so, what basic terms (interest rate, some of the credit line, etc.) it can give. FICO uses ML both to build the FICO ranking, which is used by most banks to take credit decisions and to decide the precise risk assessment for individual clients. MIT researchers have found that machine learning can be used to minimize losses of a bank on delinquent consumers by up to 25%.
Some of the examples are in Social Networking
The service will automatically highlight faces when you upload images to Facebook and recommend friends to Etiquette. How can it identify immediately which of your friends is on the photo?
Facebook uses Facial Recognition AI. Facebook explores the use of artificial neural networks — ML algorithms that imitate the structure of the human brain — to power facial recognition software in a short video highlighting their AI research (below).
The business has invested heavily in this area not only on Facebook but also through the purchase of facial recognition companies such as Face.com, which Facebook purchased in 2012 for a reported $60 million, Masquerade. And Faciometrics (2016, sum undisclosed).
Pinterest uses computer vision, an AI program where machines are trained to “see,” to automatically recognize objects in pictures (or “pins”) and then suggest visually identical pins.
Other machine learning applications at Pinterest include spam prevention, search, and discovery, ad success and monetization, as well as email marketing.
Instagram, purchased by Facebook in 2012, uses machine learning to define the subjective significance of emoji, which has been gradually replacing slang (for example, a laughing emoji might replace “lol”).
Instagram can build and auto-suggest emojis and emoji hashtags by algorithmically defining the feelings behind emojis.
In 2015, Snapchat launched Face Filters, called Lenses. These filters monitor facial movements, enabling users to add creators or digital masks that will change when moving their faces.
This technology is motivated by Looksery ‘s 2015 acquisition (for a reported $150 million), a Ukrainian company with patents in using machine learning to track video gestures.
Artificial Intelligence and Machine Learning are both theoretical and empirical goods. The belief that computers should think and perform tasks just as human beings do is centuries old.
Neither are the cognitive truths embodied in AI and Machine Learning systems unique. Via engineering, it may be easier to interpret these developments as applying strong and long-established cognitive concepts.
We should agree that as a Rorschach test we place anxieties and expectations on what constitutes a healthy or successful universe, there is a temptation to ignore all significant developments.
But AI and machine intelligence’s capacity for good lies not solely, or even mainly, within its technologies. It lies primarily in the consumers.
If we trust (mainly) how our communities are working at the moment, then we have no reason not to believe ourselves in doing well with those technologies.