Artificial intelligence applications are developing rapidly – and businesses are waking up to the potential of the technology in HR and payroll.
In the first half of 2016, around £1 billion was invested in AI firms in the UK – with the expectation that the industry will boost the country’s economy by over £230 billion in the next 12 years. Similarly, in the US, 2016 saw over 550 startups raise around $5 billion in funding to incorporate AI as a core component of their services. The capabilities and usability of AI has developed dramatically over the past few years: today it helps us plan our schedules, calculate bills, compile shopping lists, heat our homes, and more. The technology is also becoming ubiquitous – with Amazon estimated to have its ‘Echo’ device in over 10 million homes, and Google’s ‘Assistant’ app now installed on over 2 billion devices.
HR & Payroll Applications
With such innovation and potential at our fingertips, it’s easy to see why business across the world are starting to take note of the commercial capabilities of AI. From delivering products to customers and clients, to enhancing essential internal processes, the ways in which AI might be useful to business operations are numerous. HR and payroll processing are two of the processes which might benefit most from the automated, algorithmic possibilities of AI tech – since both involve the coordination and handling of large amounts of data, and the need to navigate a spectrum of complicated compliance issues.
The proximity and interconnectivity of payroll and HR means that the role AI plays within both involves significant crossover. With that in mind, let’s explore ways in which AI tech is already playing a part in payroll and HR infrastructures – and how it is promising to dramatically change the way these processes are carried out…
AI & Human Experience
AI technology is advancing at pace – and with it, come associated concerns over the elimination of human expertise in payroll and HR. Although experts don’t see the end of the need for human administrators just yet, it’s time for businesses to start thinking more carefully about the role they want AI to play in the workplace.
It goes without saying, HR and payroll are one of the most personal aspects of business – which means humans must remain at the heart of the infrastructure your organisation builds to facilitate those processes. Taking the time to understand AI, and the role you want it to play within your business going forward, is the best way to unlock its benefits – while maintaining the trust, expertise and experience that connects your payroll and HR departments to your employees.
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Deep Bajaj got fed up of seeing his wife, mother & other women struggle with dirty public toilets. He decided to solve this real problem through a startup, and PeeBuddy was born. Faced with dirty loos, women can now stand & pee!
Episode.2 of चलो StartUp features this hero who built a real startup to solve a real problem. Rather than waste time on fancy ideas. If you’re thinking of doing a startup, do something REAL. Like Deep did.
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The history of robots combines the best of science fiction and real life technology. From Isaac Asimov to modern industrial robots, keep reading to explore the fascinating history of robotics.
When many Americans think of the word “robot,” years of science fiction portrayals and action movies immediately come to mind. And while science fiction often misses the mark, the history of robots actually owes quite a debt to science fiction masters like Isaac Asimov.
However, to truly understand the history and evolution of robotics, we have to define the term. That’s surprisingly difficult to do. For our purposes, we’re going to define a robot as a machine that’s capable of carrying out routine or complex actions that are programmed by engineers. Today, robots can be used for surgery, massage therapy, space exploration, manufacturing, and code analysis, but the earliest robots were far more primitive — they were tools that could tell time or automotoms that could perform for entertainment.
Broadly defined, humans have been developing robotics and automata for hundreds of years. With this in mind, let’s get into the curious history of robots and how far we’ve come in the branch of robotics engineering.
Invent Like An Egyptian: Early Robotics
The Egyptian water clock is one of the very first cases of “robotics” in human history. The oldest example of the water clock, found in the tomb of Amenhotep I, dates as far back as 1500 BCE.
An outflow water clock was marked along the inner container with measurement lines. The container was filled with water, which would drip over time. To tell the time, the owner would simply check the water measurement. Imagine only having to fill your watch with water when the battery died.
However, what truly made this invention remarkable wasn’t the use of water to tell time. Rather, it was that the force of the water in the clock would bang gongs or strike bells on the hour with human figurines.
Greece began using water clocks by 325 BCE. And, just 25 years later, the second known advancement in robotics was invented by Greek mathematician Archytas. Archytas designed and built what’s now called The Pigeon, a mechanical bird that could be propelled into the air by using steam.
Leonardo Da Vinci was another remarkable mind in the engineering field. In 1495, Da Vinci designed and built what’s now called the Robot Knight. According to Mark Elling Rosheim’s Leonardo’s Lost Robots, the robot could sit, stand, and move its arms using pulleys and cables.
Ducks And Trumpets: The Evolution Of Automation
In the western world, we only truly began to see the evolution of modern automation in the 17th century. Jacques de Vaucanson, a French inventor, developed three automata. The first automaton was capable of playing up to 12 songs on a flute. We can only thank De Vaucanson that it wasn’t a recorder.
The second automaton could play a tambourine, drum, and flute. And the third, and most renowned, was a duck.
The duck was capable of flapping its wings, moving, quacking, and even “eating.” The real-life movements and sounds could be compared to today’s baby doll. However, the first modern automaton would be invented in 1810 by Friedrich Kauffman of Germany. This robot was designed to look like a soldier. By using automatic bellows, the soldier would blow a trumpet.
Developments In Mechanical Programming
The development of mechanical programming was advanced by Ada Lovelace. Ada Byron, the Countess of Lovelace, was an English mathematician known for writing the first algorithm for the Analytical Engine.
The Analytical Engine was a general-purpose computer proposed by Lovelace’s husband Charles Babbage, another mathematician. It was Lovelace who was able to recognize the machine’s applications and explain the machine’s function to the British establishment between 1842 and 1843.
Lovelace died at the age of 36 and Babbage was never able to complete the Analytical Engine. However, the engine served as the precursor for today’s digital computer.
Further Advancements: Started From The 1800s And Now We’re Here
In 1898, famed inventory Nikola Tesla constructed a wireless torpedo that could be controlled with a remote. It was a process he called “tele-automation,” and the robotic torpedo was demonstrated at Madison Square Garden.
However, the term “robot” wasn’t used until 1921 when Karel Capek, a Czech writer, coined the word to describe automata in fiction. The complementary term “robotics” would later be made famous by writer Isaac Asimov in 1942. After the World Wars, Isaac Asimov’s robots didn’t just capture the popular imagination of Post-War America; they kickstarted a new era in robotics history.
As soon as 1946, the Electronic Numerical Integrator and Computer, or ENIAC, was officially constructed. The ENIAC was one of the first electronic general-purpose computers and was programmed by Betty Jennings, Frances Spence, Marlyn Wescoff, Kay McNulty, Betty Snyder, Ruth Lichterman, and various others. Notably, Adele Goldstine authored the ENIAC’s program manual.
Then in 1950, Ida Rhodes co-designed the C-10 programming language for UNIVAC I. UNIVAC I was the computer system that would later be used to determine the U.S. census. Also in 1950, George Devol would invent Unimate, the first industrial robot. Unimate could transport die castings and weld them into automobiles. Similar to modern automation in manufacturing and other industrial fields, these industrial robots would be programmed for a specific function as a means of replacing unskilled labor. Unimate was one of the most important milestones in the history of robots.
The 1960s and 1970s were the decades of arm-like automatons. Shakey (1966), the Stanford Arm (1969), and the Silver Arm (1974) gave rise to Puma350 (1985) and CyberKnife (1992), which both served as innovative robotic technology in the medical field.
In fact, these arm-like automatons resemble much of modern robotics. One such robot, developed by Albert Zhang, is the Expert Manipulative Massage Automation or Emma. A product of AiTreat, a Singaporean startup, Emma is a one-armed robot engineered to provide massage therapy to human patients.
Modern Robotics In Everyday Life
The automated side of robotics are well-known by many Americans even if they haven’t been given a name. How often have you noticed automated machines during the three hours you’ve been sucked into watching How It’s Made?
These automated machines replace repetitive manual labor to give humans the ability to learn new skills in the same field. For instance, in the shipping industry it takes 15 seconds for the average employee to assemble a complete box (including bubble wrap, tape, and barcode) for shipping.
To assemble a box in that time requires familiarity, technique, and speed. Yet such a job doesn’t pay a high salary.
Robotics that replace manual labor such as box assembly create higher-level job openings in the industry. These positions require greater skill and pay higher wages.
Further advancements in technologies since the 2000s have led to more advanced automation and artificial intelligence. Automated machines are programmed to perform one action over and over and are used today in manufacturing, maritime exploration, space exploration, military, and commercialized agriculture.
Artificial Intelligence, or AI, is programmed to assess an environment and take action to succeed at a programmed goal. Recent advancements in this field have led to software capable of preventing identity theft, producing relevant search queries for search engines, and cracking ciphers for the FBI. As we look to the future history of robotics, AI will likely play a major role.
Video-on-demand websites such as Netflix and Hulu already use predictive analytics to recommend genres and shows to viewers. Algorithms that cluster recommendations based on show similarities improve customer satisfaction.
Businesses are also prone to use sentiment analysis software to get an in-depth look into public opinions on products and services. This helps businesses market better to consumers. It also keeps them in the know regarding negative feedback so they can respond swiftly to minimize damage.
The Future Of Robotics: Where Do We Go From Here?
The future of robotics is difficult to gauge because of the rate of innovation. However, it’s predicted that robots will most likely play a greater role in the home and in the business world.
Products such as Google Home, Amazon Echo, and Apple’s Siri have grown in popularity in recent years. Smart Homes have also been gaining traction because of their convenience and ability to save on utility bills, increase comfort, and improve security.
Microsoft, Google, and Amazon have also been developing greater technologies for the business world. For instance, users of Microsoft Office 365 can now receive and make business calls within Microsoft Teams without using another app. What’s more, language recognition has seen new innovation with Google’s Pixel Buds, which translate up to 40 languages in real time.
Automated robots are also expected to become more commonplace outside of manufacturing and shipping industries. Up to 35% organizations in health, logistics, and utilities are expected to begin exploring the use of automated robots as soon as 2019.
Innovations in technologies such as self-driven cars may be less likely to take as big a stake in the future. Car accidents involving self-driven vehicles show the coexistence between impulsive human drivers and careful self-driving cars may be difficult.
Space exploration is another field where robotics are expected to improve human advancement. Since the Soviet Union’s Mars 2 became Earth’s first interplanetary robot when it landed on Mars in 1971, engineers have been developing newer and greater technologies.
For instance, one of NASA’s newest developments, the ISS Robotic External Ammonia Leak Locator, detects ammonia leaks on space stations. By using a robot to detect leaks and to repair them in the future, the risk for NASA’s crew members is reduced.
That being said, the innovations of new technology will continue to rapidly develop, but not necessarily in the way science fiction predicts. Rather, breakthroughs in automation and programming will continue to improve what humans have been seeking to advance for years: communication, education, and life itself.
Originally appeared on ctemag.com. Published with permission.
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Once we start delving into the concepts behind Artificial Intelligence (AI) and Machine Learning (ML), we come across copious amounts of jargon related to this field of study. Understanding this jargon and how it can have an impact on the study related to ML goes a long way in comprehending the study that has been conducted by researchers and data scientists to get AI to the state it now is.
In this article, I will be providing you with a comprehensive definition of supervised, unsupervised and reinforcement learning in the broader field of Machine Learning. You must have encountered these terms while hovering over articles pertaining to the progress made in AI and the role played by ML in propelling this success forward. Understanding these concepts is a given fact, and should not be compromised at any cost. Here we discuss the concepts in detail, while making sure that the time you spend understanding these concepts pays off and that you are constantly aware of what is happening during this progress towards an Artificially Intelligent society.
Supervised, unsupervised and reinforcement Machine Learning basically are a description of ways in which you can let machines or algorithms loose on a data set. The machines would also be expected to learn something useful out of the process. Supervised, unsupervised and reinforcement learning lead the way into the future of machines that is expected to be bright, and will over time assist humans in doing everyday things.
Supervised Learning
Before we delve into the technical details regarding supervised learning, it is imperative to give a brief and simplistic overview that can be understood by all readers, regardless of their experience in this growing field.
With supervised learning, you feed the output of your algorithm into the system. This means that in supervised learning, the machine already knows the output of the algorithm before it starts working on it or learning it. A basic example of this concept would be a student learning a course from an instructor. The student knows what he/she is learning from the course.
With the output of the algorithm known, all that a system needs to do is to work out the steps or process needed to reach from the input to the output. The algorithm is being taught through a training data set that guides the machine. If the process goes haywire and the algorithms come up with results completely different than what should be expected, then the training data does its part to guide the algorithm back towards the right path.
Supervised Machine Learning currently makes up most of the ML that is being used by systems across the world. The input variable (x) is used to connect with the output variable (y) through the use of an algorithm. All of the input, the output, the algorithm, and the scenario are being provided by humans. We can understand supervised learning in an even better way by looking at it through two types of problems.
Classification: Classification problems categorize all the variables that form the output. Examples of these categories formed through classification would include demographic data such as marital status, sex, or age. The most common model used for this type of service status is the support vector machine. The support vector machines set forth to define the linear decision boundaries.
Regression: Problems that can be classified as regression problems include types where the output variables are set as a real number. The format for this problem often follows a linear format.
Unsupervised Learning
Since we now know the basic details pertaining to supervised learning, it would be pertinent to hop on towards unsupervised learning. The concept of unsupervised learning is not as widespread and frequently used as supervised learning. In fact, the concept has been put to use in only a limited amount of applications as of yet.
Despite the fact that unsupervised learning has not been implemented on a wider scale yet, this methodology forms the future behind Machine Learning and its possibilities. We always talk about ML bringing forth unlimited opportunities in the future, but fail to grasp the detail behind the statements made. Whenever people talk about computers and machines developing the ability to “teach themselves” in a seamless manner, rather than us humans having to do the honor, they are in a way alluding to the processes involved in unsupervised learning.
During the process of unsupervised learning, the system does not have concrete data sets, and the outcomes to most of the problems are largely unknown. In simple terminology, the AI system and the ML objective is blinded when it goes into the operation. The system has its faultless and immense logical operations to guide it along the way, but the lack of proper input and output algorithms makes the process even more challenging. Incredible as the whole process may sound, unsupervised learning has the ability to interpret and find solutions to a limitless amount of data, through the input data and the binary logic mechanism present in all computer systems. The system has no reference data at all.
Since we expect readers to have a basic imagery of unsupervised learning by now, it would be pertinent to make the understanding even simpler through the use of an example. Just consider that we have a digital image that has a variety of colored geometric shapes on it. These geometric shapes needed to be matched into groups according to color and other classification features. For a system that follows supervised learning, this whole process is a bit too simple. The procedure is extremely straightforward, as you just have to teach the computer all the details pertaining to the figures. You can let the system know that all shapes with four sides are known as squares, and others with eight sides are known as octagons, etc. We can also teach the system to interpret the colors and see how the light being given out is classified.
However, in unsupervised learning, the whole process becomes a little trickier. The algorithm for an unsupervised learning system has the same input data as the one for its supervised counterpart (in our case, digital images showing shapes in different colors).
Once it has the input data, the system learns all it can from the information at hand. In fact, the system works by itself to recognize the problem of classification and also the difference in shapes and colors. With information related to the problem at hand, the unsupervised learning system will then recognize all similar objects, and group them together. The labels that it will give to these objects will be designed by the machine itself. Technically, there are bound to be wrong answers, since there is a certain degree of probability. However, just like how we humans work, the strength of machine learning lies in its ability to recognize mistakes, learn from them, and to eventually make better estimations next time around.
Reinforcement Learning
Reinforcement Learning is another part of Machine Learning that is gaining a lot of prestige in how it helps the machine learn from its progress. Readers who have studied psychology in college would be able to relate to this concept on a better level.
Reinforcement Learning spurs off from the concept of Unsupervised Learning, and gives a high sphere of control to software agents and machines to determine what the ideal behavior within a context can be. This link is formed to maximize the performance of the machine in a way that helps it to grow. Simple feedback that informs the machine about its progress is required here to help the machine learn its behavior.
Reinforcement Learning is not simple, and is tackled by a plethora of different algorithms. As a matter of fact, in Reinforcement Learning an agent decides the best action based on the current state of the results.
The growth in Reinforcement Learning has led to the production of a wide variety of algorithms that help machines learn the outcome of what they are doing. Since we have a basic understanding of Reinforcement Learning by now, we can get a better grasp by forming a comparative analysis between Reinforcement Learning and the concepts of Supervised and Unsupervised Learning that we have studied in detail before.
The realms in Machine Learning are endless. You can pay a visit to my YouTube channel to get to know more about the world of AI and how the future will be dictated by the use of data in machines.
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Using artificial intelligence in your own business may seem daunting, but an ever-growing range of solutions makes it easy to achieve a tangible benefit. Here are five of our favorite AI-enabled apps and services for 2018.
Artificial intelligence (AI) permeates many of the apps and services we use on a daily basis, fulfilling roles from image classification to algorithmic trading strategy to predictive maintenance. We’ve already written about the 2018 AI trends we expect will dominate, but what does this mean for businesses?
Sure, many of us only hear about AI when a robot passes a major national medical exam, but the more pedestrian use cases serve real function for businesses, including those at the small-business level. Those live chat operators at your favorite e-commerce website? More and more often, they’re chatbots. That auto-attendant who routes your call to the right person? You get the idea.
Using artificial intelligence in your own company may seem daunting, but the ever-growing range of solutions makes it easy to focus on specific aspects of your operations where intelligent features may quickly yield a tangible benefit. In many cases, it only takes one or two smart apps to save real time, streamlining existing processes and enabling data-driven decision-making on a faster timeline.
We’ve listed our five favorite AI-enabled apps and services for this year below so you can better incorporate the tech into your business.
1. Clara
This AI automates meeting schedules, confirmations, and follow-up for businesses. It’ll even reserve a conference room. The app integrates with unlimited calendars, allowing it to check availability, suggest appropriate meeting times, and interact with participants if schedules change. If your business requires a large number of meetings, the time savings here can add up quickly.
2. AnswerRocket
AnswerRocket performs “search-based data discovery,” which sounds pretty mundane until you actually dig into the product. Ask it a question in plain English, and it answers you in detailed reports and charts. AI makes turning natural language directly into surprisingly-detailed business intelligence possible—otherwise, you’d need to ask a data analyst to generate this information on your behalf.
3. Fyle
With this service, you can automate your business’s expense reporting. Fyle even includes a mobile app so employees can scan, upload, and track company receipts. From extracting the relevant information from those receipts to automating expense approvals, Fyle’s fundamental concept is intelligent expense management, with AI influencing nearly every aspect of the software. It’s a simple way to increase productivity across your whole team.
4. Acquisio
Acquisio leverages machine learning for digital advertising, using predictive algorithms to manage and optimize bids. It trots out phrases like self-improving and unimaginable to assure prospective partners its solution will achieve results a team of humans could not possibly match. This quality is no surprise in the complex, big-data world of digital ads; the future is here, and it’s really good at selling us stuff—Acquisio just makes the selling easier and better.
5. Apptus
This app provides AI-powered e-commerce optimization that integrates with many major platforms. It enhances these sites through predictive merchandising, recommendations, and streamlined search and site navigation. Much of the value proposition here is in operational efficiency, as merchandising is often a particularly time-consuming task that can tax lean teams.
AI is increasingly being implemented in SaaS offerings, and in many cases those apps are plugged into machine learning interfaces from major providers including Google, Microsoft, and Amazon. In addition, services such as Blockspring and If This Then That enable integration between apps that are AI-enabled and those that aren’t, extending the reach of AI even further.
What this means, of course, is that more AI technology is coming to more apps and services on a near-daily basis. Business, large or small, stand to win back time and efficiency by letting computer intelligence influence a range of everyday tasks—and it’s never been easier to do so.
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The word “Big data” prevailed in 2017, and it’s going to keep prevailing in the following years. In our previous post, I’ve introduced some concepts about big data, machine learning, and data mining (see post: Understanding Big data, Data mining, and Machine Learning in 5 Minutes). Now let’s dig deeper into Machine Learning with a brief walk-through of some most commonly used ML algorithms, no codes, no abstract theories, just pictures and some examples of how they are used.
The list of algorithms covered in this article include:
1. Decision Tree
Classify a set of data into different groups using certain attributes, execute a test at each node, through brach judgement, further split the data into two distinct groups, so on and so forth. Tests are done based on existing data, and when new data are being added it can be classified to the corresponding group
Classify data according to some features, whenever the process goes to the next step, there is a judging branch, and the judgement divides the data into two, and the process goes on. When tests are done with existing data, new data can be These questions are learned by the existing data, when there is new data coming in, computer can categorize data into the right leaves.
2. Random Forest
Select randomly from the original data, and form into different subsets.
Matrix S is the original data, and it contains 1-N data rows, while A, B, C are the features, and the last C stands for categories.
Create random subsets from S, let’s say we got M sets of subsets.
And we get M sets of decision trees from these subsets:
Throw new data into these trees, we can get M sets of results, and we count to see which results are the most in all M sets, we can consider that as the final result.
3. Logistic Regression
When the probability of the predicting target is larger than 0, and less than or equal to 1, it cannot be fulfilled by simple linear model. Because when domain of definition is not within certain level, the range would exceed the specified interval.
We better go with model with this kind.
So how can we get this model?
This model needs to fulfill two conditions, “Larger than or equal to 0”, “Less than or equal to 1”
And we transform the formula, we can get the logistic regressions model:
By calculating the original data, we can get corresponding coefficients.
And we get the logistic model plot.
4. Support Vector Machine
To separate the two classes from hyperplane, the best choice will be the hyperplane that leaves the maximum margin from both classes. Because Z2>Z1, so the green one is better.
Use a linear equation to express the hyperplane, class above the line is larger than or equal to 1, the other class is less than or equal to -1.
Calculate the distance between the point to the surface by using the equation in the graph:
So we get the expression of total margin as below, the aim is to maximize the margin, which we need to do is to minimize the denominator.
For example, we use 3 points to find the optimal hyperplane, define weight vector=(2, 3) – (1, 1)
And get weight vector (a, 2a), substitute these two points into the equation
When a is confirmed, the result using (a, 2a) is support vector,
Equation substituting in a and w0 is support vector machine.
5. Naive Bayes
Here’s an example of NLP:
Giving out a pieces of text, examine the text’s attitude is positive or negative.
To solve the problem, we can only look at some of the words:
And these words, will represent by only some of words and their counts.
And the original question is: Give you a sentence, which category does it belong?
By using Bayes Rules, it is going to be an easy question.
The question becomes, in this class, what’s the probability of occurrence of this sentence? And remember not to forget the other two probabilities in the equation.
Example: the probability of occurrence of the word “love” is 0.1 in the positive class, and 0.001 in the negative class.
6. k-NearestNeighbor
When comes a new datum, which category has the most points nearest to it, it belongs to which category.
For example: To distinguish “dog” and “cat”, we judge from two features, “claws” and “sound”. Circles and triangles are the known categories, what about “star”:
When K=3, these three lines connect the nearest 3 points, and circles are more, so “star” belongs to “cat”.
7. k-means
Separate the data into 3 classes, the pink part is the biggest, while the yellow is the smallest.
Pick 3, 2, 1 as default, and calculate the distance between the rest data and the defaults, and classify it into the class that has the shortest distance.
After classification, calculate the means of each class, and set it as the new center.
After some rounds, we can stop when the class no longer changes.
8. Adaboost
Adaboost is one measure of boosting.
Boosting is to gather up the classifiers that didn’t have satisfied results, and generate a classifier that may have better effect.
As the below shows, tree 1 and tree 2 don’t have good effects individually, but if we input the same data, and sum up the results, the final result will be more convincing.
An example for adaboost, in handwriting recognition, the panel can extract many features, such as the beginning direction, distance between beginning point and ending point, and etc.
When training the machine, it will get the weight of each feature, like 2 and 3, the beginnings of writing them are very similar, so this feature does little to classification, so its weight is little.
But this alpha angle has a great recognizability, so the weight of this feature will be great. The final outcome will be a result of considering all of these features.
9. Neural Network
In NN, an input may end up into at least two classes.
Neural network is formed of neures, and connections of neures.
The first layer is the input layer, and the last layer is the output layer.
In hidden layers and output layer, they both have their own classifiers.
When an input comes in the network, and being activated, the calculated score will be passed down to the next layer. Scores shown in the output layer are the scores for each class. Example below gets the result of class 1;
same input being passed to different knots generates different scores, which is because that in each knot, it has different weights and bias, and this is propagation.
10. Markov
Markov Chain consists of states and transitions.
For example, get a Markov Chain based on “the quick brown fox jumps over the lazy dog”.
First, we need to set every word under a state, and we need to calculate the probability of state transitions.
These are the probabilities calculated by one single sentence. When you use massive data of texts to train the computer, you will get a bigger state transition matrix, such as words that can follow “the”, and their corresponding probabilities.
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