How to Make Stock Price Predictions Using Reinforcement Learning?

Analyzing the stock market using artificial intelligence has been a work in progress recently. Here, we’ll discuss how you can develop an AI stock prediction model using reinforcement learning.  Analyzing the behavior of the stock market has been a subject of interest and challenge in the AI industry. Data scientists, market analysts, and financial experts have been curious to determine whether it is possible to overcome these challenges. The biggest concern is the need for an extra large dataset to build a predictive system based on supervised learning algorithms.  Furthermore, even the most advanced technologies seem to be inadequate to accurately predict the changing prices in the stock market. Yet, accurate AI stock price prediction could be possible without relying on large datasets.  In this blog, we’ll try to identify the challenges of stock market prediction and understand if we can use reinforcement learning for stock prediction and data analysis in Python, that too, using limited or no data to train the algorithm.  Before we proceed to read more about stock price prediction using machine learning, let’s understand more about the data analysis methods used to process stock market data.  Types of Data Analysis Techniques Used on Share Market Data  The stock market data is analyzed in different techniques. These are categorized as – Time Series Analysis and Statistical Data Analysis. 1. Time Series Analysis  A time series is defined as a sequence of data points that appear/ occur in successive order in a given period. It is the opposite of cross-sectional data, where the events that occur at a specific time point are captured.  The time series analysis tracks the movement of the chosen data points over the specified period. The data points are usually the price of the stock/ share/ security. The prices are collected at regular intervals to analyze the patterns.  There are various techniques to perform the time series analysis on stock market data. Let’s check them out in brief.  a. Moving Averages:  The moving average of a stock is calculated to smooth the price data and constantly update the average price. In finance, the MA (moving average) is considered a stock indicator and is used in technical analysis. The short-term price fluctuations are mitigated in this process. The MA is further divided into the following:  i. Simple Moving Average (SMA)  SMA is calculated using the arithmetic mean for a given set of values over a specific period. Here, the set of values is the stock prices. These are then added and divided by the number of prices in the set.  Formula: A1+ A2+ A3+… Ann Here, A= average in the period; nn= number of periods; SMA= n   ii. Exponential Moving Average (EMA)  The EMA gives more importance to recent prices to make the average price more relevant based on the new information. The SMA is calculated first to use in the EMA formula.  The smoothing factor is calculated next to determine the weighting of EMA- 2/(selected period+1). Formula: EMAt= [Vt×(1+ds)]+EMAy×[1−(1+ds)] Here, EMAt= today’s EMA; Vt= today’s value; EMAy= yesterday’s EMA; ds= smoothing (number of days) Some other types of moving averages are:  b. ARIMA:  It is another approach to time series forecasting. ARIMA and exponential smoothing are widely used methods as they offer a complementary approach to the problem. ARIMA describes the auto-correlations in data, while exponential smoothing relies on seasonality in data and trend description.  c. Box Jenkins Model:  This model can analyze different types of time series data for forecasting. It is a mathematical model that uses inputs from specified time series to forecast data ranges. The Box Jenkins model determines the outcomes based on the differences between data points. It identifies trends for forecasting stock prices using autoregression, moving averages, and seasonal differences.  d. Rescaled Range Analysis:  It is a statistical technique developed to assess the magnitude and nature of data variability over a certain period. The rescaled range analysis method is used to identify and evaluate persistence, randomness, and mean reversion based on the time series data from the stock markets. This insight is used to make proper investment strategies.  2. Statistical Data Analysis  It is the common value that occurs in the dataset.  It is the middle number in the dataset. For example, in 4, 6, 7, 9, and 11, the median is 7.  It is the average value of the dataset.  It is also called standard normal distribution or Gaussian distribution model. It is charted along the horizontal axis, representing the total value spectrum in the dataset. The values of half the dataset will be higher than the mean, while the other half will be longer than the mean. And the other data points will be around the mean, with a few lying on extreme/ tail ends on both sides.  It measures the asymmetry/ symmetry of the price/ data point distribution. The skewness will be zero in a standard normal distribution. A negative skewness will lead to a distorted bell curve on the left, while positive skewness will cause a distorted bell curve on the right side.  What is Reinforcement Learning? It is an area of machine learning that takes the appropriate action to maximize returns for a given situation. Many software applications and machines use reinforcement learning (RL) to identify the best behavior/ path to arrive at the desired result for a specific situation.  Reinforcement learning is different from supervised learning. In the latter, the training data is the answer key to training the model with the correct answer. However, in RL, the reinforcement agent decides which task to perform, as there is no specific answer used for training. It allows machine learning developers to train the algorithm without using a dataset. The algorithm will learn from experience and improve itself over time.  What are the Different Datasets Available for Stock Market Predictions? Fortunately, there are a few datasets available to train the algorithms. Developers can access the datasets from the following:  NIFTY-50 Stock Market Data  The data is available from 1st January 2000 to 31st April 2021. It provides

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How Connected Cars and Quantum Neural Network Can Help Drivers in Emergencies

Quantum neural networks are built based on quantum computing and classical physics to deliver accurate and reliable predictions. Our proposed model will make cars self-capable to handle emergencies. We’ll discuss the internal working and modules of our quantum neural network model for connected cars.  Quantum neural networks are created using the principles of quantum mechanics. These are typically feed-forward networks where the information collected in the previous layers is analyzed and forwarded to the next layer. Deep neural networks (DNNs) are already used in developing autonomous vehicles to define the right driving behavior for a vehicle. A Quantum neural network aims to assist drivers in effectively handling emergency situations.  Providing emergency support to car drivers using connected cars and quantum neural networks can reduce the risk of accidents and help drivers reach their destinations faster. This can potentially save lives, especially when driving to hospitals or emergency units. Quantum neural networks are more reliable and accurate than conventional neural networks.  Introduction to Connected Cars and Quantum Neural Network Model  A new system/ device will be embedded in the vehicle’s dashboard to provide support to drivers in emergency situations. The system offers second-to-second continuous support and is specifically designed to handle emergency or complex situations. The system collects data from the vehicle’s sensors and sends it to the connected cloud drive. This data will be processed using quantum neural networks built on the principles of quantum computing and classical physics. The concept of using quantum neural networks and connecting cars (through shared cloud data) is different from the existing approaches used in the industry. This model will be faster, reliable, accurate, and efficient enough to handle the worst-case scenarios people might experience when driving.  Resources Required for the Model  Data is the primary resource for this model. The quantum neural network model requires data from three sources to understand the situations, driving behavior, and the vehicle’s overall performance.  Descriptive Data  This data is about the car and its performance. The data is collected from sensors embedded in the engine, suspension, brake, tires (air pressure), etc. This data will be used to identify car’s health and quality. It also provides information about what’s happening in the car every second. The quantum neural network model will be able to provide a suitable solution when it knows the car’s strengths and limitations.  Navigational Data  This data is related to the routes, navigations, and trips you take in the car. The model collects data from maps to determine the current location, destination, route map, etc. It also gathers data from side impact detection sensors, blind spot detection sensors, cyclist and pedestrian detection sensors, etc., to pinpoint your exact current location.  Behavioral Data  Behavioral data deals with drivers’ performance and abilities. The data is extracted from the sensors embedded in the dashboard. Different sensors are used to collect data necessary for the quantum neural network model to understand the driver’s health and current condition. The sensors help determine who the driver is and suggest a solution according to their driving history (collected and stored in the connected cloud).  Heartbeat sensors, eye-tracking sensors, and fingerprint sensors on the steering wheel are used for data collection. Sensors that track the driving patterns are also used to determine the abilities of the driver. Workflow of the Proposed Model Working Process of the Quantum Neural Network Model  The entire proposed concept will have four steps or modules:   Each module has a definite purpose and streamlines the data flow within the model to arrive at the desired outcome. The second module is where the majority of the work happens. It is divided into three sub-modules. Let’s explore each module in detail.  1. Data Extraction As the name suggests, the data collected from multiple sensors in the car and stored in the cloud are extracted into the APIs. The process of collecting data from the car’s sensors and sending them to the connected cloud drive is continuous. The vast amounts of data are then directly sent to the APIs, where preprocessing occurs.  2. Data Preprocessing  The APIs transfer the data to preprocessing module, which has three sub-modules to prepare the data for analysis.  Data Cleaning  The first sub-module cleans the data extracted from the connected drive APIs. This is a necessary step to improve data quality and increase the accuracy of the quantum neural network model.  Naturally, data collected from multiple sensors will have issues such as wrong image frames, incompatible data formats, corrupt data values, incomplete/ missing data values, etc. This will affect the quality of the final outcome. This sub-module uses different techniques and tools to clean data and repair the wrong image frames. It tries to resolve the missing/ incomplete data or remove it totally. Statistical techniques are used to identify the issues with data and clean it accordingly.  Data Preprocessing  Preprocessing is similar to structuring and formatting data in large datasets. This sub-module prepares the cleaned data to make it ready for transformation, training, and predictions. The data is categorized based on its source.  For example, data from the cameras are sent to the video processing module. Data from heartbeat sensors go to the numerical processing module, and so on. New data categories will be created to sort the cleaned input data into neat segments/ types, making it easy for the quantum neural network to process.  Data Transformation  The last sub-module of the preprocessing stage is data transformation. Here, the preprocessed and sorted data is transformed to create a summary of what it contains. This helps understand the actual meaning of the data before it is fed into the quantum neural network for predictions. The transformed data is analyzed to arrive at the summary and is fed into the learning phase of the system.  3. Training and Predicting Outcomes using Quantum Neural Network Model  This module deals with training the quantum neural network to become capable of working with large datasets and delivering accurate predictions in less time. The data transformed in the previous module is fed

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Depression Analysis Using Machine Learning and AI

Depression has become one of the major global health concerns. Technology like AI and ML can be used to analyze depression data to provide better treatments to people suffering from different types of depressive disorders. We’ll discuss depression and the ML Python code used to analyze data. The changing lifestyle and social scenarios have brought many changes to our lives. We have access to too much information. We are way too connected with the virtual world, and the lines between real and virtual are blurring rapidly. While it sounds like a good thing to stay up to date and informed about anything under the sun, it also has severe side effects.  The fast-paced world has resulted in a lot of anxiety and stress, leading to different psychological issues in people. Depression and poor emotional health are now among the major concerns across the globe. Thankfully, technology is coming to the rescue yet again. Machine learning engineers and researchers are working on analyzing depression in people to detect the symptoms at earlier stages and provide better ways to cope with mental health issues.  Artificial intelligence and machine learning algorithms can be used to analyze datasets with depression-related data to deliver accurate and in-depth insights. Let’s understand what depression actually is and how ML can provide a feasible solution to help people with depression and make their lives happier.  What is Depression?  Depression is a serious mental illness that makes you feel sad, lonely, tired, or anxious. It makes you lose interest in things you previously enjoyed. Depression is a psychological disorder that increases negative thoughts and emotions, leading to other health conditions. It also reduces your productivity, alertness, and ability to think coherently. It affects how you think, feel, and act.  Depression is a common condition seen in many people. Many times, people themselves don’t realize that they are in depression. Statistics show that around 3.8% of the global population suffers from depression. This includes 5.7% of adults who are aged over sixty years and 5% of adults aged less than sixty.  To put it in figures, 280-310 million people have depression. What’s alarming is that more than 800,000 people commit suicide due to depression every year. Kids and teens are by no means safe from depression. The US is among the states with the highest depression rates around the world.  Depression (Major Depressive Disorder, MDD) is commonly known as clinical depression. MDE (Major Depressive Episode) is a measure of time a person exhibits or has the symptoms of depression. Note that mood swings and short bursts of anger/ irritation are not considered depression.  Different Types of Depression  Depression is an umbrella term that covers more than one type of mental illness/ disorder. It can be classified into the following types:  Anxiety/ Distress  Anxiety is when you feel stressed and tense throughout the day. It brings negative thoughts about how things can go wrong or that something really bad will happen to you or your loved ones. So much worry takes over your mind and your thoughts. It also leads to anxiety and panic attacks.   Agitation  You feel uneasy and uncomfortable no matter what. You cannot relax and calm down. An agitated person has jerky movements and is constantly fidgeting or in motion. You cannot sit in a position for more than a few seconds. Some people also tend to talk a lot when agitated. It doesn’t make sense, but you can’t control it either.  Melancholy  Melancholy is intense sadness or emotional pain. It fills your mind to an extent where even good things don’t cheer you up. Activities you usually enjoy also fail to make you happy. Melancholy results in loss of appetite, sad thoughts, feeling down/ low in the mornings, disturbed and irregular sleep patterns, and suicidal thoughts.  Persistent Depressive Disorder Persistent Depressive Disorder is when a person is suffering from depression for more than two years. It is a chronic condition where the person is highly vulnerable and susceptible to making harmful decisions. PDD is used to describe chronic major depression and dysthymia (low-grade persistent depression). The symptoms of this disorder are:  What is Bipolar Disorder?   Bipolar disorder is also called manic depression, as it causes extreme mood swings in a person. You might experience random bursts of energy where you feel fantastic and at the top of the world. You work and overdo things until you’re exhausted. Meanwhile, on the other end of the spectrum, you’ll feel miserable and horrible about anything and everything. You feel fatigued, tired, and worthless.  This is a vicious cycle where you alter between two contrasting moods but no middle ground. Doctors recommend mood stabilizers like lithium and calming activities like meditation to bring some sort of balance and stability to your mood.  Symptoms and Warning Signs of Depression Depression has many symptoms, some of which overlap with a general lack of mood or exhaustion after a long day of work. Naturally, all of us feel low at some point in our lives or another. But when the feelings persist and take over our lives, it is a sign of depression.  Depression isn’t general sadness or pain of loss. It is more intense and can wreak havoc in your life by gradually robbing your happiness and ability to assert yourself. You can no longer feel, think, work, enjoy, and act the way you used to do. Some people term it as ‘living in a black hole’, where the void sucks out even the last bit of energy and happiness from you.  Some feel apathetic to their surroundings. Nothing matters to them anymore. Others have a constant sense of impending doom and cannot consider a positive alternative. Men exhibit signs of anger and restlessness, while women have excessive feelings of guilt, sleepiness, hunger, etc. Obviously, this varies from person to person.  Apart from this, all the above-listed symptoms are warnings signs of depression. A person who exhibits such signs needs medical intervention as soon as possible.  Datasets Used to Analyze Depression  Using the following datasets,

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Analyzing the Brain Waves Data Using Python

The brain waves play a crucial role in sending signals to different parts of the body. Analyzing this data helps scientists uncover the intricacies and complexities of the human brain and provide solutions to help people with brain-related disorders. We’ll discuss brain waves and ways to use Python to analyze this data.  The human brain is a key organ to keep us functioning and active throughout our lives. We know that the concepts of artificial intelligence, deep learning, and artificial neural networks are derived from the working of the human brain. ANNs replicate the patterns and designs of the neural networks in the brain to allow the machines to perceive this and analyze data as the human brain does. This, in turn, helps doctors and scientists use advanced technology to understand the complexities of the human brain and identify activity related to brain diseases. We’ll deal with one such method to study the functioning of the brain and analyze the signals it sends to other parts of the body.  The brain constantly generates waves of electrical activity. The pattern of the waves changes based on the emotion a person experiences at that point. Devices like EEG kits are used to detect and record wave patterns. Computer programming languages like Python can be used to analyze this data. It helps determine how alert or focused a person is. By processing large datasets with such information, scientists can identify the causes/ reasons for brain diseases and find ways to cure them.  Let’s start by reading more about brain waves and the types before learning how Python helps analyze the brain wave data.  What are Brain Waves?  Brain waves are the electrical pulses used by neurons to communicate with each other. The neurons use electrical impulses to send signals about different human emotions and behaviors.  The frequency of each brain wave is different, depending on the emotion felt and displayed by the person. Measured in hertz (Hz) or cycles per second, we have slow and fast brain waves released by the neurons. The brain waves are given individual names to differentiate one from another based on frequency.  Types of Brain Waves There are five types of brain waves, with delta being the slowest and gamma being the fastest. The level of human awareness is determined by the frequency/ speed of the brain waves.  Delta Brain Waves  As the slowest of all, these high-amplitude brain waves have a frequency of 1 to 3 Hz and are experienced by humans when they are asleep.  Theta Brain Waves The Theta waves have a frequency range of 4 to 7 Hz and are found when a person is in a dreamy state. When the waves are close to the lower end, they represent the state when a person hovers between sleep and consciousness. It’s also known as the twilight state. Theta waves, in general, signify that mental inefficiency or that the person is either too relaxed or blanked out (zoned out) at that moment.  Alpha Brain Waves The alpha brain waves have a frequency range of 8 to 12 Hz. These are larger and slower, representing a relaxed or calm state of mind for a person ready to get into action if the need arises. The alpha brain waves are generated when someone feels peaceful after closing their eyes and picturing something they like.  Beta Brain Waves Beta brain waves are faster and smaller, with a frequency range of 13 to 38 Hz. These waves imply that the person is focused on something. They signify alertness, where the person is in their senses and displays all signs of concentration and mental activity.  Gamma Brain Waves Gamma brain waves are the fastest ones, with a frequency range of 38 to 42 Hz. These are subtle compared to the other brain waves and work on the consciousness and perception of the person. The waves occur when a person is highly alert and can feel every minute change in their surroundings.  Waveforms of Different Brain Waves: Capturing Brain Waves EEG (Electroencephalography) is a popular and most used method to capture brain waves and record the electrogram of the electrical activity on the scalp. It represents the macroscopic activity of the brain waves inside the brain. The electrodes are placed on the scalp to record the activity. EEG is typically a non-invasive process. However, Electrocorticography (Intracranial EEG) is an invasive process.  The method measures the fluctuations in the voltage of the ionic current released within the neurons. In clinical terms, EEG is the recording of spontaneous electrical activity in the brain over a period. This data is collected through the numerous electrodes placed on the scalp of a person.  The focus of the diagnosis is either on spectral content or the event-related potentials, during a particular duration or for a particular event. Spectral content, on the other hand, analyzes the type of neural oscillations or the brain waves.  Applications Used to Analyze Brain Waves  Two major applications analyze the brain waves, where each application focuses on a different aspect of analysis.  Emotion Analysis  Human emotions are determined by the brain. The brain waves carry messages with emotions that make the person feel something. This ‘emotion’ can be understood by analyzing brain waves. However, the concept of emotion and what it represents varies from one person to another based on cultural and environmental backgrounds. A classification algorithm is vital to accurately analyze the emotional aspect of brain waves.  Brain-Computer Interface (BCI)  The function of BCIs is to collect the brain waves, analyze the messages, translate the messages to commands and relay them to the output devices. BCIs don’t use neuromuscular pathways for this process. That’s because the purpose of using BCIs is to restore the functioning of the neuromuscular pathways in people suffering from brain diseases.  For example, cerebral palsy, stroke, amyotrophic lateral sclerosis, or spinal cord injury can damage the pathways and affect the transmission of brain waves. BCIs aim to restore the damage done so that the person can

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Quantum Computing Concepts and Implementation in Python

Quantum computing is a fascinating concept in the science and technology industry. There’s a huge scope to use quantum computing in daily business processes in the future. We’ll discuss quantum computing concepts and see how it’s implemented using Python.  Quantum physics, as such, is a highly complex and extensive subject. The theories and concepts of quantum physics can confuse most of us, including the experts. However, researchers are making progress in utilizing the concepts of quantum physics in computing and building systems. Quantum computation might sound like something from the future, but we are very much proceeding in that direction, albeit with tiny steps. IBM, Microsoft, and D-Wave Systems (partnering with NASA) have developed quantum computers in the cloud for public use. Yes, you can actually use a quantum computer from the cloud for free.  Of course, it’s easier said than done. Quantum computing technology is not a substitute for classic computing. It’s an extension or a diversification, where classic computing and quantum computing go hand in hand. Given how building a single quantum computer can cost thousands of dollars, using the cloud version is the best choice for us. But where does Python come into the picture? And what exactly is quantum computing?  Let’s explore this topic further and understand how to implement quantum computing concepts in Python.  An Introduction to Quantum Computing  The term ‘Quantum’ comes from Quantum Mechanics, which is the study of the physical properties of the nature of electrons and photons in physics. It is a framework to describe and understand the complexities of nature. Quantum computing is the process of using quantum mechanics to solve highly complicated problems.  We use classic computing to solve problems that are difficult for humans to solve. Now, we use quantum computing to solve problems that classic computing cannot solve. Quantum computing works on a huge volume of complex data in quick time.  The easiest way to describe quantum computing would be by calling it complicated computation. It is a branch of Quantum Information Science and works on the phenomena of superposition and entanglement.  Superposition and Entanglement The smallest particles in nature are considered quantum. Electrons, photons, and neutrons are quantum particles.  Superposition is when the quantum system is present in more than one state at the same time. It’s an inherent ability of the quantum system. We can consider the time machine as an example to explain superposition. The person in the time machine is present in more than one place at the same time. Similarly, when a particle is present in multiple states at once, it is called superposition.  Entanglement is the correlation between the quantum particles. The particles are connected in a way that even if they were present at the opposite ends of the world, they’ll still be in sync and ‘dance’ simultaneously. The distance between the particles doesn’t matter as the entanglement between them is very strong. Einstein had described this phenomenon as ‘spooky action at a distance’. Quantum Computer  A quantum computer is a device/ system that performs quantum calculations. It stores and processes data in the form of Qubits (Quantum Bits). A quantum computer can speed up the processes of classic computing and solve problems that are beyond the scope of a classical computer.  If the classical computer takes five seconds to solve a complex mathematical problem like (689*12547836)/4587, the quantum computer will take only 0.005 seconds to give you the answer.  Quantum Bits (Qbits Concept) A quantum bit is a measure of data storage unit in quantum computers. The quantum bit is a subatomic particle that can be made of electrons or photons. Every quantum bit or Qbit adheres to the principles of superposition and entanglement. This makes things hard for scientists to generate Qbits and manage them. That’s because Qbits can show multiple combinations of zeros and ones (0 & 1) at the same time (superposition). Scientists use laser beams or microwaves to manipulate Qbits. Though the final result collapses to the quantum state of 0 or 1, the concept of entanglement is in force. When the two bits of the pair are placed at a distance, they are still connected to each other. A change in the state of one Qbit will automatically result in the change of state for the related Qbit.  Such connected groups of Qbits are powerful compared to single binary digits used in classical computing.  Classical Computing vs. Quantum Computing Since you have a basic idea of quantum computing, it’s time to delve into the differences between classical computing and quantum computing. These differences can be categorized based on the physical structure and working processes.  Architecture Level (Physical Structure) Differences  Phenomenon and Behavior  In classical/ conventional computing, the electric circuits can be only in a single state at any given point in time. The circuits follow the laws of classical physics. In quantum computing, the particles follow the rules of superposition and entanglement and adhere to the laws of quantum mechanics.  Information Storage  The information in classical computing is stored as bits (0 and 1), based on voltage/ charge. The binary codes represent information in conventional computing. The same is stored as Qubits or Qbit in quantum computing polarization of a photon or the spin of an electron. The Qbits include the binary code (0 & 1) and their superposition states to represent information.   Building Blocks  Conventional computers use CMOS transistors as basic building blocks. Data is processed in the CPU (Central Processing Unit), which contains an ALU (Arithmetic and Logic Unit), Control Unit, and Processor Registers.  Quantum computers use SQUID (Superconducting Quantum Interference Device) or quantum transistors as basic building blocks. Data is processed in QPU (Quantum Processing Unit) with interconnected Qbits.  Working Process Differences  The way data is represented is the major difference between a classical computer and a quantum computer.  The bits in classical computing can take the value of either 0 or 1. The Qbits in quantum computing can take the value of 0 or 1 or both simultaneously in a superposition

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Smart Video Generation from Text Using Deep Neural Networks

Creating animated videos doesn’t have to be a laborious process anymore. Artificial intelligence and deep neural networks process datasets to create videos in less time. The blog details the different AI models and techniques used for smart video generation from text.  It’s no surprise that creating animated videos takes time. It’s hard work and involves several man-hours. Even with the use of technology, animated videos are still not easy to produce. However, the entry of artificial intelligence has brought new developments.  Researchers from the Allen Institute for Artificial Intelligence and the University of Illinois have worked together to create an AI model called CRAFT. It stands for Composition, Retrieval, and Fusion Network. The CRAFT model took text/ description (captions) from users to generate scenes from the famous cartoon series, The Flintstones. CRAFT is entirely different from the pixel-generation model where the pixel value is determined by the values generated by previous pixels to create a video. It uses the text-to-entity segment retrieval method to collect data from the video database. The model was trained on more than 25,000 videos where each clip was three seconds and 75 frames long. All videos were individually annotated with the details of the characters in the scene and information about what the scene dealt with. That is still labor-intensive as the team has to work on adding the captions to each scene. How can AI experts help generate video from text using automated video generation models? First, let’s take a look at the problems in creating videos from different POVs. Problems in Creating Videos The major problems in creating animated videos can be categorized into the following: Problems from the General Point of View Time Consuming and Effort-Intensive There’s a high demand for animated videos, leading to a gap between demand and supply. Kids and adults love animated videos, games, etc. But the supply isn’t as much as the viewers would like.  This is because the technology still hasn’t reached the stage where we can generate content in minutes and meet the increasing expectations. Video generation is still a time-consuming and laborious process that requires a lot of resources and input data. Computers are Not Enough It might seem that computers are an answer to everything. However, computers and the existing software are not advanced enough to change the video creation process. While researchers and experts are working on creating new applications to create videos in quick time, we still need to wait to experience a higher level of innovation. Problems from Deep Learning Point of View Manually Adding Text Artificial intelligence has helped develop video generation software to speed up the process. However, even AI doesn’t offer a solution to everything as yet. For example, some videos don’t have captions. But you still need to create a video from existing clips. What do you do? Well, you’ve got to manually add the captions so that the software can convert the text to video. Imagine doing that for thousands of video clips!  Improper Labeling The problem doesn’t end at manually adding captions. You’ve got to label the videos as well. Now, with so many clips to work on, it’s highly possible that you might mislabel something or give a wrong caption to a couple of videos. What if you notice the error only after the smart video is generated from the given text captions? Wouldn’t that lead to more wastage of resources, and poor-quality videos?  More than CRAFT Model While the CRAFT model is indeed a worthy invention, the world needs something better and more advanced than this. Moreover, the CRAFT model is limited to creating cartoons and cannot work with all kinds of video clips. Introduction to NLP and CV Well, we’ve seen the challenges faced by the video industries and AI researchers. Wouldn’t it be great to find a solution to overcome these challenges? Oh, yes! That’s exactly what we’ll be doing in this blog. However, we’ll first get a basic idea about the two major concepts that are an inherent part of smart video generation from the text. Yep, we are talking about NLP (Natural Language Processing) and CV (Computer Vision), the two branches of artificial intelligence. Natural Language Processing (NLP) NLP can be termed as a medium of communication between a human and a machine. This is, of course, a layman’s explanation. Just like how we use languages to communicate with each other, computers use their own language (the binary code) to exchange information. But things get complex when a human has to communicate with a machine. We are talking about how the machine processes and understands what you say and write.  NLP models can train a computer to not only read what you write/ speak but also to understand the emotions and intent behind the words. How else will a computer know that you’re being sarcastic? Applications like Sentiment Classification, Named Entity Recognition, Chatbots (or our virtual friends), Question- Answering systems, Story generations, etc., have been developed using NLP models to make the computer smarter than before.  Computer Vision (CV) Computer vision is yet another vital aspect of artificial intelligence. Let’s consider a scenario where you spot a familiar face in the crowd. If you know the person very well, you’ll mostly be able to recognize them among a group of strangers. But if you don’t? What if you need to identify someone by watching the CCTV recording? Overwhelming, isn’t it?  Now, what if the computer can identify a person from a series of videos on your behalf? It would save you so much time, effort, and confusion. But how does the computer do it? That’s where CV enters the picture (pun intended). We (as in the AI developers) provide the model with annotated datasets of images to train it to correctly identify a person based on their features.  Possible Approaches other than CRAFT model Researchers have been toiling on finding ways to use artificial intelligence and deep learning to facilitate video generation from text. The solutions involve using

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AI for Law: Can AI Replace Lawyers?

How do we describe law? It’s complex, isn’t it, just like how algorithms work? There are a set of laws, regulations, and rules on one side and cases that need to be solved on the other. The lawyers and judges work within a framework using a process similar to how computer programming or machine learning algorithms work behind the scenes. Of course, the law cannot be that easily categorized into algorithms, but ML algorithms sure can be used to help implement the law and deliver justice. Before we talk about artificial intelligence in law firms, let’s first see what a law firm is and how it functions. A law firm is an association or a team of lawyers working together as a business entity and providing legal services to the public.  The lawyers of the firm share their clients and profits, depending on their schedule, expertise, and seniority. Law firms follow a hierarchy where the main partners or shareholders sit on the top tier. The senior associates follow next, with the junior lawyers working under senior lawyers and learning from them.  A law firm can be organized in several ways, based on where the practice is located. They can follow any of the below arrangements- Law firms also hire paralegals from time to time if required for the case. A firm can have both individuals and corporations as its clients. The primary step is to understand the client’s case, explain legal rights, responsibilities, and advise them on the best way forward.  The firm can accept civil and/ or criminal cases, cases that deal with financial transactions, business complications, or any other areas of law.  What are the Application Areas of Law?  Law is classified into several finer categories, each dealing with a specific aspect of society/ life. A lawyer usually chooses an area of expertise and specialization, which helps them take up cases specific to the category and help their clients.  Animal Rights Law An animal rights lawyer can save and protect the lives of those who cannot speak for themselves. Fighting against animal cruelty, getting better facilities for animal care shelters, and exposing animal abusers are some responsibilities of an animal rights lawyer. The lawyer takes up cases to protect domestic and wild animals.  Blockchain Law  This is a new area of expertise, thanks to the increasing popularity of Bitcoin and several other altcoins and digital currency. Though there aren’t many lawyers who specialize in this area, the field is set to grow and create many opportunities for upcoming lawyers looking for a new and profitable area of law.  Civil Rights Law Civil rights are basic human rights every human being in this world is entitled to. Being a civil rights lawyer is a crucial responsibility. Even though many civil rights lawyers work for nonprofit organizations, they also take up other cases of interest, especially when human rights are being violated.  Complex Litigation Law  Complex litigation cases are difficult to handle and can go on and on for years. Civil and corporate cases with their stakes high and involving some noted individuals or entities are dealt with by complex litigation lawyers. It’s a serious area of law that demands everything from the lawyer.  Corporate Law Corporate law deals with the everyday practices of a business and its other complex affairs. Corporate law enters the scene at the very beginning, right where the business is initially being set up.  Compliance, contracts, policies, rules, and regulations, etc., come under this area of law. Corporate lawyers also take care of business liabilities and bankruptcy. Criminal Law  A criminal lawyer can choose to be a defense lawyer or a prosecutor, or sometimes both. The role and priorities of the lawyer change based on whether they are defending the client or prosecuting an accused. However, the primary role of a criminal lawyer is to protect the basic rights of the client. Criminal lawyers deal with clients from all sections of society. Environmental Law  Lawyers who practice environmental law will see more demand as the fight against climate change gets serious. Setting up new regulations, amending existing laws, etc., help protect the environment. The lawyer plays a vital role in promoting these laws and helping individuals/ businesses understand the environmental laws and how these can impact their business. Family Law Family law deals with family-related aspects, be it good or bad. From divorce to inheritance disputes to adoption and child care, it encompasses an array of elements. A lawyer who practices family law has to deal with the emotional aspects of the case as well as the financial and social factors. Healthcare Law The healthcare law deals with the healthcare sector. It includes- The healthcare lawyer works with hospitals, medical centers, doctors, and insurance providers to advise them about their legal obligations and rights.  Immigration Law  Immigration laws protect immigrants and refugees from being subject to abuse/ neglect/ racism in the country they are taking shelter in. Immigration lawyers are currently in demand as (illegal) immigration, deportation, etc., are the most discussed topics. An immigration lawyer can help families get the justice they deserve in another country.  Intellectual Property Law  Intellectual property theft is becoming quite common in recent times, and the demand for IP lawyers has gone up. Intellectual property law protects ideas, concepts, theories, formulations, equations, designs, etc., from being duplicated and stolen by others. Copyrights, trademarks, patents, and other such applications are filed to protect intellectual property. Labor Law  Labor law deals with the rights of a laborer, worker, or employee of an enterprise. Lawyers who specialize in labor law are almost always in demand because of the varied nature of atrocities faced by employees. From hazardous working environments to sexual harassment and racism (among other things), the lawyer works with clients from the entry-level to top positions in an organization.  Sports and Entertainment Law  This area of law includes IP (intellectual property) laws, royalty disputes, ownership rights, contacts between different parties involved in the project, etc. The ‘entertainment’ part covers television and

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