Artificial Intelligence – Page 10

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,

AI and Data Science: What These Machine Learning Terms Mean for Business

Artificial intelligence is now more available to businesses. The top applications of AI being used by their organizations are data protection, and process automation. Additionally AI is used in customer service. AI may dramatically reduce costs, and improve production and efficiency. It opens doors to new markets and products and enhances access to existing ones. Data science on the other hand has several advantages for a firm. One of a data scientist’s key responsibilities is to analyze and organize your company’s data so that you may get more precise insights from it. Both AI and data science are important ML terms that have several applications in businesses. What Is Artificial Intelligence (AI)? The imitation of human thinking by machines is known as artificial intelligence (AI). These devices are designed to behave and think like humans. It also refers to any computer that demonstrates characteristics of the human intellect, such as learning. With the use of machine learning (ML), which is a form of AI, software programs may anticipate outcomes more accurately. It does this without having to be explicitly instructed to do so. Machine learning algorithms use historical data as input to anticipate new output values. What Is Data Science? Data science is the study of how to extract useful information from data for business decision-making, strategic planning, and other purposes by using cutting-edge analytics tools and scientific concepts. Data science encompasses a variety of ideas, including statistical analysis, data analysis, machine learning techniques, data modeling, data preparation, etc. In addition to employing algorithms and other methods, data scientists also significantly rely on AI. They rely particularly on its subfields of machine learning and deep learning, to build models and make predictions. Impacts of Data Science on Business It Helps in Recruiting Regardless of sector, finding and keeping qualified staff is a challenge for many firms. Businesses may be quite precise about the kind of individual they are wanting to recruit. This is because of their ability to choose resumes based on a certain character and personality attributes. Data science models can “read” resumes using special algorithms to determine whether or not a prospect is worth pursuing. Natural language processing (NLP) which is a subfield of data science helps firms locate better applicants more quickly. It does this by automating parts of the hiring process such as HireLakeAI which is a smart recruitment solution that helps in screening job candidates and choosing the best as per the requirements. Helps in Making Smarter Decisions Businesses require data scientists to evaluate and extract valuable insights from the data. This is due to the exponential growth in data volume. Data science enables organizations to evaluate information on a wide scale. It also enables them to develop the appropriate decision-making methods. It provides insightful findings. Evaluation and assessment of the many relevant factors are necessary while making decisions. One advantage of data science is improved decision-making. This is due to the tools we can develop to examine data in real-time, giving business managers more agility. This is accomplished using both dashboards and the projections made feasible by the data scientist’s handling of the data. For Making Better Products to Suit Customers’ Needs. Businesses should be able to draw people to their items. Customers’ needs must be taken into account while creating products as well as innovating existing products. Businesses must always guarantee customers’ satisfaction. To create their products in the best way possible, industries need data. Analyzing user feedback helps determine which goods are the greatest match for customers. Data science’s cutting-edge analytical techniques are used for this analysis. Additionally, businesses use the prevailing market trends to develop a product for the general public. These market trends give firms information about the demand for goods right now. Innovation helps businesses grow. Industries are now able to apply a variety of cutting-edge tactics in addition to newer items thanks to the development in data. Improves Data Security and Helps Interpret Complex Data The work done in the field of data security is one of the advantages of data science. For example, data scientists work on fraud protection solutions to safeguard the clients of your business. However, he can also look for regular patterns of behavior in a business’s systems to spot any potential architectural issues. When we wish to combine different types of data to better understand the market and the business, data science is a fantastic answer. For better visualization, we can combine data from “physical” and “virtual” sources depending on the techniques we use to obtain it. How AI Impacts Businesses? It Guarantees Safe Operations Software applications with AI capabilities are being used by other industries for monitoring. For instance, firms are utilizing computer vision and AI software to track employee behavior. This makes sure they are adhering to safety rules. Similar to this, businesses of all sizes may utilize AI to analyze data collected from local Internet of Things (IoT) ecosystems. This helps to monitor workers or infrastructure. After the data is analyzed, the AI models are trained on this data. Then a model registry can be used to store this trained AI model. The AI systems keep an eye out for risky situations and warn businesses about them, such as delivery truck drivers who are texting while driving. For Smarter Supply Chains Businesses from many sectors are utilizing AI to manage their supply chains better. They are forecasting what supply will be required. They also forecast the best time to transport supplies using machine learning algorithms. In this use case, AI assists businesses in building more cost-effective, efficient supply chains. It does this by reducing, if not eliminating, overstocking and the possibility of running out of in-demand goods. For Customer Experience Service and Support Customer experience, service, and support are three of the most popular organizational use cases for AI. For instance, chatbots employ NLP and ML algorithms to comprehend user requests and answer properly. And they accomplish it more cheaply and more quickly than human labor can. AI also

What Is Computer Vision as a Service? (Only Guide You Need)

Have you ever wondered how an iPhone instantly identifies your face and unlocks the device? Or better, how do the uber-cool Tik-Tok filters work? Today, there are numerous instances where smart devices seem to have farfetched applications, but in most cases, these applications are powered by a common tech, computer vision. Advanced computing capabilities and recent developments in computer vision have propelled the domain far enough. Such is the advancements in the field that several agencies providing Computer vision as a Service have popped up worldwide. Machine vision as a service refers to outsourcing third-party computer vision algorithms instead of developing and constantly upgrading in-house. With that basic idea about computer vision as a service, here is a detailed approach to the entire scheme. This will also aid you better before rounding off to a service provider. So How Does Computer Vision Work? Computer vision is part of the umbrella term Artificial Intelligence and is neatly implemented using programming tools like Python and OpenCV. Here are the following steps in a computer application that works uninterruptedly in the making of computer-vision-enabled software. Acquiring Image Datasets The first step in functioning a computer vision algorithm is determining the acquisition strategy of the image datasets. Do you want your users to upload pictures, or is the camera self-serving? Is there a possibility of having both of these features simultaneously? The options are endless; hence, the software architecture must be designed to accommodate the best possibilities. Labeling Datasets When it comes to a machine learning algorithm, there are numerous data points to be fed to the system. The labels instruct the machine about the job. Further, labeling the dataset makes it more search-friendly, with attributes like color, intensity, and size used for the same. Processing the Data Now that you have labeled data, it is time to undergo a meticulous quality check by testing it against a training dataset. Here, the images undergo a series of automated processes that enhance the images. This involves adding or removing pixels, sorting misclassified data, or removing noise. Data Augmentation The images are further modified with various operations such as cropping, compressing, flipping horizontally or vertically, and blurring, among others. The following exercise trains the system for better image recognition capabilities. This model serves as the base of other processes, including but not limited to, testing and optimization, training models, and others. Understanding the Visuals Your model is now prepared to work autonomously with various visuals in form of images or videos. The system continues to improve when used regularly. Common Computer Vision Solutions Most advanced computer vision service providers are well equipped with various solutions. The algorithms for each service are personalized for every client, which helps attain goals in record time. On the other hand, the hassles involved in the R&D of the algorithms are done by agency experts for better results. Some of the common services include: Benefits of Getting Computer Vision as a Service Solution While vision analytics is still in its infancy, numerous companies worldwide have rolled out remarkable solutions. Computer vision and other advanced biosecurity solutions are the next waves in technology. Here are some definite reasons why computer vision as a service should not be ignored at any cost. Challenges with In-house Setups When it comes to in-house setups, there are a ton of factors that prohibit companies from doing so. These are as follows: Hardware Requirements While investing in an internal setup sounds good, an in-house setup can prove costly. Here are the two sides of the coin. Data Collection and Processing When working in-house, companies face a major problem: the lack of training data sets. In such scenarios, the company must generate a huge amount of data. However, the biggest concern is not deploying a team on the streets or forming partnerships. It all boils down to privacy and the use of the data. Use Cases of Computer Vision Services in Business While there are numerous instances where computer vision plays a vital role, here are a few examples that highlight the effect. Amazon Go Imagine a situation where you walk in, pull out all the things you need, and walk out without bothering the cashier! No, this is not part of science fiction. Amazon is set out to build thousands of such structures powered by machine learning and machine vision. The store runs with minimal user interference, and with computer vision, it keeps track of every individual’s stock, maintenance, and billing. Google Lens The industry leader in innovation, Google is known for its audacious dreams. However, one such project that failed badly was Google Glass. Aimed to bring the power of computer vision to everyone, the device was way ahead of its time and hence succumbed to death. However, the company gracefully introduced most of its features in an app called Google Lens. Google Lens can solve complex mathematical problems, identify plants, and even translate languages in real-time, making it one of the most productive apps on the internet. Tesla Cars The leading electric car manufacturer Tesla is known for its self-driving feature. The car collects information about the roads and uses it meticulously to self-drive even in the worst working conditions and hence could not be ignored at all costs. Conclusion While there is a range of opportunities that could be done with computer vision, it is best to outsource solutions unless you work on a large scale. Companies with decades-old experts in the industry understand the requirement and help both the production and enhancement of these A.I.-powered solutions.

Why Do You Need an AI Developer for Your Business?

Artificial intelligence can change a business in many ways. With more and more organizations investing in AI technology, it is vital to hire an experienced AI developer to handle the task from start to finish. We’ll discuss the need for artificial intelligence in enterprises and the role of an AI engineer in the same. Robotics, chatbots, virtual voice assistants, automation, algorithms, etc., are hot keywords in recent times. What is common here is the use of artificial intelligence technology to digitally transform a business. Many small, medium and large enterprises are adopting artificial intelligence in various verticals within their business. Statistics show that the global AI market will touch $60 billion by 2025. Accenture’s study report (from 12 developed countries) shows that using AI can double economic growth and increase productivity by 40%. Close to 85% of businesses feel that using artificial intelligence will give them an edge over competitors. In such instances, it has become a necessity for business organizations to adopt AI and go for full-scale digital transformation. Artificial intelligence solutions allow automation and customization of systems to streamline processes and increase profits for a business. But how does a business integrate AI technology with its existing systems? How should an enterprise invest in artificial intelligence to get the desired results? You hire an AI developer to provide end-to-end services and make the necessary changes in your enterprise. Many artificial intelligence companies offer offshore AI development services for businesses from different industries. This allows you to use an expert team to build an artificial intelligence system (or customize an existing one) for your business needs. Neither hiring nor training is required. The AI engineer comes with experience, does the job, and hands over the systems to you. The offshore company will also train your employees to use AI tools for day-to-day work. Continue reading to know more about why AI is crucial for your business and how hiring an AI developer can take your business to new heights. What is Artificial Intelligence? A simple definition of artificial intelligence is a technology to prepare a system or machine that can mimic human intelligence to complete tasks that are usually performed by humans. Artificial intelligence focuses on processes and abilities rather than a function. Machine learning, deep learning, artificial neural networks, etc., come under the umbrella term artificial intelligence. Data science is a separate field that goes hand in hand with AI to build a data-driven model in an enterprise. Artificial intelligence is a separate domain in the IT industry. IT professionals who learn AI technology and work with it are called AI developers or engineers. It includes building an artificial intelligence model from scratch, customizing existing models/ software, feeding quality data to the systems, and training the algorithms to deliver accurate insights. AI developers should be proficient in mathematics, statistics, and algorithms. Though you can train the existing talent in your enterprise to work with AI technology, hiring an AI engineer will be an effective choice in many ways. Why Do You Need an AI Developer for Your Business? Each enterprise has a different idea about artificial intelligence and ways to adopt it within the business. However, there are some basic aspects you need to consider before deciding whether you want an in-house team or an offshore AI engineer. Qualification and Experience AI developers need to have domain-specific skills and qualifications to build AI software/apps for your business. With an increasing demand for artificial intelligence engineers and developers, many students and professionals are joining AI courses. But can you reach out to the right talent to recruit for your organization? Many businesses start by hiring offshore AI experts because of easy access to talent and experience. Recruiting campaigns don’t need to be expensive and time-consuming. There’s no need to train the new candidates to work with the existing systems. AI service provider companies already work with different organizations from various industries. One look at the project portfolio will give you an idea about the range of projects completed by the AI developer. They are already trained and ready for the job. Moreover, their experience in working with different businesses will help in identifying the right tools for your establishment. Support Services When you hire offshore AI developers from a company, you get end-to-end services from them. The company will handle everything from planning to maintenance. The AI developer will regularly monitor the systems, iron out the glitches, and make the necessary updates to keep the software ready at all times. The developer will ensure that there is no downtime (or is at its lowest) so that your business operations will not be affected due to errors in the software or integrations. They will streamline all integrations to create a path for proper data flow within your business. Remote Collaborations You don’t need to have an in-house team working in the office when the AI developer can collaborate remotely with your employees. Offshore AI services are all about remote collaborations, irrespective of the differences in time zones. It reduces operational and travel costs. Furthermore, you can hire more than one AI engineer to build/ customize an AI tool for your business. A talented team will be allotted to work on your project once you sign an agreement with the AI service company. The other advantage of remote teams is that you can hire the best team from anywhere in the world. Geographical restrictions will not limit your search for an AI engineer. There won’t be candidates who can’t join your organization because they live in another country/ region. Cost-Effective No matter you are a startup or a more established business, working costs and revenue expenditure are vital factors that affect your finances in the short and long term. In 2018, Gartner estimated that 85% of AI projects are likely to fail by 2022. This is because organizations began adopting AI without proper planning or strategy. Having unclear objectives, investing too much money in a single AI project, and wanting to build everything

7 Innovative Applications of Computer Vision in Agriculture

The agricultural sector is among the critical and crucial industries around the globe, especially in India. For 58% of the Indians, agriculture is the primary source of income. The gross value added (GVA) of agriculture, forestry, and fisheries was estimated at Rs. 19.48 lakh crores ($276.37 billion) in FY20. According to the first flash estimates of FY22 national income, the percentage of agriculture and related sectors’ GVA (at current prices) is 18.8% of the total GVA. Consumer spending in India is set to grow again in 2021, rising by up to 6.6% post-pandemic. Agriculture and related activities recorded a growth rate of 3.6% at constant prices in FY21. India’s food industry is facing tremendous growth and increasing its contribution to the global food trade every year due to its immense value creation potential, especially in the food processing industry. The Indian food and grocery market is the sixth-largest in the world, with retail accounting for 70% of sales. India’s food processing industry accounts for 32% of the country’s total food market. Total exports of agricultural and related products in FY21 were US$41.25 billion. As digital technologies revolutionize all industries, agriculture is no exception. Like any other sector, the agricultural sector faces various challenges including climate change, labor shortages, and the disruptions caused by the pandemic. Digital technologies such as computer vision can help the agricultural sector meet these challenges and achieve efficiency, resilience, and sustainability. Computer Vision Application – What Is It and Why Does It Hold Importance? Computer vision can be called an extension of AI which enables machines with the efficiency and capability of organized thinking and interpreting the data as human counterparts. Computer vision technology requires analysis of a plethora of visuals from footage to images and it has the ability to identify, track, precisely predict and assess specific objects within the stored visuals. Smart farming is about harnessing the power of technologies like artificial intelligence, computer vision, and many more to automate and improve the quantity and quality of crops. Even with fluctuations in commodity prices, a ~5% improvement in crop yields can ensure farming remains profitable. From using computer vision technology for crop and soil monitoring to disease detection and predictive analytics, agribusiness is entering a whole new phase of evolution thanks to AI. It’s not just future potential, but growing interest and investment in the agricultural sector: Challenges That Can Be Solved By Computer Vision in Agriculture Agricultural management is a complex subject due to its vast breadth and the sheer complexity that is bound with it. Manual inspections are therefore very time-consuming and expensive. Plants, that are grown in greenhouses often use humidity, temperature, and light sensors to ensure optimal growing conditions for plants. However, many sites will continue to rely on manual logging and monitoring of environmental conditions, allowing for a quick response to changes or technical issues, such as the failure of a heating unit is prevented. In agriculture, climatic factors such as precipitation, temperature, and humidity play an important role in the life cycle of agriculture. Increasing deforestation and pollution are causing climate change and making it harder for farmers to make decisions about soil preparation, seed planting and harvesting. Each culture requires specific nutrition in the soil. The soil needs 3 main nutrients: nitrogen (N), phosphorus (P), and potassium (K). Nutrient deficiencies can lead to poor crop quality. If not properly controlled, it can lead to an increase in production costs and also absorb nutrients from the soil, which can lead to nutrient deficiency in the soil. The Main Problems to Concern About 1. Irrigation In order to successfully produce crops, suitable irrigation functionality is required. The ML algorithm can improve irrigation leading to the following : Here are some irrigation systems in the machine learning realm: 2. Predictive Analytics Making the right choice or decision is a key factor in farming. The correct decision leads to better income outcomes. Predictive analytics is a great machine learning tool that plays an important role in making the right decisions. 3. Soil Defect Diagnosis Farming is all about calculated risks, but what if risks can be calculated and cured in advance? The anomaly analysis can help you identify the strengths and weaknesses of the soil, which generates more revenue and saves a lot of time. 4. Right Seed at the Right Area Agriculture faces a serious problem today. There is a huge scope for improvement as present conditions require specific solutions. Simply put, harvests are not properly monitored. Classification analysis is the key to efficient damage control and higher ROI than before. 5. Predicting Production Based on Weather Conditions The climate has now predominantly become a data issue. Previously, inaccurate weather forecasts have yielded a devastating loss of many crops, along with wastage of invested time, effort, and money. The good news is, that technology has significantly improved and one of the key solutions for this can be Regression analysis which can help with precise and accurate forecasting. 6. Weed Detection In developing countries, around 40-50% of the total crops are lost because of post-harvest issues, crop diseases, and pests. Even in developed countries such as the US, around 20-30% of crop yield is lost to similar culprits. However, you can use image analysis to detect the present object in the field/farm. By classifying the objects, one can easily spot weeds on the farm, which can be dealt with, ensuring better crop growth. 7. Recommender System We know that acquiring a new customer is harder and more expensive than retaining a paying customer. Analysis of the recommender system will assist you in identifying the customers who’re most likely to buy your product/service and the probability of your existing customers. In addition, this leads to customer loyalty and broadens the overall range of innovations in service. 8. Water Treatment The correct minerals are the most important requirement for plant growth. Spotting anomalies through unattended analysis helps you choose the required volume of minerals, leading to faster-growing plants and helping you produce

7 Benefits of Artificial Intelligence in Advertising

In the past, advertisements were created with traditional methods such as art and copywriting. However, with the prevalence of advertising on social networking sites, it is now crucial for companies to get their message across in a way that is concise and compelling. To gain a competitive edge through distinguishing ads, they need to understand how their customers are engaged with the current ads. Brands need to be smart to not only reach their audiences but also capture their attention. Yet, the growing competition can be challenging for businesses trying to build client relationships and more importantly, loyalty. So, how are brands coping with this increasingly demanding scenario? In the current digital wave, brands are improving advertisement targeting by using machine learning techniques. It has led to a new wave of advertisements that are more realistic and engaging than ever before. According to the Times of India, “AI is predicted to take up to 80 percent of global advertising spend in the world.” Additionally, using artificial intelligence enables targeted marketing campaigns by automatically recognizing consumer preferences and behaviors. By understanding customer preferences and behavior, advertisers can create more personalized ads that are more likely to be effective. What Is Artificial Intelligence (AI) and How Does AI Work in Advertising? Artificial intelligence (AI) is a branch of computer science that leads to the designing of computer programs that can simulate human intelligence. With programs that can reason, learn, and act autonomously, they can make decisions, solve problems, and understand complex concepts like humans. Furthermore, AI is gaining popularity due to its application in different industries such as HR, education, and retail to name a few, that are helping humans resolve complex issues and develop credible solutions. With artificial intelligence (AI) evolving every day, the advertising industry is also growing. With advances in AI, companies can target ads more accurately and create more engaging content. It has led to a rise in new marketing techniques such as chatbots and natural language processing. The use of artificial intelligence for advertising also allows companies to measure the effectiveness of their advertising campaigns in real time. AI can help create targeted ads, personalize customer experiences, and optimize campaigns. But how does AI work in advertising? Benefits of Using AI in Advertising Artificial intelligence is still a relatively new technology, but its potential applications are endless. Furthermore, how ai is used in advertising is proving to be particularly useful for the advertising industry. Some benefits of using AI in advertising include: 1. Target Your Ads More Effectively Through Data Advertisers are always looking for new and effective ways to target their ads. Recently, there has been a lot of talk about using big data to help improve ad targeting. Big data is a term used to describe a large amount of data that is now available due to the growth of digital technologies. This data can be used to predict consumer behavior and preferences for creating targeted ads. According to Forbes, “The most recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks”. 2. Helps Optimize Your Campaigns for Better Results Artificial intelligence is being used increasingly in digital marketing, as it can help optimize campaigns for better results. AI can identify what is working and what isn’t in a campaign, and make changes accordingly. Optimized campaigns designed as per the audience’s needs help improve customer engagement and retention. It also provides companies with increased ROI. 3. Helps Save Money and Time With Increased Sales AI can help you save time and money because it can swiftly categorize the huge amounts of data that are available in this day and age. It also spots trends and patterns which are useful in making better advertising decisions. Companies like Amazon are using AI for driving pricing strategies that help to reduce prices for more sales. They have also used AI to understand and leverage pricing strategies, like when the demand for a product increase, they increase the product price too. Thus, helping companies augment more sales and revenue. 4. Reduce the Risk of Losses in Ads By using AI to help you with the marketing process, you can reduce the amount of money that you would otherwise lose if there were an error in your campaign. One of the benefits of using AI for ad campaigns is that it can help identify which ads are most likely to result in a sale. This allows businesses to focus their efforts on the ads that are most likely to be successful, reducing wasted money and time on ineffective campaigns. Another benefit of AI is that it can help identify potential threats early on in the campaign lifecycle. By identifying threats early, businesses can take steps to mitigate the risks. 5. Improving Customer Experience With AI, you can have better customer communication and a more personalized style of service. It can help companies learn about their customers, understand their needs and desires, and then predict their future behavior. It allows companies to provide customers with personalized and the best possible service and products. For example when Nike leveraged AI to provide its customers with the capability to design their sneakers in-store. It helped them to collect more data and design future products accordingly. Such AI-backed advertising techniques are immensely crucial for customers who are more likely to purchase when they feel that their preference is being well taken care of. 6. Reach More Customers With Your Ads In the age of social media, finding new and innovative ways to reach your target audience is more critical than ever. AI can also help you target your ads more effectively by learning what works and what does not. Like Coca-Cola, the largest beverage company analyzed 120,000 pieces of content on social media to understand the demographics and customer behaviors. They also analyzed customers who actively talked about their product and brand. Thus,

How Can Businesses Adopt AI Technology to Achieve Their Goals

Artificial intelligence is a dynamic force that keeps the industry moving forward to conquer more technologies. From manufacturing to hospitality to retail and aerospace, AI is being adopted by several organizations across all industries. However, businesses are still in varying stages of adopting AI in their enterprises. While the top companies have added AI technology as an integral part of their systems, SMEs still use AI to develop pilot projects for certain departments like sales, marketing, etc. Let’s learn about the adoption of AI in certain industries and how they leverage it to achieve their goals. Why Artificial Intelligence Is Important The social applications of AI, such as home assistants, smartphone applications, etc., are different from enterprise-level applications. The latter is on a much complex and wider scale, encompassing various systems and processes in the business. Artificial Intelligence Types Unlike, in theory, AI is not a one-size-fits-all solution. It is vital to understand what the enterprise needs before investing in AI solutions. While pre-designed software is useful, it doesn’t deliver results like customized AI systems. But to understand this difference, we need to know more about artificial intelligence, the types, and what AI aims to achieve as an end goal. AI is classified as follows- Narrow AI It is used for goal-oriented purposes such as facial recognition, speech recognition, etc., where machine learning algorithms complete the task based on a set of narrow constraints. General/ Strong AI It mimics human intelligence and behavior so that it can solve any problem and not be limited to specified tasks. We still haven’t successfully created AI machines with cognitive abilities. Artificial Superintelligence This is currently in the theoretical stages. ASI machines are supposed to not just mimic humans but become one of them by being self-aware. Artificial Intelligence vs. Machine Learning In simple terms, AI is an umbrella concept where machine learning and deep learning, understand data and make decisions based on it. ML algorithms are used to quickly process vast amounts of data and deliver valuable insights. What is the End Goal of AI? AI sounds fascinating, and we can see that large-scale enterprises and global organizations are already getting results from AI. But questions are still aplenty for new businesses that want to hire AI services to revamp their systems. The ultimate aim of using AI is to get measurable results to prove that the enterprise is better than its competitors. AI works the best with a centralized system where data is collected from several sources and is stored in a data warehouse or a data lake for further cleaning and processing. Artificial intelligence drives ROI for businesses and delivers long-term results when the technology used aligns with the systems in the enterprise. AI is not limited to scaling but also to measure the results and get the most out of data. Why Should Companies Adopt AI? What makes AI so important that companies adopt and integrate it as a part of their business processes? How does AI help organizations overcome challenges and become industry leaders? The following are some uses of artificial intelligence that empower businesses to perform better in the competitive market and grab the opportunities that come their way. 1. Artificial intelligence enables better customer service and support. AI is used to create and deploy chatbots, provide smart suggestions in emails, develop human-like interactive voice response systems, automate ticket generation and routing complaints, and make it easy for customer service agents to solve an issue in less time. The live chat option on many websites has customized chatbots to respond to users’ queries. 2. AI helps with target marketing and generating more leads. Artificial intelligence is used to create content for promotional emails, develop ads for each customer segment, provide product recommendations based on customers’ purchase and search history, speech recognition, etc. Amazon’s website is the best example of target marketing using AI. 3. Use AI in the HR department to hire eligible candidates and create customized training programs for employees. HireVue is a well-known AI-powered platform that helps more than 700 leading brands to hire some of the best talents in the market. Vodafone, Unilever, and Oracle are some companies that use HireVue for recruiting purposes. Similarly, artificial intelligence helps create different training modules based on employees’ skills. 4. AI streamlines the supply chain and shortens the delivery cycle. Amazon has the best warehouse management system powered by AI. Artificial intelligence helps in managing inventory, increasing safety for warehouse employees, planning the travel route for deliveries, finding alternate delivery methods, and so on. 5. AI makes the business more secure by enhancing cybersecurity. Many of the latest antivirus software and malware are developed using artificial intelligence. The software is trained to identify phishing attacks and malicious links before employees fall prey to cyberattacks. The FaceID access feature of iPhones is a small yet everyday example of how AI is used for cybersecurity. 6. AI technology is used to detect fraudulent transactions and minimize losses. AI processes vast amounts of data in a few milliseconds and identifies patterns in transactions. Many banking and financial institutions have started to use AI and ML-based fraud detection software to identify unscrupulous customers and cybercriminals with stolen identities. Teradata provides AI-powered fraud detection software to banks. 7. Data is put to good use through AI applications. Data collected from social media and other sources is processed in real-time to extract meaningful and actionable insights. AI tools are used for predictive analytics and data visualization. Tableau is an example of AI-based data visualization software. 8. AI makes the workplace safer for employees, especially in industries like oils and mining. Artificial intelligence is used to monitor the work environment using drones, surveillance cameras, etc., and determine that the workers are not at risk. AI is used with IoT (Internet of Things) to collect data and process it to identify any signs of accidents or risk. AI helps the management create a safer working environment in factories. 9. AI optimizes the use of resources and helps the enterprise and employees

AI in Stock Market: Predicting the Ups and Downs

If you think AI still has a long way to go before it enters the stock market, you’re mistaken. Because hundreds, probably more companies have already started hitting the plans to use AI for stock market trading. For instance, Goldman Sachs, a renowned bank in the USA, led a $72.5 million funding round in an AI and ML specialist (H20.ai) to adopt AI models within the equities trading business. And this was in 2019. You can only wonder how many more companies in the finance sector have adopted AI to benefit from the stock market. But the question is, does AI in stock market work? This blog talks about everything from what AI is and how it is impacting the stock market to AI that can predict the stock market and companies that use AI in different ways to profit from the stock market. You’ll also find some popular tools that you can use for stock market analysis and so much more. So, read in full. Before we move to our main topic, let’s briefly understand what AI is. What is AI? AI or Artificial Intelligence is a technology that lays stress on developing intelligent machines that can work like humans. Common examples of AI in real life are speech recognition, facial ID, weather prediction, etc. Machine learning, an application (or subpart) of Artificial Intelligence, enables machines to learn with experience just like humans. With machine learning, machines are fed with different sources of data which they use to learn, evolve and become better on their own. And this technology has a major impact on the stock market. How? Find out below. How is AI Impacting the Stock Market? AI Reduces Research Time for Finding Stocks When it comes to finding the right stock, a lot of research goes into it. For instance, you have to analyze financial statements, revenue, P/E ratio, dividends paid, profit, loss, and whatnot. And this requires an immense amount of time. However, AI in the stock market solves this problem. By using AI analytics (that uses machine learning), you can crunch all types of data from different sources within seconds. And this way, finding the right stock to invest in becomes a piece of cake. How is this impacting the stock market? AI has reduced both the time and effort required to invest in a stock. People, especially in India, are crazily opening dematerialized (demat) accounts as they can easily choose which one to invest in. For instance, according to Business Standard (published on 15th April 2022), the number of demat accounts in India jumped 63% to a whopping 89.7 million in the financial year 2021-2022. AI Is Completely Free from Emotional Influence Emotional investing is one of the biggest enemies of retail investors. It’s basically a phenomenon wherein individuals base their investment decisions on emotions such as greed or fear. And such financial decisions based on emotions are usually less intelligent. For instance, if you fear that a stock may go down and sell all your holdings without solid research, what you’re doing is an emotional investment. And it’s highly likely that you’ll lose money. Fortunately, AI in the stock market can help. Artificial intelligence knows no emotion. All it knows is algorithms, logic, and the data it’s fed. This way, you can refrain from making a hasty decision based on emotions and thus save money. How is this impacting the stock market? As AI is pushing emotional investing out of the picture. It’s making investors smart. People are now able to make decisions that are based on data and logic. This way, they’re becoming smarter and losing less money. AI Helps with Prediction Prediction is an integral part of the stock market in general and stock trading in specific. An investor performs technical analysis by understanding the historical data, noticing patterns and then analyzing the fundamentals to determine what will happen to the stock. And you can call this phenomenon prediction, based on which, buying/selling decisions are made. While some people are remarkably good at predicting the stock market, the risk of losing always prevails. Because missing out on even the tiniest bit of information can lead to horribly wrong outcomes. However, AI can help. AI in the stock market can help you perform technical, fundamental analysis and recognize patterns better than humans. What’s more, AI can process the latest news, blogs, and interviews to understand and predict how the stock will perform in a more accurate manner. And this improves the overall chances of you making the right prediction. How is this impacting the stock market? While you can call prediction a calculated guess, several people rely on AI for the same. And with AI in the picture, predictions are becoming more reliable, which is motivating more people to invest in the stock market. Also, it’s fueling startups operating in this domain to work harder and produce better systems. AI Helps Automate the Sale and Purchase of Stocks Don’t have time to stay in front of your TV/laptop/phone screen for stock updates? Well, AI platforms make this possible. With the help of AI tools such as eToro Copy Trading, you can automate trades and set your entire or some of your portfolios on autopilot. The platform will charge you a fee and will use AI to make buying or selling decisions. Sounds lucrative, right? Please Note. No matter how lucrative it may sound, you should always have control over the decisions you make. Completely relying on AI for anything, let alone the stock market isn’t recommended. How is this impacting the stock market? With the ability to automate trades, AI is making the stock market more accessible. More people, regardless of their profession, are entering the industry relying on AI-based automated trading tools. AI Helps Reduce the Overall Cost of Trading Hiring a stockbroker is quite costly. You need to pay a commission for every trade the stockbroker makes on your behalf. However, with AI in the stock market, you can reduce these costs to

Machine Learning for Trading – Can It Predict the Trend?

Machine learning allows machines to trace patterns and identify trends by analyzing huge datasets. Data scientists are working on building ML models to assist traders in day-to-day trades in the stock market. Let’s discuss the role of machine learning in the trading industry. Machine learning is a field of artificial intelligence that deals with how computers process large datasets and learn from them to make decisions and predictions. Machine learning is a vast concept and is classified into supervised and unsupervised-based learning. There are several algorithms designed to train machines on available data. A few of them are Simple & Multi Linear Regression, Logistic Regression, Decision Tree Classification, XgBoost, and many more. The algorithm is trained in different ways depending on the type of datasets fed into the system. Machine learning is almost everywhere in today’s world. From Netflix’s search engine to sales forecasting, supply chain management, manufacturing, healthcare, and more, machine learning is already a part of every industry in multiple verticals. The financial and trading industry also uses machine learning algorithms in different ways. FinTech is changing the way we look at banking and insurance. The crypto market has various AI-based apps that allow users to trade in cryptocurrency with the assistance of advanced algorithms. AI and ML have a role to play in the trading market too. There is no 100% automation yet; ML algorithms assist stockbrokers to offer better services to investors. AI and ML consulting companies offer consulting services to traders and trading establishments to build, customize, and fine-tune the algorithms and automate statistical work. Let’s explore the role of machine learning in trading and see how it can help stockbrokers, traders, and investors. How is Machine Learning Used In Trading? Patterns and trends are an integral part of the trading industry. Machine learning algorithms are good at processing large amounts of data to identify patterns that cannot be easily spotted by humans. The stock market is volatile and prone to be affected by several factors (social, political, economic, demographic, etc.). Being able to correctly predict the trends in advance will help traders minimize the risk of market crashes and increase returns. This makes machine learning a valuable resource in trading. ML algorithms can also be used for: Sentiment Analysis Analyzing the sentiment in the market might help traders determine whether the stock prices for a brand will increase or decrease. Data is collected from multiple sources like social media, websites, forums, news platforms, and so on. Natural Language Processing (NLP) is used to understand the context of the data to determine the market mood. Traders can use this knowledge to adjust their investments and decide if they should buy more stock, sell what they own, or wait for the patterns to establish more clearly. Pattern Detection A majority of the stock market predictions come from hours of manual processing combined with years of experience. Machine learning in stock trading reduces the need for hard labor by automating the analysis. However, human expertise is still vital to deriving insights. The trader should know where to look to identify the patterns. Whether it is designing an automated trading machine or using ML to detect patterns, human experience and intuition are very much necessary for the algorithm to deliver accurate results. ML cuts through the time-consuming tasks of collecting and processing information, but it’s ultimately people who use the insights to make decisions. Real-Time Data Forecasting The machine learning algorithms can be fed more data in real-time. The algorithms are trained to learn and fine-tune the predictions to increase accuracy. For example, aspects like the global weather conditions, political unrest, climate change and its impact on renewable energy, etc., are real-world issues that have a direct impact on the trading industry. The ML algorithms can predict the results of the world’s issues and thus provide a basis for what could happen in the stock market subsequently. Using a combination of algorithms and forecasts will provide better results as multiple factors influence each other. High-Frequency Trading Machine A high-frequency trading machine runs on artificial intelligence. It performs thousands of transactions per day by taking advantage of the subtle changes in the stock market. These changes are almost impossible for humans to track as they happen in a few seconds or minutes (at the most). However, a properly trained trading machine will be fast enough to see the change and use it. That said, similar to pattern detection, this too requires human expertise. The algorithm has to be developed to detect minute changes. Regular updates are necessary to tune the algorithm and minimize errors. The tuning can also be automated to avoid statistical work but still requires human supervision. Chatbots in Trading Another way to use Machine Learning in trading is by developing chatbots for communication. Chatbots in any industry have the same roles and responsibilities. Chatbots communicate with traders and provide the information they ask for (past deals, financial statements, investment records, etc.). The chatbots can also compile a list of trading offers, potential shares to buy, the latest prices, and much more. AI-powered chatbots are faster and more effective than human support teams. A single chatbot can handle multiple conversations at the same time and provide each trader with the required data without feeling tired, getting confused, or making mistakes. The algorithms are designed to take feedback and learn from the previous mistakes to provide better support to the traders. Is Machine Learning Suitable for Day Trading? Day trading is where a trader purchases and sells equities on the same day. Traders buy the stocks of a company in the morning and sell them by the end of the day, preferably to make a profit. Day trading requires a lot of experience and knowledge about the stock market. A minor mistake could lead to losses of thousands and millions. Machine Learning for trading is relatively a new concept, with ML engineers working on developing algorithms that can accurately offer predictions and insights. Different types of algorithms need to be used to develop