Artificial Intelligence – Page 7

6 Business Benefits of Outsourcing Your AI Projects

The biggest dilemma for a business is to choose between building an in-house team and outsourcing the AI project to an offshore expert. We’ll discuss the benefits of outsourcing in detail to understand why it is the preferred choice for most enterprises. Artificial intelligence has been a game-changer for many small, medium, and large enterprises. It has made digital transformation possible by helping businesses use data to derive actionable insights and make better decisions. AI can be used in different verticals in the organization. For example, AI applications help the sales team with demand forecasting and market trend predictions. Artificial intelligence is used by the human resource department for recruitment and tracking employee performance. AI has a role in supply chain management, finance, transportation, and customer service. According to Gartner, organizations using AI for customer service are expected to see a 25% growth in customer satisfaction by 2023. More than 90% of the leading business organizations are investing in AI. In fact, 86% of the CEOs considered AI the mainstream technology in their offices as of 2021. This raises the question of whether you need to hire an in-house team for AI projects or outsource the responsibilities to offshore AI developers in India. Outsourcing has its benefits and is a preferred choice for many businesses. Small and medium-sized businesses outsource to avoid infrastructure and maintenance costs. Large enterprises outsource to increase employee productivity. Around 70% of the projects are outsourced to reduce the budget. Flexibility is the second next reason for outsourcing. Read on to understand how artificial intelligence can help your business and why outsourcing AI projects is beneficial. Importance of Artificial Intelligence Artificial intelligence is becoming an imperative part of several businesses because of the range of advantages it offers. How you use AI within the business determines the results. Here are a few important areas where AI is prominently used by leading organizations. Automation Automation is the process of using technology to complete repetitive tasks that are otherwise handled by employees. It reduced workload to save time and energy. It reduces the risk of human error and delivers more consistent results in less time. Machine learning algorithms are used for automation in various industries like healthcare, manufacturing, retail, entertainment, etc. Market and Customer Research The sales team has a lot of responsibility. It has to predict the market trends, understand customer behavior, identify the right time for the promotional campaigns, and align everything with the business goals. AI application will simplify their job while empowering them to use the insights to plan marketing strategies more effectively. Logistics Inventory management, warehouse management, and aligning the movement of stock from the warehouse to the stores or transporting it to the distributors require constant coordination with internal and external teams. A small miscommunication at any stage of the supply chain can lead to delays and losses. Artificial intelligence prevents this by predicting delays in advance and sending automated alerts to supervisors. Unlock the Power of Data Data is available in abundance in today’s world. Enterprises can gather data from several external sources. But how you use the data determines the success of your business. You need the right tools to clean, structure, process, and analyze data to get accurate insights. AI streamlines this process and also provides insights in real-time. You can use the latest data from the market to make decisions instead of relying on outdated information. Competitive Advantage Using AI tools will give you an edge over other businesses in the same industry. It helps small and medium-sized businesses establish themselves in the market despite the strong presence of global enterprises. Artificial intelligence is ‘the key’ you need to survive competition, capture markets, and expand your customer base. Disadvantages of In-House AI Team for AI Projects Having learned how AI can help your business, you should decide between building an in-house team and outsourcing the projects to AI consulting companies in the market. It might appear tempting to start from scratch and develop an in-house team so that you will always have someone working on the AI tools. However, you should consider the following aspects before going ahead with a permanent team within the enterprise. Shortage of Skilled Professionals Despite the increasing demand for AI and ML engineers, data scientists, etc., there aren’t enough professionals to bridge the gap between demand and supply. In such instances, hiring talented and experienced professionals in a short time is almost impossible unless you are a leading global organization. Furthermore, there’s no guarantee that the professionals will stay with your business. They can move to another company if they get a better offer. This means you have to start the recruitment process once again. It will lead to more delays and cause a waste of valuable resources. Cost Intensive The cost of having an in-house team doesn’t end with recruitment. You have to pay salaries and provide the necessary infrastructure and resources for the team to work on the AI projects. You also have to deal with the cost inflations due to additional requirements/purchases, pay hikes, and losses if the project is not successful. An in-house team is expensive for a business unless you have the budget to sustain an R&D department and invest millions into it regularly. Machine learning outsourcing will cost less and deliver faster results. Involvement from Different Departments The in-house cannot work in isolation. It needs input from other departments and should have team members for the same. However, not every business has enough manpower to spare employees from one department to work in another. Asking the employee to handle everything will lead to stress and decreased productivity. Access to Quality Data for Training Models Quality data is the key to accurate AI models. The algorithm will deliver results based on the input data you provide. But where does quality data come from? Data available online cannot be directly used to train models. It needs to be cleaned, formatted, structured, and processed before being fed into the model. The in-house team will spend most of the

A Definitive Guide To Machine Learning Consulting

Planning to integrate artificial intelligence and machine learning into your daily business operations? If yes, then what’s the best way to go about it? If you are confused about how to leverage machine learning or artificial intelligence capabilities to scale your business, then you need an expert to guide you. This guide will walk you through the whole process of ML and AI consulting and whether it’s the right choice for you. What is Machine Learning Consulting? The goal of AI/ML consulting is to help businesses improve their performance and productivity by using AI technologies. The main focus of artificial intelligence is to exploit machine learning to increase companies’ value in an environment of rapid change and intense competition. AI consulting companies help their clients design and develop advanced AI-based products and solutions by developing AI-driven algorithms and machine learning tools. In order to ensure maximum results, it is essential to design, implement, and integrate Artificial Intelligence into the business environment of various organizations. The demand for artificial intelligence-driven products and systems is high at present. But, there are still limited options available. The services offered by AI consultants include: What Does A Machine Learning Consulting Firm Do? There are many reasons why companies need consultants, such as a lack of expertise, existing commitments, government regulations, or the need for an unbiased perspective. While consulting firms perform varying roles and responsibilities depending on the industry and the company’s needs, their primary goal is to improve business efficiency and profitability. Consulting firms begin their work with the following tasks. The Process Starts With Knowing What Your Business Needs Every consulting project begins with some business need. Business requirements are still one of the biggest reasons for the failure of consulting and software projects. It can be choosing the right location to install your next branch or selecting the right audience. There is a particular issue with ML consulting, as it is at the intersection of consulting and software development. Setting Up the Team and the Process Machine learning is not necessary for all business problems. Problems that cannot get reduced to a set of rules make sense for machine learning and other approaches. The performance and maintenance of rule-based systems are better than machine learning when the rules are well known. ML can be useful for solving particular problems, but the project team, stakeholders, and high-level targets needed get decided. The Process of Collecting and Exploring Data It is relatively simple to do this if the company already has the data. Else consultant has to collect all the data to understand the business problem. Afterward, consultants must verify that data labels are accurate and do not conflict with each other. If data isn’t readily available, you can also have options like leveraging online data, paying for labeling, or using novel machine learning techniques such as one-shot learning. The Model Development Process. Developing a machine learning model that performs more accurately requires thousands of experiments. It is a trial-and-error process involving the latest research, a thorough understanding of business dynamics, and data analysis skills. For accuracy testing, all models get tested against the same data set. Full-Stack Application Development The model development and integration for production require additional time and resources. In most cases, ML models get embedded into APIs which makes it easy to integrate them into applications. Application development, which implements the ML model and integrates it into decision-making processes, can be more challenging than modeling itself. When developing applications, integrating them into existing enterprise systems is necessary, which requires an external developer’s involvement. To put the model into action, it is necessary to address the scalability and data security issues. Why Does Your Business Need Machine Learning Consulting Services? If you are just getting started with machine learning, it can be difficult to differentiate a top consulting firm from an upstart. You should consider these points before working with a consulting firm. They Are A Time Saver McKinsey estimates that artificial intelligence and machine learning will replace 30% of current jobs by 2030. Machine learning service providers can save you a lot of time. You can find running a machine learning campaign difficult if you don’t have any AI training. The advantage of hiring a highly regarded machine learning company is that they have expertise in all aspects of machine learning and can launch your campaign quickly. Experienced consultants are familiar with best practices. For instance, A lean consultant can quickly pinpoint inefficiencies in a company’s manufacturing process. Business owners do not need to create something from scratch or waste valuable time on tasks an expert contractor can complete. They Are Result Driven A good machine learning firm will always take the time to understand your business, your goals, and your needs before creating a machine learning campaign for you. In this way, they will design and implement a machine-learning strategy that corresponds to your business goals and provides you with the desired outcomes. It Is Financially Profitable It is important to have a lot of data to make good decisions when creating a machine learning campaign. In order to hire a machine learning consultant, you will need access to various programs and software, which can cost quite a bit over time. When you hire a machine learning consultant, you will have access to many different programs and software, which can be expensive over time. Additionally, you pay only for the services that you need, when you need them. An ML consultant with similar expertise can save hundreds of dollars compared to hiring salaried employees. Besides, consultants with expertise in many areas can identify pinpoints where you are currently overspending and suggest ways to cut it. Providing Support For Data Preparation The data preparation and collection are essential for modeling and training ML, as well as the foundation for deep learning and AI. Also, for combining data sets, renormalizing data, and preparing your database for successful implementation, a good ML consultancy can provide you with helpful advice. Consultants

6 Reasons To Hire a Machine Learning Developer

Artificial intelligence and Machine Learning are undeniably transforming and integrating themselves into every aspect of our everyday lives, from entertainment and social interactions to the workplace. Artificial intelligence, according to experts, will advance a company into the next generation, revolutionize data management, and improve the whole consumer experience. There are many business applications for AI and machine learning, and as these applications become more widespread and sophisticated, we can only anticipate an expansion of this list. 6 Reasons to Hire a Machine Learning Developer Here are a few of the reasons you should hire a machine learning developer: 1. Supports Real-Time Decision Making Big data’s potential is expanding quickly, thus you must connect users and providers (buyers and sellers) instantly. Businesses must instead use data and analytics to quickly determine the best course of action based on the current situation. Your consumers will appreciate personalized services, which will increase profits. A machine learning developer helps businesses achieve real-time decision-making by drawing insightful conclusions from the data. 2. Enables Automation That Replaces Manual Efforts ML technology advances industrial automation by replacing manual jobs that incorporate unpredictable elements like changeable parameters, external causes, and internal system changes. Industrial automation in the 20th century focused on employing machines to reduce predictable and repetitive human tasks. An ML developer gives businesses the ability to make quick judgments and automate operations by creating predictive data models. One such is Adobe Target‘s Auto Target, a potent AI technology that employs machine learning to create automated tailored experiences. Personalization in Adobe Target is based on a set of experiences chosen by the marketer. 3. Effectively Address Security and Network Challenges Cyberattacks, network breaches, and other security flaws sometimes occur in real-time without warning, giving users little opportunity to take action. One of the biggest cyberattacks in history occurred in 2018 when a series of DDoS strikes targeted GitHub and Arbor Networks in the US. Any network intrusion must be promptly detected and stopped by enterprises in order to avoid service interruptions and data leakage. ML developers are capable of creating algorithms that are capable of tracking network activity to quickly identify anomalies so that preventative steps can be taken. Additionally, as ML algorithms take the place of manual investigation and analysis, they self-learn and adapt to change, which further enhances the condition of cyber-security. 4. Improves Marketing Results The branch of AI known as machine learning focuses on creating intelligent machines that can operate independently of human intervention. Additionally, a machine learning developer helps businesses save time and money in the marketing sector as well. In marketing initiatives, machine learning can be an amazing and potent multiplier. Constant messaging, trends and changes in consumer behavior, targeted marketing techniques, etc. are all made possible by machine learning engineers. Social media websites can be used to develop micro-marketing campaigns. 5. Manages Unstructured Data Large amounts of unstructured data can take a long time to clean up. Any organization that evolves over time gathers a lot of fresh information. For instance, if you are in the air freight forwarding business, you will need to process large amounts of data on services, rates, etc. within a short period of time in order to evaluate it and obtain insights about potential future patterns. Managing and removing massive information indexes is one of machine learning’s most important applications. The information is easier to process, obtain, and retrieve thanks to machine learning. 6. Increasing Customer Satisfaction Currently, machine learning is used to improve customer experience, which results in increased customer loyalty. It is done by looking at the customer’s buying habits. Many organizations all over the world already use this technology. According to research, 76 percent of business owners agreed that their profitability increased as a result of incorporating machine learning into their processes to understand client behavioral patterns. Google and many other large websites are using this technology. You may have observed that when using websites like YouTube, LinkedIn, etc., you frequently receive recommendations for various goods to buy or individuals to connect with. With the aid of machine learning, the ads and friend suggestions are handled. How To Hire A Machine Learning Engineer When hiring a machine learning engineer, who can be a valuable asset for your business, it is advised to exercise extreme caution if the company is investing in the development of artificial intelligence and machine learning. As a result, there are some essential factors to consider when hiring a machine learning engineer or specialist. What to take into consideration when you want to hire a machine learning developer is covered in full below. 1. Technical Skills For these professionals, full-fledged technical skill in coding and programming is essential in order to tackle such assignments. Here, you must check the applicant’s technical specialization and administer the coding test to confirm that she/he is capable of overseeing the creation of AI and ML models. Make sure you have qualified professionals to hire the best applicant so you can evaluate their technical talents. A machine learning developer should have knowledge of languages like python, frameworks like Pytorch or TensorFlow, algorithms like Scikit, and many others. Make sure you test all the necessary technical skills before hiring. 2. Interactive Communication Skills The ability to communicate with others, which is necessary for collaboration with colleagues, should be taken into account when trying to hire a machine learning engineer. Before beginning the project, AI and machine learning professionals should agree on a framework for communication with the company. Therefore, if you are concerned about a candidate’s communication skills, you should assess their capabilities to ensure effective communication. Wrapping Up Finding a suitable AI or machine learning professional would be difficult and time-consuming. To prevent confusion between data analysts, data scientists, and machine learning engineers, it provides basic stages of vacancy description with a precise set of responsibilities. In order to recruit a machine learning engineer that is an expert in his field, one must have a complete understanding of technology and competence. So, to

Why Hire an AI Software Development Company?

Whether or not a business project is a success largely depends on how solid the technical team is. Today, AI development is faster than ever, pushing businesses to gain a competitive advantage. Every project is unique and so are its requirements. A company must choose the most convenient and efficient model for AI software development, depending on the project’s scale, features, goals, and budgetary constraints. However, there is also a slew of factors that determine the success of a project. Factors Responsible for Outsourcing AI Development Needs of a Company Requirements Before you decide whether to outsource a project or handle it in-house, clearly assess the project goals. What are the features you are looking for in your project? What are the services you will require for the successful execution of the project? When are you planning to launch the project? Next, you need to assess if an in-house team has all the necessary skills to fulfill your expectations from the project or if an external agency can provide you with the level of customization you require for the project. Budget Maintaining a budget is one of the most crucial aspects of a business. So you need to figure out how much capital you can invest into developing your project. Next, calculate the expenditure required for in-house handling and outsourcing and choose the option that best fits your budget. The Results It is one of the most crucial factors you need to consider to decide which of the two options is more viable for you. Compare the services that your in-house team will offer with an external agency. Understand the complete scope of work and the exact services you will be paying for. Now, the next question that might come into your mind is, “How do you ensure that your project idea is executed in the best possible manner? Do you hire an in-house software development team? Do you partner with an AI outsourcing agency?” Let us deep dive into the pros and cons of both these options so you can decide what suits your company the best. Advantages and Disadvantages of In-House AI Development In-house AI development means hiring team members within your organization and training them with the crucial IT skills and abilities needed to handle the project. Although this method is more cost-effective in the long run, it can seriously load the company’s budget in a shorter span. Disadvantages of In-House AI Project Development Lack of Expertise In-house AI developers demonstrate lesser expertise and the skill to develop AI-based ideas as compared to ones in an agency. They do not have an interdisciplinary perspective which is a prerequisite for creating meaningful models for making business decisions and getting faster results. Need for Resources Hiring an entire team is even more difficult than it sounds. As an organization, you must equip yourself with all the resources such as infrastructure, overhead, additional employee benefits, training fees, etc. making an in-house team an expensive affair. Time Crunch Hiring an in-house AI development team is an extensive process. From evaluating potential team members to recognizing the skill gap of every member, training every member is a long process. If you run out of time to execute a project, it is natural to not go with this option. Advantages of In-House AI Project Development High level of Personalization By developing your own AI solution, you can remove all the unnecessary features and complicated settings and bring down costs just by sticking to the product requirement. Ownership of Intellectual Property (IP) If your company invests in developing advanced technology like AI by building an in-house team, you can gain patent rights for the value you add in this field. It can give you an edge over your competitors in the industry. Advantages and Disadvantages of Outsourcing AI Project Development Outsourcing means your company partners with an external AI development agency and delegates the project execution job. Though outsourcing technical projects can be expensive, it is worth the investment. You get access to domain experts with a lot of experience working on similar projects. It is especially beneficial if you do not want to wait too long to launch your product in the market. Advantages of Outsourcing AI Project Development Instant Access to High-Quality Data Tons of high-quality data are needed for developing machine learning and building deep learning models. Even if your business has access to big data, working with such massive data requires a lot of experience and is time-consuming. Only an external AI development agency can bring such expertise to the table. Before starting any AI project development, proper data cleansing and preparation are prerequisites. An outsourcing agency can feed a Machine learning platform with high-quality data sets to leverage it to your advantage. Outsourcing Is a Remedy for Invisible Costs Outsourcing might be expensive, but you pay for the skills and expertise. But while building an in-house team, you pay in terms of both time and money for additional things apart from the skills and expertise. Everything is an additional cost to the company, from recruitment and software licenses to training and employee benefits to providing refreshments. Also, hiring people in technical fields such as AI is tough. A lot of time is needed to search, interview, recruit, and onboard new employees. Your company can save all of this time and capital just by partnering with an outsourcing AI agency. Scalability and Flexibility If you are a fast-growing startup in a highly competitive industry, you need to scale dynamically to meet market needs. In such a situation, the in-house team” approach might not be helpful, especially in challenging, specialized projects. Also, if you are a small company, you probably do not have the resources needed to implement all the in-house services and systems. So to ensure that the quality of the final product is not compromised and to compete with the big players in the market, outsourcing is a better option. You can achieve high levels of scalability and

4 Best Computer Vision Use Cases for Solving Business Challenges

Computer vision is a field of artificial intelligence (AI) that can be found in an increasing number of business use cases. It enables machines to “see” objects and interpret them as humans do. Computer vision services specialize in many types of vision, including detection, identification, and supervision. Here’s a quick rundown of both how the technology works and how businesses are using it to overcome challenges and optimize workflows. How Computer Vision Works All computer vision devices and applications utilize AI, which is a simulated form of human intelligence and the basis of all machine learning. Just as a human sees an image and processes an appropriate response, the AI in computer vision detects digital images, analyzes the data, and processes a response in the blink of an eye. For a computer vision system to work effectively, developers must expose it to thousands of images with definitive traits, such as names, physical features, and labels. Pattern recognition is the name of the game. Once the system acquires a knowledge base of its particular environment, it can automatically take over detection, identification, and supervision responsibilities without human assistance. Computer vision is gaining a foothold in various industries, and business leaders have many opportunities to use the technology and get ahead of their competitors. Here are four of the most influential applications and real-world examples of how computer vision can solve companies’ challenges. Computer Vision Use Cases for Solving Business Challenges 1. Object Detection Object detection is the most well-known and widely used computer vision function. A system utilizes its AI network to identify specific objects in various settings. It eliminates much of the programming and guesswork that previous detection devices relied on, allowing for quick and accurate detections without human supervision or intervention. Self-driving cars are perhaps the most well-known examples of AI-powered object detection, but that technology has seen mixed results in practice. These other real-world applications have been more successful: Object detection is just the first step in a computer vision service’s analysis. It also classifies, monitors and verifies objects based on what the AI has learned. This technology will only get more accurate as it continues to gather information. 2. Optical Character Recognition On a smaller scale, computer vision services use optical character recognition (OCR) to identify letters, numbers, and other symbols from images. The most mainstream example of OCR that you might have already used is Google Lens. This tool enables users to pull fragments of text from digital images and printed documents and even translate foreign languages from photographs. OCR has also been a valuable tool for several key societal institutions, including education, finance, and government. Universities can scan and extract text from obscure historical documents and make more accurate translations, preserving precious knowledge from the past that otherwise might have been lost. Banks and credit unions offer OCR services so customers can scan their checks and credit cards without coming into the office. Other machine-readable personal documents — passports, driver’s licenses, green cards — also utilize OCR to help governments with border security and identification. Virtually every scannable item you can imagine can benefit from OCR, so your business is sure to find a relevant use for it. 3. Risk Management Employee safety should be the top priority for all businesses and industries, but equipment and regulations can only do so much. A computer vision system can help create a safer work environment by tracking worker activity, including how they use heavy machinery and navigate the work site. Safety-oriented computer vision applications are most common in dangerous settings, such as construction sites and warehouses. Workers use wearable AI devices to monitor their physical conditions, while supervisors use drones and cameras to identify hazards and ensure everyone follows required safety procedures. The health care industry has found a similar use for computer vision services, providing patients with wearables that automatically monitor specific conditions and send feedback in real-time to their doctors. This trend arose by necessity during COVID-19, as many people were either unable or unwilling to schedule in-person appointments. Computer vision is also a key part of some in-house hospital equipment. Medical professionals can train AI to identify the early stages of illnesses through X-rays, MRIs, and CT scans. 4. Image and Video Restoration Modern cellphones, cameras, and editing tools allow people to make drastic changes to images and videos, but none of those devices come close to computer vision’s editing capabilities. Computer vision services restore pictures and videos with extensive damage and decades of deterioration. AI in computer vision services evaluates the missing or damaged parts in the original image or video, reads a generative model of the same media, and fills in the gaps to recreate the scene. Some take the restoration a step further and build 3D sets of real settings, such as archaeological and environmental professionals. Computer vision’s restoration abilities have also proven valuable in the court of law, as forensic specialists can perform more immersive crime scene reconstructions and thus solve many cases. Computer Vision Has Massive Potential These applications of computer vision services are just a handful of successful examples. This technology has massive potential to transform many crucial industries, from health care to finance to construction. Developers are still working out some of the kinks, but business leaders should embrace this technology and add it to their operations in any way they can.

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