15 Common AI Mistakes to Avoid: Ensure a Smooth AI Journey

Artificial intelligence offers endless benefits to a business but can be overwhelming to implement without proper guidance and support. Here, we’ll discuss the common errors enterprises make in AI implementation along with the ways to avoid them.  Artificial intelligence has been a part of the global market for years. While it has several definitions, AI is mainly the science and engineering of making machines intelligent. It combines computer science and datasets to enable quicker problem-solving.  Many SMBs, MSMEs, and large enterprises are adopting AI technology and tools for various purposes. A research report shows 44% of companies in the private sector will adopt AI in 2023.  According to Statista, AI is mainly used for managing data as a business asset, establishing data culture, driving innovation, performing data analytics, and building a data-driven enterprise. However, several business organizations have suffered from artificial intelligence failures. In fact, various studies show that 70% of companies have minimal or no impact of AI, and a whopping 87% of data science projects don’t make it to production.  In a way, AI mishaps are one of the reasons for organizations to be wary of adopting advanced technology for digital transformation. For example, the automated hiring algorithm used by Amazon turned out to be biased against women and hired only white men (due to biased training data). A real estate tycoon in Hong Kong filed a lawsuit against a business that sold him an AI robot to manage his funds. The robot lost around $20 million every day instead of increasing the funds by making the right investment decisions.  It’s crucial to understand where things go wrong and avoid such mistakes to successfully implement AI technology in a business.  In this blog, we’ll read about the common AI mistakes every business should avoid and ways to achieve the desired results.  What are the Most Common AI Mistakes to Avoid? 1. Half-Hearted Attempts Artificial intelligence can offer a multitude of benefits to a business. However, it requires heavy investment in building a strong IT infrastructure and training employees to work with the latest tools. Making half-hearted attempts or investing in AI for the sake of it is a sure way to generate losses.  AI adoption requires planning, implementation, and continuous tracking to deliver results. Haphazard processes will only waste business resources and lead to additional complications. To prevent such issues, businesses should take the time to develop a strategy for AI adoption and follow it.  2. Lack of Data Quality Is your data large enough to make AI effective? This is one question many people ask since AI is associated with large datasets. However, businesses forget to consider data quality and its impact on artificial intelligence. The AI model is as good as the data used to train it. If businesses don’t provide high-quality data input, the model will not deliver accurate results.  Lack of data quality is another major reason for AI failure in enterprises. This can be avoided by investing in proper data systems and ETL (extract, transform, load) models to collect, clean, format, and process the data before it is fed into AI models for training. Data management is necessary to avoid skewed or incorrect models.  3. Unclear Business Goals Why should a business invest in AI? What problems does the top management wish to solve using artificial intelligence? Which business goals can be achieved through AI adoption? Every enterprise should have clear answers to such questions before investing in AI. Simply copying a competitor is likely to result in failure.  Establish definite and measurable business goals. Align these goals with the business vision and mission. Then create an AI adoption strategy that supports the business goals and objectives. Evaluate the impact the AI model would have on the establishment and calculate the expected ROI. The trick to avoiding AI failure is to spend more time strengthening the foundation instead of building AI on weak bases.  4. Not Focusing on Change Management AI adoption requires many changes across the organization. The internal processes, IT systems, employee working methodologies, and organizational culture have to be changed and revamped to align with how AI systems work. Quite a few businesses don’t pay enough attention to these changes. They don’t have a comprehensive plan to implement the changes cohesively at each level. This results in disruptions, miscommunication, delays, and unexpected losses.  Companies offering AI consulting services insist on developing a change management strategy to ensure employees, management, systems, and processes are in sync and work towards the same goals. It’s vital to address the cultural and behavioral aspects of AI adoption to overcome roadblocks and ensure a smoother transition from outdated methods to the latest ones.  5. Relying on Black Box Models AI models are complex and hard to understand. There’s no denying this statement. However, when a business relies on such black box models (ones that offer almost no explanation of how the algorithms work), it creates opacity in the enterprise. Lack of transparency and accountability can lead to many issues for the business. Who will be responsible for the wrong insights?  Avoid using black-box AI models in the organization. Work towards building transparent models and maintaining clear documentation to understand how things are done. Businesses that cannot afford to build AI models from scratch can partner with AI companies to customize existing tools and increase transparency. Put in extra effort to explain the process to employees and establish procedures that make people accountable.  6. Not Enough Expertise Introducing artificial intelligence in a business is no small task. AI models are best handled by experts with the necessary domain knowledge. Asking the existing talent pool to use AI technology without proper training is likely to cause errors and additional problems. When planning the digital transformation of a business, it’s important to identify the talent gap and find ways to fill it.  Organizations can hire an in-house team of AI experts to initiate the process or rely on offshore service providers. Building a team from scratch is cost-intensive and time-consuming. However, working with AI companies is quicker, cheaper, and more effective.

Read More

Artificial Intelligence vs. Machine Learning: Key Differences

Often, individuals in the technology world use the terms Artificial Intelligence and Machine Learning interchangeably. However, while AI and ML are closely related, they’re different in almost every aspect. Want to know the difference between AI and Machine Learning or how they’re different? Read along. The internet is full of definitions that fail to define AI and ML properly, which is why the confusions arise. However, in this blog, you’ll find a detailed differentiation of AI vs. Machine Learning. So much so that even a non-tech-savvy person will be able to differentiate these terms. In addition, you’ll also find the advantages or use cases/applications of AI and ML in different business domains, which will further help deepen your understanding. So, read in full. AI Vs. Machine Learning What is AI or Artificial Intelligence? Some individuals think of AI as creating a conscious sentient being via programming, which is often termed artificial general Intelligence. On the other hand, data scientists use AI at a practical scale for solving real-world problems such as customer service, data analysis, manufacturing, etc. So, we can conclude that Artificial Intelligence is an umbrella term for technology that helps individuals autonomously solve problems by simulating human Intelligence. And this is done using algorithms that analyze input data and produce outputs. Take a chatbot, for example. Whenever a customer types in his/her query, the bot comes up with a suitable answer (output) based on the question (input). What’s more amazing is that AI algorithms can react to input data in different forms. Take virtual assistants or personal assistants like Google or Siri, for example. Whether you say, “What’s the weather like” or “What’s the temperature” or maybe “How’s the weather” the assistant is going to notice the keyword and provide you with the correct answer. Note. AI has numerous subsets such as Machine Learning, deep learning (common application: virtual assistants), neural networks (common application: facial recognition), computer vision (common application: image recognition or object recognition), and natural language processing. What is Machine Learning? Machine Learning, as stated above, is one of the subsets of Artificial Intelligence. However, unlike AI, Machine Learning as a term doesn’t mean creating smart machines or computer programs that can simulate human behavior to solve problems. It means creating computer systems that learn and improve on their own, using experiences (huge volumes of data) without human intervention. We can cite the example of voice assistants to understand Machine Learning.  Earlier, the assistants used only to recognize specific statements like “What is the weather outside” and give an output. If anything else was said, the assistant wasn’t able to produce the desired output. However, because of Machine Learning, assistants can now recognize and respond to a variety of different statements having the same meaning. Another example of ML is predictive analysis.  Several companies, such as Netflix and Amazon, use predictive analysis to predict user behavior and deliver a better user experience. What’s more, banks use predictive analysis to predict fraudulent patterns and stop illicit transactions from happening.     In a nutshell, if AI is the grand vision of creating intelligent computer programs or machines, ML consists of the models, technology, and processes that are being used by experts to reach there. How are Machine Learning and Artificial Intelligence Different? Machine Learning and Artificial Intelligence can be differentiated based on several factors such as their scope, primary goal, application, the data type they deal with, etc. Here’s a table that can help you differentiate Machine Learning and Artificial Intelligence better:  Criteria Artificial Intelligence Machine Learning Primary Goal To create computer systems that simulate human intelligence for solving problems. To enable machines to learn automatically from data and improve the accuracy of outputs Hierarchy AI sits at the top with Machine Learning, deep learning, computer vision, neural networks, and natural language processing being its subsets.  ML is a subset of AI and deep learning is the subset of Machine Learning that involves deep learning algorithms, vigorous training data, and multilayered neural networks. Scope AI has a pretty broad scope. Limited scope, as the focus is on making machines more accurate. Applications Chatbots, voice assistants, humanoid robots, etc.  Google search algorithms, recommendation engines, etc. Types Weak AI, Strong AI, and General AI. Supervised learning, Unsupervised learning, and Reinforcement learning Use Cases of AI And Machine Learning? 1. Fraud Detection (Anomaly Detection) AI and ML are revolutionizing the finance industry because of predictive analysis. ML can help finance companies, especially banks, with fraud pattern recognition and raise the alarm whenever there’s something fishy, thus preventing fraud from happening. Please Note. While there’s already a rule-based system that’s used by different banks, it’s accused of causing false positives. And this costs the banks a lot of time as the employees have to mitigate the issue only to find it was a false alarm. However, fortunately, as an ML system learns from its experiences, the chances of false positives reduce which helps save time and resources. 2. Automation With AI in place, companies can easily automate redundant tasks and improve efficiency. And this is true for every domain across the globe. Here are some examples: Education Sector Teachers have to manually process attendance and create questions or exam papers redundantly, which requires a lot of time and effort. However, with AI, teachers can use attendance tools or biometric systems to mark attendance and use tools such as PrepAI for generating intuitive question papers within minutes. Manufacturing Manufacturing is highly labor intensive and has a lot of moving parts which increase the likelihood of making mistakes. However, AI makes it easy. Using automation tools or robots at a manufacturing facility, companies are drastically improving their delivery potential and reducing the error margin while ensuring worker safety and innovation. 3. Customer Support Machine Learning algorithms work behind the voice assistants such as Google’s Google Assistant, Apple’s Siri, or Microsoft’s Cortana for speech recognition using NLP or Natural Language Processing to provide you with an answer to your query. And several companies integrate voice assistants or chatbots on their

Read More

Everything You Need to Know About Large Language Models 

Large language models use NLP and deep learning technologies to deliver personalized and contextually relevant output for the given input. LLMs are powerful, robust, and useful to an enterprise. Here, we’ll discuss the benefits, challenges, applications, and examples of LLMs.  Artificial intelligence is growing leaps and bounds in recent times. Generative AI brought a revolution and disrupted the global industry. The top brands are following suit by investing heavily in AI and large language models to develop customized applications like ChatGPT.  According to a Verta, Inc. survey, 63% of business organizations plan to continue or increase their budgets for AI adoption. Based on a report by Juniper Research, ML spending has increased by 230% between 2019 and 2023. Large Language models are being extensively researched and developed by universities and leading multinational brands in the international world.  While there’s no denying the heavy expenses required to implement LLMs in an enterprise, it cannot be ignored either. LLMs are proving to be beneficial on many fronts. From R&D to customer service, large language models can be used for a variety of tasks. In this blog, we’ll find out everything we know to know about AI large language models.  What is Meant by Large Language Models? A large language model is typically an AI algorithm that uses artificial neural networks to process and understand inputs provided in the human language or text. The algorithms use self-supervised learning techniques by analyzing massive data in various formats and understanding the patterns, context, etc., to provide a relevant output as the answer.  LLMs can perform tasks like text generation, image generation (from text), audio-visual media generation, translating text, summarizing input, identifying errors in code, etc., depending on how and why it has been developed. The models can converse with humans and provide human-like answers. Large language models essentially use deep learning and natural language processing technologies to understand complex entity relationships and generate output that is semantically and contextually correct. However, developing an LLM from scratch is cost-intensive and time-consuming. Large Language Model consulting companies work with open-source LLMs and train them with the client’s proprietary data to fine-tune the algorithm as per the business requirements. Enterprises can adopt LLM applications in a quick time and gain a competitive advantage.  What are the Parts of a Large Language Model? An LLM has a highly complicated architecture with various components, technologies, and connections. However, the following parts are important in building a large language model for a transformer-based architecture:   1. Input Embeddings The input text is transformed into individual words and sub-words in a process called tokenization. These tokens are embedded in a continuous vector representation. The semantic and syntactic information of the input is captured here.  2. Positional Encoding This part deals with providing the position of each token based on the input. This ensures that the model understands the input in sequential order to retain its meaning and intent.  3. Encoder Encoder analysis is based on the neural network technique. An LLM will have multiple encoder layers. These are the core of the transformer architecture and have two stages – self-attention mechanism (identifying the importance of tokens based on attention scores) and feed-forward neural network (capturing interactions between the tokens).  4. Decoder Layers Not all LLMs have a decoder layer. However, the decoder enables autoregressive generation for the model to generate the output based on the tokens.  5. Multi-Head Attention Multi-head attention is where several self-attention mechanisms are run simultaneously to understand all possible relationships between the tokens. This allows the model to interpret the input text in multiple ways (useful when the text is vague).  6. Layer Normalization Applied to each layer in the LLM, this part stabilizes the learning process of the algorithm and makes the model more effective in generating a more generalized output for various inputs.  7. Output Layers  The output layers change from one LLM to another as they depend on the type of application you want to build.  Benefits of Using Large Language Models  Now that you know how large language models work, let’s look at the advantages of implementing LLMs in an enterprise.  1. Adaptability  Large language models can be used in different departments for varied tasks. You can fine-tune the model with different datasets and change the output layers to deliver the expected results. LLMs can be used for numerous use cases. Businesses can develop more LLMs based on the core model and add more layers to expand it and use the applications across the enterprise. Over time, the LLMs can be adopted throughout the organization and integrated with the existing systems.  2. Flexibility  Even though LLMs are yet to reach their full potential, they are already flexible and versatile. You can use an LLM application for content generation, media generation (image, audio, video, etc.), classification, recognition, innovation, and many other tasks. Furthermore, the models can process input of any size (from a single line to hundreds of pages of text). You can deploy the models in each department and assign different tasks to save time for your employees.  3. Scalability  Large language models can be expanded as the business grows. You don’t have to limit the role of LLMs in your enterprise as the business volume increases. The applications can be scaled to accommodate the changing requirements. They also can be upgraded with the latest technologies and datasets to continue providing accurate and relevant results. LLMs are easy to train as they can read and process unstructured and unlabeled data. There’s no need to spend additional resources on labeling data. However, low data quality can lead to inaccurate output and inefficient applications.  4. Performance  LLMs are robust, powerful, and highly efficient. They can generate responses in near-real-time and have low latency. Using an LLM application saves time for employees. It allows them to use the results right away and complete their tasks. For example, an employee doesn’t have to read dozens of pages to understand the content. They can use LLMs to summarize the information and read only the important points that

Read More

Leveraging AI in Media and Entertainment 

The presence of AI in the media and entertainment industry has been growing with an increase in customer demand for more flexibility, choice, personalization, and access to unlimited content. Here, we’ll discuss the role of artificial intelligence in media and entertainment.  The media and entertainment industry is flourishing in today’s world. Whether it is social media networking, news portals, or OTT platforms, the industry has lots to offer to the common public. Statistics show that the M&E industry will be $2.9 trillion by 2026.  It’s no surprise, given that the media and entertainment houses are adopting AI to cater to the changing market demands and customer requirements. Artificial intelligence is now a part of the industry and has varied roles to play. From personalizing content for users to auto-generating news and predicting future events, AI in media and entertainment is redefining how people consume content.  In this blog, we’ll look at how artificial intelligence is transforming the media industry and the future of entertainment.  What is the Market Size of AI in Media and Entertainment? According to a report by Business Wire, the M&E industry will be spending $1860.9 million on artificial intelligence by 2025, while the overall AI market is expected to touch $118.6 billion. Another report shows that AI in social media alone will touch $12 billion by 2031. There’s no denying that artificial intelligence will continue to be a prominent part of the M&E industry.  What is the Role of AI in Media? Many millennials and Gen Z users consume media on digital platforms and expect personalized customer experiences and content curated exclusively for them. This led to a greater digital revolution and the adoption of artificial intelligence by M&E businesses.  However, the media houses had to overcome quite a few challenges to ensure they provide quality content to consumers and take advantage of the benefits offered by artificial intelligence.  AI is not limited to a single role or department in the media industry. For example,  How is AI Impacting the Entertainment Industry? Many media houses and entertainment companies are investing in AI to retain their competitive edge and stay relevant in the market. Here’s how AI is impacting the entertainment industry.  1. Content Development Creating unique content takes time and effort. While it is still a human-dominated field, generative AI solutions are disrupting established practices in many ways. While online writing assistants and grammar checkers are fine-tuning human-written content, platforms like ChatGPT are churning out pieces on their own.  Narrato and Copy.ai are two examples of AI-based writing assistants and content-creation tools used by various companies and professionals. No doubt, generating written content is faster with AI.  Nevertheless, there’s no denying that artificial intelligence enhances the quality of content writing and will continue to assist human writers fine-tune their works.  2. Image Generation  DALL-E is not the only AI-based image generator in the market. Many free and paid generative AI tools can create realistic HD-quality images based on a text prompt or an input image. These images are being used in blogs, news articles, social media posts, etc., to increase viewership.  Using AI for image generation is a cost-effective move and allows entertainment companies to deliver the final product quickly to the market. However, establishments still have to deal with the challenges of plagiarism (it’s not clear how AI generates the images) and ethical AI. However, artists can use AI image generators to enhance their existing work and make it more appealing to audiences.  3. Marketing  A survey report shows that 73% of marketing executives are already using generative AI for B2B and B2C marketing. Businesses from different industries use AI for marketing. The entertainment and media industry can hugely benefit from partnering with an artificial intelligence consulting firm to streamline its sales and marketing strategies.  From identifying the right target markets to understanding customer behavioral patterns and using feedback to create compelling content, AI-powered marketing can increase the visibility and reach of an entertainment company in competitive market conditions. AI also makes it easy to manage omnichannel marketing and enhance the customer journey with the business.  4. Film Production  AI tools can now write the script and make a film on behalf of humans. It takes less time, effort, and money to use AI for film production. For example, 28 Squared Studios collaborated with Moon Ventures to create a short film titled The Safe Zone. The entire film is written and produced by ChatGPT, down to the facial expressions required, character script, outfits, camera positioning, etc. This film was made to showcase how generative AI can help the entertainment industry.  While writers and content creators are still on strike against the use of AI to replace human creativity, there’s no denying the role of artificial intelligence in storytelling. The high-end graphics and visual enhancements in movies is the best example of how useful AI is in film production.  5. Monetization  With consumers shifting from traditional platforms to digital service providers and OTTs, ensuring content monetization can be tough the production houses and streaming companies. However, using AI in entertainment helps with personalizing content, hyper-targeting audiences through marketing, and mitigating churn to keep the income flowing.  AI analytics and advertisement strategies can increase subscriptions and ensure consumers are loyal to the platform. Personalization plays a major role in monetizing content for a diverse group of users from around the globe. OTT platforms like Netflix and Amazon Prime use powerful AI engines to customize the interface and content for each user.  6. Content Optimization  Entertainment companies produce and share content to generate money. However, managing the content distribution and licensing terms can hugely affect its profits. AI helps in making the right decisions based on market demands and trends.  Predictive analytics is used to understand what type of content the target audiences will enjoy. The marketing teams rely on these insights to determine the right channel and distributor to share the content with the public. Similarly, adding optimized metatags, labels, comments, alt text, etc., will take the content to a wider group and increase views. In today’s world,

Read More

AI in Paints and Coatings Industry

Paints and coatings companies use AI to can gain competitive advantages, save costs, reduce production time, and improve product quality. It helps with color matching, formulation optimization, quality control, and predictive maintenance. Here, we’ll discuss the role of artificial intelligence in the paints and coatings industry.  The paints and coatings industry is a dynamic market and includes various sectors like buildings and construction, automobile finishes, furniture (wood and non-wood), medical products, and other equipment. From antimicrobial coatings to self-cleaning paints and solar panel paintings, the products are used in a plethora of industries. The Covid-19 pandemic affected the industrial coatings sector in 2020, resulting in a 4.2% decline in the global market size. Fortunately, the industry is regaining its position and returning to pre-pandemic levels. According to Statista, the global paints and coatings industry is expected to touch $235.06 billion by 2029. Digitalization and sustainability are two key factors driving the paints and coatings industry in today’s world. Manufacturers and retailers are adopting artificial intelligence and machine learning tools to streamline production, quality control, distribution, and sales. AI can forecast coating performance, increase production efficiency, optimize formulas, improve customer satisfaction, and save time, effort, and cost for the paints and coatings industry. In this blog, we’ll read about the role of AI in the paints and coatings industry and the need for partnering with an artificial intelligence consulting company to increase ROI and profits.  Importance of AI in the paints and coatings industry Artificial intelligence has varied roles in the paints and coatings industry. From developing corrosion and wear-resistant paints to increasing their adhesiveness and enhancing the self-lubricating nature of paints, AI can help in conducting the necessary research into material properties and creating new chemical compositions. AI and ML algorithms are being used for data modeling about the properties of different types of paints (based on their chemical composition). Factors like the toxicity of the paint and coating, price fluctuations based on market demand, environmental impact, alternative materials, etc., can also be determined using AI and ML technologies. In fact, computer vision and AI are used to analyze images in bulk to develop smart coatings. By understanding the functioning of the microstructures in the materials, a manufacturer can not only create more durable and better-quality paints but can also optimize production to reduce input costs.  An article by American Coatings Association shows how AI has been instrumental in using advanced algorithms to provide recommendations around resin formulations and recipes.  Furthermore, Eric Sapper, the assistant professor in the Department of Chemistry & Biochemistry and the Western Coatings Technology Center said “Centuries of scientific learning and discovery do not need to be tossed out the window when you’re building an AI workflow. Instead, new AI workflows can be built around the institutional and domain-specific knowledge that already exists within the organization.” This means if you’re looking to implement AI in your product development and coatings experimentation, you don’t need to start everything from scratch. And the transition from manual to AI workflows and innovation around paintings & coatings would be smooth and friction-free. Now, let’s understand how AI will help save time, effort, and money in the broad parameters of the paintings and coatings industry. How can machine learning and artificial intelligence help paints and coatings companies? The role of AI and ML in the paints and coatings industry is not limited to research and development. It can also be used to match colors and create new shades, help customers choose the right colors based on their requirements, develop the right marketing strategy for the target audiences, streamline the supply chain, and enhance customer experience.  Color Matching KCC Corporation, a South Korean paint company, developed an AI-based color matching and design system called K-Smart that can complete color mixing from any combination design in just five minutes. The company used fifty years’ worth of data to develop this AI model.  Similarly, there are other AI apps available, such as Sherwin-Williams ColorSnap Match, X-Rite Color-Eye, Datacolor MATCH TEXTILE, Nix Pro 2, etc. that help choose the best colors and combinations to paint the walls (interiors and exteriors).  Using artificial intelligence for matching and mixing colors delivers the following advantages:  1. Increased Efficiency AI-based color-matching tools can match colors within minutes, which is significantly faster than traditional color-matching methods that can take hours or even days. Traditionally, paint companies rely on an expert technician or a spectrophotometer (or both). However, investing in an AI model results in greater efficiency.  2. Improved Accuracy These tools also eliminate the potential for human error and subjectivity, resulting in more accurate and consistent color matches. This leads to fewer color mismatches and less rework. Color mismatches can be due to changes in the paint formula, weather conditions, paint quality and age, and the texture and type of surface on which it will be applied. The changes in the pigmentation levels also lead to color mismatch.  3. Enhanced Sustainability By enabling manufacturers to use only the optimum quantity of paint, these tools reduce waste and improve sustainability.  4. Better Customer Experience AI-based color-matching tools enhance the overall customer experience by satisfying their requirements. This leads to greater customer satisfaction and loyalty. 5. Increased Innovation AI companies that provide these color-matching tools help manufacturers develop new and innovative color options that were previously difficult to achieve with traditional methods. As a result, painting companies are able to stand out in the crowded market. Formulation Optimization The significance of formulation optimization in the paints and coatings industry lies in its ability to enhance the quality and effectiveness, of the final product while reducing its cost of production. While using the traditional methods, the companies had to go through a lot of trial and error. Due to this, formulations were often suboptimal, time-consuming, and expensive. With AI, optimization has become much more efficient and accurate in recent years. AI algorithms can analyze vast amounts of data on raw materials, formulations, and product properties to identify the most effective combinations and optimize

Read More

Benefits of Using AI in Solar Panel Detection From Satellite Images

AI offers a powerful solution for detecting solar panels from satellite images. In this blog, you’ll  learn about the benefits, challenges, and real-world applications of AI in solar panel detection and AI’s potential impact in accelerating renewable energy adoption. The global shift towards clean and renewable energy sources has led to the growing popularity of solar energy. Solar panels have become an increasingly popular way of generating electricity for residential and commercial use. However, as the solar energy market continues to expand, there is a growing need for detailed information about distributed rooftop solar panel installations. This information is crucial for solar power providers, customers, urban planners, grid system operators, and energy policymakers. Currently, there is a lack of granular data on solar panel installations, and solar panel installers often do not share information about their installations. This has created a demand for an effective solar panel detection algorithm that can be used to create detailed datasets of installations and their power capacities. Unfortunately, there has been limited progress in developing such algorithms for detecting solar panels from aerial or satellite images. But a few players have already taken the leap and are ready to provide the valued benefits. In this blog, we will discuss in detail the benefits of using artificial intelligence (AI) to detect solar panels from satellite images, the challenges and limitations of this technology, and real-world applications of AI in the solar panel detection. How can AI detect solar panels? AI can detect solar panels by analyzing satellite or aerial images using advanced image recognition algorithms. Image recognition involves several steps, including image preprocessing, feature extraction, and classification. During image preprocessing, the image is enhanced to improve its quality and reduce noise. Feature extraction involves identifying relevant features, such as the shape and colour of the solar panels within the image. Finally, the classification step involves using neural network architectures, such as convolutional neural networks (CNNs), to make use of extracted features & identify solar panels within the image. While executing the whole process, several types of AI algorithms are used for solar panel detection, including core deep learning algorithms, classical machine learning algorithms, and CNNs. AI can also integrate with LiDAR and GIS (Geographic Information Systems) data to detect solar panels by analyzing the 3D map created from laser beams bouncing off objects. It enhances solar panel detection by combining AI algorithms with geospatial data, such as satellite imagery and digital elevation models, to improve accuracy and identify optimal installation locations.  Benefits of using AI in solar panel detection AI offers a powerful solution for monitoring solar panel installations, enabling solar energy providers to improve the efficiency and performance of their installations enhancing their performance efficiency while improving accuracy and reducing costs. AI offers a powerful solution for monitoring solar panel installations, while reducing costs and improving accuracy. By leveraging the power of AI, the solar energy industry can continue to grow and expand, providing clean, renewable energy to communities worldwide. Here are a few benefits of using AI in solar panel detection from satellite images. 1.Increased accuracy AI algorithms can identify solar panels with high accuracy, even in complex and large-scale installations. This reduces the risk of missing critical information and enables solar energy providers to make more informed decisions about their installations. 2. Time and cost savings Manual monitoring of solar panel installations can be time-consuming and expensive. By contrast, AI algorithms can process large amounts of data quickly and accurately, saving both time and money. 3. Detection of solar panels in remote locations Traditional monitoring methods can be challenging in remote or hard-to-reach locations. With satellite images, AI algorithms can detect solar panels anywhere in the world, enabling the monitoring of installations in areas that would otherwise be difficult to access. 4. Enhanced monitoring capabilities for installations Solar farms and utility-scale installations can cover vast areas, making manual monitoring challenging. AI algorithms can analyze satellite images to monitor these installations in real time, providing detailed insights into their performance and enabling early detection of potential issues. 5. Improved safety Traditional monitoring methods can require personnel to climb onto rooftops or other elevated areas to inspect solar panels, which can be dangerous. With AI and satellite imagery, inspections can be performed remotely, reducing the risk of accidents and injuries. 6. Enhanced maintenance and repair AI algorithms can monitor solar panel installations for signs of wear and tear or damage, allowing for proactive maintenance and repair. This can help prevent downtime and extend the lifespan of solar panel installations. 7. Better energy yield estimation Accurately estimating the energy yield of a solar panel installation is crucial for energy providers. AI algorithms can analyze satellite imagery to estimate the energy yield of an installation, providing valuable data for energy planning and forecasting. 8. Improved regulatory compliance In some jurisdictions, solar energy providers must report the size and location of their installations to regulatory bodies. AI algorithms can help ensure compliance by accurately detecting and identifying all solar panel installations. Real-World Applications Several companies and organizations are already using AI for solar panel detection. For example, SunPower, a leading provider of solar power solutions, has partnered with Google to use AI and machine learning algorithms to improve solar power forecasting. The partnership uses Google’s TensorFlow platform to analyze satellite imagery and other data to predict how much solar power will be generated by a given installation. Another company, Planet, uses a network of satellites to capture high-resolution images of the Earth’s surface. The company’s AI algorithms can detect solar panels in these images, allowing customers to monitor solar panel installations and optimize energy production. Impact of AI on the solar energy industry The impact of AI on the solar energy industry is significant. By improving the efficiency and accuracy of solar panel detection, AI can help energy providers make more informed decisions about where to install new solar panel arrays and how to optimize existing installations. AI can also help reduce costs and improve the

Read More

What is a Neobank? How to Implement AI in Neobanking Market

In this article, we will learn about neobanking, explore the technologies employed by neobanks, understand the role of AI in neobanking, and the future of AI in shaping the banking sector. The world of banking is undergoing a profound transformation with the rise of Neobanks, a new genre of financial institutions that are redefining the way we interact with money. Neobanks have emerged as disruptive players in the financial industry, offering innovative and customer-centric banking experiences. With the advent of digitalization and advancements in technology, neobanks are capitalizing on the power of artificial intelligence (AI), machine learning, big data analytics, and cloud computing to provide a range of financial solutions. AI, in particular, plays a pivotal role in enhancing the efficiency, accuracy, and security of neobanking operations. From AI-powered chatbots providing 24/7 customer support to advanced fraud detection systems, AI is revolutionizing the neobanking landscape.  In this blog, we will delve into how AI can enhance the customer experience of neobanking. We will also answer questions like: ●      How does a neobank work? ●      What are the benefits of implementing AI in neobanking? ●      How is the future of AI & neobanking aligned? But first, let’s understand, What is a neo bank? What is a neobank? A neobank, also known as a digital bank or challenger bank, is a financial institution that operates exclusively online without any physical branches. Neobanks leverage technology to offer a range of banking services to customers in a convenient, user-friendly, and digital-first manner. These innovative financial institutions aim to disrupt the traditional banking industry by providing personalized and customer-centric experiences. How does a neobank work? As previously mentioned neobank is a digital-only bank, so it operates through online platforms and mobile apps. Neobanks have a distinct business model compared to traditional banks and also generate revenue through the margin between deposits and loans, similar to their conventional counterparts. It allows customers to manage their accounts, make transactions, and get support using digital tools. Neobanks focus on convenience, affordability, and personalized services, using technology to streamline operations and offer a seamless banking experience. The rise of neobanks is driven by their ability to offer cost-effective and highly personalized services through the utilization of advanced technologies such as artificial intelligence (AI), automation, and cloud computing. A survey conducted by McKinsey and Company in 2021 reveals a significant surge in the adoption of digital banking among customers in developing countries across the Asia-Pacific region. The survey findings indicate a substantial increase from 54% in 2017 to 88% in 2021, showcasing the rapid expansion of customers actively engaging with digital banking services. This growing demand for digitalization in the banking sector is poised to act as a catalyst for the further growth and development of the neobanking market. How can AI be implemented in neobanking? AI plays a crucial role in neobanking, enabling a seamless and personalized banking experience. Neobanks harness AI technologies to analyze customer data and deliver customized recommendations for financial products and services. This personalized approach helps meet individual customers’ needs and preferences, enhancing their overall banking experience. AI-driven risk management is another area where neobanks benefit. By leveraging AI algorithms, neobanks can detect and mitigate fraud and other financial crimes effectively. Real-time transaction monitoring and AI-powered analysis enable the identification of suspicious activities, ensuring the security of customers’ assets and combating financial fraud. Improving customer experience is a key focus for neobanks, and AI plays a significant role in achieving this goal. AI-powered chatbots and virtual assistants enable neobanks to provide round-the-clock customer support and quick responses to queries. This enhances customer satisfaction by providing immediate assistance and reducing the reliance on human customer service representatives. AI & neobanking: Around the world Neobanks are disrupting the global financial services industry on the back of strong trends, but their long-term success hinges on embedding data and AI capabilities extensively across all aspects of their operations. In 2022, neobank investors shifted focus from growth at all costs to profitability. Most of the world’s 291 neobanks were not able to turn a profit, and investors are losing patience. With the funding route unlikely to abate, 2023 is shaping up to be a do-or-die year. Neobanks provide highly personalized services at lower prices by utilizing technology such as artificial intelligence (AI), automation, and machine learning. For Instance, Revolut is a UK-based neobank that uses AI to analyze customer data and provide tailored recommendations for financial products and services. In addition to Revolut, NatWest has introduced a personal finance app called Mimo. This app takes advantage of open-banking APIs, AI, and data analytics to create a unique social feed that assists customers in effectively managing their finances. By utilizing these advanced technologies, Mimo helps users gain insights into their spending habits, make informed financial decisions, and stay connected with their financial goals. Industry experts, such as Gartner, predict Banking-as-a-Service (BaaS) will hit mainstream adoption within two years. Gartner also mentions that 30% of banks with greater than $1 billion in assets will launch BaaS for new revenue by the end of 2024, but half will Benefits of AI in neobanking The integration of artificial intelligence (AI) in neobanking brings numerous benefits to both customers and financial institutions. Here are some key advantages of AI in neobanking: 1. Enhanced Customer Experience: AI-powered chatbots and virtual assistants offer 24/7 customer support, providing instant and accurate responses to inquiries and improving overall customer experience. These virtual assistants can handle a wide range of queries, from account balance inquiries to transaction history and account management, ensuring prompt and efficient customer service. 2. Personalization: AI algorithms analyze customer data, financial behaviour, and preferences to deliver highly personalized financial recommendations. This level of personalization allows neobanks to offer tailored products and services, such as investment portfolios, insurance plans, and loan options, catering to individual customer needs and increasing customer satisfaction. 3. Efficient Risk Management: AI algorithms enable neobanks to assess creditworthiness and risk profiles accurately. By analyzing customer data

Read More

9 Things Start-up Leaders Should Know About Generative AI

Generative AI uses deep learning algorithms and other technologies to generate output based on the user input. It is an advanced form of general AI capable of handling versatile tasks. Here, we’ll discuss the various aspects startup leaders should know about generative AI to implement the technology in their businesses.  Artificial intelligence has been revolutionizing various industries in recent times. Statistics show that the annual AI adoption rate will grow by 37.3% from 2023 to 2030. Artificial intelligence is integral in our personal, social, and professional lives.  However, we know that AI is still in the initial stages of exploration. Generative AI is a prime example of this. ChatGPT has changed the way we look at AI. It has created many discussions across the globe. The top companies have been working hard to release their versions of generative AI tools. Other SMBs and multinational organizations are hiring AI consulting firms to understand the importance of generative AI and adopt relevant tools to boost their business.  But what is generative AI? What does it do in today’s scenario? How can CEOs and entrepreneurs use generative AI in their businesses?  Let’s find the answers to these questions.  What is Generative AI, and How Can it Help Your Business? Generative AI is a set of algorithms that can generate new content (text, image, video, audio, etc.) from the training data. The generative AI models are built on models trained using large datasets with unlabeled data. The algorithms learn to self-supervise themselves and identify the underlying patterns. It uses deep learning to complete tasks effectively compared to other AI models.  The generative AI landscape covers a range of applications that optimize business processes, create content, brainstorm ideas, write and debug codes, etc., to reduce pressure on employees and complete tasks effectively.  Enterprises can use generative AI in various ways to revamp their business model, reduce costs, increase productivity, and generate higher ROI. Generative AI is a subset of machine learning and is different from discriminative AI, which deals with data categorization.  Generative AI models help businesses in the following ways:  What are Various Techniques Used for Generative AI? Generative AI is not based on any single technique but is a combination of several techniques, such as:  These techniques allow generative AI to process input data and generate output as required by the user. This is why generative AI can create versatile types of texts, images, videos, 3D designs, etc. It is highly useful in the medical and healthcare industry as it generates 3D visualizations of DNA structures, proteins, crystals, drug combinations, and so on.  However, all of them are not in every generative AI model. AI leaders choose the necessary techniques based on the type of model they want to create and its purpose.  Should Start-Ups Explore Generative AI? The answer is yes. Startups, small businesses, and growing companies should explore generative AI to survive in competitive markets. Using the latest technology to streamline internal processes and increase ROI will strengthen the foundation and allow startups to stand alongside established enterprises. Furthermore, startups can directly develop agile and scalable IT infrastructure and save money on replacing legacy systems.  However, it’s vital to make informed decisions when bringing major changes to the business model. AI consultants recommend that CEOs understand the following aspects to know how generative AI can benefit their business.  1. Fast Developments in Generative AI  Generative AI is evolving at a rapid pace for the last few years. From OpenAI’s ChatGPT to Google’s Med-PaLM, Meta’s LLaMA, Amazon Bedrock, Google Bard, and Microsoft’s integration of ChatGPT 4 with Office 365, the developments are occurring frequently.  Entrepreneurs should understand that generative AI and the future are not fixed but constantly evolve. They need to create an infrastructure and work culture where technology and talent can keep up with the developments and adapt to the changes quickly time. Constant investment is necessary to stay up to date. Generative AI is not a one-time investment.  2. It’s More Than a Chatbot  Even though we know of generative AI as an advanced chatbot, it is a lot more complex and capable. However, generative AI takes things ahead. It can classify, edit, and summarize almost any type of content. It can answer questions, draft letters, code, outline, strategy, and more.  As the models evolve, generative AI can be integrated with enterprise workflows to automate and perform specific tasks. Some AI tools are already providing these services.  3. Scalability and Competitive Advantage  Generative AI is versatile and scalable on various levels. Firstly, it is different from other deep learning models that can perform only one specific task. Generative AI can handle multiple types of tasks, resulting in greater efficiency and productivity. This makes it easier to use a single model to perform all the necessary tasks at work and gain a competitive advantage in the market. It can also be scaled to suit the growing business needs.  4. Identifying the Right Use Cases  The success of any technology or development can be seen when it is used for the right purpose at the right time and in the right manner. CEOs should ensure that they know why, how, where, and when they want to use the tools in their startups. The budget of the project will also depend on these aspects.  For example, generative AI in the customer service department can assist representatives in quickly solving customers’ complaints and speeding up ticket closing. Similarly, the sales and marketing team can brainstorm strategies and use generative AI to create a foolproof promotional campaign and relevant content without spending too much time and resources. CEOs have to identify where to use generative so that they can decide on the next step.  5. Reworking Existing Model vs. Training New Model Is generative AI rewriting your business model? The answer depends on the use cases you finalize and how you want to achieve the results. If you already have accurate ML models, you can customize them and set up integrations with generative AI.  For example, using generative AI as a SaaS (software as a service) tool is cost-effective compared to

Read More

AI Development: In-house vs Outsourcing (Complete Guide)

In this data driven world, businesses are turning to advanced tools such as Artificial Intelligence and Machine Learning to extract valuable insights and boost productivity. AI-powered solutions offer great potential for automation, and help in decision-making. However, many companies still need to know-how to implement these strategies properly. As businesses strive to stay competitive in the marketplace, many are turning to the use of AI to gain the competitive edge. But with a wide range of opportunities and methods of development, companies generally opt for outsourcing AI development.  In this blog, we will provide a comprehensive overview of the advantages of each approach and the key considerations to take into account while deploying in house vs Outsourced AI development. What is AI Development? AI development refers to the process of creating intelligent machines that can perform tasks without human intervention. It involves a combination of machine learning, natural language processing, robotics and other technologies that allow computers to learn from data and make decisions based on that information. The goal of AI development is to advance automation in various industries, including healthcare, finance, transportation and more. Developers use algorithms and models to create systems that can analyze vast amounts of data quickly and accurately. They also work on improving the ability of machines to understand human speech or written text through NLP technology. As AI technology continues to evolve rapidly, it presents new opportunities for businesses looking for ways to streamline processes or provide better customer service experiences. Also, AI is playing an increasingly important role in business, and its impact is only going to grow. By automating tasks and providing insights that would otherwise be hidden, AI is helping businesses boost efficiency, drive growth, and gain a competitive edge. While some businesses are still hesitant to embrace AI, the benefits are becoming too great to ignore. For businesses that are looking to stay ahead of the curve, investing in AI development is a smart move. Factors to Consider While Choosing In-House vs Outsourcing AI Development There are a number of factors to consider when deciding whether to develop AI in-house or outsource it to a third-party.  What is the Difference Between Outsourcing and In-House Development? In-house AI development Are you considering in-house development for your business? As a professional, you know that having your own team with the necessary IT skills and abilities can make all the difference. On the other hand, the management of such a team is a serious load on your budget. While in-house development may be more expensive in the short term, it becomes more cost-effective in the long run. Moreover, for small businesses, an in-house IT department can often handle both the support and development of internal information systems. In-house development also allows for better communication and collaboration between the development team and other departments within the company, leading to more efficient processes and improved project results. By understanding the advantages of in-house development, organizations can make informed decisions as to the best approach for their specific project needs. Advantages of In-house AI development  Customization  In today’s competitive business environment, having the ability to customize your Artificial Intelligence (AI) solutions is a must. With the ability to tailor the features and settings of your AI solution to meet the exact needs of your business, many companies are opting to develop their own in-house AI solutions.  In accordance with the culture  Collaborating with a local development team means engaging with individuals who understand the culture, values, and standards of your organization. This type of relationship ensures employees to seamlessly integrate into the organization and add value to their roles. By hiring locally, organizations can benefit from unique perspectives and skillsets that come from having a team that is more familiar with their environment. Outsourcing AI Development Outsourcing AI development can be defined as the process of contracting with an external organization or individual to create, train, and deploy AI models on behalf of another company. There are many reasons why companies might choose to outsource AI development, including lack of internal expertise, time constraints, and cost considerations. When done correctly, outsourcing AI development can be a highly effective way to quickly and efficiently deploy AI capabilities within an organization. By leveraging the expertise of a highly-skilled, external provider, you can streamline your product development while gaining access to specialized engineering & domain expertise. With the right outsourcing partner, you can take advantage of cost savings, faster time to market, and the flexibility to scale your operations as needed. Advantages of Outsourcing AI Development  Collaboration with AI Experts When it comes to developing AI products, it becomes  crucial to ensure that you have the right experts on board. However, due to the increasing demand for AI, it is difficult to secure full-time experts. Fortunately, outsourcing AI development can help you to bypass this issue. It enables you to access an experienced and professional team without the associated costs of hiring in-house. Cost-Efficient option  One of the most obvious advantages of outsourcing is the cost savings that can be realized by making use of the services of Asian countries, where the cost of labor is often much lower than in Western countries. Furthermore, these services are often of good quality, meaning that businesses are able to benefit from low pricing without compromising on the quality of the work produced. Difference Between in House vs Outsourcing AI Development Conclusion  When it comes to making a decision about in house vs outsourcing AI development, it is important to consider several factors. Cost is certainly one of the key components, however, it is also important to examine the pros and cons of each model to make the right choice. By understanding the differences between in-house AI development and outsourcing, you can make an informed decision and ensure the best outcome for your project. As artificial Intelligence services become more widely available, businesses are facing the dillema of whether to pursue in-house development or outsourcing for their AI project. While

Read More

How AI and Metaverse Have Changed Our View of the World 

Metaverse is a virtual world personalized to provide various services to each user. Artificial intelligence is an integral part of Metaverse and can determine the extent of its role in our lives. We’ll discuss how AI and Metaverse have changed our future.  Metaverse is the buzzword in today’s market. It is a combination of various advanced technologies like AR, VR, AI, etc. Metaverse allows users to engage with 3D objects in a virtual space. AI and Metaverse are set to create a new reality for mankind.  Statistics show that the Metaverse market is likely to reach $783.30 billion by 2030. Many leading multinational giants, like Facebook, Google, etc., are working on Metaverse.  But what exactly is Metaverse? What is the role of AI in developing Metaverse, and how will it impact the world? Let’s find out. But before we delve into Metaverse, let’s start with the basics and understand what artificial intelligence is.  What is Artificial Intelligence? Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI enables machines to mimic the human mind for decision-making. Artificial intelligence helps computers process vast amounts of information to identify hidden patterns, detect trends, and process information similar to a human brain.  AI is categorized into the following:  Deep learning and machine learning are subsets of AI and are used in several applications. Artificial intelligence has been adopted by various businesses in recent years. A survey report shows that more than 86% of CEOs consider AI the mainstream technology in their organizations. Close to 85% of enterprises have increased their budget for AI compared to 2019. The global AI market was valued at $93.5 billion in 2021. It is likely to grow at a CAGR (compound annual growth rate) of 38.1% from 2022 to 2030.  AI is used by small, medium, and large enterprises from various industries. From Healthcare to manufacturing, every business needs AI to streamline its process. Artificial intelligence is used in the following ways:  What is Metaverse?  Metaverse is a blend of multiple technologies. It is not a single entity and requires a range of technologies to work together. Metaverse is a collected virtual space that merges the virtual and real worlds. In fact, meta refers to ‘virtual’, and verse is short for ‘universe’. So, Metaverse is a virtual universe. Virtual reality, augmented reality, artificial intelligence, mixed reality, etc., are used to create Metaverse.  The virtual worlds can be accessed from computers, smartphones, consoles, VR devices, and more. Metaverse can allow people to interact with the virtual world without moving in the real world. However, it’s important to note that Metaverse is still in the developing stages.  Top companies are investing in bringing Metaverse closer to common people. Some smaller organizations are updating their IT infrastructure to be ready for Metaverse by creating virtual worlds that closely resemble real life. That said, a few VR-based apps are also being termed Metaverse by businesses (especially in the gaming industry). These are hybrid models at best and not Metaverse.  Role of AI in Metaverse  With so many technologies required to develop Metaverse, what role does AI have in it? How important is AI to create Metaverse?  The role of artificial intelligence is to support the extensive features of Metaverse. AI will enhance user experience. It will make user interaction with Metaverse a seamless and worthy experience.  In short, AI will aid creation, collaboration, interaction, and personalization in Metaverse. It can make the virtual world a delightful experience for users.  The Risk of Using AI in Metaverse  While AI is great for personalization, it isn’t without risks for users. Micro-targeting can go either way if it becomes manipulative or is missed by people in power.  It can be hard to separate fact from fiction if one is not alert. Data manipulation is another risk, which can be controlled through in-built safety/ regulatory algorithms. One way to minimize this risk is by setting up strict regulations about data collection and the role of AI agents in Metaverse. If we can control the misuse of technology, there’s no denying the plethora of advantages it offers to the public.  How AI and Metaverse have changed the Future  Metaverse will use AI algorithms the way social media platforms use AI for newsfeed personalization, ad targeting, etc. NLP (natural language processing), facial recognition, voice recognition, and other actions are also executed by AI.  A Harvard University report shows how AI will affect eight areas of human activities by 2030. The areas include:  The report highlights the need for reliable hardware to handle the developments and create a safe space for the world at large to use Metaverse in daily life. Sentiment analysis, emotional recognition, and biometrics (physical + behavioral) will play a vital role in the personalization of the virtual world for a user. Mark Zuckerberg, CEO of Meta, says that it could another five or ten years to make Metaverse a mainstream technology. Right now, it is limited to interactive headsets, AR/VR games, and mixed-reality applications.  Conclusion  Metaverse will open up new possibilities in technologies and blur the lines between real and virtual. You can create a virtual avatar of yourself to interact with others in the 3D virtual world. It erases boundaries and provides opportunities for businesses to market their products to the right audiences.  Metaverse also enhances learning and can provide a user-centric experience. Artificial intelligence plays a crucial role in making this happen by bringing together other technologies to make Metaverse engaging. 

Read More
Services
Data Engineering
Azure
AWS
GCP
Big Data
Data Warehousing
AI & Machine Learning
Artificial Intelligence
Natural Language Processing
Vision Analytics
Large Language Model (LLM)
Business Intelligence
PowerBI
Tableau

DataToBiz is a Data Science, AI, and BI Consulting Firm that helps Startups, SMBs and Enterprises achieve their future vision of sustainable growth.

Hire Developers
Get In Touch
Use Cases
Digital Transformation
AI Insurance Automation
Boutique Analytics
Ecommerce Analytics
Manufacturing Analytics
Digital Lending Automation
Sales Analytics
OEE Analytics
HR Analytics
Procurement Analytics
IT JIRA tracking
Products
PrepAI
Tally BI
Marketing Cockpit
Virtual Try On Platform
HirelakeAI
SensiblyAI
DataToBiz CV Platform
Quick Links
Careers
About
Partner Program
Knowledge Base
Case Studies
Industries

DataToBiz is a Data Science, AI, and BI Consulting Firm that helps Startups, SMBs and Enterprises achieve their future vision of sustainable growth.

Hire Developers
Get In Touch
DMCA.com Protection Status
Hire Developers
Request a Quote