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The world of AI tools is exciting! It promises to revolutionize content creation. However, only a select few truly deliver on their promises, and Jasper AI is undoubtedly a leader among them.  It’s a great writing tool and is capable of delivering credible results.  While it may not be a comprehensive writing tool, Jasper AI is amazing at filling gaps in pre-written content, crafting engaging headlines and subheaders, and enhancing short-form content. With AI taking center stage, many entrepreneurs are looking to make due investments in apps like Jasper AI.  In this article, we are going to break down how much it costs to make an app like Jasper AI so you, as an entrepreneur, have a better insight.  So without further ado, let’s get into the details.

About Jasper AI 

Jasper AI is undoubtedly the frontrunner in the competitive landscape of AI writing tools, and its market presence and funding statistics certainly reinforce this leadership position. According to a recent Crunchbase report, Jasper AI has secured an impressive total funding of $131 million since its launch in 2021.  And its success story doesn't end there.  As of February 2024, Jasper AI boasts a remarkable customer base of over 100,000 businesses, including notable names such as Amplitude and HubSpot.  With such rapid growth and adoption, it's no wonder that Jasper AI is projected to hit an astonishing ARR (Annual Run Rate) of $250 million by the close of 2024.  These figures alone showcase the tool's widespread appeal and the confidence businesses have in its capabilities. With such information, we believe it’s safe to say that Jasper AI is not just a passing fad but a key player in the AI landscape. Fun Fact: Do you know that the AI content creation tools market size stands at a valuation of $840.3 million in 2024, and is estimated to reach $3007.6 million by 2034? 

How Much Does it Cost to Make an AI Writing App Like Jasper AI? 

The average cost to develop an AI writing app such as Jasper AI depends on several factors. Based on a general estimate, an AI writing app can cost anywhere from $40,000 to $250,000 to develop, especially if it is designed to meet the same standards as Jasper AI. 

Breakdown of the Jasper AI App 

Platform 

• Desktop 

User Authentication and Profile:

• Email and password sign-up/login
• Social login (Apple, Google)
• User profile with basic information and preferences
• Security features (two-factor authentication, data encryption)

AI Writing Assistant:

• Natural language processing for text generation
• Ability to create and edit various types of content, including articles, blog posts, social media copy, and marketing materials
• Suggestion engine for writing improvement and tone adjustment
• Plagiarism checker

User Interface and Experience:

• Intuitive and user-friendly design
• Seamless navigation and content organization
• Customizable themes and color schemes
• Offline mode for accessing previously generated content

Content Templates and Frameworks:

• Pre-built content templates for common content types
• Ability to create custom content frameworks
• Option to collaborate and share content frameworks with other users

Integration with Other Apps:

• Integration with popular productivity and content creation apps (e.g., Google Workspace, Microsoft Office, Dropbox)
• Sharing capabilities to social media platforms and content management systems

Data Synchronization:

• Real-time synchronization of user data across devices
• Cloud backup and data recovery options

Admin and Management:

• Robust content and user management system for admins
• Analytics and reporting dashboard to track user behavior and app performance
• Content moderation tools for user-generated content

Technology Stack:

• Backend: Python (Django or Flask), AWS Lambda (for serverless functions)
• Database: PostgreSQL or MySQL for relational data, MongoDB for unstructured data
• AI Model: Transformers (Hugging Face) or similar NLP library
• Cloud Services: AWS or Google Cloud for hosting, storage, and machine learning
• Version Control: Git

Factors That Affect the Cost of Making an App Like Jasper AI 

1. Feature Set & Complexity

Apps like Jasper AI rely on sophisticated features such as natural language processing, machine learning models, and content generation.  Implementing these features requires specialized skills and can be more costly compared to simpler apps on the market. The range of platforms you plan to support also impacts costs.  Do you want a desktop-only app or a mobile version as well? We asked a React native app development company and they suggested that developing native apps for multiple platforms can be more expensive than using cross-platform frameworks. The size and location of the development team matter too. Rates vary across countries, with developers from North America or Western Europe typically charging higher rates than those from Asia or Eastern Europe. Moreover, more complex apps may require a larger team with specialized roles, influencing overall costs.

2. App Design & UI/UX

A polished and intuitive user interface is crucial for your app's success.  Custom design elements, animations, and complex UI patterns can increase development expenses. Achieving a unique and appealing design often involves creating custom icons, illustrations, and animations, requiring skilled UI/UX designers and potentially increasing costs compared to using off-the-shelf components. Crafting a user experience includes designing effective information architecture, smooth navigation flows, and intuitive user interactions. This demands expertise in UX design and can contribute to overall expenses.

3. Integration with Third-Party Services

Writing assistant apps often enhance functionality and user experience by integrating third-party services. Common integrations include social media logins, cloud storage for user data, payment gateways for in-app purchases, analytics tools, and marketing automation platforms. Each integration brings its costs and challenges, which can vary depending on usage volume and request frequency. Apps handling sensitive user data, such as payment information or personal details, must prioritize robust security features to ensure compliance with data privacy regulations like GDPR and CCPA, further contributing to overall costs.

4. Server Infrastructure & Backend Development

The backend infrastructure and server costs are often overlooked but are an important aspect of the overall expense. Writing assistant apps require robust backend systems to handle data processing, user requests, and storage. It includes database management, server maintenance, and ensuring scalability to accommodate varying user demands. The choice of cloud service provider and their pricing plans can significantly impact expenses. Furthermore, the complexity of the backend architecture, the number of servers, and the usage of containerization or microservices can all influence costs.

5. Data Storage & Security 

Writing assistant apps process and store significant amounts of user data, including generated content, user preferences, and potentially sensitive information.  Ensuring secure and compliant data storage can be costly, especially when adhering to data privacy regulations like GDPR or industry-specific standards like HIPAA. The choice of data storage solutions, such as cloud storage providers or on-premises servers, impacts costs. Encryption, data redundancy, and backup strategies also add to the overall expenses, ensuring data security and reliability.

6. Ongoing Maintenance & Updates

Apps require ongoing maintenance and updates to remain functional and secure. It involves bug fixes, compatibility updates for new operating system versions, and addressing user feedback. Regular updates help maintain user satisfaction and app store rankings. The frequency of updates depends on various factors, including user feedback, the introduction of new features, and addressing security vulnerabilities. Each update incurs development, testing, and deployment costs, contributing to the overall expenses.

7. AI Model Development & Training 

When considering the development and training of AI models for the backend of such apps, it is important to take into account several key factors. These AI models can be intricate and often resource-intensive, resulting in varying implementation costs.  Task complexity, encompassing text generation, language translation, or sentiment analysis tasks, significantly influences model architecture and the requisite training resources.  More complex tasks typically necessitate larger datasets, more advanced algorithms, and greater computational power, all of which contribute to increased costs. The effectiveness of AI models is highly dependent on the quality of the data they are trained on. Acquiring or creating a substantial, diverse, and accurately labeled dataset can be an expensive undertaking.  Ensuring data quality and its relevance to the task at hand is of utmost importance and often involves data cleaning, preprocessing, & annotation processes, each contributing to the overall costs.  Moreover, the choice of machine learning framework (such as TensorFlow or PyTorch) can impact complexity and scalability, with more robust frameworks often requiring more extensive computational resources. Training large models on powerful hardware, including GPUs or TPUs, can expedite the training process but also lead to higher costs. Training AI models is an iterative process that involves multiple training runs and hyperparameter tuning sessions to optimize performance. This iterative nature consumes significant computational resources and time, driving up costs.  Furthermore, deploying the trained model for real-time inference may require seamless integration with the app's backend system or the utilization of cloud-based inference services, both of which have associated costs. These factors collectively contribute to ongoing operational expenses.

8. Testing & Quality Assurance

Entrepreneurs should also be mindful of the costs associated with testing and quality assurance when investing in a writing app. Rigorous testing conducted by engineers is imperative for ensuring the app's stability and reliability. Comprehensive test coverage includes addressing diverse devices, operating systems, and device configurations, each of which incurs corresponding expenses. By ensuring broad test coverage, the app's compatibility and performance across multiple platforms can be enhanced, thereby delivering a satisfying user experience.

9. App Maintenance & Support

When investing in an AI writing tool app, it is important to allocate resources for ongoing maintenance and support. All apps are susceptible to bugs, and once launched, they may require regular maintenance and updates to address user-reported issues. The frequency of updates depends on factors such as new releases, the growth of the user base, and the complexity of the app's features. Moreover, if the dedicated development team introduces new features, ongoing improvements are necessary to maintain the app's competitiveness in the market. However, implementing these improvements comes at a cost. In terms of support, expenses may arise from creating and maintaining FAQs, user guides, and other relevant documentation. Support staff or outsourcing teams may also need to be factored into the overall support costs. Furthermore, maintaining the app's backend infrastructure, including servers, databases, and any relevant cloud services, incurs ongoing expenses to ensure smooth operations. Investing in an AI writing tool app requires careful consideration of different factors, including AI model development and training costs, testing and quality assurance expenses, as well as ongoing app maintenance and support costs.  By thoroughly evaluating these aspects, entrepreneurs can make well-informed decisions and allocate resources effectively to ensure the success of their AI writing tool app ventures.

The Process of Building an App Like Jasper 

Building an app like Jasper AI demands a structured approach, giving customers an effective user experience.  Here's a step-by-step breakdown to guide businesses and entrepreneurs through the process of developing a user-friendly AI writing assistant tool. 

Step 1: Ideation and Research

Initiate the process with a clear vision. Conduct brainstorming sessions to explore and define your unique selling propositions (USPs). Perform comprehensive market research to identify existing competitors and understand customer requirements. By grasping your target audience's needs, you can strategically streamline the development process, ensuring your app aligns with their expectations.

Step 2: Planning and Designing

With a clear vision in mind, proceed to define the app's concept and create an initial wireframe. This wireframe serves as a blueprint, outlining the app's structure and functionality. Develop prototypes to experience the app's basic functionalities firsthand. Then, focus on crafting a user-centric design by carefully planning the user experience (UX) and user interface (UI) elements. It ensures your app is not only functional but also intuitive and easy to navigate.

Step 3: Development and Coding

In this phase, your vision comes to life. Assemble a skilled team of app developers, in-house or offshore, to code the app, implement features, and introduce the functionalities outlined during the planning stage. The project manager plays a pivotal role in coordinating the development process, and ensuring the use of appropriate programming languages and requested frameworks. This stage also involves estimating the costs based on the app's complexity and the expertise required.

Step 4: Testing

Place significant emphasis on the testing phase to guarantee the app's performance and reliability. Conduct thorough quality assurance (QA) tests to identify and address any bugs or usability issues. This critical step ensures your app delivers a seamless user experience across various devices and platforms. It also showcases the app's reliability and efficient customer support, reinforcing its overall quality.

Step 5: App Launch & Continuous Improvement

The final step is the launch where the app is sent online for public use. The work doesn’t stop there as it must evolve to meet the changing user requirements and the growing technological advancements. There are several other aspects which are included in the cost of developing a tool like Jasper AI.  It includes aspects like post-launch updates & maintenance. When it comes to developing an app such as Jasper AI, requires several investments such as time, resources & expertise. From ideation to continuous improvement, a successful AI-powered writing assistant only becomes a success if the app stays competitive and relevant in the market. 

Key Takeaways 

• The cost of developing an AI writing app like Jasper AI can range from $40,000 to $250,000, depending on various factors.
• The features, complexity, and range of platforms supported influence the cost, with native apps for multiple platforms being more expensive.
• App design, UI/UX, and custom elements can increase expenses, while integration with third-party services adds to the overall cost.
• Server infrastructure, backend development, data storage, and security are crucial cost components, especially with sensitive user data involved.
• Ongoing maintenance, updates, and bug fixes are necessary to keep the app functional and secure, contributing to the overall expenses.
• AI model development and training costs vary based on task complexity, dataset quality, and the choice of machine learning framework.
• Testing and quality assurance are imperative for app stability and reliability, and they incur corresponding expenses.
• App maintenance and support, including backend infrastructure maintenance and user support, are ongoing costs that need to be factored in.

Conclusion 

Developing an AI writing app like Jasper AI is an exciting yet intricate endeavor. The cost of creating such an app can vary depending on various factors, including feature complexity, design elements, third-party integrations, server infrastructure, and ongoing maintenance.  It is important for entrepreneurs to carefully consider these aspects & allocate resources effectively. By investing in a comprehensive development process that includes ideation, research, planning, designing, development, testing, & continuous improvement, businesses can create a user-friendly and successful AI writing assistant tool.  Are you an entrepreneur with a vision for the next big AI writing tool? Branex is here to help turn your vision into reality. Get in touch with Branex today to hire generative AI engineers and let's create something extraordinary together!

Imagine, you're craving a coffee and ask a chatbot for recommendations. It goes beyond just listing cafes, it becomes your personalized coffee concierge. It tells you which shop is closest, who roasts the most delicious beans, and which cafe offers the perfect ambiance to match your mood.  This is the power of generative AI.  It unlocks a transformative era where powerful algorithms analyze your data and create helpful, personalized experiences. But it doesn't stop there.  Generative AI also reimagines information by analyzing existing data and generating new content: text, images, audio, video, and more.  From ChatGPT to DALL-E, Google Gemini, Microsoft Copilot, and many other latest classes of generative AI applications. We now have multiple generative AI models, where each of these models is trained to read across vast amounts of data such as text, images, audio & more which are fed into the system in advance.  These complex generative AI models then assemble solutions that answer the end user queries in the best possible way. Almost all of today’s generative AI foundation models are built on large language models (LLM) that add more diversity and versatility to the results. They are effectively trained to respond to natural language with added clarity.  As of the McKinsey & Company report released in June 2023, generative AI holds the potential to add a positive $6.1 to $7.9 trillion to the global economy by the year 2030. Thanks primarily to its contribution to increasing worker productivity.  However, it's important to acknowledge that generative AI offers numerous benefits in boosting working efficiency. It also presents challenges such as potential inaccuracy, privacy violations, and intellectual property exposure. To understand generative AI more comprehensively, we will delve into its inner workings. Explore its most immediate applications and use cases, and share some compelling examples. We'll also discuss best practices for its responsible and effective use.  So, without further ado, let's explore everything about generative AI.

What is Generative AI? 

Generative AI, as the name implies, refers to algorithms capable of rapidly creating content based on text prompts.  Different AI tools are available to produce various content forms.  For instance, DALL-E, a generative AI tool, excels at creating high-quality images based on user prompts. Similarly, Jasper helps craft long-form content precisely, while Copy.ai empowers social media influencers and management teams to generate engaging social media posts.  These are just a few examples of what's possible with generative AI.  By interacting with these text-trained models conversationally. Similar to how you would talk to another person, you can unlock a wide range of content creation possibilities. 

A Brief History of Generative AI 

1. Eliza 1960: 

It all started back in the mid-1960s when the first conversational chatbot was introduced. Eliza, an entirely largely rule-based model, was first created by the Massachusetts Institute of Technology. The responses were limited to a set of predefined rules and templates. Although it lacked contextual understanding, it set the foundation for the present-day generative AI models emerging today.  Figuratively speaking, AI models are like blank brains with complex neural networks exposed to vast amounts of real-world data that have been preloaded to the system. These systems then eventually develop intelligence - which further leads to generating novel content in response to pre-defined prompts.  Fast forward to today, even the best AI expert is unable to precisely define how these complex algorithms work and how a blank brain becomes developed & fine-tuned to respond back, even the most critical of queries asked. 

2. The Inception of AI: 

1956 marked a pivotal moment in the history of artificial intelligence (AI) with the Dartmouth Workshop. It was there that John McCarthy first proposed the possibility of machines exhibiting intelligent behavior. This groundbreaking discussion brought together several key figures in the field, including Marvin Minsky, Nathaniel Rochester, and Claude Shannon. These "marvels of Artificial intelligence", as you aptly call them, were not just theorists; they were pioneers who actively shaped the future of artificial intelligence through their contributions.

3. The Turing Test 

The idea of machines mimicking human conversation isn't new, dating back to the 1950s. However, it received a significant boost when Alan Turing, a pioneering mathematician, computer scientist, and logician – often called the "father of modern computing" and "artificial intelligence" – proposed the Turing Test. This test involves a human evaluator judging natural language conversations between a human and a machine designed to generate human-like responses. The Turing Test became a major driving force in the early development of Generative AI.

4. WABOT-1

In 1972, Japan made a significant leap in robotics with the unveiling of WABOT-1, the world's first full-scale humanoid robot. Developed between 1970 and 1973 by Professor Ichiro Kato and his team at Waseda University. WABOT-1 represented a pioneering achievement in humanoid robotics. This remarkable robot could communicate in Japanese, a groundbreaking feature for its time. Moreover, WABOT-1 had advanced capabilities for: 

• Measuring distances and directions: WABOT-1 used external sensors to perceive its surroundings.
• Walking: Equipped with articulated lower limbs, it could move around its environment.
• Gripping and carrying objects: Tactile sensors in its hands enabled it to grasp and manipulate objects.
• Seeing: Two 525-line video cameras with a 35-degree field of view provided its "vision."

5. The AI Winter

The field of artificial intelligence (AI) has experienced periods of both excitement and skepticism. One such period, known as the "AI winter", spanned from the mid-1970s to the late 1990s. During this time, research funding and public interest in AI declined significantly. Several factors contributed to the AI winter. Firstly, early AI projects often fell short of their ambitious goals, leading to disappointment and a reevaluation of AI's capabilities. Secondly, limitations in computing power at the time hindered researchers' ability to develop more sophisticated models. Finally, a lack of clear understanding about how to achieve true intelligence further dampened enthusiasm. The consequences of the AI winter were significant. Reduced research activity slowed the pace of technological advancement in the field. Moreover, public skepticism about AI's potential hindered its adoption in various industries. However, the field eventually emerged from the AI winter, fueled by advancements in computing power, new research approaches, and a renewed appreciation for AI's potential benefits.

6. The AI Re-emergence

The year 2006 marked a turning point for artificial intelligence (AI) as it began to emerge in various practical fields. The financial sector witnessed significant advancements, with AI employed for tasks like fraud detection, credit risk assessment, and algorithmic trading. In the retail sector, recommendation engines were designed to guide consumers towards informed purchasing decisions. Social media marketing transformed with the introduction of targeted marketing and effective customer segmentation, enabling businesses to reach their target audience more efficiently. AI's reach further extended to the manufacturing industry, where it optimized internal operations, performed quality control checks, and implemented predictive maintenance. The healthcare sector also embraced the potential of AI. Utilizing generative AI to enhance medical diagnosis, research new drugs, and provide personalized medicine for individual patients. Recognizing its vast potential, venture capitalists directed significant investments toward AI research and development within leading technology companies. While the practical applications of AI were gaining traction, public interest was sparked by popular media portrayals. The 2006 film "Aeon Flux" showcased a fictional city named Monica, where advanced technology played a central role. The film's depiction of automated security systems, centralized control, and AI-powered robotics provided a glimpse into how advanced technology could shape the future.

What Are Some Types of Generative AI Use Today? 

Fast forward to the present era, AI has set the center stage. It has become an effective tool in almost all walks of life. Irrespective of which industry you’re in, AI is breaking ground almost everywhere. We see countless apps and software solutions that have a fully functional API making the tools work at the backend. But what sophisticated technology is working at the backend?   Let’s learn about the different deep-learning techniques which are included: 

1. Transformer Models:

Transformers are neural networks capable of learning context by tracking and identifying relationships in sequential data. This data can be anything such as a word, a phrase, or even a sentence. Transformer models are mostly used in tasks that are dependent on natural language processing (NLP). It underpins most foundation models of many AI tools today. 

2. Generative Adversarial Networks (GANs):

GANs work with two neural networking technologies, the generator and the discriminator. The generator is responsible for creating new content, whereas the discriminator differentiates between the real and the fake. As the generator keeps producing and the discriminator keeps separating facts from falsehood, it starts giving more fact-checked and realistic results. Although GAN models are negatively used to create deepfakes mostly, the tool can be legitimately used for many business applications. Whether it’s art, video, or any other form of content creation, GAN can be truly useful. 

3. Variational Autoencoders (VAEs):

While Transformers understand and manipulate sequences, and GANs create realistic data, Variational Autoencoders (VAEs) focus on learning the essence of a dataset. They achieve this by compressing data into a special code that captures the data's core structure. This code, unlike a standard autoencoder, allows VAEs to not just reconstruct the data, but also generate new, similar data points. This makes VAEs valuable for tasks like data compression, anomaly detection, and even creating new images or music.  By leveraging the power of these generative AI models, businesses are finding new ways to approach tasks and become more productive. One such example is that of Expedia; Expedia is one of the world’s most popular travel planning websites & apps which has integrated conversational AI assistant into its services. So now customers don’t have to spend hours searching for the perfect location for their vacations. They can simply discuss with the chatbot by sharing their preference. The app automatically creates a well-segmented list of hotels and nearby attractions so it may assist them with the planning.  Similarly, the soft drink giant Coca-Cola has formed partnerships with companies like Bain & Company who use ChatGPT to assist them with marketing. Bain & Company utilizes the application to create personalized experiences for customers by crafting personalized sales ad copy, images & messaging. SnapChat has also built a conversational AI within their product, the bot called My AI can be called up for a chat at any time to answer questions or simply for entertainment purposes. Some of the suggested uses include “advising on the perfect gift for BFFs birthday, helping plan a hiking trip, or making a suggestion for dinner.” 

Responsibilities Associated with Developing Solutions Using AI 

AI has definitely created a multitude of opportunities to improve the lives of people around the world, may it be business, healthcare, or education. And with its launch, it has raised new questions in the market such as what is the best way to create a system that is solely built on fairness, privacy, safety, and interpretability. 

1. Go for a Human-Centric Approach

Imagine you're designing a brand-new app or website that uses advanced AI to make recommendations or decisions.  At the end of the day, what really matters is how actual users experience and interact with your system in the real world. First things first, you need to build in clear explanations and user controls from the get-go. People want to understand why your AI is suggesting certain things, and they want to feel empowered to make their own choices. Transparency is huge for creating a positive experience. Next, think about whether your AI should just spit out one definitive answer, or present a handful of options for the user to choose from. Nailing that single perfect answer is incredibly hard from a technical perspective. Sometimes it makes more sense to suggest a few good possibilities and let the user decide based on their unique needs and preferences. As you're developing the system, get ahead of potential downsides by modeling user reactions and doing live testing with a small group first. That way you can make adjustments before launching to the masses. Most importantly, brings a truly diverse range of user perspectives into the process from day one. The more backgrounds and use cases you account for, the more people will feel that your AI technology is working for them, not despite them. A great user experience is all about making people feel heard, understood, and empowered.

2. Examine Your Raw Data

You know the old saying - garbage in, garbage out? Well, that applies big time when it comes to machine learning. The data you use to train your AI model is going to shape how it thinks and behaves. It's kind of like parenting - the "inputs" you expose an AI to mold its worldview. So before you even get started building a model, you need to do some serious digging into the raw data you'll be using. Search it up for errors, omissions, or straight-up incorrect labels. Those data gaps could really throw your AI for a loop down the line. Next up, make sure your training data actually represents the full range of real-world scenarios and users your AI will experience. If you only train it on data from 80-year-olds, it's probably not going to generalize well to teens. Or if the data is just from summer conditions, good luck when winter rolls around. As you're developing the model, keep a hawk-eye out for any "training-serving skew" - basically, when performance in training looks great but goes sideways once deployed in the real world. Find out such gaps and rework your AI accordingly.  Oh, and one more key thing - look for any redundant or unnecessary data points that could be simplified. The simpler and leaner you can make your training data, the better your model is likely to perform.

3. Keep Testing Till You Get It Right

When it comes to building AI systems people can really trust, we could take some lessons from how software engineers ensure code quality and reliability. It starts with putting every little piece of your AI through its paces with intensive testing, just like developers test individual code modules. But AI isn't just one component - it's a complex choreography of models interacting with each other and other software. So you'd need robust integration testing to catch any unintended code blocks from how those pieces fit together. Another obstacle is what developers call "input drift" - when the data inputs start changing in unexpected ways from what the AI was originally trained on. You'd want monitoring and testing to raise a red flag anytime curveball inputs could throw your AI for a loop. As your AI matures and your user base grows, your testing has to evolve too. Maintaining a regularly updated "golden" dataset representative of the real world is key to making sure your evaluations stay legit. And there's no substitute for continually bringing in fresh user perspectives through hands-on testing cycles. Blending comprehensive testing with a constant feedback loop from the real world? That's how you develop AI systems that don't just impress on paper, but prove their trustworthiness over years of use by real people.

Key Takeaways:

• Generative AI refers to algorithms capable of creating new content, such as text, images, audio, and video, based on existing data and user prompts.
• Transformer models, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs) are some of the key types of generative AI models used today.
• Generative AI holds the potential to boost worker productivity and add trillions of dollars to the global economy, but it also presents challenges like potential inaccuracy, privacy violations, and intellectual property exposure.
• Developing responsible and effective generative AI solutions requires a human-centric approach, with clear explanations, user controls, and consideration for diverse perspectives.
• Examining and understanding the raw data used for training AI models is crucial, as the quality and representativeness of the data shape the model's performance and behavior.
• Rigorous testing, including unit tests, integration tests, monitoring for input drift, and maintaining a representative "golden" test dataset, is essential for building trustworthy AI systems.

What’s the Future of Generative AI? 

In the near term, generative AI models will become significantly more capable of understanding and responding to nuanced human instructions.  Anthropic's constitutional AI research aims to instill models with stable values aligned with human interests. DeepMind is working on growing model capabilities while improving robustness and oversight. This will enable generative AI to tackle increasingly complex creative and analytical tasks - generating marketing campaigns, software prototypes, architectural designs, and more from high-level prompts. Expect to see wider enterprise adoption for content creation, coding assistance, and streamlining knowledge work. Longer-term, generative AI breakthroughs in areas like multimodal learning could lead to models that can learn from and generate all forms of data - text, images, audio, sensor data, etc. This could supercharge fields like robotics, autonomous systems, and simulation environments. However, major challenges around bias, privacy, transparency, and controlling explicit content must still be addressed through improvements to model governance, watermarking, and aligning AI goal structures. Regulation will likely increase, with the EU's upcoming AI Act expected to enforce strict guardrails around high-risk generative AI use cases. The U.S. is also developing an "AI Bill of Rights". Despite hurdles, generative AI is projected to be a pivotal general-purpose technology, reshaping content, media, design, science, and beyond in the 2030s and 2040s.  Searching to get your hands on the transformative power of Generative AI for your business?  Reach out to Branex, a professional generative AI development company offering a team of experts who can guide you through the process of integrating AI into your products & services. 

Want to learn more? Schedule a call with us.

Imagine living in a smart home. Have you ever wondered what life is like when you’re residing in a smart home? In such a setting, most home appliances are voice-controlled, and sensors adjust lighting and cooling systems according to the climate. Security systems can detect movement outside and alert residents. All appliances are interconnected and controllable via a smartphone with the help of Artificial Intelligence Technologies. They can even detect vehicles in the driveway and automatically open the garage door. Artificial intelligence (AI) is transformative. It's evolving significantly with each passing day. Thanks to concepts like machine learning, deep learning, and natural language processing, the responses of these highly computational machines are becoming increasingly intelligent. Deep learning enables machines to explore deeply, creating new neural links, while natural language processing (NLP) allows machines to interact in a more human-friendly manner. Today, artificial intelligence takes center stage in almost every technological aspect that we come across. May it be something as simple as taking out our smartphones to search for stuff on the Internet and interacting with personalized recommendations on streaming services to traversing unfamiliar roads with the help of AI-powered GPS? But where do you think the future will take us? What are the potential benefits of its implementation and what will be its impact in different sectors of the industry?  Let’s explore the history very briefly. 

A Brief History of Artificial Intelligence 

The story of artificial intelligence (AI) is more than just robots and science fiction movies. It's a captivating tale of human curiosity, technological leaps, and the constant quest to understand and even replicate intelligence. Let's embark on a journey through time to explore the fascinating evolution of AI:

1. Ancient Spark (Before 1950):

The seeds of AI were sown long before computers. In ancient Greece, philosophers like Aristotle pondered the nature of intelligence. Centuries later, in the 17th century, René Descartes famously proposed the idea of "thinking machines". These early thoughts laid the groundwork for future generations to explore the possibility of artificial minds. 

2. Birth of a Buzzword (1950s):

The 1950s witnessed the dawn of the AI revolution. In 1950, Alan Turing, a brilliant mathematician, proposed the "Turing Test" as a way to gauge a machine's ability to exhibit intelligent behavior. This test, while debated today, sparked conversation and ignited the field of AI research. The term "artificial intelligence" itself was coined by John McCarthy in 1955, marking the birth of a buzzword that would captivate the world.

3. Early Strides and Struggles (1960s - 1970s):

This period saw the development of the first AI programs, such as Arthur Samuel's checkers-playing program in 1952. However, these early attempts were met with both excitement and skepticism. By the late 1960s, limitations in computing power and the complexity of AI problems led to a period of "AI winter", where funding and enthusiasm waned. In 1969, shaky was the first general-purpose robot built. However, by today’s standard, it was very simple and would perform actions like turning on/off lights & pushing around boxes, and so on. 

4. Learning to Learn (1980s - 1990s):

The 1980s brought a resurgence of interest in AI with the rise of "machine learning", a technique where machines can learn from data without explicit programming. Expert systems, able to mimic human expertise in specific domains, also gained traction. The 1990s witnessed the invention of the "Support Vector Machine" and the birth of Deep Blue. The chess-playing computer that famously defeated Garry Kasparov in 1997. These advancements showcased the growing power of AI.

5. The Age of Deep Learning (2000s - Present):

The 21st century ushered in the era of "deep learning", where artificial neural networks, inspired by the human brain, started achieving remarkable feats. From recognizing faces in photos to generating realistic speech, deep learning algorithms powered a new wave of AI applications. Today, AI is woven into the fabric of our lives, powering everything from self-driving cars and recommendation systems to medical diagnosis and language translation.

What Are the Different Types of Present-World AI? 

1. The Purely Reactive:

It’s the simplest form of AI, reacting solely to the current situation without considering past experiences or future consequences. They function as a reflex action rather than taking into account past experiences. A basic thermostat is a good example. It simply senses the current temperature and turns the heating or cooling system on or off accordingly. It doesn't remember past readings or anticipate future needs.

2. The Limited Memory: 

The following AI has a limited memory which allows them to learn from recent experiences and apply their short-term pre-learned knowledge to current situations. They react based on short-term history making them slightly more complex than purely reactive AI. For example, a self-driving car with limited memory might use the last few seconds of data on traffic flow to adjust its speed or lane change decision. While it doesn't have a long-term memory, it can adapt based on immediate surroundings.

3. Theory of Mind: 

Theory of Mind is largely theoretical and refers to AI which can understand and predict the mental states, (beliefs, desires, intentions) of others. Its mind-reading ability allows the AI to interact with humans in more nuanced and socially intelligent ways. In its truest form, the theory of mind AI doesn’t exist yet. However, some advanced chatbot attempts have carved the path to analyzing conversations that are more user behavior-focused and respond accordingly. However, achieving a true understanding of human psychology remains a challenge.

4. The Self-Aware:

This hypothetical type of AI would possess consciousness and self-awareness. It would be aware of its own existence and its place in the world. This category remains entirely theoretical and highly debated, with ethical and philosophical implications. As of today, there are no real-life examples of self-aware AI. It's important to remember this category is purely speculative and subject to ongoing scientific and philosophical discussions. Imagine a Sci-Fi futuristic self-aware future generation of machines that are super intelligent, sentient & conscious like the Terminator, Ultron, or Vision.  

How AI Technology Will Change the Future? 

1. Personalized Learning: 

Imagine a future classroom where your virtual tutor isn't a one-size-fits-all program. But a dynamic AI system that understands your learning style, strengths, and weaknesses. This can be achieved through AI algorithms analyzing your performance in quizzes, your interactions with educational materials, and even your facial expressions. Based on this data, the AI can personalize your learning path by suggesting relevant content, adjusting the difficulty level, and providing targeted feedback, ultimately leading to a more engaging and effective learning experience.

2. Medical Diagnosis and Treatment: 

AI is already making waves in healthcare. Imagine a doctor using AI algorithms to analyze your medical scans alongside their expertise. These algorithms, trained on vast datasets of medical images and patient records, can identify subtle patterns and anomalies that might escape the human eye. This can lead to earlier and more accurate diagnoses, allowing doctors to intervene with personalized treatment plans tailored to your specific needs and medical history. AI can also help predict potential health risks by analyzing your genetic data and lifestyle habits, enabling preventative measures to be taken proactively.

3. Scientific Discovery: 

Scientific research often involves sifting through mountains of data and searching for hidden patterns and connections. AI can act as a powerful collaborator in this process. Imagine researchers in various fields utilizing AI to analyze data from telescopes exploring distant galaxies, DNA sequencing machines unlocking genetic secrets, or environmental sensors monitoring climate change. By sifting through this data at an unprecedented rate and scale, AI can identify previously unseen correlations and patterns, potentially leading to groundbreaking scientific discoveries that would have been difficult or even impossible for humans to uncover alone.

4. Climate Change Mitigation: 

The fight against climate change requires a multifaceted approach, and AI is emerging as a valuable tool. Imagine AI systems analyzing data from weather stations, satellite imagery, and environmental sensors around the globe. This data can be used to predict extreme weather events, track deforestation patterns, and assess the effectiveness of various climate mitigation strategies. By providing real-time insights and long-term forecasts, AI can help scientists, policymakers, and individuals make informed decisions to combat the effects of climate change and work towards a more sustainable future.

5. Robotics and Automation: 

From assembly lines in factories to operating rooms in hospitals, robots are increasingly present in various aspects of our lives. Imagine AI-powered robots that are not just programmed with predefined tasks but can actually learn and adapt to their environments. These robots can continuously improve their performance, make real-time decisions, and collaborate with humans more effectively. This can lead to increased efficiency and productivity in various industries, while also enabling robots to perform tasks that are dangerous or physically demanding for humans, improving safety and well-being in various fields.

6. Smart Cities and Infrastructure Management: 

Visualize a city where traffic lights adapt to real-time traffic flow, waste collection is optimized based on predictive analytics, and energy consumption is minimized through intelligent grid management. AI can analyze sensor data from various sources in real-time, allowing cities to optimize infrastructure usage, reduce energy waste, and improve overall efficiency, leading to a more sustainable and livable urban environment.

7. Search and Rescue Operations: 

Think AI-powered drones and robots assisting in search and rescue missions in disaster zones or remote areas. These AI systems can navigate complex terrains, identify survivors trapped under debris, and even provide medical assistance in dangerous situations. By combining AI with advanced robotics, search and rescue operations can become faster, more efficient, and potentially save more lives.

8. Agriculture and Food Production: 

Percept AI systems analyzing soil conditions, crop health, and weather patterns to optimize farming practices. These systems can suggest the most suitable crops for specific regions, predict potential yield based on real-time data, and even control irrigation systems for efficient water usage. By utilizing AI in agriculture, we can potentially increase food production, reduce waste, and ensure food security for a growing global population.

9. Entertainment and Gaming: 

Wonder AI-powered characters in video games that can adapt to your playing style, respond to your choices and create a more immersive and dynamic gaming experience. AI can also be used to personalize music recommendations, generate movie trailers based on your preferences, or even write scripts for interactive storytelling experiences. This opens exciting possibilities for the future of entertainment, where AI can enhance user engagement and create personalized and interactive experiences.

Decoding the Benefits of Artificial Intelligence 

1. Eradicating Human Error:

AI excels at performing tasks with high precision and accuracy, minimizing errors that can occur due to human fatigue, distraction, or limited information processing capabilities. This is crucial in fields like surgery, aviation, and financial transactions.

2. 24/7 Availability:

Unlike humans, AI systems don't need breaks or sleep. They can operate continuously, offering round-the-clock services and support. This is beneficial in areas like customer service, network security monitoring, and remote patient monitoring.

3. Cost Reduction:

Automating tasks with AI can lead to significant cost savings in the long run. This includes reducing human labor costs, minimizing resource waste, and optimizing operational efficiency across various industries.

4. Embodied AI in Action:

When AI is integrated into physical robots, it creates embodied AI. These robots can perform tasks in the real world, often in dangerous or difficult environments. Examples include bomb disposal robots, search and rescue robots, and industrial robots in manufacturing.

5. Automating Repetitive Tasks:

AI excels at automating repetitive tasks that are often mundane, tedious, and prone to errors for humans. This frees up human time and resources for more strategic and creative work, leading to increased productivity.

6. AI-powered Customer Service:

AI chatbots and virtual assistants can provide 24/7 customer support, answer frequently asked questions, and resolve simple issues, freeing up human agents for more complex inquiries.

7. Streamlined Workflows:

AI can analyze data and processes to identify inefficiencies and bottlenecks in workflows. This allows for optimization and improvement, leading to smoother and more efficient operations.

8. Time-saving Efficiency:

AI can automate tasks that would take humans much longer to complete, significantly reducing processing time and allowing for faster decision-making and response times.

9. Fraud Detection and Prevention:

AI algorithms can analyze financial transactions and identify patterns that might indicate fraudulent activity. This helps financial institutions and businesses detect and prevent fraud attempts, protecting consumers and organizations from financial losses.

10. Deeper Data Analysis:

AI can analyze vast amounts of data much faster and more efficiently than humans, uncovering hidden patterns, trends, and insights that traditional methods might miss. This leads to better-informed decision-making and improved outcomes across various fields.

11. Digital Assistants for Daily Life:

Virtual assistants powered by AI can help with daily tasks like scheduling appointments, setting reminders, managing calendars, and controlling smart home devices, freeing up time and simplifying daily routines.

12. Personalized Customer Experiences:

Artificial Intelligence can personalize recommendations, content delivery, and services based on individual preferences and past behavior. This enhances customer satisfaction, engagement, and loyalty in various industries.

13. Faster Decision-Making:

By analyzing data and identifying patterns, Artificial Intelligence can support faster and more informed decision-making, allowing individuals and organizations to react swiftly to changing circumstances and capitalize on opportunities.

14. Automating Repetitive Jobs:

Artificial Intelligence can automate repetitive and often physically demanding jobs and careers in AI, freeing up human workers for more creative and fulfilling tasks. This can improve worker satisfaction and well-being while enhancing overall productivity.

15. Striving for Unbiased Decisions:

While complete bias removal requires careful design and implementation, AI can be trained on diverse data sets and programmed with fairness principles to minimize the potential for bias in decision-making compared to purely human-driven processes.

16. Cost Savings Beyond Labor:

Artificial Intelligence can optimize resource allocation, identify areas of waste, and improve energy usage. These aspects, combined with reduced labor costs, contribute to significant operational cost savings for organizations.

17. AI in Risky Situations:

AI-powered robots and drones can be deployed in dangerous environments, such as nuclear power plants, firefighting, and bomb disposal, minimizing human exposure to risk.

AI Shaping the Future

AI's future holds immense potential to transform various aspects of our lives. From personalized learning and improved healthcare to efficient infrastructure management and groundbreaking scientific discoveries, AI promises to shape a future that is safer, more efficient, and holds the potential to address some of humanity's most pressing challenges. However, it is important to remember that responsible and ethical development of AI is paramount to ensuring its benefits reach all of humanity. Branex is at the forefront of this exciting journey. We offer comprehensive Generative AI development services in the United States to help businesses unlock the transformative power of Artificial Intelligence. 

We are living in a time when AI is at its boom. Artificial Intelligence has taken center stage and has opened up a myriad of future possibilities. Whether it's space exploration, melanoma detection, graphic design, or anything as complex as creating software, Artificial Intelligence has created waves across all of these industries. It has made the impossible possible. In fact, there has been steady growth in artificial intelligence careers. Even LinkedIn has listed artificial intelligence among the "jobs on the rise" in 2021. Today, almost every business needs AI engineers. Whether it's interpreting a business or market trend, sifting through data, or building tools to gain a competitive advantage, AI engineers are always in demand.  Are you someone who is seeking a career in the AI world? If your answer is yes, in this blog post, we are going to explore 7 awesome jobs in the field of artificial intelligence that you can pursue in 2024.  So without further ado, let's explore and learn about this technology in-depth.

7 Most Highly Rewarding AI Careers to Pursue in 2024 

1. Machine Learning Engineer 

A machine learning engineer is an integral part of a data science team. Their tasks usually involve researching, building, and designing new artificial intelligence systems responsible for maintaining and improving existing artificial intelligence systems. Machine learning engineers streamline effective communication between data science team members. For that matter, they are always in constant communication with data science teams who are responsible for relaying the necessary information required to build such AI models.  The job responsibilities of a machine learning engineer can differ. It often includes; 

• Implementing machine learning algorithms, 
• Running AI systems for experimentation & testing, 
• Designing & developing machine learning systems, 
• Performing statistical analysis, and so on. 

Benefits of Becoming a Machine Learning Engineer 

The machine learning field is ever-evolving, continually giving rise to newer algorithms, tools, and techniques. It keeps your mind mentally challenged and intellectually engaged. It provides you with an edge in developing creative solutions to challenging problems.  You are tasked with critical thinking, problem-solving, and analytical skills. It also offers you a sense of accomplishment and intellectual satisfaction.  Machine learning engineers also receive highly competitive salaries and better compensation packages. You also have the opportunity to progress and explore newer areas of interest.  Seeking a career in machine learning engineering can offer intellectual challenges. If you are passionate about technology, enjoy problem-solving, and desire to make a difference, machine learning can be a highly rewarding field.

2. Data Scientist 

A data scientist is responsible for formulating and identifying relevant questions, gathering useful information from a variety of data sources, and organizing this information into actionable solutions.  They also communicate their findings effectively to positively impact business decisions. Data scientists excel at extracting meaningful insights from extensive datasets by identifying patterns and trends, enabling informed decision-making and driving innovation. Here’s a series of tasks that a data scientist might do regularly; 

• Find patterns and trends in datasets to uncover insights
• Create algorithms and complex data models to predict outcomes 
• Make use of machine learning algorithms to improve the data quality 
• Communicate recommendations to other staff members & senior team members
• Deploy data tools like Python, R, SAS, or SQL for complex data analysis 

They are also responsible for developing and implementing analytical methods to address complex scientific questions, fostering intellectual growth, and providing a sense of accomplishment. Data scientists equip you with significant knowledge of new tools, advanced data mining techniques, and extracting information from diverse data sources, keeping the work engaging.

Benefit of Becoming a Data Scientist 

Becoming a data scientist offers far-reaching benefits.  They can analyze data and solve real-world problems across various sectors, from healthcare to finance, e-commerce education, and so on. Data scientists drive meaningful change by analyzing vast amounts of data and implementing practices that continually improve. It provides real-time data-driven insights to inform business strategies, leading to better outcomes and organizational success. It also enables groundbreaking research using data in areas such as autonomous vehicles, artificial intelligence, and personalized medicine. Regarding potential career growth, data science presents abundant opportunities, from making accurate predictions to delivering better insights. Data scientists have a significant impact on business decision-making. As demand increases, salaries often become more attractive, and career progression can lead to leadership roles and effective specialization in specific domains. Moreover, there is room for flexibility and remote working opportunities, enhancing work-life balance.

3. Artificial Intelligence (AI) Engineer

An artificial intelligence engineer is someone who is responsible for developing, programming & training machine learning models & deep learning neural networks so they can mimic a human brain.  The role usually involves a combination of expertise in software development, data science, software programming, and data engineering. AI engineers build tools, they do not code directly.  It means that they are capable of developing systems and frameworks for secure & efficient data sharing. Rather than manually writing lines of code for every data-sharing process, they simply build tools capable of doing it.  Since they are not directly involved in coding for data sharing, AI engineers work on other aspects like model building, training, and implementation. Whereas data-sharing tasks are handled by data engineers.  Here are some of the tasks that AI engineers are responsible to perform: 

• Create and manage AI product development & infrastructure 
• Automate infrastructure used by data science teams
• Develop machine learning applications as per requirements 
• Run artificial intelligence and machine learning experiments & testing 
• Create data transformation and data ingestion infrastructures 
• Collaborate with the electronics and robotics department 

Benefits of Becoming an Artificial Intelligence Engineer 

An artificial intelligence engineer can design and build AI systems which address real-world challenges, from healthcare to finance. They can build systems that improve patient outcomes, streamline financial transactions, optimize city infrastructure, and handle other similar activities.  As an artificial intelligence engineer, you can witness the direct impact on the job market and work on people’s lives and societal progress. They are well versed in using an array of cutting-edge technologies such as TensorFlow & PyTorch to continuously learn and adapt in this dynamic field.  Their vast expanding knowledge helps them to take up complex challenges associated with developing creative solutions, may it be personalized learning experiences or AI-powered assistants for those with disabilities.  Since AI engineers are high in demand, they receive better compensation packages.  The demand for AI is global, so once you’re equipped with the right knowledge, you can potentially open new doors to work in different parts of the world & become a part of other cultures as well. 

4. Computer Vision Engineer 

A computer vision engineer steers two boats, one for artificial intelligence and the other for machine learning. They are tasked with developing a vision for an artificially intelligent bot using deep learning methodologies to extract information from images & videos. The tasks may include practices like; 

• Identifying objects & people in images 
• Tracking the movement of objects in videos 
• Recognizing the facial expressions 
• Reading text in images 

Computer vision engineers are used in a variety of industries such as self-driving cars, security & surveillance, medical imaging, robotics, and more. Today, AI vision is successfully present in manufacturing, defense, medicine, and more where monitoring is of high significance. 

Benefits of Computer Vision Engineer 

Computer vision engineers are qualified experts bringing high demand to the table. They bring plenty of job opportunities and introduce competitive salaries for those working in the field. These engineers are well equipped with the capabilities to solve real-world problems that contribute to the improvement of the individual lives of different people. It intellectually challenges you for continuous learning & problem-solving, keeping your intellectual mind well stimulated. You will be tasked with constantly exploring new algorithms, large data sets, & techniques for pushing technological boundaries.  As a Computer Vision engineer, you will most likely work around with data engineers, developers, researchers, and other similar people who can foster collaboration & teamwork skills. 

5. Natural Language Processing (NLP) Engineer 

A natural language processing (NLP) engineer for developing and designing language understanding systems. The process usually involves identifying speech patterns, AI speech recognition, understanding natural language & developing natural language accordingly.  Think of an NLP engineer as someone who is tasked with understanding & responding to human language. They work as translators and interpreters of the digital world, bridging the gap between messy communication & structuring it as understandable for computers.  NLP implementations can be done on anything from chatbots that answer your questions to translation tools that break down language barriers. They work to fine-tune machine learning models so they can better understand the complexities of language including grammar, context & sentiment.  They make use of tools and algorithms to constantly innovate and push the boundaries of how computers interact with human language. 

Benefits of Having a Natural Language Processing (NLP) Engineer

Having a natural language processing (NLP) engineer on your team can unlock a world of possibilities. If you’re seeking a career path in NLP, the process can automate manual tasks such as text analysis, data extraction, content tagging, and freeing up valuable human resources for more strategic work.  Just imagine, a machine learning algorithm summarizing all your customer reviews, extracting key information, organizing legal documents, and generating reports based on large datasets - all handled by models & systems that you build and implement.  NLP also takes care of sentiment analysis which reveals emotional trends, and personalized experiences & improves overall customer satisfaction. NLP analyzes user manuals, reviews, and support tickets to identify pain points and inform product development.  It also enables smart search experiences which understand the natural language queries and deliver relevant results which leads to higher engagement & conversion rates.  It also powers AI chatbots & virtual assistants who resolve issues. 

6. Business Intelligence Developer

A business intelligence developer is someone who is responsible for performing business research and understanding market trends. They are masters of strong technical & analytical skills, but they also have strong communication skills. They are great at problem-solving skills and are responsible for modeling, building, maintaining, and designing complex data for highly accessible cloud-based data platforms.  Here’s what a business intelligence developer can do; 

• It can collect and clean data from sales figures to gain customer feedback. 
• They perform data analysis techniques to uncover patterns, trends & correlations. 
• They translate complex data into easy-to-understand reports, dashboards & interactive visuals. 
• BI developers also create data pipelines, BI tools & reporting systems. 

Benefits of Becoming a Business Intelligence Developer 

With businesses becoming increasingly data-driven, BI developers are in high demand, and therefore, their salary expectations are increasingly competitive. When pursuing a career as a BI developer, the role requires you to constantly learn and solve problems, keeping the intellectual mind well-stimulated. As a BI developer, your work is to directly translate complex information for business decision-makers into actionable insights so they can drive business decisions. BI skills are highly valuable in different industries, offering career flexibility, mobility, and more.

7. AI Consultant 

Imagine you're running a small business and you want to use AI to make your team more productive. But you're not sure where to start! An AI consultant can help.  They're like tech guides who understand artificial intelligence and can suggest ways to use it in your business. For example, an AI consultant could help you:

• Automate repetitive tasks, like scheduling meetings or sending emails.
• Use data analysis to predict customer behavior and improve your marketing campaigns.
• Develop chatbots to answer customer questions and provide support.

AI consultants are also great communicators and problem solvers. Let’s say, that if you’re planning to hire app developers for an AI-powered solution, you may require AI consultants to help them out.  They can work with your team to develop solutions that meet your specific needs. And they're always up-to-date on the latest AI trends, so you know you're getting the best advice.

Benefits of Becoming an AI Consultant 

The demand for AI consultants is skyrocketing creating better job security and career growth potential. Today, AI consultants are almost everywhere demanding market-competitive salaries & better benefits due to their expertise. Every AI project introduces its own set of challenges & newer opportunities to learn & adapt cutting-edge technology. They are constantly learning new things and keeping up with AI advancements, which puts them as a thought leader in the field of artificial intelligence.   The world of artificial intelligence is teeming with opportunities, with exciting new discoveries and applications emerging constantly. If you're looking for a stimulating, rewarding career filled with intellectual challenges and the potential to make a real impact, then consider diving into the fascinating realm of AI.

Key Takeaways:

• AI careers offer an intellectually stimulating environment with constant learning and problem-solving opportunities.
• You can contribute to real-world solutions across various industries, from healthcare and finance to robotics and education.
• AI roles often command competitive salaries and provide flexibility and growth potential.
• Whether you're passionate about machine learning, data science, computer vision, or other specializations, there's an AI path waiting for you.

No matter your background or experience, the world of AI welcomes curious minds and innovative thinkers.  Take the first step today by exploring the diverse AI roles mentioned above, researching further, and connecting with industry professionals.  Let the power of AI fuel your journey toward a fulfilling and impactful career! Are you looking to build an AI-powered solution for your business? Our software development company is skilled in developing intelligent software designs for your business. 

Reach out to us today so we can provide you with the best AI-powered product. 

In the platform of Google's Gemini AI, imagine visiting a shoe store and contemplating the purchase of your favorite pair for mountain hiking. However, uncertainty looms about which one would best suit your requirements. You pull out your Google Pixel 8 Pro smartphone, open Google Search, and enter your search query, “list down the best brands for mountain hiking shoes” and voila! Your phone gives you an accurate list of information. It’s not something as generic as CHAT GPT’s response explaining to you how it has limited data exposure from 2021. But, it shows you the actual list of shoes that you’re looking for, all thanks to a simple Gemini API key. Over the past couple of years, numerous organizations, including OpenAI, Microsoft, and Google, have been engaged in fierce competition, constantly unveiling new and potent AI generative models. Stepping into the limelight, Google recently launched Gemini on Wednesday, December 6, 2023. This incredibly powerful AI technology can extract vast datasets, providing accurate and refined information. According to top software development companies in the USA, Gemini’s Ultra performance on the MMMU benchmark has also outperformed the GPT-4V in the following results Art and Design (74.2), Business (62.7), Health and Medicine (71.3), Humanities and Social Science (78.3), and Technology and Engineering (53.00). In this article, we are going to explore what Gemini AI is, how to set up the Gemini AI environment & its respective advantages. So without further ado, let’s delve into the details & learn everything about it. 

What is Gemini - Google’s Largest Capable AI Model Yet? 

Gemini is an artificial intelligence model developed by Google AI, which works as a multimodal generative AI.  As a multimodal model, Gemini excels in understanding and processing information from a diverse array of sources, including text, code, audio, video, and images. Its versatility sets it apart from previous AI models launched by Google, which were constrained by their ability to comprehend only a single type of information at a given instance. The core concept behind Gemini is to usher in a new era of AI models inspired by the nuanced way people understand and communicate through AI-powered tools. The ultimate goal is to create an expert helper or assistant that surpasses the limitations of its predecessors. In the ever-expanding landscape of AI, Google emerges as a frontrunner with Gemini, offering a solution that stands out amidst the competition. Here are more details on the workings of Google's largest and most capable AI model as of yet.  During its launch, Google's CEO Sundar Pichai heralded Gemini as one of the most advanced AI models developed by the company. The aim was to create a revolutionary AI model which makes groundbreaking discoveries in AI.  As Sundar Pichai puts it,  “We’re taking the next step on our journey (as an AI-first company) with Gemini, our most capable and general model yet, with state-of-the-art performance across many leading benchmarks,"  He further added,  "Our first version, Gemini 1.0, is optimized for different sizes: Ultra, Pro, and Nano. These are the first models of the Gemini era and the first realization of the vision we had when we formed Google DeepMind earlier this year."

Gemini Is Not One Model, Its Many AIs Combined 

The only way to create such an elegant model was to combine different AI models to create one efficient multimodal AI. It is capable of combining several machine learning models such as audio processing, coding & programming, large language models, computer vision, and 3D models bringing them all to work in complete synergy. The idea was to create a single AI model that empowers all other AI models enabling developers to create new ones. 

Understanding Gemini AI 

Google's DeepMind has been dedicated to pursuing artificial intelligence with the overarching goal of creating a model that delivers collective benefits to humanity.  Today, the Gemini AI has achieved groundbreaking advancements in developing a generative AI model known for its high flexibility and ability to handle a diverse range of information. Its versatility allows the multimodal to function across different systems, from powerful data center servers to mobile devices.  The expansive language model Gemini comprises three distinct variations: Gemini Ultra, positioned as one of the largest and most capable categories; Gemini Pro, which scales up to handle a broad spectrum of tasks; and Gemini Nano, designed for specific tasks and optimized for mobile devices. Notably, Google's Gemini Ultra outshines the competition, surpassing even the GPT-4 technology across different criteria. It stands as one of the pioneering models to exceed human experts in multitasking, showcasing problem-solving abilities across 57 subject areas. This significant achievement highlights Gemini Ultra's superior comprehension and problem-solving capabilities. Here’s a brief Google Gemini Statistics by AI versions comparison performed by merca20.com to give you a more detailed insight into their respective features. 

The Software Architecture of the Gemini AI Model 

As far as the software architecture of the Gemini model is concerned. All three Gemini models work on the same software architecture. They are decoder-only transformers with significant modifications to train TPUs.  Each transformer comes with a context length of 32,768 tokens capable of accepting multiple forms of input. Since Gemini is multimodal, it can combine different modes such as text, images, and videos.  Images may appear in different resolutions. Videos can appear in the sequence of images. And Audio samples at 16 kHz. All input data is finally converted into a sequence of tokens under the Universal Speech Model. 

How to Setup the Gemini AI Environment for Yourself? 

To start using the API, obtain the API key from the Google AI for Developers.  Now click on the “Get an API key” button and it will lead you to Google AI Studio. From here, you can generate your own API key.  To access the Gemini Model, you will need a Google Cloud Account and a Google Cloud Project + billing associated with it. Also, you will need to have some familiarity with the Visual Studio Code.  Start by creating a new Gemini application template in Cloud Code:  You can achieve it by visiting the Google Cloud console and logging in. Now choose “Project” and launch “Cloud Shell” and then head on to the Editor.  Launch the VS Code command palette as shown below:  In the command palette, type in: 

Cloud Code: New Application

It will bring up Application Templates. Search up Gemini and select the option.  Here you will find two application templates, available (Node.js and Python). Choose the one as per your preference.  Since we are using Python, we will opt-in for the Gemini API Python, and select the folder path within the environment. The chosen path will download the template and associated files.  The provided file list is presented below, and you should observe a comparable set of files: To have more in-depth information, you can check the README.md file which will provide you with the necessary instructions.  Since we already have the Gemini API key, we have to follow these steps to run Gemini in the Python application.  Launch a Terminal from the Cloud Shell IDE and follow along (assuming you are in the terminal and the folder in which the main.py and requirements.txt files are present. To set up the environment variable, head on to the main.py and enter your copied API key. 

export GOOGLE_API_KEY="Your Gemini API Key"

The next step is to install Python dependencies.  Just ensure that you install the required package google-generativeai which is available in the requirements.txt file. 

pip install -r requirements.txt

Once complete, the next step is to run the application. 

python main.py

Just add your default prompt such as; 

"Please provide a list of the most influential people in the world."

And it will generate the desired result; 

How is the Gemini AI Advantageous? 

Numerous small and medium-sized businesses (SMBs) along with enterprises leverage Gemini in impactful ways, particularly in improving customer service through chatbots. Gemini is instrumental in providing product recommendations. Its capabilities extend to identifying emerging trends, enabling advertisers to strategically use information for product promotion.  Remarkably, content developers also utilize Gemini for creating campaigns and blog content. Currently, Google Gemini Pro is integrated with Google’s chatbot Bard which helps it with advanced reasoning, planning, understanding, and other capabilities. In the near future, the tool is expected to launch “Bard Advanced” which will use Gemini Ultra as the biggest update to Bard, a more empowering chatbot mimicking CHATGPT. Gemini plays an important role in trend identification, facilitating product advertising for companies. Beyond that, it serves as a valuable asset for content development, aiding in the creation of marketing campaigns and blog materials. Developers find Gemini to be a versatile tool for code generation. Its capabilities extend to harvesting data from thousands of pages, transforming this information into visual representations, and even capturing screenshots for a comprehensive understanding. Today the Gemini Ultra, the first model is outperforming many human experts on MMLU (massive multitask language understanding) where it utilizes 57 subjects like math, physics, law, history, medicine, and ethics for amalgamating both, world knowledge as well as problem-solving capabilities. Its diverse application far outweighs the disadvantages of the previous models. 

Will Gemini AI Be Better than ChatGPT? 

Gemini AI has definitely taken the lion’s share as it has outperformed almost every academic test such as understanding text, images, videos, and even speech.  In several different topics and subjects such as math, physics, and law. The Gemini AI scored a belligerent 90% overall which is higher than the Chat GPT’s 86.4% which was impressive. 

Concluding Paragraph 

Gemini AI is taking the Internet world by storm.  It is capable of carrying out conversations the same way a human mind would. It is also equipped with the capability to perform extremely complex problems and provide you with suitable solutions. The Gemini AI can understand & process different information such as code, images, music & text.  There’s much more that one can do with Google’s largest & most capable AI model available.  It is also scalable and can also protect user’s private data. You can customize Gemini as per requirements to meet your desired needs. It can translate language, change writing tone & style, and create content that answers complex questions. It is becoming a norm for custom application development to use it for coding.  The possibility of Google’s new AI child Gemini is endless, so feel free to access it at Build with Gemini.  At Branex, we believe in building a technologically sound future for tomorrow.  From breathtaking UX designs to amazing mobile app experiences, our web design and development company in the USA can create remarkable experiences that make your business stand out across all digital fronts.

Is there any chance that you have been to a McDonald's Job in New York on Third Avenue at 58th Street? If not, here is how things go by. You enter McDonald's. You place an order on a touch screen. Your order is cooked by a McRobot and served by a McRobot too. As surprising as it might seem, this is quite alarming for unskilled employees. Also, in some cases, even professional employees are being replaced by automated robots. A recent study predicts that automation will eliminate 73 million jobs by 2030. If you're not worried yet, you should be. If you’ve not yet seen Black Mirror, a Twilight Zone-esque anthology TV series about technological anxieties and possible futures.  I would suggest you log into Netflix, grab some popcorn, and binge-watch all five seasons. From the first episode of Season 5, where AI took over the feelings of an average human being to the third episode, namely, ‘Rachel, Jack, and Ashley Too’ where a famous popstar was replaced by her AI robot. Yes. A clear indication that robots are taking over our jobs. Robots are now performing automation jobs more efficiently and accurately, a task once assigned to manual labor. Companies are investing billions in automating jobs. The jobs which were once performed by a real person are now being handled by machines. Do you pump your gas? Buy groceries from a self-service shelf. Alternatively, even consult an AI-based doctor to prescribe you meds. More and more jobs are assigned to robots. Automation is not a bad thing. It is smart to replace the back-breaking work that even injured employees, or plainly worn-out workers, do. Sadly, it is replacing unskilled labor such as cashiers. Instead of charging manually on credit cards, payments are made via Apple Pay or Google Pay. Even the task of flipping burgers can be assigned to robots who are making patties by pressing them between two hot rollers. JPMorgan is using software that figures out which of the documents are relevant to a case in a matter of seconds, something that used to take lawyers more than 360,000 Hours of hard work. Before you start rushing to check if your current job is at risk, there is a tool for that as well. Ironically. Will Robots take my job, and will they help you figure out if you need to switch or pursue your career or not?

Lifesaving Tips To Keep You On The Job In The Future

Here are some lifesaving tips to keep you on the job soon:

1. Always be Learning:

Japanese use the word Kaizen, which means constant growth. If you dream of having a prosperous life in the future, you must invest your energy in growing slowly every day. Education doesn’t end at the age of 22, nor does it end at 82. CEOs of today and successful people prefer reading books over spending their free time on vacation. Companies like Zappos and Amazon give their employees extra credit if they learn something other than their core field. In a federal report in the year 2017, it was concluded that more than half a million Americans are unemployed just because they won’t accept a job which is below their skill level. Figure out ways to help employees learn new skills. Develop a culture of lifelong learning.

2. Stay Curious:

Technology is moving at an enormous rate. The interns, the CEOs, and even the managers must prepare themselves for the future. The only way to survive is to stay curious. Evolve. Widen your skillset. You might be good at analyzing data. However, if you are not willing to put in your blood, sweat, and tears to learn, you’ll end up in the dungeons with the dragons. It’s necessary to exercise the Curiosity Quotient (CQ). To stay relevant, you must ask the right questions. If you keep a company of diversified people, they will generate new ideas and even contradict your opinions, which will help you to learn something new. It’s fantastic how great of an impact passionate curiosity combined with diverse, inclusive teams can have on the success of a leader, team, and business as a whole.

3. Negotiation and Persuasion:

According to Oxford research, people who have mastered arts, creativity, negotiation, persuasion, social perceptiveness, or are adept at assisting or caring for others, are in the least danger of being overtaken by Schwarzenegger-like T-800 cybernetic beings. Unfortunately, those with cramped workspaces or those who deal in tasks entailing manual/finger dexterity are most likely to be assimilated by the Borg. Neuromarketing can help you learn about the buyer’s intent and help you take the necessary actions to persuade the potential consumer to buy from your brand. Robots cannot do any such thing. The more questions you ask your customer, the more feedback you’ll be able to gather. Moreover, based on that feedback, you can judge the emotional state of the customers. Sure, robots can take data and apply some logic to analyze the situation. But robots can’t understand the feelings of customers.

4. Psychology Tricks to Persuade Anyone:

Alibaba Founder Jack Ma said at this year's Bloomberg Global Business Forum that artificial intelligence will lead to new opportunities. https://www.youtube.com/watch?v=wL1nyEi7VwY There are some jobs that are safe from robots, which are difficult to replace.

Imagine, Because Robots Cannot

One trait that cannot be learned by robots is imagination. AI currently works by taking existing data and making logical inferences based on the parameters we give it. Imagination and dreaming are not programmable skills. Activists, entrepreneurs, visionaries, thought leaders, authors, speakers, and others have a distinct advantage over technology in this arena, and that isn't going to change any time soon. The revolution of going back to the basics of humanity is about bringing each of those skills back to our reality at home, school, and work. It is impossible to answer ‘Will robots take my job?’. However, what each one of you can do is take these skills seriously and get yourself immersed in the habit of learning and growing. It is because of those skills that we have evolved over thousands of years. Moreover, it is because of those skills that we will continue to survive and thrive in times of technological disruption.

They say artificial intelligence and machine learning prediction technologies are now becoming an emblem of the future of workplaces. However, some are really inclined at the thought that AI and Machine Learning might become the death of employment. The notion in itself is not completely true because there lie two different realities to such a perception. Either the emerging technologies of tomorrow will become a core part of the business process and can allow employees to build better strategies. They will completely replace them by introducing an automated solution, so their work is adjusted. Today, we are watching technology take part in our lives and deploy programmatic systems which are defining the best use cases within the organization. For instance, consider how marketing in organizations has transformed; It solely centers on delivering the best customer experience to your users. Many customer support options offer chatbot support to incoming users, enabling them to easily navigate their way to the solution they are seeking. According to IDC research, machine learning and AI have eventually taken a huge leap from the US $12  issued statements. And these numbers are projected to double up in the next coming year and then again at the end of this decade. The year 2020 will witness more than a million new innovations in the field of Artificial Intelligence and Machine Learning. With so much happening in the world of AI and Machine Learning, we are somewhat eager to identify how such technologies are going to alter the course of the future. Here are some potentially strong AI and machine learning predictions that will transport you to the future.

Machine Learning Will Drive Product Recommendations

Fantastic Furniture is a furniture store that shifted its online presence from an on-premise server instance to a cloud environment back in the year 2016. In just a couple of years, they realized the vast potential machine learning has and how it greatly contributes to delivering an awe-inspiring customer experience to users around the world. The head of digital marketing, Leigh McKnight, issued a statement to CMO.com.au earlier this year, asserting that machine learning drives potential product recommendations. “What we have on the mobile page for in-store shoppers is recommendations based on what merchandisers would recommend, plus what other customers purchased with that furniture item based on data from the website.” Recommender systems are becoming the new norm. A good recommender system algorithm linked to your website or mobile app will collect information from variable sources and put customer reviews in front of the audience, so they can make a better judgment call. Today, you can find large-scale recommender systems in businesses that observe a lot of user interaction, such as retail, video-on-demand, or music streaming platforms.

People in Mature Economies Can End Up Consuming False Information

Gartner predicts that by the year 2020, most people in mature economies are likely to experience consumption of more false information than original content. A key role in promoting false content will be played by AI applications. Although AI proves to be a more efficient and effective technological AI advancement in the modern era, with the emerging concepts of deep machine learning, there is a high chance that false and untrue information can result in more exclusivity. Gartner further predicts that the possibility of the machine spreading such false information will ultimately become a leading cause of major financial fraud. Such frauds, seamlessly and effectively designed by automated systems, will create a major demise in the global financial sphere. Given the extent to which Internet firms are developing, there is a high chance that a country will be able to completely mitigate the problem in less than a year. But, that’s just one of the many facets where AI will result in creating a demise. We will always be skeptical of consuming false information and then spreading it around, since an AI-intelligent system will be publishing the content on our behalf.

AI Prediction & the Future of Jobs

It is predicted that 2018 will mark the democratization of AI. Gartner believes that AI will have a more resonant impact across a large number of companies, including the government. According to a research report published by Harvard and MIT, the takeaway suggests that Artificial Intelligence is not in any way going to take away our job roles. However, it will make everyone work more efficiently, ultimately ending up with increasing productivity. According to Oracle, one of their research studies indicates that more than 79 percent of HR leaders and almost 60 percent of corporate employees will observe a failure in shifting towards the adoption of AI. After identifying the speed at which technological advancements are paving the way for new job roles and more robust occupations, the report from the debate evaluates that some of the major changes in the corporate industry are going to happen in the Fourth Industrial Revolution. Organizations and job roles which are more likely to adopt AI as a part of their business processes will more likely thrive. Here is a general assessment of how job roles have grown and declined over 5 years. The demographic is an indication that a gradual rise has been observed in the software industry over the past five years. Hence, it promises a more powerful future for the fields associated with software development. After all, AI is a by-product of the software field and is eventually going to overshadow other businesses from a perspective. It proves that the job market for labor-intensive responsibilities will significantly result in decline significantly. But those that entail innovation and development will significantly emerge and grow exponentially with the inclusion of AI in the days to come. Here is another assessment that shows how AI skills are globally gaining an extra edge in various parts of the world. The United States is leading the list when it comes to AI skill penetration, whereas Argentina is lagging behind in the block for AI skill development.

Concluding Thoughts

The scope of AI is rapidly changing the very world we live in. At an estimate, it is perceived that by the end of the year 2025. The use of Artificial Intelligence will grow as much as up to 50% from the current standpoint of 29%. Amidst the hustle and bustle created by AI, one thing is for sure: AI will hardly replace the human element. However, it still remains uncertain what kind of jobs will be created and whether prior training sessions will be required. Whether people will be able to adapt to these job roles by themselves.

As AI continues to advance, it will integrate artificial intelligence, enabling deeper customer connections and redefining the marketing landscape forever. Jordan Bitterman, CMO of The Weather Company at IBM says:

“There are twelve threats to civilization; AI is one of these twelve threats. However, AI is the only threat which can also be the antidote for the other eleven.”

Gone are the days when brands had to organize everything in person. To attend major events such as the Super Bowl, marketers had to wait for the right time to publish a tweet or send out a post for maximum engagement. Now, times have changed. We have better tools like the IFTTT, Hootsuite, Socialbakers, etc. They allow us to automate our tweets and social media posts; even better, they become their own judge and publish the post on our behalf to create the perfect buzz for social events such as the Super Bowl. Don’t we often wonder how some of these tools are capable of making critical decisions for us? Like every industry, marketing is also embracing change. Every new sunrise introduces a technological advancement that’s better than the one crafted yesterday. And when the power of technology is intertwined with marketing, the outcome is something beyond our imaginations. But, as much as AI promises a remarkable future for the generations of tomorrow. Some research analysts have realized the fact that AI can turn completely rogue on you. A project named “Sophia” (A humanoid robot) was launched by a Hong Kong-based firm, Hanson Robotics in April 2015. It is a robot built in collaboration with AI developers. The robot is capable of carrying out detailed discussions with different individuals and responding to them in a sophisticated manner. Although many believe that her responses are scripted, she is still an AI-powered robot capable of making judgments on her own. When someone asked her whether she would choose to destroy humans! Surprisingly, her response was positive. https://www.youtube.com/watch?v=W0_DPi0PmF0 Maybe, we do deserve to be wiped out from the face of the Earth and machines have already realized that (joke). However, amidst the confusion, there are major technology giants who are bold enough to take that leap of faith. Some major technological giants such as Amazon, Apple, Google, and Facebook have announced a partnered approach to implement AI initiatives which can be beneficial for people and society. Today, AI has stepped out of fiction and entered into spheres of reality. Statistics show that more than 57% of the marketing agencies in the US are willing to adopt artificial intelligence to enable them to gain a competitive edge over their rivals. It’s just a matter of time until the whole world will go full throttle with AI. But, how exactly the implementations are going to change the face of marketing? Let’s find out.

How Artificial Intelligence Will Change the Future of Marketing

Here’s how Artificial Intelligence will change the future of marketing

1. The Shift from the Programmatic Era into the Cognitive Era

The future of marketing is going to shift from the programming era to the cognitive era. Previously, when we used to visit a website and search for a product, let’s say a smartphone… that smartphone might run around us for at least 2 weeks and would show up in website ads as we surf through the Internet. It would target market us on our social media profiles. And it will show up on random websites we are visiting on a daily basis. However, now times have changed. We are living our lives in an era where the cognitive abilities of a machine are more entertaining than the ones which are programmed into the machine. Systems are being developed that are capable of identifying why you left a website, how you left that site (exit pages), and whether you bought a product or totally skipped it. Based on your actions will enable marketers to make informed decisions about crafting an ad that would be highly relevant to you. It ultimately saves us from getting bombarded with unwanted advertising altogether.

2. AI Holds the Power to Automate our Content Production

One of the many individuals who will cash the true power of AI in the marketing sphere is none other than future content developers. Wait, just hold up! I know you are getting those ideas… No, Artificial Intelligence hasn’t yet reached a stage where it will generate content on our behalf. Tough luck! It’s going to take some time to get there, and until then, you will have to write on your own. But here’s how AI holds the true power to automate our content production. “WordSmith” is an AI assistant that greatly benefits writers. It is a collection of modules which allow writers to search for patterns in languages. An estimated 1.5 billion content pieces were created with the help of WordSmith last year. Software applications like WordSmith are based on NLG (Natural Language Generation) which is an intelligent AI algorithm. On the other hand, the AI sub-field NLG (Natural Language Generation) is an algorithm which translates data into natural language based on the kind of data you feed into the system. However, the solution is more sentence-based and may fail to create the perfect writing flow that a writer may end up creating, but it still turns out to be of great assistance. To produce as such, you ultimately require a senior writer who is skilled at writing.

3. Artificial Intelligence Will Improve Sales Performance

Some professionals who are going to benefit from artificial intelligence are none other than the sales teams. Most of the sales guys spend countless hours producing confirmed leads. While sifting through a roundhouse of customers can often become a hefty task. It also shaves off a good amount of time from your clock. In fact, some sales representatives claim that more than 80% of their working hours are spent identifying which of the leads have the potential to become customers and which of them are just “mystery shoppers” and not serious buyers. Making phone calls, sending out emails, awaiting responses, and then replying to them, all at the same time, can often lead to burnt-out and frustrated sales reps. As a result, they might not be able to put their best foot forward when pitching to a highly lucrative prospective client. However, with AI, things can dramatically change for sales representatives all around. By using smart intelligent systems, first, sales representatives can avoid focusing on repetitive tasks. They will not have to go through the entire long process of monitoring, categorizing, and creating effective communication with leads. It will greatly help the sales representative as they can readily spend more of their time and energy on closing sales leads.

4. Micro-Segmented Email Marketing

Adding to the previous point, an organization communicates with its internal and external customers via email. Now, doesn’t it sound beneficial if marketers can apply all of their creativity to creating remarkable content instead of exerting their mental force in monitoring how emails are sorted and sent out? Email marketing has come a long way. From physical mail segmentation to scheduled send-outs. Today, AI can help you automate everything using state-of-the-art marketing automation tools. Besides, there are several other automated emailing tools on the market, such as Constant Contact, MailChimp, SendGrid, HubSpot’s CRM, and others which can help anyone easily comb through the jargon of email data to reach useful conclusions. Future AI applications will enable marketers to easily segment email information based on different areas of interest, demographics, regions, and other criteria. So, if a marketing specialist wants to target a specific individual, they are not going to spend hours analyzing information based on personal details, zip codes, or telephone numbers to target different users belonging to different market niches. Instead, they will leverage the power of generative AI tools that will automatically reach out to users belonging to their target niche by analyzing previously stored data, and all marketer has to do is steer the tool in the right direction.

5. Website Personalization and Offering What the Customer Wants

The world of the internet is vast. It is ever-changing and growing at an immense pace. However, audiences visiting websites day in and day out are navigating through hundreds of websites each day. What will make your website stand above the competition is how personalized your approach is toward attracting customers and how good you are at creating compelling experiences for them. What people want is a personalized experience, and if your website is failing to give them one. They will eventually switch to another alternative. Many AI-powered tools have emerged which you can integrate into your website in order to provide customers with a more personalized experience. CNET Intelligent Cross-Sell is a platform that usually associates itself with consumer-electronic goods. CNET Intelligent Cross-Sell is a software platform that handles all cross-sells and upsells based on behavioral data and other attributes such as product profitability and score reviews to make the sales process and experience better for users. According to eMarketers, 48% of marketers reported observing a sales uplift in websites that are more personalized than those which aren’t. With AI-powered personalization, you can train bots at the back end of your website, so they readily adapt to what you are searching for. They can collect information from the source the audience has reached to their platform and based on that, they can readily point the customer in the right direction. With tools like Evergage, you can personalize your offers and even personalize search results with modules like Evergage SmartSearch to meet customer preferences. In short, it is a tool that fits best in the category of content marketing when it comes to website personalization.

The Wrap Up

AI is making dramatic changes for marketers in almost every aspect of marketing. With all the hype rising on the Internet and a bit of confusion, the future of AI in the world of marketing looks promising. Some marketing departments which haven’t implemented AI technology as a part of their business practices have an ambitious plan to do the same in the future. With technology growing at a rapid pace, there is an immense concern that AI-powered solutions come with uncertainties. However, future market leaders believe that gaining better insights, doing better forecasting, and working on creative strategies can help give birth to more reliable AI solutions.

From marketing to customer support, finance to travel, chatbots are ubiquitous. Chatbots' unprecedented rise to fame is fueled by an aggressive push towards digital automation. With automation slowly but surely replacing humans, chatbots will become an integral part of your business. In fact, the success or failure of your business will depend on how you implement automation technology in the years to come. Chatbots have become a buzzword lately. Despite all the hoopla surrounding chatbots, businesses are still bamboozled as to whether the chatbot technology is worth its salt or not. They are not clear about how to use chatbots to take their business to the next level. Here is an infographic that shows you how you to capitalize on this burgeoning fad and reveal some interesting chatbot applications that your business can take inspiration from.

Artificial Intelligence is here to make our lives better. Whether it be asking Siri about a local restaurant or business, or probing Alexa about the outside weather. Netflix suggests what to watch next, or Amazon lays out all the products you could potentially be interested in; AI is reaching into our homes, and our pockets and listening to our commands. Artificial Intelligence has a plethora of uses; both business-related and personal. Facebook Messenger bots are just one example of how companies are quickly leveraging AI to streamline mundane tasks and customer service. Most modern-day consumers prefer to connect with a brand through messaging apps and are not afraid to make purchases via this channel. Many AI-driven software programs are eliminating a great deal of manual work, helping brands make better decisions about how to approach potential customers, personalize conversations, and above all, recommend existing leads that deserve follow-up. Chatbots make up the largest portion of artificial intelligence and they could save businesses a lot of money. Chatbots have the potential to save $174 billion across financial services, sales, customer service, and insurance. So, are you ready to incorporate AI into your marketing strategy? Also Read: Exploring the Potential of Artificial Intelligence in the Future If not, here are some easy ways to discover how AI can help you improve your marketing efforts and allow businesses to generate more leads in 2018.

Generate More Leads

I know it’s hard to believe for many marketers that artificial intelligence can actually help them generate more leads than their current, age-old, run-of-the-mill marketing techniques. Did you know that businesses that incorporate AI into their marketing efforts receive 52% higher conversion rates, 54% increase in user engagement and traffic, 58% more profits, and 59% better close rates for sales? In nearly every aspect, AI rules over human marketing. AI is less expensive, more efficient, and can perform many time-consuming tasks quickly. However, humans can still deliver more value and a high level of personalization that robots can’t. There will always be a special place for human marketers, writers, and customer support representatives. But regardless of the extremely high levels of human intelligence, robots are not far behind and are in fact learning fast enough. AI is going to rule lead generation in 2018 as it has the ability to give you a higher ROI for your hard-earned dollars than the average human. And that is pretty interesting.

Nurture Leads

Most people don’t simply visit your website and make a purchase. Marketers are well aware of the fact that a potential buyer will visit your website multiple times before making a final purchase decision. This is why lead nurturing has become ever more indispensable. You need to follow up on prospective leads with more offers to turn them from visitors to paying customers. Before the advent of AI technology, lead nurturing was done through manual prospect massaging, email segmentation, and email messages. Some stick-in-the-mud marketers still adhere to the notion that lead nurturing can only be performed by humans. But according to a recent study, 59% of Americans are willing to receive special offers and coupons via chatbots. As people are getting ever more comfortable with conversing with chatbots, incorporating them into your business strategy will help you grow. When it comes to customer retention, chatbots also come out successful. The average customer retention rate after one month of chatbot usage is 40-60%, while for apps, it has plummeted to 20-40%. Reason? Chatbots are tireless, supernatural beings who can deliver timely responses regardless of the time of the day and never forget to send a reply. In fact, they are simply perfect for nurturing leads and retaining customers.

Potential Customer’s Data

In today’s fierce business world, data is everything. Without valuable data, digital marketing agencies are dead. They need to know their prospects and customers through A/B testing which has now become possible with the dawn of artificial intelligence and chatbots. Since chatbots are new to the marketing world, they are highly experimental, with a plethora of untapped opportunities surrounding them. Since we are yet to find out the potential of these chatbots, continuous testing is vital. Botanalytics offers valuable chatbot data for digital marketers to improve their marketing efforts. Chatbots also boast a tremendous amount of conversations in a very short period, and each conversation is a valuable piece of data. When you choose a celebrity, it offers recommendations so you can buy products according to your celebrity’s outfit style. Every single customer who chooses a celebrity and buys a product represents a valuable piece of data. Chatbots can provide businesses with more and more meaningful data than they ever thought possible by funneling significant insights and figures into analytical tools.

Increased Brand Engagement

Would you rather respond to an email or a chatbot? Emails have been used for years, and almost every marketer has an email list which they use to send special offers to their subscribers. But chatbots are mainly untapped because they are new marketing arena. This is why customers are more interested in messages sent from chatbots rather than old email marketing tactics. Let’s consider this case study, where a company developed a bot that received a massive amount of engagement. The customers spent an average of 4.27 minutes talking to a bot, and the company received more than 780 replies. Let’s take another example of Madi, a shopping bot. When users send the bot a snap-shot of their hair and color, it offers personalized hair color suggestions drawing upon their facial structure, natural hair tone, skin tone, and myriad other factors. When users take a picture of their face and hair, a bot gives personalized hair color suggestions based on their hair color, face structure, and a variety of other features. By letting customers venture a foot forward into the awe-inspiring realm of artificial intelligence, chatbots can boost brand engagement quickly.

Outstanding Customer Support

For nearly every business, providing exceptional customer support is a big challenge. Customers have their concerns that need to be solved by a call center or a chat center representative. Businesses need to invest more to offer the best possible support to their customers. However, with the rise of chatbots, businesses can provide faster and better customer support. In the healthcare and banking sectors, chatbots are answering customers’ queries instead of humans, and they are saving 4 minutes per inquiry. How? When a robot is replying to customers’ questions, it simply saves money and time while allowing humans to perform operations that only they can do. More than 50% of consumers expect businesses and their support staff to be active round the clock to entertain their queries. Chatbots can stay up and work tirelessly. More than 71% of customers want to be able to solve service issues on their own. So, customers prefer sending a message over talking on the phone. Chatbots can save money and time and will likely be an important part of customer support in the future.

Closing Thoughts

With the increasing abilities of AI, services such as prospect data, customer support, lead generation, and nurturing are cheaper and faster. And that is good news for businesses. More and more entrepreneurs, small business owners, and startups can utilize the benefits of artificial intelligence and build a successful and profitable business. In 2018, the process of lead generation will be completely transformed by AI because chatbots have officially become smarter, better, and faster than humans.