Remember the cool filters that turn you into a cat on Snapchat? Or the art generator Dall-E that creates images based on simple input? AI is behind those too.

Maybe you’ve heard these stories and thought “AI sounds amazing, but it’s WAY too complicated for me.” Here’s a secret: you don’t need to be a tech wiz to understand AI.

With the right guidance, anyone can embark on an AI learning journey, starting from scratch. Imagine going from “AI? Huh?” to using it to solve real-world problems in Nepal. This is possible with the Fusemachines AI Fellowship Program.

This blog is your invitation to explore AI, overcome your doubts, and discover how you can turn your curiosity into valuable skills. So, put on your learning hat, get ready for a fun ride, and let’s begin your transformation from beginner to expert!

From Curious Newbie to Foundational Fighter

Remember feeling nervous and excited on your first day of school? Imagine starting an exciting new adventure, but instead of textbooks and classrooms, you’re surrounded by supportive peers and industry experts, all geared towards one goal: unlocking the world of AI.

That’s exactly what the initial phase of the Fusemachines AI Fellowship feels like. We know diving into AI can be overwhelming, so we start with building a strong foundation. Think of it as climbing a ladder, but each step equips you with the knowledge and skills to confidently take the next.

Here’s what “beginner’s steps” look like:

Building the basics: Remember building Lego castles as a kid? We start similarly, but instead of colorful bricks, we work with the building blocks of AI: programming basics (if needed), essential math concepts, and the core principles of machine learning. Don’t worry, even if you’re new to these terms, our experts will guide you patiently, step-by-step.

Learning by doing: Memorizing facts is cool, but applying them is even cooler! That’s why the program integrates hands-on projects from the very beginning. Imagine building your own mini AI program or analyzing real-world data—all while having experts by your side to answer your questions and celebrate your progress.

A supportive community: Remember those nervous first-day jitters? Well, forget them! You’ll be surrounded by fellow AI enthusiasts, just like you, creating a supportive learning environment. Ask questions, share ideas, and learn from each other.

This initial phase might seem basic, but trust us, it’s crucial. You’ll be amazed at how quickly you progress from a complete beginner to someone confidently navigating the fascinating world of AI. And that’s just the beginning of your incredible journey!

From Foundations to Future-Ready: Shaping Your AI Journey

The next step in your AI journey takes you beyond fundamentals as you begin crafting your own personalized skillset, tailored to address Nepal’s unique needs and opportunities.

This phase empowers you to:

Dive deeper into AI concepts: Explore areas that fascinate you, be it natural language processing, computer vision, or machine learning algorithms.

Shape your learning path: Collaborate with mentors and peers to design projects that challenge and inspire you, applying your skills to problems you find meaningful.

Develop a Nepal-focused perspective: Gain insights into the country’s unique challenges and opportunities, ensuring your AI knowledge has direct relevance and impact.

Engage in hands-on projects: Tackle real-world challenges alongside organizations and communities in Nepal, gaining practical experience and making a difference.

Receive expert guidance: Learn from industry professionals who share their knowledge and help you navigate the world of AI.

Build a supportive network: Connect with like-minded individuals and experts, fostering collaboration and ongoing learning within the AI community.

By the end of this phase, you won’t just be an AI enthusiast; you’ll be a future-ready AI practitioner, equipped with the tools, skills, and Nepal-focused perspective to:

• Contribute to innovative solutions for Nepal’s challenges.
• Become a leader in shaping the future of AI in your community.
• Embark on a fulfilling career that makes a lasting impact.

Transformation and Expertise: Your AI Journey Starts Now

Ready to transform your curiosity into AI expertise and contribute to a brighter future for Nepal? Imagine yourself joining a supportive community of aspiring AI professionals, guided by industry experts, and equipped with the skills to tackle real-world challenges. This is the transformative power of the Fusemachines AI Fellowship Program.

More than just learning AI:

The program goes beyond teaching technical skills. It fosters:

Problem-solving mindset: Identify and analyze real-world challenges unique to Nepal, developing innovative AI solutions that make a tangible impact.

Critical thinking and innovation: Push the boundaries of AI applications, exploring creative solutions tailored to your community’s needs.

Growth mindset: Embrace continuous learning and adapt to the ever-evolving world of AI, becoming a lifelong learner and leader in the field.

Your transformation story can begin today. Applications for the next cohort of the Fusemachines AI Fellowship Program are now open! Apply now!

Here’s your chance to:

Learn from the best: Gain mentorship from industry experts and renowned faculty, acquiring practical knowledge and industry insights.

Collaborate with peers: Join a vibrant community of aspiring AI professionals, fostering learning, support, and lifelong connections.

Tackle real-world projects: Apply your skills to hands-on projects, addressing Nepal-specific challenges and making a difference in your community.

Bottom line

Don’t just read about transformation, embrace it. Join the Fusemachines AI Fellowship and unlock your potential to impact Nepal’s future. Apply now, applications close on [date]. 

The post From Beginner to Expert: Your AI Learning Journey appeared first on Fuse AI.

Artificial Intelligence Engineers are responsible for developing, programming, and training complex sets of networks and algorithms. An AI Engineer has expertise in areas such as software development, programming, data science, and data engineering. 

While AI Engineering is almost identical to Data Engineering, code composition for scalable data sharing seldom needs AI Engineers. All things considered, AI Engineers find and pull information from an assortment of sources and create and test AI models. They also use Application Program Interface (API) calls or embed code to construct and carry out AI applications. To know more about AI and its various subsets, you can check this article- What is AI.  

Why Choose the AI Engineering Field? 

Increased computing power and complex datasets have prompted the formation of new AI models and algorithms. The ever-evolving computing systems have also prompted our society as a whole to settle on fast and significant choices. Think about it, most of modern society is now used to instant gratification. Unlike before, we do not resort to browsing physical copies of encyclopedias to look something up now. Instead, we use our smartphones to instantly find any and all information instantly.

The development of AI machines will only go forward from there. When it comes to innovations, the sky’s the limit. That is why the field of AI is one of the most robust options to build a solid career.   

AI Engineering Challenges 

However, one must also consider the difficulties of AI machines and their translation from ideas into tangible functioning products. Often, the abilities of AI machines tend to generally work only in controlled conditions. They are hard to recreate, confirm, and approve in reality. Thus, we crucially require an engineering sector that can direct and deploy AI capacities effectively.  

For instance, let’s take autonomous vehicles as an example. While an autonomous vehicle works well cruising down a vacant race track on a bright, sunny day, how might it work during a hail storm in the busy streets of Kathmandu? 

Hence, AI Engineering aims to provide a stable structure and device to plan AI frameworks. They use it to create models for intricate, uncertain, and dynamic conditions. AI Engineering works towards fostering frameworks across many different sectors. This field of work also aims to guarantee human requirements are converted into moral, justifiable, and dependable AI. 

As mentioned, AI Engineering is a highly lucrative field and can open new and exciting career opportunities. The field also comes with many new innovative technologies, such as cybersecurity and data breaches, AI chatbots, facial recognition, and more. So if you are someone who has an interest in this field, choosing AI Engineering is a great idea.  

Why are AI Engineers Important?

Machine Learning (ML) and Artificial Intelligence (AI) are developing fields and can have a huge impact on the success of an organization. Advanced Machine Learning models can provide valuable recommendations and insight into an organization’s future issues or decisions. As a matter of fact, AI and ML have already been deployed in many organizational sectors, such as- 

• Finance:

  Many organizations in the finance industry use AI to learn user habits so they can better identify suspicious and fraudulent activity. Many consumers are also looking for financial independence. The adoption of AI in personal finance also provides the ability to better manage one’s financial health. Whether offering 24/7 financial guidance via chatbots powered by Natural Language Processing or personalizing insights for wealth management solutions, AI is essential for any financial institution looking to be a top player in the industry.

• Manufacturing:

  AI models are used by many manufacturing companies to rethink the supply chain and predict maintenance issues. The AI models are also used for integration with technological systems. As a result, companies can manufacture products more safely and inexpensively. For example- factories that create complex products, such as microchips and circuit boards, use the Machine Vision models. Equipped with high-resolution cameras, these models can pick up minute details and errors much more reliably than the human eye.

• Health Care:

  Healthcare organizations can reduce the cost and time associated with things like drug discovery with the help of AI. For example- a robot was used in eye surgery for the first time in Oxford’s John Radcliffe Hospital. Furthermore, the most advanced surgical robot, the Da Vinci allows doctors to perform complex procedures with greater control than conventional approaches. Likewise, Brain-Computer Interfaces (BCIs), backed by Artificial Intelligence, could restore the nervous system and provide alternative options for patients affected by neurological disease and trauma. The use of AI in healthcare is numerous.  

• Enterprises:

  Many businesses utilize AI to identify important insights in unstructured data, such as social media. AI-powered enterprises can also enhance customer service, sharpen cybersecurity, maximize sales, free up workers from mundane tasks, optimize supply chains, improve existing products and point the way to new products.  According to IDC– by 2025, the volume of data generated worldwide will reach 175 zettabytes. That is an astounding 430% increase over the 33 zettabytes of data produced by 2018. Thus, companies committed to data-driven decision-making can use these large data sets to yield in-depth business intelligence to drive improvements, but they cannot do so without AI. 

If you want to know about the skills required to be an AI Engineer, you can check this article that elaborates on this subject- Skills required to become an AI Engineer.   

Conclusion 

The synergy that exists between the development of society and AI is not likely to stop anytime soon. Arguably, the very distinction between what is human intelligence and what is artificial will probably evaporate. AI Engineering is one of the most sought-after careers in this regard, and the opportunities that this field provides are endless. 

Learn more about the responsibilities of an AI Engineer in this blog- What are the responsibilities of an AI Engineer.  

The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. The proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these will help engineers become leading AI industry experts, and also aid them in achieving a fulfilling and ever-growing career in the field.  

The post What is AI Engineering and Why You Should Join This Field appeared first on Fuse AI.

How to Become an AI Engineer

There are many ways to become an AI developer. Most, if not all, AI jobs require an analytical thought process. A key characteristic is the ability to solve problems with cost-effective and efficient solutions. AI Engineers should be aware that technological innovations need to be able to translate to state-of-the-art programs. 

To elaborate, let’s dive into innovations. It is apparent, initial product innovation first needs an introduction of a new product. You cannot, however, introduce just any new product. The new product must solve an existing problem in an exciting and modern way. Or the product needs to solve a completely new problem that has come to light.

For example, at the 2016 Consumer Electronics Show, the electronics company LG introduced a new type of flexible screen that you can roll up like a newspaper. When you first hear it, it sounds like science fiction. But it’s not in the realm of fiction anymore, it’s an actual real product that could become readily available to everybody in a short time. So what’s innovative about this, you ask? This innovative product solves the portability problem- instead of large, unwieldy screens, people can instead show videos on screens they can fold up when they are done and put in their bags. 

New innovative products can introduce new technologies or new ways to do something. And this is what AI Engineers need to be aware of- translating technological innovations into tangible practical products. 

The following topics provide an understanding of the prerequisites that you must obtain to get the AI job of your dreams.  You can also check this blog that elaborates on what AI Engineering is- What is AI Engineering. If you are hoping to begin a career as an AI Engineer, here are the requirements. 

Education Prerequisites Required to be an AI Engineer 

You first need to earn a bachelor’s degree to become an AI Engineer. You can earn a degree in the following subjects:

• Computer Science 
• Mathematics 
• Information Technology 
• Statistics 
• Finance 
• Economics 
• Data Science
• Cognitive Science

You can also take additional courses and pursue relevant certifications. There are many online courses and certifications created to further your AI knowledge and skills. For example, you might consider taking the AI2GO programming course by Fusemachines, an online course designed for individuals to learn about AI and its applications with no computer background knowledge necessary. 

Technical Skills Required to Become an AI Engineer

You will need in-depth knowledge of various technical skills to be a successful AI Engineer. You must know and use various software development techniques and practices, along with programming skills. As an AI Engineer, you will have a set of responsibilities within your workplace. To know more about the responsibilities of an AI Engineer, you can check out this article- What are the Responsibilities of an AI Engineer? 

Make sure you have a firm grasp of the following topics: 

• Programming Languages
• Statistical Knowledge
• Applied Maths and Algorithms
• Natural Language Processing
• Deep Learning and Neural Networks

Programming Skills 

The first skill you need to become an AI Engineer is programming. Learning programming languages such as Python, R, Java, and C++ is a must to build and implement models. It is also good to know about classes and data structures. In addition, you might encounter projects where you need to leverage hardware knowledge. Thus, you must be familiar with basic algorithms, classes, memory management, and linking. 

Applied Math and Statistical Skills

Technical and statistical skills include matrices, vectors, and matrix multiplication. To understand and implement AI models, you must know Linear Algebra, Probability, and Statistics. A good understanding of derivatives and integrals is necessary as well. Statistics are empirical, and tend to come up a lot. Make sure you are familiar with Gaussian Distributions, Means, and Standard Deviations as they are the fundamentals of AI Engineering. You must also have a firm understanding of Probability as this can help you understand models such as:

• Gaussian Mixture Models
• Naive Bayes
• Hidden Markov Models

The next technical skill you need is in-depth knowledge of algorithm theory and how algorithms work. To completely grasp and understand AI, you will need to know subjects such as the Gradient Descent, Quadratic Programming, Partial Differential Equations, Lagrange, and so on. Unlike front-end development, Machine Learning and Artificial Intelligence are much more math-intensive.  

Natural Language Processing Skills 

NLP incorporates two major fragments of Machine Learning and Artificial Intelligence: Linguistics and Computer Science. As an AI Engineer, the possibility of working with either text, audio, or video is highly probable. Therefore, it is important to have a good grasp of libraries such as Gensim, NLTK, and techniques such as word2vec, Sentimental Analysis, and Summarization. 

Deep Learning and Neural Networks Skills 

While working as an AI Engineer, you might need Machine Learning for assignments that are too complex for humans to code directly. This is where Neural Networks come in. Neural Networks are inspired by the human brain and identify numerical patterns based on sensory data. 

Artificial Intelligence has advanced from single-layer Neural Networks to Deep Learning Neural Networks. Data is passed through manifolds for complex pattern recognition in Deep Learning Neural Networks. They are the most accurate way of approaching complex problems such as Translation, Speech Recognition, and Image Classification. 

Aside from technical skills, AI Engineers also require business and non-technical skills to successfully navigate within companies and organizations.

Conclusion 

When it comes to the best jobs for the future, only a few industries stand out as much as Artificial Intelligence and AI Engineering. The Fuse.ai center is an AI research and training center that offers blended AI courses through its proprietary Fuse.ai platform. The proprietary curriculum includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Certifications like these will help engineers become leading AI industry experts, and also aid them in achieving a fulfilling and ever-growing career in the field.  

The post Skills Needed to Become an AI Engineer | A Guide to Technical Skills you need to become an AI Engineer appeared first on Fuse AI.